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Fine particulate matter (PM2.5) poses a public health risk, disproportionately impacting low- and middle-income countries (LMICs). In Peru, where ambient concentrations in urban areas significantly exceed the World Health Organization's annual guideline of 5 µg/m3, lack of air pollution monitoring hinders exposure assessment, health effect research, and policy development. Here, we review efforts to create a national database of estimated ambient PM2.5 in other LMICs, and then discuss our efforts in Peru. We highlight the Peru-based NIH-funded GeoHealth Hub's efforts to establish a nationwide low-cost sensor (LCS) network of 176 PurpleAir monitors. We then describe a hybrid approach for modeling ambient PM2.5 exposure across Peru, leveraging data from LCS, satellite remote sensing, chemical transport models, and advanced machine learning methods. The ground-monitoring network includes sensors in both urban (62.5%) and rural (37.5%) areas, in the 24 Regions of the country, set up in collaboration with national environmental agencies. Initial application of our hybrid approach in Lima demonstrated good prediction for the years 2010-2023, with an R² of 0.88 with existing regulatory ground monitors. We are working to extend the model across Peru at a daily level and at a 5-km2 resolution for 2024-2026. The sustainability of these efforts will depend on building local capacity, securing long-term funding, and integrating the LCS network within the current regulatory environmental monitoring network. The hybrid approach offers a scalable solution to address data scarcity and enable high-resolution exposure modeling in Peru and other LMICs.

Informal e-waste recycling releases complex mixtures of hazardous substances, including heavy metals that bioaccumulate in exposed populations-especially among e-waste workers. Emerging evidence links these metals to telomere shortening, a key marker of cellular aging and DNA damage which can lead to noncommunicable diseases (NCDs). This study therefore examined the effects of metal exposure on telomere length among e-waste workers in Agbogbloshie compared to non-e-waste workers in Madina. A total of 78 samples (53 e-waste workers and 25 controls), each with three repeated measurements, were selected from the GEOHealth II study and analyzed for telomere length using quantitative polymerase chain reaction (qPCR) technique. Restricted cubic spline (RCS) modeling was employed to assess the association between metal exposure and telomere length. This study observed consistently shorter relative telomere length among the e-waste workers, particularly those involved in burning activities. Lead (Pb) and chromium (Cr) levels were negatively associated with relative telomere length and zinc (Zn) showed a positive association while magnesium (Mg) exhibited a nonlinear relationship with telomere length. The consistently shorter relative telomere length among e-waste workers coinciding with higher concentrations of Cr and Pb implicates the role of metals in telomere shortening. Larger, long-term studies are recommended for future studies.

Soil contamination with toxicants such as lead (Pb) is notoriously patchy and requires considerable effort to map. In the present study of three mining-impacted Peruvian towns, nearly 2,000 students collected and tested 1,500 soil samples using a field kit that combines simulated gastric extraction with visual detection of a purple Pb rhodizonate precipitate. An additional 2,000 soil samples were tested by our team's field staff. The combined results were mapped and compared with total soil Pb concentrations measured by X-ray fluorescence (XRF). The degree of soil contamination varied across sites and within each site. Quality control was provided by reanalyzing a subset of 120 soil samples from the three sites in the laboratory. Overall, total Pb concentrations were highest in Cerro de Pasco with 64% and 30% of samples in the 201-2,000 and 2,001-20,000 mg/kg range, respectively. Total Pb concentrations were somewhat lower in La Oroya with 88% of samples in the 201-2,000 mg/kg range and even lower in Callao with 79% below 200 mg/kg. However, extractable Pb proportions followed an inverse pattern, with the sum of the proportions of medium and high visual readings increasing from 46% to 57% and 69% in Cerro de Pasco, La Oroya, and Callao, respectively. This trend relates more closely to blood-Pb concentrations in children measured at the three sites 2 decades ago than total Pb concentrations. The study demonstrates that student-conducted soil screening can effectively identify Pb contamination patterns while providing valuable science education. Nearly 2,000 high school students used a simple visual test to check their soil for dangerous lead contamination in the mining‐impacted towns of Cerro de Pasco, La Oroya, and Callao in Peru. Their results revealed widespread contamination at all three sites, with geographic patterns within the towns and across the towns that were consistent with blood‐lead measurements in children conducted over 2 decades ago. Beyond identifying contamination, the project taught students valuable science skills and helped raise community awareness about the risks of lead exposure. Students presented their findings to parents and local officials, with some even organizing their own awareness campaign.

Malaria imposes a major health burden in the Peruvian Amazon, and its early warning is essential for effective disease prevention. The tropical sea surface temperature (SST) variability, fundamentally shaping the global weather patterns, may also alter malaria transmission and potentially improve its long-lead predictability. In this study, we propose a machine learning-based methodology that leverages comprehensive tropical SST variability for malaria prediction in the Peruvian Amazon. First, we demonstrate that significant correlations broadly exist between tropical SST anomalies and Peruvian malaria occurrence across different seasons and time lags, confirming the potential predictability from the tropical ocean. Then, we apply the self-organizing map to synthesize the spatiotemporally varying SST-malaria relationship and identify a unique dynamic SST index for Peruvian malaria. The dynamic SST index provides better performance (higher correlation coefficients and lower root mean square errors) in the generalized linear model, compared to the traditional El Niño-Southern Oscillation (ENSO) index, with lead times exceeding 3 months. Furthermore, the dynamic SST index captures the evolution of the ENSO life cycle from its precursor climate mode (Pacific Meridional Mode) and appears to influence Peruvian malaria by altering the local near-surface air temperature and specific humidity. Such underlying mechanisms provide the physically plausible basis for the long-lead predictability of Peruvian malaria using a machine learning-based remote predictor. Last but not least, we provide open-source code for broad applications in linking tropical SST variability and vector-borne disease transmission, or other climate-sensitive socioeconomic issues. Malaria poses a serious health risk in the Peruvian Amazon, and its early warning system is vital for implementing effective prevention strategies. In this study, we explore the remote predictor for Peruvian malaria from the tropical ocean via a machine learning clustering algorithm (Self‐organizing map; SOM). First, we demonstrate that significant correlations broadly exist between tropical sea surface temperature (SST) and Peruvian malaria occurrence across different seasons and time lags, indicating the potential predictive power from the ocean. Then, we identify a dynamic SST index by applying SOM to synthesize the complex SST‐malaria relationship. Compared to the traditional El Niño–Southern Oscillation (ENSO) index, the dynamic SST index shows higher prediction performance in the single‐predictor generalized linear model, with lead times exceeding 3 months. Moreover, we illustrate the underlying mechanism of how the dynamic SST index alters the local climate conditions and malaria transmission, providing the physically plausible basis for this data‐driven remote predictor.

Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, with environmental risk factors playing a significant role in their prevalence. This review aims to critically evaluate the current methodologies employed in spatiotemporal analyses of CVDs and provides recommendations to enhance the accuracy and practical application of these models. A systematic search of the literature was conducted using Scopus, PubMed, and Embase databases. Studies were selected based on their use of spatiotemporal models to assess the relationship between environmental factors and CVDs. We evaluated the methodological quality of included studies using the Spatial Methodology Appraisal of Research Tool (SMART). Significant challenges were noted, including the need for higher spatial resolution data sets and improved methods for addressing the modifiable areal and temporal unit problems and ecological bias. Additionally, the visualization of spatiotemporal data remains underutilized and underdeveloped, limiting the practical utility of the findings. We also discuss combining parameters to form an indicator that better represents environmental conditions, as well as cases where ground, satellite, or modeled data products are suitable. These recommendations could extend to other acquired chronic diseases and their relationship with environmental risk factors to improve the utility of spatiotemporal models. While spatiotemporal modeling holds considerable promise in understanding and mitigating CVD risks associated with environmental factors, appropriate data selection, addressing methodological pitfalls and reporting spatial and temporal model outcomes are necessary to enhance their reliability and impact. Cardiovascular diseases (CVDs), like heart attacks and strokes, are major causes of death worldwide, with environmental factors such as air pollution and temperature linked to their occurrence. The occurrence of CVDs and environmental factors are closely linked to the geographical location, as well as changes over time. This review looks at how researchers are using models that track changes over space and time to study these links. Our review highlights key challenges in these models, such as the need for more precise data on where people live and better methods to account for the way different time periods and regions are grouped. We also found that tools for visualizing this data are often underdeveloped, making it harder for researchers and policymakers to apply the findings in real‐world settings. We provide recommendations on choosing the best data sources to reflect environmental conditions accurately and combining several factors into one indicator to better represent environmental risks. These recommendations could improve the way we model and understand how CVDs and environmental factors are connected, benefiting research into other chronic diseases as well. By enhancing the data and methods used in these models, we can better understand and ultimately reduce CVD risks related to environmental factors.

Earth System Models provide spatiotemporally continuous environmental exposure data but remain underused in environmental epidemiology because of uncertainty from measurement errors. We developed a novel latent-variable approach to correct for measurement error characterized by spatiotemporal error covariance, which was derived from comparisons between Coupled Model Intercomparison Project Phase 6 (CMIP6) monthly fine particulate matter (PM2.5) simulations and station-based monitoring data from 5,661 global sites. To demonstrate the utility of the framework, we associated these exposures to birthweight records from 132 Demographic and Health Surveys. The results showed variable correlations between the models and the observations (r = 0.40-0.68) as well as widely varying effect estimates across Earth System Models, from a 0.01 g (95% confidence interval: -0.85-0.87) reduction to a 15.11 g (12.69-17.54) reduction in birthweight per 10 μg/m3 increase in PM2.5. After correcting measurement error, the optimal estimate indicated a more precise and consistent reduction of 3.34 g (2.57-4.11) in birthweight per 10 μg/m3. These findings demonstrate that the negative association between PM2.5 exposure and birthweight is robust to different levels of measurement error embedded in CMIP6-based exposures, and that correction for measurement error in environmental epidemiology can help avoid misestimating the effect by reducing bias and improving consistency. Climate models are frequently used to simulate environmental conditions, such as air pollution, for health research, but these computer simulations often contain discrepancies when compared to real‐world measurements. This study introduces a novel statistical approach to identify and correct these “measurement errors” by comparing simulation data with observations from thousands of ground‐level monitoring stations worldwide. We used this method to analyze the impact of fine particulate matter (PM2.5) on the birthweight of newborns and found that different climate models produced vastly different results without correcting for errors. However, after applying the correction method, the results became consistent, confirming that exposure to higher levels of PM2.5 results in lower birthweights. This new method enables researchers to evaluate environmental health risks more accurately using climate model data.

The spatial resolution of environmental exposure and sociodemographic population data is often mismatched given limited publicly available population data that complies with privacy requirements for individuals. To address this limitation, we developed a novel matching algorithm to construct a synthetic population at the address-level. To demonstrate how our approach can improve environmental justice (EJ) analyses and health impact assessments (HIAs), we examined sociodemographic patterns of residential proximity to major roadways in Greater Boston (Massachusetts) and HIA results, comparing our method with a random address allocation method. The synthetic population was developed at a census tract-level using US Census microdata and combinatorial optimization methods and then downscaled to address-level parcels by matching building attributes to synthetic households. We designated households within 50 m of a major road "high exposure" and households below state median household income "low income".We found misclassification for individual households (21% of the high exposure/low-income households in the matched data set were identified as such in the random allocation data set). We found modest aggregate differences in matched allocation (3.3% of low-income households had high exposure) compared to random allocation (3.4%). In a HIA, the difference between random and matched allocation would be stronger when there is a strong interactive effect between a sociodemographic effect modifier and exposure on the outcome. Address-level exposure assignment based on synthetic populations can provide more significant and nuanced health impact and EJ analyses. Our novel method can be applied to other regions of the US and expanded to other dimensions of population vulnerability. Communities and decision makers often need to identify if there are disparities in the distribution of hazardous exposures and associated health outcomes. To do so requires understanding of both spatial patterns of exposures and of the attributes of exposed populations. While environmental exposure data are available at increasingly higher spatial resolution, data on high‐resolution population sociodemographic characteristics are limited by privacy requirements in the US. To support the investigation of environmental exposures and health outcomes across sociodemographic characteristics at address‐level resolution, we used publicly available US Census data to simulate an address‐level population with sociodemographic information. In a case study looking at proximity to major roadways in Greater Boston (Massachusetts), we compared exposure patterns between our approach and approaches where household attributes were not used for address assignment. We found large differences in how individual households were identified but modest differences in the percent of households identified as high‐exposure and low‐income. We also showed that differences in estimated health impacts would depend on whether there was a strong interaction between the environmental exposure and sociodemographic variable. The methods used to create the address‐level synthetic population can be replicated in other regions of the US using the same census data resources.

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Long-term exposure to particulate matter (PM) pollution may directly increase the risk of developing tuberculosis (TB). Despite the known link, the multi-scale spatiotemporal variations in the burden of TB attributable to long-term PM exposure remain largely unclear in China. In this study, we conducted a nationwide, multi-scale risk assessment of the burden of TB attributable to long-term PM2.5, PM2.5-10, and PM10 exposure from 2013 to 2019, employing the multivariate distributed lag nonlinear model (MVDLNM), Lorenz curve and Gini index. Our health impact assessments indicate that PM exposure has resulted in significant increases in TB burden. Specifically, approximately $1,202 million (95% CI: 801-1,573 million), $486 million (95% CI: 398-572 million), and $944 million (95% CI: 767-1,115 million) of health economic costs could be attributed to long-term exposure to PM2.5, PM2.5-10, and PM10, respectively. Although the overall the burden of TB attributable to PM exposure was significantly reduced from 2013 to 2019, regional inequalities have become more pronounced. The Gini index reveals a clear disparity in the burden of TB related to PM exposure across provincial, city, and county levels. These disparities are most pronounced at the county level (0.4914-0.6801), followed by the city level (0.4135-0.6382), and are least evident at the province level (0.3672-0.6078). Overall, the regional inequalities in the burden of TB are more pronounced at finer spatial scales. Our study highlights the health impacts of long-term exposure to PM on the incidence of TB across different spatiotemporal scales, and the findings provide strong scientific evidence for pollution mitigation and efforts to reduce regional inequality. Ambient particulate matter (PM) pollution is a significant environmental risk factor contributing to the high tuberculosis (TB) burden in China. Although substantial improvements in air quality have been achieved in recent years, the impact of these improvements on TB incidence remains unclear, and regional exposure inequity has seldom been explored. This study systematically evaluates how regional disparities in health economic costs attributable to long‐term exposure to different sizes of PM (PM2.5, PM2.5–10, and PM10) vary over time and across spatial scales, including the macro‐scale (provincial level), meso‐scale (city level), and micro‐scale (county level). It found that long‐term PM exposure caused billions of dollars in TB‐related health costs, with PM2.5 being the largest contributor. While overall PM‐related TB burden decreased nationwide from 2013 to 2019, inequalities between regions grew, especially at finer scales like counties. Disparities in TB burden were highest at the county level, demonstrating that local conditions strongly influence health risks. This study highlights the urgent need for targeted air quality and health policies in high‐risk areas to reduce TB burden and address health inequalities.

Heavy metal contamination in rivers mostly originates from anthropogenic sources such as industrial activities, mining, agriculture, and urban runoff. These metals accumulate in river sediments and impact living organisms and the surrounding ecosystem. Historically, some rivers in Colombia have faced issues related to heavy metal contamination, in particular from gold mining activities, which can lead to the release of heavy metals into waterways. To assess the current status of heavy metal presence in the Sinú River, one of the most important rivers in northern Colombia, we analyzed sediment samples collected in a section of the river between the towns of Santa Isabel and Montería. ICP-MS analysis found concentrations of Hg, Cd and Ni up to five times larger than the baseline concentrations, while concentrations of Pb, Zn and Cr were consistently below the baseline. The measured concentrations varied with both geographical location and sampling depth. During the dry season heavy metals accumulate near the surface, while rains spread them below the surface and into the groundwater, and across the surrounding land. These abnormal concentrations of potentially toxic elements are probably related to the widespread use of Hg in illegal gold mining activities, which are prevalent in the region, while Cd and Ni contamination probably derives from mining, as well as from agricultural practices and industrial discharges. The results point to the necessity to continuously monitor the state of the river and develop management strategies to reduce heavy metal contamination, protecting the health of the ecosystem and of the neighboring communities. Heavy metal contamination in rivers is a significant threat to the environment, and may affect the surrounding environment and the health of living organisms. The Sinú River is a vital waterway for northern Colombia, supplying water for the population and irrigation for agriculture and enabling the transport of goods. Here, we report our analysis of the presence of heavy metals in a section of the Sinú River between the towns of Santa Isabel and Montería. Soil samples were collected during the rainy and dry seasons of 2021 from the surface and at a depth of 0.5 m. ICP‐MS analysis found concentrations of Hg, Cd and Ni often much larger than the baseline, while those of Pb, Zn and Cr were consistently below these limits. Measured concentrations depend on the location and depth of the samples, and vary seasonally with rainfall due to surface runoff. The abnormal concentrations of these potentially toxic elements are related to anthropogenic activities, such as their widespread use in mining, agricultural practices and industrial discharges. The results point to the necessity of a continuous monitoring of the river and to the implementation of effective management strategies to protect the ecosystem and neighboring communities.

This study aimed to elucidate the association between wildfire smoke exposure and healthcare utilization for respiratory diseases in Samcheok (City), Gangwon Province, South Korea, focusing on a major wildfire that occurred on 6-9 May 2017. The relative risks (RRs) of healthcare utilization for respiratory diseases in a direct-exposure area (Samcheok) during (6-9 May 2017) and post (10 May-6 June 2017) wildfire periods, relative to the pre-wildfire period (22 April-5 May 2017) were analyzed. The post-wildfire period was divided into immediate and extended, each with a 2-week interval. Additionally, the relative risk ratios (RRRs) of healthcare utilization were analyzed for the same period in 2018, when no wildfire occurred. In the direct-exposure area (Samcheok), there were increased RRs of respiratory disease healthcare utilization for all ages in the wildfire (RR&#xa0;=&#xa0;1.81, 95% confidence intervals [CI]: 1.67-1.96) and extended post-wildfire (RR&#xa0;=&#xa0;1.26, 95% CI: 1.20-1.33) periods. The highest risk was observed in children aged <9&#xa0;years in the wildfire (RR&#xa0;=&#xa0;2.20, 95% CI: 2.04-2.38) and extended post-wildfire (RR&#xa0;=&#xa0;1.44, 95% CI: 1.37-1.52) periods. Compared with that of the corresponding periods in 2018, significant increases in the RRRs were observed during the wildfire (RRR&#xa0;=&#xa0;1.30, 95% CI: 1.15-1.45) and extended post-wildfire (RRR&#xa0;=&#xa0;1.75, 95% CI: 1.61-1.91) periods. The wildfire in Gangwon province significantly increased healthcare utilization for respiratory diseases during the wildfire and post-wildfire periods. A major wildfire occurred in Samcheok, South Korea, from 6 to 9 May 2017, leading to increased healthcare visits for respiratory problems. This study examined how wildfire smoke affected people's need for medical care in the city of Samcheok, where the fire occurred, and in Donghae, a nearby city. The research compared medical visits during the wildfire, immediately after, and weeks later to a period before the fire. The results showed a clear rise in hospital visits for breathing issues, especially among children under 9&#xa0;years old, whose risks were highest during and after the fire. Even in Donghae, a city less directly affected, a small increase in respiratory healthcare visits was observed. By comparing these findings to the same time in 2018, when there was no wildfire, the study confirmed that wildfire smoke had both immediate and lingering impacts on health. This research highlights the serious health risks posed by wildfire smoke, particularly to vulnerable groups like children, and the need for strong public health measures during and after such events.

Influenza remains a global public health and socio-economic threat, and air pollution is believed to increase its risk. However, relatively few studies have focused specifically on laboratory-confirmed influenza (LCI), which limits the precision and reliability of existing evidence on this association. We sought to explore the relationship between air pollution and LCI cases in Anhui province, China. We employed a two-stage time series analysis to evaluate the associations between PM2.5, PM10, CO, SO2, O3, NO2 and daily LCI cases between January 2015 and March 2023. In the first stage, we applied distributed lag nonlinear model to characterize the lagged effects of each air pollutant in individual cities, followed by a meta-analysis to pool city-specific estimates in the second-stage. We performed sub-group analysis by age and gender, and explored modification effect of population density, median pollution levels, longitude, and latitude, using a meta-regression. A total of 43, 872 LCI cases were reported in Anhui over study period. There was a positive associations between influenza and a 10&#xa0;&#x3bc;g/m3 increase in PM2.5: RR&#xa0;=&#xa0;1.0045 (95%CI: 1.0035-1.0054), SO2: RR&#xa0;=&#xa0;1.0035 (95%CI: 1.0001-1.0069), NO2: RR&#xa0;=&#xa0;1.0122 (95%CI: 1.0881-1.0162), PM10: RR&#xa0;=&#xa0;1.0025 (95%CI: 1.0017-1.0033) and CO: RR&#xa0;=&#xa0;1.2641 (95%CI: 1.1294-1.4150) in a single-day lag model. In the cumulative-day lag model, a 10&#xa0;&#x3bc;g/m3 increase in PM2.5; RR&#xa0;=&#xa0;1.0043 (95%CI: 1.0018-1.0068), NO2; RR&#xa0;=&#xa0;1.0076 (95%CI: 1.0006-1.0145), PM10; RR&#xa0;=&#xa0;1.0024 (95%CI: 1.0001-1.0046), and CO; RR&#xa0;=&#xa0;1.3026 (95%CI: 0.8139-2.0849) positively associated with LCI cases. The associations were more pronounced in males and ages 5-24&#xa0;years. Population density modified the associations. Our findings suggest the need to integrate air quality management in intervention strategies for influenza. Influenza is a major public health issue worldwide, and air pollution may increase the risk of infection. However, few studies have examined laboratory&#x2010;confirmed influenza (LCI), which provides more reliable evidence. This study analyzed the relationship between air pollution and LCI cases in Anhui Province, China, from January 2015 to March 2023. We used a two&#x2010;stage time series model to assess the effects of six air pollutants (PM2.5, PM10, CO, SO2, O3, and NO2) on daily influenza cases. First, we applied a statistical model to individual cities, and then combined the results using meta&#x2010;analysis. We also explored differences by age, gender, and environmental factors. A total of 43,872 LCI cases were reported during the study period. Higher levels of PM2.5, PM10, CO, SO2, and NO2 were linked to an increased risk of influenza, with stronger associations in males and individuals aged 5&#x2013;24&#xa0;years. Population density influenced these associations. These findings suggest that air pollution plays a role in influenza risks, highlighting the need to consider pollution levels and population density in disease prevention strategies. Policymakers should integrate air quality improvements into public health interventions to reduce influenza risks.

Wildfires are a source of air pollution, including PM2.5. Exposure to PM2.5 from wildfire smoke is associated with adverse health effects including premature death and respiratory morbidity. Air quality modeling was performed to quantify seasonal wildfire-PM2.5 exposure across Canada for 2019-2023, and the annual acute and chronic health impacts and economic valuation due to wildfire-PM2.5 exposure were estimated. Exposure to wildfire-PM2.5 varied geospatially and temporally. For 2019-2023, the annual premature deaths attributable to wildfire-PM2.5 ranged from 49 (95% CI: 0-73) to 400 (95% CI: 0-590) due to acute exposure and 660 (95% CI: 340-980) to 5,400 (95% CI: 2,800-7,900) due to chronic exposure, along with numerous non-fatal cardiorespiratory health outcomes. Per year, the economic valuation of the health burden ranged from $550M (95% CI: $19M-$1.2B) to $4.4B (95% CI: $150M-$9.9B) for acute impacts and $6.4B (95% CI: $2.2B-$12.9B) to $52B (95% CI: $18B-$100B) for chronic impacts. Additionally, a long-term average annual exposure for 2013-2023 was estimated using air quality modeling. From this, more than 80% of the population had an average seasonal wildfire-PM2.5 exposure of at least 1.0&#xa0;&#x3bc;g/m3 and there were 1,900 (95% CI: 980-2,800) attributable premature deaths and a total economic valuation of $18B (95% CI: $6.1B-$36B), per year. Evaluating and understanding the health impacts of wildfire-PM2.5 is important given the sizable contribution of wildfire smoke to air pollution in Canada, as well as the anticipated increases in wildfire activity due to climate change. Wildfires are a source of air pollution that can cause adverse effects on human health. For this study, air quality modeling was used to quantify the exposure levels of particulate matter (PM) from wildfires in Canada. From the exposure levels, the health burden attributable to PM in wildfire smoke was estimated. Hundreds to thousands of premature deaths per year are attributable to wildfire smoke exposure along with many non&#x2010;fatal cardiorespiratory health impacts, such as emergency room visits for respiratory issues. The economic burden of the health impacts from wildfire smoke is estimated in the millions to billions of dollars per year. Having an understanding of the health burden of wildfire smoke is important&#x2014;it is a large source of air pollution that is expected to increase as climate change will increase the frequency and severity of wildfires.

The Atoyac river basin is one of the most polluted watershed basins in Mexico. Recent studies have reported the presence of organochlorine pesticides (OCPs) in this highly urbanized region through environmental monitoring, raising concerns about potential health risks, particularly for children and adolescents. We still lack information about its human exposure through biological samples that represent a more realistic measure of OCPs body burden. To evaluate, we compared the serum concentrations of OCPs in children and adolescents living within and outside the Atoyac watershed basin. We included 428 individuals under 20&#xa0;years old who participated as controls in a population-based study conducted in three central-southern Mexican states (2021-2024). We collected sociodemographic characteristics through face-to-face interviews and obtained serum samples in which we quantified 24 OCPs by gas chromatography. To georeference, we classified the participants as living within or outside the Atoyac basin and compared their respective serum concentrations for those OCPs detected over 10% of samples. We found two heterogeneous spatial distribution patterns of OCPs serum concentrations. HCB, dieldrin, oxychlordane, and endosulfan sulfate were higher in the Atoyac basin, with the two formers being statistically significant. In contrast, p,p'-DDE was significantly higher outside the Atoyac basin. The two patterns of exposure between the two regions emphasized one pattern driven by industry and agriculture, and the second driven by vector-borne disease control. It reinforces the need for regulation and increased monitoring in the Atoyac river basin to provide information about adverse health effects in children and adolescents. The Atoyac River basin is one of the most polluted areas in Mexico. Organochlorine pesticides (OCPs) are harmful chemicals used in the environment, which may pose health risks&#x2010;especially to the youth. However, there is little knowledge about how these chemicals are actually getting into people's bodies. Researchers tested blood samples from 428 children under the age of 20. These participants lived either inside or outside the Atoyac basin and were part of a larger study in three states in central&#x2010;southern Mexico between 2021 and 2024. The researchers also gathered information about the participants' backgrounds through interviews. We tested the blood for 24 different OCPs and compared the levels between those living in and outside the Atoyac basin. We found that some pesticides&#x2010;like HCB and dieldrin&#x2010;were higher in those living in the Atoyac basin. Others, like p,p&#x2032;&#x2010;DDE, were higher in people living outside the basin. These results suggest there are different sources of pesticide exposure: in the Atoyac basin, pollution likely comes from industry and farming, while in other areas, it may come from efforts to control disease&#x2010;carrying insects. The study highlights the need for stronger pollution controls and monitoring in the Atoyac River basin to protect children's health.

Growing global demand for natural gas has driven the expansion of liquefied natural gas (LNG) export terminals, which emit pollutants that can pose health risks to nearby communities. This study presents a novel modeling framework using the AMS/EPA Regulatory Model (AERMOD) to assess near-source nitrogen dioxide (NO2) exposure, health impacts, and equity implications at the block-group level. We apply this methodology to four LNG export terminals in the United States, simulating NO2 concentrations within a 50&#xa0;km radius. Results show that LNG terminals substantially contribute to near-source air pollution, with simulated 1-hr maximum NO2 concentrations reaching up to 16% of the EPA's National Ambient Air Quality Standard (100 ppb). Site-specific maximum concentrations were 15.7 ppb (Site A), 1.6 ppb (B), 10.7 ppb (C), and 0.3 ppb (D). Comparing NO2 concentrations with demographic patterns, Sites A and D showed higher concentrations, higher proportions of People of Color and low-income populations, and greater health burdens in communities closer to the LNG facilities, indicating potential disproportionate impacts. The other sites showed weak or no spatial inequity patterns. Estimated annual NO2-attributable all-cause mortality rates per 100,000 people were 8.2 (A), 0.6 (B), 2.2 (C), and 0.1 (D); annual NO2-attributable pediatric asthma rates per 100,000 children were 75.5 (A), 6.2 (B), 21.8 (C), and 1.1 (D). This study demonstrates how regulatory dispersion models like AERMOD can be adapted to evaluate near-source health and equity impacts of industrial emissions and offers a transferable methodology for similar analyses across other high-emitting facilities. Liquefied natural gas (LNG) export terminals emit nitrogen dioxide (NO2), a pollutant linked to health risks. This study developed a methodology to evaluate near&#x2010;source health and equity impacts with the regulatory dispersion model, AMS/EPA Regulatory Model, and applied it to four U.S. LNG export terminals. We modeled NO2 levels near the&#xa0;terminals. We further conducted equity analysis comparing NO2 exposure with the proportion of people of color and low&#x2010;income populations at the block group level. We finally estimated potential health impacts from facility emissions, measured as NO2&#x2010;attributable all&#x2010;cause mortality and pediatric asthma.

Monitoring cyanobacteria is crucial for assessing water quality, safeguarding public health, and understanding ecosystem dynamics impacted by harmful algal blooms. This study explores the potential of satellite remote sensing (SRS) to assess risks of cyanotoxin exposure in California's recreational waters from 2002 to 2011 and 2016 to 2023. Utilizing SRS data, we compared cyanobacteria abundance across five lakes with cyanotoxin data and advisories from the California Department of Water Resources (DWR). SRS-based advisories were aligned with DWR/in situ based advisories 54%-100% of the time. Lake-specific assessments of agreement showed Lake Oroville with the highest overall accuracy (100%) and Pyramid Lake with the lowest (54%). SRS generally overpredicted DWR-based alerts in about 30% of instances and under-detected DWR-based alerts at a rate of 42%, likely due to differences in the way satellites sample across continuous spatial domains but at coarse resolutions versus in situ sampling at discrete locations. We extended our SRS monitoring capability to an additional 71 lakes to conduct a statewide assessment of toxin alerts over time. There were 10 lakes that experienced cyanobacteria alerts 12%-88% of the time across our study. When comparing 2002 to 2011 and 2016 to 2023, we observed higher rates of toxin alert frequency, duration, and a shift toward earlier starts of the year for high-risk blooms across all regions of California, with the greatest in southern California. Despite limitations in spatial resolution, SRS provides consistent, near-real-time data essential for timely cyanotoxin risk assessments and public health alerts, complementing traditional in situ sampling. This study investigates the potential of satellite remote sensing (SRS) to assess risks of cyanotoxin exposure in California's recreational waters from 2002 to 2011 and 2016 to 2023. We validated the SRS&#x2010;derived cyanobacteria biomass estimates against concurrent field measurements from the California Department of Water Resources (DWR) across five key California lakes, demonstrating strong statistical agreement. Subsequently, we utilized the validated SRS data to evaluate high&#x2010;risk bloom frequency and duration against established public health thresholds for potential cyanotoxin exposure. Our analysis revealed satellite&#x2010;based warnings agreed with DWR's alerts between 54% and 100% of the time, with Lake Oroville showing the best agreement and Pyramid Lake the lowest. While satellites sometimes predicted alerts when DWR did not (&#x223c;30%) or missed them when DWR issued one (&#x223c;42%), this is likely due to the different ways they collect data. We expanded to 71 other lakes, where 10 had high&#x2010;risk cyanobacteria alerts 12%&#x2013;88% of the time. When comparing both periods, we observed higher rates of toxin alert frequency, duration, and an earlier onset of high&#x2010;risk blooms across all regions, with the greatest seen in southern California. Despite some differences, satellite data offers a powerful tool for quickly and consistently assessing cyanotoxin risks.

To examine racial disparities in weather-related mortality in Virginia from 2005 to 2020. An ecological descriptive study using daily mortality data from the Virginia Department of Health and weather data from the National Climatic Data Center. Generalized additive models and distributed lag nonlinear models were used to estimate the relative risk of mortality as the primary endpoint associated with temperature extremes over a 21-day lag period, stratified by race. Black residents of the state had a higher risk of dying at both high and low temperatures compared to white residents; however, the risk was more profound with low temperatures. On the coldest days, the mortality risk for the Black population was more than three times that of the white population. Notably, the impact of cold on the Black population extended through lag day 15, while for white people, the impact only lasted through lag day 5. Heat-related mortality risk for Black individuals also exceeded that for white individuals, but only when the minimum temperature exceeded 20&#xb0;C. Racial disparities exist in weather-related mortality in Virginia, with the Black population experiencing a disproportionately higher risk of death as well as poorer health outcomes, especially during extreme cold weather events. Policymakers should consider developing and evaluating policies that protect vulnerable communities when they are subject to weather extremes. Our research evaluated how extreme temperatures&#x2014;both hot and cold conditions&#x2014;affect death rates among Black and white residents in Virginia from 2005 to 2020. We used weather and death records to see if there were racial differences in how temperature impacts that likelihood of residents dying following hot or cold periods. We found that Black residents are more likely to die from both extreme heat and cold compared to white residents, with cold weather posing the highest risk. On the coldest days, Black people were three to four times more likely to die than white people. The effects of low temperatures also lasted longer for Black individuals&#x2014;up to 15&#xa0;days after exposure, compared to just 5&#xa0;days for white individuals. High temperatures were slightly protective for white people but not for Black people.

Downscaled climate projections provide valuable information needed to better understand the impacts of climate change on health outcomes and to inform adaptation and mitigation strategies at local to regional scales. Because downscaled climate products vary in their representations of fine-scale spatiotemporal patterns, due to of multiple interacting factors, epidemiologic&#xa0;analyses need to consider how differences across downscaling approaches impact projections of health impacts into the future. We evaluate the projected seasonality of coccidioidomycosis in response to projected temperature and precipitation estimated using global climate models from CMIP6 included in California's Fifth Climate Change Assessment, downscaled using two approaches: (a) dynamical downscaling using the Weather Research and Forecasting model; and (b) hybrid statistical downscaling using the Localized Constructed Analogs approach. Our results indicate that by end of century, coccidioidomycosis transmission is projected to start earlier, end later, and last longer across the California endemic region; however, the magnitude of these changes varies by downscaling method. Specifically, LOCA2-hybrid projected a season onset that is 4.2&#xa0;weeks earlier and an end that is 4.1&#xa0;weeks later than historical conditions, while the dynamical approach projected a 4&#xa0;week earlier onset and a 3.8&#xa0;week later end compared to the historical period. Overall, the LOCA2-hybrid product estimates that the transmission season will last about 0.3&#xa0;weeks longer than what is projected using dynamical downscaling by end of century. This analysis highlights the sensitivity of coccidioidomycosis seasonality projections to choice of downscaling product, underscoring the need to account for these differences in mitigation and adaptation planning. Downscaled climate data are essential for projecting future local and regional climate&#x2010;related health impacts, but multiple downscaling approaches exist and each capture fine&#x2010;scale climate patterns differently. We examined how two common approaches for resolving global climate model data into local projections shape predictions of Valley fever seasonality in California. While both approaches suggest the transmission season will lengthen by the end of the century, they differ in how much change they predict. These differences matter because they can influence the timing of health warnings, resource allocation, and adaptation strategies. Our findings highlight the importance of considering climate data methods when using projections to guide climate and health research and planning.

Addressing impacts on human health from climate change will require engaged communities capable of co-creating actionable science. This is particularly the case in Jordan, one of the most vulnerable countries to climate change with a hot, dry climate and rapidly growing population. A key demographic for building capacity to address climate-health challenges is youth. To engage Jordanian youth in developing knowledge and skills related to climate-health science, the Global Center on Climate Change and Water Energy Food Health Systems (GC3WEFH) implemented The DataJam, an annual project-based data science learning program and competition developed in the United States. The GC3WEFH enrolled 87 students from 21 schools in The DataJam Jordan. Fifty-four students in teams of three completed 18 projects over a 2-year period while 33 students started The DataJam but did not complete a project. The aim of the intervention was to build data science capacity to address issues at the intersection of climate and health. To explore the outcomes of this intervention, we used the Consolidated Framework for Implementation Research to identify the primary determinants. This analysis revealed that the complexity of The DataJam and the work infrastructure of the implementation impacted communication across the intervention, which shaped the topics students researched and their use of data science. Importantly, the DataJam increased both the confidence and interest of students in engaging in climate change related challenges facing their communities. Therefore, The DataJam is a positive example of engaging youth through the international translation of a STEM learning program. Building young people's capacity to engage in science at the intersection of climate change and human health can contribute to knowledge and interventions that are more likely to be adopted and sustained. The Global Center on Climate Change and Water Energy Food Health Systems engaged Jordanian high school students using The DataJam, a project&#x2010;based data science learning program and competition developed in the United States. This study analyzed the effectiveness of The DataJam in Jordan for engaging youth in climate&#x2010;health science using data science. After showing projects tended not to focus on the intersection of climate change and health and some projects did not include data science, we employed the Consolidated Framework for Implementation Science to identify the aspects of the implementation that impacted the outcomes. We found the complexity of The DataJam and the organization of the intervention led to communication challenges throughout the process. Despite these challenges, students reported a positive experience that resulted in greater interest in climate&#x2010;health science and confidence in their ability to be engaged in their communities.