Jianbo Gao, Xin Hou, Yang Cheng, Yu Ye, Yuxiao Wang, Jingsong Kong
With the many health implications of droughts and floods known, and the many adverse secondary and tertiary effects of the Covid-19 pandemic still lingering, it is important to study the complex interactions of epidemics, droughts, and floods. To gain insights into this issue, we have constructed epidemic, drought, and flood indices for the Ming and Qing dynasties of China in a period of more than 400 years. Using adaptive fractal analysis, we find that the time series of epidemic, drought, and flood indices possess long-range correlations of different degrees in different regions of China. More importantly, the scaling behavior for the cross correlations between the epidemic and the drought indices in Northern China is characterized by a non-stationary emergent behavior rather than by a long-range correlation, while this scaling behavior is close to the boundary of stationarity and non-stationarity in the Central China. This scaling is up to about 16 years, highlighting that on average, outbreak of large-scale epidemics may occur not shorter than once every 32 years. Interestingly, the emergent behavior can be characterized as a Zipf's law for the ranked size of the epidemics, mostly in the Northern China, and sometimes also involving some regions in the Central China.
{"title":"Emergence From the Complex Interactions of Epidemics, Droughts, and Floods: Insights From Ming and Qing Dynasties of China During 1470–1911","authors":"Jianbo Gao, Xin Hou, Yang Cheng, Yu Ye, Yuxiao Wang, Jingsong Kong","doi":"10.1029/2024GH001224","DOIUrl":"https://doi.org/10.1029/2024GH001224","url":null,"abstract":"<p>With the many health implications of droughts and floods known, and the many adverse secondary and tertiary effects of the Covid-19 pandemic still lingering, it is important to study the complex interactions of epidemics, droughts, and floods. To gain insights into this issue, we have constructed epidemic, drought, and flood indices for the Ming and Qing dynasties of China in a period of more than 400 years. Using adaptive fractal analysis, we find that the time series of epidemic, drought, and flood indices possess long-range correlations of different degrees in different regions of China. More importantly, the scaling behavior for the cross correlations between the epidemic and the drought indices in Northern China is characterized by a non-stationary emergent behavior rather than by a long-range correlation, while this scaling behavior is close to the boundary of stationarity and non-stationarity in the Central China. This scaling is up to about 16 years, highlighting that on average, outbreak of large-scale epidemics may occur not shorter than once every 32 years. Interestingly, the emergent behavior can be characterized as a Zipf's law for the ranked size of the epidemics, mostly in the Northern China, and sometimes also involving some regions in the Central China.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 12","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145585032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arnab K. Dey, Anna Dimitrova, Anita Raj, Tarik Benmarhnia
We investigate the effect of extreme heat on home births in India, proposing that such extreme weather events may impede access to health facilities for childbirth. Utilizing geocoded data from the 2019–2021 Demographic and Health Survey for India, we identified the place of delivery of 208,368 births as home versus health facility. We incorporated maximum values for gridded wet-bulb globe temperatures (WBGTmax) and dry-bulb temperatures (DBTmax) corresponding to delivery dates and maternal residences. We defined context-specific extreme heat events using several percentile-based thresholds (between 80th and 95th) over varying durations (1–5 days). We used Generalized Estimating Equations (GEE) with inverse probability of treatment weighting, incorporating socioeconomic factors and state-level fixed effects, and adjusted for seasonality. We tested for effect-measure-modification by socio-economic factors (e.g., caste, wealth), healthcare access factors (e.g., rural/urban place of residence, difficultly in accessing healthcare), and contextual factors (e.g., long-term mean temperature, prevalence of institutional delivery). Both WBGTmax and DBTmax-based heatwave exposures were associated with increased likelihood of home births, with WBGT exposures demonstrating an earlier onset of significant associations at lower percentile thresholds while DBT showed stronger associations at higher thresholds and longer durations. Effect modification analyses revealed heightened impacts in warmer regions, states not designated as high-focus under the Janani Suraksha Yojana program, and non-Hindu populations. We find that extreme heat may compromise delivery at health facilities in India. Findings call for improved health system preparedness via early warning systems and advanced resource allocation to mitigate some of these effects.
{"title":"Heatwaves and Home Births: Understanding the Impact of Extreme Heat on Place of Delivery in India","authors":"Arnab K. Dey, Anna Dimitrova, Anita Raj, Tarik Benmarhnia","doi":"10.1029/2025GH001540","DOIUrl":"https://doi.org/10.1029/2025GH001540","url":null,"abstract":"<p>We investigate the effect of extreme heat on home births in India, proposing that such extreme weather events may impede access to health facilities for childbirth. Utilizing geocoded data from the 2019–2021 Demographic and Health Survey for India, we identified the place of delivery of 208,368 births as home versus health facility. We incorporated maximum values for gridded wet-bulb globe temperatures (WBGT<sub>max</sub>) and dry-bulb temperatures (DBT<sub>max</sub>) corresponding to delivery dates and maternal residences. We defined context-specific extreme heat events using several percentile-based thresholds (between 80th and 95th) over varying durations (1–5 days). We used Generalized Estimating Equations (GEE) with inverse probability of treatment weighting, incorporating socioeconomic factors and state-level fixed effects, and adjusted for seasonality. We tested for effect-measure-modification by socio-economic factors (e.g., caste, wealth), healthcare access factors (e.g., rural/urban place of residence, difficultly in accessing healthcare), and contextual factors (e.g., long-term mean temperature, prevalence of institutional delivery). Both WBGT<sub>max</sub> and DBT<sub>max</sub>-based heatwave exposures were associated with increased likelihood of home births, with WBGT exposures demonstrating an earlier onset of significant associations at lower percentile thresholds while DBT showed stronger associations at higher thresholds and longer durations. Effect modification analyses revealed heightened impacts in warmer regions, states not designated as high-focus under the Janani Suraksha Yojana program, and non-Hindu populations. We find that extreme heat may compromise delivery at health facilities in India. Findings call for improved health system preparedness via early warning systems and advanced resource allocation to mitigate some of these effects.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 11","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025GH001540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145581310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shannon Lindsey, Diane A. Garcia-Gonzales, Michael Jerrett, Claire Bekker, Miriam E. Marlier, Jason G. Su, Emily Gaw, Yang Li
California wildfires have grown increasingly frequent and intense over recent decades, raising serious public health concerns. In response, the California Air Resources Board (CARB) 2022 Scoping Plan outlines land management strategies to reduce wildfire risk and associated emissions under various climate change scenarios. This study evaluates the health benefits of CARB's official mitigation pathway, the S3 scenario, compared to a business-as-usual approach, using three global climate models (GCMs) and three future time slices. We apply the GEOS-Chem model to estimate fire-induced PM2.5 concentrations and use the U.S. EPA's BenMAP-CE tool, along with a wildfire-specific chronic mortality dose-response function, to assess associated morbidity and mortality. Results suggest that S3 can significantly reduce fire-related PM2.5 exposure, particularly in northern and central California where concentrations are typically highest—and where S3 treatments are most effective. In 2035 under the second generation Canadian Earth System Model GCM, for instance, S3 is associated with 1,927 fewer premature deaths and substantial reductions in asthma- and respiratory-related emergency room visits. However, health benefits vary by GCM and year, underscoring the influence of meteorological conditions on fire activity and health outcomes. These findings point to the importance of strategically timed and located land management actions and integrating climate variability into future mitigation planning.
{"title":"Assessing Air Quality and Health Benefits of Enhanced Management of Forests, Shrublands, and Grasslands Against Wildfires in California","authors":"Shannon Lindsey, Diane A. Garcia-Gonzales, Michael Jerrett, Claire Bekker, Miriam E. Marlier, Jason G. Su, Emily Gaw, Yang Li","doi":"10.1029/2025GH001475","DOIUrl":"https://doi.org/10.1029/2025GH001475","url":null,"abstract":"<p>California wildfires have grown increasingly frequent and intense over recent decades, raising serious public health concerns. In response, the California Air Resources Board (CARB) 2022 Scoping Plan outlines land management strategies to reduce wildfire risk and associated emissions under various climate change scenarios. This study evaluates the health benefits of CARB's official mitigation pathway, the S3 scenario, compared to a business-as-usual approach, using three global climate models (GCMs) and three future time slices. We apply the GEOS-Chem model to estimate fire-induced PM<sub>2.5</sub> concentrations and use the U.S. EPA's BenMAP-CE tool, along with a wildfire-specific chronic mortality dose-response function, to assess associated morbidity and mortality. Results suggest that S3 can significantly reduce fire-related PM<sub>2.5</sub> exposure, particularly in northern and central California where concentrations are typically highest—and where S3 treatments are most effective. In 2035 under the second generation Canadian Earth System Model GCM, for instance, S3 is associated with 1,927 fewer premature deaths and substantial reductions in asthma- and respiratory-related emergency room visits. However, health benefits vary by GCM and year, underscoring the influence of meteorological conditions on fire activity and health outcomes. These findings point to the importance of strategically timed and located land management actions and integrating climate variability into future mitigation planning.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 11","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025GH001475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145581311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Liu, Ziying Chen, Huan Fan, Ruiyun Li, Zhe Lou, Hong Ji, Jianli Hu
Previous research has primarily focused on the impact of climatic variables and air pollution on Hand, Foot, and Mouth Disease (HFMD). However, there remains limited understanding of how air pollution levels modify these relationships across different regions and populations. This study employed a two-stage, multi-city time-series analysis using data from 13 cities in Jiangsu Province (2015–2023) to explore these effects. A multistage analytical approach, including the distributed lag non-linear model, multivariate meta-regression, and attributable risk calculation, was used to quantify the association between climatic variables, air pollutants, and HFMD. Findings indicated that HFMD incidence is closely associated with meteorological conditions, with peak risk at 24.8°C for average temperature and 89.2% for average relative humidity. Low average wind speed and short sunshine hours (SH) also contributed to increased risk. Air pollutants, such as PM2.5, SO2, and O3, significantly modified these associations. For example, PM2.5 and SO2 increased HFMD risk at higher temperatures, while O3 reduced risk. Under low humidity, some pollutants exhibited protective effects, though risk increased with high humidity. NO2 had the strongest influence in reducing variability, while high PM2.5 and SO2 concentrations weakened the protective effects of SH. These findings emphasize the non-linear influence of climatic variables on HFMD risk and suggest that air pollution's modification of these relationships varies by gender, age, and location. This provides important insights for developing targeted, timely public health warnings.
{"title":"Impact of Air Pollution in Modifying the Relationship Between Climatic Variables and Hand, Foot and Mouth Disease: A Multi-City Time Series Study in Jiangsu Province, China","authors":"Xin Liu, Ziying Chen, Huan Fan, Ruiyun Li, Zhe Lou, Hong Ji, Jianli Hu","doi":"10.1029/2024GH001265","DOIUrl":"https://doi.org/10.1029/2024GH001265","url":null,"abstract":"<p>Previous research has primarily focused on the impact of climatic variables and air pollution on Hand, Foot, and Mouth Disease (HFMD). However, there remains limited understanding of how air pollution levels modify these relationships across different regions and populations. This study employed a two-stage, multi-city time-series analysis using data from 13 cities in Jiangsu Province (2015–2023) to explore these effects. A multistage analytical approach, including the distributed lag non-linear model, multivariate meta-regression, and attributable risk calculation, was used to quantify the association between climatic variables, air pollutants, and HFMD. Findings indicated that HFMD incidence is closely associated with meteorological conditions, with peak risk at 24.8°C for average temperature and 89.2% for average relative humidity. Low average wind speed and short sunshine hours (SH) also contributed to increased risk. Air pollutants, such as PM<sub>2.5</sub>, SO<sub>2</sub>, and O<sub>3</sub>, significantly modified these associations. For example, PM<sub>2.5</sub> and SO<sub>2</sub> increased HFMD risk at higher temperatures, while O<sub>3</sub> reduced risk. Under low humidity, some pollutants exhibited protective effects, though risk increased with high humidity. NO<sub>2</sub> had the strongest influence in reducing variability, while high PM<sub>2.5</sub> and SO<sub>2</sub> concentrations weakened the protective effects of SH. These findings emphasize the non-linear influence of climatic variables on HFMD risk and suggest that air pollution's modification of these relationships varies by gender, age, and location. This provides important insights for developing targeted, timely public health warnings.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 11","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sophia D. Arabadjis, Frank Davenport, Ana Maria Vecedo Cabrera, Zachary Shahn, Ellen Brazier, Andrew Maroko, Avantika Srivastava, Gad Murenzi, Timothy John Dizon, Keri N. Althoff, Antoine Jaquet, Aggrey S. Semeere, Yanink Caro Vega, Mark K. U. Pasayan, Sheri D. Weiser, Denis Nash
Extreme weather events (EWEs) continue to threaten the health and well-being of populations across the globe. However, risk from drought and floods is not evenly distributed spatially nor are all populations equally at risk for poor health outcomes. Globally, people living with HIV/AIDS (PLHIV) face a particular set of challenges with EWE exposure including increased susceptibility to disease progression from care disruptions and medication adherence, and general population concentration in areas where rainfall is both highly variable and key to economic well-being. To mitigate the impacts of EWE exposure on PLHIV, it is necessary to understand the historical EWE exposure patterns at HIV care clinics. In this paper, we link open-source measures of drought and flood events to clinic locations from the International epidemiology Databases to Evaluate AIDS (IeDEA) network, a longitudinal study of over 2 million people living with and at risk for HIV in 44 different countries around the globe enrolling in HIV care from 2006 to present. Using generalized additive models fit to clinic-level drought and flood exposures, we show how exposures vary across and within countries, model each clinic's probability of exposure to a drought or flood to identify high-risk areas, and describe how this historical exposure record could ultimately be used to identify at-risk populations for a wide variety of study designs. While EWEs occurred at HIV care clinics around the globe, we found that clinic locations in Southern Africa are particularly vulnerable to flood and drought events as compared to other IeDEA clinic regions and locations.
{"title":"Modeling the Burden of Extreme Weather Events in a Large Network of International HIV Care Cohorts","authors":"Sophia D. Arabadjis, Frank Davenport, Ana Maria Vecedo Cabrera, Zachary Shahn, Ellen Brazier, Andrew Maroko, Avantika Srivastava, Gad Murenzi, Timothy John Dizon, Keri N. Althoff, Antoine Jaquet, Aggrey S. Semeere, Yanink Caro Vega, Mark K. U. Pasayan, Sheri D. Weiser, Denis Nash","doi":"10.1029/2025GH001514","DOIUrl":"10.1029/2025GH001514","url":null,"abstract":"<p>Extreme weather events (EWEs) continue to threaten the health and well-being of populations across the globe. However, risk from drought and floods is not evenly distributed spatially nor are all populations equally at risk for poor health outcomes. Globally, people living with HIV/AIDS (PLHIV) face a particular set of challenges with EWE exposure including increased susceptibility to disease progression from care disruptions and medication adherence, and general population concentration in areas where rainfall is both highly variable and key to economic well-being. To mitigate the impacts of EWE exposure on PLHIV, it is necessary to understand the historical EWE exposure patterns at HIV care clinics. In this paper, we link open-source measures of drought and flood events to clinic locations from the International epidemiology Databases to Evaluate AIDS (IeDEA) network, a longitudinal study of over 2 million people living with and at risk for HIV in 44 different countries around the globe enrolling in HIV care from 2006 to present. Using generalized additive models fit to clinic-level drought and flood exposures, we show how exposures vary across and within countries, model each clinic's probability of exposure to a drought or flood to identify high-risk areas, and describe how this historical exposure record could ultimately be used to identify at-risk populations for a wide variety of study designs. While EWEs occurred at HIV care clinics around the globe, we found that clinic locations in Southern Africa are particularly vulnerable to flood and drought events as compared to other IeDEA clinic regions and locations.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 11","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12583239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145453672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. A. Hoque, K. K. Khaing, M. Fowler, M. S. Sultana, C. C. Myint, A. Swe, P. Dennis, S. Shahid, G. R. Fones
The Ayeyarwady Delta in Myanmar, home to an estimated 12 million people, faces widespread arsenic contamination similar to other Asian deltas namely Bengal, Red River, and Mekong. Arsenic here primarily results from reductive dissolution of iron minerals in anoxic conditions driven by organic carbon. Here, we used digital elevation model (DEM) data to investigate how drainage density and hierarchical recharge pathways influence arsenic distribution, supported by combined data set of 136 wells (81 new, 55 from a prior study)—up to 215 m deep—along a 170 km west-to-east transect across the delta. Findings indicate arsenic hotspots in the mid-central region of the delta, where high drainage density appears to facilitate focused recharge, delivering organic carbon to underlying aquifers. Compared with other deltaic regions across Asia, the Ayeyarwady has fewer high-arsenic wells, with only 21% of our data set exceeding the local 50 μg/l limit. National screening data from 123,962 wells indicate that while only 8% exceed the regulatory limit of 50 μg/l set by Myanmar, 71% exceed the 10 μg/l guideline recommended by the World Health Organization (WHO). This highlights widespread exposure risk not addressed under the current national standard, particularly for rural communities. The observed variability in arsenic concentrations, driven by complex redox dynamics and groundwater flow patterns, indicates that contamination can occur even within short spatial intervals. A blanket-screening program focused on hotspot regions is essential to ensure that at-risk populations are not unknowingly exposed to unsafe drinking water.
{"title":"Mapping Arsenic Risks in the Ayeyarwady (Irrawaddy) Delta, Myanmar: Implications for Public Health","authors":"M. A. Hoque, K. K. Khaing, M. Fowler, M. S. Sultana, C. C. Myint, A. Swe, P. Dennis, S. Shahid, G. R. Fones","doi":"10.1029/2024GH001326","DOIUrl":"https://doi.org/10.1029/2024GH001326","url":null,"abstract":"<p>The Ayeyarwady Delta in Myanmar, home to an estimated 12 million people, faces widespread arsenic contamination similar to other Asian deltas namely Bengal, Red River, and Mekong. Arsenic here primarily results from reductive dissolution of iron minerals in anoxic conditions driven by organic carbon. Here, we used digital elevation model (DEM) data to investigate how drainage density and hierarchical recharge pathways influence arsenic distribution, supported by combined data set of 136 wells (81 new, 55 from a prior study)—up to 215 m deep—along a 170 km west-to-east transect across the delta. Findings indicate arsenic hotspots in the mid-central region of the delta, where high drainage density appears to facilitate focused recharge, delivering organic carbon to underlying aquifers. Compared with other deltaic regions across Asia, the Ayeyarwady has fewer high-arsenic wells, with only 21% of our data set exceeding the local 50 μg/l limit. National screening data from 123,962 wells indicate that while only 8% exceed the regulatory limit of 50 μg/l set by Myanmar, 71% exceed the 10 μg/l guideline recommended by the World Health Organization (WHO). This highlights widespread exposure risk not addressed under the current national standard, particularly for rural communities. The observed variability in arsenic concentrations, driven by complex redox dynamics and groundwater flow patterns, indicates that contamination can occur even within short spatial intervals. A blanket-screening program focused on hotspot regions is essential to ensure that at-risk populations are not unknowingly exposed to unsafe drinking water.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 11","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Global bottom-up anthropogenic emission inventories show substantial spatial and temporal differences of short-lived pollutant emissions, which results in uncertainties in terms of air quality and human health impacts. In this study, we compare the emissions of trace gases and aerosols for the year 2015 from three different global emission inventories, the Community Emissions Data System (CEDS), the Copernicus Atmosphere Monitoring Service Global Anthropogenic Emissions (CAMS-GLOB-ANT), and Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants version 6b (ECLIPSEv6b). We then employ the Community Atmosphere Model with chemistry version 6.0 within the Community Earth System Model version 2.2.0 to quantify the atmospheric chemistry and air quality impacts from the above three anthropogenic emission inventories, with a focus on PM2.5 (particulate matter with aerodynamic diameters equal or less than 2.5 μm) and ozone (O3). Our results indicate that differences between emission inventories are largest for black carbon, organic carbon, ammonia and sulfur dioxide, in terms of global annual total emissions. These differences in emissions across CEDS, CAMS, and ECLIPSEv6b lead to substantial variations in global annual totals and spatial distribution patterns. This study shows that the global annual total PM2.5-induced premature mortality is three times higher than that from O3 mortality, indicating that PM2.5 is the primary contributor compared with O3. An inter-comparison of global human health impacts from CEDS, CAMS and ECLIPSEv6b indicates that 80% (CEDS), 81.2% (CAMS), and 77.6% (ECLIPSEv6b) of premature deaths due to anthropogenic activities are associated with Asia and Africa continents.
{"title":"Impact of Anthropogenic Emission Estimates on Air Quality and Human Health Effects","authors":"Halima Salah, Ying Xiong, Debatosh Partha, Noribeth Mariscal, Like Wang, Simone Tilmes, Wenfu Tang, Yaoxian Huang","doi":"10.1029/2024GH001223","DOIUrl":"10.1029/2024GH001223","url":null,"abstract":"<p>Global bottom-up anthropogenic emission inventories show substantial spatial and temporal differences of short-lived pollutant emissions, which results in uncertainties in terms of air quality and human health impacts. In this study, we compare the emissions of trace gases and aerosols for the year 2015 from three different global emission inventories, the Community Emissions Data System (CEDS), the Copernicus Atmosphere Monitoring Service Global Anthropogenic Emissions (CAMS-GLOB-ANT), and Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants version 6b (ECLIPSEv6b). We then employ the Community Atmosphere Model with chemistry version 6.0 within the Community Earth System Model version 2.2.0 to quantify the atmospheric chemistry and air quality impacts from the above three anthropogenic emission inventories, with a focus on PM<sub>2.5</sub> (particulate matter with aerodynamic diameters equal or less than 2.5 μm) and ozone (O<sub>3</sub>). Our results indicate that differences between emission inventories are largest for black carbon, organic carbon, ammonia and sulfur dioxide, in terms of global annual total emissions. These differences in emissions across CEDS, CAMS, and ECLIPSEv6b lead to substantial variations in global annual totals and spatial distribution patterns. This study shows that the global annual total PM<sub>2.5</sub>-induced premature mortality is three times higher than that from O<sub>3</sub> mortality, indicating that PM<sub>2.5</sub> is the primary contributor compared with O<sub>3</sub>. An inter-comparison of global human health impacts from CEDS, CAMS and ECLIPSEv6b indicates that 80% (CEDS), 81.2% (CAMS), and 77.6% (ECLIPSEv6b) of premature deaths due to anthropogenic activities are associated with Asia and Africa continents.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12538235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145349344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigated the socioeconomic disparities of asthma incidence attributable to ambient particulate matter in aerodynamic diameter ≤2.5 μm (PM2.5) exposures among schoolchildren in California, U.S. We found that schoolchildren attending public schools in more vulnerable communities, characterized by higher proportions of people of color, low educational attainment, and poverty, experienced elevated PM2.5 exposures by 2.07–2.96 μg/m3. The disproportionate PM2.5 exposures were likely driven by higher traffic-related emissions and point-source facility emissions in these communities. Using school-specific PM2.5 concentrations, student enrollment numbers, and model-estimated (not directly observed) baseline age-specific asthma incidence rates, we calculated that the asthma incidence rate attributable to 2016 PM2.5 exposures was 562 new cases per 100,000 schoolchildren [95% confidence interval (CI) = 311–854]. In absolute terms (i.e., asthma incidence), it was equivalent to 34,537 PM2.5-related new asthma cases (95% CI = 19,090–52,493) among all schoolchildren. On average, more vulnerable communities experienced 140 excess new asthma cases per 100,000 schoolchildren (i.e., the difference in average asthma cases per 100,000 schoolchildren between more and less vulnerable groups) across all demographic factors considered. Examining health disparities separately by each demographic factor revealed that race/ethnicity was associated with the largest disparities (209 new cases per 100,000 schoolchildren), followed by educational attainment (128) and poverty (85). Our findings indicate the substantial socioeconomic disparities of asthma incidence attributable to PM2.5 among schoolchildren in California. Addressing these health disparities could benefit from sustained and long-term emission reduction strategies, such as adopting zero-emission vehicles, which contribute to lower PM2.5 levels.
本研究调查了美国加利福尼亚州学童空气动力学直径≤2.5 μm (PM2.5)环境颗粒物暴露导致哮喘发病率的社会经济差异。研究发现,在有色人种比例较高、受教育程度低、贫困的弱势社区,公立学校学童的PM2.5暴露量增加了2.07-2.96 μg/m3。不成比例的PM2.5暴露可能是由这些社区较高的交通相关排放和点源设施排放造成的。利用学校特定PM2.5浓度、学生入学人数和模型估计(非直接观察)的基线年龄特异性哮喘发病率,我们计算出2016年PM2.5暴露导致的哮喘发病率为每10万名学童562例新发病例[95%置信区间(CI) = 311-854]。从绝对值(即哮喘发病率)来看,在所有学龄儿童中,相当于34,537例与pm2.5相关的新哮喘病例(95% CI = 19,090-52,493)。在考虑到所有人口因素的情况下,较脆弱社区平均每10万名学龄儿童中有140例额外的新哮喘病例(即,较脆弱群体和较不脆弱群体之间每10万名学龄儿童中平均哮喘病例的差异)。按每个人口因素分别检查健康差异显示,种族/民族与最大差异有关(每10万名学童中有209例新病例),其次是受教育程度(128例)和贫困(85例)。我们的研究结果表明,加州学龄儿童中PM2.5导致的哮喘发病率存在显著的社会经济差异。解决这些健康差异可以受益于持续和长期的减排战略,例如采用有助于降低PM2.5水平的零排放车辆。
{"title":"Socioeconomic Disparities of Asthma Incidence Attributable to PM2.5 Exposures for Schoolchildren in California","authors":"Hyung Joo Lee, Keita Ebisu, Hye-Youn Park","doi":"10.1029/2024GH001099","DOIUrl":"10.1029/2024GH001099","url":null,"abstract":"<p>This study investigated the socioeconomic disparities of asthma incidence attributable to ambient particulate matter in aerodynamic diameter ≤2.5 μm (PM<sub>2.5</sub>) exposures among schoolchildren in California, U.S. We found that schoolchildren attending public schools in more vulnerable communities, characterized by higher proportions of people of color, low educational attainment, and poverty, experienced elevated PM<sub>2.5</sub> exposures by 2.07–2.96 μg/m<sup>3</sup>. The disproportionate PM<sub>2.5</sub> exposures were likely driven by higher traffic-related emissions and point-source facility emissions in these communities. Using school-specific PM<sub>2.5</sub> concentrations, student enrollment numbers, and model-estimated (not directly observed) baseline age-specific asthma incidence rates, we calculated that the asthma incidence rate attributable to 2016 PM<sub>2.5</sub> exposures was 562 new cases per 100,000 schoolchildren [95% confidence interval (CI) = 311–854]. In absolute terms (i.e., asthma incidence), it was equivalent to 34,537 PM<sub>2.5</sub>-related new asthma cases (95% CI = 19,090–52,493) among all schoolchildren. On average, more vulnerable communities experienced 140 excess new asthma cases per 100,000 schoolchildren (i.e., the difference in average asthma cases per 100,000 schoolchildren between more and less vulnerable groups) across all demographic factors considered. Examining health disparities separately by each demographic factor revealed that race/ethnicity was associated with the largest disparities (209 new cases per 100,000 schoolchildren), followed by educational attainment (128) and poverty (85). Our findings indicate the substantial socioeconomic disparities of asthma incidence attributable to PM<sub>2.5</sub> among schoolchildren in California. Addressing these health disparities could benefit from sustained and long-term emission reduction strategies, such as adopting zero-emission vehicles, which contribute to lower PM<sub>2.5</sub> levels.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate change, rapid urbanization, and population growth are increasingly influencing the quality and quantity of surface water resources, especially in vulnerable reservoir systems. This study investigates the spatiotemporal changes in water features and quality of three key drinking water source lakes-Rawal, Simly, and Khanpur (RSK), located in and around Islamabad, Pakistan. Using Level 2 Landsat 5, 7 and 8 satellite data from 1991 to 2020, changes in lake surface area were assessed through the Google Earth Engine (GEE) platform. Thresholding and geospatial analysis in ArcGIS 10.8 were used to extract and visualize water bodies and surface feature changes. The study found that lake surface areas were directly linked to rainfall levels and decreased with rising temperatures especially during 1991, 2000 2010, and 2020. Water quality was assessed using standard laboratory procedures. Notably, higher bacterial counts were recorded during the wet season, indicating increased microbial contamination likely due to surface runoff. Among the heavy metals analyzed (Fe, F, As, Cu, Zn, Mn, Cr, Pb, Ni, B, Cd, P, Hg), only boron (B), nickel (Ni), and chromium (Cr) were detected above background levels, though within permissible limits. The study highlights the significant influence of climatic variables on both the physical extent and microbial quality of drinking water lakes. These findings offer critical insights for policymakers and water resource managers, providing a replicable framework for monitoring and managing similar reservoirs in other climate-sensitive regions.
{"title":"Geochemical and Climatic Influences on Spatiotemporal Water Quality Changes in Drinking Water Source Lakes in Pakistan: Implications for Environmental and Public Health","authors":"Toqeer Ahmed, Saif Ullah, Zulqarnain Satti, Zheng Siyue, Anwar Eziz, Alishir Kurban, Mumtaz Ahmed, Hifza Rasheed","doi":"10.1029/2025GH001595","DOIUrl":"10.1029/2025GH001595","url":null,"abstract":"<p>Climate change, rapid urbanization, and population growth are increasingly influencing the quality and quantity of surface water resources, especially in vulnerable reservoir systems. This study investigates the spatiotemporal changes in water features and quality of three key drinking water source lakes-Rawal, Simly, and Khanpur (RSK), located in and around Islamabad, Pakistan. Using Level 2 Landsat 5, 7 and 8 satellite data from 1991 to 2020, changes in lake surface area were assessed through the Google Earth Engine (GEE) platform. Thresholding and geospatial analysis in ArcGIS 10.8 were used to extract and visualize water bodies and surface feature changes. The study found that lake surface areas were directly linked to rainfall levels and decreased with rising temperatures especially during 1991, 2000 2010, and 2020. Water quality was assessed using standard laboratory procedures. Notably, higher bacterial counts were recorded during the wet season, indicating increased microbial contamination likely due to surface runoff. Among the heavy metals analyzed (Fe, F, As, Cu, Zn, Mn, Cr, Pb, Ni, B, Cd, P, Hg), only boron (B), nickel (Ni), and chromium (Cr) were detected above background levels, though within permissible limits. The study highlights the significant influence of climatic variables on both the physical extent and microbial quality of drinking water lakes. These findings offer critical insights for policymakers and water resource managers, providing a replicable framework for monitoring and managing similar reservoirs in other climate-sensitive regions.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12516088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rome Thorstenson, James Montgomery, Christie Klimas
Lead exposure remains a persistent environmental health threat. Soil contamination is recognized as an overlooked yet critical reservoir of childhood lead exposure due to a legacy of historical lead use in gasoline, paint, and industry. However, it is unclear whether measuring soil lead is an effective way to screen for risk at the community or neighborhood level, nor if soil lead is a significant predictor of elevated blood lead levels (EBLLs) beyond other socioeconomic and physical environment covariates. Building on prior soil sampling and conducting extensive citywide sampling and analysis, we assemble the largest data set of soil lead to date (n = 1,750) in Chicago. Combined with BLL data reported by the Chicago Department of Public Health (CDPH), municipal data, and census data, we investigated the association between soil lead concentrations, predicted BLLs from the EPA's Integrated Exposure Uptake Biokinetic (IEUBK) model, and EBLL from CDPH blood testing among children in Chicago at the community area scale. We present city-scale soil lead and IEUBK risk maps for Chicago. Furthermore, while median household income remains the strongest single predictor of EBLL prevalence in our models, we provide evidence that soil lead independently contributes significant predictive power. Our findings position systematic soil monitoring as a practical tool for primary prevention, complementing existing prevention and intervention strategies and accelerating progress toward safer cities.
{"title":"What's in Your Soil? A Citywide Investigation of the Importance of Soil Lead for Predicting Elevated Blood Lead Levels in Chicago","authors":"Rome Thorstenson, James Montgomery, Christie Klimas","doi":"10.1029/2025GH001572","DOIUrl":"https://doi.org/10.1029/2025GH001572","url":null,"abstract":"<p>Lead exposure remains a persistent environmental health threat. Soil contamination is recognized as an overlooked yet critical reservoir of childhood lead exposure due to a legacy of historical lead use in gasoline, paint, and industry. However, it is unclear whether measuring soil lead is an effective way to screen for risk at the community or neighborhood level, nor if soil lead is a significant predictor of elevated blood lead levels (EBLLs) beyond other socioeconomic and physical environment covariates. Building on prior soil sampling and conducting extensive citywide sampling and analysis, we assemble the largest data set of soil lead to date (<i>n</i> = 1,750) in Chicago. Combined with BLL data reported by the Chicago Department of Public Health (CDPH), municipal data, and census data, we investigated the association between soil lead concentrations, predicted BLLs from the EPA's Integrated Exposure Uptake Biokinetic (IEUBK) model, and EBLL from CDPH blood testing among children in Chicago at the community area scale. We present city-scale soil lead and IEUBK risk maps for Chicago. Furthermore, while median household income remains the strongest single predictor of EBLL prevalence in our models, we provide evidence that soil lead independently contributes significant predictive power. Our findings position systematic soil monitoring as a practical tool for primary prevention, complementing existing prevention and intervention strategies and accelerating progress toward safer cities.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025GH001572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}