Geohealth最新文献

Climate change will continue to increase the frequency and intensity of flood events in North Carolina for the foreseeable future. The extreme flooding in Western North Carolina caused by Tropical Storm Helene in September of 2024 is a recent and devastating example of this trend. Communities of color and low-income populations are more likely to reside in flood-prone areas due to structural factors, including residential racial segregation and economic inequality. As such, the adverse health and financial consequences of flood exposure overburden historically marginalized communities, which may have a more limited adaptive capacity to anticipate, respond to, and recover from flood events. Exposure to severe flooding further exacerbates chronic health conditions by impeding access to vital healthcare infrastructure and services. This study examines the spatial patterning of coastal and inland flood risk, neighborhood-level structural determinants (i.e., racial and economic inequality), and flood-sensitive health conditions in North Carolina using bivariate local indicators of spatial autocorrelation (LISA) statistics. High-high clusters capture areas where neighborhoods with high racial or economic inequality surround elevated flood risks. These clusters are distinguished by select sociodemographic characteristics and concentrated in the eastern coastal and western mountainous regions of North Carolina. Cluster locations are priority areas for targeted resource allocation and interventions that strengthen the adaptive capacity of these communities in the context of climate change.

West Nile virus (WNV) infection has caused over 30,000 human cases of the severe, neuroinvasive form of the disease (West Nile virus Neuroinvasive Disease; WNND) and nearly 3,000 deaths in the U.S. Despite known links to observable climate factors, no effective nationwide WNV or WNND forecast exists. We aimed to produce a skillful, nationwide WNND forecast built upon regionally varying relationships between climate factors and WNND. After examining the relationships between climate conditions and annual WNND caseload for 11 regions in the U.S., we incorporated the most salient climate factors-most commonly drought and temperature-into a regionally determined nationwide WNND statistical forecast model using a Bayesian regression framework. We retrospectively generated forecasts from 2005 to 2022 and compared forecast skill against various benchmarks, including a simple, historical case-driven model. Our regional, climate-informed WNND retrospective forecasts outperformed a benchmark model only informed by historical WNND case data across all regions, as well as in a nationally aggregated score (univariable: 18.8% [4.7%-27.7%], bivariable: 21.8% [7.0%-30.7%] improvement). The regional forecasts also outperformed an ensemble model generated from a recent WNV forecasting competition and a parallel, county-level, regional climate-informed forecast outperformed forecasts from the same competition. Importantly, our approach to WNND forecast development aggregated county-level data to broader regions to boost statistical signal and capture the regionally varying influences of climate conditions on annual WNND caseload. The advances here represent a potential path toward actionable broad-scale WNV forecasts.

Salmonella infections contribute significantly to gastrointestinal-related hospitalisations in Australia and remain a major global public health concern. Although seasonal patterns in Salmonella incidence have been documented globally, there is limited evidence on the influence of climatic factors, particularly rainfall, humidity, flooding, and temperature, in the Australian context. This study investigated the relationship between climatic extremes and Salmonella infections across Local Health Districts (LHDs) in New South Wales (NSW), Australia, using a Spatial Bayesian Distributed Lag Non-Linear Model. Spatial modeling revealed a marked geographical heterogeneity in the risk of Salmonella related to climate in NSW. High ambient temperatures consistently increased risk, with 99th-percentile contrasts typically yielding relative risks (RR) of 2.4–4.8 across LHDs. Monthly rainfall showed the opposite direction statewide: very dry months were associated with a higher risk, whereas very wet months were generally protective (RR1). In contrast, discrete flooding events were strongly and positively associated with risk (99th-percentile flood index RR $��\sim ��$18–23.5), with the greatest effects in some LHDs of the metropolitan/coastal region. Humidity displayed modest but consistent positive associations (99th-percentile RR $��\sim ��$1.1–1.5). Temperature and humidity exhibited J-shaped exposure-response relationships, where the lowest risk occurred at moderate values. This contrasts with rainfall, which demonstrated an inverse (protective) association, and flooding, which showed a monotonic increase in risk with intensity. These results have important public-health implications under a warming, flood-prone climate.

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 = 1.81, 95% confidence intervals [CI]: 1.67-1.96) and extended post-wildfire (RR = 1.26, 95% CI: 1.20-1.33) periods. The highest risk was observed in children aged <9 years in the wildfire (RR = 2.20, 95% CI: 2.04-2.38) and extended post-wildfire (RR = 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 = 1.30, 95% CI: 1.15-1.45) and extended post-wildfire (RRR = 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.

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.