During the first two decades of the 21st century, the level of polycyclic aromatic hydrocarbon (PAH) pollution in urban atmospheric particulate matter (PM) in China significantly increased. By combining data from more than 6,695 individual samples covering 89 typical cities (population>0.5 million people) across China, this study focuses on evaluating the health risks to urban residents and the ecological risks to the surrounding environment from PAHs in PM using the methods of the United States Environmental Protection Agency and sediment quality standards. The PAH contents and contamination levels in Central China (CC) were lower than those in South China (SC) and North China (NC). NC exhibited the most severe PAH pollution and greatest ecological risk, while CC had the highest population density and gross domestic product. The incremental lifetime cancer risk and hazard index values for people in NC were greater than those for people in CC and SC, and the health risk increased with increasing latitude. Based on ecological risk criteria and standard assessment methods, PAHs in PM in China pose a potential ecological risk, and the risk of harmful biological effects follows the order of NC>CC>SC. Given the significant risks of PAHs to people, animals and plants at both the national and global scales, under the guidance of the One Health concept of the World Health Organization, it is necessary to comprehensively manage PAHs in PM and reduce their threats to humans and ecosystems.

Extreme heat has been linked to many health outcomes, including preterm and early term birth. We examine associations between acute heat wave exposure and risk of preterm (PTB) (28-36weeks) or early term (ETB) (37-38weeks) birth, stratified by individual-level and area-level factors. Daily ambient mean temperature was linked to maternal residence in state vital records for preterm and early term births in California, Florida, Georgia, Kansas, Nevada, New Jersey, North Carolina, and Oregon between 1990 and 2017. Heat waves were identified during the four-day exposure window preceding birth using the 97.5th percentile mean temperature for zip code tabulation areas (ZCTA). We used a time-stratified case-crossover design, restricted to the warm season (May through September) and stratified by maternal age, maternal education, ZCTA-level impervious land cover or social deprivation index. We pooled estimated odds ratios across states using inverse-variance weighting. The PTB and ETB analyses included up to 945,836 and 2,966,661 cases, respectively. Heat-related ETB risk was consistently highest among women <25years of age, women with ≤high school education, and women living in areas of higher social deprivation and impervious land cover. PTB associations were also elevated in these subgroups, but positive associations were also observed among older, more educated mothers, and in areas with less social deprivation. Across all subgroups and outcomes, the change in odds associated with heat waves ranged from no increase to a 7.9% increase. Heat-related early term birth risk is enhanced among subgroups associated with socioeconomic disadvantage, but patterns of vulnerability were less consistent for preterm birth.

Fire activities introduce hazardous impacts on the environment and public health by emitting various chemical species into the atmosphere. Most operational air quality forecast (AQF) models estimate smoke emissions based on the latest available satellite fire products, which may not represent real-time fire behaviors without considering fire spread. Hence, a novel machine learning (ML) based fire spread forecast model, the Fire Intensity and spRead forecAst (FIRA), is developed for AQF model applications. FIRA aims to improve the performance of AQF models by providing realistic, dynamic fire characteristics including the spatial distribution and intensity of fire radiative power (FRP). In this study, data sets in 2020 over the continental United States (CONUS) and a historical California fire in 2024 are used for model training and evaluation. For application assessment, FIRA FRP predictions are applied to the Unified Forecast System coupled with smoke (UFS-Smoke) model as inputs, focusing on a California fire case in September 2020. Results show that FIRA captures fire spread with R-squared (R 2) near 0.7 and good spatial similarity (∼95%). The comparison between UFS-Smoke simulations using near-real-time fire products and FIRA FRP predictions show good agreements, indicating that FIRA can accurately represent future fire activities. Although FIRA generally underestimates fire intensity, the uncertainties can be mitigated by applying scaling factors to FRP values. Use of the scaled FIRA largely outperforms the experimental UFS-Smoke model in predicting aerosol optical depth and the three-dimensional smoke contents, while also demonstrating the ability to improve surface fine particulate matter (PM2.5) concentrations affected by fires.

No abstract is available for this article.

Wet-bulb globe temperature (WBGT)-a standard measure for workplace heat stress regulation-incorporates the complex, nonlinear interaction among temperature, humidity, wind and radiation. This complexity requires WBGT to be calculated iteratively following the recommended approach developed by Liljegren and colleagues. The need for iteration has limited the wide application of Liljegren's approach, and stimulated various simplified WBGT approximations that do not require iteration but are potentially seriously biased. By carefully examining the self-nonlinearities in Liljegren's model, we develop a zero-iteration analytic approximation of WBGT while maintaining sufficient accuracy and the physical basis of the original model. The new approximation slightly deviates from Liljegren's full model-by less than 1°C in 99% cases over 93% of global land area. The annual mean and 75%-99% percentiles of WBGT are also well represented with biases within °C globally. This approximation is clearly more accurate than other commonly used WBGT approximations. Physical intuition can be developed on the processes controlling WBGT variations from an energy balance perspective. This may provide a basis for applying WBGT to understanding the physical control of heat stress.

Per- and poly-fluoroalkyl substances (PFAS), which are long-lasting environmental contaminants that are released into the environment during the e-waste disassembly process, pose a threat to human health. Human milk is a complex and dynamic mixture of endogenous and exogenous substances, including steroid hormones and PFAS. Therefore, in this study, we aimed to investigate the association between PFAS and steroid hormones in human milk from women living close to an e-waste disassembly area. In 2021, we collected milk samples from 150 mothers within 4weeks of delivery and analyzed them via liquid chromatography-tandem mass spectrometry to determine the levels of 21 perfluorinated compounds and five steroid hormones (estrone, estriol, testosterone, progesterone, and androstenedione [A-dione]). We also performed multiple linear regression analysis to clarify the association between maternal PFAS exposure and steroid hormone concentrations. Our results indicated that PFOA and PFOS were positively associated with estrone (β, 0.23; 95% CI, 0.08-0.39) and A-dione (β, 0.186; 95% CI, 0.016-0.357) concentrations in human milk, respectively. Further, the average estimated daily intake of PFOA and PFOS were 36.5ng/kgbw/day (range, 0.52-291.7ng/kgbw/day) and 5.21ng/kgbw/day (range, 0.26-32.3ng/kgbw/day), respectively. Of concern, the PFAS intake of breastfeeding infants in the study area was higher than the recommended threshold. These findings suggested that prenatal exposure to PFAS from the e-waste disassembly process can influence steroid hormones levels in human milk. Increased efforts to mitigate mother and infant exposure to environmental pollutants are also required.

Air pollution in India is a foremost environmental risk factor that affects human health. This study first investigates the geographical distribution of ambient and household air pollution (HAP) and then examines the associated mortality risk. Data on fine particulate matter (PM2.5) concentration has been extracted from the Greenhouse Gas Air Pollution Interactions and Synergies (GAINS) model. HAP, mortality and socio-demographic data were extracted from the National Family and Health Survey-5, India, 2019-2021. Regression models were applied to see the difference in age-group mortality by different pollution parameters. The districts with PM2.5 concentration above the National Ambient Air Quality Standard (NAAQS) level of 40μg/m3 show a higher risk of neonatal (OR-1.86, CI 1.418-2.433), postneonatal (OR-2.04, CI 1.399-2.971), child (OR-2.19, CI 0.999-4.803) and adult death (OR-1.13, CI 1.060-1.208). The absence of a separate kitchen shows a higher probability of neonatal (OR: 1.18, CI 1.074-1.306) and adult death (OR-1.06, CI 1.027-1.088). The interaction between PM2.5 levels above NAAQS and HAP leads toa substantial rise in mortality observed for neonatal (OR 1.19 CI 1.051-1.337), child (OR 1.17 CI 1.054-1.289), and adult (OR 1.13 CI 1.096-1.168) age groups. This study advocates that there is a strong positive association between ambient and HAP and mortality risk. PM2.5 pollution significantly contributes to the mortality risk in all age groups. Children are more vulnerable to HAP than adults. In India, policymakers should focus on reducing the anthropogenic PM2.5 emission at least to reach the NAAQS, which can substantially reduce disease burden and, more precisely, mortality.

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

The increasing frequency and severity of wildfires due to climate change pose health risks to migrant farm workers laboring in wildfire-prone regions. This study focuses on Sonoma County, California, investigating the effectiveness of air monitoring and safety protections for farmworkers. The analysis employs AirNow and PurpleAir PM2.5 data acquired during the 2020 wildfire season, comparing spatial variability in air pollution. Results show significant differences between the single Sonoma County AirNow station data and the PurpleAir data in the regions directly impacted by wildfire smoke. Three distinct wildfire pollution episodes with elevated PM2.5 levels are identified to examine the regional variations. This study also examines the system used to exempt farmworkers from wildfire mandatory evacuation orders, finding incomplete information, ad hoc decision-making, and scant enforcement. In response, we make policy recommendations that include stricter requirements for employers, real-time air quality monitoring, post-exposure health screenings, and hazard pay. Our findings underscore the need for significant consideration of localized air quality readings and the importance of equitable disaster policies for protecting the health of farmworkers (particularly those who are undocumented migrants) in the face of escalating wildfire risks.

Mental distress among young people has increased in recent years. Research suggests that greenspace may benefit mental health. The objective of this exploratory study is to further understanding of place-based differences (i.e., urbanity) in the greenspace-mental health association. We leverage publicly available greenspace data sets to operationalize greenspace quantity, quality, and accessibility metrics at the community-level. Emergency department visits for young people (ages 24 and under) were coded for: anxiety, depression, mood disorders, mental and behavioral disorders, and substance use disorders. Generalized linear models investigated the association between greenspace metrics and community-level mental health burden; results are reported as prevalence rate ratios (PRR). Urban and suburban communities with the lowest quantities of greenspace had the highest prevalence of poor mental health outcomes, particularly for mood disorders in urban areas (PRR: 1.19, 95% CI: 1.16-1.21), and substance use disorders in suburban areas (PRR: 1.35, 95% CI: 1.28-1.43). In urban, micropolitan, and rural/isolated areas further distance to greenspace was associated with a higher prevalence of poor mental health outcomes; this association was most pronounced for substance use disorders (PRRUrban: 1.31, 95% CI: 1.29-1.32; PRRMicropolitan: 1.47, 95% CI: 1.43-1.51; PRRRural 2.38: 95% CI: 2.19-2.58). In small towns and rural/isolated communities, poor mental health outcomes were more prevalent in communities with the worst greenspace quality; this association was most pronounced for mental and behavioral disorders in small towns (PRR: 1.29, 95% CI: 1.24-1.35), and for anxiety disorders in rural/isolated communities (PRR: 1.61, 95% CI: 1.43-1.82). The association between greenspace metrics and mental health outcomes among young people is place-based with variations across the rural-urban continuum.