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Scientific and Technologic Advances

INTRODUCTION

Obtaining reliable estimates of exposures of large populations on multiple scales of space and time requires detailed information on emissions or transformation products from a source, on the locations of receptors (personal or ecosystem) in time and space, and on the activity levels of the receptors (as proxies for inhalation rate, ingestion potential, or dermal interaction) at the time when they are affected by a source. Additional information may be needed on how individual heterogeneity influences exposures—including such susceptibility characteristics as genetics, pre-existing health conditions, and psychosocial stress—because these factors may also influence exposure. Information on body burden, obtained by collecting exposure biomarkers, also is essential for understanding the dose from a specific source and the influence of environmental exposures on health risks.

Efforts to characterize exposure have focused on ambient conditions, and an individual is typically assigned to a home address in an epidemiologic health study or a species is assigned to a region it inhabits in an ecosystem. Although those exposure assignments have revealed important health risks, reliance on proxy methods may impart large exposure-measurement error—that is, a modeled exposure may be an inaccurate and potentially biased estimate of the true exposure. Depending on the exposure-error type, health-effect estimates may be attenuated and biased toward a null result, and the true benefits of control measures may be obscured. Obtaining more accurate estimates of internal exposure reduces exposure-measurement error and provides a more realistic understanding of potential health effects of environmental and occupational exposures (Carroll et al. 2006).

Efforts to gather information on personal exposures have relied on specialized equipment that is expensive and cumbersome and thus limits the wear time or number of subjects that can be monitored. Because of those limitations, many studies have used questionnaires (Wacholder et al. 1992) or simple information



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5 Scientific and Technologic Advances INTRODUCTION Obtaining reliable estimates of exposures of large populations on multiple scales of space and time requires detailed information on emissions or transfor- mation products from a source, on the locations of receptors (personal or ecosys- tem) in time and space, and on the activity levels of the receptors (as proxies for inhalation rate, ingestion potential, or dermal interaction) at the time when they are affected by a source. Additional information may be needed on how individ- ual heterogeneity influences exposures--including such susceptibility character- istics as genetics, pre-existing health conditions, and psychosocial stress-- because these factors may also influence exposure. Information on body burden, obtained by collecting exposure biomarkers, also is essential for understanding the dose from a specific source and the influence of environmental exposures on health risks. Efforts to characterize exposure have focused on ambient conditions, and an individual is typically assigned to a home address in an epidemiologic health study or a species is assigned to a region it inhabits in an ecosystem. Although those exposure assignments have revealed important health risks, reliance on proxy methods may impart large exposure-measurement error--that is, a mod- eled exposure may be an inaccurate and potentially biased estimate of the true exposure. Depending on the exposure-error type, health-effect estimates may be attenuated and biased toward a null result, and the true benefits of control meas- ures may be obscured. Obtaining more accurate estimates of internal exposure reduces exposure-measurement error and provides a more realistic understand- ing of potential health effects of environmental and occupational exposures (Carroll et al. 2006). Efforts to gather information on personal exposures have relied on special- ized equipment that is expensive and cumbersome and thus limits the wear time or number of subjects that can be monitored. Because of those limitations, many studies have used questionnaires (Wacholder et al. 1992) or simple information 106

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Scientific and Technologic Advances 107 on location, such as home address, that is related to exposures. Those techniques have well-known limitations, but they are often the only methods available, par- ticularly for reconstructing historical exposures. Innovations in science and technology provide opportunities to overcome limitations and guide exposure science in the 21st century to deliver knowledge that is effective, timely, and relevant to current and emerging environmental- health challenges. Personalized medicine1 and telemedicine will increase the pace of innovation in scientific and technologic methods that will benefit the field of exposure science. For example, many new genomic methods for moni- toring individual metabolic and exposure phenotypes will be critical for future individualized medicine. In telemedicine, cellular-telephone technologies in- creasingly contribute to improving diagnostics and patient care and hence to improving our ability to anticipate the effects of exposures (Wootton and Bon- nardot 2010). Similarly, new developments in geographic information science and technologies are leading to rapid adoption of new information obtained from satellites via remote sensing, which provides immediate access to data on poten- tial environmental threats. Improved information on physical activity and loca- tions of humans and other species obtained with global positioning systems (GPS) and related geolocation technologies is increasingly being combined with cellular-telephone technologies. Many of these advances are integrated through powerful geographic information systems (GIS)2 that operate either through stand-alone computing platforms or through the World Wide Web. Biologic monitoring and sensing increasingly offer the potential to assess internal expo- sures. The convergence of these scientific methods and technologies raises the possibility that in the near future embedded, ubiquitous, and participatory sens- ing systems will facilitate individual-level exposure assessments on large popu- lations of humans or other species. The new technologies and methods also may help to operationalize the concept of the exposome (see discussion in Chapter 1). Establishing a more complete record of exposures based on internal biomarkers as theorized in the exposome (Wild 2005) requires tools that can also assess external environmental exposures. Many important exposures lead to no internal biomarkers but can be associated with environmental health risks (Peters et al. 2012) (for example, noise, heat, and electromagnetic fields). There is also a need to continue to link sources to exposures; this is the basis of mitigation efforts to protect public health. The committee envisions that many of the new technologies discussed in this chapter will help to broaden the exposome to the "eco-exposome" concept discussed in Chapter 2, and help to quantify exposure indicators to address those concerns. 1 Personalized medicine is an emerging practice of medicine that uses information about a person's genetic profile and environmental exposures to prevent, diagnose, and treat disease (Offit 2011). 2 GIS is defined as a system for performing numerous operations involving the acquisi- tion, editing, analysis, storage, and visualization of geographic data (Longley et al. 2005).

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108 Exposure Science in the 21st Century: A Vision and A Strategy In this chapter, the committee reviews some of the newest technologies for exposure science and, in considering their strengths and limitations, identifies near-term and long-term innovations that will guide exposure science in the 21st century. The review is organized according to the framework in Chapter 1 that describes the scope of exposure science from characterizing external concentra- tions to personal exposures and finally to understanding how internal exposures affect dose. Figure 5-1 expands on the framework by identifying the technolo- gies that will be presented. The discussion begins with a review of geographic information technologies, which help in characterizing sources and concentra- tions and also can improve understanding of stressors and receptors when used in concert with other methods and information. Ubiquitous sensing systems, ecologic momentary assessment (participatory methods that are used to query subjects about their perceptions and experiences while in the exposure field us- ing cell phones or other real-time devices), and nanosensors are addressed next; these can help in characterizing personal exposures. We then discuss biomoni- toring, which can improve our understanding of internal exposures and, when combined with other technologies, can help to identify sources. Finally, models and information-management tools are addressed in the context of their ability to help in interpreting and managing the massive and often complex interactions among receptors and environmental stressors. Many of the technologies in this chapter are illustrated in connection with air pollution, inasmuch as this is one of the most developed sectors of exposure science. As shown in Figure 5-1, how- ever, the committee's framework and vision are intended to be broadly applica- ble and relevant to all media to reflect the expected needs for the technologies, and many other illustrative examples are presented. TRACKING SOURCES, CONCENTRATIONS, AND RECEPTORS WITH GEOGRAPHIC INFORMATION TECHNOLOGIES Three major technologic advances in geographic information technolo- gies--remote sensing, global positioning and related locational technologies, and GIS--have dramatically affected exposure science. As outlined by Good- child (2007), they are inspiring a new emphasis on spatial information in rela- tion to social and scientific inquiry. Over the last 10 years, the technologies have contributed to improvements in exposure science, and they will probably con- tinue to move the field toward more refined exposure assessments that are more comprehensive, more accurate, and more relevant to and valuable in policy- making and in the everyday lives of large populations. Remote Sensing for Exposure Assessment Remote sensing (RS) has emerged as a key innovation in exposure sci- ence. RS has been defined as "the acquisition and measurement of data/inform- ation on some property(ies) of a phenomenon, object, or material by a recording

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Scientific and Technologic Advances 109 device not in physical, intimate contact with the feature(s) under surveillance" (Short 2011). The field encompasses the capture, retrieval, analysis, and display of information on subsurface, surface, and atmospheric conditions that is col- lected with satellite, aircraft, or other technologies designed to sense energy, light, or optical properties at a distance (Jerrett et al. 2009). RS is an important tool for enhancing the capacity to assess human and ecologic exposures because it provides global information on the earth's surface, water, and atmosphere. It is also widely used for subsurface investigations (for example, electromagnetic imaging of karst in water resource investigations). It also provides exposure estimates in regions where sparse ground observation systems are available. With respect to air pollution, the most common aerosol characteristic measured with a satellite is the aerosol optical depth (AOD), which quantifies the extinction of electromagnetic radiation from aerosols in an atmospheric col- umn at a given wavelength (Emilli et al. 2010). Six primary satellite sensors provide information on particulate pollution (MODIS, Landsat, IKONOS, Orbview, SPOT, and GOES). Box 5-1 discusses evaluation of the reliability of AOD compared with PM2.53 mass concentrations measured on the ground. Box 5-2 and Figure 5-2 demonstrate how the results of a 1-km retrieval of the MODIS AOD substantially improve the resolution and thus the utility of remote sensing for health and ecologic studies; the current grid size has a 10-km re- trieval. FIGURE 5-1 Selected scientific and technologic advances considered in relation to the conceptual framework. 3 PM2.5 are fine particles in the ambient air that are 2.5 microns or less in diameter.

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110 Exposure Science in the 21st Century: A Vision and A Strategy BOX 5-1 Evaluating the Reliability of Aerosol Optical Depth Against Ground Observations Hoff and Christopher (2009) reviewed more than 30 papers that exam- ined the relationship between total column AOD and surface PM2.5 measure- ments on a station by station basis. Their results underscored "the range of measurements from across the globe and the range of correlations between AOD and mass". They found a wide range of uncertainty between the two measures of AOD and mass. The studies used simple linear regressions and correlations between the AOD values and the PM2.5 mass concentrations measured on the ground. In some cases the correlations were strong and the AOD served as a predictor of pollution on the ground. In other cases, either because the satellite product itself was not sufficiently accurate or because the particles observed in the total column were in layers aloft, the satellite derived AOD was a poor predictor of pollution at the earth's surface. The au- thors suggested conducting a study of the controlling extrinsic factors for each region that would aid in understanding the PM2.5AOD relationship. The litera- ture continues to grow with efforts to combine information from multiple satel- lite sensors and models (van Donkelaar et al. 2010) or to introduce auxiliary information, such as meteorologic data (Pelletier et al. 2007) or boundary layer height (Engel Cox et al. 2006). Lee et al. (2011) have hypothesized that the inherent variability in the PMAOD relationship is due to changes in parti- cle size and composition, earth surface properties, vertical distribution of par- ticle concentrations, and other factors. To account for the variability of these factors, they proposed a daily calibration technique that is based on the spa- tial variability of ground PM measurements and would make it possible to obtain quantitative estimates of PM concentrations by using AOD measure- ments. BOX 5-2 Evaluation of MODIS 1 km Product The development of the 3 km and 1 km products provides an opportunity to test the capabilities of the satellite data to provide the resolution needed for exposure assessment and health related studies. For example, the 10 km aerosol product offered by MODIS is sufficient for climate applications but insufficient for detailed exposure assessment from sources that are variable over small areas, such as traffic emissions. In that regard, Hoff and Christo- pher (2009) stressed the importance of a finer resolution product on a local urban scale. It is expected that a 3 km product will become publicly available in 2013. To attain 1km resolution AOD from MODIS, the Multi Angle Implementa- tion of Atmospheric Correction algorithm was applied (Lyapustin et al. 2011a,b). The 1 km product was generated for the New England area during 2003. Figure 5-2 compares the 10 km and 1 km retrievals. It clearly shows that considerably more detail is obtained with the 1 km product.

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Scientific and Technologic Advances 111 Kilometers 0 30 60 120 180 240 AOD at 550 nm 0 0.8 FIGURE 5-2 Aerosol optical depth (AOD) derived from MODIS data for the New Eng- land region with the standard 10-km algorithm (left) and the experimental 1-km algo- rithm (right) for June 25, 2003. Remote Sensing for Health, Exposure, and Ecologic Studies Several studies and reviews (for example, Maxwell et al. 2010) have sug- gested that higher-resolution data enhance efforts to identify timespace patterns that are the basis of many risk assessments for diseases (Wilson 2002). In many studies, remote sensing data were used to derive three variables: vegetation cover, landscape structure, and water bodies. The ability to sense vegetation remotely from space is important in that nearly all vectorborne diseases are linked to the vegetative environment during their transmission cycle. Further- more, crop-type information may be important for studying the effects of pesti- cides (for example, vector resistance and illnesses caused by exposure to toxins) (Beck et al. 2000). Ward et al. (2000, 2006) and Maxwell et al. (2010) used crop location to identify where pesticides were applied in relation to residential loca- tions (Maxwell et al. 2010). Remote sensing of vegetation cover combined with GIS has also been used to develop management strategies to reduce herbicide application (Gmez-Casero et al. 2010) and to assess potential exposure of fish and wildlife to pesticides and metals (Focardi et al. 2006). Green cover is also associated with higher levels of physical activity, and RS has been used with geolocation technologies to show associations between physical activity of children and their exposure to green cover (Almanza et al. 2012).

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112 Exposure Science in the 21st Century: A Vision and A Strategy Hyperspectral Imaging Hyperspectral imaging collects and processes information from a wide portion of the electromagnetic spectrum. It has been used to assess human health risks associated with infectious diseases or environmental hazards. Ong et al. (2003) used hyperspectral airborne techniques to quantify dust loadings on man- groves originating from mining. The authors found that they could detect and quantitatively map the distribution of iron oxide. Ferrier (1999) showed that mine tailings (which contain potentially toxic materials) had been dispersed from the mine workings extending down the Rambla del Playazo to within 600 m of the beach at El Playazo (Spain). Other researchers have used hyperspectral data that were collected over "Ground Zero" for rapid assessment of the potential asbestos hazards associated with the dust that settled over lower Manhattan after the collapse of the World Trade Center towers (Clark et al. 2001; Swayze et al. 2006). Malley et al. (1999), Winkelmann (2005), and van der Meer et al. (2002) have reported soil contamination by hydrocarbons. Wu et al. (2005) studied mercury contamina- tion in suburban agricultural soils in the Nansing region of China. Finally, Chudnovsky et al. (2009, 2011) used the Hyperion satellite data to separate the spectral features of the Saharan dust storm from the underlying surface. HI sensing has been used to examine exposures of coral reefs to stressors such as sea surface temperature, ultraviolet radiation, wind, sediment load, chlo- rophyll, acidification, salinity, and coastal development (Maina et al. 2008; Eakin et al. 2010). Sediment load/water clarity (Doran et al. 2011), stressor ex- posures in benthic ecosystems (Goetz et al. 2008), and other water quality pa- rameters (Bagheri and Yu 2008; Odermatt et al. 2012) have also been analyzed using HI techniques. More recently HI techniques were utilized to assess the extent of the Deepwater Horizon Oil Spill and possible exposures to oil in pe- lagic and nearshore ecosystems (Bradley et al. 2011; Lavrova and Kostianoy 2011; Bulgarelli and Djavidnia 2012; Mishra et al. 2012). In 2015, two new hyperspectral sensors will be launched: the National Aeronautics and Space Administration HyspIRI (NASA 2011) and European ENMAP (EnMAP 2011) missions. With their improved hyperspectral and mul- tispectral capabilities, these sensors will increase the ability to monitor the ef- fects of urbanization on the environment and to assess land-cover characteristics that could indicate the presence of or risks posed by vectorborne and animal- borne diseases on a global scale. Conclusions To improve data quality for RS and increase its utility for exposure stud- ies, technologic improvements are needed, including Breakthroughs in electro-optics technologies.

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Scientific and Technologic Advances 113 Improvement of the current AOD retrievals (to achieve near-laboratory air-quality data) by obtaining accurate and reliable atmospheric vertical profile information. Retrieval of high-resolution AOD to discern spatial patterns of pollu- tion in urban environments through frequent daily temporal coverage based on orbital sensors. Global Positioning System and Geolocation Technologies Launched in the 1980s for defense applications, the GPS offers exposure scientists a simple means of tracking the geographic position of a person or other species. GPS receivers are now embedded into many cellular telephones, vehicle navigation systems, and many other instruments (Goodchild 2007). The GPS is a utility owned by the US government, and it consists of three compo- nents: a space segment with at least 24 satellites that transmit one-way signals to the earth; a control segment that maintains ground stations to track the satellites, reset their clocks, and maintain their positions; and a user segment that consists of individual devices that users deploy to receive the signals and calculate three- dimensional positions and times (GPS 2011). GPS signals can be augmented or complemented by land-based navigation systems that use cellular-telephone triangulation to provide positions when satellite signals are unavailable because of, for example, topographic obstruction or weather conditions (Shoval and Isaacson 2006). Radiofrequency identification can also be used for local track- ing of goods, animals, or people (Goodchild 2007). Collectively, these systems are referred to here as geolocation technologies. Hundreds of studies have used geolocation technologies to improve as- sessment of environmental exposures, including exposure to infectious-disease vectors (Vazquez-Prokopec et al. 2009) and air pollution (Paulos et al. 2007); to analyze how physical activity is related to different built environments (Jones et al. 2009); and to inform simulation models of potential pesticide exposures (Leyk et al. 2009). Many other applications are found in the literature. The main contribution of geolocation technologies is to reduce exposure measurement error and to move closer to a "timegeography of exposure" (Hagerstrand 1970; Briggs 2005). That is, geolocation technologies offer the possibility to know, with a high degree of accuracy, an individual's location in time and space and to provide a window into the moment of contact between a source (that is, an environmental intensity) and a receptor. When data obtained on environmental intensities (for example, air or water quality) are combined with geolocation information and physical activity measurements (obtained with accelerometers), more detailed estimates of potential chemical, biologic, or physical exposures can be made by using data on inhalation rate, ingestion po- tential, or dermal contact. There are many examples of how geolocation technologies have improved our understanding of exposures through their use in defining a person's location

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114 Exposure Science in the 21st Century: A Vision and A Strategy in time and space. They have revealed important limitations of survey-based assessments of location. For example, a study by Elgethun et al. (2007) com- pared timeactivity diaries with actual measurements from GPS and found se- vere underreporting in the diaries regarding the amount of time spent outdoors at home. Such errors may result in substantial exposure misclassification to such pollutants as ozone that have low penetration ratios from outdoors to indoors, which make time outdoors a key determinant of exposure. The technologies provide more accurate information on geocoded locations of subjects and a bet- ter understanding of likely sources of error when points are used to represent large structures, such as schools and day-care facilities (Houston et al. 2006). They are also used in studies in which exposures are measured as study subjects walk, ride bicycles, or drive with pollution monitors. A study by McCreanor et al. (2007) demonstrated the effect of walking through polluted areas on asth- matic symptoms and biomarkers--such as exhaled nitric oxide, a marker of lung inflammation--in London, England. The study provided increased support for the hypothesis that ambient air-pollution concentrations can elicit changes in asthmatic symptoms. Geolocation technologies have already made important contributions to the understanding of exposures at the point of contact between source and receptor, and they appear poised to play an increasingly integral role in widespread population-based individual sensing (discussed below). Geographic Information Systems GIS combines topologic geometry, capable of manipulating geographic in- formation, with automated cartography and enables users to compile digital or hard-copy maps. GIS plays a central role in integrating data into coherent data- bases that connect different attribute data (for example, exposure and health at- tributes) by geographic location. Input data used to derive exposure surfaces, such as road locations and industrial land uses, also are stored and manipulated in GIS. GIS increasingly serves as the storage and integrative backbone of re- mote sensing, geolocation technologies, and sophisticated modeling outputs, such as for collecting measurements on the fate and transport of contaminants through ecosystems (Gallagher et al. 2010). Another important role of GIS in exposure assessment is the quantification of topologic relationships. For example, buffer functions that measure the dis- tance between a source, such as a roadway, and a receptor, such as a house, en- able analysts to relate the geographic position of a study subject in space and time with the subject's likely exposure on the basis of an overlay of location information (Jerrett et al. 2005). That type of buffering, which provides the dis- tance between a source and a receptor, is used to characterize proximity to roadways, factories, water bodies, and other land uses or modifications that have either potentially adverse exposures (for example, pesticide transport from agri- cultural fields) (Gunier et al. 2011) or potentially favorable exposures (for ex- ample, parks and health-food stores in cities) (Morland and Evenson 2009). GIS

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Scientific and Technologic Advances 115 can provide information that is stored by the user, both before and after a major change (for example, in land use) or a catastrophe (for example, a tsunami). Fig- ure 5-3 demonstrates a road buffer that was used to characterize human expo- sures to traffic-related air pollution in Hamilton, Canada (Jerrett et al. 2005). Ecologic studies have combined modeling results with overlay techniques to examine potential exposure exceedances of threatened and endangered species (for example, see Figure 5-4 for cadmium exposures of the Little Owl) (Lahr and Kooistra 2010). Web-Based Geographic Information Systems for Exposure Assessment Web-based GIS is becoming more common (Maclachlan et al. 2007) and can serve as a tool in policy-making and in educating and empowering commu- nities to understand and manage their environmental exposures better. (See Chapter 6 for additional discussion of community engagement.) For example, to promote active commuting, Metro Vancouver has collaborated with the Univer- sity of British Columbia to develop a cycling-route planner (Cycling Metro Vancouver 2007), which allows cyclists to select routes that have the most green vegetation, the least traffic pollution, and the least or greatest elevation, all specified by the user. That empowers cyclists to choose the routes that best suit their fitness levels, minimize exposure to traffic pollution, and reduce their car- bon dioxide output (Su et al. 2010). The Web site runs on the backdrop of Google Maps--an illustration of the potential synergies between new private- sector technologies and public-health protection. FIGURE 5-3 Example of a binary buffer overlay showing people likely to experience traffic-related air-pollution exposure. The circles represent people. People assigned a "0" are outside a prespecified distance, while people assigned a "1" are within a given dis- tance. Adapted from Jerrett et al. 2005. Reprinted with permission; copyright 2005, Jour- nal of Exposure Science and Environmental Epidemiology.

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116 Exposure Science in the 21st Century: A Vision and A Strategy FIGURE 5-4 Map of a flood plain in the Netherlands showing secondary risk of poison- ing by cadmium in Little Owls developed using a combination of measured cadmium concentrations, food web modeling, knowledge of foraging in different habitats, and probabilistic risk assessment. Source: Lahr and Kooistra 2010. Reprinted with permis- sion; copyright 2010, Science of the Total Environment. In addition to human health concerns, web-based GIS has been used to monitor ecologic exposures. For example, Google Earth and Google Fusion Ta- bles with Airborne/Visible Infared Imaging Spectrometer data (AVIRIS 2012) were used to provide public, real mapping of the Deepwater Horizon Oil Spill (Bradley et al. 2011). With the new technologies--such as cellular telephones, GPS, and com- puters that apply complex data-mining techniques--private companies are in- creasingly collecting data that are potentially useful for exposure science, such as location and mobility information, and in some cases direct measurements of exposure through sensing networks. Issues of data ownership, use, informed consent, and data-sharing remain to be addressed. Increased cooperation with private-sector entities offers great potential for enhancing the data available for exposure science.

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Scientific and Technologic Advances 143 Davis, A.P., B.L. King, S. Mockus, C.G. Murphy, C. Saraceni-Richards, M. Rosenstein, T. Wiegers, and C.J. Mattingly. 2011. The Comparative Toxicogenomics Data- base: Update 2011. Nucleic Acids Res. 39(suppl.1):D1067-D1072. de Nazelle, A, E. Seto, D. Donaire, M. Mendez, J. Matamala, M. Portella, D. Rodriguez, M. Nieuwenhuijsen, and M. Jerrett. 2011. Improving Estimates of Travel Activity and Air Pollution Exposure through Ubiquitous Sensing Technologies. Abstract S- 0035 in Abstracts of the 23rd Annual Conference of the International Society of Environmental Epidemiology (ISEE), September 13-16, 2011, Barcelona, Spain [online]. Available: http://ehp03.niehs.nih.gov/article/fetchArticle.action?articleURI =info%3Adoi%2F10.1289%2Fehp.isee2011 [accessed Sept. 4, 2012]. Demokritou, P., I.G. Kavouras, S.T. Ferguson, and P. Koutrakis. 2001. Development and laboratory performance evaluation of a personal multipollutant sampler for simul- taneous measurements of particulate and gaseous pollutants. Aerosol Sci. Technol. 35(3):741-752. Doran, M., M. Babin, O. Hembise, A. Mangin, and P. Garnesson. 2011. Ocean transpar- ency from space: Validation of algorithms estimating Secchi depth using MERIS, MODIS, and SeaWiFS data. Remote Sens. Environ. 115(12):2986-3001. Dunton, G.F., Y. Liao, S. Intille, J. Wolch, and M. Pentz. 2011. Social and physical con- textual influences on children's leisure-time physical activity: An ecological mo- mentary assessment study. J. Phys. Act. Health 8(suppl. 1):S103-S108. Eakin, C.M., C.J. Nim, R.E. Brainard, C. Aubrecht, C. Elvidge, D.K. Gledhill, F. Muller- Karger, P.J. Mumby, W J. Skirving, A.E. Strong, M. Wang, S. Weeks, F. Wentz, and D. Ziskin. 2010. Monitoring coral reefs from space. Oceanography 23(4):118- 133. Edwards, R., K.R. Smith, B. Kirby, T. Allen, C.D. Litton, and S. Hering. 2006. An inex- pensive dual-chamber particle monitor: Laboratory characterization. Air Waste Manage. Assoc. 56(6):789-799. Elgethun, K., M.G. Yost, C.T. Fitzpatrick, T.L. Nyerges, and R.A. Fenske. 2007. Com- parison of global positioning system (GPS) tracking and parent-report diaries to characterize children's time-location patterns. J. Expo. Sci. Environ. Epidemiol. 17(2):196-206. Emilli, E., C. Popp, M. Petitta, M. Riffler, S. Wunderle, and M. Zebisch. 2010. PM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region. Remote Sens. Environ. 114(11):2485-2499. Emond, C., L.S. Birnbaum, and M.J. Devito. 2006. Use of a physiologically based phar- macokinetic model for rats to study the influence of body fat mass and induction of CYP1A2 on the pharmacokinetics of TCDD. Environ. Health Perspect. 114(9):1394-1400. Engel-Cox, J.A., R.M. Hoff, R. Rogers, F. Dimmick, A.C. Rush, J.J. Szykman, J. Al- Saadi, D.A. Chu, and E.R. Zell. 2006. Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization. Atmos. Environ. 40(40):8056-8067. EnMAP (Environmental Mapping and Analysis Program). 2011. EnMAP Hyperspectral Imager [online]. Available: http://www.enmap.org/ [accessed Oct. 28, 2011]. EPA (U.S.Environmental protection Agency). 2009. Guidance on the Development, Evaluation, and Application of Environmental Models. EPA/100/K-09/003. Office of Science Advisor, Council for Regulatory Environmental Modeling, U.S. Envi- ronmental Protection Agency [online]. Available: http://www.epa.gov/crem/library/ cred_guidance_0309.pdf [accessed Jan. 25, 2012].

OCR for page 106
144 Exposure Science in the 21st Century: A Vision and A Strategy EPA (U.S. Environmental Protection Agency). 2012a. SHEDS-Multimedia: Stochastic Human Exposure and Dose Model for Multimedia, Multipathway Chemicals. Hu- man Exposure and Atmospheric Sciences, National Exposure Research Labora- tory, U.S. Environmental Protection Agency [online]. Available: http://www.epa. gov/heasd/products/sheds_multimedia/sheds_mm.html [accessed May 1, 2012]. EPA (U.S. Environmental Protection Agency). 2012b. ACTor. National Center for Com- putational Toxicology, U.S. Environmental Protection Agency [online]. Available: http://actor.epa.gov/actor/faces/ACToRHome.jsp [accessed Jan. 12, 2012]. EPA (U.S. Environmental Protection Agency). 2012c. ExpoCastDB [online]. Available: http://actor.epa.gov/actor/faces/ExpoCastDB/Home.jsp [accessed June 1, 2012]. Esteve-Turrillas, F.A., A. Pastor, V. Yusa, and M. de la Guardia. 2007. Using semi- permeable membrane devices as passive samplers. Trends Anal. Chem. 26(7):703- 712. Feng, C.H., and C.Y. Lu. 2011. Modification of major plasma proteins by acrylamide and glycidamide: Preliminary screening by nano liquid chromatography with tandem mass spectrometry. Anal. Chim. Acta 684(1-2):80-86. Fenner, K., M. Scheringer, M. Macleod, M. Matthies, T. McKone, M. Stroebe, A. Beyer, M. Bonnell, A.C. Le Gall, J. Klasmeier, D. Mackay, D. Van De Meent, D. Pen- nington, B. Scharenberg, N. Suzuki, and F. Wania. 2005. Comparing estimates of persistence and long-range transport potential among multimedia models. Environ. Sci Technol. 39(7):1932-1942. Ferrier, G. 1999. Application of imaging spectrometer in identifying environmental pol- lution caused by mining at Rodaquilar, Spain. Remote Sens. Environ. 68(2):25- 137. FGDC (Federal Geographic Data Committee). 2011. Standards [online]. Available: http:// www.fgdc.gov/standards [accessed May 11, 2012]. Finkelstein, M.E., K.A. Grasman, D.A. Croll, B.R. Tershy, B.S. Keitt, W.M. Jarman, and D.R. Smith. 2007. Contaminant-associated alteration of immune function in black- footed albatross (Phoebastria nigripes), a North Pacific predator. Environ. Toxi- col. Chem. 26(9):1896-1903. Focardi, S., I. Corsi, S. Mazzuoli, L. Vignoli, and S.A. Loiselle. 2006. Integrating remote sensing approach with pollution monitoring tools for aquatic ecosystem risk as- sessment and management: A case study of Lake Victoria (Uganda). Environ. Monit. Assess. 122(1-3):275-287. Fustinoni, S., L. Campo, P. Manini, M. Buratti, S. Waidyanatha, G. De Palma, A. Mutti, V. Foa, A. Colombi, and S. M. Rappaport. 2008. An integrated approach to bio- monitoring exposure to styrene and styrene-(7,8)-oxide using a repeated measure- ments sampling design. Biomarkers 13(6):560-578. Gallagher, L.G., T.F. Webster, A. Aschengrau, and V.M. Vieira. 2010. Using residential history and groundwater modeling to examine drinking water exposure and breast cancer. Environ. Health Perspect. 118(6):749-755. Gangwal, S. 2011. ExpoCastDB: A Publicly Accessible Database for Observational Ex- posure Data. Presented at Computational Toxicology Community of Practice, Sep- tember 22, 2011 [online]. Available: http://www.epa.gov/ncct/download_file s/chemical_prioritization/ExpoCastDB_CommPractice_09-22-2011-Share.pdf [ac- cessed Dec. 7, 2011]. Genualdi, S.A., K.J. Hageman, L.K. Ackerman, S. Usenko, and S.L.M. Simonich. 2011. Sources and fate of chiral organochlorine pesticides in western U.S. National Park ecosystems. Environ. Toxicol. Chem. 30(7):1533-1538.

OCR for page 106
Scientific and Technologic Advances 145 Georgopoulos, P.G., A.F. Sasso, S.S. Isukapalli, P.J. Lioy, D.A. Vallero, M. Okino, and L. Reiter. 2009. Reconstructing population exposures to environmental chemicals from biomarkers: Challenges and opportunities. J. Expo. Sci. Environ. Epidemiol. 19(2):149-171. Goetz, S.J., N. Gardiner, and J.H. Viers. 2008. Monitoring freshwater, estuarine and near- shore benthic ecosystems with multi-sensor remote sensing: An introduction to the special issue. Remote Sens. Environ. 112(11):3993-3995. Gomez-Casero, M.T., I.L. Castillejo-Gonzalez, A. Garcia-Ferrer, J.M. Pena-Barragan, M. Jurado-Exposito, L. Garcia-Torres, and F. Lopez-Granados. 2010. Spectral dis- crimination of wild oat and canary grass in wheat fields for less herbicide applica- tion. Agron. Sustain. Dev. 30(3):689-699. Goodchild, M.F. 2007. The Morris Hansen Lecture 2006: Statistical perspectives on spa- tial social science. J. Off. Stat. 23(3):269-283. GPS (Global Positioning System). 2011. The Global Positioning System [online]. Avail- able: http://www.gps.gov/systems/gps/ [accessed Oct. 28, 2011]. Gunier, R.B., M.H. Ward, M. Airola, E.M. Bell, J. Colt, M. Nishioka, P.A. Buffler, P. Reynolds, R.P. Rull, A Hertz, C. Metayer, and J.R. Nuckols. 2011. Determinants of agricultural pesticide concentrations in carpet dust. Environ. Health Perspect. 119(7):970-976. Hagerstrand, T. 1970. What about people in regional science. Pap. Regul. Sci. 24:1-21 (as cited in Briggs 2005). Hill, M., A. Parizek, R. Kancheva, M. Duskova, M. Velikova, L Kriz, M. Klimkova, A. Paskova, Z. Zizka, P. Matucha, M. Meloun, and L. Starka. 2010. Steroid me- tabolome in plasma from the umbilical artery, umbilical vein, maternal cubital vein and in amniotic fluid in normal and preterm labor. J. Steroid Biochem. Mol. Biol. 121(3-5):594-610. Hjort, N.L., and G. Claeskens. 2003. Frequentist model average estimators. J. Am. Stat. Assoc. 98(464):879-899. Hoeting, J.A., D. Madigan, A.E. Raftery, and C.T. Volinsky. 1999. Bayesian model aver- aging: A tutorial. Statist. Sci. 14(4):382-417. Hoff, R.M., and S.A. Christopher. 2009. Remote sensing of particulate pollution from space: Have we reached the promised land? J. Air Waste Manage. Assoc. 59(6): 645-675. Houston, D., P. Ong, J. Wu, and A. Winer. 2006. Proximity of licensed child care facili- ties to near-roadway vehicle pollution. Am. J. Public Health 96(9):1611-1617. Hsieh, M.D., and E.T. Zellers. 2004. Limits of recognition for simple vapor mixtures determined with a microsensor array. Anal. Chem. 76(7):1885-1895. Iglesias, R.A., F. Tsow, R. Wang, E.S. Forzani, and N. Tao. 2009. Hybrid separation and detection device for analysis of benzene, toluene, ethylbenzene, and xylenes in complex samples. Anal. Chem. 81(21):8930-8935. Intille, S.S. 2007. Technological innovations enabling automatic, context-sensitive eco- logical momentary assessment. Pp. 308-337 in The Science of Real-Time Data Capture: Self-Report in Health Research, A.A. Stone, S. Shiffman, A. Atienza, and L. Nebeling, eds. Oxford: Oxford University Press [online]. Available: http://www. ccs.neu.edu/home/intille/teaching/AMB/papers/Stone_Chapter16.pdf [accessed Jan. 20, 2012]. IPCS (International Programme on Chemical Safety). 2008. Uncertainty and Data Quality in Exposure Assessment, Part 1. Guidance Document on Characterizing and Communicating Uncertainty of Exposure Assessment. IPCS project on the Har- monization of Approaches to the Assessment of Risk from Exposure to Chemicals.

OCR for page 106
146 Exposure Science in the 21st Century: A Vision and A Strategy Geneva: World Health Organization [online]. Available: http://www.who.int/ipcs/ publications/methods/harmonization/exposure_assessment.pdf [accessed May 14, 2012]. Jaworska, J.S., R.S. Boethling, and P.H. Howard. 2003. Recent developments in broadly applicable structure-biodegradability relationships. Environ. Toxicol. Chem. 22 (8):1710-1723. Jerrett, M., A. Arain, P. Kanaroglou, B. Beckerman, D. Potoglou, T. Sahsuvaroglu, J. Morrison, and C. Giovis. 2005. A review and evaluation of intraurban air pollution exposure models. J. Expo. Anal. Environ. Epidemiol. 15(2):185-204. Jerrett, M., S. Gale, and C. Kontgis. 2009. An environmental health geography of risk. Pp. 418-445 in A Companion to Health and Medical Geography, T. Brown, S. McLafferty, and G. Moon, eds. Oxford, UK: Wiley-Blackwell. Jin, C., and E.T. Zellers. 2008. Limits of recognition for binary and ternary vapor mix- tures determined with multi-transducer arrays. Anal. Chem. 80(19):7283-7293. Jin, H., B.J. Webb-Robertson, E.S. Peterson, R. Tan, D.J. Bigelow, M.B. Scholand, J.R. Hoidal, J.G. Pounds, and R.C. Zangar. 2011. Smoking, COPD and 3-nitrotyrosine levels of plasma proteins. Environ. Health Perspect. 119(9):1314-1320. Jones, A.P., E.G. Coombes, S.J. Griffin, and E.M. van Sluijs. 2009. Environmental sup- portiveness for physical activity in English school children: A study using Global Positioning Systems. Int. J. Behav. Nutr. Phys. Act. 6(1):42. Judson, R.S., M.T. Martin, P.P. Egeghy, S. Gangwal, D.M. Reif, P. Kothiya, M.A. Wolf, T. Cathey, T.R. Transue, D. Smith, J. Vail, A. Frame, S. Mosher, E.A. Cohen- Hubal, and A.M. Richard. 2012. Aggregating data for computational toxicology applications: The U.S. Environmental Protection Agency (EPA) Aggregated Com- putational Toxicology Resource (ACToR) system. Int. J. Mol. Sci. 13(2):1805- 1831. Keller, M., D.S. Schimel, W.W. Hargrove, and F.M. Hoffman. 2008. A continental strat- egy for the National Ecological Observatory Network. Front. Ecol. Environ. 6(5):282-284. Khanna, V.K. 2012. Nanosensors: Physical, Chemical, and Biological. Boca Raton, FL: CRC Press. Kidd, K.A., P.J. Blanchfield, K.H. Mills, V.P. Palace, R.E. Evans, J.M. Lazorchak, and R.W. Flick. 2007. Collapse of a fish population after exposure to a synthetic estro- gen. Proc. Natl. Acad. Sci. U.S.A. 104(21):8897-8901. Kim, S.K., H. Chang, and E.T. Zellers. 2011. Microfabricated gas chromatograph for the selective determination of trichloroethylene vapor at sub-parts-per-billion concen- trations in complex mixtures. Anal. Chem. 83(18):7198-7206. Kim, S.K., D.R. Burris. H. Chang, J. Bryant-Genevier, and E.T. Zellers. 2012a. Micro- fabricated gas chromatograph for on-site determinations of trichloroethylene in in- door air arising from vapor intrusion. 1. Field evaluation. Environ.Sci. Technol. in press. 46(11):6065-6072. Kim, S.K., D.R. Burris, J. Bryant-Genevier, K.A. Gorder, E.M. Dettenmair, and E.T. Zellers. 2012b. Microfabricated gas chromatograph for on-site determinations of TCE in indoor air arising from vapor intrusion. 2. Spatial/temporal monitoring. Environ. Sci. Technol. 46(11):6073-6080. Klaper, R., B.J. Carter, C.A. Richter, P.E. Drevnick, M.B. Sandheinrich, and D.E. Tillitt. 2010. Corrigendum: Use of a 15 k gene microarray to determine gene expression changes in response to acute and chronic methylmercury exposure in the fathead minnow Pimephales promelas Rafinesque (72(9):2207- 2008). J. Fish Biol. 77(1): 310.

OCR for page 106
Scientific and Technologic Advances 147 Kolok, A.S., H.L. Schoenfuss, C.R. Propper, and T.L. Vail. 2011. Empowering citizen scientists: The strength of many in monitoring biologically active environmental contaminants. BioScience 61(8):626-630. Kopecky, K.J., S. Davis, T.E. Hamilton, M.S. Saporito, and L.E. Onstad. 2004. Estima- tion of thyroid radiation doses for the Hanford thyroid disease study: Results and implications for statistical power of the epidemiological analyses. Health Phys. 87(1):15-32. Korrick, S.A., L.M. Altshul, P.E. Tolbert, V.W. Burse, L.L. Needham, and R.R. Monson. 2000. Measurement of PCBs, DDE, and hexachlorobenzene in cord blood from in- fants born in towns adjacent to a PCB-contaminated waste site. J. Expo. Anal. En- viron. Epidemiol. 10(6 Pt 2):743-754. Kratz, T.K., P. Arzberger, B.J. Benson, C.Y. Chiu, K. Chiu, L. Ding, T. Fountain, D. Hamilton, P.C. Hanson, Y.H. Hu, F.P. Lin, D.F. McMullen, S. Tilak, and C. Wu. 2006. Towards a Global Lake Ecological Observatory Network. Publications of the Karelian Institute 145:51-63 [online]. Available: http://www.gleon.org/Gleon _Kratz_etal_2006.pdf [accessed Jan. 17, 2012]. Krause, A.R., C. Van Neste, L.R. Senesac, T. Thundat, and E. Finot. 2008. Trace explo- sive detection using photothermal deflection spectroscopy. J. App. Phys. 103(9):094906. Lahr, J., and L. Kooistra. 2010. Environmental risk mapping of pollutants: State of the art and communication aspects. Sci. Total Environ. 408(18):3899-3907. Lammel, G. 2004. Effects of time-averaging climate parameters on predicted multicom- partmental fate of pesticides and POPs. Environ. Pollut. 128(1-2):291-302. Lavrova, O.Y., and A.G. Kostianoy. 2011. Catastrophic oil spill in the Gulf of Mexico in April-May 2010. Izv. Atmos. Ocean. Phys. 47(9):1114-1118. Leamer, E.E. 1978. Specification Searches: Ad Hoc Inference with Nonexperimental Data. New York: Wiley. Lee, H.J., Y. Liu, B.A. Coull, J. Schwartz, and P. Koutrakis. 2011. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmos. Chem. Phys. 11(5):7991-8002. Lewis, P.R., P. Manginell, D.R. Adkins, R.J. Kottenstette, D.R. Wheeler, S.S. Soko- lowski, D.E. Trudell, J.E. Byrnes, M. Okandan, J.M. Bauer, R.G. Manley, and C. Frye-Mason. 2006. Recent advancements in the gas-phase MicroChemLab. IEEE Sensors Journal 6(3):784-795. Leyk, S., C.R. Binder, and J.R. Nuckols. 2009. Spatial modeling of personalized expo- sure dynamics: The case of pesticide use in small-scale agricultural production landscapes of the developing world. Int. J. Health Geo. 8:17. Li, M., H.X. Tang, and M.L. Roukes. 2007. Ultra-senstive NEMS-based cantilever array for sensing, scanned probes, and very high-frequency applications. Nature Nanotech. 2(2):114-120. Litton, C.D., K.D. Smith, R. Edwards, and T. Allen. 2004. Combined optical and ioniza- tion measurement techniques for inexpensive characterization of micrometer and submicrometer aerosols. Aerosol Sci. Technol. 38(11):1054-1062. Longley, P., M. Goodchild, D. Maguire, and D. Rhind. 2005. Geographic Information Systems and Science. New York: Wiley. Lyapustin, A., J. Martonchik, Y. Wang, I. Laszlo, and S. Korkin. 2011a. Multi-angle implementation of atmospheric correction (MAIAC): Part 1. Radiative transfer ba- sis and look-up tables. J. Geophys. Res. 116: D03210, doi:10.1029/2010JD014985.

OCR for page 106
148 Exposure Science in the 21st Century: A Vision and A Strategy Lyapustin, A., Y. Wang, I. Laszlo, R. Kahn, S. Korkin, L. Remer, R. Levy, and J.S. Reid. 2011b. Multi-angle implementation of atmospheric correction (MAIAC): Part 2. Aerosol algorithm. J. Geophys. Res. 116: D03211, doi:10.1029/2010JD014986. Maclachlan, J.C., M. Jerrett, T. Abernathy, M. Sears, and M.J. Bunch. 2007. Mapping health on the internet: A new tool for environmental justice and public health re- search. Health Place 13(1):72-86. MacLeod, M., D. Woodfine, J. Brimacombe, L. Toose, and D. Mackay. 2002. A dynamic mass budget for toxaphene in North America. Environ. Toxicol. Chem. 21(8):1628-1637. MacLeod, M., M. Scheringer, T.E. McKone, and K. Hungerbhler. 2010. The state of multimedia mass-balance modeling in environmental science and decision making. Environ. Sci. Technol. 44(22):8360-8364. Maina, J., V. Venus, M.R. McClanahan, and M. Ateweberhan. 2008. Modeling suscepti- bility of coral reefs to environmental stress using remote sensing data and GIS models. Ecol. Model. 212(3-4):180-199. Malley, D.F., K.N. Hunter, and G.R. Webster. 1999. Analysis of diesel fuel contamina- tion in soils by near-infrared reflectance spectrometry and solid phase microextrac- tion-gas chromatography. Soil Sediment Contam. 8(4):481-489. Mattingly, C.J. 2009. Chemical databases for environmental health and clinical research. Toxicol. Lett. 186(1):62-65. Mattingly, C.J., T.E. McKone, M.A. Callahan, J.A. Blake, and E.A. Cohen-Hubal. 2012. Providing the missing link: The exposure science ontology ExO. Environ. Sci. Technol. 46(6):3046-3053. Maxwell, S.K., J.R. Meliker, and P. Goovaerts. 2010. Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research. J. Expo. Sci. Environ. Epidemiol. 20(2):176-185. McCreanor, J., P. Cullinan, M.J. Nieuwenhuijsen, J. Stewart-Evans, E. Malliarou, L. Jarup, R. Harrington, I.K. Svartengren, P. Ohman-Strickland, K.F. Chung, and J. Zhang. 2007. Respiratory effects of exposure to diesel traffic persons with asthma. N. Engl. J. Med. 357(23):2348-2358. McKone, T.E., and M. MacLeod. 2003. Tracking multiple pathways of human exposure to persistent multimedia pollutants: Regional, continental and global-scale models. Annu. Rev. Environ. Resour. 28:463-492. McKone, T.E., R. Castorina, M.E. Harnly, Y. Kuwabara, B. Eskenazi, and A. Bradman. 2007. Merging models and biomonitoring data to characterize sources and path- ways of human exposure to organophosphorous pesticides in the Salinas Valley of California. Environ. Sci. Technol. 41(9):3233-3240. Mishra, D., C. Cho, S. Ghosh, A. Fox, C. Downs, P. Merani, P. Kirui, N. Jackson, and S. Mishra. 2012. Post-spill state of the marsh: Remote estimation of the ecological impact of the Gulf of Mexico oil spill on Louisiana Salt Marshes. Remote Sens. Environ. 118:176-185. Molitor, J., M. Jerrett, C.C. Chang, N.T. Molitor, J. Gauderman, K. Berhane, R. McCon- nell, F. Lurmann, J. Wu, A. Winer, and D. Thomas. 2007. Assessing uncertainty in spatial exposure models for air pollution health effects assessment. Environ. Health Perspect. 115(8):1147-1153. Morland, K.B., and K.R. Evenson. 2009. Obesity prevalence and the local food environ- ment. Health Place 15(2):491-495. Mun, M., S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West, and P. Boda. 2009. PEIR, the personal environmental impact report, as a platform for participatory sensing system research. Pp. 55-68 in Proceedings of the

OCR for page 106
Scientific and Technologic Advances 149 7th Annual International Conference on Mobile Systems, Applications and Ser- vices-MobiSys `09, June 22-25, 2009, Krakow, Poland. New York: ACM. NASA (National Aeronautics and Space Administration). 2011. HyspIRI Mission Study. Jet Propulsion Laboratory. California Institute of Technology [online]. Available: http://hyspiri.jpl.nasa.gov/ [accessed Oct. 28, 2011]. Nebert, D.W., T.P. Dalton, A.B. Okey, and F.J. Gonzalez. 2004. Role of aryl hydrocar- bon receptor-mediated induction of the CYP1 enzymes in environmental toxicity and cancer. J. Biol. Chem. 279(23):23847-23850. Nicholson, J.K., and J.C. Lindon. 2008. Systems biology: Metabonomics. Nature 455 (7216):1054-1056. Noy, N.F., and D.L. McGuinness. 2001. Ontology Development101: A Guide to Creating Your First Ontology. KSL-01-05. Knowledge System Laboratory, Stanford Uni- versity, CA [online]. Available: http://www.ksl.stanford.edu/KSL_Abstracts/KSL- 01-05.html [accessed May 31, 2012]. NRC (National Research Council). 2007. Models in Environmental Regulatory Decision Making. Washington, DC: National Academies Press. NRC (National Research Council). 2009. Science and Decisions: Advancing Risk As- sessment. Washington, DC: National Academies Press. OBO Foundry (The Open Biological and Biomedical Ontologies Foundry). 2012. Expo- sure Ontology [online]. Available: http://obolibrary.org/cgi-bin/detail.cgi?id=exo [accessed June 4, 2012]. Odermatt, D., A. Gitelson, V.E. Brando, and M. Schaepman. 2012. Review of constituent retrieval in optically deep and complex waters from satellite imagery. Remote Sens. Environ. 118:116-126. Offit, K. 2011. Personalized medicine: New genomics, old lessons. Hum. Genet. 130(1):3-14. Ong, C.H., T.J. Cudahy, M.S. Caccetta, and M.S. Piggott. 2003. Deriving quantitative dust measurements related to iron ore handling from airborne hyperspectral data. Min. Technol. IMM Trans. Sect. A 112(3):158-163. Pastorello, G.Z., G.A. Sanchez-Azofeifa, and M.A. Nascimento. 2011. Enviro-net: From networks of ground-based sensor systems to a web platform for sensor data man- agement. Sensors 11(6):6454-6479. Patel, C.J., J. Bhattacharya, and A.J. Butte. 2010. An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus. PLoS One 5(5):e10746. Patel, S.V., T.E. Mlsna, B. Fruhberger, E. Klaassen, S. Cemalovic, and D.R. Baselt. 2003. Chemicapactive microsensors for volatile organic compound detection. Sen- sor. Actuat. B Chem. 96(3):541-553. Paulos, E., R.J. Honicky, and E. Goodman. 2007. Sensing atmosphere. The 5th ACM Conference on Embedded Network Sensor Systems-AMC SenSys, November 6-9, 2007, Sydney, Australia [online]. Available: http://www.paulos.net/papers/2007/Se nsing%20Atmosphere%20(Sensys%202007%20Workshop).pdf [accessed Jan. 18, 2012]. Pelletier, B., R. Santer, and J. Vidot. 2007. Retrieving of particulate matter from optical measurements: A semiparametric approach. J. Geophys. Res. Atmos. 112:D06208, doi:10.1029/2005JD006737. Peters, A., G. Hoek, and K. Katsouyanni. 2012. Understanding the link between envi- ronmental exposures and health: Does the exposome promise too much? J. Epide- miol. Community Health 66(2):103-105.

OCR for page 106
150 Exposure Science in the 21st Century: A Vision and A Strategy Richardson, D.B., and A. Ciampi. 2003. Effects of exposure measurement error when an exposure variable is constrained by a lower limit. Am. J. Epidemiol. 157(4):355- 363. Roberts, A.P., J.T. Oris, G.A. Burton, and W.H. Clements. 2005. Gene expression in caged fish as a first-tier indicator of contaminant exposure in streams. Environ. Toxicol. Chem. 24(12):3092-3098. RTI International. 2008. New Technology Used to Increase Accuracy, Ease Measurement of Harmful Environmental Exposure. RTI International News: September 16, 2008 [online]. Available: http://www.rti.org/page.cfm?objectid=4749BFB4-CCC0-2F2 C-9D8D787BCE30A49D [accessed Jan. 11, 2012]. Rundel, P.W., E.A. Graham, M.F. Allen, J.C. Fisher, and T.C. Harmon. 2009. Environ- mental sensor networks in ecological research. New Phytol. 182(3):589-607. Sanchez, Y.A., K. Deener, E. Cohen Hubal, K. Knowlton, D. Reif, and D. Segal. 2010. Research needs for community-based risk assessment: Findings from multi- disciplinary workshop. J. Expo. Sci. Environ. Epidemiol. 20(2):186-195. Sanchez, W., W. Sremski, B. Piccini, O. Palluel, E. Maillot-Marechal, S. Betoulle, A. Jaffal, S. Ait-Aissa, F. Brion, E. Thybaud, N. Hinfray, and J.M. Porcher. 2011. Adverse effects in wild fish living downstream from pharmaceutical manufacture discharges. Environ. Int. 37(8):1342-1348. Sarangapani, R., J. Teeguarden, K.P. Plotzke, J.M. McKim, Jr, and M.E. Andersen. 2002. Dose-response modeling of cytochrome p450 induction in rats by octamethylcy- clotetrasiloxane. Toxicol. Sci. 67(2):159-172. Schenker, U., M. MacLeod, M. Scheringer, and K. Hungerbhler. 2005. Improving data quality for environmental fate models: A least-squares adjustment procedure for harmonizing physicochemical properties of organic compounds. Environ. Sci. Technol. 39(21):8434-8441. Scheringer, M., F. Wegmann, K. Fenner, and K. Hungerbhler. 2000. Investigation of the cold condensation of persistent organic pollutants with a global multimedia fate model. Environ. Sci. Technol. 34(9):1842-1850. Scheringer, M., M. Stroebe, F. Wania, F. Wegmann, and K. Hungerbhler. 2004. The effect of export to the deep sea on the long-range transport potential of persistent organic pollutants. Environ. Sci. Pollut. Res. Int. 11(1):41-48. Scholz, S., and I. Mayer. 2008. Molecular biomarkers of endocrine disruption in small model fish. Mol. Cell. Endocrinol. 293(1-2):57-70. Schwartz, D., and F. Collins. 2007. Environmental biology and human disease. Science 316(5825):695-696. Senesac, L., and T.G. Thundat. 2008. Nanosensors for trace explosive detection. Materi- als Today 11(3):28-36. Seto, E., E. Martin, A. Yang, P. Yan, R. Gravina, I. Lin, C. Wang, M. Roy, V. Shia, and R. Bajcsy. 2010. Opportunistic Strategies for Lightweight Signal Processing for Body Sensor Networks. Proceedings of the 3rd International Conference on Perva- sive Technology Related to Assistive Environments-PETRA, June 23-25, 2010, Samos, Greece [online]. Available: http://www.eecs.berkeley.edu/~yang/paper/Se toPETRAE2010.pdf [accessed Jan. 12, 2012]. Seto, E., P. Yan, P. Kuryloski, R. Bajcsy, T. Abresch, E. Henricson, and J. Han. 2011. Mobile Phones as Personal Environmental Sensing Platforms: Development of the CalFit Systems. Abstract S-0034 in Abstracts of the 23rd Annual Conference of the International Society of Environmental Epidemiology (ISEE), September 13 - 16, 2011, Barcelona, Spain [online]. Available: http://ehp03.niehs.nih.gov/article/

OCR for page 106
Scientific and Technologic Advances 151 fetchArticle.action?articleURI=info%3Adoi%2F10.1289%2Fehp.isee2011 [accessed Sept. 4, 2012]. Shalat, S.L., A.A. Stambler, Z. Wang, G. Mainelis, O.H. Emoekpere, M. Hernandez, P.J. Lioy, and K. Black. 2011. Development and in-home testing of the Pretoddler In- halable Particulate Environmental Robotic (PIPER Mk IV) sampler. Environ. Sci. Technol. 45(7):2945-2950. Shelley, S. 2008. Update. Nanosensors: Evolution, not revolution...yet. CEP 104(6):8-12. Shi, Q., H. Hong, J. Senior, and W. Tong. 2010. Biomarkers for drug-induced liver in- jury. Expert Rev. Gastroenterol. Hepatol. 4(2):225-234. Shipler, D.B., B.A. Napier, W.T. Farris, and M.D. Freshley. 1996. Hanford environ- mental dose reconstruction projectan overview. Health Phys. 71(4):532-544. Short, N.M., Sr. 2011. Introduction: Technical and Historical Perspectives of Remote Sensing. Remote Sensing Tutorial [online]. Available: http://rst.gsfc.nasa.gov/Intr o/Part2_1.html [accessed Jan. 18, 2012]. Shoval, N., and M. Isaacson. 2006. Application of tracking technologies to the study of pedestrian spatial behavior. Prof. Geog. 58(2):172-183. Simon, S.L., J.E. Till, R.D. Lloyd, R.L. Kerber, D.C. Thomas, S. Preston-Martin, J.L. Lyon, and W. Stevens. 1995. The Utah leukemia case-control study: Dosimetry methodology and results. Health Phys. 68(4):460-471. Smith, P.N., G.P Cobb., C. Godard-Codding, D. Hoff, S.T. McMurry, T.R. Rainwater, and K.D. Reynolds. 2007. Contaminant exposure in terrestrial vertebrates. Envi- ron. Pollut. 150(1):41-64. Soltow, Q.A., F.H. Strobel, K.G. Mansfield, L. Wachtman, Y. Park, and D.P. Jones. In press. High-performance metabolic profiling with dual chromatography-Fourier- transform mass spectrometry (DC-FTMS) for study of the exposome. Metabolom- ics in press. Stahl, R.G., T.S. Bingman, A. Guiseppi-Elie, and R.A. Hoke. 2010. What biomonitoring can and cannot tell us about causality in human health and ecological risk assess- ments. Hum. Ecol. Risk Assess. 16(1):74-86. Steenland, K., and D. Savitz. 1997. Topics in Environmental Epidemiology. New York: Oxford University Press. Stram, D.O., and K.J. Kopecky. 2003. Power and uncertainty analysis of epidemiological studies of radiation-related disease risk in which dose estimates are based on a complex dosimetry system: Some observations. Radiat. Res. 160(4):408-417. Su, J.G., M. Winters, M. Nunes, and M. Brauer. 2010. Designing a route planner to facili- tate and promote cycling in Metro Vancouver, Canada. Trans. Res. Part A 44(7):495-505. Swayze, G.A., R.N. Clark, S.J. Sutley, T.M. Hoefen, G.S. Plumlee, G.P. Meeker, I.K. Brownfield, K.E. Livo, and L.C. Morath. 2006. Spectroscopic and x-ray diffraction analyses of asbestos in the World Trade Center dust: Asbestos content of the set- tled dust. Pp. 40-65 in Urban Aerosols and Their Impact: Lessons Learned from the World Trade Center Tragedy, J.S. Gaffney, and N.A. Marley, eds. American Chemical Society Symposium Series 919. Oxford: Oxford University Press. Tan, Y.M., K.H. Liao, and H.J. Clewell, III. 2007. Reverse dosimetry: Interpreting triha- lomethanes biomonitoring data using physiologically based pharmacokinetic mod- eling. J. Expo. Sci. Environ. Epidemiol. 17(7):591-603. Tang, Z., H. Wu, D. Du, J. Wang, H. Wang, W.J. Qian, D.J. Bigelow, J.G. Pounds, R.D. Smith, and Y. Lin. 2010. Sensitive immunoassays of nitrated fibrinogen in human biofluids. Talanta 81(4-5):1662-1669.

OCR for page 106
152 Exposure Science in the 21st Century: A Vision and A Strategy Teeguarden, J.G., P.J. Deisinger, T.S. Poet, J.C. English, W.D. Faber, H.A. Barton, R.A. Corley, and H.J. Clewell, III. 2005. Derivation of a human equivalent concentra- tion for n-butanol using a physiologically based pharmacokinetic model for n-butyl acetate and metabolites n-butanol and n-butyric acid. Toxicol. Sci. 85(1):429-446. Thomas, D.C., D. Stram, and J. Dwyer. 1993. Exposure measurement error: Influence on exposure-disease. Relationships and methods of correction. Annu. Rev. Public Health 14:69-93. Timchalk, C., J.A. Campbell, G. Liu, Y. Lin, and A.A. Kousba. 2007. Development of a non-invasive biomonitoring approach to determine exposure to the organophos- phorus insecticide chlorpyrifos in rat saliva. Toxicol. Appl. Pharmacol. 219(2- 3):217-225. Todaka, T., H. Hirakawa, J. Kajiwara, T. Hori, K. Tobiishi, D. Yasutake, D. Onozuka, S. Sasaki, C. Miyashita, E. Yoshioka, M. Yuasa, R. Kishi, T. Iida, and M. Furue. 2010. Relationship between the concentrations of polychlorinated dibenzo-p- dioxins, polychlorinated dibenzofurans, and polychlorinated biphenyls in maternal blood and those in breast milk. Chemosphere 78(2):185-192. van der Meer, F.D., P.M. van Dijk, H. van der Werrf, and H. Yang. 2002. Remote sens- ing and petroleum seepage: A review and case study. Terra Nova 14(1):1-17. 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 concentra- tions from satellite-based aerosol optical depth: Development and application. En- viron. Health Perspect. 118(6):847-855. Vazquez-Prokopec, G.M., S.T. Stoddard, V. Paz-Soldan, A.C. Morrison, J.P. Elder, T.J. Kochel, T.W. Scott, and U. Kitron. 2009. Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus. Int. J. Health Geo. 8:68. Villenueuve, D.L., and N. Garcia-Reyero. 2011. Vision and strategy: Predictive ecotoxi- cology in the 21st century. Environ. Toxicol. Chem. 30(1):1-8. Wacholder, S., D.T. Silverman, J.K. McLaughlin, and J.S. Mandel. 1992. Selection of control in case-control studies. III. Design options. Am. J. Epidemiol. 135(9):1042-1050. Walt, D.R. 2005. Electronic noses: Wake up and smell the coffee. Anal. Chem. 77(3):45A. Wang, Z., S.L. Shalat, K. Black, P.J. Lioy, A.A. Stambler, O.H. Emoekpere, M. Hernan- dez, T. Han, M. Ramagopal, and G. Mainelis. 2012. Use of a robotic sampling platform to assess young children's exposure to indoor bioaerosols. Indoor Air 22(2):159-169. Wania, F., and D. Mackay. 1993. Global fractionation and cold condensation of low vola- tility organochlorine compounds in polar regions. Ambio 22(1):10-18. Wania, F., and D. Mackay. 1999. The evolution of mass balance models of persistent pollutant fate in the environment. Environ. Pollut.100(1-3):223-240. Wania, F., and Y. Su. 2004. Quantifying the global fractionation of polychlorinated bi- phenyls. Ambio 33(3):161-168. Ward, M.H., J.R. Nuckols, S.J. Weigel, S.K. Maxwell, K.P. Cantor, and R.S. Miller. 2000. Identifying populations potentially exposed to agricultural pesticides using remote sensing and a geographic information system. Environ. Health Perspect. 108(1):5-12. Ward, M.H., J. Lubin, J. Giglierano, J.S. Colt, C. Wolter, N. Bekiroglu, D. Camann, P. Hartge, and J.R. Nuckols. 2006. Proximity to crops and residential exposure to ag- ricultural herbicides in Iowa. Environ. Health Perspect. 114(6):893-897.

OCR for page 106
Scientific and Technologic Advances 153 Whitehead, A., B. Dubansky, C. Bodinier, T.I. Garcia, S. Miles, C. Pilley, V. Raghuna- than, J.L. Roach, N. Walker, R.B. Walter, C.D. Rice, and F. Galvez. 2011. Ge- nomic and physiological footprint of the Deepwater Horizon oil spill on resident marsh fishes. Proc. Natl. Acad. Sci. USA [online]. Available: http://www.pnas.org/ content/early/2011/09/21/1109545108.full.pdf [accessed Feb. 16, 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. Williamson, C.E., J.E. Saros, and D.W. Schindler. 2009. Sentinels of change. Science 323(5916): 887-888. Wilson, M.L. 2002. Emerging and vector-borne diseases: Role of high spatial resolution and hyperspectral images in analyses and forecasts. J. Geograph. Syst. 4(1):31-42. Winkelmann, K.H. 2005. On the Applicability of Imaging Spectroscopy for the Detection and Investigation of Contaminated Sites with Particular Consideration Given to the Detection of Fuel Hydrocarbon Contamination in Soil. Ph.D. Dissertation, Bran- denburgischen Technischen Universitt, Cottbus. Wootton, R., and L. Bonnardot. 2010. In what circumstances is telemedicine appropriate in the developing world? JRSM Short Rep. 1(5):37. Wu, Y.Z., J. Chen, J.F. Ji, Q.J. Tian, and X.M. Wu. 2005. Feasibility of reflectance spec- troscopy for the assessment of soil mercury contamination. Environ. Sci. Technol. 39(3):873-878. Yuan, Y.M., W. Xiong, Y.H. Fang, T.G. Lan, and D.C. Li. 2010. Detection of oil spills on water by differential polarization FTIR spectrometry [in Chinese]. Guang Pu Xue Yu Guang Pu Fen Xi 30(8): 2129-2132. Zartarian, V.G., H. Ozkaynak, J.M. Burke, M.J. Zufall, M.L. Rigas, and E.J. Furtaw, Jr. 2000. A modeling framework for estimating children's residential exposure and dose to chlorpyrifos via dermal residue contact and nondietary ingestion. Environ Health Perspect. 108(6):505-514. Zhang, X., M.E. Monroe, B. Chen, M.H. Chin, T.H. Heibeck, A.A. Schepmoes, F. Yang, B.O. Petritis, D.G. Camp II, J.G. Pounds, J.M. Jacobs, D. J. Smith, D. J. Bigelow, R.D. Smith, and W. Qian. 2010. Endogenous 3,4-dihydroxyphenylalanine and dopaquinone modifications on protein tyrosine: Links to mitochondrially derived oxidative stress via hydroxyl radical. Mol. Cell Proteomics 9(6):1199-1208.