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Adverse health outcomes caused by extreme heat represent the most direct human health threat associated with the warming of the Earth's climate. Socioeconomic, demographic, health, land cover, and temperature determinants contribute to heat vulnerability; however, nationwide patterns of residential and race/ethnicity disparities in heat vulnerability in the United States are poorly understood. This study aimed to develop a Heat Vulnerability Index (HVI) for the United States; to assess differences in heat vulnerability across geographies that have experienced historical and/or contemporary forms of marginalization; and to quantify HVI by race/ethnicity. Principal component analysis was used to calculate census tract level HVI scores based on the 2019 population characteristics of the United States. Differences in HVI scores were analyzed across the Home Owners' Loan Corporation (HOLC) “redlining” grades, the Climate and Economic Justice Screening Tool (CEJST) disadvantaged versus non‐disadvantaged communities, and race/ethnicity groups. HVI scores were calculated for 55,267 U.S. census tracts. Mean HVI scores were 17.56, 18.61, 19.45, and 19.93 for HOLC grades “A”–“D,” respectively. CEJST‐defined disadvantaged census tracts had a significantly higher mean HVI score (19.13) than non‐disadvantaged tracts (16.68). The non‐Hispanic African American or Black race/ethnicity group had the highest HVI score (18.51), followed by Hispanic or Latino (18.19). Historically redlined and contemporary CEJST disadvantaged census tracts and communities of color were found to be associated with increased vulnerability to heat. These findings can help promote equitable climate change adaptation policies by informing policymakers about the national distribution of place‐ and race/ethnicity‐based disparities in heat vulnerability. Plain Language Summary As the Earth's climate warms, extreme heat is the most direct threat to human health. Due to various socioeconomic, demographic, health, and environmental factors, some individuals and populations are more vulnerable to adverse health events caused by extreme heat. Publicly available data were obtained for each of these factors, and statistical analysis yielded a quantitative measure of heat vulnerability for 55,267 U.S. census tracts. Of these census tracts, those that have experienced historical and/or contemporary forms of marginalization were associated with increased vulnerability to heat. Additionally, non‐White race/ethnicity groups were associated with increased vulnerability to heat and were overrepresented in the census tracts with the highest vulnerability. These results can inform policymakers of the places and race/ethnicity groups most vulnerable to heat, and can therefore be used to develop equity‐promoting climate change adaptation policies. Key Points Historically “redlined” and contemporary Climate and Economic Justice Screening Tool disadvantaged communities were found to be associated with increased vulnerability to heat Communities of color were associated with increased vulnerability to heat and were overrepresented in the most vulnerable census tracts Identifying place and race/ethnicity‐based disparities in heat vulnerability can help promote equitable climate change adaptation policies

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source‐specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source‐specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data‐Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on‐road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult—since source‐specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models. Plain Language Summary Air pollution sources lead to adverse health impacts and inequities. To better understand these effects, researchers have created various models to quantify air pollution exposure from specific sources. These models are used in studies that look at the health effects of pollution, help assess risks, and address environmental justice issues. We identified six types of models used to estimate exposure from pollution sources like cars, power plants, factories, and wildfires. The models use different approaches to approximate the physical processes that dictate exposure. They also vary in the exposure metrics they produce—some use concentration units, while others rely on indirect indices. Researchers should consider and state explicitly what information might be gained or lost depending on which model they use. Evaluating these models is important but can be challenging, as it often requires comparing results with other models, which can be time‐consuming and resource‐intensive. Key Points Six source‐specific air pollution exposure assessment approaches are identified Evaluation methods are recommended for source‐specific exposure models Examples of exposure assessment applications in health studies are provided

Influenza is an acute respiratory infection that spreads rapidly and widely in densely populated areas with low vaccination coverage. The trends and drivers of the spatial and temporal dynamics of influenza incidence among residents of mainland China have not been systematically studied. This study comprehensively analyses the dynamics and spatial correlation of influenza using long‐term scale data and spatial panel data. It then identifies the interactive process of socio‐economic and natural elements on the incidence of influenza. The highest prevalence of influenza was found in the 0–4 years age group in mainland China (mean prevalence, 67.56/100,000). In addition, influenza in mainland China shows seasonality, with fall and winter being the periods of high incidence. Between 2014 and 2017, influenza incidence was clustered in Hubei and Anhui provinces, and the spatial clustering was statistically significant (Z value > 1.96, P < 0.05). Moreover, the directionality of influenza onset continued to increase each year. Specifically, the clustering of influenza onset was stronger in the northwest‐southeast direction than in the southwest‐northeast direction between 2014 and 2018. The significant role of socioeconomic factors as a primary influence on influenza incidence, while their interaction with natural factors, such as air quality (NOx and PM2.5) and climatic conditions can exacerbate regional outbreaks. This study provides a novel perspective for better prevention and control of influenza disease among mainland Chinese residents. Plain Language Summary The incidence of influenza in mainland China is currently on the rise. On 30 November, the Chinese Center for Disease Control and Prevention reported 401 cases nationwide in the 47th week of the year alone. This highlights the importance of studying influenza patterns in China to protect public health. The study analyzed influenza cases in mainland China from 2004 to 2018 and identified key trends. It found that influenza cases peak in March, suggesting that colder temperatures and increased population movement contribute to the spread of the virus. Over time, the regions with the highest flu rates have shifted from northern and central China to the southeast, influenced by factors such as population density and economic disparities. The study also shows a link between influenza and environmental factors, including air pollution, economic growth and weather conditions. It suggests that energy use, which affects the environment and climate, indirectly influences the spread of influenza. To reduce future outbreaks, public health policies should integrate environmental management with traditional disease prevention strategies. The incidence rate of urban influenza in Chinese Mainland shows a seasonal fluctuation of six months. On the spatial scale, the agglomeration area is located in cities in the middle and lower reaches of the Yangtze River. Urbanization and air pollution are the main driving factors of the incidence rate of influenza.

As the United States (US) has increased its domestic production of natural gas, transmission pipeline infrastructure continues to expand. Previous research highlights the environmental justice implications of this situation in the US, including fugitive methane emissions and the disproportionate concentration of pipelines in counties with high social vulnerability. However, gaps in publicly‐available data make it difficult to understand the intersection of social factors, pipeline prevalence, and race, particularly in rural areas and at community‐level spatial scales. This study begins to address this gap by examining the relationship between natural gas pipeline prevalence and demographics at the census block group level. This study uses North Carolina as a case study due to the state's dramatic increase in natural gas consumption driving an increase in pipeline infrastructure in recent years. This work highlights two critical findings: First, African American and American Indian people make up a disproportionately large share of the population living in block groups characterized by high social vulnerability and high densities of natural gas pipelines. Second, our main finding is insensitive to the threshold used to determine disproportionality, suggesting these results are robust. These data demonstrate a need for more equitable methods for energy infrastructure planning and maintenance. These results underscore the need for geospatial analysts to critically evaluate their methods for identifying disparities. Plain Language Summary As the United States (US) has produced and used more natural gas, more pipelines are being built to transport this gas. Previous research shows the environmental justice (EJ) implications of this situation in the US, including gas leaks and more pipelines in counties with high social vulnerability. Missing information from public data makes it difficult to understand the intersection of social factors, pipeline prevalence, and race, especially in rural areas and in community‐level detail. We begin to address this gap by examining relationships between pipeline prevalence and social characteristics at the census block group level. This study focuses on North Carolina because the state's increase in natural gas use has caused increased pipeline construction in recent years. We found two critical findings: First, African American and American Indian people make up a disproportionately large share of populations living in block groups with many pipelines and limited ability to handle the negative consequences of these. Second, our main finding is true any way we define “high risk” block groups, meaning these results are reliable. Our results suggest a need for more equitable methods for planning energy infrastructure. We recommend that analysts use sensitivity analyses to justify their methods for identifying EJ concerns. Key Points African American and American Indian populations are overrepresented in block groups with high social vulnerability index and high pipeline density Half of the pipeline mileage in North Carolina is within rural block groups. Rural areas are understudied in the literature on pipelines It is critical for those who conduct environmental justice analyses to understand the sensitivity of disproportionality results to user‐defined thresholds

Over the past two decades, the surge in warehouse construction near seaports and in economically lower‐cost land areas has intensified product transportation and e‐commerce activities, particularly affecting air quality and health in nearby socially disadvantaged communities. This study, spanning from 2000 to 2019 in Southern California, investigated the relationship between ambient concentrations of PM2.5 and elemental carbon (EC) and the proliferation of warehouses. Utilizing satellite‐driven estimates of annual mean ambient pollution levels at the ZIP code level and linear mixed effect models, positive associations were found between warehouse characteristics such as rentable building area (RBA), number of loading docks (LD), and parking spaces (PS), and increases in PM2.5 and EC concentrations. After adjusting for demographic covariates, an Interquartile Range increase of the RBA, LD, and PS were associated with a 0.16 μg/m³ (95% CI = [0.13, 0.19], p < 0.001), 0.10 μg/m³ (95% CI = [0.08, 0.12], p < 0.001), and 0.21 μg/m³ (95% CI = [0.18, 0.24], p < 0.001) increase in PM2.5, respectively. For EC concentrations, an IQR increase of RBA, LD, and PS were each associated with a 0.021 μg/m³ (95% CI = [0.019, 0.024], p < 0.001), 0.014 μg/m³ (95% CI = [0.012, 0.015], p < 0.001), and 0.021 μg/m³ (95% CI = [0.019, 0.024], p < 0.001) increase. The study also highlighted that disadvantaged populations, including racial/ethnic minorities, individuals with lower education levels, and lower‐income earners, were disproportionately affected by higher pollution levels. Plain Language Summary Over the past 20 years, more warehouses have been built near ports and in areas where land is cheaper. This has increased truck traffic and goods movement, which has worsened air quality and affected the health of nearby communities that often lack resources and health services. From 2000 to 2019, our study in Southern California examined how this rise in warehouses has impacted air pollution, focusing on very small pollution particles known as PM2.5 and a harmful component of these particles called elemental carbon. Using satellite data to analyze pollution levels across different areas, we discovered that larger warehouses, more loading docks, and increased parking spaces are associated with higher levels of pollution. We also found that this rise in pollution particularly affects disadvantaged groups in these communities, including racial/ethnic minorities, those with less education, and those with lower incomes. This research underscores the long‐term trend of warehouse expansion and its effects on air pollution. It highlights the urgent need for careful planning in warehouse construction and better protection for vulnerable communities, particularly those most at risk from increased pollution. Key Points Warehouse expansion over the last two decades was associated with elevated PM2.5 and elemental carbon concentrations in their ZIP code regions Disadvantaged populations living near warehouses are disproportionately exposed to higher levels of air pollution Targeted emission control interventions and protective measures are especially needed for vulnerable populations near warehouses

Human populations and ecosystems are extensively exposed to pesticides. Most nations lack the capacity to control pesticide contamination and have limited availability of pesticide use information. Ecuador is a country with intense pesticide use with high exposure risks to humans and the environment, although relative or combined risks are not well understood. Here, we analyzed the distribution of application rates in Ecuador and identified regions of concern because of high potential exposure. We used a geospatial analysis to identify grid cells (∼8 km × 8 km) where the highest pesticide application rates and density of human populations overlap. Furthermore, we identified other regions of concern based on the number of amphibian species as an indicator of ecosystem integrity and the location of natural protected areas. We found that 28% of Ecuador's population dwelled in areas with high pesticide application rate. We identified an area of ∼512 km2 in the Amazon region where high application rates, large human settlements, and a high number of amphibian species overlapped. Additionally, we distinguished clusters of pesticide application rates and human populations that intersected with natural protected areas. Ecuador exemplifies how pesticides are disproportionately applied in areas with the potential to affect human health and ecosystems' integrity. Global estimates of population dwelling, pesticide application rates, and environmental factors are key in prioritizing locations to conduct further exposure assessments. The modular and scalable nature of the geospatial tools we developed can be expanded and adapted to other regions of the world where data on pesticide use are limited. Plain Language Summary Pesticide exposures are a concerning issue that threatens ecosystem integrity and human health. However, most countries cannot assess, monitor, and control pesticide contamination. We studied this threat in Ecuador, a country with one of the highest application rates of pesticides worldwide, an export‐bound agricultural industry, a large population at risk, remarkable biodiversity, and a limited understanding of the nationwide extent of pesticide contamination. We assessed the geographic distribution of pesticide application rates and identified regions where the potential risk of exposure to human populations and ecosystems requires detailed exposure assessments. Using publicly available global data sets that locate human populations, biodiversity, natural parks, and pesticide use rates, we mapped areas where high levels of pesticide use and high density of human population overlap. We also assessed areas where natural parks and amphibian species may be threatened. Around 28% of Ecuador’s population lived in areas with a high pesticide application rate. We found widespread intensive use of pesticides in Ecuador in regions that overlap with human populations and ecosystems at risk of exposure. The methods developed relied on open‐source software and publicly available data. Thus, our approach can be applied to other regions where data on pesticide use are limited. Key Points Close to 30% of the population in Ecuador lives in areas with high pesticide application rates High pesticide use areas create risks for human populations, biodiversity and protected ecosystems within national parks The accessible, modular, and scalable methods developed facilitate reproducing population‐level assessments across the world

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. Plain Language Summary Lead poisoning is a serious health risk for children. While there are many exposure pathways, one underappreciated source is lead in the soil, left over from flaking leaded paint, leaded gasoline, and industry. We wanted to know if testing the soil for lead could help predict which communities are most at risk for elevated childhood lead exposure. We created a map of soil lead across Chicago community areas by analyzing 1,750 soil samples. We then compared this map to children's blood lead test results from the Chicago Department of Public Health and to community area information like median income. Our findings show community area median income remains a very strong predictor of lead exposure risk. Crucially, we also demonstrate that the amount of lead in the soil provides additional, independent predictive power. This means that even after accounting for socioeconomic (and other physical environment) factors, high soil lead levels are linked to higher risk. This study shows that testing soil is a practical tool for directing prevention. Cities can use soil testing to identify high‐risk neighborhoods before children are exposed, helping focus public health efforts like educating families and building safer environments. Key Points Soil lead is a persistent predictor of elevated blood lead among children, even controlling for socioeconomic and environment covariates Pairing soil testing with the EPA's IEUBK model enables proactive, community‐scale screening for lead exposure risk More than half (54%) of 1,750 soil samples tested had lead exceeding the EPA's 200 ppm residential screening level

Heat is associated with increased risk of morbidity and mortality. People who are incarcerated are especially vulnerable to heat exposure due to demographic characteristics and their conditions of confinement. Evaluating heat exposure in prisons, and the characteristics of exposed populations and prisons, can elucidate prison‐level risk to heat exposure. We leveraged a high‐resolution air temperature data set to evaluate short and long‐term patterns of heat metrics for 1,614 prisons in the United States from 1990 to 2023. We found that the most heat‐exposed facilities and states were mostly in the Southwestern United States, while the prisons with the highest temperature anomalies from the historical record were in the Pacific Northwest, the Northeast, Texas, and parts of the Midwest. Prisons in the Pacific Northwest, the Northeast, and upper Midwest had the highest occurrences of days associated with an increased risk of heat‐related mortality. We also estimated differences in heat exposure at prisons by facility and individual‐level characteristics. We found higher proportions of non‐white and Hispanic populations in the prisons with higher heat exposure. Lastly, we found that heat exposure was higher in prisons with any of nine facility‐level characteristics that may modify risk to heat. This study brings together distinct measures of exposure, vulnerability, and risk, which would each inform unique strategies for heat‐interventions. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for who is included in fights for environmental justice. Plain Language Summary Heat is a direct and increasing threat to human health. People in prison are especially vulnerable to heat as an increasingly older and disabled population with limited agency over their conditions of confinement, healthcare, or access to resources to decrease heat exposure. We use an air temperature data set to measure short and long‐term patterns of various heat metrics for 1,614 prisons in the United States from 1990 to 2023. We find that the patterns of highs and lows greatly differ based on the metric of choice. We also estimated differences in heat exposure at prisons by facility and individual‐level characteristics. We found higher proportions of non‐white and Hispanic populations in prisons with higher heat exposure. We also found higher temperatures are in prisons that have characteristics that can modify exposure or vulnerability to increase overall risk. Distinct measures of exposure, vulnerability, and risk can each inform unique strategies for heat‐interventions in United States prisons. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for which populations and prisons are included in efforts to reduce heat risk. Key Points Prisons, incarcerated populations, and staff are exposed to heat and changing climates as measured through a variety of metrics Higher temperatures are found in prison landscapes that have characteristics that can modify exposure or vulnerability, increasing overall risk Distinct measures of exposure, vulnerability, and risk, can each inform unique strategies for heat‐interventions in US prisons

Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste to nearby farmlands, with severe environmental health impacts of swine CAFOs and proximity to disadvantaged communities well documented in some US regions. Most studies documenting the impacts of CAFOs rely almost exclusively on CAFO locations known from incomplete public records. Poultry CAFOs generate dry waste and operate without federal permits; thus, their environmental justice (EJ) impacts are undocumented. North Carolina (NC), a leading poultry producer, has seen a significant increase in poultry CAFOs, particularly since the 1997 swine CAFO moratorium. Using literature‐derived heuristics, this study refined the locations of poultry CAFOs derived based on Earth Observation (EO) data and deep learning, reducing the overestimation of poultry CAFO density by 54% after heuristic adjustments. We removed 51.8% of misclassified features in NC and 61.5% across the US, significantly improving data set accuracy. Spatial analysis, including Local Indicators of Spatial Association, revealed that poultry CAFOs often cluster in census tracts with high Social Vulnerability Index (SVI) scores, indicating potential EJ issues. Notably, one‐third of NC's census tracts with high poultry CAFO density also have high SVI, primarily in rural eastern regions. Similar patterns were observed in the South and Southeast of the US. However, not all high‐density CAFO areas correspond with high SVI, suggesting a complex relationship between CAFO locations and community vulnerabilities. This study highlights the critical need for comprehensive, high‐quality data on unpermitted poultry CAFOs derived using AI algorithms to fully understand their impacts on communities and accurately inform EJ evaluations. Plain Language Summary This study explores the environmental and social impacts of poultry concentrated animal feeding operations (CAFOs) across North Carolina and the United States. These operations, often unregulated, contribute significantly to local pollution levels, particularly in areas with high social vulnerability. Using literature‐derived heuristics on Earth Observation data and deep learning techniques, we identified the precise locations of poultry CAFOs and analyzed their distribution in relation to socially vulnerable communities. The findings reveal a significant concentration of poultry CAFOs in certain regions, particularly where social vulnerabilities are already high, highlighting potential environmental justice concerns. Key Points Using satellite data and literature‐derived heuristics, we accurately identified unregulated poultry Concentrated Animal Feeding Operations (CAFOs) Advanced data techniques show a significant overestimation of CAFO locations prior to heuristic adjustment We found high concentrations of poultry CAFOs in socially vulnerable areas of North Carolina and Southeast US

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.