Abstract Climate change is increasing risks to health systems in all countries with potentially catastrophic implications for public health. Complex climate events, which are expected to increase with ongoing warming, can push health systems, and the broader systems that support health, such as energy, transportation, food, and water, beyond tipping points, leading to severe consequences for individual and community health. This is particularly the case in jurisdictions already facing health and social system vulnerabilities. Guided by the World Health Organization Operational Framework for Climate Resilient and Low Carbon Health Systems we examined the literature using Embase, Medline, CAB Abstracts + Global Health, and Scopus databases to identify examples of adaptation measures for reducing risks to health and health systems from complex climate events. We also identified key gaps, in hopes of informing future research into resilience-building strategies. We found that information exists that health decision makers can use in their efforts to plan for future events. However, within the existing literature there is unequal coverage of adaptation options over health system functions as well as limited recognition of impacts from complex climate events and specific adaptation needs. Our findings revealed important deficits in knowledge on the effectiveness of adaptation measures. Many of the proposed adaptation actions do not explicitly address the interacting dynamics characteristic of complex events that may lead to more severe consequences for health and health systems. Our findings suggest that health authorities can utilize existing climate change and health guidance and tools with new approaches, such as the use of scenarios of complex climate events in table-top stress testing simulations, to plan for much higher impact emergencies and disasters. The adaptation options identified may be considered by health authorities in their vulnerability assessment and adaptation planning processes to bolster the resilience of individuals and health systems. Further research into the health implications of complex climate events and the effectiveness of adaptation options is urgently needed to inform the education and training of health professionals as they chart a path forward in preparing for a warmer world.
Elevated ambient temperatures are associated with preterm birth, which disproportionately occurs among ethno-racially and socio-economically marginalized populations. Existing literature on the reproductive health effects of heat primarily explores ambient temperatures, neglecting individual-level differences in exposure to extreme temperatures due to factors like housing, occupation, and neighborhood land cover. To address these individual differences, we linked 2015–2019 Los Angeles County administrative birth records to 2022 tax parcel data to estimate individual-level housing thermal performance, occupation in high-heat-risk professions, tract-level urban-heat-island intensity, and zip-code-level maximum and minimum ambient temperature exposures among birth parents ( N = 504 761). We use logistic regression to assess disparities in these four heat-exposure metrics by sociodemographic variables previously associated with preterm birth. Black, Latine, Mexican immigrant, and less formally educated parents had consistently higher odds of heat exposure across all four dimensions, relative to their White, US-born Latine, and highly formally educated counterparts. Survival analysis using Cox proportional hazards model indicated that exposure to weeks with an extreme heat day was associated with a 41% (95% confidence interval [CI]: 8%–83%) and 75% (95% CI: 16%–165%) higher hazard of very and extremely preterm births, respectively, relative to pregnancies that did not experience an extreme heat day. We did not find strong evidence for extreme heat exposure and late preterm births. When jointly exposed to poor housing thermal performance and an extreme heat day, the hazard of an extremely preterm birth rose to 177% (95% CI: 15%–567%). We did not find evidence of a joint effect for late or very preterm births. These findings illuminate how housing, occupation, and land cover compound heat-risk exposures among ethno-racially marginalized and socio-economically disadvantaged birth parents in Los Angeles, contributing to preterm birth.
Differentials in exposure to air pollution across population subgroups is a source of environmental health inequality. An important question is the generalisability of the trends between air pollution exposure and demographic attributes across different populations: comparisons are often confounded by different study methodologies. A key challenge is that individual-level demographic data are not usually available. In this study, we apply identical methods to a single cohort of 56 734 individuals to compare trends between exposures to ambient NO _2 , PM _2.5 and O _3 and sex, age, ethnicity, and socioeconomic status (SES) in the cities of Glasgow and Edinburgh, UK. We also use individual-level information on place of work or study (where that applies) to derive more realistic estimates of exposures than studies using exposures at place of residence only. We find that whilst some broad trends in exposure-demographic relationships are similar between Glasgow and Edinburgh—for example that younger adults, those of lower SES and of non-White ethnicity are, on average, exposed to higher concentrations of NO _2 and PM _2.5 (and to lower concentrations of O _3 )—our results also confirm heterogeneities in these relationships between the two cities. Importantly, we show that this geographical specificity extends to between-city geographical distributions of places of work and study as well; for example, males and females have equal exposures to air pollutants in each city based on residence location only, but have different patterns of exposure in the two cities when place of work is included. The heterogeneities we observe presumably arise from the particular way in which the urban fabric of a given city develops over time. The policy message is that it is not appropriate to assume geographically transferable relationships between air pollution exposure and SES, ethnicity, age and sex, even when the population demographic profiles are broadly similar.
Household air pollution (HAP) contributes to the global cardiovascular disease burden. Early life exposures may impact future disease risk; however, evidence of HAP's impact on blood pressure (BP) among children in low-resource settings is limited. We assessed baseline cross-sectional associations between personal exposure to fine particulate matter (PM2.5) and black carbon (BC) and children's (8-15 years) systolic and diastolic BP (SBP, DBP) from the Sustainable Household Energy Adoption in Rwanda study. We enrolled 626 households in rural Eastern Rwanda that used traditional biomass fuels for cooking. Children wore Ultrasonic Personal Aerosol Samplers for 48 h monitoring of personal exposure, followed by BP measurement using standard protocols. Multiple linear regression was used to characterize associations between PM2.5 and BC exposure and BP in separate models; effect modification by age and sex was evaluated. Among 622 children (mean age = 11.8 years, standard deviation [SD] = 2.1; males = 303, females = 319), the average SBP and DBP were 105.8 mmHg (SD = 10.0) and 68.3 mmHg (SD = 7.6), respectively. BP percentiles were higher than those in a US reference population (e.g. 59.0% and 85.5% had SBP and DBP percentiles >50th, respectively; 24% were classified as having elevated BP and/or were hypertensive, defined as ⩾90th percentile). The median 48 h PM2.5 and BC concentrations were 192.9 µg m-3 (25th percentile [Q1] = 119.7, 75th percentile [Q3] = 336.0) and 9.85 µg m-3 (Q1 = 6.56, Q3 = 13.23), respectively. We did not observe evidence of associations between HAP and BP levels (e.g. PM2.5-SBP = 0.51 mmHg per interquartile range [IQR, 211.7 μg m-3] increase, 95% confidence interval: -0.46, 1.49). We did not observe clear evidence of effect modification. Exposures well above international health-based guidelines may have limited our ability to observe associations if the true exposure-response is flat in that part of the global exposure continuum. Importantly, our BP data suggest an elevated cardiovascular disease burden among children in this low-resource setting compared to in the US, demonstrating a need for more research and the development of more appropriate reference data in these settings. ClinicalTrials.gov Identifier: NCT05668624.
Residential greenness has been associated with improved renal function, whereas traffic-related air pollutants such as PM _2 . _5 and NO _2 have been linked to declines in renal function. However, their interrelationship remains unclear. This study examined whether PM _2 . _5 and NO _2 mediate the association between greenness and renal function. We combined the nationwide dataset of the Taiwan Biobank between 2008 and 2020 with the information on residential greenness and air pollutant exposures based on township levels of each participant’s residential address. A total of 113 164 eligible adults, aged 30–70 years, were included in the analysis. Renal function was evaluated using the Taiwan-specific estimated glomerular filtration rate (eGFR_Taiwan) while exposure variables included residential greenness, quantified using the normalized difference vegetation index (NDVI), and concentrations of PM _2.5 and NO _2 , estimated through land-use regression models employing machine learning techniques. Multiple regression and causal mediation analyses were applied. Higher NDVI was significantly associated with increased eGFR_Taiwan (estimate = 0.17, p < 0.0001). Conversely, PM _2 . _5 and NO _2 exposures were linked to reduced eGFR_Taiwan (estimates = −0.17 and −0.15, p < 0.0001 and p < 0.001, respectively). PM _2 . _5 partially mediated the NDVI-eGFR_Taiwan association, accounting for 30.8% of the total effect. Beyond its direct association with renal function, residential greenness has also been associated with better renal health, potentially through the reduction in ambient concentrations of PM _2 . _5 .
Standards for air pollution are becoming more stringent. Canada will update the annual average fine particulate matter (PM2.5) standard to 8 µg m-3 in 2030, which questions the adequacy of existing PM2.5 monitoring capacity for evaluating potential exceedances and exposure risk at community level. Although PM2.5 monitoring has expanded beyond regulatory monitors to include low-cost sensors, concerns remain regarding inequalities in its effective coverage and thus data representativeness for exposure estimations. Understanding the connections between PM2.5 monitor density, surface exposure, and neighbourhood socio-economic deprivation and health vulnerability is important for addressing compounded inequities. In this study, we applied locally adaptive kernel density estimation to calculate total PM2.5 monitor density from 2020 to 2024 in Ontario, Quebec, and Atlantic Canada. Local bandwidths were defined for each monitored location by considering the surrounding variability of surface PM2.5 concentrations and its sensitivity to distinguish exceedances. Suburban communities of Montreal, Toronto, near-border regions of Windsor and Niagara, southeast Kitchener, and remote communities in northwest Ontario had observed multiple disparities, with more pronounced socio-economic marginalization, higher surface exposure, but inadequate monitoring. Based on mixed-effects logistic regression, residential instability and situationally vulnerable populations had 25% and 54% higher odds of being deprived in PM2.5 monitoring capacity, respectively, after controlling for surface PM2.5 and emission facility density. Assessments of random-effects also suggested the importance of locally adapted strategies to account for provincial and inter-municipality variations in monitoring disparities among socio-economically marginalized groups and populations with physical or psychological disabilities. Overall, our findings inform vulnerable communities in need of monitoring expansion for precise air quality control and health risk assessments in light of the new standard.
Abstract Background: Evidence of the impact of early pregnancy exposure to polycyclic aromatic hydrocarbon (PAH) mixtures on gestational diabetes mellitus (GDM) risk is limited, and the potential role of the pre-pregnancy body mass index (BMI) as an effect modifier remains unclear. Methods: This nested case-control study of the prospective Zunyi Birth Cohort included 145 women with GDM and 885 healthy controls. Ten PAH metabolites were measured in the first-trimester urine samples from participants before GDM diagnosis. Logistic regression, restricted cubic splines (RCS), quantile-based g-computation (QGC) and interaction models were applied. Results: The QGC model indicated that each quartile increase in PAH mixture was associated with a 41% higher risk of GDM [adjusted odds ratio(OR)=1.41, 95% confidence interval(CI):1.04-1.90].1-hydroxyphenanthrene (1-OH-PHE), 4-hydroxyphenanthrene (4-OH-PHE), and 9-hydroxyfluorene (9-OH-FLU) were consistently identified as key contributors to GDM risk, with RCS analyses revealing nonlinear exposure-response relationships.Importantly, the pre-pregnancy BMI significantly modified these associations. A significant synergistic interaction was observed between BMI and 1-hydroxypyrene (1-OH-PYR) [(relative excess risk due to interaction (RERI) = 1.69; 95% CI: 0.50-3.37)].The effects of 1-OH-PYR reversed the protective effects in normal-weight women against harmful effects in overweight or obese women. Conclusions: Our findings suggest that first-trimester exposure to PAH individuals and mixtures is associated with an increased risk of GDM. Pre-pregnancy BMI is a critical effect modifier capable of reversing the risk direction of specific PAHs such as 1-OH-PYR. These results highlight the importance of considering both environmental chemical exposure and maternal metabolic characteristics for GDM prevention.
Existing studies have documented associations between daily temperature fluctuations and pain, but less is known about the sustained effects of temperature patterns over longer periods. This study is among the first to examine how prolonged exposure to extreme cold and heat over months and years shapes high-impact chronic pain (HICP) outcomes, including HICP reporting, two-year HICP incidence, and two-year HICP recovery. We linked Health and Retirement Study data (1998–2022) with census tract-level temperature data and applied hierarchical logistic models. Results indicate that high long-term (12-month) exposure to extreme cold is associated with a 4% increase in the odds of reporting HICP (95% CI: 0%–9%, p = 0.045). Intermediate-term (3-month) exposure to extreme cold and both intermediate- and long-term exposure to extreme heat are not significantly associated with HICP reporting in the overall population. Interaction analyses reveal that individuals with higher education and those living in cold or very cold climate zones are more likely to report HICP under sustained extreme cold. In contrast, people in the lowest wealth quartile and rural residents experience higher HICP incidence under cumulative exposure to extreme heat, whereas extreme heat exposure is associated with lower HICP incidence and higher recovery among individuals in the wealthiest groups. These findings provide novel evidence on environmental determinants of chronic pain and underscore the importance of climate-adaptive strategies for pain prevention and management.
Fine particulate matter (PM _2.5 ) is a major human health risk factor. Despite significant efforts to reduce air pollution in Europe the aging population, urbanization, and climate change, demand an assessment of potential impacts of future pollution trends. The interactions of climate change and PM _2.5 pollution, and their effects on adverse health outcomes require a comprehensive approach to effectively address these environmental and public health challenges and link them with future scenarios. In this study, we use three shared socio-economic pathways (SSPs) to examine potential changes in societal, demographic, and economic trends over the century in combination with three future radiative forcing trajectories. We use three scenarios: the SSP1-2.6, SSP2-4.5, and SSP5-8.5. We examine the consequences of these pathways on population exposure to PM _2.5 in the coming decades, focusing on the years 2030, 2040, 2050 and 2100. The study evaluates excess mortality across various health outcomes, such as cardiovascular and respiratory diseases, using novel relative risk functions and public health data. We find that air pollution in Europe will continue to decline over the coming decades across all scenarios. The projected excess mortality attributable to PM _2.5 exposure varies by region, future pathway, and period, and is mainly driven by the projected demographic changes of each scenario. After the mid-century, excess mortality will almost stabilize under the SS1-2.6 and SSP2-4.5 pathways and increase under SSP5-8.5. Population aging and urbanization will amplify vulnerability, particularly in densely populated areas, highlighting the need to protect the elderly. We project that by 2100, excess mortality in Europe will be 199 (56–561), 323 (98–719), 808 (341–1489) thousands per year according to SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively. Our findings suggest that current or additional air pollution control measures will offset the excess mortality burden in Europe only under the SSP1-2.6 or SSP2-4.5 pathways.
Changing precipitation patterns can intensify chronic basement wetness, amplifying health impacts in neighborhoods already affected by disinvestment and inequitable infrastructure decisions. The Health and High Water project examined links between basement wetness, microbial and radon exposures, indoor air quality, and residents’ health and risk perceptions in two predominantly Black communities in Pittsburgh, PA: flatter, poorly draining Homewood and the steeper, better-drained Hill District. The study was conducted as an academic-community partnership using a community-engaged research design, with resident data collectors from the study neighborhoods conducting household surveys and environmental sampling. Surveys and environmental sampling were conducted in 125 households with basements across both neighborhoods between March 2024 and February 2025. Household surveys captured measures of experiences with basement wetness, risk perceptions, mitigation behaviors, housing conditions, and psychological distress. Environmental sampling included 30 d of continuous indoor air quality monitoring and dust collection for microbial analysis. Over three-quarters of households reported experiencing basement wetness, yet few reported taking meaningful mitigation actions (the most common, foundation waterproofing, was reported by 28% of respondents). In the flatter Homewood neighborhood, wetness was more frequent, more severe, and more impactful. Homes in Homewood also showed higher concentrations of mold, bacteria, and nontuberculous mycobacteria than those in the Hill District. Nearly one-quarter of all sampled homes exceeded the U.S. Environmental Protection Agency radon action level of 4 pCi L ^−1 . Residents reporting neighborhood flooding or major home environment problems were more likely to meet criteria for psychological distress, while direct experiences of basement wetness were not significantly associated with distress. Findings reveal accumulating environmental health risks in these communities, with widespread basement wetness, microbial and radon exposures, and limited mitigation action occurring alongside few accessible remediation resources. Policy responses should expand and enhance healthy homes repair programs, subsidize basement wetness and radon mitigation, and better integrate housing, health, and stormwater planning.
PM _2.5 (particulate matter (PM)with a diameter of 2.5 micrometres or smaller) air pollution is a major contributor to the global burden of disease and poses particular risks to children due to its impacts on respiratory health and overall well-being. Children’s exposure to PM _2.5 in two highly polluted cities (Bandung, Indonesia and Kathmandu, Nepal) was investigated over a week-long period. The research aimed to determine (1) children’s overall PM _2.5 exposure levels, (2) the environments or activities contributing most to such exposure, and (3) any differences in exposure between the two study populations. Low-cost PM _2.5 sensors (PurpleAir) and Global Positioning System devices were carried by 61 children aged 6–13 years in custom-designed backpacks, alongside questionnaires used to characterize home and school environments and the child’s routine. Children were recruited from four schools, at which PurpleAir sensors were also installed outdoors to measure ambient PM _2.5 concentrations. Mean personal PM _2.5 concentrations were 48.0 µ g m ^−3 in Bandung and 45.8 µ g m ^−3 in Kathmandu. School environments had the lowest mean PM _2.5 (41.2 and 33.7 µ g m ^−3 , respectively) whereas home, commuting, and other microenvironments showed higher mean PM _2.5 concentrations, up to 53.9 µ g m ^−3 . Comparison with World Health Organization guidelines suggests these children are regularly exposed to PM _2.5 concentrations exceeding guidance levels. No specific source driving the elevated concentrations in the home was identified. While commuting, travelling by scooter or walking were associated with the highest PM _2.5 levels. These findings emphasize the urgent need for effective interventions to protect children from high pollution levels in urban environments.
Environmental volunteering yields co-benefits for human and planetary health and can be embedded into community-based health promotion efforts. This study aimed to evaluate the feasibility, acceptability and potential scalability of an environmental volunteering intervention supporting afforestation projects among adults with obesity and mental health conditions in Malta. This multiple single-case intensive longitudinal study enrolled adults with obesity (BMI ⩾30 kg m ^−2 ) and mental health conditions into a 3-month volunteering program. Feasibility was assessed by recruitment and retention rates. Acceptability was evaluated through attendance logs, semi-structured interviews, and adherence to a measurement protocol using ecological momentary assessments (EMA) and wrist-worn activity monitors. Recruitment proved challenging with a low enrollment rate of 10.8% (11 of 102 contacted), but retention in the intervention among participants was high (8/11: 73%). The intervention was highly acceptable; qualitative feedback revealed positive experiences driven by social connection and a sense of purpose, with time constraints being the main barrier. The measurement protocol was also acceptable, showing strong adherence (92% EMA compliance and 99% step compliance), though some participants (4/11: 36%) found the daily questions repetitive. The environmental volunteering intervention and its intensive measurement protocol are feasible and acceptable for a selected group of adults with obesity with mental health conditions in Malta. High retention signals the intervention’s health promotion potential. However, to become a scalable public health tool, its critical recruitment barriers must be overcome with targeted outreach.
Climate change presents multiple threats to mental health, including not only direct impacts of extreme weather but also more diffuse effects associated with awareness of the changing climate and of human culpability for those changes. This awareness can include a moral evaluation recognizing the ‘wrongness’ of climate change. The concept of moral distress, or the more intense experience of moral injury, refers to the deleterious effects of witnessing or participating in actions that violate one’s ethical principles or deeply held values; research has demonstrated that moral distress can impair mental health and well-being. We argue that moral evaluations and moral distress are an important part of the human response to the climate crisis, distinct from, but relevant to climate anxiety, and that climate moral distress likely constitutes one way in which climate change threatens mental health. We offer an agenda to encourage further research into this phenomenon. If climate moral distress contributes to distress about climate change and is a mental health threat, moral repair should be considered as part of an adaptive response to climate change.
Rising global temperatures have emerged as a critical concern in recent decades and are recognized as one of the biggest threats to human health. In Africa, low-income communities face disproportionate exposure and vulnerability to extreme temperatures due to poorly planned housing structures and limited adaptive capacity. As passive heat adaptation interventions gain traction across the continent, this review evaluates their technical effectiveness and community feasibility within low-income African communities. Using the Joanna Briggs Institute feasibility, appropriateness, meaningfulness, and effectiveness framework, the review examines how building modification interventions have been developed, tested, and implemented, their capacity to improve indoor thermal comfort, and the socio-technical factors influencing their uptake and scalability. The findings indicate that greening systems, house insulation, screened windows, reflective surfaces and window opening have the most potential for widespread and sustainable implementation in low-income communities. In contrast, interventions such as solar chimneys, metal roofs, wind towers, nozzle air funnels, closed eaves, open eaves, thatched roofs, bottle houses, earthbag houses and passive solar houses demonstrate higher context-specific and structural limitations despite their technical effectiveness. The review identifies critical gaps in long-term performance, scalability and community acceptability of technically effective passive heat adaptation interventions, especially in low-income communities. Overall, the study provides an analytical foundation for guiding the selection of effective, contextually appropriate passive heat adaptation interventions and underscores the need for participatory and inclusive implementation approaches to enhance heat resilience in vulnerable African communities.
Background. Diarrhoeal disease remains a leading cause of morbidity and mortality among children under five years (U5) in Rwanda, contributing substantially to healthcare utilisation and imposing considerable economic burdens on households and the health system. National estimates indicate that approximately 14.3% of children under five experienced diarrhoea in the two weeks preceding the 2019–2020 Rwanda Demographic and Health Survey, and diarrhoea has been reported as the third leading cause of death in this age group. Climate variability may influence the risk of diarrhoeal disease, but evidence on how specific climatic factors affect diarrhoeal incidence across Rwanda’s diverse ecological zones and seasons remains limited. This study examined associations between maximum temperature, minimum temperature, rainfall, and relative humidity and U5 diarrhoeal incidence at the sector level in Rwanda. Methods. Monthly counts of U5 diarrhoeal cases reported by health facilities across 416 administrative sectors in Rwanda from January 2015 to December 2024 were analysed together with satellite-derived climate data. Spatio-temporal statistical models were used to evaluate associations between standardised climate variables and diarrhoeal incidence rates while accounting for geographic and seasonal variation. Model comparison relied on both goodness-of-fit metrics and out-of-sample predictive diagnostics. Results. Diarrhoeal incidence showed clear spatial and seasonal patterns, with persistently higher rates in northern and eastern Rwanda. In the final model maximum temperature was positively associated with increased diarrhoeal incidence, with a one-standard-deviation increase corresponding to a 5.6% rise in the estimated incidence rate ratio (RR = 1.056; 95% CrI: 1.02–1.09). Relative humidity showed a protective association (RR ≈ 0.919; 95% CrI: 0.89–0.94), while rainfall showed limited immediate effects. Conclusion. Under-five diarrhoeal incidence in Rwanda showed marked spatial and seasonal variability. Maximum temperature was positively associated with diarrhoeal incidence rates, while relative humidity showed an inverse association that may partly reflect unmeasured confounding by drought, water access, and WASH infrastructure. External validation is needed before the modelling framework can be applied operationally. These findings highlight the importance of preparedness during hotter periods and strengthened prevention and surveillance efforts in high-risk areas.
Reliance on biomass and other polluting cooking fuels remains widespread in India and continues to impose substantial health burdens through both household and ambient air pollution, undermining progress toward Sustainable Development Goals (SDGs) 3, 5, and 7. Using nationally representative data from the Indian Human Development Survey (2011–2012), we construct a composite measure of individual functional health and estimate its association with cooking fuel and stove types while accounting for socioeconomic characteristics, housing conditions, cooking roles, and district-level ambient PM $ _{2.5}$ exposure. These estimates are combined with a structural household energy choice model and air pollution scenarios to predict stove and fuel adoption under alternative policy pathways and to assess the associated population health outcomes in 2030. The results show that biomass- and kerosene-based cooking is associated with significantly poorer health outcomes, with larger adverse effects observed among women responsible for cooking. Intermediate technologies such as improved biomass stoves provide limited health gains but remain inferior to clean alternatives. Scenario comparisons for 2030 indicate that policy pathways aligned with SDG 7 targets and the Paris Agreement are associated with substantially better population health outcomes than business-as-usual scenarios, which yield only modest improvements. Therefore, universal access to clean cooking technologies offers important co-benefits for population health, gender equality, and climate mitigation, supporting progress toward multiple SDGs in India.
While the relationship between race disparity and green space access is well-established in the United States (U.S.), its spatial-temporal variations and local drivers are still unclear. This study examines how racial disparities in access to green space changed between 2010 and 2020 across the contiguous U.S. and identifies both global (the whole study area) and local (individual spatial units) drivers of these disparities. By integrating high-resolution satellite-derived land cover metrics with census tract-level sociodemographic data, we evaluated these disparities using geospatial analysis, linear regression, and explainable machine learning. We find that the racial disparity in green cover neither narrowed nor widened over the decade, despite substantial national demographic diversification, indicating that demographic change alone was insufficient to reduce inequity. Our results reveal a consistent pattern: census tracts with higher proportions of White residents have significantly greater green cover, while areas with greater non-White populations have considerably less. Feature importance analysis identified socioeconomic vulnerabilities, particularly housing burden, transportation access, and economic disadvantage, as primary drivers of these disparities, highlighting the role of structural barriers in perpetuating environmental injustice. Local explainability analyses further revealed substantial spatial heterogeneity in these relationships: single-parent families remained the dominant local predictor nationally, PM _2.5 declined sharply in influence across the western U.S., and forest and shrub cover emerged as a new leading predictor in parts of the Mountain West, Pacific region, and Texas by 2020. Our findings highlight the need for equity-centered policies, targeted interventions, and community-informed urban planning to dismantle these disparities and ensure equitable access to health-promoting natural environments for all communities.
Blood lead levels (BLL) in children in the United States have decreased in recent decades; yet, lead is toxic at any concentration, and disparities in lead poisoning persist across population groups. As a result, lead exposure continues to be a major environmental public health concern. Because the relative contributions of different lead exposure sources are rarely evaluated together, mitigation efforts may be fragmented, with limited resources not always directed toward the highest-impact interventions. The objective of this study was to investigate the combined impacts of housing characteristics, soil lead concentrations, and water service line materials on childhood BLLs using tree-based machine-learning methods. Milwaukee, Wisconsin, was selected as a case study due to its high prevalence of elevated pediatric BLLs and large population at risk from older housing stock. Using existing data, we applied extreme gradient boosting model with SHapley Additive exPlanation (SHAP) to quantify the relative contributions of multiple lead exposure sources among children aged 1-5 years and to assess whether exposure profiles interact to produce higher BLLs. Our results indicate that housing age and property values, among other housing characteristics, were more strongly associated with elevated childhood BLLs than soil lead concentrations or the presence of lead service lines. Children living in homes older than 90 years with lead service lines exhibited increased exposure risk, whereas similar homes with copper service lines showed substantially reduced risk. Overall, our findings demonstrate that interpretable machine-learning methods can provide cost-effective insights to guide more targeted and impactful pediatric lead mitigation strategies.
Nitrogen dioxide (NO2) affects a range of population health outcomes. However, comprehensive assessment of exposure patterns and associated health burdens has often been constrained by limited geographic coverage of air monitoring networks, particularly in low- and middle-income countries. To calculate the health impacts of NO2 exposure in Mexico using multiple newly available models of ground-level concentrations, assessing how the magnitude, geographic distribution, and sociodemographic distribution of health risks differ across alternative datasets. We analyzed exposures and health risks using two globally modeled ground-level NO2 datasets, and additionally used TROPOMI tropospheric NO2 column densities to compare spatial patterns across datasets. For both ground-level data sets, we investigated differences in population-weighted exposures among various sociodemographic and geographic subgroups, examined spatial variations of NO2 concentrations across Mexico, and calculated health impacts attributable to NO2. Across Mexico, we estimated between 24 000 (95% CI: 12 000-36 000) and 33 000 (95% CI: 17 000-50 000) annual premature deaths attributable to NO2 exposure among adults aged 25 years and older, depending on the exposure dataset. In both models, most of the health burden was concentrated in Mexico City, though the relative contribution of each state varied by dataset. When mortality was modeled at the state level, the proportion of NO2-attributable premature deaths occurring in Mexico City and the State of Mexico-together comprising most of the metropolitan area-ranged from 52% to 81%, depending on the exposure dataset. Our analysis demonstrates substantial NO2-related health burdens across Mexico, including both urban and rural areas. We highlight the variation in results associated with exposure modeling, particularly for subnational and local analyses. These results indicate the need to select exposure datasets that are fit for the purpose of the research questions and test the sensitivity of health impact estimates to dataset choice.
Mesoscale modeling applications assessing urban-heat-reducing strategies, such as cool roofs, usually assume uniform implementation. While informative at the city scale, this approach offers limited insight into practical deployment and usually does not account for either local needs or existing disparities. In contrast, evaluating spatially and socioeconomically focused interventions within mesoscale modeling frameworks can provide more tangible and just pathways to urban cooling. In this context, the current study employed the Weather Research and Forecasting model with explicit urban canopy representation at 400 m spatial resolution to simulate three cool-roof scenarios (white/cool-colored paint, reflective coating, super-cool material) targeting heat-stress relief and health-relevant implications in the vulnerable western suburbs of the Athens Urban Area (AUA), Greece. Simulations were conducted for a nine-day extreme heat wave (28 July-5 August 2021), influenced by varying wind circulations (Etesians and sea breeze), which informed the spatial configuration of the cooling measures. Specifically, the intervention area was positioned within a strategically located industrial zone to facilitate sea-breeze-driven cooling toward the heat-sensitive western AUA. The targeted cool-roof scenarios produced clear atmospheric cooling, with mean daytime near-surface air-temperature reductions in the broader influence zone (West Athens, Central Athens, Piraeus) reaching 0.54 degrees C under Etesians and 0.73 degrees C during sea-breeze conditions. Through integration of thermophysiologically grounded epidemiological evidence and demographic data, these physical cooling effects were linked to population-wide heat-stress responses and associated health-beneficial implications, affecting 2.5%-20% of West Athens for all considered populations (male and female adults and seniors) and cool-roof strategies when sea breeze prevails. In total, the benefited population comprised 134 661 (white/cool-colored paint) to 197 787 (super-cool material) people, with heat-vulnerable female seniors exhibiting larger numbers than their male counterparts. These outcomes demonstrate how a targeted, health-relevant modeling framework can generate meaningful, needs-based insights to advance just resilience in urban environments.
