uri,href,identifier,attributes,caption,chapter_identifier,create_dt,lat_max,lat_min,lon_max,lon_min,ordinal,report_identifier,source_citation,submission_dt,time_end,time_start,title,url,usage_limits
/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/climate-change-and-health-vibrio,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/climate-change-and-health-vibrio,climate-change-and-health-vibrio,,"This conceptual diagram for an example of infection by <i>Vibrio</i> species (<i>V.</i> <i>vulnificus</i><i>,</i> <i>V. parahaemolyticus,</i> or <i>V. alginolyticus</i>) illustrates the key pathways by which humans are exposed to health threats from climate drivers. These climate drivers create more favorable growing conditions for these naturally occurring pathogens in coastal environments through their effects on coastal salinity, turbidity (water clarity), or plankton abundance and composition. Longer seasons for growth and expanding geographic range of occurrence increase the risk of exposure to <i>Vibrio</i>, which can result in various potential health outcomes (center boxes). These exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Key factors that influence vulnerability for individuals are shown in the right box and include social determinants of health and behavioral choices. Key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change. See <a href=""https://health2016.globalchange.gov/node/9"">Ch. 1: Introduction</a> for more information.",water-related-illnesses,2015-03-16T00:00:00,N/A,N/A,N/A,N/A,1,usgcrp-climate-human-health-assessment-2016,,,,,"Climate Change and Health - Vibrio",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness,links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness,,"Precipitation and temperature changes affect fresh and marine water quantity and quality primarily through urban, rural, and agricultural runoff. This runoff in turn affects human exposure to water-related illnesses primarily through contamination of drinking water, recreational water, and fish and shellfish.",water-related-illnesses,2014-10-30T14:42:00,N/A,N/A,N/A,N/A,2,usgcrp-climate-human-health-assessment-2016,,,,,"Links between Climate Change, Water Quantity and Quality, and Human Exposure to Water-Related Illness",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/locations-of-livestock-and-projections-of-heavy-precipitation,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/locations-of-livestock-and-projections-of-heavy-precipitation,locations-of-livestock-and-projections-of-heavy-precipitation,,"This figure compares the geographic distribution of chicken, cattle, and hog and pig densities to the projected change in annual maximum 5-day precipitation totals (2046–2065 compared to 1981–2000, multi-model average using RCP8.5) across the continental United States. Increasing frequency and intensity of precipitation and subsequent increases in runoff are key climate factors that increase the potential for pathogens associated with livestock waste to contaminate water bodies. (Figure sources: adapted from Sun et al. 2015 and USDA 2014).<tbib>b63c9720-f770-4718-89cc-53b3616e2bec</tbib><sup>,</sup><tbib>1002d699-e8a9-4572-aec0-16524400e7a5</tbib>",water-related-illnesses,2015-12-09T14:13:00,49.38,24.50,-66.95,-124.80,3,usgcrp-climate-human-health-assessment-2016,,,2012-12-31T23:59:59,2012-12-31T00:00:00,"Locations of Livestock and Projections of Heavy Precipitation",,"Copyright protected. Obtain permission from the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-vibrio-occurrence-and-abundance-in-chesapeake-bay,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-vibrio-occurrence-and-abundance-in-chesapeake-bay,projections-of-vibrio-occurrence-and-abundance-in-chesapeake-bay,,"Seasonal and decadal projections of abundance <i>of V. parahaemolyticus</i> in oysters of Chesapeake Bay (top) and probability of occurrence of <i>V. vulnificus</i> in Chesapeake Bay surface waters (bottom). The circles show average values in the baseline period (1985–2000) and future years averaged by decadal period: 2030 (2025–2034), 2050 (2045–2054), and 2095 (2090–2099). (Figure source: adapted from Jacobs et al. 2015).<tbib>8640a3db-35fa-4089-8fb5-d52dc8b35c71</tbib>",water-related-illnesses,2015-12-11T10:51:00,,,,,4,usgcrp-climate-human-health-assessment-2016,,,2099-12-31T23:59:59,2014-12-31T00:00:00,"Projections of Vibrio Occurrence and Abundance in Chesapeake Bay",,"Copyright protected. Obtain permission from the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/changes-in-suitable-coastal-vibrio-habitat-in-alaska,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/changes-in-suitable-coastal-vibrio-habitat-in-alaska,changes-in-suitable-coastal-vibrio-habitat-in-alaska,,"<i>Vibrio</i> growth increases in temperatures above 15°C (59°F). These maps show the low and high end of the ranges for projected area of Alaskan coastline with water temperature averages in August that are greater than this threshold. The projections were made for the following future time periods: 2030 (2026–2035), 2050 (2046–2055), and 2090 (2086–2095). On average, the models project that by 2090, nearly 60% of the Alaskan shoreline in August will become suitable <i>Vibrio</i> habitat. (Figure source: adapted from Jacobs et al. 2015)<tbib>8640a3db-35fa-4089-8fb5-d52dc8b35c71</tbib>",water-related-illnesses,2015-10-21T14:22:00,N/A,N/A,N/A,N/A,5,usgcrp-climate-human-health-assessment-2016,,,2090-12-31T23:59:59,2030-01-01T00:00:00,"Changes in Suitable Coastal Vibrio Habitat in Alaska",,"Copyright protected. Obtain permission from the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projected-changes-in-caribbean-gambierdiscus-species,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projected-changes-in-caribbean-gambierdiscus-species,projected-changes-in-caribbean-gambierdiscus-species,,"Water temperature data from 1990–2013 were collected or reconstructed for buoy sites in the western Gulf of Mexico, Yucatan channel, and eastern Caribbean Sea. These data were then used in calculations to project average annual water temperature and average growth rates for three Caribbean <i>Gambierdiscus</i> species (<i>G. caribaeus</i>, <i>G. belizeanus</i>, <i>G. carolinianus</i>) for the period 2014–2099. (Figure source: adapted from Kibler et al. 2015).<tbib>1dfd14e0-eae8-46d9-9c3e-0fa3f0c37da4</tbib>",water-related-illnesses,2015-12-11T12:54:00,,,,,6,usgcrp-climate-human-health-assessment-2016,,,,,"Projected Changes in Caribbean Gambierdiscus Species",,
/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-growth-of-alexandrium-in-puget-sound,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-growth-of-alexandrium-in-puget-sound,projections-of-growth-of-alexandrium-in-puget-sound,,"Seasonal and decadal projections of growth of <i>Alexandrium</i> in Puget Sound. The circles show average values in the baseline period (2006–2013) and future years averaged by decadal period: 2030 (2025–2035), 2050 (2045–2055), and 2095 (2090–2099). Growth rate values above 0.25_d<sup>-1</sup> constitute a bloom of <i>Alexandrium</i> (Figure source: adapted from Jacobs et al. 2015)<tbib>8640a3db-35fa-4089-8fb5-d52dc8b35c71</tbib>",water-related-illnesses,2015-12-11T12:57:00,,,,,7,usgcrp-climate-human-health-assessment-2016,,,2099-12-31T23:59:59,2014-12-31T00:00:00,"Projections of Growth of Alexandrium in Puget Sound",,"Copyright protected. Obtain permission from the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/farm-to-table,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/farm-to-table,farm-to-table,,"The food system involves a network of interactions with our physical and biological environments as food moves from production to consumption, or from “farm to table.” Rising CO<sub>2</sub> and climate change will affect the quality and distribution of food, with subsequent effects on food safety and nutrition.",food-safety-nutrition-and-distribution,2014-11-20T00:00:00,N/A,N/A,N/A,N/A,1,usgcrp-climate-human-health-assessment-2016,,,,,"Farm to Table",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/climate-change-and-health-salmonella,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/climate-change-and-health-salmonella,climate-change-and-health-salmonella,,"This conceptual diagram for a <i>Salmonella</i> example illustrates the key pathways by which humans are exposed to health threats from climate drivers, and potential resulting health outcomes (center boxes). These exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Key factors that influence vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change. See <a href=""https://health2016.globalchange.gov/node/9"">Ch. 1: Introduction</a> for more information.",food-safety-nutrition-and-distribution,2015-03-05T00:00:00,N/A,N/A,N/A,N/A,2,usgcrp-climate-human-health-assessment-2016,,,,,"Climate Change and Health--Salmonella",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/seasonality-of-human-illnesses-associated-with-foodborne-pathogens,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/seasonality-of-human-illnesses-associated-with-foodborne-pathogens,seasonality-of-human-illnesses-associated-with-foodborne-pathogens,,"A review of the published literature from 1960 to 2010 indicates a summertime peak in the incidence of illnesses associated with infection from a) <i>Campylobacter</i>, b) <i>Salmonella</i>, and c) <i>Escherichia coli</i> (<i>E. coli</i>). For these three pathogens, the monthly seasonality index shown here on the y-axis indicates the global disease incidence above or below the yearly average, which is denoted as 100. For example, a value of 145 for the month of July for Salmonellosis would mean that the proportion of cases for that month was 45% higher than the 12 month average. Unlike these three pathogens, incidence of norovirus, which can be attained through food, has a wintertime peak. The y-axis of the norovirus incidence graph (d) uses a different metric than (a–c): the monthly proportion of the annual sum of norovirus cases in the northern hemisphere between 1997 and 2011. For example, a value of 0.12 for March would indicate that 12% of the annual cases occurred during that month). Solid line represents the average; confidence intervals (dashed lines) are plus and minus one standard deviation. (Figure sources: a, b, and c: adapted from Lal et al. 2012; d: adapted from Ahmed et al. 2013)<tbib>84097f67-e3ee-4293-a657-b7f7d2b91e29</tbib><sup>,</sup><tbib>04230d65-7ec8-4b53-a59a-fa960649b9c4</tbib>",food-safety-nutrition-and-distribution,2015-01-07T01:00:00,N/A,N/A,N/A,N/A,3,usgcrp-climate-human-health-assessment-2016,,,,,"Seasonality of Human Illnesses Associated With Foodborne Pathogens",,"Copyright protected. Obtain permission from the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/effects-of-carbon-dioxide-on-protein-and-minerals,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/effects-of-carbon-dioxide-on-protein-and-minerals,effects-of-carbon-dioxide-on-protein-and-minerals,,"Direct effect of rising atmospheric carbon dioxide (CO<sub>2</sub>) on the concentrations of protein and minerals in crops. The top figure shows that the rise in CO<sub>2</sub> concentration from 293 ppm (at the beginning of the last century) to 385 ppm (global average in 2008) to 715 ppm (projected to occur by 2100 under the RCP8.5 and RCP6.0 pathways),<tbib>30b72411-16f2-400d-a1f1-deddf0ef757b</tbib> progressively lowers protein concentrations in wheat flour (the average of four varieties of spring wheat). The lower figure—the average effect on 125 plant species and cultivars—shows that a doubling of CO<sub>2</sub> concentration from preindustrial levels diminishes the concentration of essential minerals in wild and crop plants, including ionome (all the inorganic ions present in an organism) levels, and also lowers protein concentrations in barley, rice, wheat and potato. (Figure source: Experimental data from Ziska et al. 2004 (top figure), Taub et al. 2008, and Loladze 2014 (bottom figure)).<tbib>de07adc8-7f48-4455-8b2a-6707520acd59</tbib><sup>,</sup><tbib>d763a364-656a-4a46-96cc-82800edc3ac2</tbib><sup>,</sup><tbib>6f0fe842-95ce-481a-b3f6-473975719843</tbib>",food-safety-nutrition-and-distribution,2014-11-21T08:00:00,N/A,N/A,N/A,N/A,4,usgcrp-climate-human-health-assessment-2016,,,,,"Effects of Carbon Dioxide on Protein and Minerals",,"Copyright protected. Obtain permission from the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mississippi-river-level-at-st-louis-missouri,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mississippi-river-level-at-st-louis-missouri,mississippi-river-level-at-st-louis-missouri,,"Mississippi River gauge height at St. Louis, MO, from October 2007 through October 2014 showing low water conditions during the 2012 drought and water levels above flood stage in 2013. (Figure source: adapted from USGS 2015)<tbib>b2c1fa72-8eb0-4983-9281-331db52c5b8e</tbib>",food-safety-nutrition-and-distribution,2014-10-08T19:00:00,N/A,N/A,N/A,N/A,5,usgcrp-climate-human-health-assessment-2016,,,2014-10-08T23:59:59,2007-10-01T00:00:00,"Mississippi River Level at St. Louis, Missouri",,
/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mycotoxin-in-corn,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mycotoxin-in-corn,mycotoxin-in-corn,,,food-safety-nutrition-and-distribution,2009-08-31T12:00:00,N/A,N/A,N/A,N/A,6,usgcrp-climate-human-health-assessment-2016,,,,,"Mycotoxin in Corn",,
/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/low-water-conditions-on-mississippi-river,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/low-water-conditions-on-mississippi-river,low-water-conditions-on-mississippi-river,,,food-safety-nutrition-and-distribution,,,,,,7,usgcrp-climate-human-health-assessment-2016,,,,,"Low Water Conditions on Mississippi River",,
/report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/climate-change-and-mental-health,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/climate-change-and-mental-health,climate-change-and-mental-health,,"This conceptual diagram illustrates the key pathways by which humans are exposed to health threats from climate drivers, and potential resulting mental health and well-being outcomes (center boxes). These exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Key factors that influence health outcomes and vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence health outcomes and vulnerability at larger community or societal scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors may also be affected by climate change. See <a href=""https://health2016.globalchange.gov/node/9"">Chapter 1: Introduction</a> for more information.",mental-health-and-well-being,2015-02-05T00:00:00,N/A,N/A,N/A,N/A,1,usgcrp-climate-human-health-assessment-2016,,,,,"Climate Change and Mental Health",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/the-impact-of-climate-change-on-physical-mental-and-community-health,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/the-impact-of-climate-change-on-physical-mental-and-community-health,the-impact-of-climate-change-on-physical-mental-and-community-health,,"At the center of the diagram are human figures representing adults, children, older adults, and people with disabilities. The left circle depicts climate impacts including air quality, wildfire, sea level rise and storm surge, heat, storms, and drought. The right circle shows the three interconnected health domains that will be affected by climate impacts—Medical and Physical Health, Mental Health, and Community Health. (Figure source: adapted from Clayton et al. 2014).<tbib>f66b946f-c672-4a4b-8f71-1b05738e029e</tbib>",mental-health-and-well-being,2014-12-05T01:00:00,N/A,N/A,N/A,N/A,2,usgcrp-climate-human-health-assessment-2016,,,,,"The Impact of Climate Change on Physical, Mental, and Community Health",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/determinants-of-vulnerability,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/determinants-of-vulnerability,determinants-of-vulnerability,,"Defining the determinants of vulnerability to health impacts associated with climate change, including exposure, sensitivity, and adaptive capacity. (Figure source: adapted from Turner et al. 2003)<tbib>b6a2f8d3-a113-4e46-b62c-7fbaf90b4f59</tbib>",populations-of-concern,2015-10-06T11:03:00,N/A,N/A,N/A,N/A,1,usgcrp-climate-human-health-assessment-2016,,,,,"Determinants of Vulnerability",,"Copyright protected. Obtain permission from the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/intersection-of-social-determinants-of-health-and-vulnerability,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/intersection-of-social-determinants-of-health-and-vulnerability,intersection-of-social-determinants-of-health-and-vulnerability,,"Social determinants of health interact with the three elements of vulnerability. The left side boxes provide examples of social determinants of health associated with each of the elements of vulnerability. Increased exposure, increased sensitivity and reduced adaptive capacity all affect vulnerability at different points in the causal chain from climate drivers to health outcomes (middle boxes). Adaptive capacity can influence exposure and sensitivity and also can influence the resilience of individuals or populations experiencing health impacts by influencing access to care and preventive services. The right side boxes provide illustrative examples of the implications of social determinants on increased exposure, increased sensitivity, and reduced adaptive capacity.",populations-of-concern,2015-10-19T08:49:00,N/A,N/A,N/A,N/A,2,usgcrp-climate-human-health-assessment-2016,,,,,"Intersection of Social Determinants of Health and Vulnerability",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/vulnerability-to-the-health-impacts-of-climate-change-at-different-lifestages,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/vulnerability-to-the-health-impacts-of-climate-change-at-different-lifestages,vulnerability-to-the-health-impacts-of-climate-change-at-different-lifestages,,"Children’s vulnerability to climate change results from distinct exposures, biological sensitivities (developing bodies and immune systems), and limitations to adaptive capacity (dependency on caregivers) at different life stages.",populations-of-concern,2014-11-17T12:55:00,N/A,N/A,N/A,N/A,3,usgcrp-climate-human-health-assessment-2016,,,,,"Vulnerability to the Health Impacts of Climate Change at Different Lifestages",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-social-vulnerability,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-social-vulnerability,mapping-social-vulnerability,,"CDC Social Vulnerability Index (SVI): This interactive web map shows the overall social vulnerability of the U.S. Southwest in 2010. The SVI provides a measure of four social vulnerability elements: socioeconomic status; household composition; race, ethnicity, and language; and housing/transportation. Each census tract receives a separate ranking for overall vulnerability at the census-tract level. Dark blue indicates the highest overall vulnerability (the top quartile) with the lowest quartile in pale yellow. (Figure source: ATSDR 2015)<tbib>90ee72cf-ab21-486c-bb40-45780e31b45f</tbib>",populations-of-concern,2014-12-01T01:00:00,,,,,4,usgcrp-climate-human-health-assessment-2016,,,2010-12-31T23:59:59,2010-01-01T00:00:00,"Mapping Social Vulnerability",,
/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-communities-vulnerable-to-heat-in-georgia,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-communities-vulnerable-to-heat-in-georgia,mapping-communities-vulnerable-to-heat-in-georgia,,"Vulnerability to heat-related illness in Georgia extends beyond urban zones. The map on the left shows a composite measure of social vulnerability for the Atlanta, Georgia Metropolitan Area (darkest colors indicate the most vulnerable areas). The six state-wide maps on the right show the following six vulnerability factors: 1) percent population below the poverty level, 2) percent aged 65 and older living alone, 3) heat event exposure with Heat Index over 100¼F for two consecutive days, 4) percent dialysis patients on Medicare, 5) hospital insufficiency based upon accessibility of hospital infrastructure, and 6) percent impervious surface. Areas located in rural southern Georgia experienced more hazardous heat events, had less access to health care, and had a higher percentage of people living alone. (Figure source: adapted from Manangan et al. 2014)<tbib>399cfb21-5e6d-425a-98ec-55f42e32401a</tbib>",populations-of-concern,2014-11-01T01:00:00,,,,,5,usgcrp-climate-human-health-assessment-2016,,,,,"Mapping Communities Vulnerable to Heat in Georgia",,
/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/examples-of-climate-impacts-on-human-health,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/examples-of-climate-impacts-on-human-health,examples-of-climate-impacts-on-human-health,,"The diagram shows specific examples of how climate change has already affected or will continue to affect human health in the United States. The examples listed in the first column are those described in each underlying chapter’s Exposure Pathway diagram (see Guide to the Report). Moving from left to right along one health impact row, the three middle columns show how climate drivers affect an individual or a community’s exposure to a health threat and the resulting change in health outcome. The overall climate impact is summarized in the final gray column. For a more comprehensive look at how climate change affects health, and to see the environmental, institutional, social, and behavioral factors that play an interactive role in determining health outcomes, see chapters 2–8.",executive-summary,2015-09-09T10:00:00,,,,,1,usgcrp-climate-human-health-assessment-2016,,,,,"Examples of Climate Impacts on Human Health",,"Free to use with credit to the original figure source."
/report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/scenarios-of-future-temperature-rise,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/scenarios-of-future-temperature-rise,scenarios-of-future-temperature-rise,,"Projected global average temperature rise for specific emissions pathways (left) and concentration pathways (right) relative to the 1901_1960 average. Shading indicates the range (5thto 95th&nbsp;percentile) of results from a suite of climate models. Projections in 2099 are indicated by the bars to the right of each panel. In all cases, temperatures are expected to rise, although the difference between lower and higher pathways is substantial.<br /> 	<br />The left panel shows the two main CMIP3 scenarios (SRES) used in this assessment: A2 assumes continued increases in emissions throughout this century, and B1 assumes significant emissions reductions beginning around 2050. The right panel shows the newer CMIP5 scenarios using Representative Concentration Pathways (RCPs). CMIP5 includes both lower and higher pathways than CMIP3. The lowest concentration pathway shown here, RCP2.6, assumes immediate and rapid reductions in emissions and would result in about 2.5°F of warming in this century. The highest pathway, RCP8.5, roughly similar to a continuation of the current path of global emissions increases, is projected to lead to more than 8°F warming by 2100, with a high-end possibility of more than 11°F. (Data from CMIP3, CMIP5, and NOAA NCEI). (Figure source: adapted from Melillo et al. 2014)<tbib>dd5b893d-4462-4bb3-9205-67b532919566</tbib>",appendix-1--technical-support-document,2014-07-02T08:38:00,90.00,-90.00,180.00,-180.00,1,usgcrp-climate-human-health-assessment-2016,,,2100-12-31T23:59:59,1900-01-01T00:00:00,"Scenarios of Future Temperature Rise",,
/report/usgcrp-climate-human-health-assessment-2016/chapter/front-matter/figure/understanding-the-exposure-pathway-diagrams,https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/front-matter/figure/understanding-the-exposure-pathway-diagrams,understanding-the-exposure-pathway-diagrams,,"The center boxes include selected examples of climate drivers, the primary pathways by which humans are exposed to health threats from those drivers, and the key health outcomes that may result from exposure. The left gray box indicates examples of the larger environmental and institutional context that can affect a person’s or community’s vulnerability to health impacts of climate change. The right gray box indicates the social and behavioral context that also affects a person’s vulnerability to health impacts of climate change. This path includes factors such as race, gender, and age, as well as socioeconomic factors like income and education or behavioral factors like individual decision making. The examples listed in these two gray boxes can increase or reduce vulnerability by influencing the exposure pathway (changes in exposure) or health outcomes (changes in sensitivity or adaptive capacity). The diagram shows that climate change can affect health outcomes directly and by influencing the environmental, institutional, social, and behavioral contexts of health.",front-matter,,,,,,1,usgcrp-climate-human-health-assessment-2016,,,,,"Understanding the Exposure Pathway Diagrams",,"Free to use with credit to the original figure source."