6
Human Health and Security

OVERVIEW

Virtually every aspect of human health and well-being is linked to Earth, be it through the air we breathe, the climate or weather we experience, the food we eat, the water we drink, or the environs in which we live, work, or play. Diverse environmental factors affect the distribution, diversity, incidence, severity, and persistence of diseases and other health effects—something that has been recognized for millennia. Yet in the United States today, an estimated 1.8 to 3.1 years of life are lost to people living in the most polluted cities because of chronic exposure to air pollutants (Pope, 2000). Roughly 9 million cases of waterborne disease occur in the United States each year (Rose et al., 2001). Exposure to ultraviolet (UV) radiation may be the most important preventable factor in a person’s risk of skin cancer in the United States (American Academy of Dermatology, 2006); more than 1 million new cases occur each year (American Cancer Society, 2006). The 1995 heat wave in Chicago caused nearly 700 excess deaths (Whitman et al., 1997), and perhaps as many as 15,000 people died in 2003 during a prolonged heat wave in France (Fouillet et al., 2006). The annual number of industrial accidents involving the release of hazardous substances from facilities required to have risk management plans ranged from 225 to more than 500 over the 9-year period ending in 2003 (EPA, 2005). Although diseases transmitted by arthropod vectors (mosquitoes, sand flies, and so on) may be less important in the United States than elsewhere in the world, they still present an important health concern. In developing countries, malaria kills 1 million to 2 million people each year, and dengue fever afflicts as many as 80 million people globally each year (Pinheiro and Corber, 1997). Those are several of the important examples identified by the Panel on Human Health and Security and discussed later in this chapter. The examples critically link observations of Earth’s environment to human health and security risks, and indicate the opportunities that space-based observations offer to better assess and manage those risks.

The current unprecedented rate of global environmental change and the growing rates of global population growth and resource consumption indicate that analyses of such changes are important to human well-being. Global movement of people, pollutants, and lifestyles has exacerbated the role of environmental factors that affect human health. The urgency of obtaining global data—often obtained via space-



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 152
6 Human Health and Security OVERVIEW Virtually every aspect of human health and well-being is linked to Earth, be it through the air we breathe, the climate or weather we experience, the food we eat, the water we drink, or the environs in which we live, work, or play. Diverse environmental factors affect the distribution, diversity, incidence, severity, and persistence of diseases and other health effects—something that has been recognized for millennia. Yet in the United States today, an estimated 1.8 to 3.1 years of life are lost to people living in the most polluted cities because of chronic exposure to air pollutants (Pope, 2000). Roughly 9 million cases of waterborne disease occur in the United States each year (Rose et al., 2001). Exposure to ultraviolet (UV) radiation may be the most important preventable factor in a person’s risk of skin cancer in the United States (American Academy of Dermatology, 2006); more than 1 million new cases occur each year (American Cancer Society, 2006). The 1995 heat wave in Chicago caused nearly 700 excess deaths (Whitman et al., 1997), and perhaps as many as 15,000 people died in 2003 during a prolonged heat wave in France (Fouillet et al., 2006). The annual number of industrial accidents involving the release of hazardous substances from facilities required to have risk management plans ranged from 225 to more than 500 over the 9-year period ending in 2003 (EPA, 2005). Although diseases transmitted by arthropod vectors (mosquitoes, sand flies, and so on) may be less important in the United States than elsewhere in the world, they still present an important health concern. In developing countries, malaria kills 1 million to 2 million people each year, and dengue fever afflicts as many as 80 million people globally each year (Pinheiro and Corber, 1997). Those are several of the important examples identified by the Panel on Human Health and Security and discussed later in this chapter. The examples critically link observations of Earth’s environment to human health and security risks, and indicate the opportunities that space-based observations offer to better assess and manage those risks. The current unprecedented rate of global environmental change and the growing rates of global population growth and resource consumption indicate that analyses of such changes are important to human well-being. Global movement of people, pollutants, and lifestyles has exacerbated the role of environmental factors that affect human health. The urgency of obtaining global data—often obtained via space-

OCR for page 152
based methods—on land-use changes, climate changes, weather extremes, episodes of atmospheric and surface water pollution, and other observations has become critical to understanding how population and economic changes throughout the globe affect our common well-being. The panel considered issues of environmental factors pertinent to its charge, identified various kinds of human health and security risks, and then evaluated how remote sensing data from space might contribute to a better understanding of relationships between those factors and risks. Over the past couple of decades, health and environmental scientists have used remote sensing data in diverse analyses of how environmental factors have altered the risk of various health effects in time and space, and how these insights might eventually be used to make observations and evaluate and manage risk. The basis for such research is the long-term availability of remote sensing data, combined with in situ observations (such as disease surveillance and reporting) that permit analyses necessary to uncover patterns and develop forecasts (NRC, 2007). Such research is impossible without continued capture and dissemination of remote sensing data, information that has served as the basis for understanding many larger-scale spatial environmental patterns. These data, combined with in situ epidemiological observations of disease morbidity and mortality, have served as the mainstay of research on environmental factors and disease and recommendations related to human health and security. Many studies successfully demonstrate the application of remote sensing data to identification of spatial or temporal variation in disease incidence or to assessment of the quantity or quality of air, food, and potable water, for example. The aim of such studies typically is to enhance forecasts of future outbreaks or to understand pathways by which environmental features are linked to increased health risks. Although the research being undertaken has been productive in identifying environmental links to human health risk, the confidence with which most diseases and other health effects can be forecast is still very weak. For this reason, the continued availability of space-based observations of land use and land cover, oceans, weather and climate, and atmospheric pollutants is critical to further enhancing the understanding of links to diseases and to expanding capacity for early warning of times and places where risk is elevated. Only through analyses of long-term time series will such patterns be understood and capabilities developed that will be useful to risk managers and health responders. In general, knowledge of changing risk across regions and habitats, or over weeks to a few years, should improve forecasts, and hence detection capabilities and possible interventions and adaptation. For example, new higher-spatial-resolution satellite data may increase understanding of some infectious diseases whose risk to people is influenced by changes in microhabitat conditions. Also, such data can enhance understanding of relationships between human health effects and UV radiation dosage levels. Likewise better remote sensing capabilities should enhance the capacity to detect and track risk agents, including local drought conditions, harmful algal blooms, regional air pollution, and many acute releases of environmental contaminants. Anticipated health and security benefits currently drive most of the basic research agenda that employs space-based observations, yet public health practitioners and risk managers are only slowly expanding their use of these results. Future research is more likely to be useful if it is closely linked with the needs of the public health community, risk mangers, emergency responders, and specific components of human well-being. Prioritization of Needs The approach taken by this panel differed somewhat from that of most other panels, in that it intentionally began by identifying important health threats that are related to environmental factors and desired health outcomes (societal benefits). The panel then identified the kinds of Earth observation parameters and variables (environmental data) it considered important to informing relevant research and applications, and finally determined which platforms, sensors, and remote sensing data could provide the appropriate

OCR for page 152
data (missions). Thus, the panel’s discussions focused on various kinds of health effects and the Earth environmental factors and contaminants that might contribute to those effects. The discussions also focused on determining which people are at risk, and where and when. Thus, mission recommendations from this panel correspond to those of many of the other panels, in that climate, weather, ecosystems, and water resources, in particular, directly and indirectly affect the range of human health and security issues identified here. This approach led to the following considerations: Setting priorities among the sensors, platforms, or missions essential to human health and security is difficult. The importance of existing and future sensors depends on the environmental health effects that society considers to be of greatest concern, which in turn determines the environmental data that best inform exposure and risk assessments, and efforts to predict and prevent or mitigate health effects. At a minimum, continuity of existing sensors is critical to developing observational and forecast capabilities for most diseases and other health risks. Although environmental links with more direct, short-term health effects are reasonably well understood (e.g., temperature and heat stress or atmospheric pollutants and some respiratory symptoms), many other environment-disease associations involve complex pathways requiring extensive analyses of time series to develop sound predictive associations. Continued research is needed to firmly establish the predictive relationships between remotely sensed environmental data and patterns of environmentally related health effects. Beyond these research needs, preservation of existing sensors (e.g., AVHRR/MODIS, Landsat) will permit continued development and implementation of early warning or detection capacity for some better-understood limits between environmental exposures and health effects. The research agenda of many human health and environmental scientists who analyze remote sensing data and in situ data increasingly involves time-space modeling and statistical analysis of associations, suggesting that federal agencies should vigorously support such efforts. Accordingly, enhanced funding for research on and application of space-based observations to health problems should be an important part of NASA’s and NOAA’s missions to achieve societal benefits. Field evaluation of analytic results and forecasts is important to developing more comprehensive and accurate models of diverse complex environment-disease dynamics. Such efforts may eventually serve as a basis for developing improved observation systems. The need for higher-spatial-resolution data depends on the health problems to be addressed. Exceptions might occur where global transport of risk agents (by water or air plumes or the migration of birds) could be monitored by multiple sensors over large areas. Many health applications of remote sensing data will use data relevant to applications identified by other panels. There is an important synergy between many of the data needs identified by this panel and by other panels. Overarching Issues The World Health Organization defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” Thus, human health and security should be thought of in the larger context of multiple factors that affect people through various direct and indirect pathways. The Earth sciences agenda that relates to human health involves at least how environmental factors affect the more limited notion of human health. However, the manner in which those factors help to shape and define social, economic, and psychological aspects of people’s existence also alters their health. Thus, the value of remote sensing data cannot be considered independently of the more encompassing meaning of health and security, nor of data coming from other sources—demographic, occupational, insurance, housing, and other surveys and analyses.

OCR for page 152
The panel therefore considered the overarching issues to involve much more than the more narrow conceptions of human health. Indeed, it considered that the societal benefits accruing through improved human health should be fundamental to defining the research and applications goals of the Earth science agenda, including the need for an intellectual framework that directs bridging research between the Earth system framework and the public-health response and decision-maker community. It is critical for Earth scientists to interact more openly and effectively with public health and security officials, to help determine the needed understanding, the desired analyses, and the applications through which remote sensing data can contribute to prediction, detection, and mitigation of threats to health and security. With such conceptual, research, planning, and policy interactions, the Earth science community, and NASA and NOAA, will be better able to contribute to improving human health and security, thus achieving the desired societal benefits. Developing such a reliable observational and predictive capacity, based on remote sensing data used in the context of human health risk, should be a goal of future space mission decisions and agency responsibilities. Critical Questions Given these contextual issues, the panel discussed the following questions, among others that were part of its charge: How can remote sensing data be enhanced to assist detection and prediction of the places where disease risk is elevated or times when disease outbreaks are likely? Might such data enhance the rapid detection of events that threaten health or security? How can risk maps derived from space-based observations be used to enhance public-health efforts directed at education and prevention? What new exchanges can expand interactions between remote sensing system designers and public-health analysts that will help identify spatial and temporal risk patterns? What new understanding derived from remote sensing data can be used to target interventions aimed at reducing the vulnerability of human communities to health risks? STATUS AND REQUIREMENTS Status of Current Understanding and Strategic Thinking To illustrate the importance of space-based observations in addressing human health and security, this section provides a few examples of past efforts, discusses the need to assimilate space-based observations with data from other sources, identifies the role of spatial and temporal scale, and stresses the importance of moving research toward operations. Uses of Space-based Observations to Address Human Health Concerns In addressing human health and security concerns, space-based observations are most useful when used along with many other sources of data. Public-health and risk management decision making has benefited from space-based technologies, and can benefit further with improvements in these technologies, through applications that include:

OCR for page 152
Prediction of occurrence of disease or disease outbreaks. Space-based observations provide spatial and temporal data on environmental changes that affect the conditions related to disease occurrence and can be combined within predictive frameworks to forecast health emergencies. Rapid detection and tracking of events. Given sufficient temporal or spatial detail, space-based observations can provide data to support rapid detection of environmental changes or pollution events that affect human health. Construction of risk maps. The spatial extent of space-based observations provides a means to identify spatial variability in risk, potentially improving the scale of environmental observations so that they match the scale of activities in human communities. Targeting interventions. Activities to reduce the vulnerability of human communities to health risks, including environmental, behavioral, educational, and medical interventions, can be guided, improved, and made more efficient by use of available and proposed space-based observational systems. Enhancing knowledge of human health-environment interactions. Basic research on the causes of disease is ongoing, and remote sensing of environmental parameters that affect health is crucial for investigations that improve understanding of the spatial and temporal dynamics of health risk. Assimilation of Space-based Observations with Other Data Sources and Models Space-based observations are most effective as inputs to public-health decision making when they are used in concert with other data systems, including ground-based observations of environmental and epidemiological conditions, demographic data, data collected from aircraft, and outputs from numerical models.1 Investments are needed for the coordination of data collection efforts from multiple sources for specific purposes. Specifically, research on public-health decision support systems needs to address the limitations in how current data systems interface, and the opportunities for coordinating observations. The Importance of Appropriate Spatial and Temporal Resolution Effective incorporation of remote sensing data into public-health and risk management practices requires measurements that are at spatial and temporal resolutions appropriate to the scale of the problems at hand. That often means that data are needed at more finely detailed spatial and temporal resolutions than current technology allows. When rapid response to events is required or continuous monitoring can be used to identify anomalous environmental conditions, fine temporal resolution is required. Accuracy of measurements can also be improved through aggregation of multiple observations over time; frequent observations can be used for this purpose as well. Experience with risk management applications (e.g., warnings on harmful algal bloom and famine early-warning systems) suggests that fine-spatial-resolution data are required to target forecasts and warnings to specific geographical locations; such targeted warnings have been shown to be more effective than blanket warnings over entire regions, as discussed later in this chapter. The Importance of Moving Toward Operational Systems To realize the potential benefits of space-based operations for improving human health, remote sensing has to move from research to operations. Making the data collection operational, in the service of improving 1 For example, NASA’s SEDAC, the socio-economic data and applications center, and its activities, such as the development of the Gridded Population of the World (GPW), the Human Footprint Dataset and the Global Distribution of Poverty, provide examples of effective translation of Earth observation data.

OCR for page 152
BOX 6.1 LESSONS FROM LANDSAT The history of the Landsat program provides useful lessons on how long-term data continuity and user training affect the application of satellite data to real-world problems. As the Landsat technology evolved from the late 1970s to the late 1980s, the scientific literature based on Landsat data increased and its user community grew in size and expertise. By the mid-1990s it had a very large base of knowledgeable users and formed a central theme of much of the teaching about remote sensing at universities. Despite later setbacks (privatization in the late 1980s, the loss of Landsat 6 on launch, and scan-line corrector problemson Landsat 7), it continued its dominance into the new century, including an increase in interdisciplinary applications, such as health, demographics, and geology. At this writing however, the Landsat era is threatened. With only Landsat 5 still operating properly and no replacement expected to be ready in the near future, Landsat dominance in high-resolution environmental monitoring may be over. University training programs are redoing their teaching materials to focus on new sensors. Change-detection research programs are experiencing difficulties as new sensors are not back-compatible with Landsat. Interdisciplinary research scientists now find that their hard-won expertise in Landsat data analysis is obsolete, and they must seek out new collaborators with expertise in new systems. human health and security, requires that they be used to address the five sets of activities listed above and that accurate information products be delivered to public-health practitioners, risk managers, emergency responders, and the public in a timely manner. The data also have to be analyzed so that they are understandable in the context of the problems faced by decision makers and on the scale of human decision making. The data need to be reliably available so that they can be evaluated sufficiently to be trusted by the public-health community and other users and relied on as tools for supporting decisions that have life-and-death consequences. The panel believes that emphasis on three key investments would improve the benefits of remote sensing for this purpose, as well as the development of new sensing systems. First, continuity of systems that provide data to health-related programs and research is important. The existing base of users of space-based observations in the health community has experience with such sensor systems as Landsat, AVHRR, and MODIS, and the availability of these data products is necessary to ensure that the users have access to data they understand (see Box 6.1). Research and applications in public health often require long time series of data to evaluate or predict how environmental changes affect health. Sensor systems with a long-term archive of observations are most useful in such cases. Second, when new sensing systems are brought online, the public-health community, risk mangers, and emergency responders have to be trained to make the best possible use of them. Third, research that develops decision-making frameworks, tools to analyze space-based observations, and tests of efficacy in the context of real-world health interventions are all needed. Status of Existing and Planned Products and Needed Improvements Many, but not all, of the desired satellite sensors relevant to human health and security already exist. However, because the sensors are beginning to fail, plans should be devised for, at a minimum, maintaining these sensors (or their equivalents) so that long-term, time-series research linking environmental processes to health risks or disease patterns can be continued. In addition, these time-series data maintained into the

OCR for page 152
future will be critical for early warning of when and where risk mitigation efforts are warranted. As has been pointed out in other parts of this report, existing sensors are becoming nonfunctional, and replacement of equivalent or enhanced satellites and sensors is in some cases highly uncertain. The need for continued availability of the kinds of atmospheric and surface environmental data that have proved so valuable for understanding health linkages cannot be overstated. New sensors are being recommended—including some that will gather similar data at a higher spectral resolution or over a different horizontal span or time frequency. PRIORITY OBSERVATIONS, MEASUREMENTS, AND TECHNOLOGY DEVELOPMENT This section identifies various needs for space-based observational data that will help to address human health problems in six areas of application: Ultraviolet radiation and cancer, Heat stress and drought, Acute toxic pollution releases, Air pollution and respiratory/cardiovascular disease, Algal blooms and water-borne infectious diseases, and Vector-borne and zoonotic disease. These are linked to the missions (Table 6.1) that are discussed in detail elsewhere in this report. The rationale and means for application of data and information for societal benefits are outlined in each of these health domains. Ultraviolet Radiation and Cancer Mission Summary—Ozone Processes: Ultraviolet Radiation and Cancer Variables: Stratospheric ozone; water vapor; short-lived reactive species (OH, HO2, NO2, CIO, BrO, IO, HONO2, HCI, and CH2O); isotope observations (HDO, H218O, H2O); benchmark tracer data (O3, CO2, CO, HDO/H2O, NOy, N2O, CH4, halogen source molecules); spectrally resolved radiance; cloud and aerosol particles Sensor: Spectrally resolved radiometer (200–2,000 cm−1) Orbit/coverage: LEO/global Panel synergies: Climate, Ecosystems, Weather Background and Importance The need to forecast ultraviolet (UV) dosage levels at Earth’s surface is a first-order public-health issue, as skin cancer occurs with a high frequency and is also a form of cancer with an increasing incidence of occurrence despite the efforts of medical research. The American Cancer Society (2006) estimated that, in 2006, more than 1 million new cases of basal and squamous cell cancers would be diagnosed. In addition, 60,000 cases of melanoma, the most serious form of skin cancer, are diagnosed each year. The catalytic destruction of ozone that has been observed to occur predominantly in the lower stratosphere at high- and midlatitudes over highly populated regions is extremely sensitive to temperature through the potential catalytic conversion of inorganic halogens to free-radical form on cold aerosols and ice particles. Recognition of that sensitivity has created a strong mechanistic link between the forcing of

OCR for page 152
climate by increases in CO2 and H2O that radiatively cool the lower stratosphere, and studies of the loss of ozone by free-radical catalysis. The strong links between skin cancer incidence, ozone loss by catalytic destruction in the stratosphere, and the response of the climate system to CO2 forcing has linked research communities in the pursuit of global UV dosage forecasts. Role of Remotely Sensed Data The panel considered in three parts the problem of understanding and forecasting human health effects of UV. First, it addressed the mechanisms that control catalytic destruction of ozone in the stratosphere (Figure 6.1). That region of the atmosphere is important because evidence gathered over the last 30 years has shown that it has experienced the greatest loss. Second, it considered the impact that climate change will have on the processes that control ozone. Third, it reviewed what is known about the human response to increasing UV dose. Ozone (O3) is controlled in an important way by transport processes that move it poleward and downward at low latitudes. This large-scale transport is summarized in Figure 6.2, which shows convective injection of tropospheric air into the tropical lower stratosphere and into the midlatitude lowermost stratosphere (or “middle world”). Although this illustrates meridional transport, there are also important longitudinal variations coupled to such large-scale events as monsoon structures and seasonal oscillation. The longitudinal variations tend to drive gyres that bring lower stratospheric air masses to amplify catalytic activity. Those observations, of highly increased water convected in the cold lower stratosphere, raise the obvious potential of amplifying the destruction of O3 by catalytic loss. An example of the water-vapor observations is shown in Figure 6.3. The key concern that emerges from the observations is that the combination at lower temperatures and high water-vapor concentrations can dramatically enhance the CIO concentration in particular. That effect is captured in Figure 6.4 (from Kirk-Davidoff et al., 1999), which plots the logarithmic increase in the reaction rate converting HCI and ClONO2 to Cl2 (and then to CIO) and HONO2. CIO is amplified by heterogeneous conversion of HCI and ClONO2 to Cl2 and HONO2, but the mechanism may well not be capable of sufficiently amplifying ozone loss (Smith et al., 2001). However, the link between the BrO and CIO cycles, rate-limited by the reaction CIO+BrO→Cl+Br+O2 (McElroy et al., 1986) may provide an explanation. As Figure 6.1 reveals, small increases in BrO resulting from direct injection of short-lived organic bromines or BrO itself may well provide the solution to the puzzle of what has controlled changes in the ozone column concentration over the last two decades. Figure 6.5 (from Salawitch et al., 2005) shows the impact of small additional amounts of BrO on the loss of ozone column resulting from the addition of aerosol precursors into the stratosphere by volcanic injection. Only with the addition of BrO at 8 ppt can the large losses observed in the ozone column be quantitatively explained. Panel’s Recommended Objectives for UV Dosage Forecasting Those observations provide the foundation of a strategy needed for the forecast of UV dosage at Earth’s surface over the next decades. The following objectives must be achieved: Catalytic destruction of O3 under conditions of low temperature and increased water vapor by the combination of chlorine, bromine, and iodine must be defined by observing the CIO, BrO, and IO concentrations in the lower stratosphere in the presence of increased water-vapor concentrations.

OCR for page 152
TABLE 6.1 Human Health and Security Panel Priorities (Unranked) and Associated Space-based Missions Summary of Mission Focus Variables Type of Sensor(s) Coverage Spatial Resolution Frequency Synergies with Other Panels Related Planned or Integrated Space-based Missions Ozone processes: Ultraviolet radiation and cancer Stratospheric ozone; water vapor; short-lived reactive species (OH, HO2, NO2, ClO, BrO, IO, HONO2, HCl, and CH2O); isotope observations (HDO, H218O, H2O); benchmark tracer data (O3, CO2, CO, HDO/H2O, NOy, N2O, CH4, halogen source molecules); spectrally resolved radiance; cloud and aerosol particles Spectrally resolved radiometer (200-2,000 cm–1) Global 5 km horizontal; 2-3 km vertical TBD Climate Ecosystems Weather GACM ACE ASCENDS CLARREO GEO-CAPE GPSRO Heat stress and drought Rainfall; soil moisture; vegetation state; temperature Microwave sensors, radar, hyperspectral, imagers Global 1 km Twice daily Ecosystems Weather Climate DESDynI GEO-CAPE HyspIRI LIST PATH SMAP GPM LDCM NPP/NPOESS Acute toxic pollution releases Visible atmospheric or hydrospheric plumes; ocean color; particle size; gross vertical structure High-resolution imager (multispectral: UV-near-IR) Geostationary for Western Hemisphere 1 km (aerosols, ocean state, surface layers) Daily Ecosystems GEO-CAPE ACE GACM         1–20 m (multispectral, high resolution) Multi-day   GOES-R         30–50 m (high resolution, particles) 15 min.    

OCR for page 152
Air pollution in lower troposphere linked with respiratory and cardiovascular diseases Aerosol composition and size; NO2, HCHO, VOCs, CO, SO2; tropospheric ozone Multispectral UV/visible/near-IR/thermal IR, lidar Regional and global 10 km horizontal; boundary layer sensitivity Hourly (regional) Climate GACM ACE GEO-CAPE 1 km with vertical structure ~Days (global)   Glory Algal blooms and waterborne infectious diseases Coastal ocean color; sea-surface temperature; atmospheric correction; coastal ocean phytoplankton; river plumes Multispectral Regional 1 km Daily Ecosystems Water SWOT 100 m Weekly   ACE GEO-CAPE PATH SMAP       LDCM NPP/NPOESS Vector-borne and zoonotic disease Meteorological conditions (surface temperature, precipitation, wind speed); soil moisture; landcover status; vegetation state Hyperspectral; high-resolution multispectral, radar, lidar Global 10s of meters >Monthly Ecosystems Weather Water SMAP 1 km (surface temperature, soil moisture, vegetation state) Twice daily   DESDynI HyspIRI LIST PATH SWOT       LDCM NOTE: As in similar tables from the other panels, the missions and instruments shown in this table constitute only the space-based portion of the required observation system. For example, in situ ozone measurements are critical to dynamical and chemical process studies of the atmosphere as well as to validation of aircraft and satellite remote ozone measurements.

OCR for page 152
FIGURE 6.1 Ozone photochemistry. Enhanced bromine: increased ozone (O3) depletion due mainly to BrO+CIO cycle. BrO+HO2 cycle becomes a significant O3 sink below 16 km (BrO+HO2 does not drive O3 depletion if BryTrop is constant. In the left-most panel, BRyTrop=0 ppt; in the middle panel, BRyTrop=8 ppt. SOURCE: Salawitch et al. (2005). Copyright 2005 American Geophysical Union. Reproduced by permission of American Geophysical Union. Mechanisms controlling the dynamic coupling between the troposphere and stratosphere must be established with a combination of in situ isotopes, long-lived tracers, and reactive intermediates to establish how the irreversible flux of water vapor into the stratosphere will change, given increased forcing of the climate system by CO2, methane, and so on. The role of convective injection of short-lived compounds through the tropical tropopause and by convection at midlatitude continental sites must be established. Those objectives require the following combination of high-spatial-resolution observations: The short-lived reactive species OH, HO2, NO2, CIO, BrO, IO, HONO2, HCI, and CH2O to pin down the chemical-catalytic-transport structure of the TTL and the injection of short-lived species into the overworld and middleworld from the troposphere; Isotope observations of HDO, H2O obtained simultaneously in the condensed and vapor phases; Benchmark tracer data (O3, CO2, CO, HDO/H2O, NOy, N2O, CH4, and halogen source molecules) to quantify the extent of horizontal mixing and entrained ambient air and to establish the spatial pattern of the age of the air; Benchmark water vapor and total water based on instruments capable of measuring water-vapor mixing ratios accurately and precisely both outside and inside clouds. Uncertainties in measurements of relative humidity are directly proportional to uncertainties in measurements of water vapor;

OCR for page 152
Resolution requirements for air-quality observations from space include a horizontal pixel size of 1–10 km with continental to global coverage, ability to observe the boundary layer, and a return time of a few hours or less. Those requirements are defined by the need to observe the development of pollution episodes, the variation of emissions, and the state of atmospheric composition for purposes of forecasting. Hourly resolution in polluted regions is highly desirable, inasmuch as it matches the temporal resolutions of surface monitoring data, regional models, and the metrics used in air-quality standards. Outside these regions, temporal resolution can be relaxed to a few times per day for observation of long-range transport. For trace gases, multispectral methods combining UV/visible, near-IR, and thermal IR can offer boundary-layer information at least for ozone and CO. Active (lidar) observations can provide high vertical resolution for aerosols and ozone but with sparse horizontal coverage compared with passive techniques. All the above requirements cannot be met from a single platform. Within the framework of existing or readily developable technology, the highest priority is for a GEO mission, with North America being of prime domestic interest. The satellite should have spectral observation capabilities ranging from the UV-A to the thermal IR. Two shortcomings of GEO are lack of global coverage and limited vertical resolution. Those shortcomings should be overcome with a companion LEO platform that include a high-spectral-resolution lidar for vertical resolution of the boundary-layer aerosol and free tropospheric plumes and multispectral passive sensors ranging from the UV-A to the thermal IR for global observation of pollutant transport. Algal Blooms and Waterborne Infectious Diseases Mission Summary—Algal Blooms and Waterborne Infectious Diseases Variables: Coastal ocean color, sea-surface temperature, atmospheric correction, coastal ocean phytoplankton, river plumes Sensor: Multispectral Orbit/coverage: GEO/regional Panel synergies: Ecosystems, Water Background and Importance The rapid proliferation of toxic or nuisance algae, termed harmful algal blooms (HABs), can occur in marine water, estuarine waters, and freshwaters and are among the scientifically most complex and most economically significant water issues facing the United States. HAB toxins can cause human illness and death, halt the harvesting and sale of fish and shellfish, alter marine habitats, and adversely affect fish, endangered species, and other marine organisms. Previously, only a few regions of the United States were affected by HABs, but now virtually every coastal state reports major blooms (Ecological Society of America, 2005) (Figure 6.10). Economic losses associated with HABs are expected to exceed $1 billion over the next several decades, and a single HAB event can cause millions of dollars in damages in coastal economies through direct and indirect effects (Anderson et al., 2000). In addition to HABs, waterborne pathogens cause human disease and are transmitted in drinking water, through recreational exposure to contaminated water, and through ingestion or inhalation (NRC, 2004). More than 9 million cases of waterborne diseases are estimated to occur in the United States each year (Rose et al., 2001). Most waterborne pathogens are enteric and spread through fecal-oral pathways from animal and human fecal sources and are introduced to waterways through sewage discharges, urban and agricultural runoff, and vessel ballast. Some of the more severe waterborne diseases are hepatic, lymphatic, neurologic, and endocrinologic diseases, including infection with Vibrio cholerae (Lobitz et al., 2000). To develop microbial risk-assessment models for water-borne diseases, it is necessary to study the fate and transport of these pathogens, or the conditions that promote them, across the landscape via aquatic

OCR for page 152
FIGURE 6.10 Harmful algae. Global distribution of harmful algae from the early 1970s to 2005. The red lines indicate areas where harmful algal blooms have been documented. SOURCE: Courtesy of Daniel G.Baden, University of North Carolina at Wilmington and of NOAA National Ocean Service. systems. Table 6.7 lists selected events demonstrating the use of remote sensing to detect and monitor harmful algal blooms (red tides) and waterborne pathogens. Role of Remotely Sensed Data Chief among the needs to mitigate the effects of HABs and waterborne pathogens is the ability to detect, monitor, and forecast them in a cost-effective and timely manner to protect human health. Ocean-color and sea-surface temperature satellite imagery are useful for detecting and tracking HABs (Stumpf and Tomlinson, 2005; Tang et al., 2003). In ocean-color imagery, algal blooms are detected on the basis of differential absorption and backscatter of irradiance; some species are more amenable to detection because of reflectance characteristics of the cells (Carder et al., 1986). A new operational HAB forecast has been used in the Gulf of Mexico since 2004; it provides twice-a-week or daily forecasts, if conditions warrant, of bloom intensity and location (Stumpf, 2001; Stumpf et al., 2003). Information is relayed via a bulletin (www.csc.noaa.gov/crs/habf/) to local managers who use it to optimize sampling locations, focus resources, and notify the public of potential bloom conditions (Backer et al., 2003) (see Box 6.3). Detection of phytoplankton blooms with remote sensing relies on the spectral quality, thermal signature, and hydrographic features of the waters surrounding them. Blooms are often found along frontal zones, and these hydrographic features may be coherent over scales of 102–103 km2. The physical and biological factors affecting bloom dimensions are critical because resolution of patches smaller than about 5–10 km2 is generally not possible with current technology. Major ocean-current systems are often implicated in the transport of harmful algal blooms and indicative of conditions that support Vibrio cholerae. Those currents can be tracked most simply and reliably with thermal AVHRR imagery (Lobitz et al., 2000; Tester and Steidinger, 1997). Remote sensing is most commonly used to track the transport and dispersion

OCR for page 152
TABLE 6.7 Selected Events Demonstrating the Use of Remote Sensing to Detect and Monitor Harmful Algal Blooms (Red Tides) and Waterborne Pathogens Year Eventa 1975–1986 Five citations of papers on remote sensing and red tides 1975 First use of thermal imagery to identify ocean frontal zones where harmful algae were concentrated (Murphy et al., 1975) 1987–1996 28 citations of papers on remote sensing and red tides 1987 First use of thermal imagery to track oceanic currents responsible for the transport of harmful algae (Tester et al., 1991) 1988–1990 NOAA’s Coastwatch Program developed to provide timely access to nearly real-time satellite data for U.S. coastal regions (http://coastwatch.noaa.gov/) 1997–2006 76 citations of papers on remote sensing and red tides 2000 Use of remote sensing for detection of Vibrio cholerae by indirect measurement (Lobitz et al., 2000) 2001 Experimental forecast of harmful algal blooms (Stumpf et al., 2003) 2006 First operational forecast of harmful algal bloom (www.csc.noaa.gov/crs/habf/; see also Box 6.3) aSource of citations is Cambridge Abstracts-Aquatic Sciences, Cambridge University Library, Cambridge, United Kingdom. of waterborne pathogens by using storm-water runoff plumes as surrogates for direct detection. Thermal, ocean-color, Landsat Thematic Mapper (TM) and synthetic-aperture radar imagery has successfully tracked storm-water plumes (DiGiacomo et al., 2004; Nichol, 1993), but remote sensing imagery is not yet widely used in public-health programs. Panel’s Recommendations for Satellite Detection of HABs Public-health officials and marine-resource managers expect regional HAB forecasts to be available for all coastal areas in the United States within a decade. To accomplish that, additional sensors, missions, and resources are needed. The GOES-R Coastal Water Imager (https://osd.goes.noaa.gov/coastal_waters.php) may be the most important advance for satellite detection of HABs in coastal and estuarine waters; the GOES-R platform offers frequent repeated views of an area to reduce the effects of cloud cover. The coastal zone needs higher resolution than the 1 km produced by MODIS and the proposed roughly 0.7 km of VIIRS (Visible Infrared Spectrometer) on NPOESS (see http://www.ipo.noaa.gov/Technology/viirs_summary.html). Typically, the first two pixels nearest the shoreline are lost, so with VIIRS scenario the proposed resolution of GOES-R is about 0.3 km at the equator, which means 0.4–0.45 km for most U.S. coastal waters. The detection of blooms along the coast in turbid, pigment-rich water requires more information than is available from SeaWiFS, MODIS, and the proposed VIIRS. Atmospheric correction is extremely difficult in coastal areas and requires more bands than are currently planned. The set of ocean-color instruments—Sea WiFS, MODIS, and VIIRS—were designed for open-ocean work. They have two near-IR bands for atmospheric correction and most bands in the blue, where the open ocean (“blue water”) changes color substantially. Along the coast, three near-IR bands are needed for atmospheric correction. Red bands are needed to identify algae and separate them from turbidity and tannic acids. At least 10 bands are needed for an effective coastal sensor (three blue, two green, two red, and three near-IR); a 12-band sensor would be optimal (three blue, three green, three red, and three near-IR). In summary, more frequent imagery (GOES-R with a coastal sensor) with higher resolution and sensors with additional bands specifically for resolving chlorophyll signals in coastal waters would be optimal.

OCR for page 152
BOX 6.3 RED TIDES SOURCE: Red tides image courtesy of GeoEye and a NASA SeaWiFs Project; text and graph on wind conditions courtesy of NOAA.

OCR for page 152
Vector-borne and Zoonotic Disease Mission Summary—Vector-borne and Zoonotic Disease Variables: Meteorological conditions (surface temperature, precipitation, wind speed); soil moisture; land-cover status; vegetation state Sensors: Hyperspectral; high-resolution multispectral, radar, lidar Orbit/coverage: Multiple/global Panel synergies: Ecosystems, Weather, Water Background and Importance Infectious diseases still account for more than 25 percent of deaths globally. Remote sensing at moderate to coarse spatiotemporal resolution focused on the visible and near-IR portion of the spectrum has shown exceptional promise in applications in many aspects of public health, especially in risk assessment related to infectious diseases caused by pathogens transmitted to people by arthropods (such as insects and ticks) or animals (as in mammal or bird reservoirs). Those diverse and widespread infectious diseases are grouped here into the broad category of vector-borne and zoonotic (VBZ) diseases. VBZ diseases—such as malaria, dengue, and filariasis—are believed responsible for millions of deaths and tens of millions of illnesses each year. The introduction and spread of West Nile virus through North America by mosquitoes during the last 5 years and recent concerns about the worldwide dissemination of H5N1 avian influenza are key recent examples of how human populations have come to be at risk for VBZ diseases over extensive geographic regions in short periods. The recent appearance and spread of Chikungunya virus by mosquitoes among the islands of southeast Africa and the Indian Ocean demonstrate the explosive growth of vector-borne diseases under permissive environmental conditions (http://www.who.int/csr/don/2006_03_17/en/). During a 1-year period (March 2005 to March 2006), it is estimated that 204,000 of La Reunion’s population of 770,000 became ill from this mosquito-borne virus. Similar epidemics occurred during the same time in Mayotte, Seychelles, and other islands throughout the region, and the illness was exported to at least five European countries by travelers. Even in the absence of high mortality, morbidity associated with explosive epidemics taxes the health-care and economic infrastructures of affected regions. The very suspicion of vector-borne disease outbreaks often engenders substantial economic losses; the report of bubonic plague around Surat, India, in 1994 was estimated to cost the government $600 million in lost revenues from lost exports, tourism, and jobs. Attempts to control VBZ disease epidemics with available resources are hindered by lack of ability to set priorities among areas and target them for intervention. From a practical perspective, satellite observations offer an important opportunity to assess the likelihood of spatial diffusion of disease and to monitor its timing and pattern. Identifying and validating the relationship between remote sensing data and health outcomes remain a major public-health research focus. Space-based applications to VBZ diseases run the gamut from basic research to identify environmental-risk signatures to strategies for integrating remote sensing data into operational decision-support systems. The major goal of such efforts is to establish relationships between environmental conditions, as monitored by satellites, and risk to human populations from VBZ diseases. That requires improved characterization of land use, ecological changes, and changing weather at finer spatial and temporal scales.

OCR for page 152
Role of Remotely Sensed Data Some of the earliest attempts to use remote sensing data were nearly 25 years ago, when satellite sensors were used to identify breeding sites of mosquito species responsible for VBZ diseases. For example, Linthicum et al. (1999) used AVHRR data to locate increased breeding and later Rift Valley fever (RVF) virus activity in East African mosquitoes. RVF poses both a human and an agricultural risk. Washino and Wood (1993) demonstrated that Landsat could identify agricultural sites that were most likely to produce mosquito vectors of malaria. The underlying rationale for using remote sensing data to examine VBZ disease patterns is that environment, land use and land cover, weather, and human behavior determine the distribution and spread of many of the most important infectious agents. Environmental structure and meteorologic conditions affect the distribution and abundance of humans, environmental sources, arthropod vectors, and animal reservoirs of infectious agents. Each of those interacting components can be analyzed with statistical or simulation monitoring of case data and enhanced by using remote sensing land-use pattern data that are integrated with other in situ data. Environmental conditions have been characterized with satellite observations primarily by monitoring reflectance patterns in the visible and near-IR spectrum. Spectral resolution has been coarse, historically relying on Landsat TM, MSS, AVHRR, and SPOT sensors for environmental monitoring. However, empirical studies indicate substantial success in characterizing environmental conditions conducive to disease transmission. For example, Beck and colleagues (1997) used Landsat TM data to identify localized areas, on the basis of vegetation and soil-moisture characteristics, that were at risk for Lyme disease in a spectrally complex residential environment. Radar and lidar have received substantially less evaluation in this field, although their potential utility in complex environments that experience substantial cloud cover during times of interest (such as tropical regions) has been recognized. Imagery with moderate (over 20 m) to low (100–1,000 m) spatial resolution has been most commonly used to characterize environmental conditions, including land cover, elevation, temperature, and vegetation condition. Higher-resolution imagery (less than 10 m) offers utility to identify individual features, especially those related to human activities, and has been used to document the spatial distribution of human populations in regions undergoing rapid, often undocumented, development and land-cover change. Temporal resolution of 1–16 days has proved satisfactory for many of the disease systems studied; in part, this reflects the biological processes associated with pathogen amplification and the population-growth time frame for insect vectors and other animals. Typically, a sufficient environmental signal has been detected to distinguish sites with increased likelihood of disease. An intraday repeat interval for meteorological variables has been assessed with in situ monitoring systems for environmental conditions. Also, satellite-observation capabilities, in combination with in situ observations, allow for an integrated observational approach for use by emergency responders. Future Needs Future applications will require sensors that characterize meteorological conditions (at least maximumal and minimumal surface temperature, daily precipitation, and wind speed) and soil moisture two to four times per day; these appear to be major drivers of short-term vector and animal demographic responses. Those data serve as inputs to calibrate models of VBZ disease dynamics to identify time and space of risk. Many VBZ diseases (such as the Chikungunya virus) begin in tropical and subtropical regions and can spread globally. Those regions often have substantial cloud cover, which makes space-based monitoring of meteorological conditions difficult. Hyperspectral monitoring of land cover is needed to improve characterization of vegetation classes and condition. Repeat coverage on a weekly to about semi-monthly basis is

OCR for page 152
appropriate in that target populations typically respond to changing land-cover conditions relatively slowly. For both types of data streams, moderate spatial resolution (20–500 m) captures much of the information needed for study on regional scales, although 20–100 m would be preferable to resolve spatial details needed for calibration with VBZ disease models. A high-resolution sensor (less than 5 m) with multispectral capability to distinguish general land-cover characteristics is needed to identify detailed patterns of human land use and distribution and locate at-risk populations. Such a system would need a low return rate (less than monthly) to characterize changes in human population occupancy and use patterns. OTHER IMPORTANT ISSUES In addition to the specific mission recommendations, the panel discussed the importance of funding for research and applications aimed at societal benefits that are not specifically related to sensors, satellites, new remote sensing data, or particular missions. It identified the importance of support for capture, synthesis, and analysis of remote sensing data aimed at understanding health and security problems. The principal U.S. government agency charged with human health research, the National Institutes of Health, focuses more on the fundamental determinants of causation and risk than on the environmental causes that the panel considers critical. Even the Environmental Protection Agency has little research funding available for investigation of remote sensing data that might affect human health. The Centers for Disease Control and Prevention encourages studies that are aimed at applications of remote sensing data to specific diseases, but historically it has not had extensive extramural funds for such research. The panel considers the role of NASA, NOAA, and other partner agencies to be critical in funding environment and health scientists in the use of remote sensing data. The societal benefits that we all seek may not be achieved, even if remote sensing data are obtained, unless substantial and sustained support is provided for identifying Earth science determinants of the diverse health risks that can be understood. Another aspect of the broad study charge is to enhance epidemiological and disease surveillance efforts that use remote sensing data in a research or early-warning program. The panel believes that the societal value of such data will be enormously increased if support is offered to health scientists who acquire and study remote sensing data, because they understand how such insights can be used to analyze and anticipate disease outbreaks. Those scientists lack adequate support because they too often fall between the cracks of intellectual domains, research activities, and associated funding. There is an important opportunity for NASA and NOAA to expand their research and application focus to explicitly involve studies of human health and security to a much greater extent. Investment in research on these societal benefits will expand and enhance the value of the agencies to meeting the needs of citizens of the United States and the world. Related to those suggestions, which are critical to the panel’s discussions but not an explicit part of the study charge, is the role of aircraft (for example in Europe’s MOSAIC program) and other nonsatellite sensors in providing data for human health research and disease prevention. Such data sources were not explicitly identified in any of the six disease categories, but they are important for understanding patterns of other human health and security risks. Data collected through the use of aircraft supplement satellite imagery in important ways: they are used for prelaunch sensor tests, postlaunch ground truth, annual high-resolution state surveys, emergency high-resolution mapping (e.g., for observations of chemical spills, ocean blooms, and forest fires). Ground- and ice-penetrating sensors, below-cloud surveys, and special field projects with combined flight-level data and airborne remote sensing also illustrate the importance of aircraft as platforms for the collection of remote sensing data. The aircraft facilities and trained personnel must be maintained and enhanced if the space-borne and airborne environmental monitoring system is to be flexible and resilient.

OCR for page 152
SUMMARY The overall recommendations of the Panel on Human Health and Security are presented in Table 6.1 above. The panel identified many aspects of human health and security that would be enhanced by the availability, analysis, and application of remote sensing data. It considered six broad categories of health-effects mitigation that have been enhanced by application of space-based observations to such diverse health risks. Maintaining the types of remote sensing data that have allowed identification of environment-disease links, in time and space, is critical for future understanding and forecasting of U.S. and global risks. In addition, new sensors that have finer spatial or spectral resolution have been identified and justified for the scientific and social benefits that will probably accrue. Relevant agencies should consider how to engage health and social scientists who are using satellite observations in a manner that encourages analyses that produce societal benefits. Such efforts should also promote interdisciplinary exchanges of data and analytic methods. BIBLIOGRAPHY Adams, R. 2002. Homeland Defense Info Kit, Part 1: Chemical Weapons. National Fire and Rescue, May/June. SpecComm International, Inc., Raleigh, N.C. Available at http://www.nfrmag.com. American Academy of Dermatology. 2006. Ultraviolet index: what you need to know. Available at http://www.aad.org/public/Publications/pamphlets/U ltravioletIndex.htm. American Cancer Society. 2006. Cancer Facts and Figures 2006. American Cancer Society, Atlanta, Ga. Anderson, D.A., Y.Kaoru, and A.W.White. 2000. Estimated Annual Exonomic Impacts from Harmful Algal Blooms (HABs) in the United States. Technical Report WHOI-2000–11. Woods Hole Oceanographic Institution, Woods Hole, Mass. Available at http://www.whoi.edu/redtide/pertinentinfo/Economics_report.pdf. Backer, L.C., L.E.Fleming, A.Rowan, Y.S.Cheng, J.Benson, R.H.Pierce, J.Zaias, J.Bean, G.D.Bossart, D.Johnson, R.Quimbo, and D.G.Baden. 2003. Recreational exposure to aerosolized brevetoxins during florida red tide events. Harmful Algae 2:19–28. Beck, L.R., M.H.Rodriguez, S.W.Dister, A.D.Rodriguez, R.K.Washino, D.R.Roberts, and M.A.Spanner. 1997. Assessment of a remote sensing based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am. J. Trop. Med. Hyg. 56:99–106. Beniston, M. 2004. The 2003 heat wave in Europe: A shape of things to come? An analysis based on Swiss climatological data and model simulations. Geophys. Res. Lett. 31:2022–2026. Boone, J.D., K.C.McGwire, E.W.Otteson, R.S.DeBaca, E.A.Kuhn, P.Villard, P.F.Brussard, and S.C.St. Jeor. 2000. Remote sensing and geographic information systems: Charting Sin Nombre virus infections in deer mice. Emerg. Infect. Dis. 6:248–257. Borman, S., and R.Stevenson. 1998. Spatial resolution enhancement of low-resolution image sequences: A comprehensive review with directions for future research, Technical report. Laboratory for Image and Signal Analysis. University of Notre Dame, Notre Dame, Ind. Brook, R.D., B.Franklin, W.Cascio, Y.Hong, G.Howard, M.Lipsett, R.Luepker, M.Mittleman, J.Samet, S.C.Smith, Jr., and I.Tager. 2004. Air Pollution and cardiovascular disease: A statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association. Circulation 109:2655–2671. Brooker, S., and E.Michael. 2000. The potential of geographical information systems and remote sensing in the epidemiology and control of human helminth infections. Adv. Parasitol. 47:245–288. Brooker, S., M.Beasley, M.Ndinaromtan, E.M.Madjiouroum, M.Baboguel, E.Djenguinabe, S.I.Hay, and D.A.P.Bundy. 2002. Use of Remote sensing and a geographical information system in a national helminth control programme in Chad. Bull. WHO 80:783–789. Brownstein, J.S., H.Rosen, D.Purdy, J.R.Miller, M.Merlino, F.Mostashari, and D.Fish. 2002. Spatial analysis of West Nile Virus: Rapid risk assessment of an introduced vector-borne zoonosis. Vector Borne Zoonotic Dis. 2:157–164. Brownstein, J.S., T.R.Holford, and D.Fish. 2003. A climate-based model predicts the spatial distribution of the Lyme disease vector ixodes scapularis in the United States. Environ. Health Perspectives 111:1152–1157. Buchanan-Smith, M. 1994. What is a famine early warning system? Can it prevent famine? In Usable Science: Food Security, Early Warning, and El Niño (M.H. Glanz, ed.). Proceedings of the Workshop on ENSO/FEWS, Budapest, Hungary, October 25–28, 1993. National Center for Atmospheric Research, Boulder, Colo. Carder, K.L., R.G.Steward, J.H.Paul, and G.A.Vargo. 1986. Relationships between chlorophyll and ocean color constituents as they affect remote-sensing reflectance models. Limnol. Oceanogr. 31:403–413. Claborn, D.M., P.M.Masuoka, T.A.Klein, T.Hooper, A.Lee, and R.G.Andre. 2002. A cost comparison of two malaria control methods in Kyunggi Province, Republic of Korea, using remote sensing and geographic information systems. Am. J. Trop. Med. Hyg. 66:680–685.

OCR for page 152
Clennon, J.A., C.H.King, E.M.Muchiri, H.C.Kariuki, J.H.Ouma, P.Mungai, and U.Kitron. 2004. Spatial patterns of urinary schostosomiasis infection in a highly endemic area of coastal Kenya. Am. J. Topr. Med. Hyg. 70:443–448. Correia, V.R.M., M.S.Carvalho, P.C.Sabroza, and C.H.Vasconcelos. 2004. Remote sensing as a tool to survey endemic diseases in Brazil. Cad. Saúde Pública 20:891–904. Dabberdt, W.F., G.L.Frederick, R.M.Hardesty, W.-C.Lee, and K.Underwood. 2004. Advances in meteorological instrumentation for air quality and emergency response. Meteor. Atmos. Phys. 87(1–3):57–88, doi 10.1007/s00703–003–0061–8. Danson, F.M., P.Giradoux, and P.S.Craig. 2006. Spatial modeling and ecology of Echinococcus multilocularis transmission in China. Parasitol. Int. 55:S227-S231. DiGiacomo, P.M., L.Washburn, B.Holt, and B.H.Jones. 2004. Coastal pollution hazards in southern California observed by SAR imagery: Storm water plumes, wastewater plumes, and natural hydrocarbon seeps. Mar. Pollut. Bull. 49:1013–1024. Ecological Society of America. 2005. Harmful Algal Research and Response National Environmental Science Strategy 2005–2015, available at http://www.esa.org/HARRNESS/. Edwards, D., P.DeCola, J.Fishman, D.Jacob, P.Bhartia, D.Diner, J.Burrows, and M.Goldberg. 2006. Community input to the NRC decadal survey from the NCAR Workshop on Air Quality Remote Sensing From Space: Defining an Optimum Observing Strategy. Community Workshop on Air Quality Remote Sensing from Space: Defining an Optimum Observing Strategy, February 21–23, 2006, National Center for Atmospheric Research, Boulder, Colo. Available at http://www.acd.ucar.edu/Events/Meetings/Air_Quality_Remote_Sensing/Reports/AQRSinputDS.pdf. Elnaiem, D.A., J.Schorscher, A.Bendall, V.Obsomer, M.E.Osman, A.M.Mekkawi, S.J.Connor, R.W.Ashford, and M.C. Thomson. 2003. Risk mapping of visceral leishmaniasis: The role of local variation in rainfall and altitude on the presence and incidence of kiala-Azar in eastern Sudan. Am. J. Trop. Med. Hyg. 6:10–17. Emery, W. 2003. CitySat mission design and operations, Technical Report, 31 pp., Aerospace Engineering Sciences Dept., University of Colorado, Boulder, Colo., 80309 EPA (Environmental Protection Agency). 2003. National Air Quality and Emission Trends Report. Report No. EPA 454-R-03–005. EPA, Research Triangle Park, N.C. EPA. 2004. Air Quality Criteria for Particulate Matter. Report No. EPA 600/P-99/002aF-bF. EPA, Washington, D.C. Available at http://cfpub.epa.gov/ncea/. EPA. 2005. 2004 Year in Review: Emergency Management: Prevention, Preparedness and Response. Report No. EPA-550-R-05–001. EPA, Washington, D.C. EPA. 2006. Air Quality Criteria for Ozone and Related Photochemical Oxidants. Report No. EPA/600/R-05/004aF-cF. EPA, Washington, D.C. Available at http://cfpub.epa.gov/ncea/. Fouillet, G., G.Rey, F.Laurent, G.Pavillon, S.Bellec, C.Guihenneuc-Jouyaux, J.Clavel, E.Jougla, and D.Hemon. 2006. Excess mortality related to the August 2003 heat wave in France. Int. Arch. Occup. Environ. Health 80(1):16–24, doi 10.1007/s00420– 006–0089–4. Franck, D.H., D.Fish, and F.H.Moy. 1998. Landscape features associated with Lyme disease risk in a suburban residential environment. Landscape Ecol. 13:27–36. Fuentes, M.V., J.B.Malone, and S.Mas-Coma. 2001. Validation of a mapping and prediction model for human fasciolosis transmission in Andean very high altitude endemic areas using remote sensing data. Acta Trop. 79:87–95. Glass, G.E., J.E.Cheek, J.A.Patz, T.M.Shields, T.J.Doyle, D.A.Thoroughman, D.K.Hunt, R.E.Enscore, K.L.Gage, C.Irland, C.J. Peters, and R.Bryan. 2000. Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome. Emerg. Infect. Dis. 6:238–247. Glass, G.E., T.L.Yates, J.B.Fine, T.M.Shields, J.B.Kendall, A.G.Hope, C.A.Parmenter, C.J.Peters, T.G.Ksiazek, C.S.Li, J.A.Patz, and J.N.Mills. 2002. Satellite imagery characterizes local animal reservoir populations of Sin Nombre virus in the southwestern United States. Proc. Natl. Acad. Sci. U.S.A. 99:16817–16822. Guerra, M., E.Walker, C.Jones, S.Paskewitz, M.R.Cortinas, A.Stancil, L.Beck, M.Bobo, and U.Kitron. 2002. Predicting the risk of Lyme disease: Habitat suitability for Ixodes scapularis in the north central United States. Emerg. Infect Dis. 8:289–297. Hay, S.I., and A.J.Tatem. 2005. Remote sensing of malaria in urban areas: Two scales, two problems. Am. J. Trop. Med. Hyg. 72:655–657. Herbretau, V., G.Salem, M.Souris, J.-P.Hugot, and J.-P.Gonzalez. 2005. Sizing up human health through remote sensing: Uses and misuses. Parassitologia 47:63–79. Herman, A., V.B.Kumar, P.A.Arkin, and J.V.Kousky. 1997. Objectively determined 10-day African rainfall estimates created for famine early warning systems. Int. J. Remote Sensing 18:2147–2159. IPCC (Intergovernmental Panel on Climate Change). 2001. Climate Change 2001: Synthesis Report. IPCC, Geneva, Switzerland. Available at http://www.grida.no/climate/ipcc_tar/vol4/english/index.htm. Kalkstein, L.S., P.F.Jamason, J.S.Greene, J.Libby, and L.Robinson. 1996. The Philadelphia hot weather-health watch/warning system: Development and application, Summer 1995. Bull. Am. Meteor. Soc. 77(7):1519–1528. Kirk-Davidoff, D.B., E.J.Hintsa, J.G.Anderson, and D.W.Keith. 1999. The effect of climate change on ozone depletion through changes in stratospheric water vapour. Nature 402:399–401. Kitron, U. 2000. Risk maps: Transmission and burden of vector-borne diseases. Parasitol. Today 16:324–325. Klinenberg, E. 2002. Heat Wave: A Social Autopsy of Disaster in Chicago. University of Chicago Press, Chicago, III. Kogan, F.N. 1997. Global drought watch from space. Bull. Amer. Met. Soc. 78(4):621–636.

OCR for page 152
Koppe, C., S. Kovats, G, Jendritzky, and B.Menne. 2004. Heat-waves: Risks and Responses. World Health Organization, Copenhagen. Available at http://www.euro.who.int/document/e82629.pdf. Linthicum, K.J.H., A.Anyamba, C.J.Tucker, P.W.Kelley, M.F.Myers, and C.J.Peters. 1999. Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science 235:1656–1659. Lo, C.P., D.A.Quattrochi, and J.C.Luvall. 1997. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. Int. J. Remote Sensing 18:287–304. Lobitz, B., L.Beck, A.Huq, B.Wood, G.Fuchs, A.S.G.Faruque, and R.Colwell. 2000. Use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proc. Natl. Acad. Sci. U.S.A. 97:1438–1443. Lundquist, J.K., M.Leach, F.Aluzzi, M.Dillon, S.Larsen, H.Walker, G.Sugiyama, and J.Nasstrom. 2006. Personal communication, National Atmospheric Release Advisory Center, Lawrence Livermore National Laboratory, February 22, 2006. McElroy, M.B., R.J.Salawitch, S.C.Wofsy, and J.A.Logan. 1986. Reductions of Antarctic ozone due to synergistic interactions of chlorine and bromine. Nature 321:759–762. Murphy, E.B., K.A.Steidinger, B.S.Roberts, J.Williams, and J.W.Jolley, Jr. 1975. An explanation for the Florida east coast Gymnodinium breve red tide of November 1972. Limnol. Oceanogr. 20:481–486. Mushinzimana, E., S.Munga, N.Minakawa, L.Li, C.C.Feng, L.Bian, U.Kitron, C.Schmidt, L.Beck, G.Zhou, A.K.Githeko, and G. Yan. 2006. Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malar. J. 5:13. NCAR (National Center for Atmospheric Research). 2006. Community Input to the NRC Decadal Survey from the NCAR Workshop on Air Quality Remote Sensing from Space: Defining an Optimum Observing Strategy. Available at http://www.acd.ucar.edu/Meetings/Air_Quality_Remote_Sensing/Reports/. Nichol, J.E. 1993. Remote sensing of water quality in the Singapore-Johor-Riau growth triangle. Remote Sens. Environ. 43:139–148. Nicholls, N. 2004. The changing nature of Australian droughts. Climatic Change 63(3):323–336. NRC (National Research Council). 2003. Tracking and Predicting the Atmospheric Dispersion of Hazardous Material Releases: Implications for Homeland Security. The National Academies Press, Washington, D.C. NRC. 2004. Indicators for Waterborne Pathogens. The National Academies Press, Washington, D.C. NRC. 2007. Contributions of Land Remote Sensing for Decisions About Food Security and Human Health: Workshop Report. The National Academies Press, Washington, D.C. Oddiit, M., P.R.Bessell, E.M.Fevre, T.Robinson, J.Kinoti, P.G.Coleman, S.C.Welburn, J.McDermott, and M.E.Woolhouse. 2006. Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda. Trans. R. Soc. Trop. Med. Hyg. 100:354–362. Park, S.C., K.K.Park, and M.G.Kang. 2003. Super-resolution image reconstruction: A technical overview. Signal Processing 20(3):21–36. Pinheiro, F.P., and S.J.Corber. 1997. Global situation of dengue and dengue haemorrhagic fever, and its emergence in the Americas. World Health Stat. Q. 50(3–4):161–169. Pope, C.A. 2000. Epidemiology of fine particulate air pollution and human health: Biologic mechanisms and who’s at risk? Environ. Health Perspect. 108:713–723. Pope, K.O., E.J.Sheffner, K.J.Linthicum, C.L.Bailey, T.M.Logan, E.S.Kasischke, K.Birney, A.R.Njogu, and C.R.Roberts. 1992. Identification of central Kenyan Rift Valley fever virus vector habitats with Landsat TM and evaluation of their flooding status with Airborne Imaging Radar. Remote Sens. Environ. 40:185–196. Rodriguez, A.D., M.H.Rodriguez, J.E.Hernández, S.W.Dister, L.R.Beck, E.Rejmánková, and D.R.Roberts. 1996. Landscape surrounding human settlements and malaria mosquito abundance in southern Chiapas, Mexico. J. Med. Entomol. 33:39–48. Rogers, D.J., S.E.Randolph, R.W.Snow, and S.I.Hay. 2002. Satellite imagery in the study and forecast of malaria. Nature 415:710– 715. Rose, J.B., P.R.Epstein, E.K.Lipp, B.H.Sherman, S.M.Bernard, and J.A.Patz. 2001. Climate variability and change in the United States: Potential impacts on water and food borne diseases caused by microbiologic agents. Environ. Health Persp. 109:211–221. Salawitch, R.J., D.K.Weisenstein, L.J.Kovalenko, C.E.Sioris, P.O.Wennberg, K.Chance, M.K.W.Ko, and C.A.McLinden. 2005. Sensitivity of ozone to bromine in the lower stratosphere. Geophys. Res. Lett. 32:L05811, doi:2004GL021504. Sherwood, S.C., and A.E.Desslert. 2004. Effect of convection on the summertime extratropical lower stratosphere. J. Geophys. Res. 109:D23301. Smith, J.B., E.J.Hintsa, N.T.Allen, R.M.Stimpfle, and J.G.Anderson. 2001. Mechanisms for midlatitude ozone loss: Heterogeneous chemistry in the lowermost stratosphere? J. Geophys. Res. 106(D1):1297–1309. Stumpf, R.P. 2001. Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms. Hum. Ecolo. Risk Assess. 7:1363–1368. Stumpf, R.P., and M.C.Tomlinson. 2005. Remote sensing of harmful algal blooms. Pp. 277–296 in Remote Sensing of Coastal Aquatic Environments: Technologies, Techniques and Applications (R.L.Miller, C.E.Del Castillo, and B.A.McKee, eds.). Remote Sensing and Digital Image Processing Series, Vol. 7. Springer, The Netherlands. Stumpf, R.P., M.E.Culver, P.A.Tester, M.Tomlinson, G.J.Kirkpatrick, B.A.Pederson, E.Truby, V.Ransibrahmanakul, M.Soracco. 2003. Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data. Harmful Algae 2:147–160.

OCR for page 152
Tang, E.L., H.Kawamura, M.A.Lee, and T.Van Dien. 2003. Seasonal and spatial distribution of chlorophyll-a concentrations and water conditions in the Gulf of Tonkin, South China Sea. Remote Sens. Environ. 85:475–483. Tester, P.A., and Steidinger, K.A. 1997. Gymnodinium breve red tide blooms: Initiation, transport, and consequences of surface circulation. Limnol. Oceanogr. 42:1039–1051. Tester, P.A., R.P.Stumpf, F.M.Vukovich, P.K.Fowler, and J.T.Turner. 1991. An expatriate red tide bloom: transport, distribution, and persistence. Limnol. Oceanogr. 36(5):1053–1061. Thomson, M.C., F.J.Doblas-Rewyes, S.J.Mason, R.Hagedorn, S.J.Connor, T.Phindela, A.P.Morse, and T.N.Palmer. 2006. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439:576–579. Vandewalle, P., L.Sbalz, S.Süsstrunk, and M.Vetterli. 2006. Registration of aliased images for super-resolution imaging. Pp. 13–23 in Proceedings of SPIE 2006: Visual Communications and Image Processing (J.G.Apostolopoulos and A.Said, eds.). Vol. 6077. International Society for Optical Engineering (SPIE), Bellingham, Wash. Available at http://spiedl.aip.org. Ward, M.P., B.H.Ramsey, and K.Gallo. 2005. Rural cases of equine West Nile virus encephalomyelitis and the normalized difference vegetation index. Vector Borne Zoonotic Dis. 5:181–188. Washino, R.K., and B.L.Wood. 1993. Application of remote sensing to vector arthropod surveillance and control. Am. J. Trop. Med. Hyg. 50:134–144. Whitman, S., G.Good, E.R.Donoghue, N.Benbow, W.Shou, and S.Mou. 1997. Mortality in Chicago attributed to the July 1995 heat wave. Am. J. Public Health 87:1515–1518. WHO (World Health Organization). 1948. Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, June 19–22, 1946; signed on July 22, 1946, by the representatives of 61 States; and entered into force on April 7, 1948. Official Records of the World Health Organization, No. 2, p. 100. Wood, B.L., L.R.Beck, B.M.Lobitz, and M.R.Bobo. 2000. Education, outreach and the future of remote sensing in human health. Adv. Parasitol. 47:331–344. World Meteorological Organization. 2003. Scientific Assessment of Ozone Depletion: 2002. Global Ozone Research and Monitoring Project Report No. 47. World Meteorological Organization, Geneva. Yang, G.J., P.Vounatsou, X.N.Zhou, J.Utzinger, and M.Tanner. 2005. A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China. Acta Trop. 96:117–129. Zaitchik, B.F., A.K.Macalady, L.R.Bonneau, and R.B.Smith. 2006. Europe’s 2003 heat wave: A satellite view of impacts and Land-Atmosphere Feedbacks. Int. J. Climatol. 26:743–769, doi:10.1002/joc.1280.