Geohealth最新文献

{"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,&nbsp;James Montgomery,&nbsp;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}

{"title":"Heavy Metal Exposure During Pregnancy and Its Association With Adverse Birth Outcomes: A Cross-Sectional Study","authors":"Tianao Sun,&nbsp;Zhanyue Zheng,&nbsp;Minli Yang,&nbsp;Minglian Pan,&nbsp;Qitao Tan,&nbsp;Yongjie Ma,&nbsp;Yingjie Zhou,&nbsp;Muxue He,&nbsp;Yan Sun","doi":"10.1029/2025GH001471","DOIUrl":"https://doi.org/10.1029/2025GH001471","url":null,"abstract":"<p>Prenatal exposure to heavy metals (HMs) has been the focus of international research. However, current studies tend to examine individual metals in isolation and rely on traditional linear regression models, which may not adequately reflect the complex, non-linear and interactive effects of mixed metal exposure. The aim of this study was to investigate the relationship between maternal mixed urinary HM exposure levels during pregnancy and adverse birth outcomes such as preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA) infants using advanced machine learning methods. This study was conducted at a tertiary hospital in Guilin, from 2022 to 2023. A total of 489 pregnant women were enrolled. First-trimester urine samples were collected to quantify HM concentrations using Inductively coupled plasma mass spectrometry. Demographic and clinical data were obtained through structured questionnaires. Bayesian Kernel Machine Regression analysis revealed a significant cumulative effect of mixed metal exposure on adverse pregnancy outcomes, with distinct dose-response relationships. The risk of PTB and LBW increased monotonically with higher exposure levels; the adjusted odds ratios were elevated as metal mixture concentrations increased from the 25th to the 75th percentile. In contrast, the association with SGA exhibited a non-monotonic pattern—higher risk at lower exposure levels and a marked decline in risk at higher concentrations. Inorganic arsenic was identified as the primary toxic component in univariate models. Multivariate response modeling demonstrated the joint influence of metal mixtures on adverse outcomes (AUC = 0.697), with no significant statistical interactions between individual metals, as indicated by parallel dose-response curves (<i>p</i> &gt; 0.05).</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025GH001471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223884","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}

{"title":"The Impact of Extreme Weather on Dengue Fever in Guangzhou, China: A Zero-Inflated Negative Binomial Spatial Lag Analysis","authors":"Xinqiu Ouyang,&nbsp;Fang Shi,&nbsp;Yang Qiu,&nbsp;Guangran Deng,&nbsp;Shujun Zhang","doi":"10.1029/2025GH001330","DOIUrl":"10.1029/2025GH001330","url":null,"abstract":"<p>Climate change intensifies extreme weather, which in turn influences infectious disease transmission. As a dengue fever (DF) hotspot, Guangzhou lacks research on how extreme weather characteristics and spatial factors interact to shape DF patterns. This study analyzed DF distribution in Guangzhou from 2017 to 2019, using a zero-inflated negative binomial spatial lag (ZINB-SAR) regression model to assess the effects of daytime heatwaves (DHW), nighttime heatwaves (NHW) and extreme precipitation (EP) on DF. Results revealed that DF cases were predominantly clustered in central urban areas, with an epidemic season from May to November. The ZINB-SAR model outperformed negative binomial regression and spatial econometric models, with all spatial effect coefficients significantly positive. Analysis of lagged effects showed that each additional DHW event increased DF cases by up to 10.80% (95% CI: 6.22%–15.59%) at a 2-month lag, while NHW events increased DF by 2.73% (95% CI: −1.59%–7.23%). Threshold analysis indicated DHW intensity shifted from promoting to inhibiting DF between 0.66°C and 0.76°C, while NHW intensity transitioned between 0.95°C and 2.28°C. EP demonstrated the strongest effects at a 3-month lag, increasing DF cases by 12.05% (95% CI: 9.03%–15.17%), although its intensity was not statistically significant. Seasonal and spatial variations in DF incidence were evident. In conclusion, DHW and EP impacts were primarily driven by event frequency rather than intensity, whereas NHW effects were more dependent on intensity. These findings highlight the complex spatiotemporal interplay between extreme weather and DF in Guangzhou, providing critical evidence for developing targeted climate-adaptive disease control strategies.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214071","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}

{"title":"Short-Term Exposure to Fine Particulate Matter (PM2.5), Cause Specific-Mortality, and High-Risk Populations: A Nationwide Time-Stratified Case-Crossover Study","authors":"Seoyeong Ahn,&nbsp;Jieun Oh,&nbsp;Hyewon Yun,&nbsp;Harin Min,&nbsp;Yejin Kim,&nbsp;Cinoo Kang,&nbsp;Sojin An,&nbsp;Ayoung Kim,&nbsp;Dohoon Kwon,&nbsp;Jinah Park,&nbsp;Whanhee Lee","doi":"10.1029/2024GH001214","DOIUrl":"https://doi.org/10.1029/2024GH001214","url":null,"abstract":"<p>Numerous studies have reported that short-term exposure to fine particulate matter (PM_2.5) is associated with mortality risk; however, results on high-risk populations and regions have been mixed. This study performed a nationwide time-stratified case-crossover study to assess the association between short-term PM_2.5 and mortality in South Korea (2015–2019) by each cause of death and age group. A machine-learning ensemble PM_2.5 prediction model was used to cover unmonitored districts. We estimated the excess mortality and Years of Life Lost (YLL) attributable to PM_2.5 and non-compliance with the 2021 WHO guidelines (&gt;15 μg/m³). We examined the effect modifications by district-level accessibility to green spaces and medical facilities in the living sphere. In the total population, PM_2.5 was positively associated with mortality for non-accidental causes (OR: 1.008 with 95% CI: 1.006–1.010), circulatory diseases (1.007, 95% CI: 1.003–1.011), and respiratory diseases (1.007, 95% CI: 1.001–1.013). Based on the point estimates, the association was generally greater in younger age groups (0–59 or 60–69 years) than in older age groups (70–80 and 80 years or older), and this pattern was pronounced in mortality for cerebrovascular diseases and pneumonia. The excess mortality fraction and YLL due to non-compliance with WHO guidelines were 0.80% and 186,808.52 years. Our findings suggest high risk populations and benefits for establishing stricter PM_2.5 standards and action plans.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146725","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}

{"title":"Climate Change Is Expected to Expand Malaria Transmission Range and Population at Risk in Papua New Guinea","authors":"S. Karl,&nbsp;E. B. Skinner,&nbsp;S. McEwen,&nbsp;J. Keven,&nbsp;J. Kisomb,&nbsp;L. J. Robinson,&nbsp;M. Laman","doi":"10.1029/2025GH001541","DOIUrl":"https://doi.org/10.1029/2025GH001541","url":null,"abstract":"<p>Warming temperatures are expanding the potential for malaria transmission into higher altitudes, with important implications for malaria control planning. In Papua New Guinea (PNG), malaria is widespread in lowland areas but rarely transmitted above 1,600 m. This study assessed changes in malaria transmission suitability across PNG from 1960 to 2019 and projected shifts through 2040, using satellite-derived temperature data and climate models. We applied a temperature-dependent basic reproduction number (<i>R</i>₀) to identify shifts in geographic suitability, estimate the population at risk, and evaluate the effectiveness of interventions. Malaria temperature suitability ranges have subtly changed between 1960 and 2019, with the proportion of people living in suitable areas increasing from 58% to 61% (equivalent to an additional 249,125 people). Under a conservative climate change model, this proportion is expected increase to 74% by 2040 (equivalent to an additional 2,802,709 people). Interventions had a larger impact on malaria incidence in areas with <i>R</i>₀ &lt; 0.3, mitigating the current and future impact of climate change. Nevertheless, the number of people requiring access to malaria control is expected to double by 2040, to 13.4 million with 2.8 million attributed to climate change alone. The impacted areas are densely populated highlands regions with a more susceptible population and an increased potential for epidemics and clinical disease. These findings underscore the challenges of climate change for malaria elimination in PNG and highlight the need to accurately guide preparedness and forecast the additional resource requirements.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 10","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025GH001541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146726","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}

{"title":"The Correlation Between Three Teleconnections and Dengue Incidence in the Western Province of Sri Lanka, 2005–2019","authors":"N. D. B. Ehelepola,&nbsp;Kusalika Ariyaratne,&nbsp;R. M. P. Ratnayake","doi":"10.1029/2024GH001144","DOIUrl":"10.1029/2024GH001144","url":null,"abstract":"<p>Dengue is an arboviral fever. Weather modulates dengue transmission by influencing the life cycles of vector mosquitoes and the virus. Three teleconnections are known to affect the weather in Sri Lanka. Those are El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and ENSO Modoki. We studied correlations between dengue incidence (DI) in the Western Province (WP) of Sri Lanka as a whole and three districts of the province and indices of ENSO, IOD and ENSO Modoki. We used four indices of ENSO and one index each of IOD and ENSO Modoki. We acquired notified dengue cases in WP, population data and monthly indices of three teleconnections for the 2005–2019 period. We used wavelet time series analysis to determine correlations between indices of teleconnections and DI. Two indices of ENSO were correlated with the DI of the WP and all three districts of the WP individually. The other two indices were correlated with the DI of two districts. The index of IOD was correlated with DI of two districts. The index of ENSO Modoki was correlated with the DI of WP and one district of it. Both positive and negative extremes of at least one teleconnection index were followed by the rise of DI in all districts. We concluded that three teleconnections modulate DI of different districts of WP in different ways. Monitoring of indices of these teleconnections and escalating dengue preventive work after extremes of indices can potentially blunt impending dengue peaks.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 9","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135499","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}

{"title":"Assessing the Impact of Wildfire Emissions on the Seasonal Cycle of CO and Emergency Room Visits in Alberta and Ontario, Canada","authors":"Victoria A. Flood,&nbsp;Kimberly Strong,&nbsp;Rebecca R. Buchholz,&nbsp;Grace Kuiper,&nbsp;Sheryl Magzamen","doi":"10.1029/2024GH001317","DOIUrl":"10.1029/2024GH001317","url":null,"abstract":"<p>Exposure to wildfire smoke is a well-known concern for public health and is anticipated to worsen with an increase in wildfire activity related to climate change. This study uses satellite and ground-based carbon monoxide (CO) measurements from 2004 to 2019 to evaluate a change in its seasonal cycle due to wildfire emissions. Monthly average CO total columns from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument over Alberta and Ontario, and from a ground-based Fourier transform infrared spectrometer in downtown Toronto are compared before and after 1 January 2012, following previous literature. Between the two time periods, a new peak emerges in the seasonal cycle of CO, centered around August. Monthly emergency room admissions from Alberta and Ontario for nine cardiovascular and respiratory diseases are assessed with a difference in difference analysis, using MOPITT CO as the exposure metric. This analysis was used to calculate the change in monthly hospital admissions per 100,000 people, given a 1 ppb increase in XCO post-2012 compared to pre-2012, along with the 95% confidence interval (CI). For Ontario, this term is positive and significant for hypertension (change = 1.88, CI = 1.18–2.57), ischemic heart disease (0.50, CI = 0.12–0.88), arrhythmia (0.12, CI = 0.03–0.22), and asthma (0.31, CI = 0.05–0.57). For Alberta, there is a significant and positive interaction for arrhythmia (0.48, CI = 0.12–0.85). These results indicate that there was a statistically significant increase in adverse health outcomes for five of the eighteen disease-province pairings associated with the increase in atmospheric CO after 2011 coinciding with enhanced wildfire emissions.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 9","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001317","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111010","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}

{"title":"Impacts of Improved Cookstove Interventions on Personal Exposure to Carbon Monoxide and Particulate Matter in Zambia","authors":"Stephanie Parsons,&nbsp;Wesley Hayes,&nbsp;Gillian Kabwe,&nbsp;Francis Yamba,&nbsp;Nancy Serenje,&nbsp;Robert Bailis,&nbsp;Pamela Jagger,&nbsp;Andrew P. Grieshop","doi":"10.1029/2024GH001178","DOIUrl":"10.1029/2024GH001178","url":null,"abstract":"<p>Eighty-four percent of sub-Saharan African households rely on polluting fuels (e.g., wood, charcoal) for cooking, leading to high levels of household air pollution (HAP). While switching to modern fuels/stoves could decrease HAP levels, they are not always available or affordable. Improved biomass cookstoves could provide an intermediate step supporting transitions from traditional biomass to clean burning fuels/stoves. We conducted two stove intervention trials in Lusaka, Zambia using targeted marketing/incentives to motivate participants to use improved biomass stoves, either the Mimi Moto (pellet) or the EcoZoom (charcoal). Before the intervention, 65% of participants exclusively used charcoal, while 27% relied on electricity to some extent for cooking. We measured 24-hr personal exposure to CO (<i>n</i> = 747) and PM_2.5 (<i>n</i> = 90) of primary cooks. We implemented several statistical approaches to estimate the effects of interventions on exposure: household-specific endline minus baseline exposure, ranksum testing, difference-in-differences analyses, and cross-sectional analyses. We found that switching from traditional charcoal stoves to either intervention stove was not associated with significantly reduced exposures. However, cooks using electric stoves independent of the intervention did have significantly lower CO exposures than those using traditional charcoal, with greater electric stove use corresponding to greater exposure reductions. Variability in exposure was dominated by seasonal, regional, and neighborhood differences rather than household stove/fuel choices. A focus on HAP exposure from cooking in urban settings is unlikely to yield expected exposure reductions. Policy makers should consider pollution reduction policies/interventions that target ambient air quality in tandem with HAP-mitigating strategies to address air pollution health burden.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 9","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110790","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}

{"title":"Source Attribution and Health Burden of PM2.5 in Mainland Thailand","authors":"Worapop Thongsame,&nbsp;Daven K. Henze,&nbsp;Mary Barth,&nbsp;Gabriele Pfister,&nbsp;Rajesh Kumar,&nbsp;Ronald Macatangay,&nbsp;Sherin Hassan Bran","doi":"10.1029/2024GH001315","DOIUrl":"10.1029/2024GH001315","url":null,"abstract":"<p>PM_2.5 is a critical air pollutant that significantly impacts human health and the environment. To develop effective air quality management and mitigation strategies, understanding PM_2.5 source attribution and associated health risks is essential. This study investigates the source attribution and health burden of PM_2.5 focusing on Mainland Thailand (MT), North Thailand (NT), and the Bangkok Metropolitan Region (BMR), using the WRF-Chem model and a brute-force method for source attribution. PM_2.5 contributions from biomass burning including both crop and non-crop burning are quantified, along with contributions from transportation, industry, energy, residential, and other anthropogenic sectors. This study focuses on the haze season (February–April) in 2019. Our research shows that in-domain foreign country's biomass burning is a major contributor to PM_2.5, accounting for 23%–38% of PM_2.5 concentrations in MT. In NT, non-crop burning within MT contributes the most (21%–36%) to PM_2.5 levels, while crop burning within MT has a minimal impact (less than 6%). In the BMR, PM_2.5 is strongly impacted by sources outside the model domain. Overall, industrial and transportation emissions are the most impactful anthropogenic sources. We further estimate the total health burden, associated with long-term PM_2.5 exposure during the haze season contributes to 46% of this PM_2.5 health burden in MT in 2019, 66% in NT, and 37% in the BMR. These findings suggest that reducing biomass burning within MT and from in-domain foreign countries during February–April could reduce the annual health burden in MT by up to 20%.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 9","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101609","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}

{"title":"Source-Specific Air Pollution Emissions Inequalities From 2011 to 2020 in Virginia","authors":"Lucas R. F. Henneman,&nbsp;Ryah Nadjafi,&nbsp;Xiaorong Shan,&nbsp;Jenna R. Krall","doi":"10.1029/2025GH001431","DOIUrl":"10.1029/2025GH001431","url":null,"abstract":"<p>Air quality has improved in recent decades across most of the United States. However, decreases in pollution have not been uniform, potentially exacerbating inequalities in air pollution exposure by race and ethnicity. These inequalities exist, in part, because of spatial differences in source(s), for example, power plants or roadways. Determining which sources are driving inequality across racial and ethnic groups is critical to determining which policies (e.g., targeting power plant vs. vehicle emissions) would reduce inequalities. Our study determines which pollutant sources should be decreased to address inequalities in four pollutants (NO_x, SO₂, VOCs, and PM_2.5) in the Commonwealth of Virginia. We derived emissions from eight source categories for 134 Virginia counties from the National Emissions Inventory and the MOtor Vehicle Emissions Simulator mobile source emissions model. We used race and ethnicity data from the American Community Survey from 2011 to 2020. We applied the Atkinson Index to obtain a single summary of inequality for each source-pollutant pair (e.g., NO_x from electricity generation) across all race and ethnic groups. Most source category emissions were unequally distributed for at least once pollutant. Compared to other sources, electricity generation resulted in the largest inequalities across pollutants. Mobile sources increased in inequality from 2011 to 2020 even as emissions decreased. These results show the importance of identifying sources that contribute most to inequalities when developing policies to promote environmental justice.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"9 9","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081785","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}