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COMMENT pubs.acs.org/est The Calculus of Unsustainability S ustainability is a squishy term—and difficult to operation...

Author: Jerald L. Shnoor

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COMMENT pubs.acs.org/est

The Calculus of Unsustainability

S

ustainability is a squishy term—and difficult to operationalize for public policy. As I give talks to civic groups and students, I am often asked, “Exactly what is it that is not sustainable?” Perhaps the underlying problem is the public’s lack of understanding about material stocks and flows, integrals and derivatives, i.e., the calculus of unsustainability. In the U.S., we simply do not seem to get it. On October 31 of this year, according to the United Nations, the global population reached 7 billion (http://www.unfpa.org/ public/home/news/pid/7597). What a milestone! When I was born there were 2.5 billion people on earth, so we’ve almost trebled population in one (old) editor’s lifetime. But emissions of greenhouse gases have increased even more, greater than 6-fold since that time. That rate of change in emissions (the derivative) makes for a huge accumulation of greenhouse gases in the atmosphere as shown in the figure (Source: Scripps-NOAA, 2010). Clearly, such accumulation (the integral—area under the curve) of a radiatively important trace gas is not sustainable over the long run. It is as simple as that. The good news is that the rate of population change is decreasing, and demographers tell us we may reach a plateau around 9 billion people sometime in the middle of the 21st century. The bad news is that the plateau is likely to not be sustainable either—it depends on how we use our resources. Resource utilization (land, labor, capital) defines our economics. My favorite economist is Herman Daly, of ecological economics fame at the University of Maryland. Daly is the one who wrote A Steady State Economy (http:// www.sd-commission.org.uk/publications.php?id=775) to which no one seems to have paid much policy attention. If economics really is the “dismal science”, then environmental science must be “depressing science on a suicide watch”. Contributing to our depression is the exploitation of natural capital like nonrenewable resources. Deaccumulation (an integral of a declining supply curve) can be perilous and also UNSUSTAINABLE. The static reserve index, that is how many years remain of proven reserves at current global usage rates, is only about 64 for petroleum, 13 for indium in LCD displays, 30 for antimony in drugs, and 29 years for silver (http:// en.wikipedia.org/wiki/Oil_reserves;http://img.labnol.org/files/ how_many_years.jpg). But that does not tell the whole story because nonrenewable resource usage is not static, rather the consumption rate is usually increasing exponentially. Fortunately, we are finding more “proven reserves” and recycling, reusing, and substituting for them. But can a mining company ever be sustainable? (See answer below). Erdmann and Graedel published a fascinating review article in ES&T arguing that static reserves are not the best way to view unsustainability of metals. Rather criticality, which considers both vulnerability and risk of supplies, is more meaningful. They show that the Rare Earth Elements (REE), Platinum Group Elements (PGE), niobium, cobalt, scandium, tungsten, gallium, and antimony are probably the critical ones we should be worrying about (Erdmann and Graedel, Environ. Sci. Technol. 2011, 45, 7620 7630, dx.doi.org/10.1021/es200563g). So many precious r 2011 American Chemical Society

metals are utilized in the exploding electronic consumer products of today. For example, the computer chip industry consumed about 11 metals in 1980 and now uses 60 elements today— almost 2/3 of the natural periodic table! (Schmitz and Graedel, Environment360, 2010, http://e360.yale.edu/content/feature. msp?id=2266) Likewise, the accumulation of greenhouse gases in the atmosphere is unsustainable in the absence of substitution for fossil energy. But U.S. Presidential candidate and Texas Governor Rick Perry said in a recent debate, “The science is not settled” on human-induced global warming. It reminds me of the principal error in The Skeptical Environmentalist—where Lomborg fails to appreciate that the accumulation of greenhouse gases (and climate change) will continue unimpeded for centuries in the absence of mitigation toward preindustrial levels (The Skeptical Environmentalist, 2001, Cambridge University Press). Perry and Lomborg fail the calculus test. We have warmed the earth so much already (in the early stages) that the ocean is absorbing an immense amount of heat. If every person on earth (7 billion) ran 40 industrial strength hair dryers of 1400 W each, it would be equivalent to the warming we add to the planet each year (http://www.climatestorytellers.org/stories/james-hansen-makikosato-perceptions-of-climate-change/). The U.S. could use some more math—the calculus of unsustainability. Our future depends upon it. (Answer: Yes, a mining company can be sustainable if they continuously discover new ways to recycle and/or substitute for nonrenewable minerals and avoid nonrecoverable, dissipative uses. Europe is approaching 100% recycle of lead. When that happens, it obviates the need for more lead mining and the company evolves into a recycling enterprise rather than an extractive industry.) Jerald L. Schnoor Editor-in-Chief

’ AUTHOR INFORMATION Corresponding Author

[email protected].

Published: December 05, 2011 10289

dx.doi.org/10.1021/es2038118 | Environ. Sci. Technol. 2011, 45, 10289–10289

LETTER pubs.acs.org/est

Comment on “Do Some NOx Emissions Have Negative Environmental Damages? Evidence and Implications for Policy” n their Viewpoint titled “Do Some NOx Emissions Have Negative Environmental Damages? Evidence and Implications for Policy”,3 the authors represent as novel the finding in two studies that marginal reductions in NOx emissions from particular sources in certain areas may increase PM2.5 levels and yield negative health impacts. The authors then entreat the EPA to “commission a review of these studies”, to be evaluated by the EPA Science Advisory Board and submitted as a report to Congress. We argue that the Viewpoint in general, and these recommendations in particular, incompletely consider atmospheric science and do not account for current best practices in air quality management. PM2.5 formation is governed by complex nonlinear chemistry, and so the impact of reducing precursor emissions differ across receptors for three reasons: (1) levels of SO2, NOx, NH3; (2) meteorology (particularly temperature and relative humidity); and, (3) availability of ozone and related oxidants (e.g., OH, H2O2, etc.). Transient spatial and temporal increases in PM2.5 from decreases in NOx emissions are well documented in literature.1,4 Under certain atmospheric conditions, a reduction in NOx (particularly near-surface mobile NOx emissions under cold climate conditions) sometimes leads to increases in ozone and result in localized increases in PM2.5 concentrations, though this depends on baseline levels of NOx and VOC.1,4,5 For this reason, reductions of NOx within NOx-rich urban city centers (e.g., Chicago) may lead to localized increases in PM2.5 levels and may increase population exposure to PM2.5, thus resulting in the disbenefit reported in Fann et al.2 Photochemical models used by EPA and State air agencies account for these nonlinear effects. While there is clear policy relevance to the negative benefit per ton estimates reported in Fann et al.2 and elsewhere, this effect should be considered within the broader context of air quality management policy. In its 2004 report on air quality management in the U.S., the National Academies noted that air quality

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attainment strategies should balance emission reductions across multiple pollutant precursors and spatial scales6. To this end, benefit per ton estimates may inform the development of specific control strategies that aim to maximize human health benefits while achieving air quality targets. Indeed, EPA recently completed a pilot project for the city of Detroit, in which it employed a risk-based and multiple pollutant approach to air quality management.2,7 This strategy weighed local and regional emission reductions among a variety of sources and across several PM2.5 and ozone precursors, and toxic air pollutants, to design an implementation plan that maximized human health benefits and achieved a more equitable distribution of risk. Rather than reacting to incomplete evidence, approaches to air quality management such as these illustrate an approach to attainment plans that accounts for all of the best available science. Neal L. Fann* Air Quality Analysis Division, U.S. Environmental Protection Agency, Mail Drop C539-07 109 T.W. Alexander Drive Durham, North Carolina 27711, United States

(2) Fann, N.; Fulcher, C. M.; Hubbell, B. J. The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution. Air Qual. Atmos. Health 2009, 2 (3), 169–176. (3) Fraas, A.; Lutter, R. Do some NOx emissions have negative environmental damages? Evidence and implications for policy. Environ. Sci. Technol. 2011, 45 (18), 7613– 7614. (4) Myslieiec, M. J.; Kleeman, M. J. Source apportionment of secondary airborne particulate matter in a polluted atmosphere. Envrion Sci. Technol. 2002, 36 (24), 5376–5384. (5) Pun, B. K.; Seigneur, C.; Bailey, E. M.; Gautney, L. L.; Douglas, S. G.; Haney, J. L.; Kumar, N. Response of atmospheric particulate matter to changes in precursor emissions: A comparison of three air quality models. Envrion Sci. Technol. 2008, 42 (3), 831–837. (6) U.S. National Research Council (NRC). Air Quality Management in the United States; National Academies: Washington, DC, 2004. (7) Wesson, K.; Fann, N.; Morris, M.; Fox, T.; Hubbell, B. A multipollutant, risk-based approach to air quality management: case study for Detroit. Atmos. Pollut. Res. 2010, 1, 296–304. (8) Fann, N.; Roman, H. A.; Fulcher, C. M.; Gentile, M. A.; Hubbell, B. J.; Wesson, K.; Levy, J. I. Maximizing health benefits and minimizing inequality: incorporating local-scale data in the design and evaluation of air quality policies. Risk Analysis 2011, 6 (31), 908–922.

Dr. Sharon B. Phillips Air Quality Analysis Division, U.S. Environmental Protection Agency

Dr. Carey Jang Air Quality Analysis Division, U.S. Environmental Protection Agency

Dr. Farhan H. Akhtar Health and Environmental Impacts Division, U.S. Environmental Protection Agency


*Phone: (919) 541-0209; fax: (919) 5415315; e-mail: [email protected].

’ REFERENCES (1) Ansari, A. S.; Pandis, S. P. Response of inorganic PM to precursor concentrations. Envrion Sci. Technol. 1998, 32 (18), 2706–2714.

This article not subject to U.S. Copyright. Published 2011 by the American Chemical Society

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Received: October 19, 2011 Accepted: October 24, 2011 Published: November 11, 2011

dx.doi.org/10.1021/es203710m | Environ. Sci. Technol. 2011, 45, 10290–10290

LETTER pubs.acs.org/est

Speculation on the Origin of Monochloro-Nonabromodiphenyl Ethers. Letter to the Editor regarding Comment on “Identification of Monochloro-Nonabromodiphenyl Ethers in the Air and Soil Samples from South China” a Guardia et al.1 commented on the detection of three nonabromochlorodiphenyl ethers (NBCDEs) in air and soil samples from Guangzhou China and at an e-waste recycling area,2 and speculated in this and a prior publication3 that these compounds were impurities in Albemarle Corporation’s commercial decabromodiphenyl ether (DecaBDE) product. Albemarle would like to set the record straight. Yes, Albemarle filed the patent application described in La Guardia et al. (2011). Albemarle has an active research and development program, files many patent applications as a result of this active research and development, and holds numerous patents. A substantial number of these patents and patent applications include processes that are never commercialized, including the process described in the cited patent application.4 In fact, Albemarle abandoned the cited patent application some time ago, and a simple check of the public patent databases would have revealed this. Our manufacturing process for DecaBDE does not use bromine chloride or mixtures of bromine and chlorine. Albemarle has never commercialized a bromochlorodiphenyl ether and has no intention to do so, nor do we do manufacture a “decahalodiphenyl oxide” product as indicated in the authors’ 2010 publication on page 4663. Albemarle does not manufacture DecaBDE in China. To our knowledge, the authors did not contact Albemarle to ascertain whether their speculation was based on fact. Rather, La Guardia et al. (2010, 2011) assumed a patent equated to a commercial product, a commercial product’s market introduction was that of the patent date, and a business presence in a country equaled manufacture of a product in that country. Those assumptions are incorrect. We recommend the authors, and the editors of Environmental Science & Technology, do a better job of fact checking prior to publication. Erroneous publications such as these divert attention and research dollars from meaningful pursuits.

Marcia L. Hardy,†,* Niomi L. Krystowczyk,† Steve W. LeVan,‡ and David W. Clary§

L

†

Health, Safety and Environment, Albemarle Corporation, 451 Florida Street, Baton Rouge, Louisiana

‡

Advocacy, Albemarle Corporation, 451 Florida Street, Baton Rouge, Louisiana

§

Chief Sustainability Officer, Albemarle Corporation, 451 Florida Street, Baton Rouge, Louisiana


*E-mail: [email protected].

’ REFERENCES (1) La Guardia, M.; Hale, R.; Harvey, E.; Harvey, E.; Chen, D. Comment on “Identification of monochloro-nonabromodiphenyl ethers in the air and soil samples from South China. Environ. Sci. Technol. 2011, 45, 6707–6706. (2) Yu, Z.; Zheng, K.; Ren, G.; Wang, D.; Ma, S.; Peng, P.; Wu, M.; Sheng, G.; Fui, J. Identification of monochloro-nonabromodiphenyl ethers in the air and soil samples from South China. Environ. Sci. Technol. 2011, 45 2619–2625. (3) La Guardia, M.; Hale, R.; Harvey, E.; Chen, D. Flame-retardants and other organohalogens detected in sewage sludge by electron capture negative ion mass spectrometry. Environ. Sci. Technol. 2010, 44, 4658–4664. (4) WIPO, WO/2008/027780, Preparation of decahalodiphenyl oxide. http://www. wipo.int/pctdb/en/wo.jsp?WO=2008027780 (as cited by La Guardia et al. 2011).

r 2011 American Chemical Society

Received: October 28, 2011 Accepted: October 31, 2011 Published: November 15, 2011 10291

dx.doi.org/10.1021/es203846d | Environ. Sci. Technol. 2011, 45, 10291–10291

VIEWPOINT pubs.acs.org/est

China Needs Forest Management Rather Than Reforestation for Carbon Sequestration Guanglei Gao, Guodong Ding,* Haiyan Wang, Yintong Zang, and Wenjun Liang Key Laboratory of Soil and Water Conservation & Desertification Combating, Ministry of Education (Beijing Forestry University), Beijing 100083, P. R. China

s a “close-to-nature” approach for carbon sinks, planted forests (afforestation and reforestation) have a priority to combat climate change in China. During the past decade, the Chinese government invested billions of dollars in a large-scale tree-planting (e.g., the Six Key Forestry Programs). At the U. N. Climate Summit (New York, 2009), Hu, China’s President, also committed that China should endeavor to increase forest coverage by 40 million ha to energetically increase forest carbon sinks by 2020 from the 2005 levels. Obviously, it is afforestation that makes remarkable contributions to carbon sinks in China.1 However, excessive and monoculture afforestation to implement China’s carbon sequestration programs may be inefficient and cause unintended disastrous environmental consequences, especially in arid and semiarid regions.2 In fact, forest’s functions in carbon stock increasing are addressed by two Kyoto Protocol activities: afforestation/reforestation and forest management. Afforestation/reforestation has a top priority for carbon sink in China, whereas forest management has been almost thoroughly ignored. Further, from 2000 to 2010, although roughly 15 million ha of plantation were planted, which prompted the total forest coverage and forest stock being increased from 16.55% to 20.36% and 11.27 to 13.36 billion m3, respectively; China’s average forest stock, forest biomass carbon and forest carbon density still remain far less than international

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level. For example, China’s average woody forest volume was 85.88 m3/ha accounting for only 78% of the world, the plantation was even lower; meanwhile, the mean forest carbon stock in biomass (40.4 t/ha) was much lower compared to the global average of 71.6 t/ha. The gaps indicate the poor quality of China’s forest, which however implies the huge potential for carbon sink in the activities of forest management. Shao et al.1 estimated that if China followed forest management activities of the U.S., the increasing forest productivity would boost China’s forest carbon sequestration from 96 to 152 Tg C/yr without requiring additional forestland area (Table 1). In addition, if the existing carbon stock of forest biomass can be increased by 10% between 2010 and 2020, the cumulated carbon sinks will be much larger than 683 Mt of the Chinese official afforestation target; moreover, this amount is also much less than 65% of the latest international level. Scholars have been questioning that large-scale afforestation efforts in China have failed in the environmental restoration and carbon sequestration because of the negative chain ecological problems. For example, afforestation with unmatched species in afforestation regions may damage the local water balances, even exhaust limited groundwater resulting in trees death or dying; in arid or semiarid regions, it will finally lead to an enlarging desertification.3 In addition, monoculture plantations or exotic species can also reduce biodiversity when it replaces natural ecosystems. Compared with this above, forest management may have many positive impacts on environmental recovery. Forest management emphasizes natural approaches instead of monoculture tree planting for environmental restoration. Afforestation can be replaced by the native vegetation recovery. In northern China’s arid and semiarid regions, it is much more reasonable that small halophytic shrubs, savanna and steppe vegetation, and some herbaceous plants grow on aeolian sands and other land vulnerable to wind erosion.4 Meanwhile, a better mixture of plant species and appropriate measure of human activities will make a promotion to increasing biodiversity. In the socioeconomic aspect, with a cumulative afforestation cost and decreasing suitable forestland, forest management can reduce the investment, as well as provide excessive jobs in a large area and promote rural development. Hence, forest management rather than the large-scale afforestation meets the complex requirements of environmental restoration; and it is an efficient approach for forestry carbon Received: October 12, 2011 Accepted: November 2, 2011 Published: November 16, 2011 10292

dx.doi.org/10.1021/es203897f | Environ. Sci. Technol. 2011, 45, 10292–10293

Environmental Science & Technology

VIEWPOINT

Table 1. The U.S.-China Comparisons in Forest Area, Carbon Stock in Forest Biomass and Their Annual Change5 forest area 2010 106 ha

carbon stock in living forest biomass

annual change/103 ha 1990 2000

1990

1995

2000

2010

106 t

2000 2010

Annual change/103 t 1990 2000

2000 2010

U.S.

304

386

383

16 951

17 998

18 631

19 308

105

131

China

207

1986

2986

4414

5295

5802

6203

88

91

In the last decade, both the U.S. and China implemented forestry carbon sequestration programs to reduce the carbon print. However, compared to China’s large-scale afforestation, the U.S. has a much more carbon sinks with little additional forest area because of its emphasis on carbon sequestration activities of forest management. sequestration. The preservation and restoration of existing ecosystem should be the primary goal of carbon sequestration, and the destruction of these ecosystems by large-scale afforestation may be counterproductive.2 However, in fact, it is the government who has authority over policy making. It is hard for government officials facing urgent tasks to give up the short-time benefits which can show their merits and achievements. Thus, in China, although an increasing number of people have realized that monoculture afforestation is not appropriate, government attitudes still changed slowly. Based on deep examinations, officials, scholars, managers, and citizens should have common understandings of China’s environmental restoration strategy. In a word, forest management has larger potential carbon sink ability than large scale afforestation, and can avoid the potential large risk to ecosystem health. It is suggested that forest management should be a sustainable and sagacious choice for China’s forestry carbon sequestration.


*Phone: 86-10-6233-7777; e-mail: [email protected].

’ ACKNOWLEDGMENT This work was supported by the Special Fund for Forestry Scientific Research in the Public Interest, State Forestry Administration of P. R. China (200804022A). We thank Prof. Dr. Xiangdong Lei (Beijing, China) for editing an early version of this paper. ’ REFERENCES (1) Shao, G. F.; Dai, L. M.; Dukes, J. S.; Jackson, R. B.; Tang, L. N.; Zhao, J. Z. Increasing forest carbon sequestration through cooperation and shared strategies between China and the United States. Environ. Sci. Technol. 2011, 45, 2033–2034. (2) Wang, Y.; Cao, S. Carbon sequestration may have negative impacts on ecosystem health. Environ. Sci. Technol. 2011, 45, 1759–1760. (3) Cao, S. Why large-scale afforestation efforts in China have failed to solve the desertification problem. Environ. Sci. Technol. 2008, 42, 1826–1831. (4) Wang, X.; Chen, F.; Hasi, E.; Li, J. Desertification in China: An assessment. Earth-Sci Rev 2008, 88, 188–206. (5) State of the World’s Forest 2011; Food and Agriculture Organization of the United Nations: Rome, 2011.

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Controlling Air Pollution from Coal Power Plants in China: Incremental Change or a Great Leap Forward Zhang Bing,† Elizabeth Wilson,‡ and Bi Jun*,† † ‡

State Key Laboratory of Pollution Control & Resource Reuse, School of Environment, Nanjing University, Nanjing, 210046, P. R. China Humphrey School of Public Affairs, University of Minnesota, MN, United States

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hina is in the midst of the largest and fastest period of economic growth in global history. While this growth at 9 10% GDP per year has lifted hundreds of millions of people from poverty, the all out prioritization of economic prosperity has created serious air and water pollution that are affecting the health of the local population, and also have increasing global climate impacts. A World Health Organization (WHO) report estimated that diseases triggered by indoor and outdoor air pollution kill 656 000 Chinese citizens each year.1 In addition to local criteria air pollutants like sulfur dioxide (SO2) and nitrogen oxide (NOx), China is now the largest emitter of greenhouse gases (GHGs) in the world. Strategies and policies to control air pollutants have been on the books for decades, but shifting local government focus away from the sole prioritization of economic growth and including environmental protection has proven very challenging. China has been implementing the single pollutant control strategy and focus on short-term main pollution control target. From the early to take soot as the main control object to total pollution control of SO2 since 9th five-year plan. After years of effort, pollution control achieved some success. Thermal power plants have been effectively controlled dust emission since 2000, while sulfur dioxide also reached its turning point of decline in 2007 and reduced 14.3% of SO2 during the 11th five-year plan period (see Figure 1). Although control of sulfur dioxide has made great progress, other kinds of air pollution and CO2 increased a lot in r 2011 American Chemical Society

the past decade. The NOx, mercury, and CO2 from thermal power sector in 2010 were 1.51 times that of 2005. The rapid development of China will not stop. In the next 10 years, China’s GDP will increase to 4 times that of 2010. Without new pollution control policies, the NOx, mercury, and CO2 from thermal power industry sector will be 1.28 times that of 2010. Since 12th fiveyear plan, China starts to control NOx and target to reduce 10% of NOx. In addition, the international pressure on carbon reduction push China to promised to reduce its carbon intensity— the amount of CO2 it emits for each dollar of economic output— by 45%. There are significant advantages of a pollutant by pollutant approach, which allows operators and designers to target system design and hone operational features of new technologies. The single pollution control target will induce enterprises invest on end-of-pipe pollution control. In such condition, technology innovation and multipollution control technologies will be less cost-effective than single pollution control. During the 11th fiveyear plan period (2005 2010), China has installed 500000 MW desulfurization that 86% units have flue gas desulfurization (FGD) systems. However, the end-of-pipe pollutant controls will increase other pollutions, for example, controlling SO2 increases coal use, which increases associated CO2, mercury, and NOx. The used 600 MW unit in China should use average 1.58% auxiliary electricity for desulfurization. That is the removal of 1 kg SO2 will bring 6.1 kg CO2 and 0.015 kg NOx. In addition, the significant reductions in SO2 emissions will reduce the cooling impact of reflective aerosols.2 Thus, the single pollution control strategy will press the bottle gourd, but played a gourd ladle. In addition, with the increasing pollution control types, resulting in continued expansion of pollutant purification equipment, regardless of investment or operating costs, the complexity of purification systems are facing great difficulties. Meanwhile, since air pollutants and GHGs often derive from the same sources—fossil fuel combustions—there is an opportunity to address the two problems simultaneously. Control technologies that are capable of simultaneously reducing emissions of multiple pollutants may also offer the potential to achieve this at lower cost and reduced footprint when compared to conventional controls. Taking other air pollution and GHGs’ environmental impact into consideration, the total costs of coalfired plants will be not lower than nuclear power, hydropower, or wind power. Thus, one of most important implication of Received: October 24, 2011 Accepted: October 31, 2011 Published: November 23, 2011 10294

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VIEWPOINT

Figure 1. SO2 discharge in China and emission discharge from thermal power sector (NOx, mercury and CO2) from 1995 2020. The historical emissions discharge is from China’s Year Book. The trend of emission discharge is estimated based on IEA’s report.

co-control of air pollution and GHGs will make investors rethink about the cost-benefit of different energy sources. Co-control of air pollution and GHGs will reduce the total social cost and make more investment on new energy technology. In addition, cocontrol strategies will also make technologies which can improve energy efficiency more attractive, such as clean coal technology, etc. Thus, the co-control strategies by using new energy or technologies will be more cost-effective than single pollution control strategy. Both the U.S. and Europe experience has shown that an integrated pollutants co-control strategy will be more cost-effective than a single pollution control strategies.3 On the other hand, technology enables co-control of a variety of possible contaminants, such as catalytic reduction (SCR) or selective noncatalytic reduction (SNCR) technology to achieve combined desulfurization and denitrification, and the pulse corona plasma (PPCP) can removal of NOx and SO2 and PM together, electro-catalytic oxidation (ECO) technology can effectively reduce SO2, NOx, PM2.5, and mercury emissions together.4 If taking into account the reduction of carbon dioxide, flue gas desulphurization and denitrification with ultrasupercritical power generation technology (SC/USC + FGD + SCR), circulating fluidized bed boiler (CFBC), pressurized fluidized bed combined cycle (PFBC CC), integrated gasification combined cycle (IGCC) will achieve higher desulfurization and denitrification rate, as well as provide coal-fired power plants a more feasible way to treat CO2 and mercury. Therefore, cocontrol strategies can effectively improve the efficiency of air pollution and GHGs reduction in a long-term perspective.5 It is easier to imagine Chinese policies continuing to pursue power plant efficiency and a continued push to fastrack P3 (SO2 + NOx + mercury) controls throughout the country, as has already been piloted in some provinces like Shanxi. However, without stricter environmental regulations and multipollutant control strategy, it appears unlikely that the P4 pathway (SO2 + NOx + mercury + CO2) will be the road taken. While economists in the U.S. have been proffering similar advice for decades, China may have a better chance of actually implementing the longer term planning and stable perspectives to guide power plant construction policies.


*E-mail: [email protected].

’ REFERENCES (1) Platt, K. H., Chinese air pollution deadliest in world. National Geographic News 2007, http://news.nationalgeographic.com/news/ 2007/07/070709-china-pollution.html. (2) Zhao, Y.; McElroy, M. B.; Xing, J.; Duan, L.; Nielsen, C. P.; Lei, Y.; Hao, J. Multiple effects and uncertainties of emission control policies in China: Implications for public health, soil acidification, and global temperature. Sci. Total Environ. 2011, 409, 5177–5187. (3) McCarthy, J. E.; Parker, L. B. Costs and benefits of clear skies: EPA’s analysis of multi-pollutant clean air bills, CRS Report for Congress 2005, http://www.policyarchive.org/handle/10207/bitstreams/2639.pdf. (4) Boyl, P. D., Multi-pollutant control technology for coal-fired power plant. In Clean Coal and Power Conference, Washington, DC 2005. (5) Echeverri, D. P.; Fischbeck, P.; Krirgler, E. Economic and environmental costs of regulatory uncertainty for coal-fired power plants. Environ. Sci. Technol. 2009, 43, 578–584.

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Attraction of Night-Migrating Birds to Light-Blue Structures Causes Mass Bird Deaths Zhijiang Wang,† Aijun Lin,‡ Qipeng Yuan,‡ Wenbin Zhou,§ Wenbo Zhang,|| and X. Jin Yang‡,* †

Lushunkou Bureau of Agriculture, Forestry and Irrigation, Dalian, China; Beijing University of Chemical Technology, Beijing, China; § Nanchang University, Nanchang, China; Eco-humanity Alliance, Beijing, China.

)

‡

M

ass bird deaths have been occurring in numerous regions across the globe and have been raising significant public health and ecological security concerns. Avian influenza virus has been identified as one of the major causes of mass migratory bird deaths1 and communication towers and guy wires are proving to be a deadly hazard to birds, in particular migratory birds.2 It is estimated that between 5 and 50 million migrating birds are killed in the U.S. each year by colliding and crashing with communication towers and guy wires during their night migration.2 However, the majority of mass bird deaths have been mysterious and unexplained. In the early morning of August 30, 2006, about 70 birds were found dead beside three buildings in the Port of Laotieshan, Dalian (121°440 -121°490 E, 39°010 -39°040 N), China. In the following days the tragedy of about a hundred birds death per day continued. The incidents caused significant scares to the public as the bird flu outbreak was epidemic across the world. The transmission of highly pathogenic H5N1 influenza A viruses to humans was proved3 and 12 cases of human infections had been reported in mainland China by the time of the incident. The dead birds were collected and sent to the laboratory. The laboratory examination ruled out the possibility of any disease r 2011 American Chemical Society

infections or food poisoning. Screening for bird flu infections in the poultry industry around the area showed no signs of infections. It was found that the birds were all night-migrating birds and the cause of death was due to severe injuries on the head of the birds. Inspections of the building walls showed residues of bloods and feathers. We therefore concluded that the birds committed a mass “suicide” by crashing into the buildings. Building A experienced the majority of bird kills and is a size of 10 m length 6 m wide 4.5 m high, the smallest and lowest of the three incidental buildings. A 12 m high and 120 m long bridge is located only 6 m away on the northeast of Building A (Figure 1) and a five-floor building is just 50 m on the south of Building A. The bird crashes on Building A occurred on the south wall and it was surprising that none of the birds collided with the bridge and the five-floor building. Therefore, it was unlikely that the birds were accidentally colliding with the three buildings due to visibility reasons. The three buildings having bird deaths were all in light-blue color (see the insert of Figure 1) and the bridge and the five-floor building were concrete gray color. Around the three buildings there are 10 night lighting posts (25 m high). At night the area was very brightly lit and there were heavy mists on the days when the migratory bird deaths happened. Therefore, it was speculated that the birds flew at lower altitude in mist, were attracted by the light-blue buildings and were disorientated. While there is currently no empirical evidence to support this hypothesis of structures color attraction, the effect of the color of artificial night lighting on the attraction of migrating birds has been well documented.4,5 The duration and color of lighting at night are critical to whether birds are or are not attracted to lights. Solid white lights are more attractive to birds than colored or flashing lights, but solid or pulsating red lights attract night-migrating birds at a much higher rate than white strobe lights. It was reported that the two television towers near Awendaw, South Carolina had substantial bird kills when they had red incandescent lighting and the mass bird kills stopped after the red lights were changed to white strobe lights. An average of 2300 birds was killed each year over a 10-year period at lighted smoke-stacks near Kingston, Ontario. The bird kills ended after the lights were changed to white strobes. To prove the hypothesis of the structure light-blue color attraction and disorientation to the Received: November 1, 2011 Accepted: November 3, 2011 Published: November 16, 2011 10296

dx.doi.org/10.1021/es203854q | Environ. Sci. Technol. 2011, 45, 10296–10297


VIEWPOINT

Figure 1. Photo of the bird kill incident site. Dead birds were found on the ground beside Buildings A, B, and C and no dead birds were observed under the bridge. Building A is in 10 m length 6 m wide 4.5 m high, and B and C are 3- and 2-floors structures, respectively; the insert was the original color of the three buildings, which were all repainted in milk-yellow after the incident.

night-migrating birds, the three buildings were painted in milkyellow (Figure 1 A, B, C). Since the milk-yellow painting, no birds have ever been observed smashing into the buildings in the past four years. Scientific basis to establish policy regulation on the height, configuration and lighting of communications towers to protect migratory birds has been developed.2 The finding here adds supporting evidence to regulate the coloring and nightlighting scheme of buildings and structures in migratory pathways of night-migrating birds to protect the migratory birds.


*Phone/fax: +86-10-64421030; e-mail: [email protected].

’ REFERENCES (1) Liu, J.; Xiao, H.; Lei, F.; Zhu, Q.; Qin, K.; Zhang, X. W.; Zhang, X. L.; Zhao, D.; Wang, G.; Feng, Y.; Ma, J.; Liu, W.; Wang, J.; Gao, G..F. Highly pathogenic H5N1 influenza virus infection in migratory birds. Science 2005, 309, 1206. (2) Longcore, T.; Rich, C.; Gauthreaux, S.A. 2005. Scientific basis to establish policy regulating, communications towers to protect migratory birds. http://www.abcbirds.org/newsandreports/special_reports/LPPtowerkill. pdf (accessed November 12, 2011). (3) Ferguson, N. M.; Cummings, Derek A.T.; Cauchemez, Simon; Fraser, Christophe; Riley, Steven; Meeyai1, Aronrag; Iamsirithaworn, Sopon; Burke, Donald S. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 2005, 437, 209–214. (4) Gauthreaux, S. A. Jr.; Belser, C. Effects of artificial night lighting on migrating birds. In Ecological Consequence of Artificial Night Lighting; Rich, C.; Longcore, T., Eds.; Island Press: Covelo, CA, 2005. (5) Gehring, J.; Kerlinger, P.; Manville, A. M. Communication towers, lights, and birds: successful methods of reducing the frequency of avian collisions. Ecol. Appl. 2009, 19, 505.

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Check Dam in the Loess Plateau of China: Engineering for Environmental Services and Food Security. Yafeng Wang,*,† Bojie Fu,† Liding Chen,† Yihe L€u,† and Yang Gao‡ †

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, P. R. China ‡ Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, P. R. China

Figure 1. The amount of check dam in seven provinces of Loess Plateau.

I

n the world, dams and reservoirs are often controversial projects because of their social and environmental impacts, in addition to the high cost of construction and river diversion. But in Loess Plateau, check dams have attractive advantages because of unique environmental settings and regional food supply needs. Check dams are the most widespread structures for conserving soil and water in the Loess Plateau. The Loess Plateau covers an area of some 640 000 km2 in the upper and middle reaches of the China’s Yellow River. Over 60% of the land is susceptible to soil and water losses and the soil of this region has been called the “most highly erodible soil on earth”.1 Constructing check dams in the gullies is an effective strategy for reducing sediment loss. More than 100 000 check dams have been built over the last 50 years in the Loess Plateau.2 After 50 years of construction, there are about 110 thousand check dams storing 21 billion m3 sediments in the Loess Plateau. The check dams distribute mainly in seven provinces including Shaanxi (36 816), Shanxi (37 820), Gansu (6630), Inner Mongolia (17 819), Ningxia (4936), Qinghai (3877), and Henan (4147) which account for 82.5% of the total 2 (Figure 1). It is highly significant that the check dams curb sediments from flowing into the Yellow River at a rate about 3 5 million tons r 2011 American Chemical Society

annually, and they have intercepted 28 billion tons sediment since 1950s in the Loess Plateau.2 Generally, a small watershed is used as a planning and construction unit for check dams. They are constructed step by step from downstream to upstream; the combination of large, medium, and small dams can effectively mitigate flood damage and sedimentation downstream. The check dams can essentially raise the base level of the controlled watershed and thus reduce gully and headward erosion. The check dams can potentially serve as carbon storage and sequestration structures. The average organic carbon content in sediment trapped by check dams is 3.4 g kg 1. If estimated at this rate, the carbon storage in check dams of the Loess Plateau can amount to 0.952 Gt (1Gt = 109 t = 1015 g), which amounts to 18 24% of the total carbon storage of forest vegetation in China.3 The existing reseach results indicated that carbon sequestration was 2.3 Tg (1 Tg = 1012 g) in the new plantations in the upper Yellow River Basin from 1998 to 2004.4 Therefore, we indicated Received: November 2, 2011 Accepted: November 8, 2011 Published: November 16, 2011 10298


Environmental Science & Technology that the carbon sequestration is more than 400 times by the check dam than by the new plantations under the “Grain to Green” program in the Loess Plateau. Otherwise, this carbon will be transported to the Yellow River and carbon release to the atmosphere is inevitable during the transport process in the water environment to potentially contribute to global warming. When check dams are filled up with sediments, man-made plain lands come into being and can be reclaimed as high quality croplands because of the nutrient enrichment during soil erosion processes on hillslopes and improved water availability. By 2002, 3200 km2 of dam croplands had been created.2 According to the monitoring data from the Suide Soil and Water Conservation Experiment Station of the Yellow River Conservancy Commission, the soil water content in dam cropland is 1.86 times of that in slope cropland. The food production in dam cropland is 2 3 times higher than that in terrace cropland, and 6 10 times higher than that in slope cropland. The average yield is 45 000 kg ha 1 with some even up to 105 000 kg ha 1. Accordingly, dam cropland accounts for about 9% of the total cropland area in the Loess Plateau, whereas the food production amounts to 20.5% of the total food production.2 With the productive dam cropland, farmers can grow high profit crops or developing fresh water aquaculture to raise family income. Therefore, farmers’ dependence on slope farming is largely reduced, which has facilitated the effective implementation of the large scale vegetation restoration program known as Grain to Green that is motivated by the Chinese central government and recognized as the world's largest payment for ecosystem service initiative.4 The results indicated that the vegetation restoration efforts had significantly improved land coverage grass, scrub, and woods resulting in an effective control of soil erosion.5 Accordingly, the check dam as a hydro-engineering approach for soil erosion control has actually brought about services for environmental conservation and human welfare in the Loess Plateau of China. To sustain these benefits of check dams, participatory approach and adaptive management is required for planning, construction, use, and maintenance of the anthropogenic structure. The case of check dams in the Loess Plateau region verified the possibility toward building harmonious relationships between man and nature by well designed engineering systems. Therefore, ecologically and environmentally friendly design is promising to adapt to a changing world.

VIEWPOINT

(2) CMWR (Ministry of Water Resource of P.R. China). Programming for check dams in the Loess Plateau (Technical Report), 2003; pp 47 48. (In Chinese) (3) Zhao, M; Zhou, G. S. Carbon storage of forest vegetation in china and its relationship with climatic factors. Climate Change 2006, 74, 175–189. (4) Liu J, et al. Ecosystem Services Special Feature: Ecological and socioeconomic effects of China’s policies for ecosystem services. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 9477-9482. (5) Fu, B. J.; Chen, L. D.; Ma, K. M.; Zhou, H. F.; Wang, J. The relationships between land use and soil conditions in the hilly area of the Loess Plateau in northern Shannxi, China. Catena. 2000, 1, 69–78.


*Phone: 86-10-62849102; fax: 86-10-62849102; e-mail: yfwang@ rcees.ac.cn.

’ ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (No. 40901098) and the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KZCX2-YW-QN408). We thank Geoffrey Hart (Montreal, Canada) for editing an early version of this paper. We are also grateful for the comments and criticisms of the journal’s anonymous reviewers and my colleagues. ’ REFERENCES (1) Laflen, J. M., Tian, J. L., Huang, C. H. Soil Erosion and Dryland Farming; CRC press: Boca Raton, FL, 2000; p 736. 10299

dx.doi.org/10.1021/es2038992 |Environ. Sci. Technol. 2011, 45, 10298–10299

POLICY ANALYSIS pubs.acs.org/est

Rationale for Control of Anthropogenic Nitrogen and Phosphorus to Reduce Eutrophication of Inland Waters William M. Lewis, Jr.* Cooperative Institute for Research in Environmental Sciences, Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado 80309-0216, United States

Wayne A. Wurtsbaugh Department of Watershed Sciences and the Ecology Center, Utah State University, Logan, Utah 84322-5210, United States

Hans W. Paerl Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead City, North Carolina 28557, United States ABSTRACT: Concentrations of phosphorus and nitrogen in surface waters are being regulated in the United States and European Union. Human activity has raised the concentrations of these nutrients, leading to eutrophication of inland waters, which causes nuisance growth of algae and other aquatic plants. Control of phosphorus often has had the highest priority because of its presumed leading role in limiting development of aquatic plant biomass. Experimental evidence shows, however, that nitrogen is equally likely to limit growth of algae and aquatic plants in inland waters, and that additions of both nutrients cause substantially more algal growth than either added alone. A dual control strategy for N and P will reduce transport of anthropogenic nitrogen through drainage networks to aquatic ecosystems that may be nitrogen limited. Control of total phosphorus in effluents is feasible and is increasingly being required by regulations. The control strategy for nitrogen in effluents is more difficult, but could be made more feasible by recognition that a substantial portion of dissolved organic nitrogen is not bioavailable; regulation should focus on bioavailable N (nitrate, ammonium, and some dissolved organic nitrogen) rather than total N. Regulation of both N and P also is essential for nonpoint sources.

’ INTRODUCTION The United States and European Union are simultaneously moving toward nutrient regulation for inland waters with the goal of controlling eutrophication. The primary symptom of eutrophication is excessive growth of aquatic autotrophs, including suspended algae (phytoplankton), attached algae (periphyton), and aquatic vascular plants (macrophytes). Secondary symptoms include deep water anoxia in lakes, increased risk of harmful algal blooms, impairment of water treatment (taste and odor, filtration problems), and changes in the composition of aquatic communities.1 Nutrient pollution has raised global algal biomass and photosynthesis in lakes by approximately 60% over background conditions;2 streams and rivers are similarly affected. Within populated or agriculturally productive regions aquatic primary production and biomass often are many times greater than background.3 Two elements, phosphorus (P) and nitrogen (N), explain most of the experimentally diagnosed nutrient limitation of algal growth in inland waters under natural or human-modified conditions. Some research also suggests the potential for deficiencies r 2011 American Chemical Society

of other elements such as iron in inland waters,4,5 but this type of limitation is likely confined to special situations. Although the scientific basis of nutrient regulation seemingly was settled in the 1970s with emphasis on phosphorus control, strong controversy now has emerged about the alternative possibilities for controlling one nutrient preferentially (P) or two nutrients with equal emphasis (P, N). We provide here a perspective on nutrient control as it applies to algae, first for lakes and then for flowing waters. Regulation of total P concentrations is a well established practice.6,7 Regulation of nitrogen for control of eutrophication has been a lower priority, but has developed in a few places by control of total nitrogen concentrations (e.g., New Zealand8). National and international organizations (U.S. Environmental Protection Agency, Organisation for Economic Co-operation and Development) Received: January 24, 2011 Accepted: November 9, 2011 Revised: October 17, 2011 Published: November 09, 2011 10300

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POLICY ANALYSIS

recognize the significance of both elements, but current regulatory practice emphasizes phosphorus control. We describe lines of evidence showing that nutrient control based on both P and N offers a broader range of strategies and reduces the potential for corollary damage caused by anthropogenic mobilization of N.

’ COMPARISONS OF PHOSPHORUS AND NITROGEN AS LIMITING NUTRIENTS IN LAKES The limiting nutrient concept (Liebig’s Law of the Minimum9,10) holds that nutrient deficiency at any given time in a photosynthetic organism can be traced to a single element, which is the element available in the least amount relative to the needs of the organism. Therefore, in controlling excessive algal growth, it is important to know which element limits the expansion of algal populations when their growth stops because of nutrient depletion. The limiting nutrient concept is more complex for an entire community or ecosystem than it is for a single organism. For example, species may differ, even among organisms of similar type (e.g., algae), in their optimal internal N: P ratios11 13 and their ability to store critical nutrients or to take up a nutrient at low concentrations.14,15 Thus, it is possible in a mixed community of algae for some species to be limited by phosphorus and others to be limited by nitrogen. In addition, it is possible for an environment to be very near the nutrient limitation thresholds for N and P simultaneously. Thus, a slight enrichment with one element could cause the other element to become limiting (e.g., refs 16 18). A third possibility is that seasonal or spatially heterogeneous changes may occur in the relative availability of potentially limiting nutrients (19). All of these circumstances have been documented experimentally and in nature.20 Much more attention has been given to P limitation than to N limitation in inland waters for three reasons:20 (1) phosphorus is more easily removed from anthropogenic sources than nitrogen, (2) N2 fixation by cyanobacteria (also known as blue-green algae) has been assumed to make N control ineffective, and (3) the correlation between chlorophyll (an index of algal abundance) and total P among lakes is stronger than the correlation between chlorophyll and total N.3 A high proportion of total phosphorus can be removed (to concentrations as low as 30 μg/L) from waste streams by flocculation and sedimentation.21 Thus, phosphorus limitation can be induced even in a lake that is nitrogen limited by restricting the phosphorus supply to such an extent that phosphorus limitation overtakes nitrogen limitation.22,23 This is an effective strategy when the main source of phosphorus is wastewater effluent, which can be readily treated. It is less feasible where diffuse (nonpoint) sources are important, and may be entirely infeasible where background phosphorus concentrations are high.24 27 Nutrient enrichment experiments (bottle bioassays, mesocosms, whole lakes) for lakes from all parts of the world now show that nitrogen limitation is globally as common as phosphorus limitation (Figure 1, refs 28,18, and 20). The occurrence of nitrogen limitation in lakes globally raises questions about the presumption that nitrogen limitation is self-correcting through the growth of N2-fixing cyanobacteria.29 Studies of the nitrogen fixation rates for cyanobacteria show that they are unable to compensate fully for nitrogen limitation in lakes,30,31 most likely because the process of N2 fixation can be influenced by factors other than nitrogen and phosphorus, including turbulence coupled

Figure 1. Growth response ratios (natural log of ratio of treatment to control, with standard error) of freshwater phytoplankton for worldwide bioassay studies (redrawn from ref 18; n = >500 for each treatment).

with low transparency, trace metal or iron deficiency, or organic matter availability.32 Eutrophic lakes that are nitrogen limited may even be dominated by cyanobacterial taxa that cannot fix N2.33 Another important factor that works against N accumulation in lakes is microbial denitrification that converts nitrate, which is bioavailable, to N2 or N2O which are not. Denitrification is stimulated by nitrate enrichment of lakes.34 Thus, nitrogen fixation and nitrogen limitation can coexist in lakes, and suppression of N availability may suppress total algal biomass even when cyanobacterial N2 fixers are present. N2 fixers may become a larger portion of the algal community if nitrogen availability is suppressed sufficiently to cause N limitation, even if total biomass is reduced.35 The risk of inducing a shift in community composition favoring N2 fixers is a possible undesirable byproduct of induced nitrogen limitation. Presence of N fixers at moderate abundances is common over a wide trophic range,36 however, and is not exclusively a symptom of impairment. The correlation between phosphorus and mean or peak chlorophyll among lakes has been erroneously interpreted as showing cause and effect. In fact, the correlation reveals little about nutrient limitation because phosphorus is a mandatory component of algal biomass, as is chlorophyll.20 Therefore, chlorophyll and phosphorus will always be present together (as will all other biomass components), whether phosphorus is limiting or not (Figure 2). Nutrient limitation cannot be inferred from such correlations. Algae excrete phosphatases at the cell surface and into the surrounding water that allow them to assimilate phosphorus derived from cleavage of phosphorus from organic matter.36 Algae also can take up 10 or more times the minimum amount of P needed for synthesis of protoplasm37 and store the excess P as polyphosphate. Thus, toward the end of the growing season, most of the phosphorus in the upper water column of lakes is incorporated into algal biomass, except in lakes that are so strongly polluted with P as to exceed algal capacity for P uptake.34 For nitrogen, a significant portion of the dissolved fraction is refractory (not bioavailable, e.g., ref 39). Dissolved inorganic N (DIN, Table 1) typically is the main N source for algal growth in inland waters, but both unpolluted and polluted inland waters also contain substantial amounts of dissolved organic N (DON). Because DON persists even when phytoplankton show nitrogen stress, as indicated by very low concentrations of DIN, DON had until recently been considered entirely refractory, but experimental evidence now has shown that a significant portion of DON is 10301

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POLICY ANALYSIS

Table 1. Concentrations of Total N and P (μg/L) in a Representative Municipal Effluent with Secondary Treatment Plus 50% Nitrification and in Representative Unpolluted US Streams and Rivers68 71 nutrient

effluent

unpolluted streamsa

range total P, μg/L fractionation, %

2000 4000

total P total dissolved P dissolved inorganic P dissolved organic P particulate P

10 30

100

100

96

63

88

30

8 4

33 37

10 000 15,000

100 500

range total N, μg/L fractionation, % total N

Figure 2. Simulation of the relationship between P and phytoplankton chlorophyll a among a hypothetical population of lakes (seasonal averages) for which P is not limiting (r2 = 0.70, from ref 20).

available to algal cells,38 40 including not only DON from natural sources but also anthropogenic DON such as urea, which is widely used in agriculture.41 Some algal taxa have exoenzymes (amino acid oxidases, proteolytic enzymes) at the cell surface or excreted from the surface so that ammonium or small organic molecules can be released from large organic molecules and enter the cell; some taxa also are able to take up organic nitrogen by pinocytosis or phagocytosis.42 In addition, some components of DON are converted to DIN by photodegradation, but other components of DON resist photodegradation.40 Thus, the persistence of DON in the absence of DIN indicates fractional turnover of the DON pool rather than complete unavailability of DON over time scales ranging from days to months during a growing season. Natural waters vary greatly in amount of refractory nitrogen in the DON pool. A study of rivers in the eastern U.S. showed two rivers with no detectable bioavailability and seven rivers with a mean of 23% ( 4% bioavailability as determined by change in DON concentrations in six-day incubations; an accompanying literature survey for 18 sites on rivers in Europe and the U.S. showed a mean of 30% ( 4 for the labile fraction as judged mostly by 14 day incubations.43 Thus, DON of natural waters must be viewed as potentially important nutritionally to algae under nitrogen stress, yet includes a significant refractory component. Fractions of N and P differ in their potential to predict experimentally diagnosed nutrient limitation in lakes. For phosphorus, total P and total soluble P are equally accurate indicators. For nitrogen, dissolved inorganic nitrogen (almost entirely composed of nitrate plus ammonium) is an indicator superior to total nitrogen or total dissolved nitrogen.44 This is not surprising, given the unavailability of a substantial portion of DON to algae.

’ CONTROL OF N, P, OR BOTH Sole focus on phosphorus as a means of controlling algal biomass may seem advantageous because it is much less expensive than control of both N and P.45 Some researchers also continue to argue that nitrogen control does not work because N2 fixation can provide algae with labile nitrogen.46 According to this argument, lakes that are N deficient will accumulate N over time, thus eventually reaching P limitation. Lake 227 of the Canadian Experimental Lake Area, which offers the longest record of whole

total dissolved N dissolved inorganic N

100

96

79

77

29

NO3 -N

61

23

NH4+-N dissolved organic N

16 19

6 50

4

21

particulate N a

100

Unpolluted lakes will show lower DIP, DIN, PP.

lake manipulation, is cited as an example of evolving N sufficiency under P enrichment,46 but a contrary interpretation of the data has been proposed.31 Multiyear whole lake enrichment experiments with P only document persistence of N limitation in lakes with substantial P and populations of N2 fixers. For example, whole lake fertilization of several Swedish lakes with P only (multiple years), yielded no higher biomass or only slightly higher biomass than was found before fertilization.47 The same lakes developed biomass 15 - 60 times higher with P + N fertilization (refs 47 and 41 give other examples). Focus on phosphorus control presumes that phosphorus loading of a lake can be reduced sufficiently to induce and sustain phosphorus control of algae. Where nonpoint phosphorus or background phosphorus sources are strong enough to sustain eutrophic conditions, phosphorus control measures may not provide enough phosphorus recovery to reduce algal biomass. In addition, allowing the balance between nitrogen and phosphorus to be strongly distorted over entire regions by selective control of phosphorus may change the species composition or diversity of aquatic communities,13,48 which often reflect a close balance between nitrogen and phosphorus availability.18,49 Finally, because nitrogen limitation is quite common in fresh waters and even more common in coastal waters and saline lakes,50 52 allowing nitrogen to be released indiscriminately from one water body to another through the drainage network could cause widespread stimulation of algal growth by providing nitrogen to algal communities downstream that otherwise would be nitrogen limited.53,54 Thus, dual nutrient control has multiple advantages.

’ STRATEGIES FOR LIMITING PHOSPHORUS AND NITROGEN IN THE ENVIRONMENT Use of total P as an index of P availability in lakes is defensible for lakes because most of the phosphorus in the growth zone of lakes is available to algae; it consists of total dissolved P (TDP) 10302


Environmental Science & Technology with its two components, dissolved inorganic P (DIP, often designated soluble reactive P, SRP) and dissolved organic P (DOP) plus particulate P (PP), which consists mostly of phytoplankton with their internal phosphorus stores. In lakes the particulate fraction of N also consists mainly of phytoplankton, and can be counted as bioavailable, as can DIN and some DON. Thus, the concept of bioavailability suggests that water quality standards for P in lakes can be based on total P, but for N they should be based on total N minus refractory DON. Regulating total N without adjusting for unavailable DON would be equally effective, but would lower the feasibility and raise the cost of N control. For nutrient control we focus here on effluents as nutrient sources because regulation of effluents is feasible through established permitting processes and because the technological basis for regulation nonpoint of sources, which may be dominant nutrient sources in some cases,34 is weak.

’ EFFLUENT REGULATION THAT IS CONSISTENT WITH STANDARDS BASED ON BIOAVAILABILITY Point source effluents, which are the main target for discharge permitting, are rich in bioavailable total dissolved P (Table 1). For the dominant treatment technologies (i.e., with the exception of oxidation ponds or ditches), particulate P is not a major concern because of the efficiency of particle removal during treatment. Thus, permits written on the basis of total phosphorus in effluent typically will translate well into a limitation on bioavailable phosphorus in lakes. For nitrogen, the presence of dissolved organic N in effluent is a complicating factor. DON in municipal effluent is derived partly from the influent waste stream and partly from microbial metabolism that occurs during treatment.55 Effluents appear to be similar to inland waters and nearshore marine waters in having both refractory and labile components. One study of a domestic treatment effluent from a treatment facility with low nitrogen output attained by combined nitrification and denitrification showed a median labile component near 40% (range, 18 61%) based on 14-day bioassays.55 Other studies have shown a similar range for bioavailable N in municipal effluent.56,57 If the total N limits are strict enough to be fully effective in protecting lakes from enrichment with labile N, wastewater treatment facilities will find that the limiting factor in their ability to produce low nitrogen effluent is DON, which is more difficult to remove than DIN. In fact, the ultimate baseline for DON concentration, as estimated by time course bioassays for a wastewater facility operating at low nitrogen output, may approach 1 mg/L.53 To regulate the refractory component of DON with stringency equal to that of DIN or labile DON overlooks the very different potential effects of the refractory and labile fractions of total dissolved nitrogen. A regulatory system that takes into account the relative abundance of refractory DON in setting effluent limits for nitrogen would require a standardized analysis of refractory DON. Bioassays could be used for this purpose according to a rationale very similar to the long accepted CBOD5 (5 day) and CBODu (ultimate) analyses for organic carbon.55 For both nitrogen and carbon, improved technology also offers new possibilities through the use of fluorescence spectroscopy 58 60 which, if calibrated with bioassay, might allow rapid analysis of large numbers of samples for both DOC and DON.

POLICY ANALYSIS

Figure 3. Response of attached algae in streams to experimental enrichments with N, P, or N+P (n = 237; redrawn from ref 63).

Table 2. Summary of Three Possible Effluent and TMDLRelated Regulatory Strategies for Nutrients basis of regulation

feasibility

cost

total P

high

moderate

allows N pollution

total P, Total N

low

high

may require removal

total Pa, total

high

high

of refractory N focuses on bioavailable

N

refractory N

comments

nutrients

a

TDP may be a better option for stream monitoring and lake loading limits where PP is mostly adsorbed onto mineral particles.

’ STREAMS Although rivers and slowly flowing streams may produce phytoplankton populations comparable to those of lakes, periphyton (attached algae) also are important and may be dominant, especially in streams of small to intermediate size. Excessive growth of periphyton can be a byproduct of nutrient enrichment in streams or rivers. As in the case of lakes, extensive study at many sites has shown that phosphorus and nitrogen are about equally likely to be limiting to the growth of periphyton (Figure 3; refs 61 63). For stream periphyton, unlike lake phytoplankton, as much as half of experimentally tested locations show no nutrient limitation. As in the case of lakes, however the strongest responses to nutrient addition typically are for addition of both N and P. The stimulation threshold for nitrogen and phosphorus enrichment response in streams appears to be higher than in lakes.64 67 Thus, protective nutrient standard concentrations may justifiably be higher for streams than for lakes, but will differ among distinct categories of streams. The arguments regarding fractions of phosphorus and nitrogen in lakes as given above are likely applicable to flowing waters as well. One exception is the consistently greater proportion of mineral particulate phosphorus (there is no significant mineral fraction for N) that is carried in suspension by flowing waters (Table 1). It may be preferable to use total soluble phosphorus rather than total phosphorus as a basis for regulation of P in flowing waters and for development of loading restrictions on lakes, given that mineral phosphorus is much less available to algae. Assessment of eutrophication in streams and rivers has lagged behind that of lakes. Additional research will be necessary to 10303


Environmental Science & Technology identify protective standards for them. Nevertheless, many of the issues surrounding nitrogen in streams and rivers are the same as for lakes. Regulation of eutrophication in flowing waters should be based on N and P controls and recognition of refractory DON as a regulatory consideration.

’ CONCLUSION Restriction of the anthropogenic release of both N and P to inland waters is a means of controlling excessive algal growth. P regulation should be based on total P (for lakes) or total dissolved P (preferred for flowing waters). N regulation should be based on bioavailable N rather than total N; regulation of total N will likely be infeasible or will require unrealistically high standards (Table 2). ’ AUTHOR INFORMATION Corresponding Author

*Phone: 303-492-6378; fax: 303-492-0928; e-mail: lewis@spot. colorado.edu.

’ REFERENCES (1) Kalff, J. Limnology: Inland Water Ecosystems; Prentice Hall: NJ, 2002. (2) Lewis, W. M., Jr. Global primary production of lakes: 19th Baldi Memorial Lecture. Inland Waters 2011, 1, 1–28. (3) OECD. Eutrophication of Waters—monitoring, Assessment and Control; Paris, France: Organization for Economic Co-operation and Development. 1982. (4) North, R. L.; Guildford, S. J.; Smith, R. E. H.; Havens, S. M.; Twiss, M. R. Evidence for phosphorus, nitrogen, and iron colimitation of phytoplankton communities in Lake Erie. Limnol. Oceanogr. 2007, 52, 315–328. (5) Wurtsbaugh, W. A.; Horne, A. J. Iron in eutrophic nitrogen fixation and growth. Can. J. Fish. Aquat. Sci. 1983, 41, 1419–1429. (6) Welch, E. B.; Lindell, T. Ecological Effects of Wastewater: Applied Limnology and Pollutant Effects; E & FN Spon: London, England, 2000. (7) Cooke, G. D.; Welch, E. B.; Peterson, S. A.; Nichols, S. A. Restoration and Management of Lakes and Reservoirs, 3rd ed.; Taylor & Francis Group, LLC: London, 2005. € (8) Abell, J. M.; Ozkundakci, D.; Hamilton, D. P. Nitrogen and phosphorus limitation of phytoplankton growth in New Zealand Lakes: Implications for eutrophication control. Ecosystems 2010, 13, 966–977. (9) Hutchinson, G. E. Eutrophication: The scientific background of a contemporary practical problem. Am. Sci. 1973, 61, 269–279. (10) Sterner, R. W.; Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere; Princeton University Press: Princeton, NJ, 2002. (11) Smith, V. H. The nitrogen and phosphorus dependency of algal biomass in lakes: An empirical and theoretical analysis. Limnol. Oceanogr. 1982, 27, 1101–1112. (12) Suttle, C. A.; Harrison, P. J. Ammonium and phosphate uptake rates, N:P ratios, and evidence for N and P limitation in some oligotrophic freshwater lakes. Limnol. Oceanogr. 1988, 33, 186–202. (13) Pick, F. R. Species specific phytoplankton responses to nutrient enrichment in limnetic enclosures. Arch. Hydrobiol. Beih. Ergebn. Limnol. 1989, 32, 177–187. (14) Sterner, R. W.; Hessen, D. O. Algal nutrient limitation and the nutrition of aquatic herbivores. Ann. Rev. Ecol. Syst. 1994, 25, 1–29. (15) Burger, D. F.; Hamilton, D. P.; Hall, J. A.; Ryan, E. F. Phytoplankton nutrient limitation in a polymictic eutrophic lake: Community versus species-specific responses. Fund. Appl. Limnol. 2007, 169, 57–68. (16) Bergstr€om, A.; Jansson, M. Atmospheric nitrogen deposition has caused nitrogen enrichment and eutrophication of lakes in the northern hemisphere. Global Change Biol. 2006, 12, 635–643.

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(17) Elser, J. J.; Andersen, T.; Baron, J. S.; Bergstrom, A.-K.; Jansson, M.; Kyle, M.; Nydick, K. R.; Steger, L.; Hessen, D. O. Shifts in lake N:P stoichiometry and nutrient limitation driven by atmospheric nitrogen deposition. Science 2009, 326, 835–837. (18) Elser, J. J.; Bracken, M. E. S.; Cleland, E. E.; Gruner, D. S.; Harpole, W. S.; Hillebrand, H.; Ngai, J. T.; Seabloom, E. W.; Shurin, J. B.; Smith, J. E. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 2007, 10, 1135–1142. (19) Stoddard, J. L. Long-term changes in watershed retention of nitrogen its causes and aquatic consequences. Adv. Chem. Ser. 1994, 237, 223–284. (20) Lewis, W. M., Jr.; Wurtsbaugh, W. W. Control of lacustrine phytoplankton by nutrients: Erosion of the phosphorus paradigm. Int. Rev. Hydrobiol. 2008, 93, 446–465. (21) Stumm, W., Morgan, J. J. Aquatic Chemistry, Chemical Equilibria and Rates in Natural Waters, 3rd ed.; Wiley: New York, 1996. (22) Golterman, H. L. Physiological Limnology. an Approach to the Physiology of Lake Ecosystems; Elsevier: New York, 1975. (23) Ryding, S. O., Rast, W. The Control of Eutrophication of Lakes and Reservoirs, Man and the Biosphere series; Parthenon: New York, 1989; Vol. 1. (24) James, C.; Fisher, L. J.; Moss, B. Nitrogen driven lakes: The Shropshire and Cheshire meres?. Arch. Hydrobiol. 2003, 158, 249–266. (25) Bunting, L.; Leavitt, P. R.; Hall, V.; Gibson, C. E.; McGee, E. J. Nitrogen degradation of water quality in a phosphorus-saturated catchment: The case of Lough Neagh, Northern Ireland. Verh. Int. Verein. Limnol. 2005, 29, 1005–1008. (26) NRC. Endangered and Threatened Fishes in the Klamath River Basin: Causes of Decline and Strategies for Recovery; National Academies Press: Washington, DC, 2004. (27) Lewis, W. M., Jr.; Saunders, J. F., III; McCutchan, J. H., Jr. Application of a nutrient-saturation concept to the control of algal growth in lakes. Lake Res. Manag. 2008, 24, 41–46. (28) Elser, J. J.; Marzolf, E. R.; Goldman, C. R. Phosphorus and nitrogen limitation of phytoplankton growth in the freshwaters of North America: A review and critique of experimental enrichments. Can. J. Fish. Aquat. Sci. 1990, 47, 1468–1477. (29) Schindler, D. W. Evolution of phosphorus limitation in lakes: Natural mechanisms compensate for deficiencies of nitrogen and carbon in eutrophied lakes. Science 1977, 195, 260–262. (30) Howarth, R. W.; Marino, R.; Lane, J.; Cole, J. J. Nitrogen fixation in freshwater, estuarine, and marine ecosystems: Rates and importance. Limnol. Oceanogr. 1988, 33, 669–687. (31) Scott, J. T.; McCarthy, M. J. Nitrogen fixation may not balance the nitrogen pool in lakes over timescales relevant to eutrophication management. Limnol. Oceanogr. 2010, 55, 1265–1270. (32) Paerl, H. W. Physiological ecology and regulation of N2 fixation in natural waters. Adv. Microb. Ecol. 1990, 11, 305–344. (33) Paerl, H. W.; Xu, H.; McCarthy, M. J.; Zhu, G.; Qin, B.; Li, Y.; Gardner, W. S. Controlling harmful cyanobacterial blooms in a hypereutrophic lake (Lake Taihu, China): The need for a dual nutrient (N & P) management strategy. Water. Res. 2011, 45, 1973–1983. (34) Downing, J. A.; McCauley, E. The nitrogen: Phosphorus relationship in lakes. Limnol. Oceanogr. 1992, 37, 936–945. (35) Smith, V. H. Low nitrogen to phosphorus ratios favor dominance by blue-green algae in lake phytoplankton. Science 1983, 221, 669–670. (36) Reynolds, C. S. Ecology of Phytoplankton; Cambridge: New York, 2006. (37) Healey, F. P. Characteristics of phosphorus deficiency in Anabaena. J. Phycol. 1973, 9, 383–394. (38) Berman, T.; Chava, S. Algal growth on organic compounds as nitrogen sources. J. Plankton Res. 1999, 21, 1423–1437. (39) Seitzinger, S. P.; Sanders, R. W.; Styles, R. Bioavailability of DON from natural and anthropogenic sources to estuarine plankton. Limnol. Oceanogr. 2002, 47, 353–366. (40) Bronk, D. A.; See, J. H.; Bradley, P.; Killberg, L. DON as a source of bioavailable nitrogen for phytoplankton. Biogeoscience 2007, 4, 283–296. 10304


Environmental Science & Technology (41) Finlay, K.; Patoine, A.; Donald, D. B.; Bogard, M. J.; Leavitt, P. R. Experimental evidence that pollution with urea can degrade water quality in phosphorus- rich lakes of the Northern Great Plains. Limnol. Oceanogr. 2010, 55, 1213–1230. (42) Bronk, D. A.; Steinberg, D. Nitrogen regeneration. In Nitrogen in the Marine Environment; Capone, D. G., Bronk, D. A., Mulholland, M., Carpenter, E., Eds.; Elsevier: New York, 2008; pp 385 468. (43) Wiegner, T. N.; Seitzinger, S. P.; Gilbert, P. M.; Bronk, D. A. Bioavailability of dissolved organic nitrogen and carbon from nine rivers in the eastern United States. Aquat. Microb. Ecol. 2006, 43, 277–287. (44) Morris, D. P.; Lewis, W. M., Jr. Phytoplankton nutrient limitation in Colorado mountain lakes. Freshwater Biol. 1988, 20, 315–327. (45) Wang, H. J.; Wang, H. Z. Mitigation of lake eutrophication: Loosen nitrogen control and focus on phosphorus abatement. Prog. Nat. Sci. 2009, 19, 1445–1451. (46) Schindler, D. W.; Hecky, R. E.; Findlay, D. L.; Stainton, M. P.; Parker, B. R.; Paterson, M. J.; Beaty, K. G.; Lyng, M.; Kasian, S. E. M. Eutrophication of lakes cannot be controlled by reducing nitrogen input: Results of a 37-year whole-ecosystem experiment. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 11254–11258. (47) Holmgren, S. K. Experimental lake fertilization in the Kuokkel Area, Northern Sweden. Phytoplankton biomass and algal composition in natural and fertilized subarctic lakes. Int. Rev. Gesamten Hydrobiol. Hydrogr. 1984, 69, 781–817. (48) Berman, T. The role of DON and the effect of N:P ratios on occurrence of cyanobacterial blooms: Implications from the outgrowth of Aphanizomenon in Lake Kinneret. Limnol. Oceanogr. 2001, 46, 443–447. (49) Sterner, R. W. On the phosphorus limitation paradigm for lakes. Int. Rev. Hydrobiol. 2008, 93, 433–445. (50) Conley, D. J.; Paerl, H. W.; Howarth, R. W.; Boesch, D. F.; Seitzinger, S. P.; Havens, K. E.; Lancelot, C.; Likens, G. E. Controlling eutrophication: Nitrogen and phosphorus. Science 2009, 323, 1014–1015. (51) Paerl, H. W. Controlling eutrophication along the freshwatermarine continuum: Dual nutrient (N and P) reductions are essential. Estuarine Coasts 2009, 32, 593–601. (52) Hammer, U. T. Saline Lake Ecosystems of the World; Junk Publishers: Dordrecht, 1986, P. 616. (53) Elmgren, R.; Larsson, U. Nitrogen and the Baltic Sea: Managing nitrogen in relation to phosphorus. In The Scientific World, Special edition; Balkema Publishers: 2001; Vol. 1(S2), pp 371 377. (54) Paerl, H. W.; Valdes, L. M.; Piehler, M. F.; Lebo, M. E. Solving problems resulting from solutions: The evolution of a dual nutrient management strategy for the eutrophying Neuse River Estuary, North Carolina, USA. Environ. Sci. Technol. 2004, 38, 3068–3073. (55) Urgun-Demirtas, M.; Sattayatewa, C.; Pagilla, K. R. Bioavailablity of dissolved organic nitrogen in treated effluents. Water Environ. Res. 2008, 80, 397–406. (56) Bronk, D. A.; Roberts, Q. N.; Sanderson, M. P.; Canuel, E. A.; Hatcher, P. G.; Mesfioui, R.; Filippino, K. C.; Mulholland, M. R.; Love, N. G. Effluent organic nitrogen (EON): Bioavailability and photochemical and salinity-mediated release. Environ. Sci. Technol. 2010, 44, 5830–5835. (57) Filippino, K. C.; Mulholland, M. R.; Bernhardt, P. W.; Boneillo, G. E.; Morse, R. E.; Semcheski, M.; Marshall, H.; Love, N. G.; Roberts, Q.; Bronk, D. A. The bioavailability of effluent-derived organic nitrogen along an estuarine salinity gradient. Estuarine Coasts 2011, 34, 269–280. (58) Chen, J.; Gu, B.; LeBouef, E. J.; Pan, H.; Dai, S. Spectroscopic characterization of the structural and functional properties of natural organic matter fractions. Chemosphere 2002, 48, 59–68. (59) Stedman, C. A.; Markager, S.; Bro, R. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Mar. Chem. 2003, 82, 239–254. (60) Cory, R. M.; McKnight, D. M. Fluorescence spectroscopy reveals ubiquitous presence of oxidized and reduced quinines in dissolved organic matter. Environ. Sci. Technol. 2005, 39, 8142–8149. (61) Dodds, W. K.; Welch, E. B. Establishing nutrient criteria in streams. J. N. Am. Benthol. Soc. 2000, 19, 186–196.

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(62) Tank, J. L.; Dodds, W. L. Nutrient limitation of epilithic and epixylic biofilms in ten North American streams. Freshwater Biol. 2003, 48, 1031–1049. (63) Francoeur, S. N. Meta-analysis of lotic nutrient amendment experiments: Detecting and quantifying subtle responses. J. N. Am. Benthol. Soc. 2001, 20, 358–368. (64) Lewis, W. M., Jr.; McCutchan, J. H., Jr. Ecological responses to nutrients in streams and rivers of the Colorado mountains and foothills. Freshwater Biol. 2010, 55, 1973–1983. (65) Dodds, W. K.; Lopez, A. J.; Bowden, W. B.; Gregory, S.; Grimm, N. B.; Hamilton, S. K.; Hershey, A. E.; Marti, E.; McDowell, W. H.; Meyer, J. L.; Morrall, D.; Mulholland, P. J.; Peterson, B. J.; Tank, J. L.; Valett, H. M.; Webster, J. R.; Wollheim, W. N uptake as a function of concentration in streams. J. N. Am. Benthol. Soc. 2002, 21, 206–220. (66) Mulholland, P. J.; Steinman, A. D.; Elwood, J. W. Measurements of phosphorous uptake length in streams: Comparison of radiotracer and stable PO4 releases. Can. J. Fish. Aquat. Sci. 1990, 47, 2351–2357. (67) O’Brien, J. M.; Dodds, W. K.; Wilson, K. C.; Murdock, J. N.; Eichmiller, J. The saturation of N cycling in Central Plains streams: 15N experiments across a broad gradient of nitrate concentrations. Biogeochemistry 2007, 84, 31–49. (68) Lewis, W. M., Jr. Yield of nitrogen from minimally disturbed watersheds of the United States. Biogeochem. 2002, 57/58, 375–385. (69) Meybeck, M. Carbon, nitrogen, and phosphorus transport by world rivers. Am. J. Sci. 1982, 282, 401–450. (70) Dodds, W. K.; Oakes, R. M. A technique for establishing reference nutrient concentrations across watersheds affected by humans. Limnol. Oceanogr.: Methods 2004, 2, 333–341. (71) Smith, R. A.; Alexander, R. B.; Schwarz, G. E. Natural background concentrations of nutrients in streams and rivers of the conterminous United States. Environ. Sci. Technol. 2003, 37, 3039–3046.

10305



Should We Pretreat Solid Waste Prior to Anaerobic Digestion? An Assessment of Its Environmental Cost Marta Carballa,* Cecilia Duran, and Almudena Hospido* Department of Chemical Engineering, School of Engineering, University of Santiago de Compostela, Rua Lope Gomez de Marzoa s/n, E-15782 Santiago de Compostela, Spain

bS Supporting Information ABSTRACT: Many studies have shown the effectiveness of pretreatments prior to anaerobic digestion of solid wastes, but to our knowledge, none analyzes their environmental consequences/costs. In this work, seven different pretreatments applied to two types of waste (kitchen waste and sewage sludge) have been environmentally evaluated by using life cycle assessment (LCA) methodology. The results show that the environmental burdens associated to the application of pretreatments prior to anaerobic digestion cannot be excluded. Among the options tested, the pressurize-depressurize and chemical (acid or alkaline) pretreatments could be recommended on the basis of their beneficial net environmental performance, while thermal and ozonation alternatives require energy efficiency optimization to reduce their environmental burdens. Reconciling operational, economic and environmental aspects in a holistic approach for the selection of the most sustainable option, mechanical (e.g., pressurize-depressurize) and chemical methods appear to be the most appropriate alternatives at this stage.

’ INTRODUCTION Anaerobic digestion (AD) is a very promising option for the treatment of solid organic wastes due its ability to transform organic matter into biogas (with 60 70% CH4), with the concomitant reduction of the amount of final solids to be disposed.1 Moreover, solid byproduct (digestate) can be further used for agricultural purposes.2 However, AD of solid wastes is often limited by long retention times (20 30 days) and/or low overall degradation efficiencies (30 50%), probably associated with the hydrolysis stage.3 Therefore, significant effort has been dedicated in recent years to find alternatives to improve AD of solid wastes.4 Among the different options, the use of pretreatments is the most studied and its operational effectiveness has been demonstrated by many authors.1,5 8 All pretreatments entail the use of resources (chemicals and/or energy), thus deriving not only financial but also environmental costs. Preliminary economical analyses of several pretreatments have been recently published;8,9 however, to the best of our knowledge, their environmental evaluation has not been addressed yet. Life cycle assessment (LCA) is one of the most widely known and internationally accepted methodologies to compare environmental impacts of processes and systems.10 Several LCAs have focused on examining sewage sludge (SS) treatment options and/or end uses,11 16 most of them concluding that the combination of AD and agricultural application is the preferable alternative from an environmental point of view. This methodology has been also applied as a decision support tool in the selection of the most adequate municipal solid waste (MSW) management strategy for several countries or regions.17 21 In most cases, the r 2011 American Chemical Society

recycling of valuable materials together with the AD of the organic fraction of MSW, mostly consisting of food waste or kitchen waste (KW), turned to be the best scenario in environmental terms. Other studies were only focused on the conversion and management options of the organic fraction of MSW,22 24 concluding that AD was environmentally more favorable than incineration or aerobic composting. So, up to now, much LCA has validated AD and agricultural application as the best options for treatment and final disposal of organic solid wastes, respectively, but no information could be found on the environmental impact of the use of pretreatments prior to AD of solid wastes. In this study, we aim to fulfill this information gap by using LCA to evaluate the environmental consequences associated to the use of seven different pretreatments before AD of two organic solid wastes (kitchen waste and sewage sludge), and consequently, add the environmental vector to the technical and the economic evaluation toward a more sustainable decision making process.

’ EXPERIMENTAL SECTION Functional Unit Definition. The functional unit (FU) is usually defined in terms of the system output;25 however, when dealing with waste management systems, the FU might Received: June 1, 2011 Accepted: October 31, 2011 Revised: September 26, 2011 Published: October 31, 2011 10306

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POLICY ANALYSIS

Scenarios Description. The system included the different pretreatments applied, the anaerobic digestion process with energy recovery and the disposal of the digestate in agricultural land. Based on the different pretreatments and waste used (KW and SS), 16 scenarios (8 per type of waste) were considered for comparison (Table 2), including the reference scenario. The latter refers to the anaerobic treatment of 10 L of waste without being previously pretreated. A detailed description of the pretreatments applied to KW and SS can be found in Ma et al. (2011)8 and Carballa et al. (2006,5 20076), respectively. It is important to make clear that not all scenarios were experimentally tested; instead, estimations were conducted on the basis of the experimentally tested pretreatments. A detailed description of the assumptions required for those estimated alternatives can be found in the Supporting Information (SI). AD was carried out in a lab-scale continuously stirred tank reactor at thermophilic conditions (55 ( 2 °C) and with a sludge retention time (SRT) of 10 and 20 days for SS and KW, respectively. A detailed description of the equipment and their performance can be found elsewhere.8,27 However, due to the lack of information, the infrastructure related to lab-scale operation was excluded from the analysis. Final disposal on agricultural land was modeled on the basis of literature data (see the Inventory Analysis section). The generation of SS and KW was left beyond the boundaries of the system, and the common elements within the system boundaries, such as digested solids conditioning, transportation and the spreading procedure, were not included in the analysis for comparative reasons. Inventory Analysis. In this stage, the raw materials consumed, the energy used, the products and coproducts obtained, and the emissions to air, water and soil, were identified and quantified for each scenario. Lab-scale experimental data of the KW and SS characteristics for each scenario and AD performance were provided by Duong (2009),28 Ma et al. (2011),8 and Carballa et al. (2006;5 2007;6 200929), except for the levels of nutrients and heavy metals in KW that were taken from literature.30 Since

be defined in terms of the system input, that is, the waste to be managed.26 Accordingly, the management (i.e., pretreatment, anaerobic digestion, and agriculture application) of 10 L of solid waste has been chosen in this work. For KW, the 10 L consisted of food waste provided by Trans Vanheede Environmental Group (Belgium) diluted with wastewater coming from a sewage treatment plant (Ossemeersen, Belgium) in order to achieve the required organic loading rate (OLR) in the digester (for further information, see Ma et al., 20118). In the case of SS, 10 L of a mixture of primary and secondary sludge (70:30, v/v) collected from the two thickeners existing in a sewage treatment plant (Galicia, NW Spain) was considered (for a more detailed description, see Carballa et al., 200727). The physic-chemical parameters, nutrients and heavy metals content in the KW and SS are reported in Table 1. Table 1. Main Characteristics of Sewage Sludge (n = 20) and Kitchen Waste (n = 10, except Nutrients and Heavy Metals Which Come from Literature30) Used in the Experiments parameter

kitchen waste

sewage sludge

pH

3.8 ( 0.2

5.6 ( 0.2

CODt (g/kg KW or L SS)

268 ( 20

50 ( 18

CODs (g/kg KW or L SS) TS (g/kg KW or L SS)

75 ( 7 166 ( 14

4(2 53 ( 19

VS (g/kg KW or L SS)

155 ( 13

34 ( 13

N (g/kg TS)

31.6

21.5

P (g/kg TS)

5.2

30.5

Zn (mg/kg TS)

76

868

Cu (mg/kg TS)

31

293

Cd (mg/kg TS)

1

2

Cr (mg/kg TS) Pb (mg/kg TS)

2 4

167 93

Ni (mg/kg TS)

2

79

Table 2. Description of the Pre-Treatments Applied in the Different Scenarios of KW and SS OLR scenariosa (kg COD m‑3d 1) KW1b

3.0

SS1

4.3

pretreatment

description Reference (nonpre-treated)

KW2b

4.0

alkaline

Addition of lime until pH 12, checking this value after 24 h, and neutralization with HCl (10 N)

SS2 KW3b

3.6 4.0

acid

before feeding the digester. Doses: 0.125 g CaO g 1 VSS and 0.058 g HCl g 1 VSS. Addition of HCl (10 N) until pH 2, checking this value after 24 h, and neutralization with NaOH (10 N)

SS3

4.0

KW4b

3.0

thermal

Heating and maintenance at 120 °C during 30 min. Cooling to room temperature before

thermo-acid

Acidification with HCl (10 N) until pH 2 followed by thermal treatment at 120 °C during 30 min. Freezing at

SS4

8.7

KW5b

3.0

before feeding the digester. Doses: 0.026 g HCl g

1

VSS and 0.040 g “extra” NaOH g

3.0

KW6b

5.0

freeze thaw

SS6 KW7b

5.0 5.0

feeding the digester. pressurize depressurize Pressurization to 10 bar with airc and quick depressurization to atmospheric pressure.

SS7

5.0 5.0

SS8

6.6

VSS.

feeding the digester.

SS5

KW8b

1

Cooling to room temperature and neutralization with NaOH (10 N) before feeding the digester.

ozone

20 °C for 6 h and thawing at 55 °C for 30 min. Cooling to room temperature before

Ozonation in a 10 L bubble column operated in batch for 2 h at room temperature. The ozone dose was set approximately at 20 mg O3 g

1

TSS.

a

Names in italics indicate the scenarios that have not been experimentally tested but inventoried based on estimations. b NaOH (10 N) was used in all KW scenarios in order to neutralize the influent prior to feed the digester. c Air was considered instead of CO2 because no chemical effect (e.g., increase in pH) was observed from the use of CO28. 10307

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POLICY ANALYSIS

Table 3. Summary of the Inventory Data for the 16 Scenarios Compared (see Table 2 for the Description of the Scenarios)a KW1 KW2 KW3 KW4 KW5

KW6

KW7

KW8

24

24

24

SS1

SS2

SS3

SS4

SS5

SS6

SS7

SS8

chemicals (g FU 1) NaOH

24

40

HCl

23

CaO

50

24

10

36 8

13

4

382

O2

355

22

energy used (kJ FU 1)

CH4 (%)

30

7

28

Na2S2O3 biogas production (L FU 1)

34

160

176

280

959

948

1,866

68

1,476

220

240

280

400

185

20

202 58.3

222 62.3

354 65.0

4,375

4,584

5,536

68

1,476

350

303

354

505

234

65.0

65.0

65.0

65.0

65.0

65.0

65.0

65.0

59.4

65.0

65.0

65.0

65.2

N

9.5

15.1

15.1

10.2

11.6

19.6

20.2

19.9

8.9

10.4

9.6

12.2

9.6

9.6

9.6

6.7

P

1.6

2.5

2.5

1.7

1.9

3.2

3.3

3.3

12.7

14.8

13.6

17.3

13.6

13.6

13.6

9.5

nutrients content (g FU 1)

a

Names in italics indicate that those scenarios have not been experimentally tested but inventoried based on estimations.

nutrients and heavy metals content in sludge varied during the experimental period, the average values reported in Table 1 were considered for all scenarios. One operational advantage of the use of pretreatments is that it allows operating the anaerobic digesters at higher OLR. Consequently, the OLR varied among the different scenarios, each corresponding to “optimal operational conditions”, that is, the highest OLR enabling steady state performance and conversion of at least 50% of the COD input into biogas (Table 2). Energy was not only used in some pretreatments but also during the AD process for stirring and heating. In this study, it was assumed that the biogas produced in the reference scenarios (KW-1 and SS-1) was sufficient to cover the heating and stirring requirements of the digester. The “extra” biogas produced as a consequence of the pretreatment application was considered to be burned in a cogeneration plant (gas engine) in order to produce both electricity and heat31 (power generation rate of 40% and a heat generation rate of 50%), and the associated air emissions of CO, CO2, CH4, and NMVOC (nonmethane volatile organic compounds) from the biogas combustion were included by using emission factors reported in literature.32 Regarding digestate application in agriculture, P and N were regarded as organic fertilizers that reduce the need of synthetic fertilizers by 70% and 50%, respectively.33 Nutrientrelated emissions (N to air as N2O and NH3 and P to water as PO43‑) were estimated by means of emission factors from literature.32 Background data related to the production of chemicals, energy, and fertilizers came from the Ecoinvent Database v2.34 36 Table 3 shows a summary of the input and output data collected or calculated for the 16 scenarios. A more detailed compilation of data can be found in the SI (Tables S1 and S2). Life Cycle Impact Assessment. This stage characterizes the environmental pressures related to the inventory by means of impact assessment models, and makes use of category indicators to condense and explain the inventory results. In this study, a well-established midpoint methodology was applied, the CML 2 baseline 2000 v2.05 implemented in the SimaPro 7.3 software (http://www.pre.nl/content/simapro-lca-software). Among the impact categories described by this method,37 the following were selected: abiotic resource depletion potential (ADP), eutrophication potential (EP), global warming potential (GWP), human toxicity potential (HTP) and terrestrial ecotoxicity potential (TTP).

’ RESULTS Table 4 displays the outcomes of the classification and characterization steps of the impact assessment stage. The selected category indicators are separately presented since each one is expressed in its corresponding unit of reference. For a quick comparison of the different alternatives examined, the relative performance of the individual scenarios within each category indicator (stated as the percentage ratio between the value of the scenario and the maximum value of that indicator within the same waste group) is also shown. Pressurize-depressurize (KW7, SS7) and chemical pretreatments (KW2, KW3, SS2, SS3) shared the top positions with minimum or even positive net impact on the environment, except for eutrophication, where the references (KW1, SS1) were the best scenarios. The latter is probably related to the OLR applied in each scenario, because taking into account that nutrients are hardly removed during AD (some precipitation can occur), the higher the OLR applied, the higher the amount of solids entering the digester, and thus, higher nutrients levels being discharged per FU. Among KW scenarios, KW-8 (ozone) was located at the end of the ranking in all categories, which can be explained by the high use of chemicals and energy. In the case of SS, the greatest impacts were observed in the most energydemanding scenarios, that is, SS4 (thermal), SS5 (thermo-acid) and SS6 (freeze thaw). Abiotic Resource Depletion Potential (ADP). ADP covers all potential impacts of the extraction of mineral and fossil fuels.37 Figure 1 shows the contribution of the different elements to this impact category (see Table S3 in the SI for detailed information). Taking into account that the Spanish energy profile is heavily reliant on fossil energy (80%), it was not surprising that the energy use was the main contributor to this category indicator. Therefore, the most damaging alternatives were those scenarios consuming large amounts of energy, that is, thermal (KW4, KW5, SS4, SS5) and freeze thaw (KW6, SS6) pretreatments. This fact was particularly remarkable in the SS scenarios because the volume of SS pretreated was higher (KW was pretreated without dilution). On the contrary, the consumption of chemicals had a minor impact, except for ozonation, with contributions of 62% and 57% in KW8 and SS8, respectively. This is mainly due to the higher amounts of oxygen consumed (Table 3) rather than the emission factor associated to its manufacture process (0.00301 g Sb-eq g 1 O2), which is 10308



POLICY ANALYSIS

Table 4. Absolute and Relative (Within Each Waste Group) Results from the Impact Assessment Stage (See Table 2 for the Description of the Scenarios). Positive Values Mean a Negative Impact on the Environment, While Negative Values Mean a Positive Impact (Avoided Impact) on the Environmenta category indicator scenario

3‑

ADP (g Sb-eq/FU)

EP (g PO4 -eq/FU)

KW1

0.20

7.4%

1.27

35.8%

99.8

20.2%

38.1

KW2

0.16

5.7%

1.85

52.1%

106.7

21.6%

22.1

KW3

1.21

43.7%

2.06

58.0%

23.1

KW4

0.50

18.2%

1.65

46.4%

170.7

KW5 KW6

0.46 0.58

16.4% 20.9%

1.92 3.01

54.1% 84.7%

176.8 237.1

KW7

3.25

117%

2.48

KW8

2.77

100%

3.55

69.8% 100%

GWP (g CO2-eq/FU)

232.5 494.5

4.68%

HTP (g 1,4-PDB-eq/FU) 14.7% 8.53%

TTP (g 1,4-PDB-eq/FU) 1.8

22.1%

1.7

21.0%

60.8

23.2%

3.1

38.4%

34.5%

113.1

43.6%

4.6

56.8%

35.8% 48.0%

139.1 180.8

53.6% 69.6%

5.4 7.9

66.4% 97.9%

3.4%

2.7

47.0% 100%

8.7 259.6

100%

8.1

33.2% 100%

SS1

0.50

12.1%

1.71

46.1%

11.3

1.6%

548.1

49.4%

19.3

49.5%

SS2

0.66

15.8%

2.04

55.2%

21.8

3.1%

652.0

58.7%

22.7

58.4%

57.0%

22.2

SS3

2.03

49.1%

2.07

SS4

2.66

64.2%

3.70

SS5 SS6

4.15 3.97

100% 95.9%

3.53 3.54

SS7

4.67

113%

SS8

2.20

53.0%

56.1% 100% 95.5% 95.6%

128.4

18.4%

521.2

74.7%

1,110

633.1

698.0 697.5

100% 99.9%

1,029 1,047

100% 92.7% 94.3%

38.9 35.3 36.6

57.0% 100% 90.6% 94.0%

1.78

48.1%

420.7

60.3%

552.3

49.8%

20.7

53.1%

2.31

62.3%

388.6

55.7%

626.8

56.5%

19.8

50.9%

a

Names in italics indicate that those scenarios have not been experimentally tested but inventoried based on estimations. ADP: Abiotic Resource Depletion Potential; EP: Eutrophication Potential; GWP: Global Warming Potential; HTP: Human Toxicity Potential; TTP: Terrestrial Ecotoxicity Potential.

Figure 1. Contribution of different elements to abiotic resource depletion (ADP).

the second lowest among all chemicals used in this study. Actually, oxygen was responsible for 66% and 85% of the impact associated to chemicals consumption in KW8 and SS8, respectively (SI Table S3). In most scenarios, the impact was compensated by the enhancement of the AD performance, which is reflected in the avoided products (energy and fertilizers). In this category, the avoided energy derived a greater benefit than the avoided fertilizers in most cases. Yet, it was not enough to balance the impact associated to the energy requirements of the pretreatments, except for the pressurize-depressurize method. In fact, the best results were achieved in these scenarios (KW7, SS7), which were net energy and fertilizers producers (Table 4).

Eutrophication Potential (EP). EP covers all potential impacts of excessively high environmental levels of macronutrients (mainly N and P), which may cause an undesirable shift in species composition and elevated biomass production.37 In all scenarios, nutrient-related direct emissions from the application of digestates on agricultural soil dominated the impact in this category (Figure 2 and SI Table S4). The poorest performance was observed in those scenarios where higher OLRs were applied, that is, higher solids entering the digester, and therefore, higher amounts of nutrients per FU. This impact is correlated with the benefit derived from the provision of a biosolids product that displaces the use of N- and P-based fertilizers, as both elements directly 10309



POLICY ANALYSIS

Figure 2. Contribution of different elements to eutrophication potential (EP).

Figure 3. Contribution of different elements to global warming potential (GWP).

depend on the nutrient content (Tables 3, SI S1 and S2). The use of energy and chemicals shared the second position, each being more relevant in the energy-using (KW4, KW5, KW6, SS4, SS5, SS6) and chemical (KW2, KW3, KW8, SS2, SS3, SS8) pretreatments, respectively. In this category, the avoided products did not compensate the environmental impact, and consequently, the reference scenarios were the most preferable options for both types of waste (Table 4). Global Warming Potential (GWP). GWP is defined as the impact of human emissions on the radiative forcing of the atmosphere.37 In this work, the emissions of biogenic CO and CO2 have been disregarded (characterization factors equal to 0) according to Docka (2010).32 As expected, Figure 3 shows a relationship between energy use and the GWP, being the worst scenarios KW6, KW8, SS4, SS5, and SS6 (Table 4), all of them characterized by a high energy demand (>1.4 MJ FU 1). Behind the background emissions associated with energy production, this category is dominated by the direct emissions from biogas burning (CH4 and NMVOC) and digestate application in the soil (N2O) (SI Table S5). Only in KW8 and SS8 (ozonation), the emissions derived from the use of chemicals accounted for more

than 40% of total impact (Figure 3), once again due to the use of oxygen, whose production is highly energy intensive. In this category, the environmental benefit (negative values in figures) was always higher in the pretreatment scenarios than in the reference, and the rate of this benefit was mainly related to the increased biogas produced as a consequence of the application of the pretreatment (Figure 3 and Table 3). For both types of waste, pressurize-depressurize scenarios (KW7, SS7) performed the best, followed by the acid scenarios (KW3, SS3), all of them having net positive impact (i.e., negative category indicators) on global warming potential (Table 4). Human and Terrestrial Toxicity Potential (HTP and TTP). HTP covers the impacts of toxic substances present in the environment on human health, while TTP refers to impacts of toxic substances on terrestrial ecosystems.37 In these impact categories, there is a significant difference between both wastes (Figure 4), caused by the extremely high contribution of the direct emissions of heavy metals to soil in the SS scenarios (>63% in HTP, SI Table S6, and >58% in TTP, SI Table S7). This pattern can be explained by the nature of heavy metal pollution, mostly diffuse with industrial origin, and thus making very difficult 10310



POLICY ANALYSIS

Figure 4. Contribution of different elements to human toxicity potential (A) and terrestrial toxicity potential (B).

the control of heavy metals presence in sewage sludge.38 On the contrary, in order to protect human health, food products are very controlled and, consequently, the presence of heavy metals is not expected. This dominance of heavy metals has been found by other LCA studies,11,12 and consequently, the application of digestates in sectors where no risk of contaminating the animal or human food chain exists (e.g., floriculture) has been suggested. 13 In addition, the environmental burdens associated to HTP (Figure 4A) were more than 25-fold those of TTP (Figure 4B), which positions the human being in a more protective perspective than the ecosystem. Overall, the relative position of the different scenarios within these two categories was quite similar, being the pressure-depressure pretreatment (SS7, KW7) one of the best options, whereas thermal and freeze thaw methods showed the poorest performance due to the indirect emissions of toxic substances of the background processes associated (electricity production).

’ DISCUSSION Sixteen scenarios combining pretreatment, anaerobic digestion and agricultural disposal were environmentally modeled using life cycle assessment. The results show that the environmental burdens associated to the application of any pretreatment

prior to anaerobic digestion cannot be excluded. If for illustrative purposes, we value all the selected indicators equally, pressurizedepressurize and chemical (acid or alkaline) methods could be recommended on the basis of their net environmental performance. On the contrary, thermal, freeze thaw and ozonation alternatives would entail an environmental damage as the improvement of the AD process does not compensate the environmental burdens associated to the pretreatment. Energy vs Chemicals. Any pretreatment makes use of some form of energy (pressure, translational, rotational, thermal, or electrical) and/or chemicals and both resources can have a very diverse effect on the environment and humans. In this study, the scenarios using energy in their pretreatments possess a higher impact than those using chemicals. Therefore, though thermal pretreatments appear to be the most suitable for the improvement of waste stabilization, efforts must be done to enhance the energy balance by using the waste residual heat to maintain the temperature of the digester, by applying more efficient methods for waste disintegration such as microwave heating9 and for biogas utilization39 or by making use of a more sustainable energy production profile, that is, less dependent on fossil fuels. Among energy-using pretreatments, mechanical disintegration (i.e., pressurize-depressurize) is preferred over thermal methods due to the lower energy demand without compromising the increase in 10311


Environmental Science & Technology biogas production. Among chemical processes, category indicators invert the option preferences between acid and alkaline methods. The former performs better in terms of ADP and GWP, while the latter does in EP and toxicity potentials. Further research on alternative chemicals as well as on the optimization of the required dose are likely to entail a reduction of the environmental burdens of these scenarios. Biogas Production vs Digestate Quality. The two beneficial impacts derived from AD of solid waste are the biogas production (avoided energy) and a biosolids product (digestate) suitable for agricultural application (avoided fertilizers). When analyzing the operational effectiveness of the use of pretreatments, most researchers focus on the increase in biogas production and scarce information is provided on digestate quality. In terms of environmental performance, this study shows that the benefit derived from pretreatments application regarding biogas production is greater than in terms of improving the fertilizing capacity of digestates when both avoided products were measurable. This is probably related to the intrinsic purpose of pretreatments, that is, making organic matter available to boost biogas production. However, the more stringent limitations for biosolids application in agriculture would make practitioners to work hard to control digestate quality. Substrate Characteristics. Overall, no significant differences were observed between the analysis of kitchen waste and sewage sludge scenarios. Yet, there are some aspects that need to be addressed. Organic solids content in the waste is relevant not only because it determines the amount of nutrients and pollutants contained in the waste, affecting particularly eutrophication and toxicity potentials, but also because it establishes the loading rate at which the digesters can be run, and consequently, the biogas production rate. Moreover, substrate composition (i.e., biodegradability) will determine the effect of the pretreatments, since those wastes containing poorly digestable materials (e.g., cellulose or lignin), such as agroindustrial residues, would experience a more significant improvement in biogas production at the same cost (same energy and/or chemical consumption), thus yielding a more positive environmental impact. On the other hand, literature data were required to fill the gap of metals content in KW and differences are likely to exist between food wastes due to, for example, food legislation or habits of consumption. This demonstrates that general recommendations should be avoided and individual analyses should be conducted. Consideration of Offsets. One of the uncertainties of this study is the extent of potential offsets. The biogas produced in the reference scenarios was assumed to be sufficient to cover the energy needs of heating and stirring of the digester; however, Hospido et al. (2010)16 only compensated the heating demand and reported values for stirring of 1.68 and 0.84 kWh FU 1 for SRT of 20 and 10 days, respectively. Soda et al. (2010)40 showed that AD coupled to power generation processes resulted in excess energy production if high sludge-loading rates were applied. The inclusion of energy for stirring in this study would increase significantly the environmental burdens of all scenarios, especially those of KW due to the 2-fold SRT applied. Yet, since this study is focused on a comparative analysis, the ranking of the scenarios would not be affected. Methodology Aspects. In this study, biogenic CO2 and CO emissions have been excluded from the impact assessment. However, according to Griffith et al. (2009),41 around 20% of the total organic carbon found in wastewater has a fossil origin and therefore the figures obtained on GWP for SS scenarios would have

POLICY ANALYSIS

been underestimated. A sensitivity analysis was performed on the SS scenarios assuming the distribution reported,41 that is, 20% of fossil origin and 80% of biogenic C source, and the results revealed that, although the direct emissions associated to biogas combustion increased 3-fold, the general conclusions and the option preferences would not be reversed. Another aspect to be mentioned is the high significance that, in general, impact assessment methodologies give to heavy metals in comparison to other pollutants,42 44 probably as a combined result of the well-established toxicity models existing for these compounds and their bioaccumulative character. The FU has been here defined in terms of the system input as the function of our system was to guarantee the appropriate management of a waste stream rather than obtaining a particular product. In fact, our system had a combined objective: increase biogas production and provide a final substance with fertilizing value. Therefore, the definition of the FU in an output basis as recommended by other authors25 was not appropriate. Finally, we have used a “well to tank” approach when defining the boundaries related to the final use of energy (both heat and electricity) instead of a “well to wheel” system as suggested by other authors25 due to the comparative characteristics of this study. Sustainable Pretreatments Application. Reconciling operational, economic and environmental aspects for sustainable pretreatment application is not straightforward. Yet, this study in combination with financial and operational data from literature5,6,8,9,45,46 suggests that mechanical (e.g., pressurizedepressurize) and chemical pretreatments appear to be the most suitable at this stage. Further work may examine full-scale experience and a more integrated and energy-efficient scheme of waste management with the inclusion of subsequent digested solids treatment processes (dewatering, transportation, spreading) and biogas utilization pathways.

’ ASSOCIATED CONTENT

bS

Supporting Information. Assumptions made during the inventory analysis of nonexperimentally tested scenarios, additional inventory data for KW and SS scenarios and contribution of different elements to abiotic resource depletion potential, eutrophication potential, global warming potential, human toxicity potential and terrestrial toxicity potential. This material is available free of charge via Internet at http://pubs.acs.org.


*Phone: +34-881-816020; fax: +34-881-816015; e-mail: marta. [email protected] (M.C.), [email protected] (A.H.). Author Contributions

Both authors equally contributed to the work.

’ ACKNOWLEDGMENT This research was funded by postdoctoral contracts from the Xunta de Galicia for Dr. Marta Carballa (IPP-08-37) and Dr. Almudena Hospido (IPP-06-57). We also thank the Spanish Ministry of Education and Science (Project CTM2010-17196) and the Xunta de Galicia (Projects 09MDS010262PR and GRC2010/37) for its financial assistance. Dr. Jingxing Ma and 10312


Environmental Science & Technology Thu Hang Duong are acknowledged for their well-done experimental work.

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(42) Dreyer, L. C.; Niemann, A. L.; Hauschild, M. Z. Comparison of three different LCIA methods: EDIP97, CML2001 and Ecoindicator 99. Does it matter which one you choose? Int J. Life Cycle Assess. 2003, 8 (4), 191–200. (43) Pizzol, M.; Christensen, P.; Schmidt, J.; Thomsen, M. Ecotoxicological impact of “metals” on the aquactic and terrestrial ecosystem: a comparison between eight different methodologies for Life Cycle Impact Assessment (LCIA). J. Cleaner Prod. 2011, 19, 687–698. (44) Pizzol, M.; Christensen, P.; Schmidt, J.; Thomsen, M. Impacts of “metals” on human health: a comparison between nine different methodologies for Life Cycle Impact Assessment (LCIA). J. Clean Prod. 2011, 19, 646–656. (45) M€uller, J. A. Prospects and problems of sludge pre-treatment processes. Water Sci. Technol. 2001, 44 (10), 121–128. (46) Perez-Elvira, S. I.; Nieto Diez, P.; Fdz-Polanco, F. Sludge minimisation technologies. Rev. Environ. Sci. Biotechnol. 2006, 5, 375–398.

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Beach Monitoring Criteria: Reading the Fine Print Meredith B. Nevers* and Richard L. Whitman U.S. Geological Survey, Great Lakes Science Center, Lake Michigan Ecological Research Station, 1100 N. Mineral Springs Road, Porter, Indiana 46304, United States ABSTRACT: Beach monitoring programs aim to decrease swimming-related illnesses resulting from exposure to harmful microbes in recreational waters, while providing maximum beach access. Managers are advised by the U.S. EPA to estimate microbiological water quality based on a 5-day geometric mean of fecal indicator bacteria (FIB) concentrations or on a jurisdiction-specific single-sample maximum; however, most opt instead to apply a default single-sample maximum to ease application. We examined whether re-evaluation of the U.S. EPA ambient water quality criteria (AWQC) and the epidemiological studies on which they are based could increase public beach access without affecting presumed health risk. Single-sample maxima were calculated using historic monitoring data for 50 beaches along coastal Lake Michigan on various temporal and spatial groupings to assess flexibility in the application of the AWQC. No calculation on either scale was as low as the default maximum (235 CFU/100 mL) that managers typically use, indicating that current applications may be more conservative than the outlined AWQC. It was notable that beaches subject to point source FIB contamination had lower variation, highlighting the bias in the standards for these beaches. Until new water quality standards are promulgated, more site-specific application of the AWQC may benefit beach managers by allowing swimmers greater access to beaches. This issue will be an important consideration in addressing the forthcoming beach monitoring standards.

’ INTRODUCTION Passage of the U.S. BEACH Act1 required that all coastal recreational waters be monitored for fecal indicator bacteria (FIB) starting in 2004. With the initiation of numerous programs and expansion of existing programs, a wealth of data has been generated by monitoring agencies from coastal beaches, including Great Lakes beaches. With the increase in data generation, more instances of high FIB concentrations have been detected, resulting in a higher overall number of beach closures and the impression that beach water quality is universally declining,2 despite a lack of supporting information. Negative publicity, combined with known limitations of using FIB as an indicator—i.e., lengthy analysis time, natural sources3—has been a likely disincentive to expand beach monitoring beyond minimal requirements. While the existing ambient water quality criteria (AWQC) originally developed in 1986 are under revision by U.S. EPA, beach managers are obliged to monitor their beaches using the currently accepted FIB standards until new standards are promulgated; however, there is underused flexibility already integrated into the existing AWQC that could benefit the public and management. The AWQC developed by the U.S. EPA for freshwater were derived from epidemiological studies conducted in 19791982 at four beaches on two lakes directly influenced by point source contamination.4 The criteria define an acceptable illness rate of 8/1000 swimmers and an FIB standard; water with FIB concentrations in This article not subject to U.S. Copyright. Published 2011 by the American Chemical Society

excess of the criteria are out of compliance, and beach managers typically close the beach to swimming or issue a swimming advisory. The AWQC primarily recommend that beach management decisions be based on a geometric mean calculation of water quality of at least five samples collected over the previous 30-day period to estimate the steady-state mean; this is to prevent unnecessary closures/advisories due to day to day fluctuations in FIB. Decisions about implementing the AWQC standards are the responsibility of the individual states, and most states opt to use a single sample maximum limit (ssmax) (e.g., refs 510), also presented in the AWQC, rather than the five-day mean despite the document’s urging that such a decision “may be erroneous”.11 Further, most states use the default ssmax (ssmaxEPA) of 235 colony-forming units (CFU) E. coli/100 mL water calculated by EPA and based on standard deviations derived in the epidemiological studies, disregarding the AWQC recommendation that “each jurisdiction should establish its own standard deviation for its conditions which would then vary the single sample limit”.11 FIB may exhibit high spatial-temporal variation in beach waters, the extent of which varies among beaches.1214 For this reason, Received: July 25, 2011 Accepted: November 7, 2011 Revised: October 27, 2011 Published: November 07, 2011 10315

dx.doi.org/10.1021/es202568f | Environ. Sci. Technol. 2011, 45, 10315–10321

Environmental Science & Technology the AWQC provide flexibility in the form of establishing jurisdiction-specific ssmax (ssmaxCALC). An understanding of variation in local bacteria densities would benefit swimmers and beach managers because a monitoring standard can be developed that may be as protective of public health risk as the default criteria outlined in the AWQC. In this study, we examine historical beach monitoring data for beaches along southern Lake Michigan, extending from Chicago through coastal Indiana, and we calculate ssmaxCALC, partitioning data in a number of different spatial and temporal scales to highlight the potential variation in monitoring outcomes. Further, we explore the implications of this assessment on beach monitoring outcomes, expectation of health protection, and beach access. The results of this study emphasize typically overlooked details included in the AWQC and the adaptability of monitoring protocols, with implications for both the current and forthcoming ambient water quality criteria.

’ METHODS Beach monitoring data were obtained from several existing databases maintained by the Chicago Park District, the Indiana Department of Environmental Management, and the Indiana Dunes National Lakeshore;15,16 included in the analysis were 50 beaches that cover the majority of the Great Lakes coasts of Illinois and Indiana. These beaches are monitored for E. coli, one of the fecal indicator bacteria, as recommended for freshwater beaches in the AWQC. Frequency of monitoring at a given beach ranged from 1 to 7 days a week, and the available historical monitoring data ranged from 6 to 21 years. Beach monitoring is under the jurisdiction of numerous regulatory agencies, which use different sampling replication and averaging protocols and have different regulations regarding swimming restrictions when water quality is out of compliance.3 Although sampling depth, location, and time can have a significant impact on E. coli results outcome,3 as well as analytical method (defined substrate or membrane filtration),17 we evaluate the data “as is”; that is, as submitted to regulatory agencies and U.S. EPA to satisfy requirements of the BEACH Act and therefore considered adequate for monitoring recreational water quality. For the time scale analysis, data from five beaches in Indiana were used that were collected during a period of 21 years (1990 2010). These beaches are directly influenced by the outfall of the Little Calumet River (Burns Ditch): from west (closest to the river outfall) to east, the beaches include Ogden Dunes (Ogden), West, Wells Street (Wells), Marquette Park (Marquette), and Lake Street (Lake) Beaches. In further analyses of spatial patterns, data collected from 2004 to 2010 at 50 subject beaches were used for more intensive analyses because of the higher collection frequency and presumed better characterization of overall water quality. Beaches in this region are affected in different ways by point and nonpoint sources of fecal contamination. Spatial analysis of Lake Michigan beaches included grouping the 50 beaches into 5 geographic regions: Chicago (22 beaches in Chicago, IL); Lake (7 beaches in western Lake County, IN); Burns Ditch (5 beaches influenced by the Burns Ditch outfall of the Little Calumet River in IN); Indiana Dunes (8 beaches in the Indiana Dunes State Park and National Lakeshore); and LaPorte (8 beaches in LaPorte County, IN). These designations are based jointly on geographic location, management jurisdiction, and specific point source influence.

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Single-sample maxima (ssmax) for freshwater were calculated based on the AWQC11 developed in epidemiological studies conducted at two locations in the United States:4 ssmax ¼ 10∧ ðlog10 GM þ fZ SDgÞ or ssmax ¼ GM 10∧ ðZ SDÞ where ssmax = the single sample maximum limit; GM = 126, geometric mean E. coli concentration developed in the epidemiological study for acceptable illness rate of 8 per 1000; Z = 0.675, the 75% calculated one-sided confidence level for a designated beach area; and SD is the calculated standard deviation of the singlesample log10 E.coli concentrations. In the AWQC, a geometric mean E. coli concentration for a minimum of 5 samples collected over a 30-day period in excess of 126 CFU/100 mL is considered to be out of compliance (GM = 126) and corresponds to an acceptable swimming-associated illness rate of 8 per 1000 swimmers. The AWQC also consider a single sample with a concentration in excess of 235 CFU/100 mL to be out of compliance. This is based on a control chart approach, with the upper control limit being the 75th percentile for a geometric mean of 126, and using a standard deviation of 0.4 log, as calculated from the epidemiological studies.4 With the recommendation of a beach- or jurisdiction-specific calculation, a beach that has a higher standard deviation in E. coli concentrations, indicative of higher variation overall, would have a higher ssmax, regardless of the water quality. Similarly, a beach with a lower standard deviation and lower variation in E. coli concentrations would have a lower ssmax. Data were analyzed using Systat 12.018 and SPSS 12.019 software. Overall FIB concentrations were compared across beaches in the spatial variance section using analysis of variance with P < 0.05. Standard deviation for monitoring data was calculated by bootstrapping a subset of the entire 20042010 data set. A total of 100 calculations of standard deviation were made using 100 randomly selected monitoring results without replacement, after this sample size was determined to be adequate to represent confidence limits in a power analysis. The standard deviation and 95% confidence interval are reported. The number of instances of FIB concentration exceeding the ssmaxCALC vs the ssmaxEPA were compared using the nonparametric McNemar test or the Fisher exact test where data distribution was uneven (P < 0.05).

’ RESULTS Temporal Variation. A review of data across time for a group of five Indiana beaches directly influenced by a point source (Burns Ditch beaches) indicates that variation in the ssmaxCALC depends on time range considered in the calculation. E. coli means and standard deviations calculated from all available monitoring data were highly variable year to year and did not follow a general trend, so ssmaxCALC fluctuated from a low of 253 in 1991 to a high of 430 in 2003 (Figure 1). Calculating across a 4-year, moving average smoothed the variation, resulting in a lower range of ssmaxCALC (296387; Figure 1). Cumulative calculation over the 21 years further smoothed variation, resulting in a narrower range but higher overall ssmaxCALC for any given year (304353; Figure 1). Regardless of calculation method, standard deviation and therefore ssmaxCALC for these beaches was consistently 10316



POLICY ANALYSIS

Figure 1. Single-sample maxima (ssmaxCALC), as calculated from the EPA ambient water quality criteria,5 for five Indiana beaches along Lake Michigan using three methods of subdividing the data. Annual is an ssmaxCALC for each individual year; cumulative is the ssmaxCALC for each year using the target and all previous years of available data; 4 yr-running is the ssmaxCALC using the target year and previous three. The ssmaxEPA is the default 235 ssmax provided in the AWQC and is provided for reference. N for each individual year ranged from 33 (in 1991) to 383 (in 2004).

Figure 2. Calculated single-sample maxima (ssmaxCALC), as defined in the EPA ambient water quality criteria5 for individual beaches along southern Lake Michigan and the entire area. N = 100 for each beach; box and lines indicate the ssmaxCALC with 95% confidence limit.

higher than the ssmaxEPA currently used in the local beach monitoring programs. Spatial Variation. The increase in monitoring frequency in 2004 significantly increased the number of samples considered, therefore, our examination of spatial calculations of ssmaxCALC incorporated monitoring data from 2004 to 2010. Analysis of data across a range of spatial scales also showed high variation in E. coli concentrations and ssmaxCALC (Figure 2). Of the 50 beaches studied, four stood out with significantly higher E. coli concentrations: Jeorse Park, 63rd Street, Washington Park, and Buffington beaches (F = 64.694, df = 49, P < 0.01), all of which have a history of frequently elevated E. coli events.2,20 Only Washington Park, however, is situated immediately adjacent to a point source outfall; FIB sources at any of these beaches have not been definitively identified. For individual beaches, ssmaxCALC included a wide range: 261 CFU/100 mL at Washington Park, IN to 407

CFU/100 mL at Buffington Harbor, IN (Figure 2). Beaches with lower standard deviations/ssmaxCALC often included locations directly influenced by a point source outfall (Washington Park, Ogden, Lake). For a subset of beaches known to be directly influenced by a point source outfall,21 Burns Ditch, there was relatively less variation between ssmaxCALC for individual beaches (range 287302 CFU/100 mL). E. coli concentrations in general were significantly higher at Lake and Marquette than at the other three beaches (impacted by Burns Ditch) according to an ANOVA (F = 40.373, df = 4, P < 0.01). Lake had the lowest standard deviation and therefore the lowest ssmaxCALC: 285 CFU/100 mL; the highest ssmaxCALC was associated with West (300 CFU/100 mL). When beaches were divided into five groupings, analysis of variance revealed a significant difference across regions (F = 110.973, df = 4, P < 0.01), with Lake County and Chicago beaches having 10317



POLICY ANALYSIS

Table 1. Values of ssmaxCALC for Each of Five Designated Regions along Southern Lake Michigan, Using Monitoring Data Collected 20042010a region Chicago

N

region ssmaxCALC

% exceeding ssmaxEPA

% exceeding region ssmaxCALC

difference in number of beach days

11248

394

17.4

11.3

681

Lake Burns Ditch

3951 2217

401 304

24.2 7.9

17.9 6.0

248 43

Indiana Dunes

2003

318

11.5

7.9

73

LaPorte

3149

328

9.2

5.9

102

a

Percent of beach days exceeding the default ssmaxEPA and exceeding ssmaxCALC specifically for a given region are provided. Using a region-specific ssmaxCALC would result in a higher number of beach days meeting the AWQC.

Figure 3. Outline of southern Lake Michigan beach locations showing variation in the standard deviation of E. coli concentrations (color gradations) and % difference in number of days exceeding the E. coli concentration ssmaxEPA vs ssmaxCALC (size gradations).

significantly higher means (Figure 2). The lowest standard deviation and ssmaxCALC were associated with the Burns Ditch beaches (303 CFU/100 mL); this characteristic supports the application of the monitoring standard at point source locations, although the standard deviation was notably higher (0.567) than the 0.4 used in the AWQC. The highest standard deviation and ssmaxCALC was associated with the Lake County beaches (400 CFU/100 mL); these beaches are subject to frequently elevated E. coli concentrations and high fluctuations overall, although no source of contamination has been adequately identified. Using data from the entire 50 beach data set, the ssmaxCALC for the entire southern Lake Michigan crescent was 376 CFU/100 mL (SD = 0.703).

Effect of Alternate ssmax on Number of Beach Advisories. Use of an ssmax based on local water quality would have resulted in an increase in beach access (open beaches) because in all instances ssmaxCALC was higher than the default 235 CFU/ 100 mL in the AWQC. Results of a nonparametric McNemar test indicated that use of ssmaxCALC for all regions (P < 0.01) and the southern Lake Michigan region (McNemar chi-squared = 1125.0, df = 1, P < 0.01) resulted in significantly fewer instances of beaches exceeding the ssmax (Table 1). Results for individual beaches also indicated that the higher ssmaxCALC resulted in significantly fewer out-of-compliance events (P < 0.01) (Figure 3), according to the McNemar test. Beaches 10318


Environmental Science & Technology associated with natural, undeveloped areas without a nearby point source were more likely not to exhibit a significant difference and included Central, Dunbar, Kemil, Long Beach, and Porter. Kemil had no samples collected with an E. coli count that exceeded either standard. Based on these historical monitoring data, use of a regional ssmaxCALC decreased the percent of instances when the beach water was out of compliance, when compared to the ssmaxEPA. This decrease ranged from 2 to 6%, which was the equivalent of 43681 beach days (Table 1). Using ssmaxCALC for individual beaches, the number of days out of compliance decreased in a range from 0 to 11% (Figure 3).

’ DISCUSSION Regardless of how the data were partitioned, temporally or spatially, all ssmaxCALC were higher than the standard ssmaxEPA currently applied by beach managers in southern Lake Michigan to determine when swimming water is out of compliance, according to the AWQC. This indicates that many current monitoring applications in this region and perhaps nationwide may be overly conservative relative to the recommendations of the AWQC. The AWQC are currently under revision by the U.S. EPA; until any upcoming changes are promulgated, beach managers are likely to monitor their beaches using the currently accepted FIB standards since retrospective analyses and applications for revisions are cumbersome. The AWQC, as we highlight, provide allowances for the wide range of waters subject to assessment; these could benefit practical beach management and should be carefully considered in development of the new criteria. The great discrepancy between ssmaxEPA and ssmaxCALC may stem from the calculation used in epidemiological studies.4 The epidemiological studies were conducted at four freshwater beaches over two years, plus one additional beach/year, and included the collection of an unidentified number of days of indicator bacteria data along with interviews from thousands of beachgoers.4 These data were simplified to a single mean E. coli concentration and number of illnesses for each beach/year, and from the resulting nine data pairs, regression analysis was conducted.4 Further, the standard deviation from which the ssmaxEPA is calculated is based on the standard deviation of the log10 mean E. coli concentrations. This type of averaging will reduce variation, resulting in a lower standard deviation, and perhaps explaining the difference between those results and the analyses presented here. Calculation of ssmaxCALC must consider the temporal and spatial extent over which data are averaged. Neither of these factors are specifically mentioned in the AWQC, but Chawla and Hunter22 recommended periods of 34 years; this was subsequently included in the European Union directive.23 Using this approach, we calculated a 4-year running average, and it was notable that in the earlier years the standard deviation was higher (0.530.79) and the ssmaxCALC range was wider (335387 CFU/100 mL), prior to the added influx of data starting in 2004 (range SD 0.510.66; ssmaxCALC 296324 CFU/100 mL). The increase in number of observations, in general, increases confidence in the estimate of overall water quality.11 Use of a single year of data could result in highly variable ssmaxCALC due to interyear variation in FIB concentrations resulting from rainfall, sewer overflows, differences in sources, and hydrometeorological effects.

POLICY ANALYSIS

Basing overall FIB estimates of variation on sources or similar geographic areas may provide a better estimate of overall variation in FIB. Research has indicated that background FIB fluctuations are similar across beach regions as long as 35 km,20,24 but variation within and between beaches may warrant use of shorter lengths of coast to account for local sources and circulation patterns.13,14 In each beach group in this analysis, the ssmaxCALC was higher than 300 CFU/100 mL. Simplifying across the entire 50 beaches resulted in an ssmaxCALC well above the default 235 CFU/100 mL generally used (Figure 3). Use of an ssmaxCALC for specific regions resulted in increased access (i.e., fewer days out of compliance) to beaches at all regional divisions, with an increase, for example, of as many as 681 days of beach access overall for Chicago beaches. One of the five designated regions directly downcurrent of a point source (Burns Ditch) had the least overall benefit because it had the lowest ssmaxCALC. The higher ssmaxCALC for all regions, even an ssmaxCALC for the entire southern Lake Michigan region, would allow for more beach access days. Previous estimates of regionspecific ssmaxCALC also indicated improvements in prediction success using empirical predictive models that can potentially provide results in less than an hour compared to current analysis techniques that can take 2448 h.25 Because of the high variation in E. coli among beaches, use of the default ssmaxEPA despite the recommendation for a sitespecific ssmaxCALC results in the application of differential illness risk across beaches. Two components of the ssmaxEPA are the acceptable illness rate set at 8/1000 and the 75% confidence limits (around the geometric mean of 126 CFU/100 mL); use of ssmaxEPA when the standard deviation of a water body is different from 0.4 affects both of these assumptions. If the confidence limits are controlled at 75%, use of ssmaxEPA in a situation where ssmaxEPA < ssmaxCALC results in the application of a lower level of risk tolerance, that is, management is “overprotective”. For example, at Buffington, where logSD = 0.755 and ssmaxCALC = 407 CFU/100 mL, use of ssmaxEPA (235 CFU/100 mL) for beach management would effectively protect for an acceptable illness rate of 5.74/1000. Alternately, if the acceptable illness rate is controlled at 8/1000, use of ssmaxEPA when ssmaxEPA < ssmaxCALC results in a reduction of the confidence limit; from 75% in the AWQC standards to 64% in the case of Buffington. The implications of the violation of these assumptions will need to be considered in the development of new standards because of the potential for uneven human health protection across beaches, regions, or states. Accuracy of risk estimates in the AWQC are hindered by the lack of data collected that correspond to high illness rates: because illness rate must be projected beyond a certain level, there is a high rate of error in estimates at higher bacteria/illness levels. Therefore, accuracy in presumptions of illness risk decreases at higher levels. Interpretation of higher illness rates and higher calculations of ssmaxCALC should take this into consideration. For this reason, studies that estimate risk based on concentrations of known pathogens (e.g, quantitative microbial risk assessment, QMRA26) may be a viable solution for more accurately estimating acceptable water quality. These evaluations determine the public health outcome with exposure to water contaminated with fecal pathogens (e.g., Norovirus, Cryptosporidium, Giardia). This technique may allow estimates of illness rates at higher levels of indicator bacteria, without extensive epidemiological studies. Although not considered in this analysis, the original AWQC also outlined the use of wider confidence intervals for swimming 10319


Environmental Science & Technology areas with lower use, although use is not quantified in the criteria. The beaches considered in this analysis have a wide range of use, so those allowances may apply to some of these waters. Numerous beaches in Great Lakes locations could further consider these wider confidence intervals in calculating and using an ssmaxCALC for monitoring. Use of the current FIB monitoring standard has been questioned due to inconsistencies in development and application: specifically, use of data from rain-free days, time period averaging, and the presence of a point source sewage impact. The low ssmaxCALC at the Burns Ditch beaches is reflected in a related study of E. coli variation conducted by U.S. EPA at one of these beaches (West);27 here, the ssmaxCALC, based on the standard deviation (0.538), was comparable to the results presented here: 291 CFU/100 mL. At the other freshwater beach studied by Wymer et al.,27 Belle Isle in Michigan, the resulting ssmaxCALC was 255 CFU/100 mL (SD = 0.453). The lowest ssmaxCALC for an individual beach in this study was Washington Park (261 CFU/100 mL; SD = 0.469), which is also affected by a point source, Trail Creek. These comparisons support the strength of this statistic for point source impacted beaches. It should be noted, however, that all coastal beaches are required to incorporate these standards into a beach monitoring program, regardless of whether a point source is present. Our study illustrates that data averaging spatially and temporally is an important consideration in the development and implementation of new water quality criteria for both fresh and marine waters. Others have suggested the use of a more flexible system of standards with more gradations to designate water quality,28 as opposed to the binary system inherent in the AWQC. Even with the adoption of new standards, however, implementation will likely take some time, and beach managers may re-evaluate the variable application of the current AWQC to allow greater access to beaches presumably without additional public health risk.


*Phone: 219-926-8336 ext. 425; fax: 219-929-5792; e-mail: [email protected].

’ ACKNOWLEDGMENT This research was funded by the USGS Ocean Research Priorities Plan and the Great Lakes Restoration Initiative through USGS. Engaging discussions with Shannon Briggs (Michigan Department of Environmental Quality) and reviews by Samir Elmir (Florida Department of Health), Jean Adams (USGS), and anonymous reviewers helped us to improve this manuscript. Photo of North Avenue Beach, Chicago, Illinois; credit: Antonio Vernon. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This article is Contribution 1670 of the USGS Great Lakes Science Center. ’ REFERENCES (1) Beaches Environmental Assessment and Coastal Health Act, 33 USC 1251. Public Law 106284, 114 Stat. 870877, 2000; Vol. 33 USC 1251. (2) Dorfman, M.; Rosselot, K. S. Testing the Waters: A Guide to Water Quality at Vacation Beaches, 19th ed.; Natural Resources Defense Council: New York, 2009.

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(3) Nevers, M. B.; Whitman, R. L. Policies and practices of beach monitoring in the Great Lakes, USA: A critical review. J. Environ. Monit. 2010, 12 (3), 581–590. (4) Dufour, A. P. Health Effects Criteria for Fresh Recreational Waters; EPA-600/1-84-004; U.S. EPA: Cincinnati, OH, 1984. (5) New York State Department of Health. Public Health Law. Subpart 62 Bathing Beaches, Section 225 62.15, 2004. (6) State of Illinois, Swimming Pool and Bathing Beach Act 210 ILCS 125. 77: Public Health Section 820.400 minimum Sanitary Requirements for Bathing Beaches; Illinois General Assembly, 2004. (7) State of Indiana. Water Quality Standards. 327 IAC 21-6, Indiana Administrative Code, 2008. (8) State of Michigan. Michigan Natural Resources and Environmental Protection Act. Part 31, Water Quality Standard, Part 4 Rules, Rule 323.1062 (1), 1997; Vol. PA 451. (9) State of Minnesota. Minnesota Administrative Rule 7050.0222. Minnesota Office of the Revisor Statutes, 2008. (10) Wisconsin Department of Natural Resources. Water Quality Standards for Wisconsin Surface Waters; Department of Natural Resources: Madison, WI, 2001. (11) U.S. EPA. Ambient Water Quality Criteria for Bacteria; EPA 440/5-84-002; Office of Water Regulations and Standards: Washington, DC, 1986. (12) Boehm, A. B.; Grant, S. B.; Kim, J. H.; Mowbray, S. L.; McGee, C. D.; Clark, C. D.; Foley, D. M.; Wellman, D. E. Decadal and shorter period variability of surf zone water quality at Huntington Beach, California. Environ. Sci. Technol. 2002, 36 (18), 3885–3892. (13) Boehm, A. B. Enterococci concentrations in diverse coastal environments exhibit extreme variability. Environ. Sci. Technol. 2007, 41 (24), 8227–8232. (14) Whitman, R. L.; Nevers, M. B. Escherichia coli sampling reliability at a frequently closed Chicago beach: Monitoring and management implications. Environ. Sci. Technol. 2004, 38 (16), 4241–4246. (15) Indiana Department of Environmental Management. Accessed Sept. 15, 2011; https://extranet.idem.in.gov/beachguard/. (16) Illinois Department of Public Health. Accessed Sept. 15, 2011. http://app.idph.state.il.us/envhealth/ilbeaches/public/. (17) Eckner, K. F. Comparison of membrane filtration and multipletube fermentation by the Colilert and Enterolert methods for detection of waterborne coliform bacteria, Escherichia coli, and enterococci used in drinking water and bathing water quality monitoring in southern Sweden. Appl. Environ. Microbiol. 1998, 64, 3079–3083. (18) Systat 12.0; SYSTAT Software, Inc.: Chicago, IL, 2007. (19) SPSS version 12; SPSS Inc.: Chicago, IL, 2003. (20) Whitman, R. L.; Nevers, M. B. Summer E. coli patterns and responses along 23 Chicago beaches. Environ. Sci. Technol. 2008, 42 (24), 9217–9224. (21) Nevers, M. B.; Whitman, R. L. Nowcast modeling of Escherichia coli concentrations at multiple urban beaches of southern Lake Michigan. Water Res. 2005, 39 (20), 5250–5260. (22) Chawla, R.; Hunter, P. R. Classification of bathing water quality based on the parametric calculation of percentiles is unsound. Water Res. 2005, 39, 4552–4558. (23) EC (Commission of the European Communities). Directive 2006/7/EC of the European Parliament and of the council of 16 February 2006 concerning the management of bathing water quality and repealing Directive 76/160/EEC. Commission of the European Communities, 2006. (24) Nevers, M. B.; Whitman, R. L. Coastal strategies to predict Escherichia coli concentrations for beaches along a 35 km stretch of southern Lake Michigan. Environ. Sci. Technol. 2008, 42 (12), 4454– 4460. (25) Nevers, M. B.; Whitman, R. L. Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches. Water Res. 2011, 45 (4), 1659–1668. (26) Schoen, M. E.; Ashbolt, N. J. Assessing pathogen risk to swimmers at non-sewage impacted recreational beaches. Environ. Sci. Technol. 2010, 44 (7), 2286–2291. 10320



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(27) Wymer, L. J.; Brenner, K. P.; Martinson, J. W.; Stutts, W. R.; Schaub, S. A.; Dufour, A. P. The EMPACT Beaches Project: Results from a Study on the Microbiological Monitoring of Recreational Waters; EPA 600/ R-04/023; U.S. EPA, Office of Research and Development: Cincinnati, OH, 2005. (28) Kim, J. H.; Grant, S. B. Public mis-notification of coastal water quality: A probabilistic evaluation of posting errors at Huntington Beach, California. Environ. Sci. Technol. 2004, 38 (9), 2497–2504.

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Risk and Markets for Ecosystem Services Todd K. BenDor* Department of City and Regional Planning and UNC Institute for the Environment, University of North Carolina at Chapel Hill, New East Building, Campus Box #3140, Chapel Hill, North Carolina 27599-3140, United States

J. Adam Riggsbee RiverBank Ecosystems, Inc., Austin, Texas 78755, United States

Martin Doyle Nicholas School of the Environment, Duke University, Durham, North Carolina 27708 ABSTRACT: Market-based environmental regulations (e.g., cap and trade, “payments for ecosystem services”) are increasingly common. However, few detailed studies of operating ecosystem markets have lent understanding to how such policies affect incentive structures for improving environmental quality. The largest U.S. market stems from the Clean Water Act provisions requiring ecosystem restoration to offset aquatic ecosystems damaged during development. We describe and test how variations in the rules governing this ecosystem market shift risk between regulators and entrepreneurs to promote ecological restoration. We analyze extensive national scale data to assess how two critical aspects of market structure (a) the geographic scale of markets and (b) policies dictating the release of credits affect the willingness of entrepreneurs to enter specific markets and produce credits. We find no discernible relationship between policies attempting to ease market entry and either the number of individual producers or total credits produced. Rather, market entry is primarily related to regional geography (the prevalence of aquatic ecosystems) and regional economic growth. Any improvements to policies governing ecosystem markets require explicit evaluation of the interplay between policy and risk elements affecting both regulators and entrepreneurial credit providers. Our findings extend to emerging, regulated ecosystem markets, including proposed carbon offset mechanisms, biodiversity banking, and water quality trading programs.

’ INTRODUCTION Efforts to improve environmental protection policy have sparked widespread interest in market-based environmental policies.1 These market structures take many forms, including publicly funded payments for ecosystem services (PES), voluntary environmental improvement programs (e.g., voluntary carbon markets), cap and trade programs, and regulated ecosystem offset markets. The United States has begun moving toward “regulated offset markets,” which induce demand for ecosystem services (see Chart 1) by requiring environmental conservation, preservation, or restoration (hereafter “conservation”) to offset environmental destruction elsewhere. While many have been proposed, in reality, few ecosystem markets are operational, and most lessons for proposed markets are drawn from the well-established markets for aquatic ecosystems streams and wetlands in the United States.2 Because other ecosystem r 2011 American Chemical Society

markets include few genuine trades,1,3 aquatic ecosystem markets provide some of the best primary empirical data for evaluating ecological effects of markets,4 landscape-scale market trading behavior,5 and regulatory behavior and decision-making capacity for overseeing ecosystem service markets.6 Since 1988, United States water policy has sought to attain “no net-loss” of aquatic ecosystems. Regulations have gradually evolved to require offsets, usually through ecological conservation, for aquatic ecosystems impacted or destroyed during land development (for a review of policies creating this market, see ref 2). For example, if a land developer impacts 10 ha of wetlands Received: September 13, 2011 Accepted: November 1, 2011 Revised: November 1, 2011 Published: November 01, 2011 10322

dx.doi.org/10.1021/es203201n | Environ. Sci. Technol. 2011, 45, 10322–10330

Environmental Science & Technology Chart 1

as part of a project, that developer must provide at least 10 ha of ecological conservation offsets to fulfill the no-net-loss requirement. Developers can either provide ecological conservation themselves or purchase offsets credits from a “mitigation bank”. Mitigation banks are private, entrepreneurial firms (but can be public entities) that speculatively conserve large tracts of aquatic ecosystems (largely through restoration), thus creating a bank of compensation ‘credits’. These credits can then be sold to multiple individuals seeking to impact aquatic ecosystems elsewhere. Compensatory mitigation, as this regulatory process is known, now comprises the largest tradable ecosystem service market in the United States.1 Aquatic ecosystem markets trade nearly $3 billion worth of wetland and stream conservation annually1,7 nearly 10 times that of the Endangered Species Act habitat programs conserving approximately 20,000 wetland ha (1999 2003 average), and over 73 km of streams annually.7 As aquatic ecosystem markets have grown nationwide, their regulation has begun to mirror financial markets, as greater regulatory standards and outside investment have increased transparency and standardization of trades.8 It is important to draw a distinction between broader “pollution markets”. Pollution markets trade commodities based on pollution weight, volume, or concentration (e.g., water quality trading and the U.S. SO2 market), while ecosystem service markets trade environmental services measured through ecological assessment criteria (including point to nonpoint water pollution). Ecosystem service markets also tend to trade in commodities of area of entire, bundled ecosystems (e.g., area of wetlands or endangered species habitat, length of stream or riparian buffer) rather than particular pollutants (e.g., nitrogen), although this is not a firm distinction. One major ongoing debate concerns the extent to which traded ecosystems should rigidly mimic each other’s ecological functions. Trading ecosystems in this ‘in-kind’ manner creates trade-offs between preserving specific functions and characteristics (e.g., replacing a cold-water stream with a cold-water stream) and inadvertently ‘thinning’ markets for certain ecosystems,9 since certain ecosystems (e.g., groundwater fed wetlands) become nonexchangeable due to their inherent uniqueness. We analyze the factors affecting the prevalence of mitigation banking, which now forms the backbone of the compensatory mitigation industry.10 We collected data from regulators, industry associations, and performed the first comprehensive survey of the national mitigation banking community. Our goal is to understand the risk considerations in this market and policies that modify risk (whether intentionally or not) and encourage mitigation banking by lowering market entry. Our results have important implications for proposed and emerging analogous ecosystem markets in the U.S. and worldwide.

’ PROGRAM IMPLEMENTATION: ECOLOGICAL RISK VS ENTREPRENEURIAL RISK Risk management is an important framework for understanding the success of environmental policy.11 Risk in aquatic

POLICY ANALYSIS

ecosystem markets is derived from two primary forms: regulatory risk and entrepreneurial (or ‘banker’) risk. Regulatory risk is the likelihood that the goal of no net-loss of ecosystem services will not be met. Given that regulators are enforcing environmental protection regulations, regulatory risk is very much a proxy for ecological risk. The task for regulatory agencies is to minimize ecological risk. Conversely, entrepreneurial risk is the likelihood that conservation activities (production of credits) will not be profitable or worthwhile financial investments. The primary regulator of aquatic markets is the Army Corps of Engineers (hereafter Corps), the federal agency administering Section 404 of the Clean Water Act.6 When the Corps permits aquatic ecosystem impacts, they encounter risk that an impact will not be fully offset by the conservation provided by mitigation credits. Net ecosystem loss can result from three types of failure: a) failure to conserve ecosystems (including the same type of ecosystems) sufficiently or altogether,2,12 b) failure to perform timely conservation,13 or c) failure to maintain long-term viability of a conserved site.14 Addressing these types of failures has been a goal of evolving federal policy, which recently adopted mitigation banking as a technique for reducing some of these ecological risk factors (ref 15 p 19594). Historically, compensation was provided by permittees (i.e., developers) themselves, known as ‘permitteeresponsible mitigation’,2 or governments, who typically run ‘inlieu fee’ programs, which collect and pool fees for aquatic impacts to fund future restoration projects.16 These approaches typically produced offset sites that imposed substantial regulatory burdens and produced little ecological success (i.e., small, fragmented, and widely dispersed offset sites2). Moreover, programs historically did little to ensure that the aquatic ecosystems services being lost were replaced by “equivalent” services (known as ‘in-kind’ mitigation, which is now an important component of mitigation programs nationwide due to substantial criticism6,17,18). Mitigation banks were initially proposed to solve these problems by creating ecosystem credits in advance of impacts,19 as opposed to a contract for future conservation, thus reducing or eliminating the first two types of failure and associated ecological risk. However, risk reduction for the regulator shifts risk to the mitigation banker entrepreneurial risk. Mitigation bankers enter markets with heavy up-front capital investments, including substantial legal and planning work, land acquisition, design, and construction. Mitigation banks also rely on economies of scale, necessitating large, contiguous tracts of wetlands or stream reaches (typically measured in gross terms as hectares [wetlands] or linear meters [streams]), established years in advance, in order to produce credits for sale. Uncertainty around these investments and potential payoffs represent multiple sources of entrepreneurial risk. Mitigation bankers must weigh these investments against potential future demand for ecosystem credits, which are driven by urban, transportation, and land development, and are reliant on local, regional, and macroeconomic growth, i.e., other sources of uncertainty and entrepreneurial risk. Mitigation bank investments are also weighed against regulator behavior (which institutional economists might consider as “sovereign risk”), which can significantly affect credit demand. While federal policy establishes broad rules, a large degree of autonomy in interpreting and implementing the policy is left to local-level (district) staff within the Corps, a source of variability in how mitigation banks are regulated. For example, regulators 10323

dx.doi.org/10.1021/es203201n |Environ. Sci. Technol. 2011, 45, 10322–10330

Environmental Science & Technology can slow the bank approval process, which has the effect of driving up the legal and planning costs. They can also alter the ecological standards required of banks, thereby forcing ad hoc adjustments to restoration investments. Among the policies that vary by Corps districts is the credit release schedule in that regulators now typically disregard the original definition of banking as advance mitigation and, rather, allow scheduled credit releases whereby sale of a percentage of total bank credits is allowed prior to project completion. In fact, credits can be released by regulators prior to any verification that a bank has met any ecological standards set by regulators (in order to get initial advance credit releases, however, bankers must obtain conservation easements, produce financial assurances, and present detailed project designs and plans15). For example, policy may allow a bank to sell 30% of credits prior to achieving any ecological criteria thresholds and the rest in stages, as other criteria are achieved. This practice reduces entrepreneurial risks by increasing ecological risks. Regulations can also influence credit demand in several ways. The first is through variations in geographic service areas, which are “...the geographic area[s] within which impacts can be mitigated at a specific mitigation bank...” [refs 10 and 15 Part 332.2]. Large geographic service areas increase potential demand for credits; small service areas reduce demand, and like the release schedule, each district has the ability to set its own service area policy. Large service areas allow conservation to be distant from impacts and thus higher ecological risk than small service areas. An interesting side note here is that recent work in Chicago has demonstrated the regulatory consequences of having few suppliers in a given service area, whereby spatial monopolies tend to form for banks.10 Finally, and perhaps most importantly, although regulators are directed under federal rules to prefer mitigation bank credits (ref 15 Part 332.3(b)(2 6)), they can instruct or allow the use of alternative forms of compensation, which can dramatically reduce the demand for mitigation bank credits. In sum, there is a distinct trade-off between regulatory risk and entrepreneurial risk. As originally conceived, mitigation banking practice involves substantial entrepreneurial risk. However, regulators’ ability to release credits before project completion and adjust geographic service areas has potentially reduced entrepreneurial risk but at the cost of increasing regulatory (i.e., ecological) risk.

’ METHODS AND DATA We developed comprehensive national statistics on supply and demand for aquatic ecosystem credits and its variation with market regulation. We collected national scale data on credit demand, including data on federal permitting behavior (FY2006 2008 permits/year; the only permit years for which data were available for each district in similar, comparable formats), and urban and transportation construction, which is measured as annual building permit (U.S. Census building permit data from 2005 to 2008) and transportation construction rates (total lane-km constructed 2000 2007). Road construction was ascertained using 2000 2007 data on road lane-km (a lane-kilometer is one lane-width for a linear km) from the Highway Performance Monitoring System,20 which includes 12 different road types for each U.S. county, ranging in size from local rural roads to interstate highways.

POLICY ANALYSIS

On the supply side, we collected extensive data on prevalence of banking and wetlands, relative mitigation banking costs, advance credit release policies, and the geographic scale of markets (‘service areas’). Estimates of banking activity levels (number of banks and credits produced by banks) in districts were established using data from the RIBITS federal banking database21 in combination with additional data collected by Madsen et al.,1 who assembled the first national census of the type, location, and size of stream and wetland mitigation banks. As of September, 2010, 11 districts (Albuquerque, Baltimore, Charleston, Ft. Worth, Los Angeles, Pittsburgh, San Francisco, St. Paul, Tulsa, Walla Walla, and Wilmington) had not been incorporated into the RIBITS regulatory database and therefore could not augment the Madsen et al.1 database. As of the date of data collection, RIBITS did not reliably account for stream banking at a national level. Our analysis augmented this database with additional, available regulatory data as RIBITS continues to expand into nationwide use. To our knowledge, this is the most comprehensive and representative mitigation banking database available. Wetland data were collected from the National Wetlands Inventory, established by the U.S. Fish and Wildlife Service.22 This database is somewhat incomplete in the western U.S. and does not exist for the State of Wisconsin (resulting in substantial incomplete data for the St. Paul District). Data were collected for all available advance credit release policies (as of mid-2009) in the 36 districts of the contiguous United States (not including districts in Hawaii and Alaska). Districts that contained banks, but had no formal advance release policies, were asked for at least four recent mitigation bank instruments (legal documents formally describing the bank and its operation). Here, individual bank early releases were averaged to approximate a de facto formal release policy. Data on service area size were previously collected for all Corps districts by Womble and Doyle.23 We also sought direct input on how regulations were interpreted by ecosystem market participants. Between April and May 2009, we administered a Web-based survey to mitigation banking professionals (N = 156, 47.7% response rate24) to better understand banker perceptions of recent regulations and the cost framework associated with mitigation banking. Bankers were asked to disaggregate banking costs into nine separate categories, including legal and site approval, land acquisition, baseline ecological monitoring, physical restoration (hydrological/stream channel construction), biological restoration (vegetation establishment), postrestoration ecological monitoring, and site maintenance. The Corps is divided into 36 regulatory districts in the contiguous United States, each of which operates largely autonomously through the direction of regulatory staff. It is this autonomous rule interpretation that creates nationwide variability in mitigation bank regulation and, essentially, an experiment in how different regulations affect credit production and the number of mitigation bankers entering the market. As a result, data on permitting, credit release policies, and banking prevalence were collected (and only available) at the district level, while wetland prevalence (percentage of total land area) and building and road construction data were spatially aggregated to districts. The aggregation process for wetlands and building and road construction data involved allocating counties into districts, the analysis unit for this study. Counties divided by districts were placed into the district containing the larger part. This process was completed independently by two coders and compared for 10324



POLICY ANALYSIS

Table 1. Regression Analyses on Bank Credits and Number of Banksa total bank credits (n = 30; R2= 0.80) coef. 1) % wetland area

129791.80

2) rigorous market area size (dummy)

2991.75

3) % advance release

a b

77.10

std. error

number of banks (n = 32; R2= 0.46) t d

coef.

std. error 73.28

4.02d

10.26

0.69

34714.36

3.74

294.43

4870.98

0.61

7.09

192.27

0.40 c

0.92

0.42

t

2.21c

4) road construction

0.89

0.41

2.19

0.00

0.00

5) building construction

0.23

0.11

1.97b

0.00

0.00

0.81

0.01

0.36

6) regulatory permitting

0.14

2.56

0.05

7) 8-digit HUC market area (dummy)

4298.42

5696.71

0.75

8) intercept

8309.08

9616.23

0.86

0.00 0.59 38.12

11.97 20.22

0.96

0.05 1.89b

Case-wise data on total bank credits and early release were only available for 30 districts, while data on bank counts were available for 32 districts. p < 0.1. c p < 0.05. d p < 0.01.

inter-rater reliability (97.13% match rate), whereby all inconsistencies were rectified. To understand the relationships between the factors discussed above, we implemented two ordinary least-squares (OLS) regression models to control for intervening effects of building permitting rates, total road construction, regulatory permitting rates, wetland area as percentage of total land area, percentage of credits released before meeting ecological performance criteria, whether a district used HUC-8 boundaries (dummy variable), and whether a district had a strict policy defining bank service areas (dummy variable). The first model regressed these factors on total bank credits constructed in each district, while the second model regressed these factors on the number of individual banks constructed (see Table 1 for variable lists). No significant collinearity was found between variables (VIF < 3.5). Aquatic ecosystem markets, which are an amalgamation of ecological, regulatory, and entrepreneurial interests, are difficult to understand, partly because data are difficult to acquire and unequivocal conclusions can rarely be drawn from the fragmented data sets. The unavailability of longitudinal data on policy, regulatory decisions, and permitting at the district level precludes the use of regression techniques that could causally link policies to bank and credit establishment (inability to establish or measure Granger causality).25 Indeed, data for mitigation are notoriously incomplete, and severe data collection and quality issues have hindered past evaluations.5,26,27 However, exploratory data analysis and simple linear regression were adequate for understanding broad relationships between market geography, phased credit sale policies, and banking prevalence at the district level. Thus, we utilized available information that could be used to indicate supply and demand sites of mitigation and how these responded to regulatory variability. Although our statistical analysis is noncausal, it represents a critical analysis for understanding market dynamics, which would otherwise necessitate years of wide-scale and costly posthoc data collection by the Corps.

’ RESULTS National Development Patterns. Average aquatic impact permitting rates ranged between 376 per year in the New Orleans district and 6,350 per year in the Jacksonville District (Figure 1A), both of which are wetland-dense regions (Figure 1B). New Orleans has historically had some of the highest permit counts in the nation, but development and/or permitting activities were

largely curtailed following Hurricane Katrina in August 2005 (immediately preceding FY 2006). In FY 2008, the New Orleans District granted 578 permits, signaling an upward climb in postKatrina permitting. Between 2000 and 2007, nearly 366,000 km of roads were constructed in the United States (Figure 1C and D), primarily in the Southwest (Los Angeles and Albuquerque Districts), the upper Great Plains (Omaha, St. Paul, and Kansas City Districts), and the Southeast (Jacksonville, Wilmington, and Savannah Districts). High growth regions, including the Jacksonville, Los Angeles, Sacramento, and Ft. Worth districts saw high rates of building construction, particularly single family housing, which accounted for the vast majority of building permitting in all measured years in these regions. Slow-growing areas in central-southern and midwestern districts, including Memphis, Little Rock, St. Louis, and Pittsburgh, had the lowest average building permit rates. Variability in Mitigation Banking. There were approximately 201 new banks since the publication of Madsen et al.,1 yielding a total of 994 banks containing 379,956 wetland credits within the contiguous U.S (Figure 1E; the Madsen et al.1 data set contained information on N = 809 total banks, 793 of which contained useful location information). Banking was particularly prevalent in the Southeast, and while some Southern districts (e.g., Ft. Worth, Galveston) do not contain a large number of banks, they have accrued extensive wetland credit inventories in large banks (i.e., few large banks). All districts with operating banks allowed early credit releases, ranging from 15 to 60% of total bank credits (mode = 30, mean = 36.7%; Figure 1F). As of August 2009 (when credit release data collection was finalized), the Albuquerque, Tulsa, Pittsburgh, and New England Districts did not contain any federally authorized mitigation banks (they still may contain state or locally authorized banks). All districts without formal policies allowed advance releases to multiple banks, often at very similar rates, thereby creating a de facto advance release policy. Although wording and the specific thresholds for staged credit release varied among districts, a common series of steps allowed incremental credit releases as banks increasingly achieved ecological standards. Even so, there was significant variation between districts: the New Orleans District allowed the highest fraction of credits released prior to satisfying ecological performance criteria (to the left of the vertical dashed line), while other districts allowed only 30% (Figure 2 Left Panel). The Charleston District had a larger number of stages after which credits could be 10325



POLICY ANALYSIS

Figure 1. Panel A: Total impact permits granted, by Regulatory District, Panel B: Relative Wetland Density (% of total district land area), Panel C: Building permits granted (Avg. 2005 2008), Panel D: Total lane km construction (2000 2007), Panel E: Number of mitigation banks and total credits (enumerated on map), Panel F: Advance release rates (% of total credits in a bank), Panel G: Geographic scale and rigor of policies determining bank market size (‘service area’), policy rigor is measured as rigorous, lenient, or a mix (‘divided districts’), Panel H: Costs to gain access to advance release credits (% of total bank construction costs).

released (incrementally over five years of monitoring and bank closeout), although the New Orleans District required 15 years of monitoring and successful ecological establishment in order to sell the final 20% of bank credits. As shown in the Right Panel of Figure 2, there was also significant variation between districts as

the required investments of each incremental step can incur very different cost structures across districts. In the Forth Worth District, for example, a much higher percentage of the costs (83.8%) are generated prior to meeting ecological performance thresholds than in the Chicago District (35.9%). 10326



POLICY ANALYSIS

Figure 2. Left Panel: Examples of Credit Release Stages for Mitigation Banks (‘Credit Release Schedules’) with pre-ecological threshold marked with a dashed line (percentage of total credits on vertical axis). For example, bankers can sell 30% of the units in a Chicago wetland bank to developers before any ecological threshold has been met. Right Panel: Comparison of Bank Creation Cost Trajectories for Two Example Districts.

The lowest advance credit release rates occurred in the Norfolk, Omaha, and St. Louis Districts, which allowed only 15% of credits to be sold prior to meeting ecological performance standards. One quarter of the districts (n = 9) granted a 30% advance credit release, and nearly one-half (n = 16) allowed 30 35%. The highest advance releases (60%) occurred in the New York and Rock Island Districts. Geographic service area regulation also varied from restricting transactions to a single watershed, basin, eco-region, or other government-defined boundary, or any combination thereof

(Figure 1G). Most districts (∼70%) relied on 8-digit watersheds (HUC28) to define market sizes (HUC-8 watersheds are ∼1,800 km2). While ‘rigorous’ districts employed strict service area policies, most districts (∼64%) were more ‘lenient,’ allowing case-by-case variations. The Kansas City, Huntington, and Philadelphia Districts are divided regarding their enforcement of stringent service area boundaries. Many districts also use multiple geographic boundaries to determine service areas, including physiographic or EPA defined eco-regions,29 state-defined service areas (and watershed management or resource inventory areas), counties, or cities. 10327


Environmental Science & Technology Response rates for cost-related survey questions were low (18.7%), as mitigation bankers were reluctant to reveal bank construction cost information, even in confidential, aggregate forms; this reluctance is consistent with other studies of banker investment costs (e.g., refs 30 and 31). Responses yielded information for 11 districts (N = 29; Panel H of Figure 1) in which 75.9% to 93.8% of total costs were expended on activities prior to reaching performance standards, which we defined to include legal and planning costs, land acquisition, restoration design and implementation, and baseline ecological monitoring (see Right Panel of Figure 2 for examples of incremental cost structures in Forth Worth and Chicago). Although these results demonstrate substantial variation in cost structures throughout the country, due to the low response rate received, we must note that we did not use the cost-related survey data in our regression analyses. Integrating Demand and Policy with Bank Prevalence. Regression analyses (Table 1) showed that advance credit release rates had no significant relationship to total bank credits and an inverse relationship with the prevalence of individual banks (a proxy for number of bank firms). Additionally, there was no relationship between bank prevalence or credit production and policies rigorously enforcing a geographic service area size or mandating a common and fairly large service area (the 8-digit hydrological unit28). Again, we note that lack of time series data precludes causally focused regression analysis. However, studying the relationships between policy and outcomes is still meaningful for drawing lessons about landscape-scale market activity and incentives. The Environmental Paradox of Third-Party Offset Production. U.S. aquatic ecosystem markets give us some insight into how emerging markets might balance regulatory risk (a proxy for ecological risk) and entrepreneurial risk. If regulators seek to facilitate markets, they may begin by allowing advance credit sales or larger geographic market areas, thereby absorbing risk from entrepreneurs. The tension currently afflicting these ecosystem market policies lies between the goal of incentivizing credit supplier market entry versus ensuring that high quality offsets occurs well in advance of impacts and where they are needed most. However, our findings suggest that increasing ecological risk by allowing the early sale of credits, within a range of 15 60%, does not increase market activity and therefore cannot be justified for that reason alone. Early releases above or below this range may or may not have an effect. Our analysis was forced to consider the average credit release rates for each district and therefore does not allow us to determine if regulators consistently provide standardized releases that are independent of restoration project effort or quality. Economic theory suggests that if mitigation bankers encounter significant market entry barriers (e.g., high investment costs, uncertain profit margins, and credit demand32), and there is no way to overcome these barriers through advance credit sales, bankers will be less likely to locate in a given market area. In the case that credit suppliers fail to enter markets, credit purchasers would be forced to seek alternative techniques for creating conservation; systemic ecological and implementation failure has often plagued these alternative techniques.2 Our data show that regulators, in an attempt to attract more bankers, have typically adopted policies that allow bankers to sell a large fraction of their credits prior to demonstrating establishment of ecological functions and over a wide array of geographic service areas. However, our results do not demonstrate a

POLICY ANALYSIS

significant link between these policies that attempt to incentivize market entry and actual rates of market entry, as measured by number of banks and credit production. Rather, market entry is primarily related to regional geography (the prevalence of aquatic ecosystems) and regional economic growth (construction rates), i.e., demand for offsets. It appears that policies intended to increase market entry have not overcome the fundamental constraints created by the regional landscapes and economic dynamics that ultimately drive market demand. This study goes as far as possible to understand ecosystem markets in the U.S. with available national data. In order for further ecosystem market research to be possible, regulatory records must be more complete, understandable (e.g., few districts maintain high quality geo-spatial data), and contain time-indicated information on regulatory decision-making.5 Analyzing additional markets (e.g., carbon, endangered species habitat) in similar detail is not possible given the current scarcity of basic data. The lack of national, time-series market data (e.g., date of policy adoption, date of bank establishment) inhibits direct assertions about absolute causal linkages between individual district policies and market entry patterns. Our findings pose several questions that need to be addressed by any type of ecosystem service market regulatory structure: What are the trade-offs of different forms of risk and failure when using markets for environmental protection? If we discover and quantify these trade-offs, what should regulators be willing to risk in order to enhance market entry? The crux of the matter in regulating ecosystem markets that rely on private investment in ecosystem conservation involves determining whether policies that incentivize market entry are irrelevant in comparison to broader economic and ecological forces determining market behavior. In fact, policies that incentivize market entry may distort market participation, providing divergent incentives to different types of credit suppliers. This raises the more problematic issue of whether regulatory policies are actually incentivizing different qualities of conservation. The actual functional quality of ecosystem credits produced is an aspect of ecosystem markets that we have not addressed, nor has it been systematically addressed elsewhere (the closest is perhaps the 2005 U.S. Government Accountability Office33 evaluation of seven districts, where major problems were found with permit evaluations and regulatory processes), but is of critical importance and interest to both regulators and the offset industry.2 Restoration ‘quality’ can be thought of as the functional quality of ecological restoration in terms of gains to physical, chemical, or biological integrity; this is often different from the definition used by regulators which more often measures conservation actions performed (process-driven) rather than functional uplift attained (outcome-driven; see ref 14 for an empirical study of this disconnect). Moreover, the time frame during which sales occur (particularly advance credit sales) and ecological function is fully established is often very different.31 By allowing advance release, regulators sacrifice some precision in their ability to assess the quality of offset projects in exchange for more bankers that enter markets and (hopefully) produce higher quality credits than would be created using other mitigation methods. However, it is possible that policies incentivizing banker entry could disproportionally benefit mitigation bankers that create low quality credits. This process, known in economics as ‘adverse selection’,34 occurs when buyers and sellers have asymmetric information (i.e., bankers know more about their own abilities to produce credits than regulators). 10328


Environmental Science & Technology Under adverse selection, low quality credits producers will benefit under an incentive structure that lowers market entry barriers established to limit ecological risk. Assuming that market entry barriers are much higher for the creation of high quality than for low quality credits, then low quality credit producers have the most to gain from policies that lower the cost of market entry (creating high quality offsets involves greater expenditures in finding and obtaining ecologically valuable areas to conserve and elevated levels of expertise in designing and performing restoration). If regulators and credit purchases are unable to distinguish bankers based on their capability for creating high quality credits, or lack the ability to discriminate between bankers based on past conservation experience, then incentivizing market entry by decreasing entry costs may inadvertently incentivize low quality credit production.24 Example of the consequences of low quality credit production include bank failures, such as the Northlakes Park Bank in Florida, and Virginia’s Fort Lee Mitigation Bank, which sold nearly all total bank credits even though they both failed to establish proper hydrology.35 Given the increased use of market mechanisms for environmental management, scientists and policy makers need to increasingly view environmental conservation as a coupled ecologicaleconomic system. Thus, the future of conservation may be affected less by species interaction and biogeochemical cycles than by local regulatory discretion, distorted incentives, market entry, and asymmetric information.


*Phone: 919-962-4760. E-mail: [email protected].

’ ACKNOWLEDGMENTS We would like to thank Terry Chapin for his early comments on this article, as well as the National Mitigation Banking Association for their assistance with developing and distributing the national survey of mitigation bankers. ’ REFERENCES (1) Madsen, B.; Carroll, N.; Brands, K. M. State of Biodiversity Markets: Offset and Compensation Programs Worldwide; Ecosystem Marketplace: Washington, DC, 2010. (2) NRC, Compensating for Wetland Losses Under the Clean Water Act; National Academy Press: Washington, DC, 2001. (3) EPA, EPA Water Quality Trading Evaluation Final Report; U.S. Environmental Protection Agency: Washington, DC, 2008. (4) Palmer, M. A.; Filoso, S. Restoration of Ecosystem Services for Environmental Markets. Science 2009, 325 (5940), 575–576. (5) BenDor, T.; Sholtes, J.; Doyle, M. W. Landscape Characteristics of a Stream and Wetland Mitigation Banking Program. Ecol. Appl. 2009, 19 (8), 2078–2092. (6) Hough, P.; Robertson, M. Mitigation under Section 404 of the Clean Water Act: where it comes from, what it means. Wetlands Ecol. Manage. 2009, 17 (1), 15–33. (7) ELI, Mitigation of Impacts to Fish and Wildlife Habitat: Estimating Costs and Identifying Opportunities; Environmental Law Institute: Washington, DC, 2007. (8) Nolles, K. Lessons on the Design and Implementation of Environmental Markets from the Financial markets (SEELab Working Paper: WP20060707); SIRCA Experimental Economics Laboratory: Sydney, Australia, 2006. (9) Salzman, J.; Ruhl, J. B. Currencies and the Commodification of Environmental Law. Stanford Law Rev. 2000, 53, 607–694.

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(10) Robertson, M. M.; Hayden, N. Evaluation of a Market in Wetland Credits: Entrepreneurial Wetland Banking in Chicago. Conserv. Biol. 2008, 22 (3), 636–646. (11) Yin, H.; Pfaff, A.; Kunreuther, H. Can Environmental Insurance Succeed Where Other Strategies Fail? The Case of Underground Storage Tanks. Risk Anal. 2011, 31 (1), 12–24. (12) Cole, C. A.; Shafer, D. Section 404 Wetland Mitigation and Permit Success Criteria in Pennsylvania, USA, 1986 1999. Environ. Manage. 2002, 30 (4), 508–515. (13) Gutrich, J. J.; Hitzhusen, F. J. Assessing the Substitutability of Mitigation Wetlands for Natural Sites: Estimating Restoration Lag Costs of Wetland Mitigation. Ecol. Econ. 2004, 48, 409–424. (14) Reiss, K. C.; Hernandez, E.; Brown, M. T. Evaluation of Permit Success in Wetland Mitigation Banking: A Florida Case Study. Wetlands 2009, 29 (3), 907–918. (15) Corps, EPA Compensatory Mitigation for Losses of Aquatic Resources; Final Rule. http://www.epa.gov/owow/wetlands/pdf/ wetlands_mitigation_final_rule_4_10_08.pdf (accessed 12/28/08). (16) Wilkinson, J. In-lieu fee mitigation: coming into compliance with the new Compensatory Mitigation Rule. Wetlands Ecol. Manage. 2008, 17 (1), 53–70. (17) Bedford, B. L. The Need to Define Hydrolic Equivalence at the Landscape Scale for Freshwater Wetland Mitigation. Ecol. Appl. 1996, 6 (1), 57–68. (18) Race, M. S.; Fonseca, M. S. Fixing Compensatory Mitigation: What Will it Take? Ecol. Appl. 1996, 6 (1), 94–101. (19) Corps, EPA Federal Guidance for the Establishment, Use and Operation of Mitigation Banks. http://www.epa.gov/owow/wetlands/ guidance/mitbankn.html (accessed 6/13/2005). (20) HPMS, Highway Performance Monitoring System (HPMS); U.S. Federal Highway Administration: Washington, DC, 2009. (21) Corps, Regional Internet Bank Information Tracking System (RIBITS); U.S. Army Corps of Engineers, Engineering Research and Development Center (Environmental Laboratory): Vicksburg, MS, 2008. (22) Tiner, R. NWI Maps: Basic Information on the Nation’s Wetlands. BioScience 1997, 47 (5), 269. (23) Womble, P.; Doyle, M. W. Setting Geographic Service Areas for Compensatory Mitigation Banking. Natl. Wetlands Newsl. In press. (24) BenDor, T.; Riggsbee, A. A survey of entrepreneurial risk in U.S. wetland and stream compensatory mitigation markets. Environ. Sci. Policy 2011, 14, 301–314. (25) Granger, C. W. J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969, 37 (3), 424–438. (26) BenDor, T.; Brozovic, N.; Pallathucheril, V. G. Assessing the Socioeconomic Impacts of Wetland Mitigation in the Chicago Region. J. Am. Plann. Assoc. 2007, 73 (3), 263–282. (27) Strand, M. Law and Policy: “Information, Please”. Natl. Wetlands Newsl. 2010, 32 (3), 24. (28) Seaber, P. R.; Kapinos, F. P.; Knapp, G. L. Hydrologic Unit Maps: U.S. Geological Survey; U.S. Geological Survey: Washington, DC, 1987. (29) EPA, Ecoregions of North America; U.S. Environmental Protection Agency: Washington, DC, 2010. (30) EEP, Ecosystem Enhancement Program 2004 2005 Annual Report; N.C. Ecosystem Enhancement Program: Raleigh, NC, 2005. (31) Robertson, M. M. Emerging Ecosystem Service Markets: Trends in a Decade of Entrepreneurial Wetland Banking. Frontiers Ecol. Environ. 2006, 4 (6), 297–302. (32) Hallwood, P. Contractual difficulties in environmental management: The case of wetland mitigation banking. Ecol. Econ. 2007, 63, 446–451. (33) GAO, Corps of Engineers Does Not Have an Effective Oversight Approach to Ensure That Compensatory Mitigation Is Occurring: Report to the Ranking Democratic Member, Committee on Transportation and Infrastructure, House of Representatives; U.S. Government Accountability Office: Washington, DC, 2005. 10329



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(34) Akerlof, G. A. The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. Q. J. Econ. 1970, 84 (3), 488–500. (35) McElfish, J. M.; Nicholas, S. Structure and Experience of Wetland Mitigation Banks. In Mitigation Banking: Theory and Practice; Marsh, L. L., Porter, D. R., Salvesen, D. A., Eds.; Island Press: Washington, DC, 1996.

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ARTICLE pubs.acs.org/est

Human-Specific E.coli Single Nucleotide Polymorphism (SNP) Genotypes Detected in a South East Queensland Waterway, Australia Maxim S. Sheludchenko, Flavia Huygens,* and Megan H. Hargreaves Cell and Molecular Biosciences, Faculty of Science and Technology, Queensland University of Technology, Brisbane, Queensland, Australia

bS Supporting Information ABSTRACT: The World Health Organization recommends that the majority of water monitoring laboratories in the world test for E. coli daily since thermotolerant coliforms and E. coli are key indicators for risk assessment of recreational waters. Recently, we developed a new SNP method for typing E. coli strains, by which human-specific genotypes were identified. Here, we report the presence of these previously described specific SNP profiles in environmental water, sourced from the Coomera River, located in South East Queensland, Australia, over a period of two years. This study tested for the presence of human-specific E. coli to ascertain whether hydrologic and anthropogenic activity plays a key role in the pollution of the investigated watershed or whether the pollution is from other sources. We found six human-specific SNP profiles and one animalspecific SNP profile consistently across sampling sites and times. We have demonstrated that our SNP genotyping method is able to rapidly identify and characterize human- and animal-specific E. coli isolates in water sources.

’ INTRODUCTION Water is a precious resource in Australia, which is the driest inhabited continent. Approximately every ten years in Australia there are three years with sufficient water supply and three years of drought, then water restrictions and other measures are put in place to ensure the supply.1 Environmentally conscientious residents are interested in effective water management strategies and therefore their water authorities are as well. Microbial source tracking (MST) is one of the tools for the identification of multiple sources of fecal pollution in water sources. Various microorganisms are known to be targets for MST.2 Some pathogenic E. coli have been transmitted via water.3 To our knowledge, there are no host-specific markers for E. coli, even among virulence genes and the recently discovered humanspecific serotype O81,4 that have been consistently detected in environmental water.5 Recently, we developed a new SNP method for typing E. coli strains, by which human-specific genotypes were identified.6 Typing methods used for MST rely on building a reference library from known host groups to identify sources in unknown water samples. The most common examples of these methods are repetitive extragenic palindromic (rep) PCR,7 ribotyping,8 and pulsed-field gel electrophoresis (PFGE).9 They are difficult to develop, interpret, and often are applied only in local geographical areas where the reference library was developed initially.10 Therefore, library-dependent methods are rarely used. Alternatively, there are library-independent methods which are usually based on polymerase chain reaction (PCR) only and do not require large reference libraries. These methods have targeted specific genes of microorganisms which are difficult to r 2011 American Chemical Society

cultivate in normal conditions such as Bacteroides spp. 16S rRNA clone groups,11 F+ RNA coliphage differentiation,2 and enteric viruses including polyomaviruses and adenoviruses.12 The advantage of these molecular markers is that they appear to be host-specific,13 although, some studies have reported humanspecific markers from nonhuman sources. For instance, humanspecific Bacteroides marker was detected in other animals such as dogs14 and fish.15 In this study, using E. coli as the target organism, we chose a molecular typing approach that is a comparative method. This method simply determines whether isolates are the same or different based on their SNP genotype profile. The SNP-typing method developed recently6 and applied here is considered by the authors to be neither library-dependent nor -independent. In fact, the authors would describe this novel genotyping approach as a culture-dependent library-based method and once developed, can be done without culture. Library-based due to the origin of the SNPs identified from housekeeping genes described in the MLST database. We report the presence of these previously described specific SNP profiles in environmental water, sourced from the Coomera River, located in South East Queensland, Australia, over a period of two years. The sampling points were suggested by the Gold Coast City Council as being problematic sites with a history of large numbers of fecal coliforms. One potential source of fecal Received: May 11, 2011 Accepted: October 25, 2011 Revised: October 24, 2011 Published: October 25, 2011 10331

dx.doi.org/10.1021/es201599u | Environ. Sci. Technol. 2011, 45, 10331–10336


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Table 1. Locations and Characteristics of Sampling Sites site name (GISa map reference)

code

site characteristics

site classification

1

Coomera Marina ( 27.861672, 153.339089)

cattle/kangaroo grazing, house-boat mooring site

rural

2

Santa Barbara ( 27.855165, 153.350612)

park, BBQ, toilets and fishing, private houses about 100 m away

recreational

3

Sanctuary Cove ( 27.851617, 153.362140)

canal estate, modern houses and apartments, modern infrastructure,

urban/suburban

commercial/light industrial area

a

4

Jabiru Island ( 27.879057, 153.380685)

disused sand mine, no houses, small park with sewered toilets

rural

5

Paradise Point ( 27.886359, 153.396596)

public swimming beach, mouth of river, much water traffic

recreational

6

Coombabah, the Estuary ( 27.896607, 153.366845)

established suburban area, uninhabited island opposite

suburban

Global information system.

Table 2. Weather Patterns on Days of Sampling variable

a

Autumn 2008

Winter 2008

Autumn 2009

Winter 2009

rainfall in mm, 24 h prior sampling rainfall in mm, 72 h prior sampling

0 0.4

1.3 33.9

3.2 15.2

1.4 1.4

tides (m)a

0.183

0.425

0.931

1.368

Personal communication (Daryl Metters, Maritime Safety Queensland, Department of Transport and Main Roads).

pollution may be the accidental sewage discharge from a large number of yachts and houseboats owned by residents who have boat moorings in the many canal estates. According to the Transport Operations (Marine Pollution) Act16 and Regulation,17 sewage discharge in canals and marinas is prohibited. Boat owners, however, may be unaware of the regulations, or noncompliant. As a result, sewage discharge into the Coomera estuary may be a continuing risk. The method described in our previous work6 allowed us to test for the presence of humanspecific E. coli in the current study.

’ EXPERIMENTAL SECTION Study Site. The Pimpana-Coomera watershed is located in South East Queensland, Australia. It is used intensively for agriculture and recreation and an anthropogenic effect can be observed in the watershed. The main water source is the Coomera River, which flows for 90 km from its headwaters in the Lamington National Park to the river mouth in the Pacific Ocean. The upper reaches of the river pass through mainly rural areas, where crop and cattle grazing are the common economic/land use activities. In the 1970s and 1980s, the river was widened 20 km upstream from the mouth as a consequence of sand and gravel extraction operations. The lower reaches of the Coomera River pass through highly developed areas including canal estates such as Santa Barbara, Hope Island, Sanctuary Cove, and the Coomera Mooring Marina. Most of the sewage collection system is gravity fed and follows natural catchment drainage lines until it reaches a centrally located wastewater treatment plant. After treatment, the water is released into the Gold Coast Seaway located south of the Coomera River estuary. Despite the existence of such an effective treatment system, large numbers of coliforms have been observed over a long period of time in the estuary by the Gold Coast City council. Environmental Water Sampling. Four seasonal trips to the Coomera catchment on the Gold Coast were undertaken to collect 24 river water samples (taken in duplicate) from May 2008 to July 2009. Sites selected for sampling included the following: Coomera Marina (1), Santa Barbara (2), Sanctuary Cove (3), Jabiru Island (4), Paradise Point (5), and Coombabah (6) (TOC Art Figure and Table 1). These were suggested by the

Gold Coast City Council as being problematic sites with a history of high concentrations of fecal coliforms. Two water samples of 600 mL each were collected in sterile bottles containing sodium thiosulphate (in case of chlorine residuals in the sample) and transported on ice to the laboratory. Samples were collected using a 1.8 m long dipper, to ensure that the sample was taken from the river rather than the edge where eddies and ephemeral contamination may have interfered with the results. Water samples were prepared for analyses immediately upon arrival at the laboratory, which was always within 6 h. Rainfall and tidal information for sampling days/times were retrieved from the Australian Bureau of Meteorology and are listed in Table 2 (http://reg.bom.gov.au/climate/data/index.shtmLand). Isolation of E. coli. The environmental water samples collected in duplicate were each mixed thoroughly, and 100 mL was filtered as described previously.18 As a high number of E. coli was expected based on previous studies of these sites (pilot study and Gold Coast Council data), samples were filtered both undiluted and diluted 1:10 to provide a countable number of colonies per filter pad. Testing according to the U.S. EPA standard19 was done on all water samples and dilutions, using 0.45-μm sterile gridded filter membranes (Millipore Corporation, Bedford, MA). The membranes were aseptically transferred to modified mTEC agar plates (BD, Sparks, MD) and were incubated aerobically at 35 °C for 2 h followed by an additional incubation at 44.5 °C for 24 h. Single magenta colonies demonstrating ß-D-galactosidase activity were selected and subcultured onto MacConkey agar #2 (Oxoid, UK) for DNA extraction as described previously.20 DNA Extraction. To prepare genomic DNA, single lactose fermenting pink colonies from MacConkey agar #2 were isolated and incubated overnight in 5 mL of nutrient broth (Oxoid, UK). A 500 μL portion of overnight culture was centrifuged at 10 000g for 1 min. Cell pellets were resuspended in 180 μL of DNAase/ RNAase-free water and used for DNA extraction on the Corbett X-tractorGene automated DNA extraction system (Corbett Robotics, Brisbane, Australia, Protocol no.141404 version 02). Quantity and purity of DNA extracts were tested on the DU 730 spectrophotometer (Beckman Coulter, USA). Identification of E. coli SNP Genotypes by Allele-Specific Real-Time PCR. The E. coli MLST database available at NIH 10332



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Figure 1. Shades of red and black indicate human-specific SNP profiles. Shades of green indicate animal-specific SNP profiles. Blue indicates unique SNP profiles isolated from the Coomera River. The remaining colors are indicative of “mixed source” SNP profiles. Uncolored spaces indicate SNP profiles that have only been detected once or twice. Circles show values of E. coli in Colony Forming Units (CFU)/mL.

(http://www.shigatox.net) currently contains 668 E. coli strains that are grouped into 231 Sequence Types (STs). Informative SNP sets were identified by using the software program called “Minimum SNPs”.21 Eight SNPs, with a Simpson’s index of Diversity (D value) D of 0.96, were determined by the program for the differentiation of E. coli isolates.6 A method for highly discriminatory SNP interrogation of E. coli, by using allelespecific real-time PCR, was developed previously by our group6

and applied to all the E. coli isolates in this study. A number of SNP profiles resolved the E. coli population on the basis of unique eight character “barcodes” within a day. From each subculture/ McConkey plate at least 10 colonies, when possible, were selected for DNA extraction followed by SNP profile identification. Statistical Analysis. Binary logistic regression analysis was used to determine the relationship between human/nonhuman SNP profiles and seasons, rainfalls (24 h and 72 h prior sampling), tide 10333



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Table 3. Summary of the Twenty Most Prevalent SNP Profiles Distributed Across Sampling Sites and Time-Pointsa

a

* indicates SNP profile numbers as previously published in Sheludchenko et al.6

levels for estimating salinity at sampling times, land use categories (suburban, urban, rural, and recreational) and distance from the river mouth to the sampling site. All calculations were done using MiniTab 16.0 (Minitab Inc.) and p-values were calculated. To calculate the SNP diversity per sampling site, any SNP profile that was only found once or twice at a specific site (clear bars in Figure 1), was excluded from this analysis; instead, SNP profiles that were frequently found at each site were included (colored bars in Figure 1). Supporting Information Table S1 provides a summary of the numbers of SNP profiles found per season per sampling site. The total number of SNP profiles found at each sampling point was used in the binary logistic analysis.

’ RESULTS AND DISCUSSION Although it is well recognized that E. coli numbers in natural waterways fluctuate in response to environmental factors, particularly rainfall events,22 there is limited research reporting the diversity of E. coli genotypes in environmental water in relation to similar hydrological conditions and land use.5,23 In the current study, a new highly discriminatory genotyping method based on SNP interrogation of E. coli6 was applied to detect host-specific E. coli genotypes in the Coomera watershed over a two-year period. In total, 165 isolates were grouped into 67 SNP profiles found at six sites collected between May 2008 and July 2009. A summary of all the SNP profiles observed in this study can be found in Table S2. A very low number of E. coli were detected in samples from site 3, which is therefore not discussed further in this paper.

Despite the fact that there was variation in E. coli numbers isolated from the various sampling sites over the period of two years (Figure 1), the SNP type diversity did not vary significantly between sites, or times of sampling. Nor was a significant relationship detected between SNP profile diversity, and rainfall, seasons, tides, rural/suburban land use, or distance from the river mouth. From a SNP profile perspective, we found the most diverse E. coli population at site 1, which also had the lowest number of E. coli. In addition, host-specific profiles were rarely detected at sites 4, 5, or 6, which had the largest number of E. coli. The 20 most prevalent SNP profiles were detected in a number of samples over the whole period (Figure 1 and Table S3). The remaining 47 SNP profiles were detected only once. Of the 20 most prevalent profiles, three human-specific profiles (29, 11, and 32) and one animal-specific profile (7) corresponded to human- and animal-specific profiles published previously.6 Table 3 is a summary of the 20 most prevalent SNP profiles found at each sampling site and at each sampling time-point. Human- and animal-specific SNP profiles were distributed across all the sampling sites, irrespective of the seasons. Five of the top 20 SNP profiles (35, 70, 37, 80 and 40) could also be considered to be representative of human fecal contamination, since these profiles were previously characterized as “mainly human-specific”.6 Also included in the most prevalent cohort were SNP profiles previously identified as “nonspecific” (9, 21, 22, 23, 26, and 34) in terms of host assignation.6 SNP profile 80, which in our previous study contained only two human isolates (and no animal), was identified in four of the six sampling sites.6 SNP profiles 14 and 82 were also detected, 10334


Environmental Science & Technology which have previously been characterized as cattle- and horsesourced, respectively.6 Interestingly, SNP profile 45 was not found at any of the sites we tested, even though this profile was shown by our previous study to be prevalent in local and international collections.6 In addition, we have not found SNP profile 76 in this or our previous study,6 despite the fact that it is a profile found in other countries. In conclusion, we found six human-specific (29, 11, 32, 70, 37, 80) SNP profiles and one animal-specific (7) SNP profile consistently across sampling sites and times. SNP profile 29 was found in the majority (44%) of samples tested in this study. SNP profile 11 was the second-most commonly encountered profile, being present in 28% of samples (Table S2). This study investigated SNP profiles, previously aligned with human or nonhuman sources,6 found in an E. coli population isolated from a natural waterway. The effect of variables such as rainfall (24 and 72 h), tide height and time, general land use (rural, suburban), seasons, and distance from the river mouth as an estimate of salinity was investigated and it was found that none of the variables significantly influenced the diversity of E. coli SNP profiles present in the water (p values >0.35). In addition, by applying our previously developed SNP genotyping method6 to genotype water-sourced E. coli, we were able to identify six human-specific E. coli SNP profiles, and four animal-specific E. coli SNP profiles in the Coomera River of South East Queensland, Australia.

’ ASSOCIATED CONTENT

bS

Supporting Information. Table S1 lists the number of SNP profiles found more frequently at each sampling point per season; Table S2 lists all the SNP profiles found in this study; Table S3 lists the abundance of SNP genotypes in the Coomera water catchment over a period of two years and four sampling events. This information is available free of charge via the Internet at http://pubs.acs.org/


*Phone: +61 7 3138 0453; fax: +61 7 3138 1534; e-mail: [email protected].

’ ACKNOWLEDGMENT We thank Melanie Robertson-Dean for providing assistance with statistical analyses. M.S.S. is in receipt of a postgraduate studentship from the Institute of Sustainable Resources, Queensland University of Technology. ’ REFERENCES (1) Department of the Environment, W., Heritage and the Arts. Australian weather and the seasons. http://www.culture.gov.au/articles/weather/. Visited April 2011. (2) Bernhard, A.; Field, K. Identification of nonpoint sources of fecal pollution in coastal waters by using host-specific 16S ribosomal DNA genetic markers from fecal anaerobes. Appl. Environ. Microbiol. 2000, 66 (4), 1587–1594. (3) King, E. L.; Bachoon, D. S.; Gates, K. W. Rapid detection of human fecal contamination in estuarine environments by PCR targeting of Bifidobacterium adolescentis. J. Microbiol. Methods 2007, 68 (1), 76–8.

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(4) Scott, T. M.; Jenkins, T. M.; Lukasik, J.; Rose, J. B. Potential use of a host associated molecular marker in Enterococcus faecium as an index of human fecal pollution. Environ. Sci. Technol. 2005, 39 (1), 283–287. (5) Havelaar, A. H.; Furuse, K.; Hogeboom, W. M. Bacteriophages and indicator bacteria in human and animal faeces. J. Appl. Bacteriol. 1986, 60 (3), 255–262. (6) Sheludchenko, M. S.; Huygens, F.; Hargreaves, M. H. Highlydiscriminatory single nucleotide polymorphism interrogation of Escherichia coli using allele-specific Real-Time-PCR and eBURST analysis. Appl. Environ. Microbiol. 2010, 76 (13), 4337–4345. (7) Walk, S. T.; Alm, E. W.; Gordon, D. M.; Ram, J. L.; Toranzos, G. A.; Tiedje, J. M.; Whittam, T. S. Cryptic lineages of the genus Escherichia. Appl. Environ. Microbiol. 2009, 75 (20), 6534–6544. (8) Clermont, O.; Lescat, M.; O’Brien, C. L.; Gordon, D. M.; Tenaillon, O.; Denamur, E. Evidence for a human-specific Escherichia coli clone. Environ. Microbiol. 2008, 10 (4), 1000–1006. (9) Ahmed, W.; Tucker, J.; Bettelheim, K. A.; Neller, R.; Katouli, M. Detection of virulence genes in Escherichia coli of an existing metabolic fingerprint database to predict the sources of pathogenic E. coli in surface waters. Water Res. 2007, 41, 3785–3791. (10) Ratajczak, M.; Laroche, E.; Berthe, T.; Clermont, O.; Pawlak, B.; Denamur, E.; Petit, F. Influence of hydrological conditions on the Escherichia coli population structure in the water of a creek on a rural watershed. BMC Microbiol. 2010, 10 (1), 222. (11) Smith, C. J.; Olszewski, A. M.; Mauro, S. A. Correlation of shiga toxin gene frequency with commonly used microbial indicators of recreational water quality. Appl. Environ. Microbiol. 2009, 75 (2), 316–321. (12) Kon, T.; Weir, S. C.; Howell, E. T.; Lee, H.; Trevors, J. T. Repetitive element (REP)-polymerase chain reaction (PCR) analysis of Escherichia coli isolates from recreational waters of southeastern Lake Huron. Can. J. Microbiol. 2009, 55 (1), 269–276. (13) D’Elia, T. V.; Cooper, C. R.; Johnston, C. G. Source tracking of Escherichia coli by 16S 23S intergenic spacer region denaturing gradient gel electrophoresis (DGGE) of the rrnB ribosomal operon. Can. J. Microbiol. 2007, 53, 1174–1184. (14) McLellan, S. L.; Daniels, A. D.; Salmore, A. K. Genetic characterization of Escherichia coli populations from host sources of fecal pollution by using DNA fingerprinting. Appl. Environ. Microbiol. 2003, 69 (5), 2587–2594. (15) Seurinck, S.; Verstraete, W.; Siciliano, S. Microbial source tracking for identification of fecal pollution. Rev. Environ. Sci. Biotechnol. 2005, 4 (1), 19–37. (16) Yan, T.; Sadowsky, M. J. Determining sources of fecal bacteria in waterways. Environ. Monit. Assess. 2007, 129 (1 3), 97–106. (17) Bernhard, A. E.; Field, K. G. A PCR assay to discriminate human and ruminant feces on the basis of host differences in BacteroidesPrevotella genes encoding 16S rRNA. Appl. Environ. Microbiol. 2000, 66 (10), 4571–4574. (18) Fong, T. T.; Lipp, E. K. Enteric viruses of humans and animals in aquatic environments: Health risks, detection, and potential water quality assessment tools. Microbiol. Mol. Biol. Rev. 2005, 69 (2), 357–361. (19) Ogorzaly, L.; Tissier, A.; Bertrand, I.; Maul, A.; Gantzer, C. Relationship between F-specific RNA phage genogroups, faecal pollution indicators and human adenoviruses in river water. Water Res. 2009, 43 (5), 1257–1264. (20) McQuaig, S. M.; Scott, T. M.; Harwood, V. J.; Farrah, S. R.; Lukasik, J. O. Detection of human-derived fecal pollution in environmental waters by use of a PCR-based human polyomavirus assay. Appl. Environ. Microbiol. 2006, 72 (12), 7567–7574. (21) Ahmed, W.; Goonetilleke, A.; Powell, D.; Gardner, T. Evaluation of multiple sewage-assoclated Bacteroides PCR markers for sewage pollution tracking. Water Res. 2009, 43 (19), 4872–4877. (22) Kildare, B. J.; Leutenegger, C. M.; McSwain, B. S.; Bambic, D. G.; Rajal, V. B.; Wuertz, S. 16S rRNA-based assays for quantitative detection of universal, human-, cow-, and dog-specific fecal Bacteroidales: A Bayesian approach. Water Res. 2007, 41 (16), 3701–3715. 10335



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(23) McLain, J. E. T.; Ryu, H.; Kabiri-Badr, L.; Rock, C. M.; Abbaszadegan, M. Lack of specificity for PCR assays targeting human Bacteroides 16S rRNA gene: Cross-amplification with fish feces. FEMS Microbiol. Lett. 2009, 299 (1), 38–43. (24) MSQ, Transport Operations (Marine Pollution) Act. Queensland, M. S., Ed. 1995. (25) MSQ, Transport Operations (Marine Pollution) Regulation. Queensland, M. S., Ed. 2008. (26) Eaton, A.; Clesceri, L. S.; Rice, E. W.; Greenberg, A. E. Standard Methods for the Examination of Water and Wastewater, 21st ed.; Washington, DC, 2005. (27) Oshiro, R. Method 1603: Escherichia coli (E.coli) in water by membrane filtration using modified membrane-thermotolerant Escherichia coli agar (Modified mTEC); EPA 600-4-85-076; Office of Water Regulations and Standards, U.S. Environmental Protection Agency: Washington, DC, 2002. (28) Vogel, J. R.; Stoeckel, D. M.; Lamendella, R.; Zelt, R. B.; Domingo, J. W. S.; Walker, S. R.; Oerther, D. B. Identifying fecal sources in a selected catchment reach using multiple source-tracking tools. J. Environ. Qual. 2007, 36 (3), 718–729. (29) Robertson, G.; Huygens, F.; Giffard, G. Identification and interrogation of highly informative single nucleotide polymorphism sets defined by bacterial multilocus sequence typing databases. J. Med. Microbiol. 2004, 53, 35–45. (30) Kelsey, H.; Porter, D., E.; Scott, G.; Neet, M.; White, D. Using Geographic Information Systems and Regression Analysis to Evaluate Relationships between Land Use and Fecal Coliform Bacterial Pollution; Elsevier: Kidlington, Royaume-Uni, 2004; p 13. (31) Kleinheinz, G. T.; McDermott, C. M.; Hughes, S.; Brown, A. Effects of rainfall on E. coli concentrations at Door County, Wisconsin beaches. Int. J. Microbiol. 2009, 2009, 9. (32) Ishii, S.; Hansen, D. L.; Hicks, R. E.; Sadowsky, M. J. Beach sand and sediments are temporal sinks and sources of Escherichia coli in Lake Superior. Environ. Sci. Technol. 2007, 41 (7), 2203–2209. (33) Lyautey, E.; Lu, Z.; Lapen, D. R.; Wilkes, G.; Scott, A.; Berkers, T.; Edge, T. A.; Topp, E. Distribution and diversity of Escherichia coli populations in the South Nation river drainage basin, Eastern Ontario, Canada. Appl. Environ. Microbiol. 2010, 76 (5), 1486–1496. (34) Wijesinghe, R. U.; Feng, Y.; Wood, C. W.; Stoeckel, D. M.; Shaw, J. N. Population dynamics and genetic variability of Escherichia coli in a mixed land-use watershed. J. Water Health 2009, 07 (3), 484–496.

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Physicochemical Characterization of Particulate Emissions from a Compression Ignition Engine: The Influence of Biodiesel Feedstock N. C. Surawski,†,‡ B. Miljevic,†,|| G. A. Ayoko,†,§ S. Elbagir,§ S. Stevanovic,†,|| K. E. Fairfull-Smith,|| S. E. Bottle,|| and Z. D. Ristovski*,† †

)

ILAQH, Institute of Health and Biomedical Innovation, Queensland University of Technology, 2 George Street, Brisbane QLD 4001, Australia ‡ School of Engineering Systems, Queensland University of Technology, 2 George Street, Brisbane QLD 4001, Australia § Discipline of Chemistry, Queensland University of Technology, 2 George Street, Brisbane QLD 4001, Australia ARC Centre of Excellence for Free Radical Chemistry and Biotechnology, Queensland University of Technology, 2 George Street, 4001 Brisbane, Australia

bS Supporting Information ABSTRACT: This study undertook a physicochemical characterization of particle emissions from a single compression ignition engine operated at one test mode with 3 biodiesel fuels made from 3 different feedstocks (i.e., soy, tallow, and canola) at 4 different blend percentages (20%, 40%, 60%, and 80%) to gain insights into their particle-related health effects. Particle physical properties were inferred by measuring particle number size distributions both with and without heating within a thermodenuder (TD) and also by measuring particulate matter (PM) emission factors with an aerodynamic diameter less than 10 μm (PM10). The chemical properties of particulates were investigated by measuring particle and vapor phase Polycyclic Aromatic Hydrocarbons (PAHs) and also Reactive Oxygen Species (ROS) concentrations. The particle number size distributions showed strong dependency on feedstock and blend percentage with some fuel types showing increased particle number emissions, while others showed particle number reductions. In addition, the median particle diameter decreased as the blend percentage was increased. Particle and vapor phase PAHs were generally reduced with biodiesel, with the results being relatively independent of the blend percentage. The ROS concentrations increased monotonically with biodiesel blend percentage but did not exhibit strong feedstock variability. Furthermore, the ROS concentrations correlated quite well with the organic volume percentage of particles a quantity which increased with increasing blend percentage. At higher blend percentages, the particle surface area was significantly reduced, but the particles were internally mixed with a greater organic volume percentage (containing ROS) which has implications for using surface area as a regulatory metric for diesel particulate matter (DPM) emissions.

1. INTRODUCTION Alternative fuels, such as biodiesel, are currently being investigated not only to address global warming1 but also to reduce DPM emissions.2 While a considerable database exists describing the impact of different transesterified biodiesel fuel types on regulated emissions (i.e., PM, NOx, CO, and HCs),3,4 limited information is available addressing the impact of different biodiesel fuel types on other particle emission properties, such as particle number and size. Regulated emissions from compression ignition engines typically exhibit strong dependencies on both feedstock and blend percentage. With PM emissions (for example), animal fat based biodiesel gives greater PM reductions than soy based biodiesel, and the PM reductions exhibit a nonlinear reduction with respect to blend percentage.4 Given these results, it is quite likely that particle emissions will display similar dependencies. At present, a detailed database is not in r 2011 American Chemical Society

existence characterizing the unregulated physicochemical characteristics of DPM such as the following: particle number emission factors, particle size distributions, surface area as well as PAHs and ROS with different biodiesel feedstocks and blend percentages. Consequently, a primary objective of this study was to explore the physicochemical properties of particle emissions from 3 biodiesel feedstocks tested at 4 different blend percentages to shed light on their potential health impacts. A combination of physical and chemical factors influences the health effects of DPM,5 where it is noted with biodiesel combustion that the particles have a much higher organic fraction.6 The organic Received: June 1, 2011 Accepted: October 31, 2011 Revised: September 2, 2011 Published: October 31, 2011 10337


Environmental Science & Technology fraction of DPM includes many compounds that are deleterious to human health such as PAHs and ROS.7 Previous research has demonstrated a correlation between the semivolatile organic component (i.e., they partition between the gas and particle phase) of particles and their oxidative potential for DPM8 and also for wood smoke particles.9 Furthermore, a correlation has been demonstrated between the oxidative potential of particles and also PAH emission factors.10,11 Typically, the chemical properties of particulate emissions, such as PAHs and ROS are detected using off-line analytical chemistry techniques. The development of a near real-time technique enabling the detection of semivolatile organic compounds would be quite useful, given their importance in assessing the health effects of DPM. As PAHs and ROS are both classed as semivolatile organic compounds, it is therefore possible that heating diluted exhaust within a TD will provide near real-time qualitative information on the presence of these components. As a result, a secondary objective of this work was to assess whether online measurements of the organic volume percentage (VORG) of DPM can provide information on genotoxic compounds on the surface of the particle that are usually measured using off-line analytical chemistry techniques. To achieve this objective, the relationship between VORG and ROS concentrations is explored. Historically, the regulation of DPM emissions has been achieved using a mass-based emissions standard;12 however, a particle number standard for heavy duty diesel engines will be introduced in the European Union at the Euro VI stage.12 While there have been studies suggesting that particle number emissions correlate with respiratory13 and cardiovascular14 morbidity from DPM more adequately than particle mass, toxicological studies have shown a strong inflammatory response from inert ultrafine particles in a size-dependent manner.15,16 Consequently, the toxicological literature suggests that particle surface area could be a relevant metric for assessing DPM health effects. Given that DPM is quite often composed of a solid elemental carbon core with internally mixed semivolatile organics,17 a surface area based metric would provide information on the ability of toxic organic compounds to adsorb or condense on the surface of the particle. Consequently, a third objective of this work was to critically examine whether regulation of the DPM surface area emitted by a compression ignition engine has merit. All of the research objectives have been undertaken by investigating particle emissions from a nonroad diesel engine operated with various biodiesel feedstocks and blend percentages.

2. METHODOLOGY 2.1. Engine and Fuel Specifications. Particulate emissions testing was performed on a naturally aspirated 4 cylinder Perkins 1104C-44 engine with a Euro II (off-road) emissions certification. The engine investigated is typical of those used in underground mines in Australia and is the same engine used in Surawski et al.11 The engine was coupled to a Heenan & Froude water brake dynamometer (DPX 4) to provide a load to the engine. Ultralow sulfur diesel (denoted ULSD hereafter, 200 nm); however, for smaller mobility diameters (

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