Geohealth最新文献

We quantify anthropogenic sources of health burdens associated with ambient air pollution exposure in South Korea and forecast future health burdens using domestic emission control scenarios by 2050 provided by the United Nations Environment Programme (UNEP). Our health burden estimation framework uses GEOS-Chem simulations, satellite-derived NO₂, and ground-based observations of PM_2.5, O₃, and NO₂. We estimate 19,000, 3,300, and 8,500 premature deaths owing to long-term exposure to PM_2.5, O₃, and NO₂, respectively, and 23,000 NO₂-associated childhood asthma incidences in 2016. Next, we calculate anthropogenic emission contributions to these four health burdens from each species and grid cell using adjoint sensitivity analysis. Domestic sources account for 56%, 38%, 87%, and 88% of marginal emission contributions to the PM_2.5-, O₃-, and NO₂-associated premature deaths and the NO₂-associated childhood asthma incidences, respectively. We project health burdens to 2050 using UNEP domestic emission scenarios (Baseline and Mitigation) and population forecasts from Statistics Korea. Because of population aging alone, there are 41,000, 10,000, and 20,000 more premature deaths associated with PM_2.5, O₃, and NO₂ exposure, respectively, and 9,000 fewer childhood asthma incidences associated with NO₂. The Mitigation scenario doubles the NO₂-associated health benefits over the Baseline scenario, preventing 24,000 premature deaths and 13,000 childhood asthma incidences by 2050. It also slightly reduces PM_2.5- and O₃-associated premature deaths by 9.9% and 7.0%, unlike the Baseline scenario where these pollutants increase. Furthermore, we examine foreign emission impacts from nine SSP/RCP-based scenarios, highlighting the need for international cooperation to reduce PM_2.5 and O₃ pollution.

Air pollution exposure is closely linked to population age and socioeconomic status. Population aging and imbalance in regional economy are thus anticipated to have important implications on ozone (O₃)-related health impacts. Here we provide a driver analysis for O₃ mortality burden due to respiratory disease in China over 2013–2050 driven by population aging and regional inequity. Unexpectedly, we find that population aging is estimated to result in dramatic rises in annual O₃ mortality burden in China; by 56, 101–137, and 298–485 thousand over the periods 2013–2020, 2020–2030, and 2030–2050, respectively. This reflects the exponential rise in baseline mortality rates with increasing age. The aging-induced mortality burden rise in 2030–2050 is surprisingly large, as it is comparable to the net national mortality burden due to O₃ exposure in 2030 (359–399 thousand yr⁻¹). The health impacts of O₃ pollution, shown as mortality burden per capita, are inequitably distributed, with more severe effects in less developed provinces than their developed counterparts by 23.1% and 21.5% in 2019 and 2030, respectively. However, the regional inequity in O₃ mortality burden is expected to be mitigated in 2050. This temporal variation reflects evolving demographic dividend characterized by a larger proportion of younger individuals in developed regions. These findings are critical for targeted improvement of healthcare services to ensure the sustainability of social development.

As urbanization progresses under a changing climate, urban populations face increasing threats from chronically higher heat exposures and more frequent extreme heat events. Understanding the complex urban thermal exposure patterns becomes crucial for effective heat risk management. The spatial advantage of satellite thermal observations positions surface urban heat islands (SUHI) as a primary measure for such applications at the city scale. However, satellite-inherent biases pose considerable uncertainties. To improve the representation of human-relevant heat exposure, this study proposes a simple but effective satellite-based measure– ground urban heat island (GUHI), focusing solely on radiant temperatures from urban ground elements. Leveraging ECOSTRESS land surface temperature product and radiation-based statistical downscaling, diurnally representative GUHIs were evaluated over NYC. The findings reveal that overall GUHI is consistently warmer than SUHI diurnally. However, GUHI exhibits complex spatial contrasts with SUHI, primarily influenced by vegetation coverage. Various indicators associated with urban structures and materials were examined, showing important but dissimilar roles in shaping the spatial dynamics of GUHI and SUHI. This study highlights the value of satellite thermal observations compared to air temperature while addressing uncertainties in widely adopted practices of using them. By improving the depiction of human-related urban heat patterns from Earth observations, this research offers valuable insight and more reliable measures to address the urgent requirements for urban heat risk management globally.

The increasing frequency and severity of wildfires due to climate change pose health risks to migrant farm workers laboring in wildfire-prone regions. This study focuses on Sonoma County, California, investigating the effectiveness of air monitoring and safety protections for farmworkers. The analysis employs AirNow and PurpleAir PM_2.5 data acquired during the 2020 wildfire season, comparing spatial variability in air pollution. Results show significant differences between the single Sonoma County AirNow station data and the PurpleAir data in the regions directly impacted by wildfire smoke. Three distinct wildfire pollution episodes with elevated PM_2.5 levels are identified to examine the regional variations. This study also examines the system used to exempt farmworkers from wildfire mandatory evacuation orders, finding incomplete information, ad hoc decision-making, and scant enforcement. In response, we make policy recommendations that include stricter requirements for employers, real-time air quality monitoring, post-exposure health screenings, and hazard pay. Our findings underscore the need for significant consideration of localized air quality readings and the importance of equitable disaster policies for protecting the health of farmworkers (particularly those who are undocumented migrants) in the face of escalating wildfire risks.

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

Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM_2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM_2.5 concentrations at 0.5° × 0.625° resolution across the IFLS domain. We assessed the association between PM_2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM_2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM_2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM_2.5 and blood pressure in Indonesia.

Mental health disorders have become a global problem, garnering considerable attention. However, the root causes of deteriorating mental health remain poorly understood, with existing literature predominantly concentrating on socioeconomic conditions and psychological factors. This study uses multi-linear and geographically weighted regressions (GWR) to examine the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that job sprawl and land mixed use are highly correlated with a lower risk of depression. Additionally, the presence of green spaces, especially in urban area, is associated with improved mental health. Conversely, higher concentrations of air pollutants, such as PM_2.5 and CO, along with increased precipitation, are linked to elevated depression rates. When considering spatial correlation through GWR, the impact of population density and social capital on mental health displays substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals nuanced determinants. In northwestern counties, depression rates are more influenced by factors like precipitation and socioeconomic conditions, including unemployment and income segregation. In southeastern counties, population demographic characteristics, particularly racial composition, are associated with high depression prevalence, followed by built environment factors. Interestingly, job growth and crime rates only emerge as significant factors in the context of high depression risks in southeastern counties. This study underscores the robust linkages and spatial variations between built and natural environments and mental health, emphasizing the need for effective depression treatment to incorporate these multifaceted factors.

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.

The urban heat island effect exacerbates independent climate change-induced shifts toward longer, stronger, and more frequent heat extremes. Environmental inequity, driven by a history of racially motivated urban planning policies, has led particular demographics to bear the worst impacts of urban heat exposure and thus also climate change. These impacts cause adverse health outcomes in the form of heat emergencies. Through a novel demographic and spatial analysis of heat-related illness Emergency Medical Services data from Richmond, Virginia, this study investigates the relationships between heat health emergencies and intra-urban heat islands quantified through three heat exposure metrics. We also evaluate the accessibility of built refuge from urban heat in the form of public transit infrastructure, libraries, and government cooling centers in relation to these emergencies. We found that heat emergencies are inequitably distributed among racial, age, and socioeconomic groups in Richmond, particularly among residents identified as Male, Black or African American, 50+ years old, and experiencing mental health, intoxication, and/or homelessness. We found significant associations between the location of these heat emergencies and urban heat islands as estimated from remotely-sensed surface and community science-derived air temperature metrics, but not a co-estimated heat index. We also found that available refuge facilities are insufficiently located to protect individuals with reduced mobility across areas with the highest number of heat-related health emergencies. Community involvement in the mitigation and management of extreme heat threats, especially for those disproportionately impacted, is necessary to decrease the number of summertime heat illnesses.

Lead exposure has blighted communities across the United States (and the globe), with much of the burden resting on lower income communities, and communities of color. On 17 January 2024, the US Environmental Protection Agency (USEPA) lowered the recommended screening level of lead in residential soils from 400 to 200 parts per million. Our analysis of tens of thousands of citizen-science collected soil samples from cities and communities around the US indicates that nearly one quarter of households may contain soil lead that exceed the new screening level. Extrapolating across the nation, that equates to nearly 30 million households needing to mitigate potential soil lead hazards, at a potential total cost of 290 billion to $1.2 trillion. We do not think this type of mitigation is feasible at the massive scale required and we have instead focused on a more immediate, far cheaper strategy: capping current soils with clean soils and/or mulch. At a fraction of the cost and labor of disruptive conventional soil mitigation, it yields immediate and potentially life-changing benefits for those living in these environments.