Green Energy and Technology
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José A. Orosa Armando C. Oliveira •
Passive Methods as a Solution for Improving Indoor Environments
123
José A. Orosa Departamento de Energía y P. M Escuela Técnica Superior de N. y M Universidade da Coruña Paseo de Ronda 51 15011 La Coruña Spain e-mail:
[email protected] ISSN 1865-3529 ISBN 978-1-4471-2335-4 DOI 10.1007/978-1-4471-2336-1
Armando C. Oliveira New Energy Tec. Unit Faculdade de Engenharia Universidade do Porto Rua Dr Roberto Frias 4200-465 Porto Portugal e-mail:
[email protected] e-ISSN 1865-3537 e-ISBN 978-1-4471-2336-1
Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2011940780 Ó Springer-Verlag London Limited 2012 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
This book was written to show the effect of passive methods, such as thermal inertia and permeable coverings, to improve indoor environments in different aspects, such as thermal comfort, energy saving, preservation of materials, hygiene and health. Specifically, the use of permeable coverings is a well known passive method but its effects and ways to improve indoor environments have been rarely analysed. Recent studies, developed by Orosa and Oliveira, revealed a new procedure to define the real effect of permeable coverings and their working periods in controlling indoor environments and their effects, such as energy saving and local thermal comfort. This new procedure, of the real behaviour of internal coverings, was covered in the PhD Thesis of José A. Orosa in 2003, under advice of the International Energy Agency (IEA) in his Annex 41 entitled ‘‘Moist Eng’’. Several related publications were made in the last three years in journals like Energy and Buildings, Building and Environment and Renewable Energy, among others. Despite the fact that real buildings reflect the expected theoretical results, the procedure was only employed in a few public buildings. In this regard, actual standards of the International Organization for Standardization proposed modifications in accordance with real case studies and, hence, a closer approach to reality was obtained. This book reveals and discusses the methodology developed and its results and future research works, such as patents and construction indications, in order to improve indoor environmental conditions. Finally, we express our appreciation to Hugo Hens and Carey C. Simonson for their attention and care offered in the journey through the knowledge of passive methods. July 2011
José A. Orosa Armando C. Oliveira
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Preface
It is understood that indoor environments must be classified and analysed using different criteria—work risk prevention, energy saving, thermal comfort, biological sources, preservation of materials and therapeutical effect. The different indoor environments may be defined as industrial environments, office and school buildings, apartments, libraries and spas. To analyse these environments, data loggers that measure indoor conditions for long periods of time were employed. Results revealed different temperature limits and relative humidity in concurrence with these criteria and, consequently, different procedures to control indoor ambiences. It is proposed to establish better behaviour habits in apartments and libraries: to open windows for a few hours to prevent the generation of excess humidity and improve the effect of passive methods, such as thermal inertia and permeable internal coverings in offices and schools. Hot environments, especially, revealed different control processes. On the one hand, a low level of air change is proposed for energy saving and low air velocity to prevent local thermal discomfort over wet skin surfaces in a spa. On the other hand, to prevent work risk increasing the degree of air changes and enforce resting periods in a controlled industrial environment are proposed. Research was carried out about general and local thermal comfort to define better models to be employed as control algorithms in Heating Ventilation and Air Conditioning Systems, in order to improve energy savings, preservation of materials and work risk prevention.
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Contents
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Thermal Comfort and Indoor Air Quality . . . . . 1.1 Thermal Comfort. . . . . . . . . . . . . . . . . . . . . 1.1.1 General Thermal Comfort Background 1.1.2 Local Thermal Comfort Background . . 1.2 Indoor Air Quality Background . . . . . . . . . . . 1.2.1 Chemical and Biological Load . . . . . . 1.2.2 Consumer Products . . . . . . . . . . . . . . 1.2.3 Other Pollutants . . . . . . . . . . . . . . . . 1.2.4 Biological Contaminants . . . . . . . . . . 1.2.5 Sensorial Load . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Indoor Air Standards and Models . . . . . . . . . . . . . . . . . . . . 2.1 Indoor Air Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 General Thermal Comfort Standards . . . . . . . . . . . 2.1.3 Local Thermal Comfort Standards. . . . . . . . . . . . . 2.1.4 Indoor Air Quality Standards . . . . . . . . . . . . . . . . 2.2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Moist Air Models . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 General Thermal Comfort Models. . . . . . . . . . . . . 2.2.4 Local Thermal Comfort Models . . . . . . . . . . . . . . 2.3 Indoor Air Quality Models . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Percentage of Dissatisfied with Indoor Air Quality . 2.4 Indoor Air Renovation Models . . . . . . . . . . . . . . . . . . . . 2.4.1 Techniques of Tracer Gas Monitoring . . . . . . . . . . 2.5 Building Simulation Models . . . . . . . . . . . . . . . . . . . . . . 2.5.1 EN ISO 13790 Models . . . . . . . . . . . . . . . . . . . . 2.5.2 Heat, Air and Moisture Tools Simulation Models . .
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2.5.3 Building Time Constant Models . . . . . . . . . . . . . . . . . . 2.5.4 Material Properties Models . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43 44 46
Indoor Environments . . . . . . . . . . . . . . . . . . . . . Indoor Environments . . . . . . . . . . . . . . . . . . . . . . Temperature and Relative Humidity. . . . . . . . . . . . Equipments and Methods to Analyse Indoor Environments . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Measuring Devices for Indoor Environments 3.3.2 Measurement Process and Standards . . . . . . 3.3.3 Statistical Analysis . . . . . . . . . . . . . . . . . . 3.4 Apartments: Indoor Air and Health . . . . . . . . . . . . 3.5 Offices and Schools: Indoor Air and Energy Saving 3.6 Libraries: Indoor Air and Material Preservation. . . . 3.7 Industrial Environments: Indoor Air and Work Risk 3.8 Spas: Indoor Air and Sports Centres . . . . . . . . . . . 3.9 General Conclusions About Indoor Environments . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Real 3.1 3.2 3.3
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Passive Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Natural Ventilation, Energy Saving and Comfort. . . . . . 4.3 Thermal Inertia and Passive Methods. . . . . . . . . . . . . . 4.3.1 Thermal Inertia and Energy Saving . . . . . . . . . . 4.3.2 Thermal Inertia and Thermal Comfort Adaptive Models. . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Thermal Inertia and Whole Building Simulation . 4.4 HVAC Improvement by Passive Methods. . . . . . . . . . . 4.5 Passive Methods and Preservation of Materials . . . . . . . 4.5.1 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Permeable Coverings . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Buildings . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Ventilation Rate . . . . . . . . . . . . . . . . . . . . 5.1.3 Data Loggers . . . . . . . . . . . . . . . . . . . . . . 5.1.4 Weather Stations. . . . . . . . . . . . . . . . . . . . 5.1.5 Working Periods . . . . . . . . . . . . . . . . . . . . 5.2 New Method to Define Permeable Covering Effect . 5.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Measuring Periods. . . . . . . . . . . . . . . . . . . 5.2.3 Statistical Analysis . . . . . . . . . . . . . . . . . .
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5.2.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . Hourly Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Improving PAQ with Permeable Coverings During the First Hours of Occupation . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . 5.5 Implementation of a Method for Building Certification . . . . 5.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . 5.6 Permeable Coverings Methods and Sick Building Syndrome 5.6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.2 Objective Parameters . . . . . . . . . . . . . . . . . . . . . . . 5.6.3 Subjective Parameters . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Future Research Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Energy Consumption, Thermal Comfort and Preservation of Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Thermal Inertia . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Advances in Natural Ventilation . . . . . . . . . . . . . 6.2.3 HVAC Improvement by Passive Methods . . . . . . 6.3 Extreme Indoor Environments: Relative Humidity . . . . . . 6.4 Extreme Indoor Environments: Temperature . . . . . . . . . . 6.5 Indoor Environments and Health . . . . . . . . . . . . . . . . . . 6.6 Other Research Works . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.1 Indoor Environments and Noise . . . . . . . . . . . . . 6.6.2 Implementation of ISO Standards . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Chapter 1
Thermal Comfort and Indoor Air Quality
1.1 Thermal Comfort In this chapter, the general and local thermal comfort background is presented with the aim of relating the thermal comfort with more recent research works [1– 3]. Thus, the first procedure is to review the definition of indices and models and how they can be implemented in later works, adopted by the International Organization for Standardization (ISO) and the American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) standards, which are presented in Chap. 2. The information is centred in general procedure laboratory studies and definition of thermal comfort models. Most of these studies are based on the subjective perception of indoor air by occupants. This is defined as a thermal sensation in general thermal comfort, and as a percentage of dissatisfied people (PD) in local thermal comfort.
1.1.1 General Thermal Comfort Background Thermal comfort was analysed by Fanger in 1967 [4]. His work was based on the thermoregulation of the human body and its related heat and mass transfer. From this relationship, Fanger could define a thermal comfort equation. This equation resulted in a new index of general thermal comfort, called predicted mean vote (PMV). To define this relationship, it was concluded that heat and mass transfer is related to activity levels, sweat rate and skin temperature in a linear way. These will be defined with six input parameters to lead to the PMV index. The initial thermal comfort equation was validated with the studies of Nevins et al. [5] and McNall et al. [6]. In these investigations, students rated their thermal J. A. Orosa and A. C. Oliveira, Passive Methods as a Solution for Improving Indoor Environments, Green Energy and Technology, DOI: 10.1007/978-1-4471-2336-1_1, Ó Springer-Verlag London Limited 2012
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1 Thermal Comfort and Indoor Air Quality
sensation in response to specific thermal environments, and their neutral thermal conditions were defined. Accordingly, this equation gives us the relationship between the heat released by the body and the heat released by the same body for the optimum comfort conditions during the same activity. Finally, in some studies, this equation was related to the thermal sensation scale of ASHRAE to define simplified models.
1.1.2 Local Thermal Comfort Background Since the origin, humans have shown a special interest in controlling indoor environments, but it was only in 1956 that the first serious study was conducted by Kerka and Humphreys [7]. During these studies, Kerka and Humphreys employed panels to assess the intensity of smell of three different fumes and smokes to snuff. The results revealed that there is a high influence of relative humidity and temperature in the intensity of odour. Furthermore, in 1979, Woods [8] concluded that it is a function of the moist air enthalpy. In 1972, Fanger expressed thermal comfort through the heat and mass transfer between the human body and the thermal environment, as reflected in ISO 7730 and in the ASHRAE standard 55-2004 [9]. In 1974, the smell adaptation to air components, when the exposition time is increased, was analysed. The same conclusions were obtained in all experiments: 1. There is no difference between air contaminants. 2. All contaminants experiment a reduction of 2.5%/sec, reaching a final value equal to 40% of the initial value after some minutes. In 1983, Cain et al. [10] investigated the effect of temperature and relative humidity on the perception of indoor air quality (IAQ). From this work, it was concluded that with an increment of temperature and relative humidity there is an increment of odour problems; specifically, when temperature is higher than 25°C and relative humidity higher than 70%. Six years after this research, Berglund and Cain [11] discussed the adaptation to pollutants over periods of time for different air humidity levels. It was concluded that relative humidity and temperature are the more important parameters and, secondly, the presence of pollutants in the air. In 1992, Gunnarse et al. [12] studied the possibility of adapting the perception of odour intensity. Four years later, Knudsen et al. [13] developed a research into the air before accepting a full body and facial exposure under a constant temperature of 22°C. Two years after Knudsen’s studies, Fang et al. [14] developed an initial experiment in a chamber (Fig. 1.1). This chamber depicted temperature and relative humidity control systems. Notably, in its experiments, 40 students were asked about the acceptability of an
1.1 Thermal Comfort
3
Fig. 1.1 First odour chamber designed
Fig. 1.2 New experimental chamber
indoor air chamber with 18°C and a relative humidity of 30%. For this experiment, students were advised not to use any strong perfumes. In these experiments, students were tested about their first impression on indoor air, revealing that relative humidity and temperature exert a higher influence on acceptability of indoor air than pollutants. In the second set of statements, this research group developed a set of experiments for a full body exposure under different levels of temperature and relative humidity to reveal the thermal comfort and acceptability (Acc) (Fig. 1.2). The new chamber depicted two zones, connected with a door. During the experiment, the students, 25 men and 10 women, went from one zone to the other, where a new temperature and relative humidity could be found, to test thermal comfort and perception of indoor air (survey shown in Fig. 1.3). During these experiments, the students wore thermal clothes to suit the environment. The survey was completed in 2.5, 5, 10, 15 and 20 min for each experiment. After 20 min, conditions in the chamber were changed. To define the perception of contaminants in a second group of experiments, PVC under lower air changes (200 l/s from 420 l/s in the previous experiment) was added. During these experiments, most of the contaminants were hidden in the chamber, so its presence was defined as a consequence of indoor air temperature and relative humidity during the first hours of occupation. However, reaching the 20-minute level, the detection of pollutants was not as a function of temperature and relative humidity and a new parameter, called adaptation, was defined.
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+3
Hot
+2
Warm
+1
Slightly warm
0
Neutral
-1
Slightly cool
-2
Cool
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Cold
No odour
1
Slight odour
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Moderate odour
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Strong odour
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Very strong odour
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Overpowering odour
Just acceptable
0
Clearly unacceptable
-1
a. thermal sensation
0
Clearly acceptable
+1
b. acceptability
c. odour intensity
Fig. 1.3 Used survey [14]
Fig. 1.4 Influence of temperature and relative humidity on the acceptability
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Acc
1 0.5 0 -0.5 -1 10
11
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In conclusion, there is better Acc when the mucous membranes are cooled and, additionally, when the temperature of the chamber is low. Furthermore, despite the fact that temperature is the main parameter, relative humidity exerts a big influence and, consequently, it is related to the Acc with moist air enthalpy (Fig. 1.4). These relations were obtained with clean and polluted indoor air, and the conclusion reached was that the Acc does not depend on the temperature and relative humidity in the other chamber zone. On the other hand, experiments with odour revealed that there is no relation between temperature, relative humidity and odour; in fact, the odour was adapted in the chamber after a few minutes, as stated by Gunnarsen [12]. From earlier studies, we conclude that energy saving is related to indoor acceptability owing to the detriment of temperature and enthalpy. For example, polluted air with low temperature and humidity depicts the same number of PD than clean air with high temperature and relative humidity. This conclusion is complemented by the fact that when temperature and humidity are low, the pollution emitted by the materials is low, as stated by Fang in 1998 [14].
1.2 Indoor Air Quality Background
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Table 1.1 Pollution caused by occupants Sensory pollution loads olf/person Sedentary, 1–1.2 met 0% smokers 20% smokers 40% smokers 100% smokers Physical exercise Low level, three met Half level, six met High level (athletes), ten met Children Preschoolers, 3–6 years, 2.7 met School, 14–16 years, 1–1.2 met
1 2 3 6
CO2 l/(h.p)
CO l/(h.p)
H2O l/(h.p)
19 19 19 19
11910–3 21910–3 53910–3
50 50 50 50
4 10 20 1.2 1.3
200 430 750 90 19
50
1.2 Indoor Air Quality Background IAQ inside a building depends on the outside air quality, design of the air conditioning system and its working conditions and the presence of pollution sources and their magnitude. The risks related to bad IAQ are exposure to toxic substances, radioactivity, infections or allergies, temperature and relative humidity conditions and odour nuisance. At the same time, the risks can be detected with the following symptoms: headache, dizziness, nausea, fatigue, dry skin, eye irritation, sinus congestion and cough. In the past few years, a European Collaborative Action was set up to identify the main problems related to the lack of air quality in an indoor environment, generally called sick building syndrome (SBS). In 1986, the World Health Organization (WHO) recognised this syndrome and defined the techniques to eliminate these pollutants, such as to control emission sources and renovate indoor air. In this regard, the main technique to eliminate these pollutants is based on an in-depth analysis of the building construction materials. For example, of all construction materials that can be employed, only those that can lower pollution emission should be selected. A lower pollution emission will improve the indoor ambience and, hence, lower the ventilation rate. There are different methods to quantify the pollution load. Some of them are chemical, some are based on the sensory effect and others are based on the biological effects. A chemical method to quantify the pollution load is needed to summarise the contamination load from each individual source. The sensory method is based on the olf unit, which expresses the effect of many chemicals as perceived by humans. Hence, one olf is defined as the pollution produced by a healthy adult in an office and in neutral thermal environment. Different pollution loads are shown in Table 1.1.
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It is well known that airborne micro-organisms are related to infections or allergic disorders, such as asthma and fever, in exposed people. For example, earlier studies show the relation between dust mite exposure and asthma. However, there is no clear relation between mould and asthma and between fungi growth and its effects in children. A lot of research works developed in the last few years revealed that the current methods are difficult to relate mould and health risk and that there is a need to improve actual standards. Particularly, it was observed that standards must implement a methodology to evaluate the exposure to fungi mould and metabolites. In general, we summarise that there is a need for more research to clarify the real effect of thermal environment on mould, fungi and mites development. Little data were found for allergy avoidance, and only breast-feeding and child growing up in smoke free environments were proposed. Furthermore, avoidance of certain types of foods and having a pet during infancy was recommended. However, it is well known that exposure to certain allergens is related to asthma. For example, a lower prevalence of asthma was obtained in ambiences without pets and mites; it was found that there were different effects from different allergens. Currently, the only way to reduce the level of mites in an indoor ambience is by removing carpets and improving IAQ. There are three methods to improve IAQ: the first consists in detecting construction failures that allow moisture to permeate the walls of a building; the second is based on improving air change. Under indoor air, the changes in relative humidity are low and gases are high. This method is related to bad HVAC design and an improvement of its equipment. Despite the fact that the data collected in household characteristics changed between studies and that constructions are different, it can be concluded that there are some common topics. The reason why it was not possible to relate the growth of mites in an indoor ambience is that we only considered weather conditions and indoor environments; however, in spite of these conditions, mites obtain water from moist air. Consequently, a relative humidity range to prevent the development of mites is proposed by most of the standards. For example, a relative humidity range of 75–95% and a temperature of 13–308C are the optimal conditions for mites’ growth and a relative humidity over 70% is optimal for fungal growth. Finally, despite the fact that there is a relationship between humidity and mites’ growth, this was not confirmed by most of the recent studies. For example, indoor conditions not only depend on weather, there are also microclimates. In conclusion, it is more complicated than a simple statistical study to obtain a relationship with all the variables implicated in this problem. To solve the problem, the IEA, in its Annex 41, developed a report for the identification and characterisation of pollution sources and humidity in indoor environments to be taken as reference. This report is based on real measured data with the aim of defining new methods for energy saving and improving IAQ.
1.2 Indoor Air Quality Background Table 1.2 Chemical contaminants in buildings Combustion products Construction materials Carbon dioxide Carbon monoxide Smoke snuff
Fibres VOCs
7
Consumer products
Others
Particulate Pesticides
Ozone Metals Radon
VOCs Volatile organic compounds
1.2.1 Chemical and Biological Load Despite the fact that building occupants are sources of pollution, other particles, such as cleaning products, cooking and cigarette smoke, cause the presence of contaminants in the building. To summarise, the main contaminants can be classified as chemical pollutants, combustion products (carbon monoxide, carbon dioxide and smoke snuff), building materials (fibres, volatile organic compounds), waste products through humans and other pollutants.
1.2.1.1 Chemical Pollutants Table 1.2 shows the most common chemical contaminants in the air inside a building, related to their possible source.
1.2.1.2 Combustion Products The presence of different chemical contaminants in the interior of a building is related to combustion from heaters, kitchens, etc. Furthermore, these contaminants are responsible for an increment in indoor air due to low air change.
Carbon Dioxide Carbon dioxide is usually related to the combustion of carbon substances, such as industrial processes, human respiration and smoking. Its main effect is related to suffocating effects.
Carbon Monoxide Carbon monoxide is related to an incomplete combustion of carbon substances. It happens, for example, in garages with combustion engines and its main effect is related to the lack of oxygen.
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1 Thermal Comfort and Indoor Air Quality
1.2.1.3 Building Materials In building materials used in general insulation, we find fibres, glasses, asbestos and different types of volatile organic compounds (VOCs) with related health effects. Fibres Fibreglass and asbestos are two different types of fibres that present a potential contamination risk. They are composed of amorphous glassy materials and employed to reinforce paper and plastic, for example, fabric thermal insulation and air conditioning systems. This concept covers materials, such as asbestos, which are currently forbidden in new buildings but present in old buildings. Consequently, maintenance of the building can be a source of dangerous contamination.
Volatile Organic Compounds This concept covers substances that evaporate at room temperature and a photochemical reaction causes oxygen in the air to be converted into smog-promoting ozone under favourable climatic conditions. Commonly found in household, institutions, industrial cleaning and maintenance products and in building materials, VOCs are a significant contributor to SBS. In enclosed spaces, VOCs can cause eye, nose and throat irritation, dizziness, headache, memory and visual impairment; some are known or suspected of causing cancer. There are two VOCs that are considered as important examples: formaldehyde and solvents. Formaldehyde Formaldehyde is employed in obtaining different plastics, and its degradation or inadequate design may cause the release of different contaminants indoors, causing irritation and respiratory allergies. Solvents Solvents are present in furniture and wood decoration among others, and can also release contaminants indoors, such as formaldehyde.
1.2.2 Consumer Products Consumer products cover different materials employed in building construction, such as paints, solvents, cleaning products, glues, etc. In this subsection, two big groups must be highlighted: particulates and pesticides.
1.2 Indoor Air Quality Background
9
1.2.2.1 Particulates Particulates are present at higher concentration in an indoor ambience than outside. Most of the times, particulates are related to respiratory problems and irritant effects. These particles can be classified in fine particles like those generated by cigarette smoke and aerosols, and larger particles like those produced by carpet fibres.
1.2.2.2 Pesticides A pesticide is composed of different hydrocarbons employed against insects and microbiological growth. In general, its effects over human health are unknown yet.
1.2.3 Other Pollutants There are different pollutants, such as ozone, metal compound and radon, which can be detected in an indoor ambience. The pollutants come from different sources, such as indoor equipments, outdoor air and building construction materials. For example, ozone is related to photocopying machines, metal compounds with external sources, and radon is related to the soil surrounding the building.
1.2.4 Biological Contaminants In the same way as chemical contaminants, microorganisms in indoor air should also be considered. The production of biological aerosols is related to two new concepts: reservoir and disseminator. The reservoir is a medium that brings together a number of conditions that allow organisms to survive in a certain environment and a disseminator is that which introduces micro-organisms in the air. Biological contaminants, on the other hand, are basically classified as infectious agents, antigens and toxins.
1.2.4.1 Infectious Agents Infectious diseases are higher when diluted in low air volumes and when there is a higher surface contact with a human body during long periods of time. Hence, infectious diseases are transmitted more easily indoors than outdoors. However, although this general concept is true, there are other parameters to be considered to prevent infectious diseases. For example, not all diseases require direct contact with a human body, such as colds and tuberculosis, where micro-organisms can live in ventilation systems.
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1 Thermal Comfort and Indoor Air Quality
Infectious diseases, usually transmitted through air, can affect the respiratory system and are a general cause of health risk. Those susceptible to these diseases are those with health problems and/or with a compromised immune system, especially the children and the elderly. To prevent these infections, it is advisable to take air samples regularly; however, this requires special equipment and experienced staff, not always available.
1.2.4.2 Antigens An Antigen is any substance which enters an animal’s organism with a mature immune system and is capable of provoking a specific immune response. Most of the antigens found in the air in enclosed environments are derived from microorganisms, arthropods or animals. These can cause diseases, such as hypersensitive pneumonia, allergic rhinitis and allergic asthma among others. The characteristic symptoms of pneumonitis hipersensitiva include fever, chills, choking, malaise and cough. The symptoms of allergic rhinitis are mucus, itchy eyes and nose and sinus congestion, whereas those of allergic asthma are shortness of breath and tightness in the chest as a result of constriction of the bronchi. Among the micro-reservoirs and multipliers for determining hypersensitivity diseases are substrates from outside, such as soil, plant materials (live and artificial) and water, and wet substrates from internal environments. Micro-organisms can grow in any stagnant water and air. Unclean surfaces are breeding grounds for the growth of fungi, forming spores which are directly exposed to the air and dispersed throughout the building.
1.2.4.3 Toxins Toxins are substances produced by micro-organisms that have some adverse effects on living organisms. Most of the microbial toxins in the air are made up of bacterial endotoxins and mycotoxins (from fungi). When the endotoxin-producing bacterium grows, it releases toxins into the water (humidifier, for example), from which they are dispersed into the atmosphere. Endotoxins are associated with some of the symptoms of pneumonia. A distinctive smell of mould from the areas in which fungi are present is due to production from these volatile substances.
1.2.5 Sensorial Load Air quality can be perceived through the nose and by its general chemical effects, related to the nose and eye membranes that appear like different agents of the air.
1.2 Indoor Air Quality Background Fig. 1.5 Percentage of dissatisfied persons for different ventilation rates
11 91 81 71
PD
61 51 41 31 21 11 1 0
5
10
15
20
25
30
25
30
Air renovation (L/s)
Fig. 1.6 Relationship between perceived air quality and the percentage of dissatisfied persons
121 101
PD
81 61 41 21 1 0
5
10
15
20
Odor intensity (Decipol)
Table 1.3 Examples of the three levels of perception of air quality
Quality level (category)
Quality of the perceived air % of dissatisfied
Decipol
A B C
10 20 30
0.6 1.4 2.5
Req. Vent.* l/s olf 16 7 4
*The ventilation examples are recounted exclusively to the quality of perceived air
The combined effects of these two sensors reveal that the indoor air is fresh and pleasant. Hence, we define the percentage of PD who feel dissatisfied in an indoor environment. This index can be defined as a function of indoor air changes and odour intensity (Figs. 1.5 and 1.6). As a related index, we define olf as pollution due to human biofluents. Hence, we express the maximum olf level to reach an inadequate indoor ambience. Once olf is defined, it is time to define the decipol. Decipol is the perceived quality of an indoor air with a contamination of one olf ventilated with 10 l/s of clean air (Fig. 1.6). Figures 1.5 and 1.6 were obtained from the model of Eq. 1.2.5.1.
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1 Thermal Comfort and Indoor Air Quality
However, there are different contamination sources that lead researchers to classify indoor ambience in types A, B and C that reveal the required ventilation levels into different types of indoor ambience (Table 1.3). PD ¼ 395 exp 1:83 q0:25 if q 0:32 l=s ð1:2:5:1Þ PD ¼ 100 exp 1:83 q0:25 if q 0:32 l=s olf There are other indices that can be employed to define indoor ambiences, for example, indoor acceptability. This index is revealed in Eq. 1.2.5.2 as a function of indoor air enthalpy and can be related to the energy consumption to change indoor air to better conditions. Acc ¼ a h þ b
ð1:2:5:2Þ
a and b are empirical coefficients whose values for clean air are a = -0.0033 and b = 1.662. From this equation, we deduce that indoor Acc increases with enthalpy. This is a benefit to air conditioning because we can detect air changes and maintain indoor air enthalpy. To do it, we need to drop relative humidity by 5% for each degree of temperature rise. However, temperature rise is limited to an indoor air enthalpy of 50 kJ/kg and a relative humidity of 55%. This relative humidity limit is related to the proliferation of fungi in accordance with ASTM 1994 and Viitanen [15]. On the other hand, this acceptability exerts a stronger effect on our indoor air perception than on thermal sensation.
References 1. Orosa JA, Oliveira A (2009) Hourly indoor thermal comfort and air quality acceptance with passive climate control methods. Renew Energ 34(12):2735–2742 2. Liao CM, Luo WC, Chen SC, Chen JW, Liang HM (2004) Temporal/seasonal variation of size-dependent airborne fungi indoor/outdoor relationship for a wind-induced naturally ventilated airspace. Atmos Environ 38:4415–4419 3. Hargreaves M, Parappukkaran S, Morawska L, Hitchins J, He C, Gilbert D (2003) A pilot investigation into associations between indoor airborne fungal and non-biological particle concentrations in residential houses in Brisbane, Australia. Sci Total Environ 312:89–101 4. Fanger PO (1970) Thermal comfort. Analysis and applications in environmental engineering. McGrawHill, New York 5. Nevins RG, Rohles FH, Springer W, Feyerherm AM (1966) A temperature-humidity chart for thermal comfort of seated persons. ASHRAE Trans 72(1):283–291 6. McNall Jr PE, Jaax J, Rohles FH, Nevins RG, Springer W (1967) Thermal comfort (and thermally neutral) conditions for three levels of activity. ASHRAE Trans 73:1–14 7. Kerka WF, Humphreys CM (1956) Temperature and humidity effect on odor perception. ASHRAE Trans 61:531–552 8. Woods JE (1979) Ventilation, health and energy consumption: a status report. ASHRAE J 21:23–27
References
13
9. ASHRAE Standard 55-2004. (2004) Thermal environmental conditions for human occupancy. ASHRAE 10. Cain WS, Leaderer BP, Isseroff R, Berglund LG, Huey RJ, Lipsitt ED, Perlman D (1983) Ventilation requirements in buildings–I. Control of occupancy odour and tobacco smoke odour. Atmos Environ 17:1183–1197 11. Berglund L, Cain WS (1989) Perceived air quality and the thermal environment. In: Proceedings of IAQ’89: the human equation: health and comfort, San Diego, pp 93–99 12. Gunnarsen L, Fanger PO (1992) Adaptation to indoor air pollution. Environ Int 18:43–54 13. Knudsen HN, Kjaer UD, Nielsen PA (1996) Characterisation of emissions from building products: long term sensory evaluation, the impact of concentration and air velocity. In: Proceedings of Indoor Air’96, Nagoya. International conference on indoor air quality and climate, vol 3, pp 551–556 14. Fang L, Clausen G, Fanger PO (1998) Impact of temperature and humidity on the perception of indoor air quality. Indoor Air 8:80–90 15. Viitanen H (1996) Factors affecting the development of mould and brown rot decay in wooden material and wooden structures. Effect of humidity, temperature and exposure time. Dissertation, Uppsala, SLU
Chapter 2
Indoor Air Standards and Models
2.1 Indoor Air Standards 2.1.1 Introduction This chapter aims to reveal the main standards applicable for indoor environments at international level. Specifically, the mean values and limiting conditions and the models for indoor environments are revealed as the main tools to be employed in real case studies. Following a methodic enumeration of standards, they are classified into general thermal comfort, local thermal comfort and indoor air quality (IAQ) standards.
2.1.2 General Thermal Comfort Standards In this section, the main standards of the American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) and International Organization for Standardization (ISO) for general thermal comfort are described. At the same time, different sections divide thermal comfort in moderate environments and work risk prevention in extreme environments. Their contents are described in the ASHRAE 55 standards.
2.1.2.1 Moderate Indoor Environments As described earlier, the main ISO [1, 2], NTP [3] and ASHRAE [4, 5] standards that may be of interest to the ergonomics or thermal comfort researcher are enumerated in the following sections. Note that ISO 7730 describes the indices and sampling procedures, in accordance with the previous chapter.
J. A. Orosa and A. C. Oliveira, Passive Methods as a Solution for Improving Indoor Environments, Green Energy and Technology, DOI: 10.1007/978-1-4471-2336-1_2, Springer-Verlag London Limited 2012
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Table 2.1 International and national standards for general thermal comfort in moderate environments ISO Standards ISO 11399:1995 Ergonomics of the thermal environment—principles and application of relevant international standards ISO 7730:2005 Ergonomics of the thermal environment—analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria ISO 9920:2007 Ergonomics of the thermal environment—estimation of thermal insulation and water vapour resistance of a clothing ensemble ISO 8996:2004 Ergonomics of the thermal environment— determination of metabolic rate ISO 7726:1998 Ergonomics of the thermal environment— instruments for measuring physical quantities ISO 10551:1995 Ergonomics of the thermal environment— assessment of the influence of the thermal environment using subjective judgment scales ASHRAE Standard ANSI/ASHRAE 55-2004 Thermal environmental conditions for human occupancy NTP 74: Thermal comfort Spanish Thermal Comfort NTP 501: Thermal environment: local thermal discomfort Standards NTP 242: Ergonomics: ergonomic office workplaces analysis NTP 503: Acoustic comfort: noise in offices NTP 358: Odours: a factor of indoor air quality and comfort
The first standard is ISO 11399. On the other hand, the main predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) indices are described in ISO 7730, in accordance with the heat that must be transferred from the whole body into real environments, as described in ISO 9920 and ISO 8996 standards. Furthermore, the general measuring methodology is described in ISO 7726 standard. We find that ASHRAE 55 is equivalent to the contents described in the ISO standards. Once the models are developed, they must be tested with respect to the real questionnaires, as reflected in ISO 10551 standard. It is interesting to define the application of these general standards to particular conditions. It is the case of the national standards developed by the Spanish ministry of work. Its standards are entitled NTP (technical standards for work risk prevention). Within the NTP standards, standards 74 and 501 are of special interest, related to the general and local thermal comfort under working conditions. In some working areas, such as office buildings, an in-depth analysis was made, reflected by the standard 242. Some new parameters to be considered at the time of evaluating IAQ and local thermal comfort, such as noise and odours, were developed (503 and 358) (Table 2.1).
2.1 Indoor Air Standards
17
Table 2.2 International and national standards for general thermal comfort in extreme environments Standard title ISO ISO 9886:2004 Ergonomics—evaluation of thermal strain by physiological measurements ISO 7933:2004 Ergonomics of the thermal environment—analytical determination and interpretation of heat stress using calculation of the predicted heat strain ISO 11079:2007 Ergonomics of the thermal environment—determination and interpretation of cold stress when using required clothing insulation (IREQ) and local cooling effects NTP NTP 387: Working conditions analysis: the ergonomic workplace analysis NTP 322: Estimation of the heat stress: WBGT NTP 350: Heat stress evaluation. Required sweating index NTP 462: Cold stress: occupational exposures evaluation NTP 18: Heat stress evaluation of severe exposures NTP 279: Thermal environmental and dehydration NTP 534: Mental workload: factors NTP 445: Mental workload: fatigue NTP 179: Mental workload: definition and measurement NTP 575: Mental workload: indicators NTP 445: Mental workload: fatigue NTP 275: Mental workload in health care workers: an assessment checklist NTP 177: Physical work load: definition and measurement NTP 295: Physical work load evaluation by continuous register of heart rate NTP 405: Human factor and accident rates. Social aspects
As for general indoor moderate environments found in ISO and NTP standards, there is an adaptation of these standards to extreme indoor environments (Table 2.2). Notably, standard ISO 9886 shows the measuring methodology to detect thermal strain. Heat and cold stress determination and interpretation are described in ISO standards 7933 and 11079. As seen in Table 2.2, there are a lot of standards at the national level to define and analyse this situation. The first general standard is the NTP 387 that describes the measuring methodology at the workplace. After that, indices for heat and cold stress evaluation are described in the summer standards, such as NTP 322, 350, 462 and 18. Notably, allegation and interpretation of mental work load is described in standards NTP 279, 534, 445, 179, 575, 475 and 275, and allegation and interpretation of physical work load in standards NTP 177 and 295. Finally, new indices that show the percentage of accidents are described in standard NTP 405 (Table 2.2).
2.1.3 Local Thermal Comfort Standards ASHRAE 55 and ISO 7730 are the only standards that define the local thermal comfort in an indoor environment, and its main indices and measuring procedures are described in this section.
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2 Indoor Air Standards and Models
Fig. 2.1 Local thermal comfort parameters
Most of the information that affects local thermal comfort is revealed in the models’ section, and here we only show the main parameters. The first step is to define local thermal comfort as that defined in some particular body zones, due to: air velocity, asymmetric thermal radiation, vertical temperature difference and soil temperature (Fig. 2.1).
2.1.3.1 Air Velocity Models Air velocity is related to sensible heat released by convection and latent heat released by evaporation and, hence, the feeling of thermal comfort is influenced by draft. For example, one bares a higher indoor air temperature when indoor air velocity is increased. So, during the summer season, ventilation helps to reduce cooling energy consumption. The opposite effect is obtained during the winter, when a higher air velocity also implies a higher heating energy consumption. To control these effects, there are two air velocity limits: indoor air velocity must never be over 0.9 m/s during the summer season and below 0.15 m/s during the winter. Another parameter to be considered, to define local thermal comfort, is the influence of temperature fluctuation. For example, in two different conditions we experience the same thermal loss, feeling higher dissatisfaction when the air temperature experiments clear changes with time, called air turbulence.
2.1.3.2 Asymmetric Thermal Radiation Asymmetric thermal radiation happens when an occupant of an indoor environment is exposed to a heat source on one side of his body during long periods of time, and experiences a certain degree of dissatisfaction. This happens, for example, in an indoor ambience with a warm roof and cold windows.
2.1 Indoor Air Standards
19
2.1.3.3 Vertical Temperature Difference The vertical temperature difference can be defined as the temperature difference that exists between the ankle and neck.
2.1.3.4 Soil Temperature Soil temperature is related to the difference in temperature between feet and ground temperature, depending on variables such as conductivity and the heat capacity of ground materials.
2.1.4 Indoor Air Quality Standards Various international organizations, such as the World Health Organization (WHO) and the International Council of Building Research, the ASHRAE, some countries, such as Sweden (the Swedish Council of Building Research), the United States, Canada and Australia, have developed guidelines and standards of exposure to indoor air pollutants. The air conditioning system has to ensure that the air contains acceptable low concentrations of pollutants. Hence, it must be properly designed and maintained to reduce pollutants to acceptable levels by dilution with clean air or elimination of foreign particles by filtration. According to ASHRAE, an acceptable indoor air is one in which there are no known contaminants in harmful concentrations, as determined by the competent authorities and a substantial majority (80% or more) of staff are not exposed to dissatisfaction. Obviously, the definition is vague, not only with regard to acceptable levels, but also to the concept of dissatisfaction. There are no standards to regulate the presence of microorganisms in the environment. The Committee for Bioaerosols of the American Conference of Governmental Industrial Hygienists (ACGIH) has recently published a guide for the assessment of bioaerosols in an indoor environment that can be used as a starting point. For those chemicals that do not have a reference value, it is acceptable (ASHRAE 62 [5, 6]) that a concentration of 1/10 TLV does not produce a significant increase in the number of complaints from members of a group of industrial workers. Table 2.3 lists the maximum concentrations of pollutants that may be present in an outdoor air and also a minimum that can be used for ventilation in an enclosed building. Information for common air pollutant internal maximum exposure limits of the Occupational Safety and Health administration (OSHA) and the ACGIH in an industrial environment is included in Table 2.4.
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Table 2.3 Reference values of external air quality by US EPA, Environmental Protection Agency Contaminant Long exposure Short exposure
SO2 CO2
Mean concentration lg/m3 ppm 80 0.03 – –
Time 1 year –
N2 O3 Pb Particulates Radon
100 – 1.5 75 0.2
1 year – 3 months 1 year Pico curies/l
0.053 – – –
Mean concentration lg/m3 ppm 365 0.14 40,000 35 10,000 9 – – 235 0.12 – – 260 –
Time (h) 24 1 8 – 1 – 24
Table 2.4 Reference values and concentrations recommended for some industrial pollutants by OSHA and ACGIH Contaminant Concentration Exposure time Origin Asbestos SO2
CO2
NO2 Formaldehydes CO
O3 Pb
0.2–2.0 libres/cm3 2 ppm 5 ppm 10,000 ppm 5,000 ppm 30,000 ppm 1 ppm 3 ppm 5 ppm 1 ppm 2 ppm 35 ppm 200 ppm 50 ppm 400 ppm 0.1 ppm 0.2 ppm 0.005 mg/m3 0.15 mg/m3
8h 8h 15 min 8h 8h 15 min 15 min 8h 15 min 8h 15 min 8h 15 min 8h 15 min 8h 15 min 8h 8h
TLV-TWA PEL-TWA PEL-STEL PEL-TWA TLV-TWA PEL-STEL PEL-STEL TLV-TWA PEL-STEL PEL-TWA TLV-STEL PEL-TWA PEL-TECHO TLV-TWA TLV-STEL PEL-TWA PEL-STEL PEL-TWA TLV-TWA
PEL permissible exposure limit; TLV threshold limit value; TWA time weighted average; STEL short term exposure limit
As seen in the previous sections, the actual standards related to IAQ are numbered (Table 2.5). To control IAQ, different standards were developed. The first standards show the vocabulary and units for the measuring process, as described in ISO 4225 and 4226. Once the basic concepts are developed, the design process is described in standards 16813 and 16814.
2.1 Indoor Air Standards
21
After the design process, the measuring process in real indoor environments must be developed, as described in standards ISO 16000-1, 16000-5 and 16000-8. We also need information on how to measure temperature, relative humidity and how to relate it with mould and its corresponding health effects, as described in standards ISO 8756, ISO 16000-16 and 7708. As in general and local thermal comfort, the contents of ISO standards are reflected in the corresponding ASHRAE standard. However, it is the ANSI/ ASHRAE 62.2-2004 that shows these contents. The NTP describes the same contents as the ISO standards and adapt these to the particular conditions of working environments. In this standard, we find the general concepts shown in NTP 243 and different procedures for the characterisation of IAQ 431. Within this characterisation, we find the detection of fungi and microbiological hazards, as described in standards NTP 488, 299, 335 and 313, among others. On the other hand, to evaluate the exchange of indoor environments, we have standards NTP 549 and 345. Finally, to detect and control sick building syndrome (SBS), new standards were developed as NTP 288, 289, 290 and 380; these standards show the new criteria to develop future ventilation standards, as described in NTP 343 (Table 2.5).
2.2 Models 2.2.1 Introduction According to ISO 7730 standard [1], thermal comfort is defined as the mental condition that expresses satisfaction with the surrounding environment. Even though it is easy to understand, it is at the same time difficult to define by equations. Equations of thermal comfort can be divided into local and general thermal comfort. General thermal comfort aims to define the mean parameters that a thermal environment must depict, so that a maximum number of persons experience a neutral thermal sensation. The neutral thermal sensation is related to the fact that there are no hot or cold sensors activated, and hence that there is no heat release, or received from internal or external heat sources. In this regard, when defining general thermal comfort we must consider that, under the same weather conditions, there are other hot and cold sources that must be considered for different indoor ambiences. Hence, we must define thermal comfort for each specific indoor condition.
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Table 2.5 Indoor air quality standards ISO standard ISO 4225:1994 Air quality—general aspects—vocabulary ISO 4226:2007 Air quality—general aspects—units of measurement ISO 16813:2006 Building environment design—indoor environment—general principles ISO 16814:2008 Building environment design—indoor air quality—methods of expressing the quality of indoor air for human occupancy ISO 16000-1:2004 Indoor air Part 1: general aspects of sampling strategy ISO 16000-5:2007 Indoor air Part 5: sampling strategy for volatile organic compounds (VOCs) ISO 16000-8:2007 Indoor air Part 8: determination of local mean ages of air in buildings for characterizing ventilation conditions ISO 8756:1994 Air quality—handling of temperature, pressure and humidity data ISO 16000-16:2008 Indoor air Part 16: detection and enumeration of moulds— Sampling by filtration ISO 7708:1995 Air quality—particle size fraction definitions for health-related sampling ASHRAE ANSI/ASHRAE 62.1-2004 Ventilation for acceptable indoor air quality Standard ANSI/ASHRAE 62.2-2004 Ventilation and acceptable indoor air quality in low-rise residential buildings NTP [3] NTP 243: Indoor air quality NTP 431: Characterisation of indoor air quality NTP 488: Indoor air quality: identification of fungi NTP 299: Method for airborne bacteria and fungi counting NTP 335: Indoor air quality: pollen grains and fungi spores evaluation NTP 313: Indoor air quality: microbiological hazards in air conditioning and ventilation systems NTP 347: Chemical contamination: Air concentration assessment NTP 315: Air quality: indoor low concentration gases NTP 521: Indoor air quality: emissions from building materials and cleaning products NTP 549: Carbon dioxide in evaluating indoor air quality NTP 345: Ventilation assessment using tracer gases NTP 289: Sick-building syndrome: risk factors NTP 288: Sick-building syndrome and building related diseases: bioareosol involvement NTP 290: Sick-building syndrome: questionnaire for its detection NTP 380: Sick-building syndrome: simplified questionnaire NTP 343: New criterion for future indoors ventilation standards
2.2.2 Moist Air Models This model is described by ASHRAE [6] and subsequently implemented by Simonson [7] to determine the influence of coatings on indoor environments. The basic equations that relate temperature and relative humidity are well known and are based on the working assumptions of the moist air model. They
2.2 Models
23
consider that air is a mixture of ideal gases that can be defined as dry air and water vapour. This water vapour presents an enthalpy equal to the enthalpy of saturated steam at the same temperature, for temperatures ranging from -10 to 50C. The relative humidity is defined after the relationship between partial pressure of water vapour in the air (Pv) and partial pressure of water vapour in the saturated air (Pvsat). Therefore, relative humidity is expressed as: RH ¼
PV PVSAT
ð2:2:2:1Þ
Furthermore, the partial water vapour pressure in the saturated condition is a function of temperature (T): Pvsat ¼ f ðT Þ ¼ eF
ð2:2:2:2Þ
where the value of F is defined by ASHRAE: 27 K\T\273 K
F¼
273 K\T\473 K
C1 þ C2 þ C3 T þ C4 T2 þ C5 T3 þ C6 T4 þ C7 ln T T
F¼
C8 þ C9 þ C10 T þ C11 T 2 þ C12 T 3 þ C13 ln T T
The values of the constants are: C1 ¼ 5674:5359C2 ¼ 6:3925247 C3 ¼ 9:677843 103 C4 ¼ 6:22115701 107 C5 ¼ 2:0747825 109 C7 ¼ 4:1635019 C8 ¼ 5800:2206 C11 ¼ 4:1764768 105 C10 ¼ 4:8640239 102 C13 ¼ 6:5459673
C6 ¼ 9:484024 1013 C9 ¼ 1:3914993 C12 ¼ 1:4452093 108
This equation shows that when temperature drops, the pressure of saturated vapour also drops. Another variable widely used is absolute humidity (w), defined as the ratio between vapour and dry air mass. Absolute humidity is calculated from the relationship between the partial pressure of water vapour and of the air (Pa): w ¼ 0:62198
pv pa
ð2:2:2:3Þ
This equation describes the relationship between temperature, humidity ratio and relative humidity, which can be graphically expressed through a psychrometric chart. Finally, the ideal gas law shows that moist air enthalpy (h) represents the sum of the energy of its components (dry air and water vapour). If the temperature and humidity increase, the enthalpy of the air increases (see also Fig. 2.2): h ¼ cpa t þ wðcpw t þ Lo Þ
ð2:2:2:4Þ
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2 Indoor Air Standards and Models
Fig. 2.2 Psychometric chart
This equation has been used in a simplified form by Simonson, expressed as h ¼ t þ wð2501:6 þ 1:865tÞ
ð2:2:2:5Þ
2.2.3 General Thermal Comfort Models Once the concept of general thermal comfort is defined, it is the moment to define its two main indices: PMV and PPD. PMV provides information about the thermal sensation of occupants in an indoor environment, experienced on a scale of seven points. This scale goes from minus three to plus three, passing through zero, which represents the neutral thermal sensation. PPD is related to the PMV model, owing to the fact that there are differences in the perception of thermal comfort between persons. At the same time, it is related to individual habits, such as food, different clothes and styles and, in general, all the differences between individuals in their daily lives. As a consequence of the earlier observation, PPD depicts a scale that goes from 0 to 100 in accordance with ASHRAE [8] and ISO 7730 [1] standards. The relationship between PPD and PMV is related to the fact that, for a PMV value of ±0.85, we find a PPD value of 20% (Fig. 2.3). Owing to these values, more standards define a thermal comfort limit of ±0.5 of PMV associated with a PPD of 10% (Table 2.6). In Table 2.6, we see that there are three thermal environment classes: A, B and C. Finally, the general thermal comfort model depicts, in accordance with ASHRAE [8], an air velocity limit of 0.2 m/s. If air velocity is higher than this value, different values of general thermal comfort indices are expected, due to the higher ability to release heat.
2.2 Models
25
Fig. 2.3 Evolution of PPD on the basis of PMV
100 90 80 70
PPD
60 50 40 30 20 10 0
-3
-2.5 -2 -1.5 -1
-0.5
0
0.5
1
1.5
2
2.5
3
PMV
Table 2.6 Predicted percentage of dissatisfied based on the predicted mean vote
Comfort
PPD
PMV range
A B C
\6 \10 \15
-0.2 \ PMV \ 0.2 -0.5 \ PMV \ 0.5 -0.7 \ PMV \ 0.7
On the other hand, under this air velocity limit, local thermal comfort must be estimated and new parameters, such as the occupants’ adaptability after some minutes in an indoor environment, must be considered. To define general thermal comfort, one needs to estimate metabolic rate and clothes’ insulation. The metabolic rate (met) is defined as the amount of energy released as a function of the level of muscular activity. It is defined as 58.15 W/m2 of body surface, in accordance with the values reflected in the ISO standard. The clo index is employed to quantify clothes’ insulation. One clo is equal to 155 m2C/W. For example, a naked person shows a clo value of zero and a person wearing typical street clothes depicts a clo value equal to one.
2.2.3.1 Sampled Parameters To define thermal comfort in an indoor environment, the clo and the met values should be defined, as observed earlier. Once these parameters are estimated, more parameters must be considered, such as mean radiant temperature, operative temperature, relative humidity and air velocity. Mean radiant temperature (tr ) is defined as the uniform temperature in an imaginary black enclosure in which a person experiences the same loss by radiation than in the real situation. The mean radiant temperature is that temperature contained in the walls and air of the compound room, which experiences the same heat transfer to the
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2 Indoor Air Standards and Models
Fig. 2.4 Mean radiant temperature t1
t4 R
R´
tr
t2
t3 Actual room
Imaginary room
atmosphere by convection and radiation; in the case when such temperatures are different, it is a real environment defined by ASHRAE. This standard proposed different calculation methods of the operative temperature. Some methods employ the equation that defines the sensitive heat loss from the body, per unit of time and surface (Eq. 2.2.3.1.1). In other cases, this parameter can be defined as the arithmetical mean of the mean radiant temperature and air temperature (Eq. 2.2.3.1.2). This simplified equation can be only employed under the following conditions: a metabolic rate from 1.0 to 1.3 met, air velocities not higher than 0.20 m/s and no direct sunlight exposition. to ¼
ðhrtr þ hc ta Þ ðhr þ hc Þ
ð2:2:3:1:1Þ
where ta, tr and to are the air, mean radiant and operative temperatures. to ¼
ðtr þ ta Þ 2
ð2:2:3:1:2Þ
The importance of operative temperature is based on the fact that it allows to define thermal comfort zones for some previously defined values of relative humidity, air speed, metabolic rate and clothes’ insulation. This zone is represented in Fig. 2.4 for air velocities no greater than 0.2 m/s. Furthermore, two zones are typically represented for 0.5 and 1.0 clo, which are related to the hot and cold seasons. These two ranges are represented by Eqs. 2.2.3.1.3 and 2.2.3.1.4. Top min;Icl ¼ ½ðIcl 0:5cloÞTmin;1:0clo þ ð1:0clo Icl ÞTmin;0:5clo =0:5clo ð2:2:3:1:3Þ Top max;Icl ¼ ½ðIcl 0:5cloÞTmax;1:0clo þ ð1:0clo Icl ÞTmax;0:5clo =0:5clo ð2:2:3:1:4Þ where Tmax;Icl is the upper operative temperature limit for clothing insulation Icl, Tmin;Icl is the lower operative temperature limit for clothing insulation Icl and Icl is the thermal insulation of the clothing in question (clo). Recent research has revealed that, to evaluate an indoor environment, great importance must be given to relative humidity. Relative humidity is related to
2.2 Models
27
Fig. 2.5 Comfort zone
0,050
Humidity Ratio (g/kg.)
0,045 0,040 0,8
0,035 0,030
0,6
0,025 0,020
0,4
0,015 0,010
0,2
0,005 0,000
PMV=+0,5
0
5
10
15
20
25
30
35
40
Top. (°C)
thermal comfort (ASHRAE [8, 9]; Wargocki in 1999 [10]), perception of IAQ [11], health of occupants [12] and energy consumption [7], as we can see in Fig. 2.5. Although it is well known that there is a relation between thermal comfort and air velocity, it has not been reflected by in depth studies. To consider this effect, ASHRAE depicts curves of higher temperature supported with different indoor air velocities. An analysis of the individual thermal balance will now be presented. Thermal comfort is based on the thermal balance of the full body and empirical equations, in accordance with ISO 7730 [1] and two conditions: (1) The neutral thermal sensation on the skin temperature and full body temperature. (2) The heat produced by metabolism must be the same as the heat lost to the atmosphere under steady state (Eqs. 2.2.3.1.5, 2.2.3.1.6). M W ¼ qsk þ qres þ S
ð2:2:3:1:5Þ
M W ¼ ðC þ R þ Esk Þ þ ðCres þ Eres Þ þ ðSsk þ Scr Þ
ð2:2:3:1:6Þ
where M is the rate of metabolic heat production (W/m2), W is the rate of mechanical work accomplished (W/m2), qsk is the total rate of heat loss from the skin (W/m2) qres is the total rate of heat loss through respiration (W/m2), C ? R is the sensible heat loss from the skin (W/m2), Cres is the rate of convective heat loss from respiration (W/m2), Eres is the rate of evaporative heat loss from respiration (W/m2), Ssk is the rate of heat storage in the skin compartment (W/m2), Scr is the rate of heat storage in the core compartment (W/m2). The heat storage in the body is defined by Eqs. 2.2.3.1.7 and 2.2.3.1.8:
28
2 Indoor Air Standards and Models
Scr ¼
ð1 ask Þmcp;b dtcr AD dh
ð2:2:3:1:7Þ
ask mcp;b dtsk AD dh
ð2:2:3:1:8Þ
Ssk ¼ where
ask is the fraction of body mass concentrated in the skin, m is the body mass (kg), cp,b is the specific heat capacity of the body (kJ/kgK), AD is the DuBois surface area (m2), tcr is the temperature of the core node (8C), tsk is the temperature of the skin node (8C), h is the time (s). To calculate thermal comfort indices, the following equations are typically used: ð2:2:3:1:9Þ PMV ¼ 0:303 e0:036M þ 0:028 L PPD ¼ 100 95 eð0:03353 PMV
4
þ0:2179 PMV2 Þ
ð2:2:3:1:10Þ
L is the thermal load on the body; L is the difference between the internal heat production and heat loss to the actual environment. Another parameter to be considered is the evaporative heat loss. This term depends on the amount of moisture on the skin and the difference between water pressure on the skin and in the environment (Eq. 2.2.3.1.11). Esk ¼
wðpsk pa Þ Re þ 1=ðfcl he Þ
ð2:2:3:1:11Þ
where w is the wet skin area (m2), psk is the water vapour pressure on the skin (kPa), Pa is the water vapour pressure in the environment (kPa), Re is the evaporative heat transfer resistance on a layer of clothing (m2 kPa)/W and he is the evaporative heat transfer coefficient (W/m2 kPa), fcl is the fraction of body area covered with clothing. The exchange of heat by respiratory convection and evaporative heat is given by: Cres ¼ 0:0014 M ð34 tÞ Eres ¼ 1:72 105 M ð5867 Pv Þ
ð2:2:3:1:12Þ ð2:2:3:1:13Þ
2.2 Models
29
Table 2.7 Methods to calculate general thermal comfort indices Method 1 Air velocity (va) Air temperature (ta) Mean radiant temperature ðtr Þ Measure Measure Calculate Method 2 Air velocity (va) Operative temperature (to) Measure Measure Method 3 Equivalent temperature (teq) Measure Method 4 Air velocity (va) Effective temperature (ET) Measure Calculate
CþR¼
ðtsk to Þ Rcl þ 1=ðfcl hÞ
Humidity (w) Measure Humidity (w) Measure Humidity (w) Measure
ð2:2:3:1:14Þ
where fcl is the clothing area factor, Rcl is the thermal resistance of clothing (m2 K)/W, tsk is the temperature of the skin (8C), h is the sum of convective and radiative heat transfer coefficients (W/m2 K). Equation 2.2.3.1.14 expresses the sensible heat loads from the skin. The thermal comfort equation can be employed to define thermal comfort in an indoor environment. However, studies show that it is too complicated to be solved with manual procedures. Furthermore, to solve this equation we must consider the need to measure indoor air temperature, relative humidity and air velocity or related parameters (Table 2.7). In Table 2.7, we find a new parameter called equivalent temperature (teq). This teq is defined as the uniform temperature of a radiant black enclosure with zero air velocity, in which an occupant has the same dry heat loss than in the actual nonuniform environment.
2.2.3.2 Alternative General Thermal Comfort Models As reflected in ASHRAE, there is a certain disagreement between the PMV model and the thermal sensation, as demonstrated by De Dear [13] and Brager and De Dear [14]. This difference, when defined with the neutral temperature, is about 1.4C. The reason is related to the fact that thermal sensation is obtained from the survey of individuals located in an environment and the PMV method only employs heat and mass transfer. PMV ¼ at þ bpv c
ð2:2:3:2:1Þ
30
2 Indoor Air Standards and Models
Table 2.8 The coefficients a, b and c are a function of spent time and the sex of the subject Time/sex a b c 1 h/man Woman Both 2 h/man Woman Both 3 h/man Woman Both
0.220 0.272 0.245 0.221 0.283 0.252 0.212 0.275 0.243
0.233 0.248 0.248 0.270 0.210 0.240 0.293 0.255 0.278
5.673 7.245 6.475 6.024 7.694 6.859 5.949 8.620 6.802
Specifically, the error in defining the neutral temperature was related to a PMV problem in defining the metabolic rate and the clo value or taking into account the insulation of the seat. Under ASHRAE contract, the Institute for Environmental Research at the Kansas State University developed a research work to define thermal comfort in sedentary regime. The main objective of this research was to define a model that expressed PMV in terms of easily measurable parameters. An investigation to 1,600 students revealed a statistical correlation between the level of comfort, temperature, humidity and exposure duration. The research was developed in groups of five men and five women, with a temperature range between 15.6 and 36.7C and relative humidity between 15 and 85%. Furthermore, the experiments were developed under air speeds below 0.17 m/s. Finally, the adaptation to a thermal environment was considered to happen when the thermal sensation was repeated every half an hour for 3 h. The result, described in Eq. 2.2.3.2.1, reveals the dependence on three constants, a, b and c, for different periods of exposure (Table 2.8). From these studies, a comfort zone close to 26C and 50% was found. However, these two indices employ a scale of seven points that goes from minus three to plus three, with zero as the neutral thermal sensation (Table 2.9). From these surveys, different regressions could be developed and, consequently, the models obtained depend on the parameters selected to develop the regression—for example, Eq. 2.2.3.2.2 [15]. Tsens ¼ 0:305 T þ 0:996 clo 8:08
ð2:2:3:2:2Þ
Another option to define general thermal comfort is the operative temperature model. Operative temperature at different activity levels can be defined as a function of operative temperature at sedentary conditions (Eq. 2.2.3.2.3.1). Equation 2.2.3.2.3 can be employed between 1.2 and 3 met and for a minimum operative temperature of 15C. toac ¼ tosed 3ð1 þ cloÞðmet 1:2Þ
ð2:2:3:2:3Þ
2.2 Models
31
Table 2.9 Thermal sensation values
Tsens
Thermal sensation
3 2 1 0 -1 -2 -3
Warm Heat Slightly hot Neutral Slightly fresh Freshness Cold
Furthermore, there are two equations for winter and summer: Terms of summer : tosed ¼ 24:5 1:6 C Terms of winter : tosed ¼ 21:8 1:8 C
ð2:2:3:2:4Þ ð2:2:3:2:5Þ
These models, applied to define general thermal comfort, are adaptive. Recent research revealed that adaptive models can be employed to define the neutral thermal conditions in indoor environments as a function of outside weather. These models can be employed only when their occupants are in near sedentary activity level (1–1.3 met) and must be able to freely adapt their clothing. However, a mechanical cooling or heating system can work only on a condition that there must not be any working mechanical ventilation system. Accordingly, windows are the main way to control thermal conditions. In this regard, Eq. 2.2.3.2.6 was proposed by Nicol and Roaf [16] for naturally ventilated buildings. The other two models proposed by Humphrey [17] were Eqs. 2.2.3.2.7 and 2.2.3.2.8, and three more by Auliciems and de Dear [13] (Eqs. 2.2.3.2.9, 2.2.3.2.10). Finally, ASHRAE proposed Eq. 2.2.3.2.12 as the model to define neutral temperature conditions. Tn;o ¼ 17 þ 0:38To
ð2:2:3:2:6Þ
Tn;1 ¼ 2:6 þ 0:831Ti
ð2:2:3:2:7Þ
Tn;o ¼ 11:9 þ 0:534To
ð2:2:3:2:8Þ
Tn;i ¼ 5:41 þ 0:731Ti
ð2:2:3:2:9Þ
Tn;o ¼ 17:6 þ 0:31To
ð2:2:3:2:10Þ
Tn; i; o ¼ 9:22 þ 0:48Ti þ 0:14To
ð2:2:3:2:11Þ
Tc ¼ 17:8 þ 0:31To
ð2:2:3:2:12Þ
ASHRAE:
32
2 Indoor Air Standards and Models 0.5 0.45
Mean air velocity (m/s)
Fig. 2.6 Average air velocity depending on temperature and degree of turbulence of thermal environments for a percentage of dissatisfied persons below 20%
0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05
Tu=0
Tu=10
Tu=20
Tu=80
0 18
20
22
24
26
28
30
Air Temperature (°C)
where Tc is the comfort temperature, To is the outdoor air temperature, Ti is the mean indoor air temperature, Tn,i is the neutral temperature based on mean indoor air temperature and Tn,o is the neutral temperature based on mean outdoor air temperature.
2.2.4 Local Thermal Comfort Models Once the mean conditions that express general thermal comfort are defined, the variables that define thermal comfort in special body zones must be considered: local thermal comfort conditions.
2.2.4.1 Air Velocity Models The percentage of PD owing to draft is obtained from Eq. 2.2.4.1.1 that is based on a study of 150 subjects. This experiment was developed within a temperature range of 20–26C, speed ranges between 0.05 and 0.4 m/s and turbulence intensities from 0 to 70%. The draft risk (DR) is given by: DR ¼ ð34 tÞðv 0:05Þ0:62 ð0:37vTu þ 3:14Þ
ð2:2:4:1:1Þ
From this equation, Fig. 2.6 can be obtained. The figure shows the draft risk of 15% of PD for different indoor air temperature levels and turbulence levels.
2.2.4.2 Asymmetric Thermal Radiation The effect of asymmetric thermal radiation is now considered. This effect can be obtained by two methods: the first is to measure in two opposite directions, employing a transducer to capture radiation that affects a small plane from the corresponding hemisphere.
2.2 Models
33
Fig. 2.7 Percentage of PD as a function of asymmetrical radiant temperature, produced by a roof or wall, cold or hot PD
100
10
1 5
10
15
20
25
30
Asymmetrical Radiant Temperature (°C) Hot Ceiling
Cold Wall
Cold Ceiling
Hot Wall
The second method consists in measuring the temperature of the surrounding surface in indoor environments, and calculate the increment of radiant temperature Dtpr . Equations 2.2.4.2.1–2.2.4.2.4 show the percentage of PD due to hot and cold ceiling, and hot and cold walls. (a) Hot ceiling Dtpr \23 C 100 5:5 1 þ expð2:84 0:174 Dtpr Þ (b) Cold wall Dtpr \15 C PD ¼
100 1 þ expð6:61 0:345 Dtpr Þ (c) Cold ceiling Dtpr \15 C PD ¼
PD ¼
100 1 þ expð9:93 0:50 Dtpr Þ
ð2:2:4:2:1Þ
ð2:2:4:2:2Þ
ð2:2:4:2:3Þ
(d) Hot wall Dtpr \35 C PD ¼
100 3:5 1 þ expð3:72 0:052 Dtpr Þ
ð2:2:4:2:4Þ
Finally, the curves obtained are shown in Fig. 2.7.
2.2.4.3 Vertical Temperature Differences Recent research works (Eq. 2.2.4.3.1) reflect the general percentage of PD with vertical temperature differences. For different increments of temperature, we find different percentages of PD (Fig. 2.8).
34
2 Indoor Air Standards and Models 100
PD
Fig. 2.8 Percentage of PD, depending on the vertical temperature difference
10
1
0
1
2
3
4
5
6
7
8
9
10
Vertical Temperature Difference (ºC)
PD ¼
100 1 þ expð5:76 0:856 DtÞ
ð2:2:4:3:1Þ
where Dt is the vertical temperature difference (8C).
2.2.4.4 Soil Temperature Standards show the percentage of PD in accordance with Eq. 2.2.4.4.1 and Fig. 2.9. PD ¼ 100 94 expð1:387 þ 0:118 tf 0:0025 tf2 Þ
ð2:2:4:4:1Þ
where tf is the floor temperature.
2.2.4.5 Percentage of Dissatisfied Persons with Local Thermal Comfort New models can define local thermal comfort, thermal sensation and perception of IAQ as a function of moist air temperature and relative humidity, under special conditions and considerations. The effect of relative humidity, under local thermal comfort, was investigated by Toftum et al. (1998) [18]. In this research, 38 individuals were provided with clean air between 20 and 29C and relative humidity between 45 and 70%. The result of the percentage of PD is defined in Eq. 2.2.4.5.1. From this equation, we conclude that the percentage of PD tends to decrease with temperature. In accordance with the ASHRAE standard [5, 7, and 20], this percentage of PD must be below 15%. PD ¼
100 1 þ eð3:58þ0:18ð30tÞþ0:14ð42:50:01pv Þ
ð2:2:4:5:1Þ
These indices and models, used by different researchers in recent works, for example, Simonson et al. [7], were employed to define the effect of internal
2.2 Models
35
Fig. 2.9 Percentage of PD, depending on floor temperature
PD
100
10
1 5
10
15
20
25
30
35
40
Floor Temperature (°C)
coverings on indoor thermal comfort and the perception of IAQ, and also to define better indoor conditions to reduce energy consumption.
2.3 Indoor Air Quality Models 2.3.1 Percentage of Dissatisfied with Indoor Air Quality Fang et al. [20] obtained the index, appearing in Eq. 2.3.1.1, from the sensory response of subjects exposed to different combinations of temperature and relative humidity. The validity range of temperature and relative humidity is from 18 to 28C and relative humidity from 30 to 70%. PDIAQ ¼
expð0:18 5:28 AccÞ 100 1 þ expð0:18 5:28 AccÞ
ð2:3:1:1Þ
PDIAQ is a function of another index Acc (Eq. 2.3.1.2), with a scale ranging from +1 (clearly acceptable) to -1 (clearly unacceptable). AccIAQ ¼ 0:033 h þ 1:662
ð2:3:1:2Þ
where h is the indoor air enthalpy in kJ/kg.
2.4 Indoor Air Renovation Models The largest number of complaints about air quality inside a building is within the area of thermal comfort and ventilation. According to the National Institute for Occupational Safety and Health, in more than 50% of the buildings studied, problems are caused by inadequate ventilation. Thermal comfort is based on a balance between physical activities and clothing on one hand, and relative humidity, temperature, air velocity and radiant temperature on the other hand. ASHRAE have developed standards for confined spaces, which would ensure the comfort level of 90% of the population. In general,
36
2 Indoor Air Standards and Models
the acceptable range of values is relatively narrow, given the relationship that exists between variables. A slight increase in air velocity, for example, can trigger a series of complaints while the temperature remains within acceptable limits. Similarly, when ventilation is incorrect, as a result of an insufficient supply of fresh air from outside, there may be a source of accumulation of various pollutants to levels that can annoy the occupants. The contribution of outside air should be sufficient enough to dilute the pollutants to levels that are below human perception, and those obviously considered harmful to health. References since the middle of the eighteenth century recommended a minimum input of fresh air per person, to dilute the concentration of human and bioeffluents to avoid inconveniences due to bad odours. For 70 years, ASHRAE published several articles recommending an injection of fresh air of at least 34 m3/h per person to prevent odours and an absolute minimum of 8.5 m3/h per person to maintain the concentration of carbon dioxide below 2,500 ppm, which is half the average permissible exposure in a work environment. Recently, ASHRAE Standard 62-1989 recommended a minimum of 25.5 m3/h per person for classrooms, 34 m3/h for offices and 42.5 m3/h for hospitals (diseased area). This standard also recommends an increase in the volume when there are problems in the mixture of air breathing zones or unusual sources of pollution. On the other hand, we must remember that the primary purpose of an air conditioning system in an office building is to provide a good level of comfort. Currently, there is a lot of interest in determining the ventilation rate in indoor environments. In this regard, there have been various models to determine the air change to link it with energy consumption (Cunningham [15]). Research techniques using a tracer gas to detect flaws in the ventilation conditions are widespread. The tracer gas used in ventilation is often colourless, odourless and inert, and should normally not be present in the atmosphere. This section describes the different models and measuring procedures for air renovation in the indoor environments and their effects on local thermal comfort.
2.4.1 Techniques of Tracer Gas Monitoring The techniques of tracer gas are the only ones that allow multiple types of quantitative assessments of ventilation, which include measures of air infiltration and renovation, the efficiency of extraction systems (foul air and smoke), as well as the dispersal of pollutants. It can also measure air velocities in ventilation ducts. Another advantage of tracer gas techniques is the ability to perform actions in occupied environments. This is an effective and accurate method because it takes into account the large effect of occupation to assess the conditions for air renovation and the impact of opening and closing doors and windows. This will also take into account the enormous impact of occupation on the conditions for air renovation. The results of appropriate measures with tracer gas in a ventilation system will provide information on the amount of air introduced in each enclosure, the efficiency of heat recovery units, the amount of air taken from
2.4 Indoor Air Renovation Models
37
recycling, the ‘‘short circuit’’ effect of air related to outlet and inlet distribution. Both the planning stage and the lack of regular checks can cause a great increase in energy consumption and, in many buildings, one can see the effect of SBS by not taking these factors into account. The flow of air through a building or room can be assessed by one of the three tracer gas methods: concentration decay method, constant concentration method and constant emission method. These three methods are based on the continuity equation, as seen in Eq. 2.4.1.1, and in its possible simplifications. V
dC ¼ FðsÞ þ NðsÞ Coa N ðsÞ CðsÞ ds
ð2:4:1:1Þ
where V is the volume of the air in the room (m3), C is the tracer gas concentration in the room air (m3/m3), s is the time (h), F ðsÞ is the rate of introduction of tracer gas in the room (m3/h), Coa is the concentration of tracer gas in outdoor air (m3/m3) and N ðsÞ is the air flow through the room (m3/h): N ð sÞ ¼
F ðsÞ V dC ds C ðsÞ Coa
ð2:4:1:2Þ
The air change rate, or number of renovations, N, is estimated by dividing the flow rate through the compound by its volume, assuming that Coa = 0. (a) Concentration decay method The concentration decay method is the most basic method used to measure air flow during renovations of discrete short periods. In this method, a small amount of tracer gas is completely mixed with the air. To ensure uniformity of the tracer gas concentration at all points in the environment, fans are employed. After this, the tracer gas concentration drops, and the time needed to reach a concentration value close to zero must be measured. When there are no new issues of tracer gas and the air flow rate entry is constant, the concentration of tracer gas falls exponentially with time. Using the natural logarithm of gas concentrations over time, we get a line where the gradient is the rate of air change N (Eq. 2.4.1.3). If a line cannot be obtained, we must consider that the tracer gas does not mix well with the indoor environment and that the results are not valid. N¼
ln Cð0Þ ln Cðs1 Þ s1
ð2:4:1:3Þ
Finally, the equipment needed is a gas monitor, a tracer gas source and fan(s) to homogenise the mix.
38
2 Indoor Air Standards and Models
(b) Constant emission method The constant emission method is employed for long periods of continuous measurement of air renovation in simple areas. Tracer gas is emitted at a constant speed during the measuring period. Therefore, if the renovation and the concentration of tracer gas are constant, the number of air changes per hour, N, will be in accordance with: N¼
F V C
ð2:4:1:4Þ
The tracer gas concentration should be the same in all areas at any given moment. (c) Constant concentration method The constant concentration method is used to assess the ongoing renovation in occupied buildings. Using a gas monitor, tracer gas concentrations are measured in each zone. With this information, the dosage of tracer gas is controlled to maintain a constant concentration. A small fan can be used to facilitate the mixture. Air changes are also expressed in Eq. 2.4.1.4. The air renovation is proportional to the tracer gas emission required to maintain constant concentration. This approach has three advantages: • It allows to get the exact average speed of change during long periods and in situations where the air change varies over time. • It can be used to assess changes in specific areas. • It is particularly appropriate to evaluate the continuous outside air infiltration in each individual zone; the exchange of unwanted air between different parts of a building can also be assessed.
2.5 Building Simulation Models 2.5.1 EN ISO 13790 Models Recently, ISO 13790 proposed some calculation procedures to certify the energy consumption of buildings. Currently, real energy consumption of buildings, with different levels of permeability of internal coverings, is used to obtain the main constants of the general equation of energy certification of buildings, in accordance with EN ISO 13790. Particularly, the simplified monthly method of EN ISO 13790 standard will be presented. In this monthly method, the heat demand of the building QH is defined for each calculation period through: QH ¼ QL gQG
ð2:5:1:1Þ
2.5 Building Simulation Models
39
where QL is the heat loss of the building, g is the utilisation factor of heat gains and QG is the total heat gain. The annual heat demand is the sum of the heat demand over the entire year and it is positive: QHa ¼
12 X
QH;m
ð2:5:1:2Þ
m¼1
ISO 13790 standard gives expressions to determine the utilisation factor for heating: gG;H ¼
1 caHH 1 þ caHH
ð2:5:1:3Þ
gL;C ¼
1 caCC 1 þ caCC
ð2:5:1:4Þ
and for cooling :
The heat gain and loss ratios for heating and cooling periods, respectively: cH ¼
QG;H QL;H
ð2:5:1:5Þ
cC ¼
QL;C QG;C
ð2:5:1:6Þ
In accordance with [21], the utilisation factor represents the portion of gains (during the heating season) or of losses (during the cooling season) that contribute to the reduction in the heating demand (during the heating season) or cooling demand (during the cooling season). The non-utilised part of the gains (in winter) or the losses (in summer) depends on the dynamic mismatch between the gains and losses, which may cause overheating (above set-point temperature) in winter or under-cooling in summer. This utilisation factor depends on coefficients a0 and time constant [22]: a ¼ a0 þ
s s0
ð2:5:1:7Þ
a0 is a numerical parameter and s0 is the reference time constant that depends on the building category. For example, both for heating and cooling in office buildings, the following equation is proposed by the standard [23]: aH ¼ aC ¼ 0:8 þ
s 70
ð2:5:1:8Þ
Therefore, aH and aC are linearly correlated to the time constant of the building s (Eq. 2.5.1.8). The EN ISO 13790 standard gives default values for the parameters a0 and s0 . For example, for the monthly calculation of continuously heated buildings, the
40
2 Indoor Air Standards and Models
values are a0 ¼ 1 and s0 ¼ 15 h. The value of these parameters can also be provided at national level; therefore, the suitability of the default values of a0 and s0 was studied under Finnish conditions [24]. Other researchers [21] revealed that the differences between the buildings can be accounted by adding a parameter to Eq. 2.5.1.8. For example, Eq. 2.5.1.9 depicts a parameter that depends on the glazed area of the envelope, because highly glazed external envelopes yield a wide-ranging hourly profile of heat losses, leading to a decrease in the internal temperature below the cooling set-point, owing to the fact that this effect is not duly taken into account through the time constant of the building. aC ¼ 8 13n þ
s 17
ð2:5:1:9Þ
where n is the ratio between the glazed area of the envelope and conditioned floor area. It should be noted that, owing to the fact that this formulation was obtained through regional multiple-regression expressions, it is only suitable for Italian national buildings with medium heat capacity (147 kJ/m2K), a time constant of 28 h and Italian climatic conditions. Hence, it must be adapted to each climate. On the other hand, the time constant, referring to a mono-capacitor model of the building, is the time required for the internal minus external temperature difference to decrease in the absence of heat gains, considering a constant external temperature. This parameter, usually expressed in hours, quantifies the change in building internal temperature when submitted to a dynamic solicitation: s¼
C H
ð2:5:1:10Þ
where C is the internal heat capacity of the building and H is the total heat loss coefficient of the building caused by transmission and ventilation heat losses.
2.5.2 Heat, Air and Moisture Tools Simulation Models To solve the balance equations of room models, the balance equations were created from the individual Building Physics Toolbox [25, 26]. Heat, air and moisture (HAM) tools library is a Simulink model upgraded version of H-Tools with a similar structure, and specially constructed for thermal system analysis in building physics. The library contains blocks for 1D calculation of HAM transfer through the building envelope components and ventilated spaces. The library is a part of IBPTInternational Building Physics Toolbox and available for free downloading [27]. This library depicts two main blocks: a building envelope construction (walls and windows) and thermal zone (ventilated spaces), which are enclosed by the building envelope. Component models provide detailed calculations of the
2.5 Building Simulation Models
41
hydrothermal state of each subcomponent in the structure, according to the surrounding conditions to which it is exposed. In Fig. 2.10, we see the main blocks employed for a building simulation, in which we see a block that represents different exterior/interior walls, floor, roof and windows components. These constructions are defined with respect to their physical properties (density of dry material and open porosity), thermal properties (specific heat capacity of the dry material and thermal conductivity) and moisture properties (sorption isotherm, moisture capacity, water vapour permeability and liquid water conductivity), in accordance with the BESTEST structure. Other parameters, for example, internal gains (convective, radiative and moisture gains), air changes and heating/cooling system are considered in the heat and moisture building balance. The mathematical model employed in these simulations is the result of whole building HAM [28–30] balance, and depends on moisture generated from occupant activities, moisture input or removal through ventilation, and moisture transported and exchanged between indoor air and the envelope [31]. The mathematical model is based on the numerical resolution of the energy and moisture balance through the building. In accordance with the following equations [29], the heat flow depicts a conductive and a convective part: q ¼ qconductive þ qconvective qconductive ¼ k
oT ox
qconvective ¼ ma cpa T þ hevap:
ð2:5:2:1Þ ð2:5:2:2Þ ð2:5:2:3Þ
where k is the thermal conductivity (W/mK), T is the temperature (C), ma is the density of moisture flow rate of dry air (kg/m2s), cpa is the specific heat capacity of the dry air (J/kg K) and hevap: is the latent heat of evaporation (J/kg). The moisture flow transfer was separated in liquid and vapour phases (Eqs. 2.5.2.4, 2.5.2.5). ml ¼ K
oPsuc ox
ð2:5:2:4Þ
where ml is the density of moisture flow rate of vapour phase (kg/m2\,s), K is the hydraulic conductivity and Psuc is the suction pressure (Pa). The vapour phase was divided into diffusion and convection: mv ¼ dp
op þ m a xa ox
ð2:5:2:5Þ
where dp is the moisture permeability (s) and xa is the water vapour content (kg/kg). The mass airflow through the structure driven by air pressure differences is described by:
42
2 Indoor Air Standards and Models
Horiz Cat
Geometry
Construction
Constructions
S
Radiation
Zone
Construction
System
Construction
Selector
Geometry
Room temperature and RH
Room air / CTH WAVO model
Gains
Construction
0
-K-
R
Radiation
Zone
Clock
Days
ROOF
Zone
HEATING/COOLING SYSTEM S
Radiation
0
Radiation
3
Construction
Zone
EXTERIOR WALL 2
Room
Horiz Cat
Gains S
Radiation
Demux
Zone
2
Double-pane window Variable solar transmittance IEA Common Excercise
Zone
Geometry
BTweather BESTEST
1 Horiz Cat
Radiation
EXTERIOR WALL 1 Geometry
Geometry
Geometry
Zone
Zone
0
Construction Out1
EXTERIOR WALL 3
Radiation
Heat supply
F
Internal gains 0
FLOOR Geometry
Construction System
Zone
Zone Radiation
Ventilation system AIR IN
S
Internal gains1
Vert Cat
EXTERIOR WALL 4
System
simout To Workspace 4 In1
Zone
Ventilation system AIR OUT
Out1
Zone out
Fig. 2.10 MATLAB blocks for building simulations
ma ¼ ra qa
ð2:5:2:6Þ
where ra is the density of the air flow rate (m3/m2s) and qa is the density of the material (kg/m3). The final energy and moisture balance is revealed through:
o oT q ¼ c q0 ox ot
ð2:5:2:7Þ
d ow m¼ ox ot
ð2:5:2:8Þ
where qo is the density of the dry material (kg/m3), c is the specific heat capacity of the material (J/kg K), w is the moisture content mass by volume (kg/m3), t is the time (s) and x is the space coordinate (m). Finally, the numerical model, based on a control volume method, lumps the thermal capacity C in the middle of the total thickness d/2 and, consequently, the thermal resistances for one half are: R¼
d=2 k
ð2:5:2:9Þ
2.5 Building Simulation Models
43
Rp ¼
d=2 dp
ð2:5:2:10Þ
Rsuc ¼
d=2 Ksuc
ð2:5:2:11Þ
The obtained heat and moisture balance equations are: Tinþ1 1 ðTi1 Ti Þ ðTiþ1 Ti Þ ðpi1 pi Þ ðpiþ1 pi Þ þ þ ¼ n hevap ... Dt C Ri1 þ Ri Riþ1 þ Ri Rp;i1 Rp;i Rp;iþ1 Rp;i ma cpa ðTi1 Ti Þn ; ma [ 0 þ ma cpa ðT1 Tiþ1 Þn ; ma \0
ð2:5:2:12Þ
psuc;i1 pisuc;i psuc;iþ1 psuc;i wnþ1 wni 1 ðpi1 pi Þ ðpiþ1 pi Þ i þ þ ¼ ... Rsuc;i1 Rsuc;i Rsuc;iþ1 Rsuc;i d Rp;i1 þ Rp;i Rp;iþ1 þ Rp;i Dt n 6:21:106 ma ðpi1 pi Þ ; ma [ 0 þ 6:21:106 ma ðpi piþ1 Þn ; ma \0
ð2:5:2:13Þ where i is the objective node, i - 1 and i ? 1 are the preceding and following nodes and n and n + 1 the previous and corresponding time steps.
2.5.3 Building Time Constant Models As seen before, the time constant is normally found from a slow cooling period with a constant low outdoor temperature such as heat capacity/heat loss factor [32]. This method is based on a seasonal steady state energy balance on the building as a whole or on a particular building zone. The thermal inertia is introduced in terms of the utilisation factor that shows the part of energy gains (solar irradiation and others) that can be stored in the building materials to be transmitted into the zone when needed. The utilisation factor g is a function of the periodic time constant of the building and the ratio Qgain/Qloss. The time constant is defined in the standard, as it was showed earlier in Eqs. 2.5.1.1 and 2.5.1.10. As [32] recommended, when we want to work in a more precise way, the logarithm of the temperature difference of indoors/outdoors is taken and matched to a straight line by the method of least squares. The time constant is the inverse of the coefficient for the independent variable (time) given by this curve fit.
44
2 Indoor Air Standards and Models
2.5.4 Material Properties Models 2.5.4.1 Moisture Storage Capacity Real moisture storage capacity, when the indoor air relative humidity changes, is a new parameter that must be defined by researchers in order to obtain the adequate building materials’ behavior prediction. In this sense, different authors [7, 33] have developed new methods centred in Eq. 2.5.4.1.1. Cm ¼ ðu60% Rh u40% RH Þ
qV1000 20
ð2:5:4:1:1Þ
where; Cm is the moisture capacity (g/%RH), u is the moisture content (kg/kg), q is the density of the material (k/m3) and V is the volume of the material (m3). On the other hand, the moisture diffusivity can be calculated in an analogous manner to thermal diffusivity by Eq. 2.5.4.1.2. am ¼
kd Cm=ð1000VÞ 100=P
ð2:5:4:1:2Þ
where; Pvsat is the saturation pressure for water vapour at 22C(Pa), Kd is the water vapour permeability (kg/(s m Pa)), Cm is the moisture capacity (g/%RH) and am is the moisture diffusivity (m2/s). Finally, we must consider that the definition of moisture diffusivity from Eq. 2.5.4.1.3 neglects the moisture storage in the air within the porous material [35, 36]. This effect is negligible for most hygroscopic materials. am ¼
kd Cm=ð1000VÞ 100=Pvsat
ð2:5:4:1:3Þ
where; Pvsat is the saturation pressure for water vapour at 22C(Pa), kd is the water vapour permeability (kg/(s m Pa)), cm is the moisture capacity (g/%RH) and am is the moisture diffusivity (m2/s). Based on these methods, researchers showed that concrete moisture buffering influenced the indoor air conditions with a moisture capacity that is half of the wooden structures and more results will be showed in our case studies.
2.5 Building Simulation Models
45
2.5.4.2 Vapour-Driving Potential As a result of the vapour-driving potential, the second term of the model of Eq. 2.5.4.2.1 includes another moisture source of indoor air. The reasons to analyse indoor air based on the partial vapour pressure were explained by [37]. They concluded that the driving potential for vapour transport is the difference in vapour pressure on the surfaces (Eq. 2.5.4.2.2). They also concluded that as the moisture is transported through a finite volume in a medium, and the amount of moisture retained by the volume is altered during any transient stage of the transport process. The basic reason for this is a change in local temperature or vapour pressure. There is even doubt whether the vapour pressure is the driving force for diffusion through walls when there is no air pressure difference. In the building literature, it is often assumed that vapour pressure is the defining variable when discussing water movement in walls; but there is evidence that it is the humidity difference that drives diffusion through absorbent materials. For these reasons, vapour partial pressure has been used for determining an index that allows comparison of the effect of coverings by means of equations defined by ASHRAE. The uncertainty of the calculated vapour pressure was ±0.07 kPa. This index will be used to consider the excesses of the indoor versus outdoor partial vapour pressure, following Hens’ work [38]. V
dcin ¼ G þ M þ nVðcout cin Þ ds
ð2:5:4:2:1Þ
where; s is the time (s), V is the volume of the room, m3 N is the air ventilation rate, s-1 c is the water vapour content, kg/m3 G is the moisture generation rate, kg/s M is the sum of moisture quantities contributed by buildings components, kg/s. Jv ¼ l gradp where; l is the water vapour permeability of the medium (kg/(s m Pa)), J is the water vapour flux density (kg/m2 s) Gradp is the driving potential (Pa/m).
ð2:5:4:2:2Þ
46
2 Indoor Air Standards and Models
References 1. ISO 7730 (2005) Ergonomics of the thermal environment—analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria 2. ISO 7726 (2002) Ergonomics of the thermal environment—instruments for measuring physical quantities 3. Normativa Técnica de Prevención (NTP) (2011) Ministerio de Industria. http://www.insht.es. Accessed 2 Feb 2011 4. ASHRAE Standard 55-2004 (2004) Thermal environmental conditions for human occupancy 5. ASHRAE Handbook (2007) HVAC applications SI units American Society of Heating, Refrigerating and Air-Conditioning Engineers, Washington DC 6. ASHRAE Handbook Fundamentals (2005) SI units American Society of Heating, Refrigerating and Air-Conditioning Engineers, Washington DC 7. Simonson CJ, Salonvaara M, Ojanen T (2001) Improving indoor climate and comfort with wooden structures. Technical Research Centre of Finland, Espoo 8. ASHRAE Standard 55-2004 (2004) Thermal environmental conditions for human occupancy. SI Units American Society of Heating, Refrigerating and Air-Conditioning Engineers, Washington DC 9. Fanger PO (1970) Thermal comfort. Doctoral thesis. Danish Technical, Copenhagen 10. Wargocki P, Wyon DP, Baik YK, Clausen G, Fanger PO (1999) Perceived air quality, sick building syndrome (SBS) symptoms and productivity in an office with two different pollution loads. Indoor Air 9:165–179 11. Fang L, Clausen G, Fanger PO (1998) Impact of temperature and humidity on perception of indoor air quality during immediate and longer whole-body exposures. Indoor Air 8(4):276–284 12. Molina M (2000) Impacto de la temperatura y la humedad sobre la salud y el confort térmico, climatización de ambientes interiores (Tesis doctoral). Universidad de A Coruña, Spain 13. De Dear RJ, Auliciems A (1985) Validation of the predicted mean vote model of thermal comfort in six Australian field studies. ASHRAE Trans 91(2B):452–468 14. Brager GS, de Dear RJ (1998) Thermal adaptation in the built environment: a literature review. Energy Build 27:83–96 15. Berglund L (1978) Mathematical models for predicting the thermal comfort response of building occupants. ASHRAE Transactions, vol 84. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Washington DC 16. Nicol F, Roaf S (1996) Pioneering new indoor temperature standard: the Pakistan project. Energy Build 23:169–174 17. Humphreys MA (1976) Comfortable indoor temperatures related to the outdoor air temperature. Build Serv Eng 44:5–27 18. Toftum J, Jorgensen AS, Fanger PO (1998) Upper limits for indoor air humidity to avoid uncomfortably humid skin. Energy Build 2:1–13 19. ASHRAE Handbook (2008) HVAC system and equipment. SI Units American Society of Heating, Refrigerating and Air-Conditioning Engineers, Washington DC, USA 20. Fang L, Clausen G, Fanger PO (1996) The impact of temperature and humidity on perception and emission of indoor air pollutants. Proc Indoor Air 4:349–353 21. Corrado V, Mechri HE, Fabrizio E (2007) Building energy performance assessment through simplified models: application of the ISO 13790 quasi-steady state method. In: Proceedings of building simulation pp 79–86 22. Corrado V, Fabrizio E (2007) Assessment of buildings cooling energy need through a quasisteady state model: simplified correlation for gain-loss mismatch. Energy Build 39:569–579 23. ISO (2005) Thermal performance of buildings-calculation of energy use for space heating and cooling. ISO/DIS 13790:2005, International Organization for Standardization, Geneva
References
47
24. Jokisalo J, Kurnitski J (2007) Performance of EN ISO 13790 utilisation factor heat demand calculation method in a cold climate. Energy Build 39:236–247 25. Nielsen TR, Peuhkuri R, Weitzmann P, Gudum C (2002) Modelling building physics in Simulink. http://www.ibpt.org. Accessed 2 Feb 2011 26. Rode C, Gudum C, Weitzmann P, Peuhkuri R, Nielsen TR, Kalagasidis AS, Hagentoft C.E. (2002). International building physics toolbox-general report. Department of Building Physics. Chalmer Institute of Technology, Sweden. Report R-02: 2002. 4 27. International building physics toolbox in Simulink. http://www.ibpt.org. Accessed on 2 Feb 2011 28. Kalagasidis AS (2002) BFTools Building physics toolbox block documentation. Department of Building Physics. Chalmer Institute of Technology, Sweden 29. Kalagasidis AS (2002) HAM-Tools. International building physics toolbox. Block documentation. Department of Building Physics. Chalmer Institute of Technology, Sweden 30. Weitzmann P, Kalagasidis AS, Nielsen TR, Peuhkuri R, Hagentoft C (2003) Presentation of the international building physics toolbox for simulink. Eight international IBPSA conference, Netherlands, pp 1369–1376 31. International Energy Agency. http://www.iea.org. Accessed on 2 February 2011 32. Norén A, Akander J, Isfält E, Söderström O (1999). The effect of thermal inertia on energy requirement in a Swedish building—results obtained with three calculation models. Int J Low Energy Sustain Build 1:1–16 33. Yongling W, Ruilun Z, Zizhong D (2000) Primary research on moisture absorption and desorption function of aerocrete as building element material for dehumidification. In: Proceedings of healthy buildings p 3 34. Simonson CJ, Salonvaara M, Ojalen T (2001) Improving indoor climate and comfort with wooden structures. Espoo. Technical Research Centre of Finland, VTT Publications 431.200p. ? app 91 p 35. Olutimayin SO, Simonson CJ (2005) Measuring and modeling vapor boundary layer growth during transient diffusion heat and moisture transfer in cellulose insulation. Int J Heat Mass Transf 48:3319–3330 36. Talukdar P, Osanyintola OF, Olutimayin SO, Simonson CJ (2007) An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials, Part II: Experimental, numerical and analytical data. Int J Heat Mass Transf (in press). http://dx.doi.org/doi:10.1016/j.ijheatmasstransfer.2007.03.025. Accessed 2 Feb 2011 37. Trechsel HR (ed) (1994) Moisture control in buildings. American Society for Testing and Materials (ASTM manual series: MNL 18), Philadelphia 38. Hens H (2011) Indoor climate in student rooms: measured values. IEA-EXCO energy conservation in buildings and community systems annex 41 ‘‘Moist-Eng’’ Glasgow meeting. http://www.kuleuven.be/bwf/projects/annex41/protected/data/ KUL%20Oct%202004%20Paper%20A41-T3-B-04-6.pdf. Accessed 2 Feb 2011
Chapter 3
Real Indoor Environments
3.1 Indoor Environments It is well known that, in accordance with the International Energy Agency (IEA), 1/3 of energy from fossil fuels is employed in about equal parts in transport, industry and indoor ambiences. In this regard, people spend year after year more time in indoor environments and, hence, the energy consumption in an indoor environment keeps increasing. In the European Union energy consumption in buildings represents 40% of the total consumption. However, an increment in the number of air conditioned buildings is not always related with better indoor ambient conditions. This is related to the difficulties in controlling real indoor environments. Recent research tried to define better Heating, Ventilation and Air Conditioning (HVAC) control systems based on neural networks and important control techniques. The results, most of the times, tend to recommend the natural adaptation of workers in accordance to each environmental change. Consequently, more complex mechanical HVAC systems, to ensure thermal comfort and air quality, are in competition and hardly ever better than natural ventilation controlled by human activity, in accordance with the number and degree of opening windows. On the other hand, most of the times complex HVAC systems need expensive maintenance, due to a high cost in working time and materials. If passive methods only need an adequate consideration at the moment of building design and construction and a nearly null maintenance during the lifetime of the building, then mechanical HVAC systems are more expensive than passive methods to control an indoor environment. Considering the uncertainty of duration of fossil fuels, it is evident that the need for new methods is necessary to reduce energy consumption and harmful emissions to the atmosphere. Indoor air energy consumption, health effects and preservation of materials are a consequence of the occupants’ activities and habits. Thus, we classify indoor J. A. Orosa and A. C. Oliveira, Passive Methods as a Solution for Improving Indoor Environments, Green Energy and Technology, DOI: 10.1007/978-1-4471-2336-1_3, Ó Springer-Verlag London Limited 2012
49
50
3 Real Indoor Environments
Fig. 3.1 Local thermal comfort parameters
environments in accordance with these activities in industrial, public and domestic buildings (Fig. 3.1). Examples of these environments are ships, offices, schools and apartments, respectively. Ships, offices, schools and apartments depict different temperature and relative humidity limits and, hence, different control methods of thermodynamic properties of moist air that exist in the ambience. These indoor environments are presented in this chapter as an introduction to the future on indoor environments and the implementation of passive methods, such as the use of permeable coverings.
3.2 Temperature and Relative Humidity It is well known that temperature and relative humidity are intimately related with general and local thermal comfort in indoor environments. Most of the present control systems are based on the control of temperature and relative humidity with the aim of reducing energy consumption, but at the same time assuring thermal comfort, preservation of materials and health in accordance with present standards. Results reveal that the minimum energy consumption happens with minimum air flow rates and a moist air relative humidity below the upper thermal comfort limit. For example, when employing air renovation to control indoor relative humidity, the lowest energy consumption happens when hybrid systems, with a desiccant wheel, are employed. As observed earlier, in accordance with temperature and relative humidity conditions, indoor environments can be classified and analysed. Hence, field studies in these different environments are reported in the next few sections.
3.3 Equipments and Methods to Analyse Indoor Environments
51
3.3 Equipments and Methods to Analyse Indoor Environments 3.3.1 Measuring Devices for Indoor Environments The variable selected for most of this study was the excess of internal partial vapour pressure related to outdoors, because this parameter allows to take into account the real effect of coverings on indoor relative humidity, temperature, air renovation and, indirectly, on the percentage of dissatisfaction and perception of indoor air quality (IAQ). Tinytag Plus 2 dual channel data loggers with thermistors and capacitive sensors were installed to record temperature and relative humidity values with accuracies of ± 0.2°C and ± 3% RH, respectively. Ventilation rate was recorded with a Brüel and Kjaer multi-sampler, consisting of a photo-acoustic infrared detection microprocessor-controlled gas analyser and a multi-sampler for air with six sampling ports. The apparatus was equipped with an SF6 filter U0988, with an accuracy of ± 0.01 ppm, which was single-point calibrated using certified calibration gas with a concentration of 10 ppm. The same device allows to define the temperature and relative humidity in different zones at the same time and, hence, the heat and mass transfer related with indoor air in different building zones is understood.
3.3.2 Measurement Process and Standards The temperature and relative humidity measuring process was carried out in different buildings throughout the year, with a measurement frequency of 5–10 min, in accordance with the objective of the different studies. When relating indoor environment with different moisture sources, one needs to measure indoor temperature and relative humidity with a frequency of 5 min to define humidity release; the humidity source was the reason for short working periods, for example, cooking time, taking a shower and different cleaning periods. Once the moisture and temperature measuring frequency was defined, different measuring points were selected, in accordance with the objective of the study. For example, when trying to relate the effect of humidity release during cooking over other building zones, a new measuring process or device must be developed; for example, when trying to measure the effect of humidity release over other rooms and, at the same time, considering air changes in each zone. Another parameter to be considered at the time of measuring temperature and relative humidity is the margin of error. For our case studies, we had temperature and relative humidity accuracies of 0.2°C and ± 3%, respectively. Data were measured in two zones of office buildings: one, where the clients are usually located, known as the clients zone, and the other where the employers
52
3 Real Indoor Environments
work, called the workers zone. In apartments, each room was a measuring zone. Finally, in ships, normal and extreme indoor environments were detected, such as the engine room, engine control room and bridge. In school buildings, a measuring process was developed with data loggers located in the center of gravity of each room, in accordance with the International Organization for Standardization (ISO) standards. Particularly, heat sources, such as computers, were taken into account to obtain a representative value of indoor temperature and relative humidity conditions. Besides, special care was taken to obtain measurements near the centre of gravity of the occupants because the values of temperature and relative humidity are used to quantify local thermal comfort and IAQ indices. These indices were explained in the model section. Finally, as observed earlier, measurements were taken into account with the American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) [1] and ISO [2] recommendations, and were recorded in the occupied zones of the buildings at locations where the occupants are known or are expected to spend their time. In this regard, a sampling process was developed in workstations or seating areas, depending on the function developed or the available space. When impossible to measure occupancy distribution, measurements were taken in the centre of the room. Once measured data were obtained and recorded, different statistical analyses were carried out to define working periods. For example, one-way ANOVA analysis was conducted to define the working periods of internal coverings.
3.3.3 Statistical Analysis To define the working periods of coverings, some statistical analyses are needed. To define the effect of internal coverings on indoor conditions, an hourly one-way ANOVA analysis was conducted. This study allowed to define the similarity between groups of indoor ambient conditions and define if their behaviour can be considered equal or different. For example, it is the responsibility of indoor air temperature and relative humidity to define which groups of internal coverings depict the same working periods over indoor ambiences. To develop these tasks, an adequate variable was selected: the moist air partial vapour pressure because this variable expresses the combined effect of temperature and relative humidity. Once the measured variable was defined, the measuring frequency must be defined too. This frequency must be selected in accordance with the effect that will be analysed. The hourly mean values of partial vapour pressure excess were taken into account as main variable to define the working periods. Once the one-way ANOVA analysis was conducted, results must be interpreted in accordance with the significance index. The significance is the square of variance and variance is the mean value of the square deviations about the mean
3.3 Equipments and Methods to Analyse Indoor Environments
53
square (MS) value, or the sum of the squared deviations about the mean (SS) value divided by the degrees of freedom: s2 ¼ MS ¼
SS df
ð3:3:3:1Þ
If the critical level of significance is lower than 0.05, one must reject the hypothesis of the same mean value and conclude that not all mean values are the same and selected groups are clearly different. To determine if groups are well defined, one must apply another test, called the Duncan post hoc analysis. To obtain the significance value of the mean partial vapour pressure difference at each hour of the year, the ANOVA and the post hoc analyses were applied using SPSS software [3]. This statistical study was employed to define the effect of moisture sources in different rooms of apartments. In these case studies, more statistical methods are typically used. For example, multiple regression and least square methods are employed to define new models based on real measured data.
3.4 Apartments: Indoor Air and Health In these buildings, the typical health problems in humid regions of Northwestern Spain and Portugal are related to indoor temperature and high relative humidity. These variables were measured in accordance with the ASHRAE standard recommendations [4]. In this study, one occupant suffered or was suffering a respiratory ailment in the 25 selected apartments. To relate health problems with indoor conditions, some parameters, such as air renovation, microbiological load and indoor air temperature and relative humidity, were measured at the same time. The measuring procedure allowed to obtain the humidity ratio in different apartment rooms in a particular period of time, in accordance with habits of the occupants; for example, defining the humidity ratio in the kitchen during breakfast (07:00–10:00), during lunch (12:00–15:00) and during dinner (20:00–21:00). During these time periods, the main humidity sources are related to the humidity released during cooking. On the other hand, other periods related to humidity sources, such as human activity, must be considered. In this regard, sleeping time, recorded in bedrooms from 0:00 to 07:00, is a clear example of this humidity source. Results obtained are summarised in Table 3.1 This table shows mean indoor conditions obtained in 25 apartments, in accordance with the previous time periods. A first conclusion obtained from these results is that the humidity in the bedroom was the highest and that indoor air temperature was about 20°C, with an outdoor mean temperature of 12°C. This is the highest value reached in an indoor environment, if we do not consider the peak humidity in the kitchen during meal
54
3 Real Indoor Environments
Table 3.1 Mean indoor conditions in apartments Humidity ratio Outdoors Living room (kg/kg)
Bedroom
Children
Kitchen
Lunch Night Dinner Breakfast
7.91 8.25 8.17 8.23
7.8 8.08 8.03 7.92
8.02 7.89 7.94 7.94
7.10 7.00 7.06 7.00
7.76 7.84 7.89 7.80
Fig. 3.2 Humidity ratio during the night in the bedroom
40 35
Time (%)
30 25 20 15 10 5 0 3-4
4-5
5-6
6-7
7-8
8-9
9-10 10-11 11-12 12-13
Humidity ratio (g/kg)
Fig. 3.3 Humidity ratio during breakfast in the kitchen
40 35
Time (%)
30 25 20 15 10 5 0 1
2
3
4
5
6
7
8
9
10
Humidity ratio (g/kg)
times. In particular, during half the resting time, the humidity ratio experiences a value between 8 and 9 g/kg with an indoor air temperature of 20°C (Fig. 3.2). If the humidity ratio in the kitchen during breakfast, Fig. 3.3, lunch, Fig. 3.4, and dinner, Fig. 3.5, is taken into consideration, one can reach new conclusions. For example, the humidity ratio experiences a lower maximum value of 6 g/kg during a quarter of the measuring period during lunch. When comparing different humidity ratios at different eating times, one can conclude that during dinner a different distribution of humidity ratios is obtained: a maximum humidity range of 7 g/kg is reached during the maximum period of time. From this humidity distribution, one may conclude that humidity during this period peaks under low ventilation rate. This is a typical problem that is usually solved with adequate air change related to opening of windows and extraction of
3.4 Apartments: Indoor Air and Health Fig. 3.4 Humidity ratio during lunch in the kitchen
55
40 35
Time (%)
30 25 20 15 10 5 0 1
Fig. 3.5 Humidity ratio during dinner in the kitchen
2
3
4 5 6 7 Humidity ratio (g/kg)
8
9
10
8
9
10
40 35
Time (%)
30 25 20 15 10 5 0 1
2
3
4
5
6
7
Humidity ratio (g/kg)
humidity released during cooking. It can be done with adequate occupants’ habits. Another conclusion obtained was that, once local thermal comfort was defined as a function of the local percentage of dissatisfied persons (PD), it resulted within the local thermal comfort limit of 15% in bedrooms and kitchens. Hence, it can be considered as a comfortable environment. An indoor environment can depict an adequate thermal comfort under inadequate air changes. This is the case of bedrooms, where air changes, estimated as a function of CO2 concentration decrease, resulted in excessively low CO2 concentration values of 3,000 ppm and, consequently, below the lowest air changes admitted for this environment. It is of special interest that these CO2 values can be related to some health problems. In this investigation, a statistical study about the relationship between an indoor ambient concentration and source was developed. Specifically, a strong relationship was obtained between the level of CO2 in the kitchen during dinnertime and in the living room. Other studies were developed for this type of apartment; for example, bacteria and fungi growth owing to humidity problems. To reach this objective, measuring processes of temperature and relative humidity were carried out, and questionnaires about perception of indoor ambiences and occupants’ habits were filled. General characteristics of the apartments are summarised in Table 3.2.
56
A B C D E F G H I J K L M N O P Q R S T U V W X Y
3
Fig. 3.6 Total bacteria measured
Apartment characteristics
Total bacterial (CFU/m )
Table 3.2 Apartment description
3 Real Indoor Environments
Pet Normal Normal Limited space Normal Humidity problems Humidity problems Limited space Normal Pet Humidity problems Limited space Normal Humidity problems Normal Normal Pet Normal Normal Humidity problems Normal Normal Humidity problems Normal Normal
1800 1600 1400 1200 1000 800 600 400 200 0
A B C D E F G H I J K L M N O P Q R S T U V W X Y
Flat
The results revealed (Figs. 3.6, 3.7) that there is no similar bacteria and fungi growth in all apartments. Furthermore, it must be related to some of the parameters, reflected in the previous table. A statistical study was developed to relate these parameters with bacteria and fungi growth. The results revealed that there was a clear relationship between bacteria growth, the presence of pets and fungi growth with humidity problems in walls and ceilings. The conclusion reached is that occupants’ habits in these naturally ventilated buildings are clearly related to indoor relative humidity and, hence, fungi growth.
3.4 Apartments: Indoor Air and Health 400 350 3
Fungi (CFU/m )
Fig. 3.7 Total fungi measured
57
300 250 200 150 100 50 0 A B C D E F G H I J K L M N O P Q R S T U V W X Y
Flat
So, to reduce humidity peaks during sleeping and cooking times, new habits, such as opening windows, were recommended. Finally, humidity problems cannot be detected only by measuring indoor air temperature and relative humidity. Hence, filling questionnaires about indoor conditions, such as that done with local thermal comfort, are important, because these questionnaires are the only proven way to detect humidity problems in walls and ceilings. Examples of questionnaires are described by most of the standards. However, questionnaires must be implemented by researchers to adapt them to their particular field of research work. Hence, an inadequate adaptation to a particular situation will imply an incorrect analysis of results and future conclusions.
3.5 Offices and Schools: Indoor Air and Energy Saving In this section, a few public indoor environments, such as schools and office buildings, are reported, as well as their energy consumption characteristics. These environments were selected because they depict high energy consumption during classes and working hours. On the other hand, during the period after classes or working hours, the energy consumption is approximately constant and nearly null. Passive methods may lead to long periods of working time and reveal advantages relatively to classic HVAC systems. All passive methods, and the particular case of using internal coverings, are analysed in Chap. 5. A new methodology to analyse the effects of passive methods over real indoor environments is presented and developed in detail in the next chapter. As observed earlier, regardless of the fact that these characteristics are general for public buildings, these are at the same time a function of climatic conditions for each region. For example, our studies are centred in the Northwest of the Iberian Peninsula with a mild climate throughout the year. In particular, the Spring season can originate temperature peaks that must be compensated with HVAC systems. To reduce energy consumption in these situations, passive methods, such as using permeable coverings, wall construction and thermal inertia, can be utilised [5]. Permeable coverings and wall construction reduce the relative humidity peaks
58
3 Real Indoor Environments 0.80 0.75
Relative humidity (%)
0.70 0.65 0.60 0.55 0.50 0.45 0.40 New School
Old School
0.35 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 Time (hours)
Fig. 3.8 Indoor air relative humidity in school buildings
during the first few hours of occupation and thermal inertia helps to store energy and release it when indoor temperature drops and vice versa. Passive methods have been considered in laboratory studies and simulations, but real case studies must be analysed in new and old public buildings, such as schools [6, 7]. These buildings were selected to analyse the thermal inertia effect on indoor conditions. As customary in Spanish regions, old schools are built of stone and concrete, while new school buildings have thin walls composed of layers of insulation, brick, concrete and plaster. Hence, higher thermal inertias are expected in old buildings than in new ones. Figures 3.8 and 3.9 show the mean indoor relative humidity and indoor temperature found in school buildings during various hours of the winter weekends. The related humidity reveals peak values during the morning hours, reaching 70% and an indoor temperature of 20°C. This effect is clearer in new buildings. In the old ones, this is reduced owing to the effect of thick walls and higher air renovation. The days represented in the charts correspond to weekends. During these days, indoor relative humidity in new and old school buildings tend to have similar values. On the other hand, we find a clear difference in indoor air temperatures between the two buildings during weekends. The new buildings have an indoor air temperature of about 21°C, whereas the old ones have a mean temperature of 19°C. This result was not expected, but is in accordance with wall construction characteristics. Notably, new buildings have thiner walls and lower air renovation. However, the internal insulation of new buildings allows a lower indoor air temperature reduction during the unoccupied period. This implies a higher indoor air temperature after the weekend, estimated at 2°C and, consequently, a reduction in the peak energy load during the first hours of occupation for the HVAC system.
3.5 Offices and School: Indoor Air and Energy Saving
59
22.5 22.0 21.5
Temperature (ºC)
21.0 20.5 20.0 19.5 19.0 18.5 18.0 17.5
New School
Old School
17.0 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 Time (hours)
Fig. 3.9 Indoor air temperature in school buildings
0.60
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0.00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 Time (hours)
Fig. 3.10 Indoor air acceptability in school buildings
22
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17 NewSchool
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16 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 Time (hours)
Fig. 3.11 Indoor air percentage of dissatisfied persons in school buildings
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Once indoor air conditions were defined, it is time to define local thermal comfort (Figs. 3.10, 3.11). For example, the percentage of PD is about 20% during daylight hours in new and old buildings. However, new school buildings experience peaks of percentage of PD, related to peaks of relative humidity under an indoor air temperature 2°C higher than in old buildings. As observed earlier, these peaks of relative humidity are related to more hermetic buildings, with a lower number of air changes. Old buildings depict a higher level of air renovation, with higher infiltrations through windows and doors. In Chap. 4, different passive methods are analysed, as well as a new methodology to validate software resources employed to simulate these parameters in real buildings.
3.6 Libraries: Indoor Air and Material Preservation The Northwest Iberian Peninsula depicts a high relative humidity throughout the year, reaching values of 88% and temperatures of 17°C during the summer months. Specifically, June and July, months of maximum library occupation with expected high indoor air temperature and relative humidity, were selected to carry out the real case study. ASHRAE standard recommendations for the storage of books and archival material for libraries were employed. In this study, the specifications revealed that library indoor conditions must be between 20 and 22°C and 40 and 55% relative humidity. Just like in apartments, ships, etc., libraries also have two zones. The first one is where students read books, where air renovation is controlled by opening/closing windows. The other one is the archive section, for the preservation of books. In this zone, the preserved books are placed on shelves, so that they are far away from the influence of occupants and heat sources, such as the sun and computers. Air renovation in libraries is only due to communication through the stairs and between floors. Results revealed that indoor temperatures in a Spanish university library were about 22°C (zone 1) and 25°C (archive section) during the measuring period (Figs. 3.12, 3.13). Furthermore, the archive section depicts temperature fluctuations due to library occupation with daily amplitudes of 1.5°C, whereas in the main library these variations were about 0.2°C. On the other hand, relative humidity reached values of 58% in the archive section, as a consequence of moisture sources (occupants of the library). However, this relative humidity is low when compared with the relative humidity values obtained in the main library, where the mean values are 70% (Fig. 3.14). As a consequence of the indoor air temperature and relative humidity, values of indoor conditions in the archive section resulted in book conservation being out of limits. Furthermore, mould growth in books and walls is expected, due to the high
3.6 Libraries: Indoor Air and Material Preservation
61
Fig. 3.12 Example of a Spanish library
temperature, despite the fact that relative humidity is lower than in the main library. The high temperature was related to the effect of solar radiation on the walls, but also due to the moisture. Employment of doors separating each zone must be proposed to reduce this humidity transfer. Once thermal comfort and local perception of IAQ were analysed, it was revealed that the percentage of PD is higher in the archive section than in the main library, by about 10% (Figs. 3.14, 3.15). The peaks of PD are related to temperate peaks, despite the fact that indoor air relative humidity is lower than in the main library zone. Finally, during weekends it was found that the temperature does not rise due to indoor occupation and, hence, the percentage of PD experiences a reduction.
3.7 Industrial Environments: Indoor Air and Work Risk Examples of industrial environments are present in power stations and in ships. Specifically, ships depict different indoor environments that change frequently. For example, inside the bridge of a ship, indoor conditions are related to weather, depending on the sea path. These environments depict heat sources that provide high temperature increments in clear weather conditions. These heat sources are related to the presence of the engines and/or boilers that can increase the surrounding air temperature, reaching a point where it becomes hazardous for engineers. Specifically, this temperature increment may cause, in accordance with the work risk prevention law, low productivity rates, health hazards and increase in the number of accidents. (Fig. 3.16). Real measured data give some insights on this rather unknown indoor environment. For example, the sea lane from Barcelona to the Canary Islands and back, from December to February, revealed the values presented in Table 3.3. The measured data must be analysed, once the mean values are known. As seen in Figs. 3.17 and 3.18, indoor air temperature and relative humidity inside the bridge are a clear function of weather conditions. However, indoor variations are
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Temperature (ºC)
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21 23-6
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Fig. 3.13 Indoor air temperature in a Spanish library 75
Relative humidity (%)
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Fig. 3.14 Indoor air relative humidity in a Spanish library Fig. 3.15 Indoor air percentage of dissatisfied persons in a Spanish library
45 40 35
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similar to outdoor ones, without any extreme variations, due to an adequate control of indoor environments with HVAC systems. From those graphs, one may conclude that indoor conditions in this zone are adequate especially for watch keeping. For some sea lanes, a minimum HVAC system and energy consumption are needed. Once the moderate indoor environments were analysed, it is time to analyse the extreme indoor environment found in the engine room. This extreme hot environment depicts mean values of indoor temperature of 32°C, implying a hazard for marine engineers.
3.7 Industrial Environments: Indoor Air and Work Risk
63
Fig. 3.16 Relationship between temperature and heat disorders
Table 3.3 Ship indoor conditions on a sea lane
Outdoor temperature Outdoor relative humidity Bridge Dining room Engine room
Fig. 3.17 Indoor air temperature in the bridge
23°C 60% 22°C 22°C 32.5°C
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9
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Fig. 3.19 Indoor air temperature in the engine room and engine control room
3 Real Indoor Environments
32 28 24 20 Engine control room
Engine room
16 0
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The engine room has higher temperature values due to the heat released by the main engine. To get away from this heat source, marine engineers retreat to an engine control room nearby, conditioned with lower temperature values. A problem occurs when the operators do not know the mean temperature adequate for the engine control room. Typical values for these indoor environments are around 18°C, which is too low, leading to thermal shock for workers coming from the engine room (Fig. 3.19). Currently, there are a lot of standards for work risk prevention that show some solutions to prevent heat stress, such as drinking water and resting times. However, there are no clear standards and methodologies that show the maximum and minimum time that a worker is required to be in this extreme environment to release the accumulated heat. In accordance with current standards, some prevention measures are typically proposed for this environment. For example, drinking water to improve the heat transfer to the environment by sweat and perspiration. Another consideration is the acclimatisation of workers in the engine control room, before beginning the work in the engine room. For resting periods, one may propose an increment in the workers surveillance by co-workers who are in a position to detect initial symptoms of heat strain (Fig. 3.20). On the other hand, to prevent these extreme conditions, design conditions for HVAC systems may be proposed. For example, an increment in the number of air changes per hour employed to cool the engine room. However, these solutions are not enough. Residential works revealed new solutions to be implemented in future standards [8]. For example, defining the heat released by the human body in the engine control room and, consequently, the maximum time that an engineer must be in the engine room and the minimum time in the engine control room to release the accumulated heat. In this regard, a new parameter, the lowest indoor temperature in the engine control room, was considered. Measured data revealed values below 18°C, which are too low and can be related to the thermal shock of a marine engineer coming from the engine room, with average temperatures of 32.5°C and peaking at 38°C.
3.7 Industrial Environments: Indoor Air and Work Risk
65
Fig. 3.20 Work risk prevention measures in extreme environments
Fig. 3.21 Maximum period of time in the engine room, as a function of its temperature
Fig. 3.22 Resting time in the engine control room
As a function of these parameters and in accordance with the Spanish National Work Risk Prevention Standards, two charts were obtained (Figs. 3.21, 3.22). The first chart represents the maximum time that a marine engineer should be in the engine room with a metabolic rate of 1.2, clo of 1.0 and air velocity of 0.5 m/s. The other chart reveals the time that a marine engineer must be in the engine control room to release the cumulative heat, as a function of its temperature.
66 Table 3.4 Water temperatures S/RITE 10.2.1.2
3 Real Indoor Environments Competition Training Education and recreation Physically handicapped Children’s pool Children 3–6 years and seniors Pregnant women
24°C 26°C 25°C 29°C 30°C 32°C 30–32°C
For the case under study, working periods of 27 min in the engine room and resting periods of 10 min in the engine control room are obtained through these charts. Similar solutions may be obtained for power station environments, as they also use engines that release heat to the working environment.
3.8 Spas: Indoor Air and Sports Centres A spa is a special environment employed as a health centre. In this environment, pools at different temperatures are used for training or relaxation. Hence, different water temperatures are reached with typical values of 26–27°C, with some health treatments requiring temperatures of 30°C Table 3.4. In this regard, general standards show general recommendations to be employed for the reduction of energy consumption and moist air condensation. This moist air condensation is of special interest, as it is related to material preservation and health problems. It is important to highlight some of these recommendations. For example, it is recommended that the surrounding air is kept between 2 and 3°C over the water temperature, to reduce evaporation and energy consumption. Furthermore, Spanish national standards state that indoor air relative humidity must be between 60 and 65% to prevent condensation on the cold surface in contact with the outdoor air. In our case studies on spas, different zones were analysed. Within these zones, we found general zones in the commercial areas called low level, level one and level two. Gyms and pools present a temperature in accordance with the expected effect (Table 3.5). Within these pool areas, we found pools designed for Thalasso therapy. This therapy is conducted in pools with seawater [9] and temperature over the normal values. This therapy aims at absorbing the elements found in seawater in high concentration, which can be absorbed in a 20 min bath, with a water temperature of 37°C. Experimental results from real case studies were obtained and compared with the data of present standards.
3.8 Spas: Indoor Air and Sports Centres
Fig. 3.23 Indoor air temperature in different zones of a spa
Temperature
Effect
Very cold (0–12°C) Cold (12–18°C) Slightly chilled (18–27°C) Neutral (27–32°C) Temperate (32–36.5°C) Heat (37–40°C) Very warm (40–43°C)
Pain sensation Sensation of cold, poor tolerance Feels nice, short exposures Feels nice Feels comfortable Heat sensation Tolerable in short exposures
34 32 Temperature (ºC)
Table 3.5 Water temperature bearable to humans
67
30 28 26 24 22 20 Low
Level 1
Level 2
Gym
Pool
Thalasso
Pool
Thalasso
Sampling zone
75 Relative Humidity (%)
Fig. 3.24 Indoor air relative humidity in different zones of a spa
70 65 60 55 50 45 40 Low
Level 1
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Sampling zone
The measured indoor air temperature revealed that Thalasso pools depict a more extreme situation (Fig. 3.23). In Thalasso pools, the surrounding indoor air temperature was 31.5°C and the relative humidity was 74.9% (Fig. 3.24), whereas standards recommend relative humidity values between 50 and 70%. Another parameter to be considered when analysing these indoor environments is the number of air changes. In this regard, the standards propose a minimum ventilation rate of 12.5 l/s per person, 1.2 decipols and a maximum CO2 concentration of 500 ppm. Also, these standards propose a minimum air flow of 2.5 l/ s m2 on the pool surface, which adds an air flow to control the relative humidity. Finally, to reduce the moist air release, a pressure of 20–40 Pa with respect to the surrounding environment is recommended. In the last few years, new standards have forbidden the use of conventional energy sources to heat pools. In this regard, they recommend employing between
68 100 90 80 70 PD (%)
Fig. 3.25 Indoor air percentage of dissatisfied persons in different zones of a spa
3 Real Indoor Environments
60 50 40 30 20 10 0 Low
Level 1
Level 2
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Thalasso
Sampling zone
30 and 70% of annual heat needs from solar energy. Furthermore, new proposals of operation are recommended in these new standards, like for example using an insulating water cover when the pools are not in use, to prevent evaporation losses. In general, present standards reveal different ways to reduce energy consumption. For example, an increase in relative humidity to reduce evaporation and condensation in walls is proposed for these environments. At the same time, air velocities and air changes must be reduced to improve general and local thermal comfort and reduce energy consumption. More research is required to analyse and help define which parameters can reduce energy consumption with passive methods, such as thermal inertia. Thermal inertia did not reveal a clear influence on the indoor environment of pools. When thermal comfort was analysed, values of 80% of PD were obtained in pools (Fig. 3.25) and in the Thalasso therapy areas. They were related to the high relative humidity and temperature and, hence, one can only stay in this indoor environment for reduced periods of time. Despite the fact that indoor temperature and relative humidity are the most important parameters to be considered in thermal comfort studies, local thermal comfort must be analysed too. In this regard, local thermal comfort revealed a certain discomfort related to temperature changes between environments. Specifically, discomfort was obtained between the pool and the corridor. In these corridors, indoor air temperature was very low and, consequently, a slight thermal shock was experienced by the occupants. For future research, the definition of new charts that reveal the maximum time that a swimmer should spend in pools is proposed. In the meantime, one has to use the corridors or dressing rooms to release the accumulated heat, as in the case of extremely hot environments of industrial areas. Besides local thermal comfort, other parameters, such as noise level, must be considered. In this regard, present standards do not define what the particular conditions of spa areas are. Therefore, some measured values were obtained (Fig. 3.26). Figure 3.26 reveals the different noise levels in different areas of a spa. Sixtyfive decibels is the lower value in the commercial areas. An intermediate value of 70 dB was obtained in the Gyms and the maximum noise levels were obtained in the Thalasso therapy pools, with values of more than 76 dB. This high value of noise level is related to the noise generated by the water that falls into the pool.
3.8 Spas: Indoor Air and Sports Centres
69
78
Fig. 3.26 Noise levels in different zones of a spa
76 74 dB
72 70 68 66 64 62 60 Low
Level 1
Level 2
Gym
Pool
Thalasso
Sampling zone
Like with the earlier parameters, future standards must be based in deep analyses of real situations and realistic solutions for spas.
3.9 General Conclusions About Indoor Environments From the review on different indoor environments, it was possible to conclude that, in accordance with the activity to be developed, new temperature and relative humidity limits must be developed for inclusion in standards. For example, different temperature limits must be defined for an industrial environment with extreme temperature values, and spas or sport centres, with extreme relative humidity values, and for libraries and office buildings. In accordance with these conditions for indoor environments, recent research has revealed the feasibility of passive methods as one of the important resources to control indoor environments. These methods are classified as passive methods to control thermal and hygroscopic inertia. Also, these two parameters depend on air changes, wall construction, weather and indoor activities. In this regard, when the origin of a moisture source occurs only for determined periods of time, natural or mechanical ventilation is the most economical method to control indoor conditions. On the other hand, when the humidity source is present for long periods of time, only thermal considerations can help reduce condensation and energy consumption. Finally, industrial environments, such as the engine room in a ship, need air renovation and resting periods in the engine control room for work risk prevention. At the end of each type of indoor environment studied, new research topics were proposed in accordance with the results obtained. For example, when trying to analyse humidity problems in apartments, it was found that most are related to local humidity problems in walls and ceilings. These could only be detected with questionnaires adapted to the specific conditions. Furthermore, measured data of temperature and relative humidity could not be related to fungi and bacteria growth. As a consequence of these results, a new procedure for fungi detection based on questionnaires, designed in accordance with the particular conditions of the indoor environment, must be employed. These questionnaires must be
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validated in real case studies and implemented in most of the current standards, to help architects and engineers to design, operate and control indoor environments. When analysing extreme indoor environments, we found two cases: indoor environments with extreme temperature, such as industrial complexes, and indoor environments with high relative humidity, such as sport centres and spas. In the first case, the extreme temperature values can be related to some of the work risk situations, such as dehydration and possibly thermal shock. Just like in the case of the engine control room and the engine room, that revealed temperature differences of 20°C. Charts that help defining the maximum time that can be spent in the engine room, and the minimum time in the engine control room, were proposed. These charts were developed as a function of the global temperature found in those indoor environments. A similar situation can be found in sports centres. There, we found extreme relative humidities and related work risk situations. Hence, new charts like those developed for industrial environments must be developed and adopted by the most common standards. On the other hand, sports centres depict a high energy consumption, just like offices and school buildings. In these environments, the main solution to improve energy consumption is to employ passive methods. These passive methods can be related to building thermal inertia, wall construction, air renovation and occupants’ habits, among others.
References 1. Talukdar P, Olutmayin S, Osanyintola OF, Simonson CJ (2007) An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials. Part I: Experimental facility and material property data. Int J Heat Mass Transf 50:4527–4539 2. ISO 7730-2005. (2005) Ergonomics of the thermal environment. Analytical determination and interpretation of the thermal comfort using calculating of PMV and PPD indices and local thermal comfort criteria. International Organization for Standardization, Geneva, Switzerland 3. Keppel G, Wickens TD (2004) Design and Analysis: A Researcher’s Handbook, 4th edn. Prentice Hall, California 4. Orosa JA (2011) A new modelling methodology to control HVAC systems. Expert Syst Appl 38(4):4505–4513 5. UNE-EN ISO 13790 (2005) Thermal performance of buildings—Calculation of energy use for space heating, 2004. International Organization for Standardization, Geneva 6. Rodriguez E, Orosa JA, Saiz JM (2006) Moisture sources evaluation in schools from northwest of Spain, IEA-Annex 41 MOIST-ENG, Working Meeting, Lyon. http://www.iea.org. Accessed 2 Feb 2011 7. Annex 41. Whole Building Heat, Air and Moisture Response (MOIST-EN). http://www.ecbcs. org/annexes/annex41.htm. Accessed 2 Feb 2011 8. Orosa JA, Iradi G, Oliveira A (2010) Thermal comfort conditions in ships. J ship production 26(1):60–65 9. Routh, IB, Bhowmik, KR (1996) Basic tenets of mineral water. A glossary of concepts relating to balneology, mineral water, and the spa. Clin Dermatol 4(6):549–550
Chapter 4
Passive Methods
4.1 Introduction In the past few years, passive methods were considered as a better solution to reduce energy consumption for conditioning an indoor environment. However, to improve these methods, concepts must be considered while designing the building and only a few can be employed once the building has been constructed. Passive methods use building design parameters, such as the shape of the building and orientation, which reduce energy consumption of air conditioning systems. Passive methods can be classified into two main groups: passive energy reception and control. The first group consists of energy reception from solar radiation and the second group consists of thermal inertia and hygroscopic inertia of building envelopes (Fig. 4.1). To improve a passive method, the designer must consider the building orientation and the presence of buildings and/or trees that may interfere in solar reception. To improve an energy control method, we have thermal and hygroscopic inertia; thermal inertia depends on the wall structure, accumulating heat when there is an excess and releasing it when needed. Air renovation and indoor habits can alter the control of energy consumption, natural or mechanical, and hence, must be considered at the time of designing the building. Natural ventilation can improve indoor environments with nearly null energy consumption if there is an adequate building design. In this chapter, the results obtained during the past few years using passive methods to improve indoor environments are presented. Specifically, the thermal inertia concept, natural ventilation, permeable coverings and the general effect of these methods to improve the ventilation and air conditioning system will be discussed. These concepts must be understood before reading Chap. 5. This next chapter is about permeable coverings as passive methods to control indoor air relative humidity and temperature. J. A. Orosa and A. C. Oliveira, Passive Methods as a Solution for Improving Indoor Environments, Green Energy and Technology, DOI: 10.1007/978-1-4471-2336-1_4, Ó Springer-Verlag London Limited 2012
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Fig. 4.1 Passive methods
4.2 Natural Ventilation, Energy Saving and Comfort Research on natural ventilation can be classified into three approaches or stages: laboratory, field measurements and simulation. Once all stages are developed, a comparison between simulation and field measurements allows to obtain a validated tool for building design. To define indoor air change, differences between indoor environments must be considered. For example, in a residential building, where occupants spend most of their time, the number of air changes must be defined in relation to the heating, ventilation and air conditioning (HVAC) system used. On the other hand, in industrial settings, the number of air changes presents a clear relation with work risk prevention measures, as observed in the earlier chapter. There are more indoor environments with some characteristics that must be considered at the time of defining the number of air changes: hospitals, schools and offices. In hospitals, the main problem is related to the risk of infection between patients. The main solution for this risk is related to the reduction or suppression and adequate treatment of indoor air recirculation. In schools and offices, the problem is related to the experimental control of two parameters: CO2 emission and a reduction of energy consumption with air changes. To improve this situation, HVAC control systems were developed based on neural networks. The results are equivalent to those obtained with occupants’ self-adjustment of air changes through windows. As observed earlier, despite the fact that most indoor environments present similar problems, there are some that reveal different needs. For instance, different ventilation methods must be defined for different situations (Fig. 4.2). Once an in-depth analysis of indoor ambiences is carried out, we find that humidity sources in bathrooms of residential buildings are higher. To solve this problem, recent research revealed that the ventilation arrangement of forcedceiling supply and wall exhaust systems presents the best method to extract odours from bathrooms.
4.2 Natural Ventilation, Energy Saving and Comfort
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Fig. 4.2 Air changes in different environments
In residential buildings, we find that bedrooms are sources of CO2 and humidity. To control these two sources, recent research has proposed different night ventilation methodologies. Furthermore, other parameters of air exchange between rooms must be considered while designing the ventilation system. For ventilation in industrial ambiences, different ventilation methods, such as mixing ventilation systems, are installed to control contaminants. Injection of cold air in the upper regions of the room, and mixing with the ambient air, determines the cooling of the whole volume, and establishes a uniform temperature level with a very small vertical temperature gradient. The results revealed in [1] were adequate for industrial ambiences. In schools and offices, a high HVAC energy consumption is verified throughout the year. In these environments, Demand Control combined with Displacement Ventilation reduces ventilation air volumes to about 65–75% compared to Constant Air Volume and, consequently, the energy demand. These ventilation methods are summarised in Fig. 4.3. After analysing the number of air changes and ventilation methodology employed in an indoor environment, there are other issues that may improve its characteristics. For example, to reduce energy consumption in natural ventilation buildings, some models are proposed [2] to predict this ventilation and energy saving with respect to the mixing ventilation procedure. The results revealed that there is energy saving when significant heat gains are detected. Another study compared two different natural ventilation models [3]—passive displacement and passive mixing in transient conditions—which revealed the interest of displacement ventilation. In recent years, new parameters for infection risks, between hospital environments, were investigated and simulated with Computational Fluid Dynamics techniques [4], also with the aim of improving building design and energy demand [5, 6]. In this regard, to develop and validate these numerical models, field measurements must be considered [7–9] and analysed.
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Fig. 4.3 Recommended ventilation methods for different environments
In these numerical models, new models for buoyancy forces are proposed [10], revealing that the hourly air wind and temperature differences between indoor and outdoor are important parameters for the natural driving forces. Another model reveals the expected natural ventilation cooling potential in some cities, predicting the effect of climate change [11]. Finally, the number of air changes and ventilation methods are not the only way to improve indoor environments. For example, to reduce energy consumption related to air changes, new heating, ventilation and air conditioning control systems have been proposed [12].
4.3 Thermal Inertia and Passive Methods Thermal inertia parameters for buildings are of special interest for designers and researchers due to their relation to passive methods and energy saving. The system with the highest thermal inertia has the lowest energy requirements, due to the fact that, in times of energy abundance from solar irradiation, energy can be stored in building structures. When there is no energy abundance, energy reception and indoor temperature decreases and the energy stored is sent indoors, reducing the energy consumption to reach an adequate thermal comfort [13].
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There is a need for further research, related to thermal inertia, for night-time ventilation [14] and other passive solar methods [15–17]. However, lack of models and software resources hinders any research to be conducted extensively. In this chapter, a new procedure to define real building thermal inertia, based on real measured data and tested with different procedures, is discussed. To reach this objective, new software resources, like Heat, Air and Moisture (HAM) Tools [18– 20], are employed, as this software offers clear advantages for engineers. We developed a simulation of the real dynamic conditions of indoor ambiences, with some software data modification. MATLAB is an open code programme, easy to understand and, specifically, if different block diagrams, present in a MATLAB toolbox entitled Simulink, are used. This represents a clear advantage over other common computer programs, due to the visual item available for solving problems. For example, modifications to building internal coverings and its buffering effect are not available in most of the general programs employed in building energy certification. Hence, this software is adequate enough for technical tasks but of no use to researchers. The Energy Conservation in Buildings and Community Systems Programme, in partnership with the International Energy Agency (IEA), launched Annexes 17 and 41 [21]. Researchers from more than 20 different countries, in the University of A Coruña and Porto [22], conducted an in-depth study of building HAM response. This research lead to the development of H-tools and HAM tools software applications. These tools, which are a more interesting version of software resources for moist air and buffering effect studies, were developed by the Chalmers Institute of Technology. Different subgroups developed real case studies to test the software resource. This software resource is of special interest as its code source is easy to understand and, based on visual resolution of block diagrams, a non-specialised researcher can develop the first simulations only with MATLAB. On the other hand, modifications for permeable coverings and building thermal inertia effects were difficult to analyse when using software resources such as TRNSYS [23] and Energy plus [24]. Hence, it was confirmed that HAM tools with structure modifications are easier to use by researchers in building analysis. As equipment, Gemini data loggers were employed to measure indoor air temperature and relative humidity. Air changes were assessed with the tracer gas decay method, based on CF6 tracer gas. Based on MATLAB Simulink, HAM tools software solves the balance equations of a room and, specifically, in a new toolbox developed by the Chalmers Institute of Technology called Individual Building Physics Toolbox [25, 26] that upgrades H-Tools. It is a one-dimensional model for calculating heat, air and moisture transfer through the building envelope and freely downloadable from the Web [27]. HAM tools are composed of two tool boxes: one for the building envelope construction and the other for the thermal zones, to simulate everything related to the HVAC system and its heat and moisture sources (Fig. 4.4). Most of these
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Horiz Cat Geometry Construction
Constructions
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Radiation
Zone
Clock
Days
ROOF
Zone Radiation
BTweather BESTEST
1 Horiz Cat
Zone Radiation
Geometry
Geometry
HEATING/COOLING SYSTEM S
Zone
0
Construction Out1
EXTERIOR WALL 3
Radiation
F
Heat supply Internal gains
0
FLOOR Geometry Construction Zone Radiation
System
Zone
Ventilation system AIR IN
S
Internal gains1
Vert Cat EXTERIOR WALL 4
simout To Workspace In1
Out1
4
System
Zone
Ventilation system AIR OUT
Zone out
Fig. 4.4 MATLAB blocks for building simulations
building structure components were defined in accordance with the BESTEST structure, to be uploaded by MATLAB.
4.3.1 Thermal Inertia and Energy Saving Thermal inertia is defined as a property of materials, accumulating and releasing heat to the surrounding environment when needed. Hence, this concept is applied to all materials currently found in the construction industry. Thermal inertia is proposed as one of the most interesting passive methods for energy saving in indoor environments. Researchers concluded that buildings and their inertia must be considered as another component in the HVAC system. Its main advantages were revealed by Dornelles and Roriz [28], where it was shown that construction systems with a high thermal inertia provide more comfortable environments and lower energy consumption. Under the same air changes and indoor habits of occupants, a building with a thicker wall structure depicts lower temperature variations. Hence, assuming that the mean temperature value corresponds to comfortable conditions, less extreme conditions occur and, therefore, better thermal comfort conditions are likely to occur. At the same time, energy consumption for heating, ventilation and air conditioning is reduced due to the same fact.
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77
This thermal inertia effect was employed in research works [13]. In this work, the effect of changing wall construction and increasing building thermal inertia of a house in a northern climate has reduced specific energy requirements. Other works developed in the past few years tried to analyse the effect of phase change materials employed as internal coverings of walls, as a means for changing indoor air temperature [29]. In these works, most software resources employ energy models that ignore moisture phase change effect. Hence, the first objective of this work was to develop new software tools for HVAC systems and building simulation. Specifically, the recent HAM tools, with open code sources, could be used to simulate those processes. These concepts are represented in HAM tools with two MATLAB Simulink Blocks. The first block consists of building envelope elements: windows, floor, walls and roofs, among others. In this block, physical properties are described, for example, density and porosity; thermal and moisture properties, in accordance with the BESTEST structure, are considered; room orientation, area and tilt of each wall are added; internal gains, air changes and heating system parameters are added in the ventilation block. These parameters were adjusted in accordance with the real measured data. However, the first step in research was to validate the software. To do it, field measurements of indoor and outdoor air temperature and relative humidity were used [30]. In accordance with the International Organization for Standardization (ISO) and ASHRAE Standards, measuring devices had adequate margins of error. In accordance with ASHRAE standards, measurements were developed where the occupants are known to, or are expected to, spend their time. Furthermore, in unoccupied rooms, the evaluator makes a guess of the future occupant locations within the room and takes appropriate measurements. When occupancy distribution cannot be estimated, the measurement locations were in the centre of the room and 1.0 m inward from the centre of walls. In exterior walls, measurements were taken 1.0 m inward from the centre of the largest window. Finally, as general consideration proposed by the standard, all measurements were taken in locations where extreme values of the thermal parameters are estimated or observed to occur, but allowing a proper air circulation around measurement sensors. Once the measured values were obtained, it was possible to define the thermal inertia of two buildings with different wall structures but similar indoor conditions. In this regard, two schools, one new and the other old, were selected [30]. Finally, in accordance with the real measurements, it was possible to define their thermal inertia. The other method to define a building’s thermal inertia is based on simulation processes. A more in-depth definition of the building structure was needed as input data for this software resource. In this regard, the old school depicted 0.90 m thick walls with no insulation; the classroom had a volume of 210 m3, with three painted wooden single pane windows and pine floor. The scheduled occupation was 26 students and 1 teacher between 08:30 and 14:10 hours, and 20 students and 1
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4 Passive Methods
Table 4.1 External wall properties of the new and old school buildings Layer Dry conductivity Dry density (kg/m3) Old school 0.5 cm plaster 0.5 cm concrete 0.43 m stone New school 0.5 cm plaster 0.5 cm concrete 12 cm brick 4 cm insulation 12 cm brick 0.5 cm concrete 0.5 cm plaster
Specific heat (J/kgK)
0.1600 0.5100 1.1300
950 1,400 2,300
840 1,000 840
0.1600 0.5100 0.8940 0.0289 0.8940 0.5100 0.1600
950 1,400 1,222 42.45 1,222 1,400 950
840 1,000 795 1,214 795 1,000 840
teacher between 16:30 and 19:30 hours, from Monday to Friday. The building central heating system operated from 08:00 to 10:30 hours and 16:30 to 18.30 hours, from February to March. On the other hand, the new school is located on the first floor and walls are thermally insulated by air space. The volume of the classroom is 150 m3, with three double glazed aluminium windows and terrazzo floor. The scheduled occupation is 25 students and 1 teacher from 08:30 to 14.10 hours from Monday to Friday, and from 16:00 to 19:00 hours on Tuesdays. The building has central heating from 08:00 to 10:30 hours, from February to April. Table 4.1 presents characteristics of both school buildings (old and new). The main results are shown in Tables 4.2, 4.3, 4.4 and 4.5. From these tables we can conclude that the old building presents the lower effective thermal inertia because of lower solar heat gains. Parameters like air change rate and permeable coverings interact with time constants and energy saving. In particular, permeable coverings provide better insulation and enable an improved effective thermal inertia and hence a greater energy saving. Furthermore, old buildings showed a distinct reduction in indoor humidity when simulated under the buffering effect of permeable coverings. Despite these improvements in thermal inertia and, at the same time, in energy consumption and thermal comfort, in an old building, an uncontrolled air parameter changes due to air infiltration, which causes a big decrease in temperature during the weekend. HAM tools are an adequate software resource to control and simulate HVAC systems and passive methods. However, new tool boxes to simulate the behaviour of new control systems based on passive methods are needed.
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Table 4.2 Outdoor/indoor dry bulb temperature (8C) statistics Outdoor Indoor new school
Indoor old school
Average RMS deviation Maximum Minimum
20.1 2.1 26.9 14.0
11.5 5.5 26.46 -4.15
19.2 2.5 26.9 13.7
Table 4.3 Outdoor/indoor relative humidity (%) statistics Outdoor Indoor new school
Indoor old school
Average RMS deviation Maximum Minimum
53.2 6.2 73.8 29.7
85.85 15.13 100.0 28.9
59.1 7.5 78.9 33.9
Table 4.4 Outdoor/indoor partial vapour pressure (Pa) statistics Outdoor Indoor new school
Indoor old school
Average RMS deviation Maximum Minimum
1266.8 243.3 2158.3 590.3
1186.40 332.8 2191.6 432.22
1327.9 225.9 2086.9 752.9
Table 4.5 Time constants for different conditions Initial Without heat conditions gains
With reduced air changes
With permeable coverings
New school Old school
185 112
188 115
178 111
37 67
4.3.2 Thermal Inertia and Thermal Comfort Adaptive Models As described earlier, thermal inertia is an interesting parameter to analyse indoor environments. For example, in the previous section, it was related to building energy consumption. In this section, its relationship with thermal comfort will be analysed and implemented in accordance with adaptive models. Thermal inertia must be taken into consideration when building design is taking place. In conclusion, the higher the thermal inertia, the higher the time lag in extreme temperatures between the inner and outer surface of the building envelope. Hence, a heavy-weight (high inertia) building can, during short periods of
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4 Passive Methods
cold weather, release or receive heat and, consequently, reduce heating system operating time. Another advantage of thermal inertia is that the room air temperature is often lower in the lightweight building. Furthermore, the amplitude of temperature fluctuation in the inner surface of buildings with thick walls is lower than in buildings with light walls. After discussing those concepts, it is the moment to analyse the different methods to define thermal inertia values. There are different methods used in the design and application of HVAC systems [14, 31]. Some of these methods depict a general procedure based on real measured data, to define the main building heat load as a function of weather and indoor conditions. As observed earlier, recent software resources were focused on the study of heat and mass transfer through building envelopes, to allow a more accurate simulation of indoor environments [15, 16]; for example, the effect of permeable coverings on indoor environment. This effect has been recently analysed as a possible tool to improve indoor environments [21, 22, 32–35]. Earlier results revealed that school buildings depict the highest energy consumption for air conditioning during the winter season; during the spring season, the heating system is employed only if it is really needed. These indoor ambiences are those where thermal inertia exerts a clear difference in building design for energy saving. As revealed earlier, HAM tools are an interesting research tool in analysing different energy-saving methods and, particularly, the effect of thermal inertia. Hence, analysing the expected thermal inertia effect in different buildings under the same weather conditions will be interesting. In the last years, thermal comfort models revealed a great accuracy and capability in defining thermal comfort conditions in indoor environments. As described in Chap. 2, thermal comfort models are a function of weather conditions. HAM tools [36, 37] represent a clear methodology to validate thermal comfort. Specifically, it is an interesting tool for defining the relationship between indoor and outdoor mean, minimum and maximum conditions related to building thermal inertia. As in the earlier work, data loggers and air renovation methods were employed. Air renovation was measured with a multi-gas monitor. The ventilation rate was assessed using the concentration decay method, using CF6 as tracer gas. The building structure description is seen in Table 4.1. The research work was divided into two experiments. The first was related to weather conditions and indoor measured values, to define the effect of minimum outdoor temperature over the minimum indoor temperature, without the influence of occasional heat loads. These results are of special interest because they relate weather conditions with building wall construction—an interesting tool for designers. The second experiment was the simulation of the two previously described buildings with HAM tools, once these buildings were tested with real measured data during 3 days of unoccupied periods. After this, both buildings were simulated under constant weather conditions, to define building time constant.
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81
This process is a new methodology employed to obtain building time constants, without the need to measure them directly. The main disadvantage of this is that we need long periods of time with constant weather conditions, which is something difficult to be obtained in real situations. Once the simulation was validated, a new tool was developed, which depicts very interesting applications. Between the main applications, it is possible to define indoor temperature and relative humidity conditions in a constructed building. From these data, different models to relate indoor conditions with weather were defined, in accordance with adaptive models. In this case, formulae were generated by multiple regressions for the indoor minimum temperature of the classrooms in the two schools, to contrast the weather model with the measured data; the indoor minimum temperature was related to the energy consumption and HVAC control system. Currently, minimum temperature and temperature drop during the day represent the rate of nocturnal radiant cooling [17]. Results revealed the prediction of indoor minimum temperature. The equation defined by Krüger et al. [17] predicts indoor conditions with respect to the outdoor minimum temperature: Tmin
in
¼ 13:12 þ 0:3028 Tmin
out
þ 0:2659 ðTdrop Þ
ð4:3:2:1Þ
where Tmin in is the minimum indoor temperature for the period under analysis (8C), Tmin out is the minimum outdoor temperature for the period (8C), Tmin out is the maximum outdoor temperature for the period (8C) and Tdrop is the temperature drop from the previous outdoor maximum Tmax out ðn 1Þ to the minimum Tmin out Various other parameters were employed to define this winter equation, such as Swing, Tdrop and RNAvg; RNAvg is the average outdoor temperatures during the previous 3 days; GT is the average temperature for each study period; Swing is the diurnal swing ðTmax out Tmin in Þ; and Tavg is the outdoor daily average temperature (8C). Results of the first experiment revealed the relationship between indoor and outdoor conditions by means of Eqs. 4.3.2.2 and 4.3.2.3. These equations reveal a similar weather model to that obtained by other authors in Eq. 4.3.2.1 T minin
new
¼ 13:7740 1:1679 T maxout þ0:9417 T minout þ0:3541 Tavgt þ 0:1895 RNaver þ 1:0234 Swing þ 0:1041 Tdrop ð4:3:2:2Þ
with R2 = 0.77
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4 Passive Methods
T minin
old
¼ 13:5134 0:5538 T maxout þ0:6676 T minout 0:3133 Tavg þ 0:4591 RNaver þ 0:6272 Swing þ 0:0118 Tdrop ð4:3:2:3Þ
with R2 = 0.76 The main conclusion is that HAM tools is an interesting software resource to be easily employed by researchers to define indoor environmental conditions. A second experiment was carried out to define building thermal characteristics. As observed in earlier experiments, the building constants are obtained with real measured data, and they will be calculated with a software resource, once validated. Specifically, the proposed methodology employed in this work is based on HAM tools that contain 1D calculation of HAM transfer through the building envelope. With HAM tools, we employed the same general methodology to define building thermal inertia. This general procedure to define building thermal inertia and its time constants is described below. The time constant is normally found in a cooling down period with constant low levels of temperature, as heat capacity/heat loss factor [13]. This method is based on a seasonal steady state energy balance on a building as a whole, or on a particular building zone. Thermal inertia is related to the heat received from energy gains, such as solar, stored in the building mass and released when needed: Qheat ¼ Qloss gQgain
ð4:3:2:4Þ
where Qheat is the heat requirement (W), Qloss is the heat loss (W), Qgain is the heat gain (W) and g is the utilisation factor, having a value between 0 and 1. The utilisation factor g is a function of the building time constant and the ratio Qgain/Qloss. The time constant is defined in the standard by Eq. 4.3.2.5. P Ci C i P ð4:3:2:5Þ s¼ ¼ Hi H i
where C is the sum of thermal capacities Ci for each construction element i, based on a 24-h periodic response, and H is the sum of heat loss factors for each construction element, including ventilation and air leakage. First, an outdoor temperature of 10°C and 80% relative humidity were selected to simulate and define the building time constant. A linear regression of the logarithm of temperature difference with respect to outdoor was carried out. The aim of this experiment was to depict the same weather conditions in the two buildings, and simulate them to obtain an equation relating indoor conditions at different initial values.
4.3 Thermal Inertia and Passive Methods Fig. 4.5 Time constant determination for the new school building
83
2.2
ln(Temperature difference)
2
1.8
1.6
1.4
y = -0.0056x + 2.1501 2 R = 0.9364
1.2 New School
Linear (New School)
1 0
20
40
60 80 Time (hours)
100
120
140
Also, thermal inertia was estimated based on the traditional method, based on real measured data. It consists of an adequate curve fit by the least squares method [13]. This time constant is the inverse of the constants related to time, obtained in the previous regression. For the second experiment, a linear regression was developed revealing a time constant of 178 and 111 in the new and old schools, respectively (Fig. 4.5). After HAM tools were validated in accordance with real measured data, new simulations were developed. These simulations tried to define indoor ambience evolution at different initial conditions, under the same weather climate. Results revealed the following correlation: Tin
new
¼ 10:30 0:01764 t þ 0:7348 Tout þ 4 ðTinitial 18Þ 48 ðTinitial 18Þ t 100000
ð4:3:2:6Þ
This correlation allows a prediction of indoor conditions from earlier outdoor mean values, with correlation factors over 0.9, it is, with great accuracy. Recent research revealed the most interesting conclusion obtained in the past few years about thermal inertia. This conclusion revealed the origin of adaptive models. This origin is related to the fact that Eq. 4.3.2.2 is a simplification of Eq. 4.3.2.6. This was confirmed when real instantaneous values of weather conditions were integrated and the results compared the indoor minimum temperature with the real measured data. More conclusions could be obtained. For example, the general idea was that old buildings present a higher thermal inertia than new ones. This was related to the fact that old buildings usually employ a high wall thickness and, in theory, must depict a higher thermal inertia. However, new buildings have lower air changes, with lower infiltration through walls and windows, and this changes the thermal inertia value (see Eq. 4.3.2.5)
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4 Passive Methods
From this analysis, we may conclude that building construction affects thermal inertia, but the general parameter to be analysed with respect to energy consumption and thermal comfort is what we may call the indoor air thermal inertia. This parameter depends on the construction and other parameters, such as air renovation. These effects are expressed by some real measured data, for example, old buildings depict a slightly low weekly mean indoor temperature than new buildings. During weekends, old ones present a faster heat loss. These effects were related to a lower solar gain. Future research should concentrate on this new methodology. For example, once HAM tools building simulations with real measured data have been defined and validated, new possible modifications can be proposed. For example, different indoor air changes due to the air conditioning system could be proposed, to lead to a better IAQ, with lower energy consumption. Finally, one important simulation is to assess the effect of internal coverings in indoor environments. Consequently, the software must simulate heat and mass transfer through the building envelope and the buffering effect. These effects are described in Chap. 5.
4.3.3 Thermal Inertia and Whole Building Simulation The methodology was centred in two school buildings, as described earlier, with different seasons analysed to relate indoor ambiences, HVAC system and building construction. ASHRAE Standard 1992 [38] and Burch [37, 39], revealed that thick exterior walls reduce heating and cooling requirements of buildings. Noteworthy is the aim of substituting the HVAC system with passive methods, during the spring season in mild climates. To reach this objective, a steady period of 3 days, a weekend and a holiday, were employed to measure and simulate indoor conditions under low ventilation rates, and testing at the same time the HAM tools software with real data. Particularly, solar heat gains and heat storage in walls under natural ventilation [40] were considered. Once the software was tested, building thermal inertia, air renovation and internal coverings were simulated to define the best indoor conditions and indices, such as the percentage of PD with local thermal comfort and IAQ. These indices were defined in accordance with equations depicted earlier in Chap. 2. The problem in defining an accurate building thermal inertia is that the real measured data need constant weather conditions for long periods of time, and this hardly ever happens in real weather conditions. Hence, a simulation software resource, once tested with the earlier data, allows to predict the real evolution of indoor ambiences under constant weather and, hence, define the real building time constant.
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85
Relative humidity 0.80 0.75
Relative humidity (%)
0.70 0.65 0.60 0.55 0.50 0.45 0.40 New School
Covering in New
Old School
Covering in Old
0.35 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00
Time (hours)
Fig. 4.6 Indoor air relative humidity when air renovations were reduced
The aim of this study is to develop a test methodology to obtain building time constants with HAM tools simulation, in accordance with real data methodology. The time constant is obtained by a curve fit of the logarithm of temperature difference between indoor and outdoor versus time. The linear regression constants give us the time constant of each building. Another simulation process was developed with the aim of analysing the effect of reducing air changes during the occupied period (Figs. 4.6, 4.7). Specifically, a reduction from 0.6 and 0.7 to 0.4 air changes per hour was simulated (Figs. 4.8, 4.9). A final group of simulations was carried out with a change in internal covering permeability and, hence, heat and mass transfer conditions through the building envelope (Figs. 4.10, 4.11, 4.12, 4.13, 4.14). Results obtained revealed time constant values of 111 and 178 for the old and new buildings, respectively. This value revealed a higher thermal inertia in the new school than in the old one, due to the heat solar gain in the new building. This solar gain is related to building room orientation and the absence of other buildings near this one, which reduces solar radiation gains. In this regard, when solar irradiation was avoided in the simulation process, new time constants of 36.9 and 66.6, for the new and old building were obtained. In this case, the old school presents a higher thermal inertia, due to its wall thickness. Another simulation was developed under lower air changes in the two buildings. Its results revealed an indoor air relative humidity reduction towards 60%, due to heat and moisture transfer through the stone walls. Indoor air temperature in the new building revealed higher minimum values than under normal conditions of air renovation. This effect can influence the indoor air enthalpy and percentage of PD.
86
4 Passive Methods Temperature 22.5 22.0
Temperature (ºC)
21.5 21.0 20.5 20.0 19.5 19.0 18.5 18.0 17.5
New School
Covering in New
Old School
Covering in Old"
17.0 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00
Time (hours)
Fig. 4.7 Indoor air temperature when air renovations were reduced
Enthalpy (kJ/kg)
Enthalpy 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 New School Covering in New School Old School Covering in Old School 34 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00
Time (hours)
Fig. 4.8 Indoor air enthalpy when air renovations were reduced
When other internal coverings, such as wood panels, were considered, more results were obtained. These results revealed that permeable coverings can reduce peaks of relative humidity. However, there was no intense effect of permeable coverings on indoor ambience due to a reduced surface of permeable coverings that represent only the surfaces of the rooms interacting with the outdoor. A general conclusion obtained is that new buildings depict an increment of indoor temperature due to a reduction of air renovation, and also related to the fact that solar heat gain is more intense. When permeable coverings are used, new buildings reveal an indoor air temperature decrease and a reduction of relative humidity.
4.3 Thermal Inertia and Passive Methods
87 PD
22 21
PD (%)
20 19 18 17 New School
Covering in New School
Old School
Covering in Old School
16 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00
Time (hours)
Fig. 4.9 Indoor air PD when air renovations were reduced
Relative humidity 0.75
Relative humidity (%)
0.70 0.65 0.60 0.55 0.50 0.45 0.40 New School
Covering in New
Old School
Covering in Old
0.35 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00
Time (hours)
Fig. 4.10 Indoor air relative humidity with and without internal coverings
In general, we confirm that the importance of time constants is related to the fact that buildings with a lower time constant react faster to weather changes, internal heat and moisture changes. Consequently, during short cold periods, a lightweight building will need a heating system working to get adequate indoor conditions. On the other hand, a building with thick walls does not need energy release, due to the fact that heat has been previously stored in the structure [13]. However, the amplitude of temperature fluctuation of inner surfaces of walls in low time constant buildings is 1°C higher, in accordance with previous results [40]. These variations are summarised in Table 4.6. According to the table, the conclusion reached is that, apart from meteorological data and general procedures,
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4 Passive Methods Temperature 22.5 22.0
Temperature (ºC)
21.5 21.0 20.5 20.0 19.5 19.0 18.5 18.0 17.5
New School
Covering in New
Old School
Covering in Old"
17.0 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00
Time (hours)
Fig. 4.11 Indoor air temperature with and without internal coverings
Enthalpy (kJ/kg)
Enthalpy
50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 New School Covering in New School Old School Covering in Old School 34 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00
Time (hours)
Fig. 4.12 Indoor and enthalpy with and without internal coverings
energy saving in HVAC system design must be done in accordance with the individual characteristics of the building.
4.4 HVAC Improvement by Passive Methods During the past few years, different indoor ambiences were analysed and results revealed that different ambiences present different problems and, hence, different needs to be met. For example, when analysing apartments, it was found that the
4.4 HVAC Improvement by Passive Methods
89
0.60
0.50
Acceptability
0.40
0.30
0.20
0.10 New School
Covering in New School
0.00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 Time (hours)
Fig. 4.13 Indoor air acceptability with and without internal coverings in new schools
0.60 0.50
Acceptability
0.40 0.30 0.20 0.10 0.00
Old School
Covering in Old School
17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 Time (hours)
Fig. 4.14 Indoor air acceptability with and without internal coverings in old schools
important objective was to improve thermal comfort, IAQ and reduce different health problems found indoors [34]. At the same time, bad habits can generate different problems in these indoor environments. For example, a bad indoor air renovation is related to moisture accumulation from kitchens and bathrooms, which can affect other rooms and the related fungi growth and allergies. In office buildings and schools, local thermal comfort, IAQ, workers productivity and energy saving is considered. The main solution to improve the environment is related to air changes during unoccupied periods [35]. To improve the
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4 Passive Methods
Table 4.6 Time constant for different modifications Without Initial heat gain conditions
Air renovation reduction
Permeable coverings
New Old
185 112
188 115
37 67
178 111
HVAC system and reduce its energy consumption, natural ventilation is being developed as the best solution for this application. In museums and libraries, the main objective is conservation [30]. What is more, if possible indoor conditions must be reached with the lowest energy consumption. In this regard, the HVAC system can be improved with the building thermal inertia and room orientation, to be employed in the archive sections of libraries. Finally, in extreme temperature indoor ambiences, work risk prevention is the main objective and, in extreme relative humidity indoor ambiences, the lowest energy consumption and prevention of condensation risk are the main objectives. To reduce the effect of these extreme environments, the HVAC system must be designed accordingly. Other solutions are related to the control of indoor habits, such as opening windows. Humidity generation periods and adequate passive methods help in reducing energy consumption, and, in some cases, replacing the HVAC system. As described earlier, for an adequate understanding of indoor ambiences, it is not enough to define the general process of air change and building construction characteristics to simulate its behaviour. Other factors, such as occupant’s habits, local weather and preservation of materials, require real measured data to be defined and employed software to be validated. Laboratory experiments of parameters, for example material properties, and questionnaires on IAQ perception, occupants habits and health problems, give a more adequate indoor ambience information and, consequently, help improving HVAC system design and operation.
4.5 Passive Methods and Preservation of Materials Currently, there is a great interest in naturally ventilated buildings and passive control methods that can be improved to achieve local microclimates with low energy costs. Despite the fact that indoor conditions can be related to the HVAC system, it is difficult to get adequate indoor conditions in a humid climate. For humid climates, coolers and dehumidifiers must be employed, despite the fact that it is difficult to have any control over these ambiences. Specifically, in libraries the problem is that the HVAC system is not continuously working because it is controlled in accordance with occupants’ thermal comfort conditions
4.5 Passive Methods and Preservation of Materials
91
and not in accordance with preservation of materials. Hence, there are moist air condensation periods related to mould growth [41]. Another parameter to be considered at the time of designing indoor environments in libraries is the material exchange. When a book is transported from the archive to the reading room, it depicts a low temperature that suddenly changes to a new temperature usually found in libraries. The solution for this problem is related to natural ventilation. This natural ventilation depicts lower energy consumption and thermal comfort is obtained when controlled by occupants. This low energy consumption is related to a better employment of parameters, such as solar illumination, and when there is no energy consumption in the HVAC system by fans, chillers and pumps. In this regard, naturally ventilated libraries tend to employ passive cooling methods [42–44], and a mechanical ventilation system is only employed to control thermal loads and ensure air quality during the day, and cooling the building during the night. The main objective is to reach an adequate indoor air temperature during the first hours of occupation and during peak thermal loads. When a centralised HVAC system is employed, it can be implemented with control algorithms and software resources. In some cases, this is based on software resources to predict indoor real ambiences, for example, ESP-r [44–46]. TRNSYS 15 is the other software employed to simulate building and HVAC system behaviour. TRNSYS 15 can consider nocturnal mechanical ventilation and window surface factor of special interest to define energy consumption. In naturally ventilated buildings, the thermal ambience is controlled by the occupants that control the opening level of windows and doors, in accordance with ASHRAE 55-2004. Of interest is that the occupants’ demands are different in an indoor ambience with natural and mechanical ventilation. The same happens under different weather conditions, in accordance with ASHRAE standard 55-2004, and is related to different thermal situations, clothing, and occupant expectations. To define the type of neutral indoor air temperature to be employed in an indoor ambience, ASHRAE proposed to employ adaptive thermal models. These models can be proposed at building design stage [44] to define design conditions. Also, other indications about natural ventilated buildings are oriented towards prevention of material deterioration [47]. In this regard, researchers concluded that material deterioration can be classified as change in size, change in the rate of deterioration by chemical reactions and change in biological deterioration sources, which are related to the three main indoor air parameters—temperature, relative humidity and indoor pollution. To control these indoor ambiences, the limit of these indoor air parameters needs to be defined in accordance with ASHRAE Handbook 2003 HVAC Applications [48]. If the relative humidity on the surface is above a critical value of 70%, especially in hot environments with static air, fungal growth is expected. This growth is related to material moisture content, local surface temperature and humidity condition in the ambience. To prevent fungi growth, the IEA has established a surface relative humidity upper limit of 80% and a lower limit of 30% [49] and the Canada Mortgage and
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Housing Corporation’s stringent rules requires a relative humidity upper limit of 65%. But, in general, these limit conditions are strict with porous materials, though they are, most of the times, clean. In libraries, the effect is not only related to the aesthetic aspect, but also to paper degradation. To detect fungi growth, electronic noses are developed [50] as an algorithm of the HVAC control system. The second parameter to be considered is the weekly temperature variation, as a reaction of the chemical processes for material deterioration. Examples of these reactions are those developed by cellulose corrosion under temperatures of more than 20°C and paper fragility due to moist air irregular relative humidity cycles [47]. However, storage zones present lower temperature limits than common zones for reading and storage. In this regard, the general temperature limits for these two activities are between 20 and 21°C. The last parameter to be considered is indoor air pollution. In libraries and museums this parameter is related to health problems and material conservation [51]. Specifically, investigations revealed that most of Volatil Organic Compounds (VOCs) are related to furniture and material packaging; so, the presence of the materials must be reduced in that specific environment.
4.5.1 Case Study In this section, a real case study is presented to characterise a real situation in libraries, improving preservation of materials with passive methods and an adequate control of natural ventilation. The library in this study was mainly occupied by university students. But, besides the fact that no relics were stored, the goal was to maintain the specified ranges of temperature and humidity, and minimise daily and weekly variations. The building depicts two zones: the first corresponds to the library and the other is where books are stored when not in use—the archive section. The library has a gross floor area of 300 m2, located in the front of the building with a porch that reduces solar incidence. Books are stored in wooden and metal bookcases (Figs. 4.15, 4.16). The archive section is located in an annex, with three parts and its only access is from stairs located at the end of the library. This section does not have open windows, except a little one to supply a slight air renovation when the librarian considers it as necessary. The archive section consists of only metal bookcases where books are stored for future use. No human heat sources were detected in this room. In both rooms, main walls are external walls and its construction has a layer of concrete, brick, air barrier, insulation, brick and painted plaster (Fig. 4.16).
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4.5 Passive Methods and Preservation of Materials
Up
ARCHIVE
Up To second floor
LIBRARY
Down To archive
Fig. 4.15 Library
Fig. 4.16 Building envelope characteristics
1 1 Outdoor 2 Concrete
2
3
4
3 Brick 4 Air Barrier
5
6 5 Insulation 6 Brick
7
8 7 Plaster 8 Indoor
During the year, the library presents a level of occupation of about 20 students; but during exams, from June to July, the seats are all occupied reaching the triple of occupants. The HVAC system consists in a water heating system whose exchangers are located in both zones and only operative during the winter season. There is no cooling system for the summer and no mechanical air renovation. All the air renovation is obtained by opening windows. As one of the adaptive model conditions, students can open or close windows when they consider it necessary. As observed earlier, this is an ASHRAE Standard 55-2004 condition to employ adaptive models for predicting indoor thermal comfort temperature. Furthermore, this standard indicates that occupants may freely adapt their clothing to the indoor and/or outdoor thermal comfort (Figs. 4.17, 4.18). Methodology was centred in adaptive and local thermal comfort models, observed earlier in Chap. 2. Main results revealed that indoor relative humidity in the library has a mean value of 65% and a maximum of 70%. In the archive section, indoor relative humidity has a lower mean value of 55%. These data reveal that, despite the fact that natural ventilation can reduce temperature peaks related to occupants and heat moisture gains, the cumulative heat and moisture sources are transported to the archive section through stairs, and as a result book preservation is affected. This
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Fig. 4.17 Library stairs
Fig. 4.18 Library archive
effect is especially clear during the occupied period and, specifically, during exam months. Indoor temperature in the library reaches extreme values between 20 and 24°C in December and August, respectively. However, temperature in the library did not show a significant daily swing, reaching values of 22°C during these two months. On the other hand, in the archive section we find that indoor temperature reveals a higher mean value of 25°C during summer and a clear influence of library occupation was observed. At the same time, acceptability depicts values out of limits in these two ambiences. For example, an Acc of -0.2 was observed, related to bad Acc in the archive section and with its mean temperature values and higher relative humidity.
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When preservation of materials was analysed, the result revealed that relative humidity depicts a mean value of 58% and temperature of 25°C. Despite the fact that the indoor air relative humidity is adequate, it depicts an excessively high temperature of more than 25°C, allowing mould growth and the worst Acc index. According to the real case study, the importance of natural ventilation is easily understood and new passive methods that let us control indoor conditions are taken into consideration; for example, night cooling by mechanical ventilation to reduce peaks of temperature, relative humidity and Acc. Another proposed research related to this work is a new control system design based on passive control methods or Acc models [52].
References 1. Caputo AC, Pelagagge PM (2009) Upgrading mixed ventilation system in industrial conditioning. Appl Therm Eng 29:3204–3211 2. Woods AW, Fitzgerald S, Livermore S (2009) A comparison of winter pre-heating requirements for natural displacement and natural mixing ventilation. Energ Build 41:1306– 1312 3. Coffey CJ, Hunt GR (2007) Ventilation effectiveness measures based on heat removal: part 2: application to natural ventilation flows. Build Environ 42:2249–2262 4. Chow TT, Yang XY (2004) Ventilation performance in operating theatres against airborne infection: review of research activities and practical guidance. J Hosp Infect 56:85–92 5. Orosa JA, Oliveira AC (2010) Implementation of a method in EN ISO 13790 for calculating the utilization factor taking into account different permeability levels of internal coverings. Energ Build 42(5):598–604 6. Orosa JA, Oliveira A (2009) Hourly indoor thermal comfort and air quality acceptance with passive climate control methods. Renew Energ 34(12):2735–2742 7. Özcan SE, Vranken E, Berckmans D (2009) Measuring ventilation rate through naturally ventilated air openings by introducing heat flux. Build Environ 44:27–33 8. Evola G, Popov V (2006) Computational analysis of wind driven natural ventilation in buildings. Energ Build 38:491–501 9. Jian Y, Alexander D, Jenkins H, Arthur R, Chen Q (2003) Natural ventilation in buildings: measurement in a wind tunnel and numerical simulation with large-eddy simulation. J Wind Eng Ind Aerodyn 91:331–353 10. Yang L, Zhang G, Li Y, Chen Y (2005) Investigating potential of natural driving forces for ventilation in four major cities in China. Build Environ 40:738–746 11. Yao R, Li B, Short A (2009) Assessing the natural ventilation cooling potential of office buildings in different climate zones in china. Renew Energ 34:2697–2705 12. Jae-Weon J, Anseop C, Sang-Tae N (2010) Improvement in demand-controlled ventilation on multi-purposed facilities under an occupant based ventilation standard. Simul Model Pract Theory 18:51–62 13. Noren A, Akander J, Isfät E, Söderström O (1999) The effect of thermal inertia on energy requirements in a Swedish Building—results obtained with three calculation models. Int J Low Energy Sustain Build 1:1–16 14. Roucoult JM, Douzane O, Langlet T (1999) Incorporation of thermal inertia in the aim of installing a natural night time ventilation system in buildings. Energ Build 29:129–133 15. Badescu V, Sicre B (2003) Renewable energy for passive house heating II model. Energ Build 35:1085–1096
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16. Badescu V, Sicre B (2003) Renewable energy for passive house heating: part I: building description. Energ Build 35:1077–1084 17. Krüger E, Givoni B (2008) Thermal monitoring and indoor temperature predictions in a passive solar building in an arid environment. Build Environ 43:1792–1804 18. Kalagasidis AS (2002) BFTools, Building physics toolbox block documentation. Department of Building Physics, Chalmer Institute of Technology, Sweden 19. Kalagasidis AS (2002) HAM-Tools, International Building Physics Toolbox, Block documentation 20. Weitzmann P, Kalagasidis AS, Nielsen TR, Peuhkuri R, Hagentoft C (2002) Presentation of the international building physics toolbox for simulink 21. Hauer A, Mehling H, Schossig P, Yamaha M, Cabeza L, Martin V, Setterwall F (2009) International energy agency implementing agreement on energy conservation through energy storage. Annex 17: advanced thermal energy storage through phase change materials and chemical reactions—feasibility studies and demonstration projects. Final report 22. International Energy Agency. http://www.iea.org./publications/index.asp. Accessed 2 Feb 2011 23. TRNSYS. A transient system simulation program. http://sel.me.wisc.edu/trnsys. Accessed 2 Feb 2011 24. EnergyPlus Energy Simulation Software. http://apps1.eere.energy.gov/building/energyplus. Accessed 2 Feb 2011 25. Nielsen TR, Peuhkuri R, Weitzmann P, Gudum C (2002) Modelling building physics in simulink. BYG DTU Sr-02-03, ISSN 1601-8605 26. Rode C, Gudum C, Weitzmann P, Peuhkuri R, Nielsen TR, Sasic Kalagasidis A, Hagentoft CE (2002) International building physics toolbox—general report. Department of Building Physics, Chalmer Institute of Technology, Sweden, Report R-02: 2002. 4 27. International Building Physics Toolbox in Simulink. Http://www.ibpt.org. Accessed 2 Feb 2011 28. Dornelles KA, Roriz M (2004) Thermal inertia, comfort and energy consumption in buildings: a case study in Sao Paulo State-Brasil. Int J Hous Sci Appl 28:153–162 29. Hed G (2003) Use of phase change material for change of thermal inertia of buildings. Building Materials Technology Centre for built environment, Sweden, KTH Research School University of Gävle, S-801 76 Gävle 30. Orosa JA, Carpente T (2009) Thermal inertia effect in old buildings. Eur J Sci Res 27(2):228– 233 31. Karlsson F, Fahlén P (2008) Impact of design and thermal inertia on the energy saving potential of capacity controlled heat pump heating systems. Int J Ref 31:1094–1103 32. Simonson CJ, Salonvaara M, Ojalen T (2002) The effect of structures on indoor humiditypossibility to improve comfort and perceived air quality. Indoor Air 12:243–251 33. Simonson CJ, Salonvaara M, Ojalen T (2001) Improving indoor climate and comfort with wooden structures, Espoo 2001. Technical Research Centre of Finland, VTT Publications. 431, 200+ app 91 34. Orosa JA, Baaliña A (2007) Passive climate control in Spanish office buildings for long periods of time. Build Environ. doi:10.1016/j.buildenv.2007.12.001 35. Orosa JA, Baaliña A (2008) Improving PAQ and comfort conditions in Spanish office buildings with passive climate control. Build Environ. doi:10.1016/j.buildenv 36. Burch MD, Chi J (1997) MOIST: a PC program for predicting heat and moisture transfer in building envelopes. NIST Special Publication 917. NIST United States Department of Commerce Technology Administration, National Institute of Standards and Technology, USA 37. Burch DM, Remmert WE, Krintz DF, Barnes CS (1997) A field study of the effect of wall mass on the heating and cooling loads of residential buildings (aka log home report). National Bureau of Standards Washington, DC 20234. In: Proceedings of the building thermal mass seminar. Knoxville, TN; 6/2-3/82. Oak Ridge National Laboratory. http://www.fire.nist.gov/ bfrlpubs/build82/PDF/b82001.pdf. Accessed 2 Feb 2011
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38. ASHRAE Handbook—Fundamentals (1993) Load and energy calculations, energy estimating methods, Chapter 28 39. Moist Software (1997). http://www.fire.nist.gov/bfrlpubs/build97/art005.html. Accessed 2 Feb 2011 40. Ya F (2004) Thermal design standard for energy efficiency of residential buildings in hot summer/cold winter zones. Energ Build 36:1309–1312 41. Cornell Library. http://www.library.cornell.edu/preservation/librarypreservation/mee/ preservation/paper.html Accessed 2 Feb 2011 42. Krausse B, Cook M, Lomas K (2007) Environmental performance of a naturally ventilated city centre library. Energ Build 39:792–801 43. Lomas KJ (2007) Architectural design of an advanced naturally ventilated building form. Energ Build 29:166–181 44. Finn DP, Connolly D, Kenny P (2007) Sensitivity analysis of a maritime located night ventilated library building. Sol Energy 81:697–710 45. Chow TT, Clarke JA, Dunn A (1997) Primitive parts: an approach to air-conditioning component modelling. Energ Build 26:165–173 46. Kalagasidis AS, Weitzmann P, Nielsen TR, Peuhkuri R, Hagentoft C, Rode C (2007) The international building physics toolbox in simulink. Energ Build 39:665–674 47. Pavlogeorgatos G (2003) Environmental parameters in museums. Build Environ 38:1457– 1462 48. ASHRAE Handbook—HVAC Applications (2007) SI units American Society of Heating, Refrigerating and Air-conditioning Engineers. Washington, USA 49. Responsibilities for environmental monitoring for collection preservation. http:// orpheus.ucsd.edu/preservation/collectionpreservation.html Accessed 2 Feb 2011 50. Canhoto O, Pinzari F, Fanelli C, Magan N (2004) Application of electronic nose technology for the detection of fungal contamination in library paper. Int Biodeterior 54:3030–3309 51. Schieweck A, Lohrengel B, Siwinski N, Genning C (2005) Salthammer organic and inorganic pollutants in storage rooms of the Lower Saxony State Museum Hanover, Germany. Atmos Environ 39:6098–6108 52. Calderaro V, Agnoli S (2007) Passive heating and cooling strategies in an approach of retrofit in Rome. Energ Build 39:875–885
Chapter 5
Permeable Coverings
5.1 Introduction As observed earlier, plastic emulsion paints are typically employed for preventing moist air transfer from indoor environments. These nearly always depict higher moisture content due to the presence of different moisture sources located indoors [1–8]. Also, the buffering effect must be considered. The buffering effect is related to the local moist air condensation in some building zones, causing damage and loss of energy in the buildings. However, recent research demonstrated the feasibility of permeable coverings as a complimentary tool of the HVAC system to control indoor environments and excess partial vapour pressure [9–11]. Furthermore, despite the fact that it is not the more important effect of the permeable coverings, it was observed that this same effect can influence indoor air temperature in ranges from 2 to 3C, as observed by Gaur [12]. To characterise this effect in laboratory studies, sorption isotherm curves were developed, revealing new constants for the materials employed [13, 14]. For example, the first constant to be defined is the constant of proportionality of moisture transfer between air and materials, and the difference in moisture content. The second constant is the material time parameter, which relates moisture content with the history of humidity evolution in indoor air, revealing the concept of memory of a material, as observed earlier about material models. In an attempt to predict indoor conditions by simulation [15, 16], laboratory studies are usually employed. Simulations results are tested against real indoor environment results. Only a few studies have tried to understand how to adapt this simulation to real situations [15, 16]. An hourly study was carried out in this work, a continuation of previous works on the effect of permeable internal coverings Also, the methodology to calculate this effect in real buildings will be developed.
J. A. Orosa and A. C. Oliveira, Passive Methods as a Solution for Improving Indoor Environments, Green Energy and Technology, DOI: 10.1007/978-1-4471-2336-1_5, Springer-Verlag London Limited 2012
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Earlier, passive methods were revealed as the best solution to reduce energy consumption in new buildings, with low cost and reaching adequate levels of thermal comfort. In this regard, we found that permeable coverings are one of the passive methods that are currently being considered as one of the best solutions for buildings construction. Passive solar methods must be considered and applied during the building design process and there are only a few opportunities to correct the design once building construction is finished. On the other hand, different ventilation methods can be improved upon during the design process and implemented once the building is constructed. In this case, natural ventilation is expected to be an adequate method to improve IAQ and complement the building characteristics to reach an adequate thermal inertia. Most of these passive methods have been analysed in previous research works, clearly revealing their hourly behaviour and their design conditions and operation. However, this is not well defined for all passive methods, such as in the case of permeable coverings. These internal coverings have been analysed in laboratory studies, where their effects and characteristic curves were developed. Some of these results were applied in buildings. The methodology was only developed for wooden structures in cold regions, where the results revealed to be adequate. However, internal coverings have not been analysed in climates with buildings using different construction materials, such as those employed in Spain, Portugal, France and Italy, among others. In these countries, we find different wall structures composed of concrete, bricks, and barrier and vapour barrier. From an earlier research developed in laboratory studies, the effect of different internal coverings relates not only to indoor temperature or relative humidity, but also to local thermal comfort and energy consumption. Moist air and local thermal comfort models revealed how to define energy consumption and perception of IAQ. The study of these parameters based on statistical studies and in an adequate selection of the data to be employed must the developed. For example, we employed mean data obtained during the occupied and unoccupied period and also the hourly data obtained during the first hours of occupation. Some of these studies are related to the perception of indoor quality when clients and workers arrive at the offices. Other studies revealed the mean reduction of the expected energy consumption. When local thermal comfort, perception of IAQ and energy consumption is analysed every hour, new conclusions are obtained. The first initial moist air parameter to be analysed is enthalpy because it is related to Acc and energy consumption, to reach the temperature and relative humidity values. Results revealed that during the occupation period in the summer, the percentage values of dissatisfaction with warm respiratory comfort with impermeable coverings is nearly 40%, whereas 15% are obtained with permeable coverings. Also, permeable coverings revealed Acc between 0.1 and 0.2, which are related to a good perception of IAQ. During the unoccupied period, impermeable coverings depicted values of -0.1 and the perception of indoor air at 14:00 h with -0.2. Hence, during the occupied period, the percentage of PD was higher with
5.1 Introduction
101
Fig. 5.1 Office building distribution Internal wall Employees’ zone
External wall
Clients’ zone
permeable coverings. This is due to a higher partial vapour pressure at the same indoor air temperature than with impermeable ones and, hence, higher enthalpy. This unexpected result can be expressed in another way: if we reduce indoor air temperature and enthalpy, we reach the same Acc as with impermeable coverings but with lower energy consumption. Finally, new parameters, such as productivity, must be taken into account in future research. Recent works revealed that 10% PD implies a productivity increase of 1.5%. In our case study, it reached values of 7.5% of worker productivity, which is a high value. In conclusion, despite the fact that impermeable coverings reveal higher acceptability in the occupied period during the winter season, they present serious disadvantages, for example, peaks of energy consumption to reach adequate indoor air conditions and a higher percentage of PD during the summer.
5.1.1 Buildings Office buildings, as an object of study, depict a heating system that controls indoor temperature during the winter season and operate with free floating temperature during the summer. Also, relative humidity is not controlled in any season, except by the expected effect of permeable coverings. The office buildings analysed have walls in brick and concrete, with two main zones (Figs. 5.1, 5.2). Building wall construction is not the only parameter that influences the buffering effect. There are other parameters, such as internal coverings, which can influence this effect. In this regard, different internal coverings were considered in these studies. Examples of internal coverings are found in building construction (Table 5.1), such as paper, plaster, wood, paint or plastic covering. As seen in Table 5.1, these internal coverings present different permeability levels that influence the wall buffering effect. Despite the fact that glass is not usually considered as an internal covering, it exerts a more clear impermeable effect and must be considered as influencing the buffering effect. Glazed areas are related to the number of windows and doors and the surface of the wall.
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Fig. 5.2 Building wall construction
1
2
1 Outdoor 2 Concrete
Table 5.1 Types of coverings and permeability
3
4
3 Brick 4 Air Barrier
5
6 5 Insulation 6 Brick
7
8 7 Plaster 8 Indoor
Type
Covering
Permeability (kg/m s Pa)
P SP E SI I
Paper and plaster Wood Paint Plastic Glass
1.44e-10 2.55e-12 1.75e-12 0.8e-12 1e-50
P permeable; I impermeable; SI/SP semi-permeable; E no effect for a relative humidity interval of 0.8–0.95
5.1.2 Ventilation Rate Ventilation rate was determined in accordance with the tracer gas decay method, in this case using CF6 as tracer gas with a Brüel and Kjaer multi-sampler. The air exchange rates depend on the number of infiltrations and the degree of air changes due to the ventilation system. To measure this, a multi-gas 1302 monitor was used, with an uncertainty less than ±0.002 h-1.
5.1.3 Data Loggers Measurements of indoor temperature and relative humidity in 25 office buildings were done by means of Tynitag data loggers (Meaco Europe), which can register indoor environments for months, with sampling periods lasting from minutes to hours with accuracies of +0.2C and +3% HR, respectively. These data loggers were located in accordance with Hens’ method [2] and International Organisation for Standardization (ISO) 7730 [17], and considering the different characteristics in office buildings, such as heat sources from computers, that may alter measured values. At the same time, temperature and humidity were also measured using an Innova 1221 data logger equipped with a temperature transducer MM0034, based on thermistor technology, and a humidity transducer MM0037 with a light-emitting diode, a light sensitive transistor, a mirror, a cooling element and a thermistor. The accuracies were +0.2 and +0.3C (dew-point temperature), respectively.
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5.1.4 Weather Stations Outdoor climatic conditions were analysed after indoor conditions were measured. In this case, values were obtained from weather stations, located next to the city, belonging to the Environmental Information System of Galicia (SIAM) [18, 19]. This organisation has 23 weather stations located in the region, where important parameters of weather conditions are measured with a frequency of 10 min and sent, in real time, to the Web.
5.1.5 Working Periods Commonly, working periods in office buildings are from 08:30 to 15:00 h and clients are attended to from 09:00 to 14:00 h. The first and last hours of occupation are of special interest, due to the fact that they are related to the first impression of IAQ. Hence, an adequate indoor air perception implies a reduction in the HVAC system energy consumption. This energy consumption is related to the effect of permeable coverings in the real indoor environments.
5.2 New Method to Define Permeable Covering Effect 5.2.1 Introduction The effect of permeable coverings was analysed in real case studies. For example, in office buildings all located in the same area with the same wall construction and indoor activities, permeable covering effect was analysed—the only difference between buildings is the type of covering used. The buildings depict different internal coverings that were classified, in accordance with their expected permeability level, as: impermeable, semi-permeable and permeable. However, their real effect must be analysed in real conditions, to include the effect of superficial treatments, such as paints, which can reduce or, in some cases, annihilate material permeability. In this regard, the real behaviour of construction materials is not well known as yet. Laboratory studies revealed that concrete can absorb moisture and volatile organic compounds (VOCs) from the surrounding air, and thus we may consider this effect to be incorporated in real buildings. Recent research tried to simulate and predict this effect as a combination of sorption isotherms, as observed by Simonson [20].With this prediction; it was possible to obtain a laboratory relationship between indoor environments when a weather change is expected.
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In this section, a new methodology to predict internal covering working periods, based on statistical analyses of real measured data in buildings, is developed.
5.2.2 Measuring Periods Measuring periods are affiliated to indoor activities. As observed earlier, these offices depict the same number of working hours and moisture sources. The working period lasts five hours, from 9:00 to 14:00 h, with the remaining hours being considered as unoccupied. The unoccupied period is of special interest because, under low ventilation rates, internal coverings clearly depict the stronger effects. In this regard, coverings and wall material moisture contents depend on the building indoor ambient history, in accordance with the time constant parameter. This parameter is related to the memory of its materials. It expresses the period of time that has to elapse, to ensure that indoor ambient conditions do not affect actual material sorption behaviour [21]. During the measuring period, indoor air temperature and relative humidity were recorded by means of data loggers. Also, the outdoor air temperature and related humidity was obtained from weather stations located near the buildings.
5.2.3 Statistical Analysis The methodology employed in this study was developed to define internal covering working periods in real environments. Consequently, it must relate simultaneous and complicated parameters that typically occur in those environments. Also, these represent more complicated conditions than laboratory studies. As observed earlier, a statistical analysis allows us to define the most important internal covering working periods and their real effect in buildings. The first step in this methodology was to define the main variables that reveal a moisture buffering effect to be employed in the statistical analysis. In this case, vapour pressure differential between indoor/outdoor conditions was selected because it combines indoor air temperature and relative humidity in a single parameter. Once the main variable was defined, statistical analyses must be selected. During statistical studies, one-way ANOVA was selected. Hence, the average comparison was based on the analysis of variance of a factor for a level of significance of 0.05. For this study, daily average values for summer and winter seasons, during occupied and unoccupied periods, were calculated, to define groups of indoor ambiences with identical behaviour. To develop these statistical analyses, SPSS and Microsoft Excel software resources were employed.
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Outdoor temperature 20
Temperature (ºC)
18 16 14 12 10 8 6 4 2 0
December
January
February
March
April
May
June
July
August
Sept
October
November
Month
Fig. 5.3 Monthly average outdoor temperature
5.2.4 Results and Discussion Results revealed a mild climate with a humid summer and winter season. Specifically, a mean outdoor temperature between 18 and 20C during the summer season and temperatures between 4 and 7C during the winter season were recorded (Fig. 5.3). At the same time, mean outdoor relative humidities between 65 and 80% can be found throughout the year. Finally, outdoor wind velocity depicts values between 1.7 and 2.5 m/s throughout the year. However, some peaks of wind speed velocity can be obtained in March, reaching a mean value of 3.5 m/s (Fig. 5.3). An average ventilation rate of 1 h-1 was recorded. On the other hand, during unoccupied periods an air change rate of 0.5 h-1 was measured (Figs. 5.3, 5.4, 5.5). In an initial study on the mean value of partial vapour pressure during these periods, it was observed that indoor partial vapour pressure is higher than outdoors. This is due to the fact that indoor humidity is the sum of that obtained from outdoor air and generated by different indoor moisture sources. Permeable coverings tend to revert these situations [2], as revealed in laboratory studies. The studied offices have an internal coating (Table 5.2), sorted as permeable (P), impermeable (I), semi-permeable (SP) or semi-impermeable (SI). The components of the walls’ structure are concrete and bricks. This buffer effect of the building envelope on IAQ may be beneficial, since peak concentrations are reduced and compounds may be stored in the walls of a house. In Table 5.2, paper plaster coverings and plastic impermeable coverings are listed. In accordance with the expected permeability level of internal coverings, different coverings were classified into three groups, as revealed earlier. Specifically, internal coverings typically employed in office buildings were subjects of this study; for example: plastic, paper and wood. In research, plastic is employed and associated with no permeable covering; it depicts the main advantages for an easy cleaning process due to the fact that it supports water effects. We find that paper and wood are typically defined as permeable coverings.
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Wind velocity (m/s)
4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00
DecemberJanuary February March
April
May
June
July
August
Sept
OctoberNovember
August
Sept
October November
Month
Fig. 5.4 Monthly average outdoor wind velocity
Outdoor relative humidity 90
Relative humidity (%)
80 70 60 50 40 30 20 10 0 DecemberJanuary February March
April
May June Month
July
Fig. 5.5 Monthly average outdoor relative humidity
Table 5.2 Examples of wall coatings in some of the studied offices
Office
Coating
P3 P4 P5 SP8 E2 SI1 SI2 I3
Paper Plaster Plastic Plastic Paint Wood Plastic Glass
However, to prevent heat and mass transfer from the indoor air to building structures, most of these coverings are externally treated with paints. Hence, permeability of these external coverings is not always found.
5.2 New Method to Define Permeable Covering Effect
107
200 100
Partial vapor pressure excess
0 -100
P1
P2
SP1
P3
P4
P5
SP2
SP3
SI1
E1
SP4
SI2
SP5
E2
SP6 SP7
E3
SP8
E4
E5
I1
E6
I2
I3
I4
-200 -300 -400 -500 -600 -700 -800 Internal covering
Fig. 5.6 Partial vapour pressure excess during the summer season
According to the initial classification (Table 5.2), plaster and paper are permeable coverings, plastic is semi-impermeable and glass is the best example of impermeable covering. After an ANOVA analysis, three groups of buildings with the same indoor air behaviour were defined as A, B and C. As expected, these groups are similar to the classification of buildings according to the expected internal covering permeability level. To understand internal covering behaviour, Fig. 5.6 must be understood. This figure reveals the partial vapour pressure excess in an indoor ambience of an office with respect to the outdoor air, during the summer season. ‘‘x’’ axis represents office buildings, analysed and ordered in accordance with internal covering, from permeable to impermeable. For example, P1 is the office building with the most permeable internal covering. The degree of permeability for each office is in agreement. The partial vapour pressure excess was calculated as a mean value for the office building during an unoccupied period. All of them were harmony with the value of this difference, as seen in the ‘‘x’’ axis. From Fig. 5.6, we can conclude that during summer three groups of internal coverings could be defined in accordance with the partial vapour pressure difference. For example, permeable coverings 1, 2, 3, 4 and 5 are located on the left side of the chart. Semi-permeable coverings are located in the middle of the figure, and impermeable on the right. Once the indoor ambience during summer was analysed, it was time to analyse winter conditions (Fig. 5.7). In Fig. 5.7, semi-permeable coverings are located on the left side and permeable coverings are located on the right. These results are not in accordance with the
108
5 Permeable Coverings 500 400
Partial vapor pressure excess
300 200 100 0 -100
SP4 SP1 SP2 SP5 SP3
I1
SP7
I2
SP8
I3
SP6
E1
I4
E6
E2
E3
SI1
E5
E4
P2
P5
SI2
P1
P3
P4
-200 -300 -400 -500 Internal covering
Fig. 5.7 Partial vapour pressure excess during the winter season
expected values. However, during winter, permeable covering effect is clearer than impermeable one; impermeable coverings cannot act as in the summer season. Consequently, impermeable coverings are located in the middle of the figure. To compare real indoor ambient evolution with time for different internal coverings and to find groups of offices that reveal the same behaviour, a one-way ANOVA study was employed. Specifically, this statistical study must employ the significance index. This index reveals if the work of each internal covering is the same as the others or not. Once the possible evolution of each index in each different office building has been analysed, the time is right to apply statistical analyses. In this regard, Tables 5.3 and 5.4 reveal the statistical analyses of significance. The study also revealed the office buildings experiences the same evolution with time and the same behaviour. To understand the effect of each group of internal coverings [6, 22], the significance index of mean, maximum and minimum values was analysed. Once the tables were analysed, we found three groups—A, B and C—that depend on season. For example, during the summer season, the permeable behaviour of group A was composed of P1, P2, P3 and SP1; group C was composed of I1 and group B, was composed of other office buildings. However, in the winter, three groups A, B and C are composed of different office buildings. For example, the permeable behaviour of group C was formed by P1, P3 and P4; the impermeable group is formed by SP1, SP2 and SP4 and the semi-permeable group is formed by other office buildings. From this study, we understand that the behaviour of the same internal covering is not the same during the two seasons. Specifically, we concluded that permeable coverings P1 and P3 experience the same permeable behaviour for the two seasons.
5.2 New Method to Define Permeable Covering Effect Table 5.3 Internal–external partial vapour pressure difference in summer Summer Group A
Group B
Group C
P1 P2 SP1 P3 P4 P5 SP2 SP3 SI1 E1 SP4 SI2 SP5 E2 SP6 SP7 E3 SP8 E4 E5 I1 E6 I2 I3 I4
109
Mean -665.2 -609.0 -592.3 -587.9 -491.1 -430.5 -402.9 -384.2 -324.1 -296.3 -243.5 -236.0 -195.5 -174.4 -170.2 -166.9 -148.3 -135.9 -62.4 -49.7 -38.8 -9.0 7.6 50.5 65.4
I impermeable; P permeable; SI semi-impermeable; SP semi-permeable; E no effect
An in-depth analysis revealed that the internal covering that experiences the same behaviour during the two seasons is the paper; this internal covering should be analysed in future research. Finally, despite the fact that groups are defined in accordance with the statistical index, we note some tendencies in the indoor air evolution. For example, as was the case of impermeable coverings I4, I2 and I3 during the summer season and, during the winter season, it was not so intense as to define a new group. However, for the summer, we note the impermeable covering effect in Table 5.3. Once the groups were defined in accordance with their behaviour during the occupied and unoccupied periods, it was time to analyse the instantaneous evolution. For this, the hourly evolution of the building was analysed and conclusions can be obtained. Specifically, permeable coverings revealed the lower maximum humidity in summer and higher minimum humidity in winter, than the impermeable case; internal coverings tend to reduce indoor air relative humidity variation (Figs. 5.8, 5.9, 5.10, 5.11).
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Table 5.4 Internal–external partial vapour pressure difference in winter Winter Mean SP4 SP1 SP2 SP5 SP3 I1 SP7 I2 SP8 I3 SP6 E1 I4 E6 E2 E3 SI1 E5 E4 P2 P5 SI2 P1 P3 P4
-379.4 -322.5 -236.5 -179.5 -166.4 -149.5 -138.3 -135.5 -84.4 -56.6 -54.7 -50.1 -43.9 -8.1 9.7 27.3 28.2 34.7 70.4 72.8 81.5 100.1 105.2 167.3 454.2
Group A
Group B
Group C
I impermeable; P permeable; SI semi-impermeable; SP semi-permeable; E no effect
In conclusion, we confirmed that permeable coverings improve indoor conditions, in spite of the use of a barriers and less permeable coverings. The conclusions are in clear accordance with the conclusion obtained for wooden structures under different weather conditions.
5.3 Hourly Study In the previous section, the mean effect of permeable coverings in office buildings during their unoccupied periods was assessed, revealing main groups of indoor environments that depict the same behaviour, due to the same permeability effect of wall internal coverings. This study was developed with the mean value of partial vapour pressure difference between indoor and outdoors, during the unoccupied period. In this section, an instantaneous study of indoor partial vapour pressure excess is carried out, with the aim of defining real hourly working periods of internal
5.3 Hourly Study
111
Partial vapour pressure (Pa)
1600
1400
1200
1000
800
600 Permeable 400 0:00
6:00
Outdoor
12:00
18:00
0:00
Time (days)
Fig. 5.8 Effect of permeable coverings during the winter season
Partial vapour pressure (Pa)
2200
2000
1800
1600
1400
1200 Permeable 1000 0:00
6:00
12:00
Outdoor 18:00
0:00
Time (Hours)
Fig. 5.9 Effect of permeable coverings during the summer season
coverings, despite the fact that occupancy level may be the parameter that controls these effects. In conclusion, from the measured data, we noted that the partial vapour difference between ambiences with permeable and impermeable coverings is more intense in summer than in winter. To define the working periods of different internal coverings for 24 h, the same procedure was developed. Hence, the hourly statistical study with one-way ANOVA was developed and the hourly significance index was obtained (Figs. 5.12, 5.13).
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5 Permeable Coverings
Partial vapour pressure (Pa)
2000
1900
1800
1700
1600
1500 Impermeable 1400 0:00
6:00
12:00
Outdoor 18:00
0:00
Time (Hours)
Fig. 5.10 Effect of impermeable coverings during the summer season
Partial vapour pressure (Pa)
1400 1350 1300 1250 1200 1150 1100 12:00
Impermeable
18:00
0:00
Outdoor
6:00
12:00
Time (days)
Fig. 5.11 Effect of impermeable coverings during the winter season
To define what the hourly working periods are, one must understand the significance index before analysing the charts. When the data of a group of indoor ambiences reveals a significance index under 0.05, the groups are considered different, the internal coverings working in different ways. This procedure allowed us to define what are the real working periods of different internal coverings, and understand which coverings experience the same behaviour with respect to its buffering effect. In this regard, if we analyse the significance index, we find that there is a clear difference in internal covering behaviour between summer and winter. For example, the significance index
5.3 Hourly Study
113
0.3
Winter
0.25
Significance
0.2
0.15
0.1
0.05
0 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Hour
Fig. 5.12 Significance value during the winter season
depicts, during the winter season, a value of 0.05 for nearly 24 h. Only from 14:00 to 18:00 h does this value experience a decrement value below 0.05 (Fig. 5.12). The meaning of this evolution is related with the fact that office buildings experience, during the winter season, a different evolution and only after closing, at 14:00, all offices tend to present the same behaviour. If we now analyse the hourly evolution of the significance index during the summer season, we find that there is a clear difference between the behaviours of the office along time, as can be seen in Fig. 5.13. When the hourly statistical analysis was done, it was concluded that indoor and outdoor ambiences experiment a minimum partial vapour pressure difference value at 5:00. Furthermore, this value is maintained in indoor ambiences till the opening moment. In this regard, we can see that during only one hour all the offices present a different behaviour. This is related with the effect of permeable coverings at first hours of occupation, and it will be analysed in the next section. The first conclusion that we obtain from the hourly study of the significance index is that all the offices have periods of common behaviour. Specifically, these periods are the summer season, and a few hours during winter. This effect is of interest during the summer season in milder climates because it is related to the temperature peaks that occur with the increment of occupation and summer heat. With this new procedure, working periods of coverings could be defined. For example, during the summer season, three working periods could be recognised: the first is the occupation period from 9:00 to 14:00 h; the second is from 14:00 to 19:00 h and the third is from 19:00 to 9:00 h. During the winter season, there were only two different periods—12:00– 19:00 h and 19:00–12:00 h. Despite the fact that these working periods are not the same in winter and summer, summer periods can be employed as references, due
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5 Permeable Coverings Significance
0.09 Summer
0.08 0.07
Significance
0.06 0.05 0.04 0.03 0.02 0.01 0 1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour
Fig. 5.13 Significance value during the summer season
to the fact that covering effects are more intense and that working periods are clearly revealed. During the summer season, it was concluded that indoor conditions in the first hours of occupation are defined by internal covering behaviour during unoccupied periods. This is directly related to a low ventilation rate during this same period (lower than 0.5 ach). During the occupied period, ventilation rate is about 1 ach and its evolution depends on initial conditions, indoor moisture sources and outdoor conditions. On the other hand, another conclusion reached was that this internal covering effect is maintained during the occupation period (09:00–14:00 h), as it was reflected by statistical studies. Once the permeable effect was defined, the energy consumption was calculated in accordance with the internal covering permeability. Results obtained revealed a clear reduction of the energy consumption for the cooling and heating seasons (Table 5.5). The main conclusions are; • in accordance with earlier research, permeable coverings depict clear advantages with respect to impermeable coverings; for example, it was concluded that permeable coverings reveal the lower maximum humidity in summer and higher minimum humidity in winter; internal coverings tend to reduce indoor air relative humidity variation, as seen in Figs. 5.12 and 5.13 • ventilation rate is one of the most important parameters to control permeable covering behaviour; in this regard, during the occupied period, ventilation rate increases indoor relative humidity during winter, reaching bad conditions in buildings with impermeable coverings;
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115
Table 5.5 Mean energy consumption in offices with different internal coverings Permeable Semi-permeable Impermeable Cooling Heating
kWh/m2 year % kWh/m2 year %
0.83 35.0 0.25 18.4
1.71 71.2 0.91 65.0
2.4 100 1.4 100
• during the unoccupied period, internal coverings begin to exert a buffering effect that control indoor ambiences in a dynamic response with air humidity changes; hence, the simultaneous effect of storage capacity and sorption speed, which determines the usefulness of a material as internal covering, is employed; • in our real case study, after the offices were closed for the night, the offices maintained their indoor conditions for about 4 h, in accordance with the results of [23] under low ventilation rate and the Moisture Admittance Model suggested by [14]. Finally, it is important to not only know which mean effect for internal covering, but also which working periods for each covering occur. In this regard, we found that internal coverings experience this effect for some periods, sometimes reaching 4 h after the excessive indoor humidity.
5.4 Improving PAQ with Permeable Coverings During the First Hours of Occupation 5.4.1 Introduction As observed in the previous section, hourly statistical analyses revealed that permeable coverings exert a clear effect during the first hours of occupation. Hence, they are important regarding energy saving and improving the perception of IAQ during the first hours of occupation. Once the statistical study was concluded, new indices for this interpretation must be employed; the same as PD and Acc described in detail in Chap. 2. These models were employed in important research works to analyse the effect in real environments. For example, Simonson [6, 11] and Hameury [3, 4] observed the buffering effect of a huge amount of wood, and concluded that moisture storage has a market effect on the indoor humidity for about 2 weeks, after a change in weather conditions. Hence, these materials control indoor relative humidity [7, 11], complementing or substituting the HVAC system and providing energy savings [8]. Moreover, this effect improves the perception of IAQ [11]. The aim of this research is to define the real effect of permeable coverings on indoor air perception
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5 Permeable Coverings
[24] and warm respiratory comfort [25], during the first hours of occupation of the office buildings analysed in a previous work [26], in accordance with models revealed in earlier chapters.
5.4.2 Methodology Measurements [26] were centred in data loggers that recorded indoor temperature and relative humidity with a time frequency of 5–10 min in each zone. Later, data of occupied and unoccupied periods were separated to apply local thermal comfort and IAQ models and define internal covering effect. An ANOVA was developed to define groups of indoor environments with the same behaviour due to internal covering effect. However, in this research, we developed this statistical analysis during the first hours of occupation, to define the real effect of internal coverings during the night and how it influences the perception of IAQ and local thermal comfort. On the other hand, indoor air temperature and relative humidity for the few hours of the unoccupied period (08:00 h) were averaged and indices were calculated in accordance with [24, 25, 27]. This period is important because of its relation to the real initial perception of indoor ambience during the first hours of occupation, and also because it is related to factors, such as adaptation to perception of indoor air, that must be analysed.
5.4.3 Results and Discussion The initial results obtained from the indoor measured data were that the office buildings, analysed in earlier works [26], present an average air change rate of 1 h-1. Mechanical ventilation does not work during the unoccupied period, and ventilation rate is lower during these periods. As a result, the same tendencies, as in an earlier work, were obtained for this initial period. For example, impermeable coverings presented a high indoor partial vapour pressure during the summer and lowest during the winter. As expected, permeable coverings depict the opposite behaviour. Also, indoor air temperature was nearly the same in the offices, with a mean value of 23C in summer and 19C in winter. When local thermal comfort was analysed, PDIAQ, PDWRC and indoor air AccIAQ and AccWRC parameters were calculated. The results revealed no clear differences between office buildings with permeable and impermeable coverings. There are differences between seasons. For example, Acc was nearly null (acceptable) during the summer, whereas this perception was improved during the winter, with value of 0.5. During winter, the percentage of PD with respect to the warm respiratory thermal comfort was slightly higher with permeable than impermeable coverings,
5.4 Improving PAQ with Permeable Coverings During the First Hours of Occupation
117
2000
1800
Pv (Pa)
1600
1400
1200
1000
800 p1 p2 p3 p4
p5 p6
s1
s2
s3
s4
s5
s6
s7
s8
s9 s10 s11 s12 s13 s14 i1
i2
i3
i4
i5
Office 8:00 Winter
8:00 Summer
Fig. 5.14 Indoor air partial vapour pressure during the first hours of occupation
reaching values of 5% of PD. These values reach 10% in summer, due to the indoor partial vapour pressure values (Figs. 5.14–5.18). As expected, internal permeable coverings not only improved indoor air temperature and relative humidity during the first hours of occupation, but the percentage of PD also improved. Specifically, the percentage of PD, due to the perception of IAQ, revealed a clear difference between permeable and impermeable coverings, reaching a differential value of 25% during the summer season. Finally, despite the fact that our office buildings depict different wall constructions than earlier laboratory and case studies [2, 11], their results revealed the same tendencies.
5.5 Implementation of a Method for Building Certification 5.5.1 Introduction The present European standard on the calculation methodology of energy use for space heating and cooling was developed by the European Committee for Standardization and the ISO [28–31]. The aim is to define a procedure for building energy use qualification. This method can be employed in a quasi-steady state methodology and in an hourly method, which is a more complex calculation procedure. For example, ISO 13790:2008 [32] reveals the calculation methodology of the annual energy use for heating and cooling applicable at building design or maintenance stage. These two calculation procedures for building energetic certification are observed in the chapter on models. On the other hand, the Energy Performance Building Directive states that the energy efficiency of buildings has to be determined in the member states in accordance with their particular climate conditions [33, 34]. Examples of this
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5 Permeable Coverings 28
Temperature (°C)
26 24 22 20 18 16 14 p1
p2 p3
p4
p5
p6
s1
s2
s3
s4
s5
s6
s7
s8
s9 s10 s11 s12 s13 s14 i1
i2
i3
i4
i5
i2
i3
i4
i5
Office 8:00 Winter
8:00 Summer
Fig. 5.15 Indoor air temperature during the first hours of occupation 35 30
PD (%)
25 20 15 10 5 0 p1 p2 p3 p4 p5 p6 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 i1
Office 8:00 Winter
8:00 Summer
Fig. 5.16 Indoor air percentage of dissatisfied persons with warm respiratory thermal comfort
adaptation were observed in research by Jokisalo and Kurnitski [35]. In these works, Finland’s conditions were adjusted in this model. In other works, glazed areas were adjusted [36] and now employed in new software resources [37]. These parameters try to affect the utilisation factor of energy, obtained from passive methods and represented by the ratio Qgain/Qloss, and thermal inertia in buildings. However, there is another parameter to be considered, which is the permeability of internal coverings.
5.5.2 Methodology 5.5.2.1 Office Buildings In this case study, offices in a seven-storey 120 m2 building in the northwest of Spain were analysed. Their construction materials were concrete, with two brick
5.5 Implementation of a Method for Building Certification
119
1
Acc
0.5
0 p1 p2 p3 p4 p5 p6 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10s11s12s13s14 i1 i2 i3 i4 i5
-0.5
-1 Office 8:00 Winter
8:00 Summer
Fig. 5.17 Indoor air acceptability with indoor air quality
35 30
PD (%)
25 20 15 10 5 0 p1 p2 p3 p4 p5 p6 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10s11s12s13s14 i1 i2 i3 i4 i5
Office 8:00 Winter
8:00 Summer
Fig. 5.18 Indoor air percentage of dissatisfied persons with indoor air quality
layers and an internal barrier. The floor, due to its contact with the soil, has no insulation.
5.5.2.2 Energy Calculation Procedure The methodology employed in this work is described earlier in the book. After a clear differentiation of indoor ambiences, based on their buffering effect, their internal covering real activity was classified as permeable, semi-permeable and impermeable [26, 38]. Also, the number of air changes and climatic conditions were obtained every 10 min. On the other hand, the energy consumption was defined in accordance with the set-point temperature [39]. Finally, the effect of permeable coverings over perception on local thermal comfort during the first and last hours of occupation is described in the previous section.
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The aim of this work was to implement the certification model, in accordance with the earlier results obtained on permeable covering effect, as done with glazed areas. The general procedure is based on the curve fit of the real measured data and energy consumption, with respect to the certification model to be defined. If found adequate, then there are new model constants for each internal covering. The first step consists of automating the calculation procedure of the building energy certification model, in accordance with ISO standards. Software resources such as MATLAB, C, Visual Basic and Microsoft Excel are utilised to develop the task. The software needed follows a closed loop to solve, by iteration, the mean constants adjusted to the energy certification model, for different building energy consumptions during the summer and winter seasons. We must keep in mind that ISO standards depict different certification models. Some are simplified methods that need less information than the other cases and depict a lower accuracy. For our case study, we selected the simplified method because the iteration process can easily be improved. Once the certification model has been implemented into a software resource, different input data to obtain the main model constants are needed. In this regard, we related the input data with real measured data. Also, we need to assess energy consumption during different weather conditions, indoor temperature, relative humidity and the description of building structure. As observed earlier, the office buildings were selected because different studies and building structure characteristics were developed earlier. Furthermore, the simulation to define constants and utilisation factor was developed for permeable, semi-permeable and impermeable internal coverings. Once the energy certification model was employed, an adequate curve fit between energy consumption, a0 and partial vapour pressure, for the winter and summer seasons, must be obtained under an adequate correlation factor. Table 5.6 shows software input data needed to simulate the case studies, in accordance with present standards. These data were classified in different groups— building constructive characteristics, the real annual heat gain, thermal zones, setpoint temperatures, the ventilation rate and different constants in accordance with standards.
5.5.3 Results and Discussion As observed earlier, each simulation to define constants and utilisation factor was developed for each internal covering. For example, the relationship between energy consumption and a0 and permeability during summer and winter was defined (Tables 5.7, 5.8, 5.9). The simplified method was selected because less input data are required and also easier for iteration process and data analysis. And, these simplified models are less sensitive than the complete model to errors [40, 41].
5.5 Implementation of a Method for Building Certification
121
Table 5.6 Simulation data for the building energetic certification Simulation parameters data for software Internal heat capacity: 150 kJ/m2K (medium heavy structure) U-value for windows 1.8 W/m2K Residential buildings 4 W/m2 The real annual heat gain Office buildings 5.7 W/m2 From people: 10 kWh/m2 year From lighting: 8 kWh/m2 From equipment: 26 kWh/m2 Total annual heat gain: 44 kWh/m2 year Thermal zones Due to temperature difference between indoor ambiences is lower than 4C, the office can be simulated as only one indoor environment Set-point temperatures Heating was 18C Cooling was 23C. We must remember that there is no cooling system and the ventilation rate to reduce indoor temperature was employed Ventilation rate From 09:00 to 14:00 h 1 volume/hour From 14:00 to 09:00 h 0.5 volume/hour Constants: in accordance with standards, two Building heated only some hours of the day constants were selected for these buildings s0 = 70 A set of values of a0 from 0.1 to 10 were proposed Building constructive characteristics
As can be seen in Tables 5.7, 5.8, 5.9, results revealed that permeable coverings depict the highest utilisation factor and impermeable coverings depict the lowest values for gain/loss ratios between 0.5 and 2. On the other hand, an adequate curve fit between energy consumption a0 and partial vapour pressure difference for the winter was obtained with a correlation factor of r2 = 0.98 (Eqs. 5.5.3.1, 5.5.3.2). Ewinter ¼ 0:220 þ
1:978 9:718 þ 2 pv a0
ð5:5:3:1Þ
where Ewinter denotes energy consumption in the winter (kWh/m2 year), pv is the partial vapour pressure difference between indoors and outdoors (Pa) and a0 is the certification equation constant. Esummer ¼ 0:8258 þ 0:00594 lnða0 Þ þ 3:3759 eða0 Þ
ð5:5:3:2Þ 2
where Esummer denotes energy consumption in the summer (kWh/m year). From these two equations, there is no clear influence of partial vapour pressure (pv) on energy consumption in the summer, whereas the opposite occurs in the winter. This effect is indirectly related with model constants a0 and s0 that are related to energy consumption and utilisation factor.
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Table 5.7 Average energy consumption in offices with different types of internal coverings Impermeable Semi-permeable Permeable Cooling (kWh/m2 year) Heating (kWh/m2 year)
2.4 1.4
1.71 0.91
0.83 0.25
Table 5.8 Calculated constants for energy consumption in accordance with the permeability level of internal coverings—heating season Impermeable Semi-permeable Permeable Heating energy (kWh/m2 year) a0 t0 gH (Heating)
1.4 1.5 70 0.65
0.91 2.6 70 0.78
0.25 10 70 0.96
Table 5.9 Calculated constants for energy consumption in accordance with the permeability level of internal coverings—cooling season Impermeable Semi-permeable Permeable Cooling energy (kWh/m2 year) a0 t0 gC (Cooling)
2.4
1.71
0.83
0.7 70 0.54
1.2 70 0.71
10 70 1.00
From this equation, new certification model constants were defined for different permeability levels of internal coverings, in accordance with Eq. 5.5.3.3 aH ¼ a C ¼ a 0 þ
s 70
ð5:5:3:3Þ
As seen in Tables 5.7, 5.8, 5.9, winter values of a0 from 4.9 for permeable coverings and 2.2 for impermeable coverings are defined, respectively. During the summer, we find values of 1.95–1.2 for permeable and impermeable coverings, respectively. The effect of these new constants on the previous model equation allows us to predict new utilisation models (Eqs. 5.5.3.4, 5.5.3.5), for different seasons and taking into consideration the internal covering permeability level of the employed constants. Defining this model for summer and winter, correlation factors of r2 = 0.92 and r2 = 0.95 were found, respectively. For the cooling season: gC ¼ 0:2267 lnða0 Þ þ 0:6373 For the heating season:
ð5:5:3:4Þ
5.5 Implementation of a Method for Building Certification Fig. 5.19 Utilisation factor during the summer season
123
1
Utilisation factor (h C)
0.8
0.6
0.4
0.2
0
0
permeable
semi-permeable
2
4 6 Gain/loss ratio
gH ¼ 0:1259 lnða0 Þ þ 0:4038
impermeable
8
10
ð5:5:3:5Þ
These two equations, as it done by most of the standards, may be represented in charts like those in Figs. 5.19 and 5.20. The main conclusion obtained from these equations is that, due to a low permeability constant a0 when impermeable coverings are employed during summer and winter seasons, a lower value of utilisation factor was obtained. Hence, actual standards for building certification must consider this. Specifically, values of 0.5 and 0.65 for winter and summer seasons were found. From these results, we expect a reduction of 15% in the utilisation factor, which must be taken into consideration for the certification processes. Finally, a research on the effect of internal coverings in an indoor ambience must be developed. Specifically, a new procedure to combine this effect with the heating, ventilation and air conditioning system must be improved, to obtain a better indoor thermal comfort, perception of IAQ and higher certification level in future buildings. This improvement includes new design methods for an initial stage. Also, some methods are employed in buildings to be constructed and, hence, can be employed during the design process and corrective methods employed once the building is finished.
5.6 Permeable Coverings Methods and Sick Building Syndrome 5.6.1 Introduction The main objective of this book is to reveal the feasibility of permeable coverings as a tool for improving IAQ, energy saving and reducing health problems with
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Fig. 5.20 Utilisation factor during the winter season
1
Utilisation factor (h H)
0.8
0.6
0.4
0.2 permeable
semi-permeable
impermeable
0 0
2
4 6 Gain/loss ratio
8
10
passive methods [26, 42–44], due to the fact that little information was obtained. Furthermore, examples of new procedures to employ these passive control methods are revealed as the basis for future research. As observed earlier, permeable coverings can improve indoor ambiences’ partial vapour pressure, improve Acc and local thermal comfort during the first hours of occupation in buildings. However, permeable coverings depict other applications, for example VOC control. In this regard, recent studies [45] revealed that ventilation rate and permeable coverings are the two adequate tools to reduce VOC concentrations. The last method is related to the sorption/desorption rates of walls surfaces. During the building occupation period, temperature is high and VOC emission rate increases. At the same time, ventilation rate is higher during this same period and VOCs are usually released out of the indoor ambient. During the unoccupied period, ventilation rate is reduced which is when the permeable covering begins to work, reducing VOCs in indoor air. In conclusion, permeable coverings can improve IAQ in different ways. As a result of these two effects, developed by permeable coverings, we relate it to an improvement of subjective parameters, such as the local thermal comfort and perception of IAQ. Also, permeable coverings improve IAQ, with VOC concentration reduction indoors. The result is that we can control the energy consumption with a reduction in ventilation rates and set-point temperature of the HVAC system, to obtain the same perception of IAQ, thermal comfort, Acc and a general reduction of SBS symptoms. SBS was defined by the WHO [46] in 1983 as the occurrence of an increased prevalence of non-specific symptoms among pollutants in determined buildings [26]. It is difficult to detect SBS, and its exact definition depends on the method employed to detect it [42, 43].
5.6 Permeable Coverings Methods and Sick Building Syndrome
125
Fig. 5.21 SBS detection methods
SBS is related to symptoms, such as irritation of eyes and nose, nausea, mental fatigue and lack of concentration, with the occupancy of some indoor ambiences [47]; for example, fleece factor, air temperature, total suspended particles, job stress, mechanical ventilation, illumination [48], dust and noise [49] are related with SBS. In conclusion, SBS is related to a conjunction of symptoms that affect occupants of indoor ambiences and its prevalence over normal values. SBS diagnosis is based on the whole building and not in individuals [47] that occupy the building. To detect the SBS, we employ objective and subjective parameters, see Fig. 5.21, as with most health problems. In this regard, objective parameters, such as air temperature, global temperature, air velocity, carbon monoxide and ozone concentration, were not adequate enough to detect SBS in a whole building. However, a clear relation between CO2 concentration and SBS was obtained [50]. Hence, it must be related to air changes in an indoor ambience and the presence of pollutants due to this low ventilation level. On the other hand, subjective methods, for example questionnaires about health state in accordance with the WHO, 1989, revealed better results for SBS detection.
5.6.2 Objective Parameters The objective of most research works is to control objective parameters to prevent SBS symptoms. In this regard, some questions, such as if we must employ natural or mechanical ventilation, and if we must improve the HVAC system designed to reduce its operating period, are looked into.
5.6.2.1 Natural or Mechanical Ventilation It is commonly accepted that occupants of buildings with mechanical ventilation depict higher SBS symptoms than in naturally ventilated buildings [51]. For example, a clear relationship between this effect and lack of ducts, chillers and humidifiers maintenance [47] (increment in dust in the HVAC system due to lack of maintenance) were detected.
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5.6.2.2 HVAC System and Building Design In conclusion, we employed natural ventilation or, in the case of mechanical ventilation, we considered an adequate HVAC system maintenance to prevent SBS symptoms [52] with duct cleaning. In this case study, a real reduction of TVOC, CO2 and fungal spore concentration were below its typical values, when a duct cleaning was done periodically. Duct cleaning relates to IAQ and air displacement in an indoor environment [53]. However, these are not the only parameters to be considered during building construction and HVAC system design and operation of IAQ. Pollution sources, indoor activity and occupants’ habits are factors related to energy saving. For example, recent studies [54, 55] revealed that an adequate IAQ can reach a reduction of 2.5 times, implying a reduction of nearly three times the ventilation rate to reach the same IAQ.
5.6.3 Subjective Parameters Within subjective parameters, we find aspects, such as job stress and perception of IAQ. Job stress resulted in a direct relationship to SBS symptoms for people working in different environmental conditions, as observed by Hedge [47]. For example, the number of hours that a computer is used by workers resulted in direct relation to the SBS symptoms [50]. When job satisfaction was analysed in recent studies, it revealed that air quality, ventilation and working area temperature parameters are related to a better satisfaction level [53], improving workers concentration and productivity [47]. To obtain this job satisfaction, questionnaires are the typical tool to obtain real data about this subjective state, as was revealed with the local thermal comfort and perception of IAQ in Chap. 2. In this regard, a questionnaire must depict the following elements: 1. A questionnaire zone related to symptoms. 2. Another zone that relates these symptoms with building construction and region. 3. Take special care to reveal a clear difference between symptoms to get an adequate conclusion. 4. The last part of questionnaire must depict indication about the frequency scale to record the occurrence of symptoms and discomfort [56]. 5. Questionnaire must be redundant in its questions to improve the time spent by an occupant for symptom description. 6. To define perception of IAQ and Acc, PD and Acc models can be employed. In general, we conclude that SBS is still an unsolved problem due to the importance of subjective parameters that make it difficult to detect, locate each cause and employ corrective actions to reduce or eliminate SBS presence.
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Like in local thermal comfort studies, this subjective perception of indoor environments is of interest when we try to detect and analyse the SBS.
References 1. Padfield T (1998) The role of absorbent building materials in moderating changes of relative humidity. PhD thesis, Department of structural Engineering and Materials, Technical University of Denmark 2. Hens H (2008) Indoor climate in students rooms: measured values. IEA-EXCO energy conservation in buildings and community systems annex 41 ‘‘moist-eng’’ Glasgow meeting. http://www.byg.dtu.dk/upload/institutter/byg/publications/rapporter/byg-r191.pdf. Accessed 2 Feb 2011 3. Hameury S (2005) Moisture buffering capacity of heavy timber structures directly exposed to an indoor climate: a numerical study. Build Environ 40(10):1400–1412 4. Hameury S, Lundstrom T (2004) Contribution of indoor exposed massive wood to a good indoor climate: in situ measurements campaign. Energ Build 36:281–292 5. Simonson CJ, Salonvaara M, Ojalen T (2001) Improving indoor climate and comfort with wooden structures. Espoo 2001, VTT Publications, Technical Research Centre of Finland, 431.200 p ? app 91 p 6. Salonvaara MH, Simonson CJ (2000). Mass transfer between indoor air and a porous building envelope: part II: validation and numerical studies. Proc Healthy Build 3:477–482 7. Aydin I, Colakoglu G, Colak S, Demirkir C (2006) Effects of moisture content on formaldehyde emission and mechanical properties of plywood. Build Environ 41(10): 1311–1316 8. Osanyintola OF, Simonson CJ (2006) Moisture buffering capacity of hygroscopic building materials: experimental facilities and energy impact. Energ Build 38:1270–1282 9. Karlsson JF, Moshfegh B (2007) A comprehensive investigation of a low-energy building in Sweden. Renew Energ 32:1830–1841 10. Haghighat F, De Bellis L (1998) Material emission rates: literature review, and the impact of indoor air temperature and relative humidity. Build Environ 5:261–277 11. Simonson CJ, Salonvaara M, Ojalen T (2002) The effect of structures on indoor humidity— possibility to improve comfort and perceived air quality. Indoor Air 12:243–251 12. Gaur RC, Bansal NK (2002) Effect of moisture transfer across building components on room temperature. Build Environ 37:11–17 13. Talukdar P, Olutmayin S, Osanyintola OF, Simonson CJ (2007) An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials: part I: experimental facility and material property data. Int J Heat Mass Tran 50:4527–4539 14. Plathner P, Littler J, Stephen R (1999) Dynamic water vapor sorption: measurement and modelling. Proc Indoor Air 1:720–725 15. Olutimayin S, Simonson CJ (2005) Measuring and modelling vapor boundary layer growth during transient diffusion heat and moisture transfer in cellulose insulation. Int J Heat Mass Tran 48:3319–3330 16. Talukdar P, Osanyintola OF, Olutmayin S, Simonson CJ (2007) An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials: part II: experimental, numerical and analytical data. Int J Heat Mass Tran 50:4527–4539 doi:10.1016/j.ijheatmasstransfer.2007.03.025 17. International Standard ISO 7730-2005 (2005) Ergonomics of thermal environment: analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD
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5 Permeable Coverings indices and local thermal comfort criteria. International Organization for Standardization, Geneva, 2005 Environmental Information system of Galicia (2007) SIAM. http://www.Siam-cma.org. Accessed 2 Feb 2011 MeteoGalicia. Anuario climatoloxico de Galicia (2002). Consellería de Medio Ambiente. Xunta de Galicia, ISBN: 84-453-3520-0 Yongling W, Ruilum Z, Zizhong D (2000). Primary research on moisture absorption and desorption function of aerocrete as building element material for dehumidification. Proc Healthy Build 3: Plathner P, Littler J, Stephen R (1999) Dynamic water vapour sorption: measurement and modelling. Proc Indoor Air Qual 99(1):720–725 Simonson CJ, Tuomo O (2000) Moisture performance of buildings envelopes with no plastic vapor retarders in cold climates. Proc Healthy Build 3:177–122 Simonson CJ, Salonvaara MH (2007) Mass transfer between indoor air and porous building materials: part I: experimental facility and material property data. Int J Heat Mass Tran 50:4527–4539 Fang L, Clausen G, Fanger PO (1996) The impact of temperature and humidity on perception and emission of indoor air pollutants. Proc Indoor Air 4:349–353 Toftum J, Jorgensen AS, Fanger PO (1998) Upper limits of air humidity for preventing warm respiratory discomfort. Energ Build 28:15–23 Orosa JA, Baaliña A (2008) Passive climate control in Spanish office buildings for long periods of time. Build Environ 43(12):2005–2012 Toftum J, Jorgensen AS, Fanger PO (1998) Upper limits for indoor air humidity to avoid uncomfortably humid skin. Energ Build 28:1–13 ASHRAE (2009) Energy estimating and modeling methods. In: ASHRAE handbook, fundamental, Chap 19, Atlanta ISO/DIS 13790:2005 (2005) Thermal performance of buildings calculation of energy use for space heating and cooling. International Organization for Standardization, Geneva Standard SFS-EN 832 (1998) Thermal performance of buildings: calculation of energy use for heating residential buildings Jokisalo UA, Kurnitskia J (2007) Performance of EN ISO 13790 utilization factor heat demand calculation method in a cold climate. Energ Build 39:2 Millet JR (2007) The simple hourly method of prEN 13790: a dynamic method for the future. In: Proceedings of Clima 2007 WellBeing Indoors Ministerio de Industria Turismo y Comercio (2007) RITE: Reglamento de Instalaciones Térmicas en los Edificios. Murcia (España) MeteoGalicia (2011) Consellería de Medio Ambiente, Territorio e Infraestruturas Santiago de Compostela. A Coruña (España). http://www.meteogalicia.es. Accessed 2 Feb 2011 Jokisalo J, Kurnitski J (2007) Performance of EN ISO 13790 utilization factor heat demand calculation method in a cold climate. Energ Build 39:236–247 Corrado V, Mechri HE, Fabrizio E (2007) Building energy performance assessment through simplified models: application of the ISO 13790 quasi-steady state method. In: Proceedings of building simulation ATECYR (2008) DTIE 7.03. Entrada de datos a los programas LIDER y CALENER VyP. Madrid (España) Orosa JA, Baaliña A (2009) Improving PAQ and comfort conditions in Spanish office buildings with passive climate control. Build Environ 44(3):502–508 Orosa JA, Oliveira AC (2009) Energy saving with passive climate control methods in Spanish office buildings. Energ Build 41(8):823–828 Van der Veken J, Saelens D, Verbeeck G, Hens H (2004) Comparison of steady state and dynamic building environment simulation program. In: Proceedings of the international buildings IX ASHRAE conference on the performance of exterior envelopes of whole buildings, Florida. https://lirias.kuleuven.be/handle/123456789/204376. Accessed 2 Feb 2011
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41. De Meulenaer V, Van der Veken J, Verbeeck G, Hens H (2005) Comparison of measurements and simulations of a passive house. In: Proceedings of the 9th international IBPSA conference: international IBPSA conference edición: 9. Montreal, Canada, pp 769–776. https://lirias. kuleuven.be/handle/123456789/204392 42. Orosa JA, Baaliña A (2009) Improving PAQ and comfort conditions in Spanish office buildings with passive climate control. Build Environ 443:502–508 43. Orosa JA, Oliveira AC (2009) Hourly indoor thermal comfort and air quality acceptance with passive climate control methods. Renew Energ 34(12):2735–2742 44. Orosa JA, Oliveira AC (2009) Energy saving with passive climate control methods in Spanish office buildings. Energ Build 41:823–828 45. Seo J, Kato S, Ataka Y, Chino S (2009) Performance test for evaluating the reduction of VOCs in rooms and evaluating the lifetime of sorptive building materials. Build Environ 44:207–215 46. World Health Organization (1989) Indoor air quality: organic pollutants. WHO Regional Office for Europe (EURO Report and Studies No. 111), Copenhagen 47. Hedge A, Erickson WA, Rubin G (1996) Predicting sick building syndrome at the individual and aggregate levels. Enviro Int 22(1):3–19 48. James PAB, Bahaj AS (2005) Smart glazing solutions to glare and solar gain: a sick building case study. Energ Build 37:1058–1067 49. Thörn A (1998) The sick building syndrome: a diagnostic dilemma. Soc Sci Med 47(9):1307–1312 50. Gupta S, Khare M, Goyal R (2007) Sick building syndrome—a case study in a multi-storey centrally air-conditioned building in the Delhi City. Build Environ 42:2797–2809 51. De Magalhaes Rios JL, Boechat JL, Gioda A, Dos Santos CY, De Aquino Neto FR, Lapae Silva JR (2009) Symptoms prevalence among office workers of a sealed versus a non-sealed building: associations to indoor air quality. Environ Int 35:1136–1141 52. Kolari S, Heikkilä-Kallio U, Luoma M, Pasanen P, Coronen P, Nykyri E, Reijula K (2005) The effect of Duct clearing on perceived work environment and symptoms of office employees in non-problem buildings. Build Environ 40:1665–1671 53. Haghighat F, Donnini G (1999) Impact of psycho-social factors on perception of the indoor air environment studies in 12 office buildings. Build Environ 34:479–503 54. Wargocki P, Bakó-Biró Z, Clausen G, Fanger PO (2002) Air quality in a simulated office environment as a result of reducing pollution sources and increasing ventilation. Energ Build 34:775–783 55. Assimakopoulos VD, Helmis CG (2004) On the study of a sick building: the case of Athens air traffic control tower. Energ Build 36:15–22 56. Raw GJ, Roys MAS, Whitehead C, Tong D (1996) Questionnaire design for sick building syndrome: an empirical comparison of options. Environ Int 22(1):61–72
Chapter 6
Future Research Work
6.1 Introduction In this chapter, future research works for different indoor environments are discussed. It is interesting to recall the previously presented classification of indoor environments (Fig. 6.1). In accordance with Fig. 6.1, structure, results and future research must be developed for these indoor environments. Specifically, two groups can be joined as one—material preservation and energy consumption. These two groups are a clear function of building constructive parameters, and how it was designed to improve HVAC system operation. The final section analyses another research in the area of passive methods and environments.
6.2 Energy Consumption, Thermal Comfort and Preservation of Materials As stated before, indoor air, energy consumption, thermal comfort and preservation of materials can be analysed as a function of some general terms; hence, we find thermal inertia, ventilation rate and HVAC system improvement.
6.2.1 Thermal Inertia As stated earlier, thermal inertia is a passive method employed for energy saving in indoor environments. Furthermore, recent research includes the same thermal inertia as another parameter to be considered while designing and operating
J. A. Orosa and A. C. Oliveira, Passive Methods as a Solution for Improving Indoor Environments, Green Energy and Technology, DOI: 10.1007/978-1-4471-2336-1_6, Springer-Verlag London Limited 2012
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Fig. 6.1 Indoor environment classification
HVAC systems, due to its clear advantages. For example, a few authors suggested that buildings with higher thermal inertia can experience better indoor environments, due to lower temperature and relative humidity variations [1]. Hence, an improvement in thermal comfort and perception of indoor environments are achieved with the same energy consumption [2]. On the other hand, thermal inertia can be modified by the effect of parameters such as building construction materials and other materials that are present in the same environment. Specifically, the effect of phase changes on moisture content in building materials can modify the thermal inertia [3]. As stated earlier, a key parameter that changes building thermal inertia is air ventilation. In this regard, in a building with higher air exchange rates thermal and moisture inertia can be significantly changed. Finally, software resources, such as Ham tools, are currently the only way to reach a clear understanding of building indoor environment. Recent research revealed that this open source software can be validated with real case studies and propose little modifications to begin designing and/or operating HVAC systems. When thermal inertia was analysed in old buildings, several conclusions could be obtained. For example, lower energy consumption in old buildings with thick walls, during the heating season, for the same thermal comfort conditions. These old buildings depict the lower maximum and the higher mean temperature in the indoor environment. Old buildings with thick walls depict a higher thermal inertia. However, this thermal inertia can be influenced by solar heat gains and ventilation. Specifically, air infiltration through cracks can alter this inertia. Hence, adequate maintenance of these buildings is needed to obtain the expected energy saving [4]. Currently, the International Energy Agency (IEA) is working on different topics related to heat and mass transfer through building envelopes, HVAC systems and ventilation, and developing new software resources based on real case studies, as a continuation of Annexes 41 and 17. Once thermal inertia was simulated with different software resources, new research works on how to improve indoor thermal comfort based on the thermal inertia effect were proposed. In this regard, relationship between weather, thermal inertia, people and sound are some new parameters for analysis.
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Recent research revealed that models of indoor minimum temperature with respect to outdoor weather conditions can be obtained by multiple regression of simulated measured data. Furthermore, a time-dependent thermal inertia model is obtained and employed by HAM tools to predict indoor conditions as a function of outdoor air temperature [5]. The more surprising conclusion was that this thermal inertia model can be simplified for a period, becoming the weather model. Furthermore, when the initial indoor temperature is 18.5C, it becomes an adaptive thermal comfort model. This result must be improved on future research, with an in-depth analysis on the optimal wall thickness to obtain the best thermal comfort conditions, in accordance with local weather conditions. This new building design methodology, based on local weather conditions to define the best indoor environments, implies a reduction in HVAC energy consumption. Hence, we conclude that HAM tools are defined as the best software tool to predict the effect of thermal inertia and its relationship with weather and thermal comfort.
6.2.2 Advances in Natural Ventilation In the past few years, based on natural and mechanical ventilation procedures, a new ventilation method named hybrid ventilation was proposed. In this regard, recent research was developed by the IEA in its Annex 35 entitled ‘‘hybrid ventilation in new and retrofitted buildings’’ with the aim of defining the ventilation level needed to improve energy consumption with a better thermal comfort and an adequate IAQ. During this research process, two models were developed: HYBCELL1.0 and SPARK. In accordance with the general methodology to validate software resources, it was evaluated with respect to real case studies. From this research on hybrid ventilation, different conclusions were obtained: • First, during winter, despite the fact that hybrid ventilation depicts higher energy consumption, it depicts lower mean and maximum carbon dioxide concentration values. • Second, during spring, it depicts a higher energy consumption with a lower difference than in winter. • Third, during summer, hybrid ventilation depicts lower energy consumption than others. However, it depicts a little higher carbon dioxide concentration in measured indoor environments and the worst air renovation process. To summarise, recent research [6] revealed that hybrid and mixing ventilation methods are adequate enough to remove indoor contaminants. Furthermore, actual models to simulate ventilation resulted in clear agreement with real case studies. Hence, it is possible to simulate with an adequate accuracy the effect of different ventilation methods in indoor environments.
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Fig. 6.2 HVAC improvement by passive methods
6.2.3 HVAC Improvement by Passive Methods To improve HVAC system operation, different actuation methods can be employed, for instance, building constructive characteristics, ventilation methods and HVAC control systems (Fig. 6.2).
6.2.3.1 Building Constructive Characteristics In the past few years, research revealed new methodologies to improve indoor environments, using passive methods. However, it is a recent concept that demonstrated its effects but without a clear definition of working periods and main parameters, as well as adjustment principles. Specifically, it is important to develop a methodology that allows to define, at the design stage, the type of internal covering materials (permeability degree, thickness, superficial treatment…) and the area needed to control indoor environment. In this regard, heat and mass transfer through the building envelope and HVAC system parameters must be considered. To define this methodology, new computer design tools are being developed by the designer [7]. Some points to be considered at the software development stage are: • • • • •
The software should be simple and easy to use. It should suggest default values when variables are unknown. Its learning time must be reduced Its calculation time must be reduced as much as possible. Output data must be simple and informatively employed and, if possible, by means of some charts and figures. • Software must employ technical concepts with an adequate language. • Once the tool is developed, it must be tested by designers and engineers based on real case studies, as it was done with HAM tools. • The software resource must be compatible with input (such as meteorological data format) and output data (such as MS Word, MS Excel…)
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Finally, these software tools are of special interest as energy saving and local and thermal comfort improvements, as well as teaching methods about building design improvement [8, 9].
6.2.3.2 Ventilation Different standards were considered, such as the ASHRAE [10] standard and the environmental specification for the storage of library and archival materials [11]. These standards recommend temperature values between 20 and 22C and a relative humidity between 40 and 55% for stack and branch libraries. Finally, this standard recommends lower fluctuations of indoor conditions to prevent damage of books. In earlier works, a practical case study of indoor conditions in libraries located in the northwest of Spain and Portugal was carried out. The aim of the research was to relate indoor conditions with indoor thermal comfort and preservation of materials in naturally ventilated buildings. Natural ventilation must be in accordance with occupants’ behaviour through opening/closing windows. However, ventilation needs to depend on building constructive characteristics. Hence, future research must take this into consideration. After the measurement of temperature and relative humidity, values were analysed and the conclusion reached was that despite the fact that natural ventilation reduces peaks of temperature in an indoor environment, occupants release moisture. Furthermore, this humidity level affects the archive section, affecting book preservation. Hence, this real effect cannot be expected in laboratory studies and must be investigated in future research works, to optimise building characteristics and HVAC working periods. For future research, design corrections are proposed to improve indoor environments. Some of these corrections are based in the HVAC system, for example, night cooling mechanical ventilation and others based on constructive characteristics and passive methods, such as permeable coverings [12] or solar heat gains. These passive methods work during the unoccupied period and allow an improvement of indoor conditions all the time.
6.2.3.3 A New Modeling Methodology to Control HVAC Systems In the past few years, new methodologies to control HVAC systems were developed, due to the fact that HVAC systems are related to indoor thermal comfort and energy savings, among other factors. For example, recent research revealed that HVAC control systems can be improved with new control algorithms. It is important that models related to thermal comfort should be treated as equal to the important HVAC system control models. This is due to the fact that an adequate control of the HVAC system allows us to reach an adequate thermal comfort with the minimum energy consumption [2, 3].
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The problem is related to the fact that thermal comfort depends on an individual’s psychology and physiology [1], being a difficult parameter to be modelled with mathematical equations. Within this area of thermal comfort, the PMV index was developed and defined in the past few years. This index depends on six thermal variables: human activity level, clothing insulation, mean radiant temperature, relative humidity, temperature and velocity of the indoor air (Fig. 6.3). Specifically, indoor air temperature and velocity are the most employed variables to control indoor conditions within thermal comfort limits. Different control methodologies are currently employed to achieve a better approach to the ideal conditions. These methodologies are classified as adaptive models, neural networks and regression methods. For example, neural networks are employed to get a better approach to the adjustment developed by building occupants. However, this methodology must be trained, during long periods of time, with specific data to be measured from specific environments. Hence, the network obtained can only be employed in this particular environment under the training conditions. Also, if some of the parameters considered constant during the training process experience some change, the network will not work in an adequate manner. Furthermore, once trained, this neural network needs complex mathematical calculations that must be done to define the set-point temperature to be employed in this process during long periods of time. These problems make neural networks, currently, a non-adequate method of HVAC control, to be employed in general environments. We conclude that an easier thermal comfort model to define set-point temperature must be defined and employed in HVAC systems. Thus, a linear PMV model defined by ASHRAE, and presented in Sect. 2.2.3.2.1, presents a special interest. These models are defined by regression methods as typically done with thermal sensation models [7, 8, 13]. Specifically, the selected model depicts two variables, such as temperature and partial vapour pressure, and three constants a, b and c (Eq. 6.2.3.3.1, Table 6.1). PMV ¼ at þ bpv c
ð6:2:3:3:1Þ
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Table 6.1 The coefficients a, b and c are a function of exposure time and sex of the subject Time/sex A B C 1 h/man Woman Both 2 h/man Woman Both 3 h/man Woman Both
0.220 0.272 0.245 0.221 0.283 0.252 0.212 0.275 0.243
0.233 0.248 0.248 0.270 0.210 0.240 0.293 0.255 0.278
5.673 7.245 6.475 6.024 7.694 6.859 5.949 8.620 6.802
These constants are general for all indoor environments, considering the time of exposure and the sex. For example, an exposure of 1 h of a man will have constants a, b and c of 0.220, 0.233 and 5.673. New modelling methods are based on trying to adapt this general model to particular indoor environments. To do it, a new patented system based on a measuring process with the more accurate thermal comfort measuring devices, and a later regression in accordance with the previous model, was developed. This regression process must consider the fact that the data employed must be separated in groups of exposure time. Once the particular PMV model for each indoor environment is obtained, related simulations were developed with MATLAB Simulink. Hence, moist air models and thermal comfort models were defined in accordance with the block diagrams method (Fig. 6.4). This same model was translated to MATLAB Simulink (Fig. 6.5). Once this model was developed, it was tested with real measured data. Specifically, two different indoor environments were tested. The first is a moderate thermal environment and the second an extreme thermal environment, such as those found in industry. When employing this modeling procedure to moderate indoor environments, the general method was used. Furthermore, the HVAC control system, once defined in accordance with the model, was inserted into HAM tools. As stated in earlier chapters, HAM tools are an open code software resource developed to simulate the building constructive characteristics and evolution of indoor environments with time. This software is a MATLAB Simulink tool developed for researchers. It was tested in Annex 41 of the IEA (Figs. 6.6 and 6.7). The main advantages of HAM tools are that our control system can be implemented to simulate a whole building. For example, air changes, building construction and, specifically, heat and mass transfer through building envelope can be included. Hence, the building model reveals a closer approach to real conditions. Once the number of occupants and solar irradiation values were included, the control system was improved and tested. In this experiment, during the winter
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season, a PMV value equal to -0.5 implied a thermal comfort within the limits and lower energy consumption. On the other hand, during the summer season, a PMV value of 0.5 implied a lower cooling energy consumption to be within comfort limits. The results obtained revealed that the lower set-point temperature must be near 18C during the winter and 23C in the summer, as we can see in Fig. 6.6. These results are in accordance with the institutes of Spain and Portugal, which indicate an indoor temperature-core energy conservation of 18C. On the other hand, during the summer season, despite the fact that in milder climates the cooling system does not employ a set-point temperature of 23C, it is a value proposed by most of the standards during the warmer seasons.
6.2 Energy Consumption, Thermal Comfort and Preservation of Materials 21.5 21 Indoor Temperature (°C)
Fig. 6.6 Indoor temperature with fixed and variable setpoint during the winter season
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With energy consumption calculated in accordance with the change in indoor air enthalpy, a new graph was obtained (Fig. 6.7). The values of energy consumption expressed as economic cost are valid for a long period of time. A comparison of energy of economic cost with a variable and a fixed set-point temperature is made during the winter season. The conclusion is that, after these long periods of time, the difference in energy cost between the two control methods was about one-third of the total energy cost. For an extreme environment case study, a ship indoor environment was selected. Specifically, ships depict indoor environments that change after short intervals of time. Furthermore, these changes in temperature present a health risk in the extreme environment of the engine room in contrast to the cold environment of the engine control room. For example, in the engine room temperature values of 28C are found, while in the engine control room set-point temperatures of 18C are commonly adjusted. Consequently, thermal shock is expected in these environments and a self-adjusted set-point temperature for the HVAC control system is needed. An example of the layout of these indoor spaces in a ship (engine room and engine control room) is shown in Fig. 6.8.
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Fig. 6.8 Extreme indoor environment of a ship MAIN ENGINE
ENGINE ROOM
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In this environment, it was proposed that a new model was adjusted to the indoor environment and, after the measuring process, simulations of the HVAC control system were carried out. Real measured data for 5 days can be seen in Fig. 6.9. During the 5 days, indoor temperature in the engine control room varied between 18 and 208C. Once the thermal comfort model was adjusted in the HVAC control system, in accordance with the ambience of the engine room with temperatures of 30C, a variable set-point temperature was defined (Fig. 6.9). As observed earlier, this extreme environment is due to the heat released by the main engine. Typically, in this and other industrial environments, the only way to control the indoor environment is by increasing air exchange with outdoor air. The resulting temperature was between 27 and 22C for the variable set-point temperature. Coming in from the engine room, the new indoor temperature in the engine control room implies a reduction in the health risk for heat stress and thermal shock of marine engineers. On the other hand, the temperature allows marine engineers to release accumulated heat and take all considerations needed for this extreme environment, for example, drinking water and surveying the engine room. Future research will reveal the main advantages of different models. Specifically, the heat stress index must be applied and for general moderate indoor environments the PMV model must be employed for work risk prevention. On the other hand, HAM tools are the correct tools to implement new control methods and obtain a closer approach to real indoor environments. Once adequate results are obtained, a prototype will be developed to be tested in laboratory and real field studies.
6.3 Extreme Indoor Environments: Relative Humidity Spas have particular indoor air relative humidity control methods that need to cope with an adequate thermal comfort and energy conservation. In this regard, the Spanish standards proposed pool water temperatures between 24 and 30C [14].
6.3 Extreme Indoor Environments: Relative Humidity 30 28 Temperature (°C)
Fig. 6.9 Temperature in the engine control room with the present and the new control
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The temperature depends on the activity developed in the pool. For example, in pools used for sports, water temperature must be between 24 and 25C. More conditions are proposed by the standard, for example, the temperature must be homogeneously distributed in the pool with a margin of error of +/-1.5C. Another proposal by the Spanish standard is that air temperature surrounding the pool must be 2–3C higher than the water, to reach a maximum relative humidity of 65%. However, it was observed in real case studies that therapeutical effect for a medical treatment ‘‘Thalasso therapy’’ needs temperatures of for surrounding air 31.5C and a relative humidity of 74.9% (Figs. 6.10, 6.11, 6.12 and 6.13) (Table 6.2) The new Spanish standard (RITE [15]) proposed that water temperature must be obtained by renewable energy with a share of 30–70% of the annual heat requirement, and that the water layer must be protected with an insulating cover when the pool is not in use. Another conclusion related to indoor air exchange in these pools is that the indoor ambience must have a temperature lower than outdoors.
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Fig. 6.12 Percentage of dissatisfied persons in different zones of the spa
100
PD (%)
90 80 70 60 50 40 30 20 10 0
Low
Level 1
Level 2
Gym
Pool
Thalasso
Pool
Thalasso
Sampling zone
Fig. 6.13 Indoor air acceptability in different zones of the spa
0.4 0.2
Acc
0 -0.2 -0.4 -0.6 -0.8 -1 Low
Level 1
Level 2
Gym
Sampling zone
Table 6.2 Water temperature S/RITE 10.2.1.2
Activity
Temperature (C)
Training Competition Education and recreation Physically handicapped Children’s pool Children 3–6 years and adults Pregnant women
26 24 25 29 30 32 30–32
When energy consumption was analysed, the conclusion reached was that if air exchange is increased, the energy consumption increases too. Furthermore, air changes are not the only parameter to be considered; there are other parameters, such as air velocity, related to the evaporation rate and local thermal comfort. As in the earlier case, there is a need for further research in the relationship between indoor air temperature and relative humidity, thermal comfort (depending on the air velocity and previous parameters), building constructive characteristics (preventing moist air condensation and its health-related effects) and energy saving (related to air exchange). Future research must define a new HVAC control system that controls and optimises the mean parameters of indoor environments.
6.4 Extreme Indoor Environments: Temperature Sometimes, thermal comfort is not the most important parameter to analyse indoor environments. It is the case, for example, of preservation of materials and work risk prevention in industrial environments. Between different indoor environments
6.4 Extreme Indoor Environments: Temperature Fig. 6.14 Measured and simulated temperature in the bridge
143
25 20 15 10 Bridge
Simulation
Outdoors
5 0
1
2
3
4
5
6
7
8
9
10
11
12
Days 100 Relative humidity(%)
Fig. 6.15 Measured and simulated humidity in the bridge
80
60
40 Bridge
Simulation
Outdoors
20 0
1
2
3
4
5
6
7
8
9
10
11
12
Day
analyses, ships depict indoor environments that change in short intervals of time and, hence, are sources of stress-related diseases. Particularly, only neutral thermal comfort temperatures were obtained in the bridge and dining room. On the other hand, the engine room and the engine control room depict a range of temperatures related to heat stress and thermal shock of marine engineers (Figs. 6.14 and 6.15). Some of the work risk preventing measures are: • Drinking water. Sources for water must be available and workers should be made aware of the importance of drinking water. • Acclimatisation of workers to this extreme environment before starting the work. • Metabolic heat can be controlled by adjusting length and frequency of breaks. • Workers must be kept under constant watch. The engine room reveals air conditions not within any recommendations from standards, namely: • The engine control room revealed limiting conditions of thermal comfort with temperature values too low related to health problems. • The engine control room has an undersized air conditioning system with a low temperature set-point. • As the outdoor air conditions are suitable, an increase in renovations with outdoor air can be proposed as preventive measure against work risks. [16–18].
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6 Future Research Work
Current research revealed that the indoor environment depends on the heat sources located on board and the weather condition on the sea lane. Hence, future research must be developed for an in-depth analysis of the heat accumulated and released by the workers, and a general procedure to adapt thermal comfort conditions and work risk prevention measures for each sea lane. Finally, these conclusions must be summarised in future standards [19].
6.5 Indoor Environments and Health It is well known that indoor air and health are intimately related. In previous works, we revealed the relationship between indoor relative humidity and fungi growth, pets’ presence and bacterial growth and HVAC system maintenance. Finally, there are new effects related to health, such as the sick building syndrome (SBS). As stated in earlier chapters, there is not much information on how to recognise the SBS in a real environment. Hence, the SBS definition, control methods and general considerations must be developed in future research, taking into consideration the main results obtained until now. Other indoor health problems must be analysed, such as in the case of fungi growth. In this regard, the growth was related to humidity problems in walls and ceiling. Our research revealed that fungi growth and humidity problems can be detected with the local version of IAQ models. Hence, new research on the presence of humidity problems as a function of the local perception of the IAQ models must be applied for new electronic detection systems. Also, humidity problems are related to occupants’ indoor habits. In this regard, occupants of indoor environments have a general idea about which is the most important parameter to be controlled in those indoor spaces. However, the general understanding of indoor environments is not enough and description of which must be the habits in each particular indoor ambience must be revealed. Furthermore, this information must be reflected in future general standards.
6.6 Other Research Works 6.6.1 Indoor Environments and Noise Once the different indoor environments have been analysed and its future research and tendencies are revealed, there are new ideas related with one or more of these concepts that must be introduced. It is the case of noise level in general and industrial environments and development of future standards. When trying to analyse noise levels in different indoor environments, see Fig. 6.16, there are different standards to be considered. It is the case of, for
Isolated rooms Whisper Quiet office Crowded shop Intense traffic Discotheque Motorcycle Launch of a missile
Pain
Danger
Rest
Fig. 6.16 Health effects of noise levels
Fatigue
145 Nuisance
6.6 Other Research Works
10 50 90 110 130 170 Decibels dB (A)
example, apartments’ indoor environments. In this environment, there are only some indications about the relationship between noise level and health effects. Also, few indications about the measuring procedure for these parameters are given. On the other hand, most standards reveal the peak noise value as reference, but the noise wave must be understood and analysed, to reach new research objectives that define the noise in rooms through composite walls. In accordance with these results, it will be possible to define which noise levels can be released by radios and musical instruments. Furthermore, a clear description about where the noise source can be located within apartments to cause the lowest possible damage in surrounding building environments, must be investigated. As a final task, the results obtained must be summarised in future standards. When analysing noise levels in industrial environments, we must remember that these environments will present extremely high noise levels during long periods of time. In the case of ships, the noise levels are related to the main engine and, as in the case of heat stress, the engine control room is the only way to reduce exposure time to this hazard. However, work risk prevention standards about these environments do not predict the maximum time of exposure and the noise levels that can be reached. For this indoor environment, it was proposed, as in the case of thermal stress, to develop new charts that reveal the maximum time a worker can stay in the engine room and the minimum time in the engine control room to prevent this hazard.
6.6.2 Implementation of ISO Standards As stated in the different chapters of this book, there are some parameters that must be considered at the time of developing future standards. Most of these modifications were revealed earlier, and the modifications related to internal coverings are described in this chapter. The first concept stated in a previous chapter was that the effect of internal coverings on energy consumption, once demonstrated, must be implemented in the certification equation to develop adequate building certifications adapted to peculiarities of different climatic regions. In this regard, values of a0 can consider parameters such as permeability level and glazed area.
146
6 Future Research Work 12
PD=5%
Acc=0.2
PD=15%
PD=10%
HR=70%
Acc=0
HR=60%
HR=50%
Humidity ratio (g/kg)
11 10 HR=40%
9 8
Acc=0.6
7
HR=30%
6 5 4 3
Acc=1
2
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Fig. 6.17 Future charts that must be developed by standards
In our case study [17], after demonstrating the effect of internal coverings on energy savings, new constants for the general certification equation were obtained, in accordance with EN ISO 13790. From these results, we could conclude that impermeable coverings depict the lowest utilisation factor of 0.65 and 0.5, for summer and winter, respectively, which represent 15% of the utilization factor on energy simulation. For future research, it is proposed to adapt the general certification equation to other construction scenarios, such as residential buildings and industrial environments. As a result, a simple constant, such as glazed area, could include the effect of permeability, with a clear difference between each permeability level. Finally, in accordance with previous sections, more information about which are the most interesting internal covering materials, surface treatment, active thickness and its clear working periods, between other parameters, must be revealed. Once the general standards consider the effect of permeable coverings in an indoor environment, new charts designed for these applications must be developed. The charts must consider local thermal comfort, related to the perception of IAQ and including moisture problems and fungi development (Fig. 6.17). Acknowledgments We thank the University of a Coruña for the sponsorship through its V Contract Programme.
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