3

Using Emerging Science and
Technologies to Address Persistent
and Future Environmental Challenges

Chapter 2 discussed some of the broad drivers and challenges that are inherent to the mission of the US Environmental Protection Agency (EPA) today and in the future. Remarkable progress has been made in the last several decades in the development of new scientific approaches, tools, and technologies relevant to addressing those challenges. The purpose of this chapter is to highlight new and changing science and technologies that are or will be increasingly important for science-informed policy and regulation in EPA.

New tools and technologies can substantially improve the scientific basis of environmental policy and regulations, but it is important to remember that many of the tools and technologies need to build on and enhance the current foundation of environmental science and engineering in the United States. In addition, addressing the complex “wicked problems” facing EPA today and in the future requires not only new science and technology but a more deliberate approach to systems thinking, for example, by using frameworks that strive to integrate a broader array of interactions between humans and the environment. From the perspective of scientific advances relevant to the future of EPA, it will be increasingly important that all aspects of biologic sciences and environmental sciences and engineering—including human health risk assessment, microbial pathogenesis, ecosystem energy and matter transfers, and ecologic adaptation to climate change—be considered in an integrated systems-biology approach. That approach must also be integrated with considerations of environmental, social, behavioral, and economic impacts.

A SIMPLE PARADIGM FOR DATA-DRIVEN, SCIENCE-INFORMED DECISIONS IN THE ENVIRONMENTAL PROTECTION AGENCY

New scientific advances, including the development and application of new tools and technologies, are critical for the science mission of EPA. Effec-



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3 Using Emerging Science and Technologies to Address Persistent and Future Environmental Challenges Chapter 2 discussed some of the broad drivers and challenges that are in- herent to the mission of the US Environmental Protection Agency (EPA) today and in the future. Remarkable progress has been made in the last several decades in the development of new scientific approaches, tools, and technologies rele- vant to addressing those challenges. The purpose of this chapter is to highlight new and changing science and technologies that are or will be increasingly im- portant for science-informed policy and regulation in EPA. New tools and technologies can substantially improve the scientific basis of environmental policy and regulations, but it is important to remember that many of the tools and technologies need to build on and enhance the current foundation of environmental science and engineering in the United States. In addition, addressing the complex "wicked problems" facing EPA today and in the future requires not only new science and technology but a more deliberate approach to systems thinking, for example, by using frameworks that strive to integrate a broader array of interactions between humans and the environment. From the perspective of scientific advances relevant to the future of EPA, it will be increasingly important that all aspects of biologic sciences and environmental sciences and engineering--including human health risk assessment, microbial pathogenesis, ecosystem energy and matter transfers, and ecologic adaptation to climate change--be considered in an integrated systems-biology approach. That approach must also be integrated with considerations of environmental, social, behavioral, and economic impacts. A SIMPLE PARADIGM FOR DATA-DRIVEN, SCIENCE-INFORMED DECISIONS IN THE ENVIRONMENTAL PROTECTION AGENCY New scientific advances, including the development and application of new tools and technologies, are critical for the science mission of EPA. Effec- 54

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Emerging Science & Technologies to Address Environmental Challenges 55 tive science-informed regulation and policy aimed at protecting human health and environmental quality relies on robust approaches to data acquisition and to knowledge generated from the data. For science to inform regulation and policy effectively, a strong problem-formulation step is needed. Once a problem is formulated, EPA scientists can evaluate what types of data are needed and then determine which available tools and technologies are appropriate for gathering the most robust data (see Figure 3-1). As described in detail in this chapter, management and interpretation of "big data" will be a continuing challenge for EPA inasmuch as new technologies are now capable of quickly generating huge amounts of data. Senior statisticians are needed in the agency to help analyze, model, and support the synthesis of that data. In many instances, large amounts of data are directly acquired as a component of hypothesis-driven research. However, many new technologies are also used for discovery-driven research-- that is, generating large volumes of data that may not be a derivative of a clear, hypothesis-driven experiment, but nevertheless may yield important new hy- potheses. In both instances, the data themselves do not become knowledge that can be applied as solutions to problems until they are analyzed and interpreted and then placed in the context of an appropriate problem or scientific theory. As depicted in Figure 3-1, there are iterations and feedback loops that must exist, particularly between data acquisition and data modeling, analysis, and synthesis. The generation of knowledge, which can take many forms depending on the question being addressed and the nature of the data, ultimately serves as the basis of science-informed regulation and policy (see Figure 3-1). The committee recognizes that scientific data constitute one--albeit important--input into deci- sion-making processes but alone will not resolve highly complex and uncertain environmental and health problems. Ultimately, environmental and health deci- sions and solutions will also be based on economic, societal, and other consid- erations apart from science. They need to take into account the variety and com- plexities of interactions between humans and the environment. But with better scientific understanding, regulations and other actions can be more effective and can have better and more cost-effective outcomes, such as improved human health and improved quality of ecosystems and the environment. In accordance with the above discussion, it is imperative that EPA have the capacity and knowledge to take advantage of the latest science and technolo- gies, which are always changing. The remainder of the chapter highlights a number of scientific and technologic advances that will be increasingly impor- tant for state-of-the-art, science-informed environmental regulation. It also in- cludes several examples of how emerging science, technologies, and tools are transforming the way in which EPA will use data to address important regula- tory issues and decision-making, and they demonstrate the need for a systems approach to addressing these complex problems. The chapter has been organized in parallel to the challenges identified in Chapter 2. The main topics that will be discussed are tools and technologies to address challenges related to 1) chemical exposures, human health, and the environment; 2) air pollution and climate

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56 Complex Challenges for the Future Problem Formulation Hypothesis Generation Needs Assessment Technical Approaches Analysis of Key Measures to Advance Knowledge Knowledge Data Acquisition Environmental Fate Impacts Ecologic Population Health Biologic Data Modeling, Exposure and Dose Physical Analysis, and Mechanism and Mode of Action Chemical Synthesis Implications Epidemiologic Costs Socioeconomic Feedback Outcomes Behavioral Behaviors Balanced Informed Decisions Informatics Decision Options Improved Health Cleaner Environment Lower Costs Systems Thinking to Assess Implications of Decisions Applying Science that Anticipates, Innovates, Takes the Long View, Is Collaborative Translation and Communication Applications, Decisions, Synthesis and Evaluation Systems Tools and Skills and Actions Sustainability Analysis Life-Cycle Assessment Policy Solution-Oriented Approaches Cumulative Risk Assessment Regulation Multiple-Criteria and Social, Economic, Behavioral, Social Change Multidimensional Tools and Decision Sciences Uncertainty Synthesis Research FIGURE 3-1 The iterative process of science-informed environmental decision-making and policy. The process starts with effective problem- formulation, which drives both the experimental design and the selection of data to be acquired. Modeling, synthesis, and analysis of the data are necessary to generate new knowledge. Only through effective translation and communication of new knowledge can science truly inform policies that can generate actions to improve public health and the environment. An evaluation of outcomes is an essential component in deter- mining whether science-informed actions have been beneficial, and it, in turn, adds to the knowledge base.

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Emerging Science & Technologies to Address Environmental Challenges 57 change; 3) water quality and nutrient pollution; and 4) shifting spatial and tem- poral scales. The chapter ends with a section on, "Using New Science to Drive Safer Technologies and Products", which discusses ways in which EPA can prevent environmental problems before they arise. The examples in this chapter are not intended to be comprehensive; rather, they are provided to illustrate from different perspectives the many ways in which new advances in science, engineering, and technology could be embraced by the agency, its scientists, and regulators to ensure that the agency remains at the leading edge of science-informed regulatory policy to protect human health and the environment. Having assessed EPA's current activities, the committee notes that EPA is well equipped to take advantage of most of the new scientific and technologic advances and that, in fact, its scientists and engineers are lead- ers in some fields. TOOLS AND TECHNOLOGIES TO ADDRESS CHALLENGES RELATED TO CHEMICAL EXPOSURES, HUMAN HEALTH, AND THE ENVIRONMENT New technologies will be important to EPA for identifying chemicals in the environment, understanding their transport and fate in the environment, as- sessing the extent of actual human exposures through biomonitoring, and identi- fying and predicting the potential toxic effects of chemicals. Current and emerg- ing tools and technologies related to these topics are discussed in the sections below. Identifying Chemicals in Environmental Media Analytic chemistry continues to improve at breakneck speed, and analytic determinations for both metals and organic chemicals have improved exponen- tially. Chemicals can now be detected at ever lower concentrations. For some organic chemicals, such as chlorinated dioxins, standard EPA methods include the routine measurement of samples in parts per quadrillion (ppq) or picograms per liter (pg/L) (EPA 1997), which allows risk managers to characterize lifetime uptake of exposure to various carcinogens and daily uptake rates in chronic haz- ard quotient assessments of chemicals that were not previously detectable. Sim- ply being able to measure concentrations of chemicals in environmental media or blood confronts EPA with new decisions on whether to set maximum con- taminant levels in drinking water or allowable daily intakes in food or whether to allow states to do so independently if health effects are uncertain. As the public learns about new methods of detection of chemicals in, for example, their blood, their children's blood, and the environment (water, air, and soil), questions arise as to what such occurrences mean. Of course, the simple detection of chemicals in relevant receptors does not necessarily imply any hu- man health or ecologic effects. To evaluate the health implications of chemical

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58 Science For Environmental Protection: The Road Ahead exposures throughout the range of exposure levels, sufficiently large epidemi- ologic studies that incorporate state-of-the-art analytic methods are needed (see the section "Applications of Biomarkers to Human Health Studies"). But, even when biologic effects are not evident (and in special cases of hormesis when there are potentially beneficial effects), the challenge for EPA is to provide meaningful and relevant information to potentially affected parties. It is now possible, while testing for emerging contaminants of interest and their metabolites, to monitor the effluent of a publicly owned wastewater- treatment plant and determine trace quantities and metabolites of substances-- such as pharmaceuticals (licit and illicit), personal-care products, and hormones (natural and synthetic)--that are being used and disposed of or excreted by peo- ple in each town (Zegura et al. 2009; Jean et al. 2012; Neng and Nogueira 2012). The mass emission factors per capita can be calculated for the chemicals without determining individual household use. However, without better knowledge of the environmental and human health risks of such low-dose exposures, the ad- vanced detection capabilities do not necessarily help the agency to interpret the results or to protect human health and the environment more effectively. One example is mercury. On one hand, from a toxicologic standpoint, mercury is one of the most studied elements (Schober et al. 2003; Jones et al. 2010). On the other hand, it is still difficult to make a conclusive assessment of the health ef- fects of mercury emitted into the environment (EPA 2011a). Finding cost- effective research opportunities for connecting data on environmental chemicals with environmental and health outcomes can contribute to an increase in knowl- edge and can inform policy. Fate and Transport of Chemicals in the Environment EPA has long been recognized as a leader in developing computer models of the fate and transport of chemical contaminants in the environment, a key component in constructing models of human exposure and health outcomes, as well as in source attribution for ecologic and human endpoints. It develops and supports models for both scientific purposes and application in environmental management. Although many of its models are well established and now backed by years of application experience, EPA and the broader environmental- modeling community face challenges to improve spatial and temporal resolu- tion, to account for stochastic environmental behaviors and for modeling uncer- tainties, to improve the characterization of transfers between environmental me- dia (air, surface water, groundwater, and soil), and to account for feedback between contaminant concentrations and environmental behavior (for example, the effects of such short-lived radiative-forcing agents as ozone and aerosols have on climate change). Furthermore, sources, properties, and behaviors of some contaminants remain poorly understood, even after years of study. EPA also faces significant challenges and opportunities for integrating models with data from new monitoring systems through data assimilation and inverse model-

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Emerging Science & Technologies to Address Environmental Challenges 59 ing techniques. Specific examples of ways in which new approaches to envi- ronmental fate and transport modeling are enhancing the understanding of health and ecologic impacts of pollutants are provided in the section on "Tools and Technologies to Address Challenges of Air Pollution and Climate Change". Assessing the Extent of Human Exposures Through Biomonitoring Historically, exposure research in EPA has focused on discrete expo- sures--in external or internal environments, concentrating on effects from sources or effects on biologic systems, and on human or ecologic exposures-- one pollutant or stressor at a time. Tools and methods have evolved for under- taking those specific challenges, but targeted approaches have led to sparse ex- posure data (Egeghy et al. 2012). The broader availability and ease of use of advanced technologies are re- sulting in a profusion of data and an overall democratization of the collection and availability of exposure data. The US Centers for Disease Control and Pre- vention (CDC) National Health and Nutrition Examination Survey (NHANES) alone has provided one of the most revealing snapshots of human exposures to environmental chemicals through the use of biomonitoring (CDC 2012). The collaboration between CDC and national and international organizations quickly expanded the breadth and depth of data available at the population and subpopu- lation level. That rapid progress was predicated on the availability of better ana- lytic methods and a national commitment to generate baseline data. Scientific and technologic advances in disparate fields--including compu- tational chemistry, climate change science, health tracking, computational toxi- cology, and sensor technology--have provided unprecedented opportunities to address the needs of exposure research. Many of the tools are more accessible and easier to use than earlier ones and are slowly being deployed by researchers and stakeholders, such as state agencies and public-interest groups. For example, advances in personal environmental monitoring technologies have been enabled because people around the world routinely carry cellular telephones (Tsow et al. 2009). Those devices may be equipped with motion, audio, visual, and location sensors that can be controlled through wireless networks. Efforts are underway to use them to create expanding networks of sensors to collect personal exposure information. As discussed in Chapter 2, biomonitoring for human exposure to chemi- cals in the environment has provided a new lens for understanding population exposures to toxicants. Although the analytic and technical methods discussed to measure human exposure to environmental toxicants will continue to improve, without better information to understand whether the dose is of sufficient magni- tude to cause an effect, simply identifying the presence of a toxic substance may raise more questions than it answers. Therefore, there are continuing advances needed to measure and understand the burden of chemicals and their metabolites in the human body.

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60 Science For Environmental Protection: The Road Ahead Recent advances in microchip capillary electrophoresis for separation and identification of nucleotides, proteins, and peptides and advances in spectromet- rics, such as nuclear magnetic resonance imaging and mass spectrometry, have changed the nature of health effects monitoring. These technologic advances-- especially in genomics, proteomics, metabolomics, bioinformatics, and related fields of the molecular sciences (referred to here collectively as panomics)-- have transformed the understanding of biologic processes at the molecular level and should eventually allow detailed characterization of molecular pathways that underlie the biologic responses of humans and other organisms to environ- mental perturbations. Advances in "omics" technologies provide EPA with a better understanding of mechanistic pathways and modes of action that can sup- port the risk assessment process. Also, the integration of those technologies with population-based epidemiologic research can contribute to the discovery of ma- jor environmental determinants, dose-response relationships, mechanistic path- ways, susceptible populations, and gene-environment interactions for health effects in human populations. Appendix C discusses some of the recent ad- vances in -omics technologies and approaches, their implications for EPA, where EPA is at the leading edge of applying the technologies to address envi- ronmental problems, and where EPA could benefit from more extensive en- gagement. New high-throughput -omic and biomonitoring technologies are providing a greater number of potential biomarkers to assess multiple exposures simulta- neously over the course of a lifetime. The biomarkers address exposures to a wide variety of stressors, including chemical, biologic, physical, and psychoso- cial stressors. The exposome is now being presented as a unifying concept that can capture the totality of environmental exposures (including lifestyle factors, such as diet, stress, drug use, and infection) from the prenatal period on by using a combination of biomarkers, genomic technologies, informatics, and environ- mental exposures (Figure 3-2) (Wild 2005; Rappaport and Smith 2010; Lioy and Rappaport 2011). The exposome, in concert with the human genome and the epigenome, holds promise for elucidating the etiology of chronic diseases and relevant contributions from the environment (Rappaport and Smith 2010). The concept of the exposome will be of particular value to EPA in assessing and comparing potential health and environmental consequences of individual chemical exposures against previously identified risks. It may also allow for more carefully designed and rational experiments to evaluate potential chemical interactions that contribute to the exposome of individuals or populations. Exposure information is a key component of prediction, prevention, and reduction of environmental and human health risks. Exposure science at EPA has been limited by the availability of methods, technologies, and resources, but recent advancements provide an unprecedented opportunity to develop higher- throughput, more cost-effective, and more relevant exposure assessments. Re- search in this field is funded by other federal agencies and international pro- grams, such as the National Institute of Environmental Health Sciences Expo-

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Emerging Science & Technologies to Address Environmental Challenges 61 FIGURE 3-2 Characterizing the exposome. The exposome represents the combined ex- posures from all sources that reach the internal chemical environment. Examples of toxi- cologically important exposome classes are shown. Biomarkers, such as those measured in blood and urine, can be used to characterize the exposome. Source: Adapted from Rappaport and Smith 2010. sure Biology Program; the National Science Foundation Environmental, Health, and Safety Risks of Nanomaterials Program; and the European Commission's exposome initiative. Those organizations provide valuable partnership opportu- nities for EPA to build capacity through strategic collaborations. Moreover, an integral need for EPA in the future will be to develop processes and procedures for effective public communication of the potential public health and environ- mental risks associated with the increasing number of chemicals, both old and new, that will undoubtedly be identified in food, water, air, and biologic sam- ples, including human tissues. Risk communication strategies should include the latest approaches in social, economic, and behavioral sciences, as discussed in Chapter 5. Applications of Biomarkers to Human Health Studies Epidemiologic research plays a central role in assessing, understanding, and controlling the human health effects of environmental exposures. In 2009,

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62 Science For Environmental Protection: The Road Ahead the National Research Council (NRC) report Science and Decisions: Advancing Risk Assessment recommended that EPA increase the role of epidemiology, sur- veillance, and biomonitoring to support cumulative risk assessment (NRC 2009). The most successful and current epidemiologic studies leverage multiple resources and use highly collaborative and multidisciplinary approaches (Semi- nara et al. 2007; Baker and Nieuwenhuijsen 2008). In the United States, a num- ber of high-quality prospective cohort studies funded mostly by the National Institutes of Health have followed millions of people and have collected bio- specimen repositories (blood, urine, nails, and DNA) and sociodemographic, genetic, medical, and lifestyle information (Seminara et al. 2007; Willett et al. 2007; NHLBI 2011). Major prospective cohort studies have also been under- taken in other countries (Riboli et al. 2002; Ahsan et al. 2006; Elliott and Peak- man 2008). With some exceptions, current prospective cohort studies generally lack information on environmental exposures. EPA can contribute to closing this gap by, for instance, adding high-quality environmental measures to studies that already have good followup and outcome measures. Examples of collaborations in which EPA plays a critical role are the Agricultural Health Study (NIH 2012), the Multiethnic Study of Atherosclerosis and Air Pollution (MESA Air) (Uni- versity of Washington 2011), and the National Children's Study (NRC/IOM 2008). In the National Children's Study, the linkage of monitoring data on toxi- cants in air, water, food, and ecosystems to individual participant data has al- ready been explored in depth in Queens, New York, one of the Vanguard Na- tional Children's Study sites (Lioy et al. 2009). Budgetary and implementation challenges for the National Children's Study will require innovative strategies for recruitment, examination, and followup without compromising the quality of the science (Kaiser 2012). Alternatively, EPA could add followup and outcome measures to studies that have good measures of exposure, although this is likely to be more time- consuming and expensive. At a minimum, EPA should ensure that environ- mental indicators, including country-wide air-monitoring and water-monitoring data, meet quality and accessibility criteria, for example, through a public data- access system. The indicators can then be merged with individual and commu- nity-level data in population-based studies by using geographic and temporal criteria. Biomonitoring and modeling approaches to predict exposure and dose and other advances in exposure science--including the exposome (Weis et al. 2005; Sheldon and Cohen Hubal 2009; Rappaport and Smith 2010; Lioy and Rappaport 2011), -omic technologies, and complex systems approaches (Diez Roux 2011)--could be incorporated into the prospective studies. By building expertise and leadership in exposure assessment and by working in collaboration with other national and international efforts, EPA can play a principal role in the incorporation of environmental exposures into prospective cohort studies and thus contribute to the discovery of major environmental determinants, dose response relationships, mechanistic pathways, and geneenvironment interac- tions for chronic diseases in human studies.

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Emerging Science & Technologies to Address Environmental Challenges 63 Environmental informatics plays an important role in the human- populationbased studies described above. Although environmental informatics received much of its momentum from central Europe in the early 1990s (Pill- mann et al. 2006), EPA has recognized its importance and has played a role in shaping its direction. The agency helped to establish the Environmental Data Standards Council, which was subsumed in 2005 by the Exchange Network Leadership Council (Environmental Information Exchange Network 2011), an environmental-data exchange partnership representing states, tribes, territories, and EPA. The council's mission includes supporting environmental information- sharing among its partners through automation, standardization, and real-time access. The scope of data exchange covers air, water, health, waste, and natural resources, and covers multiple programs. Cross-program data include data from the Department of Homeland Security, the Toxics Release Inventory, pollution- prevention programs, the Substance Registry Services System, and data obtained with geospatial technologies. The council is an example of useful and productive national efforts to generate environmental informatics data. On the basis of technologic advances and new environmental challenges discussed throughout this report, it will be necessary for EPA to begin to make data standards flexible and adaptable so that it can use data that are less structured and less groomed. Health informatics has a strong history in the United States. There are nu- merous national and state data registries on chronic and nonchronic diseases, such as the Surveillance, Epidemiology, and End Results cancer registry and the National Birth Defects registry. The Agency for Healthcare Research and Qual- ity of the Department of Health and Human Services maintains a national hospi- tal discharge database and, as previously mentioned, CDC's National Center for Health Statistics conducts the NHANES annually to study health behaviors, die- tary intake, environmental exposure, and disease status of the US population. EPA could also work with CDC's National Center for Health Statistics and the National Center for Environmental Health to facilitate the merging of environ- mental-monitoring data (on air, water, and ecosystems) with national databases that have biomarker and health data, such as NHANES. Such merging, follow- ing the NHANES model of public access, could constitute a major advance in the understanding of environmental exposures and their health effects and in informing policy regulation and the prevention and control of environmental exposures. Collaborating with other epidemiologic research efforts, EPA will have the opportunity to identify the optimal population-based prospective cohort study protocol to answer environmental-health questions, to ensure that high- quality data on environmental exposures are incorporated into large epidemi- ologic studies, and to contribute to the analysis and interpretation of exposure and health-effect associations. In addition, there are proprietary databases owned by healthcare providers and insurers, including Medicare and Medicaid. These databases lay out the foundation of health informatics in the United States and have been successfully used in environmental health research.

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64 Science For Environmental Protection: The Road Ahead Identifying and Predicting the Potential Toxic Effects of Chemicals In 2007, NRC convened a panel of experts to create a vision and strategy for toxicity testing that would capitalize on the -omics concepts described in Appendix C and on other new tools and technologies for the 21st century (NRC 2007a). Conceptually, that vision is not very different from the now classic four- step approach to risk assessment--hazard identification, exposure assessment, doseresponse assessment, and risk characterization--that was laid out in the NRC report Risk Assessment in the Federal Government: Managing the Process (commonly referred to as the Red Book) (NRC 1983) and that has been widely adopted by EPA as its chemical risk assessment paradigm (EPA 1984, 2000). However, the vision looks to new tools and technologies that would largely re- place in vivo animal testing through extensive use of high-throughput in vitro technologies that use human-derived cells and tissues coupled with computa- tional approaches that allow characterization of systems-based pathways that precede toxic responses. The computational approach to predictive toxicology has many advantages over the current time-consuming, expensive, and some- what unreliable paradigm of relying on high-dose in vivo animal testing to pre- dict human responses to low-dose exposures. Although there is generally widespread agreement that the new panomics tools (that is, genomics, proteomics, metabolomics, bioinformatics, and related fields of the molecular sciences), coupled with sophisticated bioinformatics ap- proaches to data management and analyses, will transform the understanding of how toxic chemicals produce their adverse effects, much remains to be learned about the applicability and relevance of in vitro toxicology results to actual hu- man exposures at low doses. With the fundamental mechanistic knowledge, it should be easier to distinguish responses that are relevant to humans from re- sponses that may be species-specific or to identify responses that occur at high doses but not low doses or vice versa. That knowledge would contribute to a reduction in the frequency of false-positive and false-negative results that some- times plague high-dose in vivo animal testing. A key issue in the use of such technologies is phenotypic anchoring,1 which is an important step in the validation of an assay. It is essential to validate treatment-related changes observed in an in vitro omics experiment as causally associated with adverse outcomes seen in the individual. A single exposure to one dose of one chemical can result in a plethora of molecular responses and hundreds of thousands of data points that reflect the organism's response to that exposure. Quantitative changes in gene expression (transcriptomics), protein content (proteomics), later enzymatic activity, and concentrations of metabolic 1 The concept of phenotypic anchoring arose from studies that examined the effects of chemical exposures on gene expression in tissues (transcriptomics). In that context, the term is defined as "the relation[ship between] specific alterations in gene expression pro- files [and] specific adverse effects of environmental stresses defined by conventional parameters of toxicity such as clinical chemistry and histopathology" (Paules 2003).

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96 Science For Environmental Protection: The Road Ahead Browner, C.M. 1993. Pollution Prevention Takes Center State. EPA Journal, July/September 1993 [online]. Available: http://www.epa.gov/aboutepa/history/top ics/ppa/01.html [accessed Apr. 9, 2012]. Byappanahalli, M.N., R.L. Whitman, D.A. Shively, and M.B. Nevers. 2010. Linking non- culturable (qPCR) and culturable enterococci densities with hydrometeorological conditions. Sci. Total Environ. 408(16):3096-3101. CAMRA. 2012. Welcome to the CAMRA Microbial Risk Assessment Wiki (CAM- RAwiki) [online]. Available: http://wiki.camra.msu.edu/index.php?title=Main_Page [accessed Apr. 10, 2012]. Cangelosi, G.A., K.M. Weigel, C. Lefthand-Begay, and J.S. Meschke. 2010. Molecular detection of viable bacterial pathogens in water by ratiometric pre-rRNA analysis. Appl. Environ. Microbiol. 76(3):960-962. CDC (Centers for Disease Control and Prevention). 2012. National Report on Human Exposure to Environmental Chemicals. Centers for Disease Control and Preven- tion [online]. Available: http://www.cdc.gov/exposurereport/ [accessed Mar. 30, 2012]. Christensen, V.G., P.P. Rasmussen, and A.C. Ziegler. 2002. Real-time water quality monitoring and regression analysis to estimate nutrient and bacteria concentrations in Kansas streams. Water Sci. Technol. 45(9):205-211. Cooper, S., F. Khatib, A. Treuille, J. Barbero, J. Lee, M. Beenen, A. Leaver-Fay, D. Baker, Z. Popovi, and Foldit Players. 2010a. Predicting protein structures with a multiplayer online game. Nature 466(7307):756-760. Cooper, S., A. Treuille, J. Barbero, A. Leaver-Fay, K. Tuite, F. Khatib, A.C. Snyder, M. Beenen, D. Salesin, D. Baker, Z. Popovi, and Foldit Players. 2010b. The chal- lenge of designing scientific discovery games. Pp. 40-47 in Proceedings of the Fifth International Conference on the Foundations of Digital Games (FDG 2010), June 19-21, Monterey, CA. New York: ACM [online]. Available: http://www.cs. washington.edu/homes/zoran/foldit-fdg10.pdf [accessed Mar. 30, 2012]. Cooper, S., F. Khatib, I. Makedon, H. Lu, J. Barbero, D. Baker, J. Fogarty, Z. Popovi, and Foldit Players. 2011. Analysis of social gameplay macros in the Foldit cook- book. Pp. 9-14 in Proceedings of the Sixth International Conference on the Foun- dations of Digital Games (FDG 2011), June 28-July 1, 2011, Bordeaux, France. New York: ACM [online]. Available: http://grail.cs.washington.edu/projects/pro tein-game/foldit-fdg11.pdf [accessed Mar. 30, 2012]. Coppus, R., and A.C. Imeson. 2002. Extreme events controlling erosion and sediment transport in a semi-arid sub-Andean valley. Earth Surf. Proc. Land. 27(13):1365- 1375. Crouse, D.L., P.A. Peters, A. van Donkelaar, M.S. Goldberg, P.J. Villeneuve, O. Bioron, S. Khan, D.O. Atari, J. Jerrett, C.A. Pope, M. Brauer, J.R. Brook, R.V. Martin, D. Stieb, and R.T. Burnett. 2012. Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particular matter: A Canadian national-level cohort study. Environ. Health Perspect. 120(5):708-714. CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.). 2012a Hydrologic Information System [online]. Available: http://his.cuahsi.org [accessed Apr. 23, 2012]. CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.). 2012b. CUAHSI Data Access, Publication and Analysis [online]. Available: http://www.cuahsi.org/his.html [accessed Apr. 23, 2012]. Diez Roux, A.V. 2011. Complex systems thinking and current impasses in health dispari- ties research. Am. J. Public Health 101(9):1627-1634.

OCR for page 54
Emerging Science & Technologies to Address Environmental Challenges 97 Djikeng, A., R. Kuzmickas, N.G. Anderson, and D.J. Spiro. 2009. Metagenomic analysis of RNA viruses in a fresh water lake. PLoS ONE 4(9):e7264. Dominici, F., R.D. Peng, C.D. Barr, and M.L. Bell. 2010. Protecting human health from air pollution: Shifting from a single-pollutant to a multipollutant approach. Epide- miology 21(2):187-194. Eder, B., D. Kang, R. Mathur, J. Pleim, S. Yu, T.A. Otte, and G. Pouliot. 2009. Perform- ance evaluation of the National Air Quality Forecast Capability for the summer of 2007. Atmos. Environ. 43(14): 2312-2320. Egeghy, P.P., R. Judson, S. Gangwal, S. Mosher, D. Smith, J. Vail, and E.A. Cohen Hubal. 2012. The exposure data landscape for manufactured chemicals. Sci. Total Environ. 414(1):159-166. Elliott, P., and T.C. Peakman. 2008. The UK Biobank sample handling and storage pro- tocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol. 37(2):234-244. Environmental Information Exchange Network. 2011. Exchange Network Leadership Council [online]. Available: http://www.exchangenetwork.net/about/network-manag ement/exchange-network-leadership-council/ [accessed Apr. 2, 2012]. EPA (US Environmental Protection Agency). 1984. Risk Assessment and Management: Framework for Decision Making. EPA 600/9-85-002. US Environmental Protec- tion Agency, Washington, DC. EPA (US Environmental Protection Agency). 1986. Ambient Water Quality Criteria for Bacteria-1986: Bacteriological Water Quality Criteria for Marine and Fresh Rec- reational Waters. EPA-440/5-84-002. Office of Water Regulations and Standards, US Environmental Protection Agency, Washington, DC. January 1986 [online]. Available: http://water.epa.gov/action/advisories/drinking/upload/2009_04_13_bea ches_1986crit.pdf [accessed Apr. 3, 2012]. EPA (US Environmental Protection Agency). 1997. Approval of EPA Method 1613 for Analysis of Dioxins and Furans in Wastewater. Fact Sheet, July 1997. Office of Water, US Environmental Protection Agency [online]. Available: http://water.epa. gov/scitech/methods/cwa/organics/dioxins/1613-fs.cfm [accessed Apr. 2, 2012]. EPA (US Environmental Protection Agency). 1999. Design for the Environment: Build- ing Partnerships for Environmental Improvement. EPA744-R-99-003. Office of Pollution Prevention and Toxics, US Environmental Protection Agency, Washing- ton, DC. EPA (US Environmental Protection Agency). 2000. Risk Characterization Handbook. EPA 100-B-00-002. Office of Science Policy, Office of Research and Develop- ment, US Environmental Protection Agency, Washington, DC [online]. Available: http://www.epa.gov/stpc/pdfs/rchandbk.pdf [accessed Aug. 1, 2012]. EPA (US Environmental Protection Agency). 2001. The US Environmental Protection Agency's Design for Environment Program. EPA744-F-00-020. Office of Pollu- tion Prevention and Toxics, US Environmental Protection Agency, Washington, DC. May 2001 [online]. Available: http://www.epa.gov/dfe/pubs/tools/DfEBroch ure.pdf [accessed Apr. 11, 2012]. EPA (US Environmental Protection Agency). 2002. Method 1600: Enterococci in Water by Membrane Filtration Using membrane-Enterococcus Indoxyl--D-Glucoside Agar (mEI). EPA-821-R-02-022. Office of Water, US Environmental Protection Agency, Washington DC [online]. Available: http://www.caslab.com/EPA-Metho ds/PDF/EPA-Method-1600.pdf [accessed Apr. 3, 2012]. EPA (US Environmental Protection Agency). 2005. Microbial Source Tracking Guide Document. EPA-600/R-05/064. Office of Research and Development, US Envi-

OCR for page 54
98 Science For Environmental Protection: The Road Ahead ronmental Protection Agency, Washington DC. June 2005 [online]. Available: http://www.ces.purdue.edu/waterquality/resources/MSTGuide.pdf [accessed Apr. 3, 2012]. EPA (US Environmental Protection Agency). 2006a. Implementing the BEACH Act of 2000. Report to Congress. US Environmental Protection Agency [online]. Avail- able: http://water.epa.gov/type/oceb/beaches/report_index.cfm [accessed Aug. 13, 2012]. EPA (US Environmental Protection Agency). 2006b. Design for the Environment Part- nership Highlights. US Environmental Protection Agency, March 2006 [online]. Available: http://www.epa.gov/dfe/pubs/about/dfe-highlights06b.pdf [accessed Apr. 11, 2012]. EPA (US Environmental Protection Agency). 2008a. US EPA Office of Research and Development Computational Toxicology Research Program Implementation Plan for Fiscal Years 2009 to 2012: Providing High-Throughput Decision Support Tools for Screening and Assessing Chemical Exposure, Hazard, and Risk. US En- vironmental Protection Agency [online]. Available: http://epa.gov/ncct/download_ files/basic_information/CTRP2_Implementation_Plan_FY09_12.pdf [accessed Apr. 2, 2012]. EPA (US Environmental Protection Agency). 2008b. Evaluation of EPA Efforts to Inte- grate Pollution Prevention Policy throughout EPA and at Other Federal Agencies. Office of Pollution, Prevention and Toxics, US Environmental Protection Agency. October 2008 [online]. Available: http://www.epa.gov/p2/pubs/docs/p2integration. pdf [accessed Apr. 20, 2012]. EPA (US Environmental Protection Agency). 2009. EPA's DfE Standard for Safer Cleaning Products (SSCP). US Environmental Protection Agency. June 2009 [online]. Available: http://www.epa.gov/opptintr/dfe/pubs/projects/formulat/dfe_crit eria_for_cleaning_products_10_09.pdf [accessed Apr. 10, 2012]. EPA (US Environmental Protection Agency). 2010. FY 2011-2015 EPA Strategic Plan: Achieving Our Vision. US Environmental Protection Agency, September 30, 2010 [online]. Available: http://nepis.epa.gov/Adobe/PDF/P1008YOS.PDF [accessed Apr. 2, 2012]. EPA (US Environmental Protection Agency). 2011a. National-Scale Mercury Risk As- sessment Supporting the Appropriate and Necessary Finding for Coal- and Oil- Fired Electric Generating Units, Draft. Technical Support Document. EPA-452/D- 11-002. Office of Air Quality Planning and Standards, US Environmental Protec- tion Agency, Research Triangle Park, NC. March 2011 [online]. Available: http: //yosemite.epa.gov/sab/sabproduct.nsf/02ad90b136fc21ef85256eba00436459/9F04 8172004D93BB8525783900503486/$File/hg_risk_tsd_3-17-11.pdf [accessed Apr. 4, 2012]. EPA (US Environmental Protection Agency). 2011b. Inventory of US Greenhouse Gas Emissions and Sinks: 19902009. EPA 430-R-11-005. US Environmental Protec- tion Agency, Washington, DC. April 15, 2011 [online]. Available: http://www.epa. gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2012-Main-Text. pdf [accessed Aug. 2, 2012]. EPA (US Environmental Protection Agency). 2011c. Pollution Prevention (P2): Basic Information. US Environmental Protection Agency [online]. Available: http://www. epa.gov/p2/pubs/basic.htm [accessed Apr. 9, 2012]. EPA (US Environmental Protection Agency). 2011d. Green Chemistry Program at EPA. US Environmental Protection Agency [online]. Available: http://www.epa.gov/ greenchemistry/pubs/epa_gc.html [accessed Apr. 9, 2012].

OCR for page 54
Emerging Science & Technologies to Address Environmental Challenges 99 EPA (US Environmental Protection Agency). 2011e. EPA Honors Winners of 2011 Presidential Greem Chemistry Challenge Awards. US Environmental Protection Agency, News: June 20, 2011 [online]. Available: http://yosemite.epa.gov/opa/ admpress.nsf/0/93C78AFC58096165852578B5004B1E99 [accessed Apr. 9, 2012]. EPA (US Environmental Protection Agency). 2012a. Computational Toxicology Research, Research Publications. US Environmental Protection Agency [online]. Available: http://www.epa.gov/ncct/publications.html [accessed July 10, 2012]. EPA (US Environmental Protection Agency). 2012b. Quantifying Methane Abatement Efficiency at Three Municipal Solid Waste Landfills. Final Report. EPA/600/R- 12/003. Prepared by ARCADIS US, Inc., Durham, NC, for Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC. January 2012. EPA (US Environmental Protection Agency). 2012c. Solid Waste and Emergency Re- sponse Discussion Forum. US Environmental Protection Agency [online]. Avail- able: http://blog.epa.gov/oswerforum/ [accessed Apr. 4, 2012]. EPA (US Environmental Protection Agency). 2012d. Watershed Central. Office of Wa- ter, US Environmental Protection Agency [online]. Available: http://water.epa. gov/type/watersheds/datait/watershedcentral/index.cfm [accessed Apr. 4, 2012]. EPA (US Environmental Protection Agency). 2012e. Program History. Design for the Environment. US Environmental Protection Agency [online]. Available: http:// www.epa.gov/dfe/pubs/about/history.htm [accessed Apr. 9, 2012]. EPA (US Environmental protection Agency). 2012f. Safe Product Labeling. Design for Environment, US Environmental Protection Agency [online]. Available: http://www. epa.gov/opptintr/dfe/pubs/projects/formulat/saferproductlabeling.htm [accessed Apr. 10, 2012]. EPA (US Environmental Protection Agency). 2012g. Sustainable Future. US Environ- mental Protection Agency [online]. Available: http://www.epa.gov/oppt/sf/ [ac- cessed Apr. 9, 2012]. Ferebee, M., Jr. 2011. New Tools, New Rules, New Year. Inside. Business News, December 2, 2011 [online]. Available: http://insidebiz.com/news/new-tools-new- rules-new-year-whether-its-through-new-technology-processes-behavior-modeling -les [accessed Apr. 5, 2012]. Fishman, J., K.W. Bowman, J.P. Burrows, A. Richter, K.V. Chance, D.P. Edwards, R.V. Martin, G.A. Morris, R. B. Pierce, J.R. Ziemke, J.A. Al-Saadi, J.K. Creilson, T.K. Schaack, and A.M. Thompson. 2008. Remote sensing of tropospheric pollution from space. Bull. Amer. Meteor. Soc. 89(6):805-821. Foldit. 2012. The Science behind Foldit. Solve the Puzzles for Science [online]. Avail- able: http://fold.it/portal/info/science [accessed Apr. 4, 2012]. Fong, T.T., and E.K. Lipp. 2005. Enteric viruses of humans and animals in aquatic envi- ronments: Health risks, detection, and potential water quality assessment tools. Microbiol. Mol. Biol. Rev. 69(2):357-371. Gilski, M., M. Kazmierczyk, S. Krzywda, H. Zbransk, S. Cooper, Z. Popovi, F. Khatib, F. DiMaio, J. Thompson, D. Baker, I. Pichov, and M. Jaskolski. 2011. High-resolution structure of a retroviral protease folded as a monomer. Acta Cryst. D67:907-914. Goetz, S.J., and R.O. Dubayah. 2011. Advances in remote sensing technology and impli- cations for measuring and monitoring forest carbon stocks and change. Carbon Manag. 2(3):231-244. Greenbaum, D., and R. Shaikh. 2010. First steps toward multipollutant science for air quality decisions. Epidemiology 21(2):195-197.

OCR for page 54
100 Science For Environmental Protection: The Road Ahead Gurney, K.R., R.M. Law, A.S Denning, P.J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler, Y.H. Chen, P. Ciais, S. Fan, I.Y. Fung, M. Gloor, M. Heimann, K. Hi- guchi, J. John, T. Maki, S. Maksyutov, K. Masarie, P. Peylin, M. Prather, B.C. Pak, J. Randerson, J. Sarmiento, S. Taguchi, T. Takahashi, and C.W. Yuen. 2002. Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature 415(6872):626-630. Haas, C.N. 1983. Estimation of risk due to low doses of microorganisms: A comparison of alternative methodologies. Am. J. Epidemiol. 118(4):573-582. Haas, C.H., J.B. Rose, and C.P. Gerba, eds. 1999. Quantitative Microbial Risk Assess- ment. New York: John Wiley and Sons. Hall, F.G., K. Bergen, J.B. Blair, R. Dubayah, R. Houghton, G. Hurtt, J. Kellndorfer, M. Lefsky, J. Ranson, S. Saatchi, H.H. Shugart, and D. Wickland. 2011. Character- izing 3D vegetation structure from space: Mission requirements. Remote Sens. Environ. 115(11):2753-2775. Hall, J., A.D. Zaffiro, R.B. Marx, , P. C. Kefauver, E.R. Krishnan, R. Haught, and J.G. Herrmann. 2007. On-line water quality parameters as indicators of distribution sys- tem contamination. J. Am. Water Works Assoc. 99(1):66-77. Hayes, D.J., D.P. Turner, G. Stinson, A.D. McGuire, Y. Wei, T.O. West, L.S. Heath, B. de Jong, B.G. McConkey, R.A. Birdsey, W.A. Kurz, A.R. Jacobson, D.N. Huntz- inger, Y. Pan, W. Mac Post, and R.B. Cook. 2012. Reconciling estimates of con- temporary North American carbon balance among terrestrial biosphere models, atmospheric inversions, and a new approach for estimating net ecosystem ex- change from inventory-based data. Global Change Biol. 18(4):1282-1299. He, S. 2003. Informatics: A brief survey. Electron. Libr. 21(2):117-122. Heald, C.L., D.J. Jacob, R.J. Park, B. Alexander, T.D. Fairlie, R.M. Yantosca, and D.A. Chu. 2006. Transpacific transport of Asian anthropogenic aerosols and its impact on surface air quality in the United States. J. Geophys. Res. 111:D14310, doi:10.1029/2005JD006847. Hill, V.R., A.L. Polaczyk, D. Hahn, J. Narayanan, T.L. Cromeans, J.M. Roberts, and J.E. Amburgey. 2005. Development of a rapid method for simultaneous recovery of diverse microbes in drinking water by ultrafiltration with sodium polyphosphate and surfactants. Appl. Environ. Microbiol. 71(11):6878-6884. Hystad, P., E. Setton, A. Cervantes, K. Poplawski, S. Deschenes, M. Brauer, A. van Donkelaar, L. Lamsal, R. Martin, M. Jerrett, and P. Demers. 2011. Creating national air pollution models for population exposure assessment in Canada. Environ. Health Perspect. 119(8):1123-1129. Jean, J., Y. Perrodin, C. Pivot, D. Trep, M. Perraud, J. Droguet, F. Tissot-Guerraz, F. Locher. 2012. Identification and prioritization of bioaccumulable pharmaceutical substances discharged in hospital effluents. J. Environ. Manage. 103:113-121. Jeong, Y., B.F. Sanders, and S.B. Grant. 2006. The information content of high- frequency environmental monitoring data signals pollution events in the coastal ocean. Environ. Sci. Technol. 40(20):6215-6220. Johns, D.O., L.W. Stanek, K. Walker, S. Benromdhane, B. Hubbell, M. Ross, R.B. Dev- lin, D.L. Costa, and D. S. Greenbaum. 2012. Practical advancement of multipollut- ant scientific and risk assessment approaches for ambient air pollution. Environ. Health Perspect. 102(9):1238-1242. Jones, L., J.D. Parker, and P. Mendola. 2010. Blood Lead and Mercury Levels in Pregnant Women in the United States, 2003-2008. NCHS Data Brief No. 52. US Department of Health & Human Services, Centers for Disease Control and Prevention, National

OCR for page 54
Emerging Science & Technologies to Address Environmental Challenges 101 Center for Health Statistics, Hyattsville, MD. December 2010 [online]. Available: http://www.cdc.gov/nchs/data/databriefs/db52.pdf [accessed Apr. 2, 2012]. Judson, R.S., K.A. Houck, R.J. Kavlock, T.B. Knudsen, M.T. Martin, H.M. Mortensen, D.M. Reif, D.M. Rotroff, I. Shah, A.M. Richard, and D.J. Dix. 2010. In vitro screening of environmental chemicals for targeted testing prioritization: The Tox- Cast project. Environ Health Perspect. 118(4):485-492. Judson, R.S., R.J. Kavlock, R.W. Setzer, E.A. Cohen-Hubal, M.T. Martin, T.B. Knudsen, K.A. Houck, R.S. Thomas, B.A. Wetmore, and D.J. Dix. 2011. Estimating toxic- ity-related biological pathway altering doses for high-throughput chemical risk as- sessment. Chem. Res. Toxicol. 24(4):451-462. Kaiser, J. 2012. Overhaul of the US Child Health Study Concerns Investigators. Science. 335:1032. Khatib, F., S. Cooper, M.D. Tyka, K. Xu, I. Makedon, Z. Popovi, D. Baker, and Foldit Players. 2011a. Algorithm discovery by protein folding game players. Proc. Natl. Acad. Sci. 108(47):18949-18953. Khatib, F., F. DiMaio, Foldit Contenders Group, Foldit Void Crushers Group, S. Cooper, M. Kazmierczyk, M. Gilski, S. Krzywda, H. Zbransk, I. Pichov, J. Thompson, Z. Popovi, M. Jaskolski, and D. Baker. 2011b. Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nat. Struct. Mol. Biol. 18(10):1175-1177. Koetz, B., G.Q. Sun, F. Morsdorf, K.J. Ranson, M. Kneubuhler, K. Itten, and B. Allgower. 2007. Fusion of imaging spectrometer and LiDAR data over combined radiative transfer models for forest canopy characterization. Remote Sens. Environ. 106(4):449-459. Kristiansson, E., J. Fick, A. Janzon, R. Grabic, C. Rutgersson, B. Weijdegrd, H. Sderstrm, and D.G.J. Larsson. 2011. Pyrosequencing of antibiotic-contaminated river sediments reveals high levels of resistance and gene transfer elements. PLoS ONE 6(2):e17038. Lioy, P.J., and S.M. Rappaport. 2011. Exposure science and the exposome: An opportu- nity for coherence in the environmental health sciences. Environ. Health Perspect. 119(11):A466-A467. Lioy, P.J., S.S. Isukapalli, L. Trasande, L. Thorpe, M. Dellarco, C. Weisel, P.G. Geor- gopoulos, C. Yung, S. Alimokhtari, M. Brown, and P.J. Landrigan. 2009. Using national and local extant data to characterize environmental exposures in the na- tional children's study: Queens County, New York. Environ. Health Perspect. 117(10):1494-1504. Long, S.C., and J.D. Plummer. 2004. Assessing land use impacts on water quality using microbial source tracking. J. Am. Water Resour. Assoc. 40(6):1433-1448. Longley, D., and M. Shain. 1985. Dictionary of Information Technology, 2 Ed. London: Macmillan Press. Loperfido, J.V., C.L. Just, and J.L. Schnoor. 2009. High-frequency diel dissolved oxygen stream data modeled for variable temperature and scale. J. Environ. Eng.-ASCE 135(12):1250-1256. Loperfido, J.V., P. Beyer, C.L. Just, and J.L. Schnoor. 2010a. Uses and biases of volun- teer water quality data. Environ. Sci. Technol. 44(19):7193-7199. Loperfido, J.V., C.L. Just, A.N. Papanicolaou, and J.J. Schnoor. 2010b. In situ sensing to understand diel turbidity cycles, suspended solids, and nutrient transport in Clear Creek, Iowa. Water Resour. Res. 46:W06525, doi: 10.1029/2009WR008293.

OCR for page 54
102 Science For Environmental Protection: The Road Ahead Lovett, G.M., D.A. Burns, C.T. Driscoll, J.C. Jenkins, M.J. Mitchell, L. Rustad, J.B. Shanley, G.E. Likens, and R. Haeuber. 2007. Who needs environmental monitor- ing? Front. Ecol. Environ. 5(5):253-260. Martin, R.V. 2008. Satellite remote sensing of surface air quality. Atmos. Environ. 42(34):7823-7843. Matthews, M.W. 2011. A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. Int. J. Remote Sens. 32(21):6855-6899. McHale, C.M., L. Zhang, A.E. Hubbard, and M.T. Smith. 2010. Toxicogenomic profiling of chemically exposed humans in risk assessment. Mutat. Res. 705(3):172-183. Medema, G.J., W. Hoogenboezem, A.J. van der Veer, H.A. Ketelaars, W.A. Hijnen, and P.J. Nobel. 2003. Quantitative risk assessment of Cryptosporidium in surface water treatment. Water Sci. Technol. 47(3):241-247. Mertes, L.A.K. 2002. Remote sensing of riverine landscapes. Freshwater Biol. 47(4):799- 816. Messer, J.W., and A.P. Dufour. 1998. A rapid, specific membrane filtration procedure for enumeration of enterococci in recreational water. Appl. Environ. Microbiol. 64(2):678-680. Messner, M., S. Shaw, S. Regli, K. Rotert, V. Blank, and J. Soller. 2006. An approach for developing a national estimate of waterborne disease due to drinking water and a national estimate model application. J. Wat. Health 4(suppl. 2):201-240. Morris, G.A., S. Hersey, A.M. Thompson, S. Pawson, J.E. Nielsen, P.R. Colarco, W.W. McMillan, A. Stohl, S. Turquety, J. Warner, B.J. Johnson, T.L. Kucsera, D.E. Larko, J. Oltmans, and J.C. Witte. 2006. Alaskan and Canadian forest fires exacer- bate ozone pollution over Houston, Texas, on 19 and 20 July 2004. J. Geophys. Res. 111:D24S03, doi:10.1029/2006JD007090. Napelenok, S.L., R.W. Pinder, A.B. Gilliland, and R.V. Martin. 2008. A method for evaluating spatially-resolved NOx emissions using Kalman filter inversion, direct sensivitities, and space-based NO2 observations. Atmos. Chem. Phys. 8(18):5603- 5614. Neng, N.R., and J.M. Nogueira. 2012. Development of a bar adsorptive micro-extraction- large-volume injection-gas chromatography-mass spectrometric method for phar- maceuticals and personal care products in environmental water matrices. Anal. Bioanal. Chem. 402(3):1355-1364. NHLBI (National Heart, Lung and Blood Institute). 2011. NHLBI Population Studies Database [online]. Available: http://apps.nhlbi.nih.gov/popstudies/ [accessed Oct. 11, 2011]. NIH (National Institutes of Health). 2012. Agricultural Health Study [online]. Available: http://aghealth.nci.nih.gov/ [accessed May 3, 2012]. Noble, R.T., A.D. Blackwood, J.F. Griffith, C.D. McGee, and S.B. Weisberg. 2010. Com- parison of rapid quantitative PCR-based and conventional culture-based methods for enumeration of Enterococcus spp. and Escherichia coli in recreational waters. Appl. Environ. Microbiol. 76(22):7437-7443. Nowak, P., S. Bowen, and P. Cabot. 2006. Disproportionality as a framework for linking social and biophysical systems. Soc. Natur. Resour. 19(2):153-173. NRC (National Research Council). 1983. Risk Assessment in Federal Government: Man- aging the Process. Washington, DC: National Academy Science. NRC (National Research Council). 2007a. Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: National Academies Press. NRC (National Research Council). 2007b. Models in Environmental Regulatory Decision Making. Washington, DC: National Academies Press.

OCR for page 54
Emerging Science & Technologies to Address Environmental Challenges 103 NRC (National Research Council). 2009. Science and Decisions: Advancing Risk As- sessment. Washington, DC: National Academies Press. NRC (National Research Council). 2011. Emerging Science for Environmental Health, Decisions: Workshop: Applying 21st Century Toxicology to Green Chemical and Material Design [online]. Available: http://nas-sites.org/emergingscience/workshops/ green-chemistry/workshop-presentations-green-chemistry/ [accessed Apr. 11, 2012]. NRC (National Research Council). 2012. A Research Strategy for Environmental, Health and Safety Aspects of Engineered Nanomaterials. Washington, DC: National Academy Press. NRC/IOM (National Research Council and Institute of Medicine). 2008. The National Children's Study Research Plan: A Review. Washington, DC: The National Acad- emies Press. NRDC (National Resource Defense Council). 2011. Testing the Waters: A Guide to Water Quality at Vacation Beaches, 21st Annual Report. National Resource Defense Coun- cil [online]. Available: http://www.nrdc.org/water/oceans/ttw/titinx.asp [accessed Apr. 6, 2012]. Ostby, F.P. 1999. Improved accuracy in severe storm forecasting by the Severe Local Storms Unit during the last 25 years: Then versus now. Weather Forecast. 14(4): 526-543. Paules, R. 2003. Phenotypic anchoring: Linking cause and effect. Environ. Health Per- spect. 111(6): A338-A339. Pepper, I.L., J.P. Brooks, R.G. Sinclair, P.L. Gurian, and C.P. Gerba. 2010. Pathogens and indicators in United States Class B Biosolids: National and historic distribu- tions. J. Environ. Qual. 39(6): 2185-2190. Pillmann, W., W. Geiger, and K. Voigt. 2006. Survey of environmental informatics in Europe. Environ. Modell. Softw. 21(11):1519-1527. Preuss, P. 2011. ORD Innovation. Presentation to the Science Advisory Board and Board of Scientific Counselors. June 29-30, 2011 [online]. Available: http://yosemite.epa. gov/sab/sabproduct.nsf/962C02E5D5A1587B852578BC005BD263/$File/CORREC TED+PWP+PPT+SAB-BOSC+for+posting.pdf [accessed April 10, 2012]. PSP (Puget Sound Partnership). 2011. The Action Agenda in South Central Puget Sound, Appendix C. Puget Sound National Estuary Program Management Confer- ence Overview. Draft, December 9, 2011 [online]. Available: http://www.psp. wa.gov/downloads/AA2011/120911/AA-draft-120911-appendixC.pdf [accessed Apr. 3, 2011]. Rappaport, S.M., and M.T. Smith. 2010. Environment and disease risks. Science 330 (6003):460-461. Riboli, E., K.J. Hunt, N. Slimani, N.T. Ferrari, T. Norat, M. Fahey, U.R. Charrondire, B. Hmon, C. Casagrande, J. Vignat, K. Overvad, A. Tjnneland, F. Clavel- Chapelon, A. Thibaut, J. Wahrendorf, H. Boeing, D. Trichopoulos, A. Trichopou- lou, P. Vineis, D. Palli, H.B. Bueno-De-Mesquita, P.H. Peeters, E. Lund, D. Enge- set, C.A. Gonzlez, A. Barricarte, G. Berglund, G. Hallmans, N.E. Day, T.J. Key, R. Kaaks, and R. Saracci. 2002. European Prospective Investigation into Cancer and Nutrition (EPIC): Study populations and data collection. Public Health Nutr. 5(6B):1113-1124. Ridolfi, L., P. D'Odorico, A. Porporato, and I. Rodriguez-Iturbe. 2003. Stochastic soil moisture dynamics along a hillslope. J. Hydrol. 272(1-4):264-275. Rosario, K., C. Nilsson, Y.W. Lim, Y.J. Ruan, and M. Breitbart. 2009. Metagenomic analysis of viruses in reclaimed water. Environ. Microbiol. 11(11):2806-2820.

OCR for page 54
104 Science For Environmental Protection: The Road Ahead Rose, J.B. 1988. Occurrence and significance of Cryptosporidium in water. J. Am. Water Works Assoc. 80(2):53-58. Rothamsted. 2012. The Rothamsted Archive. Rothamsted Research, Harpenden, U.K. [online]. Available: http://www.rothamsted.ac.uk/Content.php?Section=Resources &Page=RothamstedArchive [accessed Apr. 3, 2012]. Rotroff, D.M., B.A. Wetmore, D.J. Dix, S.S. Ferguson, H.J. Clewell, K.A. Houck, E.L. Lecluyse, M.E. Andersen, R.S. Judson, C.M. Smith, M.A. Sochaski, R.J. Kavlock, F. Boellmann, M.T. Martin, D.M. Reif, J.F. Wambaugh, and R.S. Thomas. 2010. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicol Sci. 117(2):348-358. Sarewitz, D., D. Kriebel, R. Clapp, C. Crumbley, P. Hoppin, M. Jacobs, and J. Tickner. 2010. The Sustainable Solutions Agenda. The Consortium for Science, Policy and Outcomes, Arizona State University, Lowell Center for Sustainable Production, University of Massachusetts Lowell [online]. Available: http://www.sustainablepro duction.org/downlods/SSABooklet.pdf [accessed Apr. 10, 2012]. Sauch, J.F. 1985. Use of immunofluorescence and phase-contract microscopy for detec- tion and identification of Gardia cysts in water samples. Appl. Environ. Microbiol. 50(6):1434-1438. Scavia, D., N.N. Rabalais, R.E. Turner, D. Justic, and W.J. Wiseman. 2003. Predicting the response of Gulf of Mexico hypoxia to variations in Mississippi River nitrogen load. Limnol. Oceanogr. 48(3):951-956. Schaepman, M.E., S.L. Ustin, A.J. Plaza, T.H. Painter, J. Verrelst, and S.L. Liang. 2009. Earth system science related imaging spectroscopy: An assessment. Remote Sens. Environ. 113(1):S123-S137. Schober, S.E.,T.H. Sinks, R.L. Jones, P.M. Bolger, M. McDowell, J. Osterloh, E.S. Garrett, R.A. Canady, C.F. Dillon, Y. Sun, C.B. Joseph, and K.R. Mahaffey. 2003. Blood mercury levels in US children and women of childbearing age, 1999-2000. JAMA 289(13):1667-1674. Schwalm, C.R., C.A. Williams, K. Schaefer, R. Anderson, M.A. Arain, I. Baker, A. Barr, T.A. Black, G. Chen, J.M. Chen, P. Ciais, K.J. Davis, A. Desai, M. Dietze, D. Dragoni, M.L. Fischer, L.B. Flanagan, R. Grant, L. Gu, D. Hollinger, R.C. Izaur- ralde, C. Kucharik, P. Lafleur, B.E. Law, L. Li, Z. Li, S. Liu, E. Lokupitiya, Y. Luo, S. Ma, H. Margolis, R. Matamala, H. McCoughey, R.K. Monson, W.C. Oechel, C. Peng, B. Poulter, D.T. Price, D.M. Riciutto, W. Riley, A.K. Sahoo, M. Sprintsin, J. Sun, H. Tian, C. Tonitto, H. Verbeeck, and S.B. Verma. 2010. A model-data intercomparison of CO2 exchange across North America: Results from the North American carbon program site synthesis. J. Geophys. Res. 115:G00H05, doi:10.1029/2009JG001229. Seminara, D., M.J. Khoury, T.R. O'Brien, T. Manolio, M.L. Gwinn, J. Little, J.P. Hig- gins, J.L. Bernstein, P. Boffetta, M. Bondy, M.S. Bray, P.E. Brenchley, P.A. Buffler, J.P. Casas, A.P. Chokkalingam, J. Danesh, G. Davey Smith, S. Dolan, R. Duncan, N.A. Gruis, M. Hashibe, D. Hunter, M.R. Jarvelin, B. Malmer, D.M. Maraganore, J.A. Newton-Bishop, E. Riboli, G. Salanti, E. Taioli, N. Timpson, A.G. Uitterlinden, P. Vineis, N. Wareham, D.M. Winn, R. Zimmern, and J.P. Io- annidis. 2007. The emergence of networks in human genome epidemiology: Chal- lenges and opportunities. Epidemiology 18(1):1-8. Shanks, O. 2011. Global Inter-lab Fecal Source Tracking Methods Comparison Study. Presentation at 2011 National Beach Conference, March 16, 2011, Miami, FL. Sheldon, L.S., and E.A. Cohen Hubal. 2009. Exposure as part of a systems approach for assessing risk. Environ. Health Perspect. 117(8):1181-1184.

OCR for page 54
Emerging Science & Technologies to Address Environmental Challenges 105 Shukla, S., C.Y. Yu, J.D. Hardin, and F.H. Jaber. 2006. Wireless data acquisition and control systems for agricultural water management projects. HortTechnology 16(4):595-604. Sivapalan, M., K. Takeuchi, S.W. Franks, V.K. Gupta, H. Karambiri, V. Lakshmi, X. Liang, J.J. McDonnell, E.M. Mendiondo, P.E. O'Connell, T. Oki, J.W. Pomeroy, D. Schertzer, S. Uhlenbrook, and E. Zehe. 2003. IAHS decade on Predictions in Ungauged Basins (PUB), 20032012: Shaping an exciting future for the hydro- logical sciences. Hydrol. Sci. 48(6):857-880. Slifko, T.R., D. Friedman, J.B. Rose, and W. Jakubowski. 1997. An in vitro method for detecting infectious Cryptosporidium oocysts with cell culture. Appl. Environ. Mi- crobiol. 63(9):3669-3675. Slifko, T.R., D.E. Huffman, and J.B. Rose. 1999. A most probable assay for enumeration of infectious Cryptosporidium parvum oocysts. Appl. Environ. Microbiol. 65(9): 3936-3941. Srinivasan, S., A. Aslan, I. Xagoraraki, E. Alocilja, and J.B. Rose. 2011. Escherichia coli, enterococci, and Bacteroides thetaiotaomicron qPCR signals through wastewater and septage treatment. Water. Res. 45(8):2561-2572. Tapscot, D., and A.D. Willams. 2010. MacroWikinomics - Rebooting Business and the World. New York: Penguin. Tian, D., D.S. Cohan, S. Napelenok, M. Bergin, Y. Hu, M. Chang, and A.G. Russell. 2010. Uncertainty analysis of ozone formation and response to emission controls using higher-order sensitivities. J. Air Waste Manage. Assoc. 60(7):797-804. Tickner, J.A. 2011. Science of problems, science of solutions or both? A case example of bisphenol. Am. J. Epidemiol. Community Health 65(8):649-650. Tornero-Velez, R., P.P. Egeghy, and E.A. Cohen Hubal. 2012. Biogeographical analysis of chemical co-occurrence data to identify priorities for mixtures research. Risk Anal. 32(2):224-236. Tsow, F., E. Forzani, A. Rai, R. Wang, R. Tsui, S. Mastrioianni, C. Knobbe, A.J. Gan- dolfi, and N.J. Tao. 2009. A wearable and wireless sensor system for real-time monitoring of toxic environmental volatile organic compounds. 9(12):1734-1740. University of Washington. 2011. MESA Air Pollution [online]. Available: http://depts. washington.edu/mesaair/ [accessed Apr. 3, 2012]. van Donkelaar, A., R.V. Martin, M. Brauer, R. Kahn, R. Levy, C. Verduzco, and P.J. Villeneuve. 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application. Environ. Health Perspect. 118(6):847-855. Vandenberghe, V., P.L. Goethals, A. Van Griensven, J. Meirlaen, N. De Pauw, P. Vanrolleghem, and W. Bauwens. 2005. Application of automated measurement sta- tions for continuous water quality monitoring of the Dender river in Flanders, Bel- gium. Environ. Monit. Assess. 108(1-3):85-98. Varma, M., R. Field, M. Stinson, B. Rukovets, L. Wymer, and R. Hauglanda. 2009. Quantitative real-time PCR analysis of total and propidium monoazide-resistant fecal indicator bacteria in wastewater. Water Res. 43(19):4790-4801. Wade, T.J., R.L. Calderon, E. Sams, M. Beach, K.P. Brenner, A.H. Williams, and A.P. Dufour. 2006. Rapidly measured indicators of recreational water quality are pre- dictive of swimming-associated gastrointestional illness. Environ. Health Perspect. 114(1):24-28. Wang, K., S.E. Franklin, X. Guo, and M. Cattet. 2010. Remote sensing of ecology, biodiversity and conservation: A review from the perspective of remote sensing specialists. Sensors 10(11):9647-9667.

OCR for page 54
106 Science For Environmental Protection: The Road Ahead WATERS Network. 2009. Living in the Water Environment: The WATERS Network Science Plan, May 15, 2009 [online]. Available: http://www.watersnet.org/docs/ WATERS_Network_SciencePlan_2009May15.pdf [accessed Aug. 8, 2012]. Weis, B.K., D. Balshaw, J.R. Barr, D. Brown, M. Ellisman, P. Lioy, G. Omenn, J.D. Potter, M.T. Smith, L. Sohn, W.A. Suk, S. Sumner, J. Swenberg, D.R. Walt, S. Watkins, C. Thompson, and S.H. Wilson. 2005. Personalized exposure assessment: Promising approaches for human environmental health research. Environ Health Perspect 113(7):840-848. Wetmore, B.A., J.F. Wambaugh, S.S. Ferguson, M.A. Sochaski, D.M. Rotroff, K. Free- man, H.J. Clewell, III, D.J. Dix, M.E. Andersen, K.A. Houck, B. Allen, R.S. Judson, R. Singh, R.J. Kavlock, A.M. Richard, and R.S. Thomas. 2012. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicol. Sci. 125(1):157-174. WHO (World Health Organization). 2004. Guidelines for Drinking-Water Quality: Vol- ume 1 Recommendations, 3rd Ed. Geneva: World Health Organization [online]. Available: http://www.who.int/water_sanitation_health/dwq/GDWQ2004web.pdf [accessed Aug. 1, 2012]. Wild, C.P. 2005. Complementing the genome with an "exposome": The outstanding chal- lenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomarkers Prev. 14(8):1847-1850 Willett, W.C., W.J. Blot, G.A. Colditz, A.R. Folsom, B.E. Henderson, and M.J. Stampfer. 2007. Merging and emerging cohorts: Not worth the wait. Nature 445(7125):257- 258. Yates, M.V., J. Malley, P. Rochelle, and R. Hoffman. 2006. Effect of adenovirus resis- tance on UV disinfection requirements: A report on the state of adenovirus sci- ence. J. Am. Water Works Assoc. 98(6):93-106. Ye, L., and T. Zhang. 2011. Pathogenic bacteria in sewage treatment plants as revealed by 454 pyrosequencing. Environ. Sci. Technol. 45(17):7173-7179. Zegura, B., E. Heath, A. Cernosa, and M. Filipic. 2009. Combination of in vitro bioassays for the determination of cytotoxic and genotoxic potential of wastewater, surface water and drinking water samples. Chemosphere. 75(11):1453-1460.