Geohealth最新文献

Wildfires are a source of air pollution, including PM_2.5. Exposure to PM_2.5 from wildfire smoke is associated with adverse health effects including premature death and respiratory morbidity. Air quality modeling was performed to quantify seasonal wildfire-PM_2.5 exposure across Canada for 2019–2023, and the annual acute and chronic health impacts and economic valuation due to wildfire-PM_2.5 exposure were estimated. Exposure to wildfire-PM_2.5 varied geospatially and temporally. For 2019–2023, the annual premature deaths attributable to wildfire-PM_2.5 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-PM_2.5 exposure of at least 1.0 μg/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-PM_2.5 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.

Global environmental changes have posed threats to ecosystems worldwide. Safeguarding terrestrial ecosystem health in particular is fundamental to achieving global sustainability targets, yet land degradation, carbon depletion and climate extremes continue to undermine resilience due to climate change and human activities. Therefore, Understanding human-environment interactions is increasingly important for enhancing the resilience of terrestrial ecosystems under global change. The collection for this special issue addresses urgent challenges of land degradation, soil carbon loss, and ecosystem vulnerability by assembling eight regionally grounded studies from diverse landscapes of Asia. Collectively, these contributions reveal how land-use transitions, restoration strategies and climate variability shape ecosystem health and carbon dynamics, while advancing methodological and governance frameworks that link science with policy. The collection offers critical insights and practical lessons for scholars and policy planners to sustainably manage land resources within the GeoHealth paradigm.

Smoke from agricultural fires is a potentially important source of fine particulate matter (PM_2.5) in the US. Sugarcane is burned in Florida to facilitate the harvesting process, with the majority of these fires occurring in the Everglades Agricultural Area (EAA), where there is only one regulatory air quality monitor. During the 2022–2023 sugarcane burning season (October–May), we used public low-cost PurpleAir sensors, regulatory monitors, and 29 PurpleAir sensors deployed for this study to quantify PM_2.5 from agricultural fires. We found satellite imagery is of limited use for detecting smoke from agricultural fires in Florida due to the cloud cover, overnight smoke, and the fires being small and short-lived. For these reasons, surface measurements are critical for capturing increases in PM_2.5 from smoke, and we used multiple smoke-identification criteria. During the study period, median 24-hour PM_2.5 concentrations increased by 2.3–6.9 μg m⁻³ on smoke-impacted days compared to unimpacted days, with smoke observed on 4%–28% of the campaign days (ranges from the different smoke-identification criteria). Further, short-term PM_2.5 increases were observed over 40 μg m⁻³ during smoke events. We contrast the region near the EAA with large populations of low-income and minoritized groups to the more affluent coastal region. The inland region experienced more smoke-impacted monitor days than the Florida east coast region, and there was a higher study-average smoke PM_2.5 concentration in the inland area. These findings highlight the need to increase air quality monitoring near the EAA.

Ambient PM_2.5 exposure poses the greatest environmental risk to public health in India. While several studies have quantified the changing patterns of exposure, the extent of inequality in exposure among population subgroups at the sub-national scale remains unknown. In this study, we examined the disparity in ambient PM_2.5 exposure across various population subgroups in urban and rural India and analyzed its changes in recent years by integrating satellite-derived PM_2.5 concentrations (1-km × 1-km) with sociodemographic information from the 4th (2015–2016) and 5th (2019–2021) rounds of the National Family Health Survey. We found a larger absolute disparity (60–90 µgm⁻³) in high socio-demographic index (SDI) states compared to middle and lower SDI states. Moreover, we discovered that ambient PM_2.5 exposure was higher (indicated by relative disparities in terms of Z_score) among the top and bottom quantiles of wealth index and the other backward caste subgroup (Z_score > ±0.02, p < 0.1) than among their demographic counterparts in middle and high SDI states. From 2015–2016 to 2019–2021, the disparity in ambient PM_2.5 exposure across subgroups increased in urban areas, while it either remained static or decreased in rural areas. India's urban-centric approach to addressing air pollution may further exacerbate disparities among diverse demographics. Therefore, we recommend the formulation of targeted policies aimed at reducing ambient PM_2.5 exposure and alleviating disparities by prioritizing actions for the vulnerable subgroups.

In the United States, private wells are not federally regulated, and many households do not test for Arsenic (As). Chronic exposure is linked with multiple health outcomes, and risk can change sharply over short distances and with well depth. Coarse maps or sparse sampling often miss exceedances. Most existing models operate at ∼1 km resolution and use groundwater chemistry or detailed geologic logs, which limits their use in undersampled areas where improved guidance is most needed. We overcome these limitations by developing a machine learning model for Minnesota, USA, that predicts As exposure risk using only surficial variables from remote sensing and global data sets. Variables related to surface water hydrology and geomorphology are selected based on mechanistic links that control redox conditions and As mobilization. Local training was essential, and surficial geology variables that are more sensitive to local conditions were needed to maximize model accuracy. The resulting complete model was sufficiently sensitive to generate accurate and detailed risk maps and depth profiles of As concentrations above the 10 μg/L maximum contaminant level. Accuracy depended on local training data density. We identified a training data density of 0.07 wells/km² as a practical target for stable county-level performance. Maps of exceedance probabilities highlight priority areas for testing that are particularly important in rural communities that have received less sampling. These results support public health action by guiding where to install wells and where to test them, how much new sampling is needed, and where treatment outreach is most urgent.

Ambient air pollution remains a leading environmental risk factor for morbidity and mortality in the U.S, though most research is conducted in urban areas. Our study assessed how sociodemographic factors indicative of social vulnerability were associated with smoke from agricultural burns in Florida. We assessed census-level sociodemographic variables among four counties adjacent to the Everglades Agricultural Area (n = 409 census tracts, 2016–2020). Smoke day counts from local agricultural fires were based on satellite plumes identified from the National Oceanic and Atmospheric Administration Hazard Mapping System. Primary analysis fit a negative binomial model with bidirectional stepwise regression, followed by an adjusted geospatial model with a Queen-continuity adjacency matrix. Sensitivity analysis focused on rural-only census tracts. Rural areas had higher concentrations of people of color and poverty compared to coastal urban areas. Median (Q1, Q3) smoke days by census tract was 36 (31, 45), with the highest concentrations in rural central and western regions. Primary model results skewed toward mostly urban tracts, where an interquartile ranges (IQR) increase in median household income was associated with a 12% decrease (95% confidence interval (CI) −14.5%, −5.2%) in smoke days. Among rural-only census tracts, an IQR increase in percentage of residents living 200% below the poverty line and non-English speaking residents were associated with 23% (95% CI: 1.2%, 37.7%) and 120% (95% CI: 20.5%, 176.5%) increases in smoke days, respectively. Sociodemographic factors associated with health and environmental vulnerability were context dependent. Within rural regions, poverty, race and ethnicity played more important roles in exposure risk, whereas wealth mitigated risk among urban areas.

The adjoint of the U.S. EPA's Community Multiscale Air Quality (CMAQ) model is extended for hemispheric scale applications and is used to estimate location-specific health impacts from primary PM_2.5, and PM_2.5 precursor emissions (NH₃, NO_X and SO₂). We estimate the monetized health burden due to mortality caused by chronic PM_2.5 exposure among adults living in the northern hemisphere, using a generalized concentration-response function. The health impact sensitivities show large spatial variability over the northern hemisphere and exhibit a great deal of seasonal variability, especially for inorganic precursor emissions. The largest marginal impacts are seen for NH₃ and primary PM_2.5. The estimated health impacts for a 10% reduction in emissions reveal a hemispheric burden of 513,700 avoided mortality and monetized health benefits at above 1.2 trillion USD₂₀₁₆. The largest regional contribution to hemispheric mortality is found to be in East and South Asia, particularly China and India (183,760 and 123,440 for a 10% reduction in emissions, respectively). Monetized health burdens are estimated to be highest in China and Europe (∼365 and ∼252 million USD for a 10% reduction in emissions) while it is relatively similar in India (∼175 million USD) as in Canada and the United States (∼177 million USD). Sectoral source contribution analysis demonstrates that the agriculture (19%) and residential (15%) sectors are the largest contributors to the northern hemispheric scale health burden, however, regional differences exist in the results. Examining location- and sector-specific health impacts can inform more effective regulatory measures.

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.

Groundwater is a major source of drinking water in the United States (US). Groundwater chemistry can contribute to lead leaching from water supply pipes due to factors such as pH and mineral content that influence corrosion. Lead exposure disproportionately affects children from low-income neighborhoods. We evaluated the association of county-level groundwater chemicals with the percentage of children with blood lead levels >5 μg/dL (BLL5%) in 1,104 US counties served by public water utilities using groundwater. Out of the 4,844 BLL5% observations, 3,525 had values of “NA” for BLL5%. We used weighted least squares regression to evaluate the associations, adjusting for covariates such as county-level median household income, educational attainment, and poverty rates. Bayesian Kernel Machine Regression (BKMR) was used to assess the joint effects of all chemicals on BLL5%. Sensitivity analyses tested the robustness of our results by imputing missing BLL5% values. A one mg/L increase in arsenic, copper, dissolved oxygen, and selenium was associated with increases in BLL5% of 0.0512% (95% CI: 0.0002%, 0.1023%), 0.0358% (95% CI: 0.0208%, 0.0508%), 0.0956% (95% CI: 0.0225%, 0.1687%), and 0.3038% (95% CI: 0.1747%, 0.4420%), respectively. Alkalinity, pH, calcium, bicarbonate, and dissolved solids were not found to be statistically significant. BKMR identified calcium, lithium, and alkalinity (posterior inclusion probabilities = 1,000) as important, though with minimal effects. Sensitivity analyses showed variability in results depending on assumptions about missing data. Our findings highlight the importance of monitoring groundwater quality and implementing interventions to reduce childhood lead exposure risks in vulnerable populations, particularly minority, and low-income children.

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.