Ria Martins

While the relationship between race disparity and green space access is well-established in the United States (U.S.), its spatial-temporal variations and local drivers are still unclear. This study examines how racial disparities in access to green space changed between 2010 and 2020 across the contiguous U.S. and identifies both global (the whole study area) and local (individual spatial units) drivers of these disparities. By integrating high-resolution satellite-derived land cover metrics with census tract-level sociodemographic data, we evaluated these disparities using geospatial analysis, linear regression, and explainable machine learning. We find that the racial disparity in green cover neither narrowed nor widened over the decade, despite substantial national demographic diversification, indicating that demographic change alone was insufficient to reduce inequity. Our results reveal a consistent pattern: census tracts with higher proportions of White residents have significantly greater green cover, while areas with greater non-White populations have considerably less. Feature importance analysis identified socioeconomic vulnerabilities, particularly housing burden, transportation access, and economic disadvantage, as primary drivers of these disparities, highlighting the role of structural barriers in perpetuating environmental injustice. Local explainability analyses further revealed substantial spatial heterogeneity in these relationships: single-parent families remained the dominant local predictor nationally, PM2.5 declined sharply in influence across the western U.S., and forest and shrub cover emerged as a new leading predictor in parts of the Mountain West, Pacific region, and Texas by 2020. Our findings highlight the need for equity-centered policies, targeted interventions, and community-informed urban planning to dismantle these disparities and ensure equitable access to health-promoting natural environments for all communities.

Background: Alzheimer's disease (AD) prevalence varies substantially across United States (US) counties, yet the combined influence of environmental and socioeconomic factors remains incompletely characterized. Objective: This study bridges this gap by employing machine learning combined with explainable artificial intelligence to identify and interpret key environmental drivers of AD prevalence across US counties. Methods: Utilizing county-level AD prevalence data linked with comprehensive environmental and socioeconomic indicators, we applied an eXtreme Gradient Boosting (XGBoost) regression framework. SHapley Additive exPlanations (SHAP) were used to evaluate global and local feature contributions. Results: The final model demonstrated stable performance (RMSE: 0.89 ± 0.04; R ² : 0.62 ± 0.03). Among 15 selected factors, chloride precipitation and median household value emerged as the most influential predictors. Additional significant drivers included land-use metrics (open land, walkability), atmospheric deposition, and localized industrial exposures. SHAP analysis revealed nonlinear, threshold-like increases in predicted AD prevalence at higher chloride levels, while geographic mapping identified regionally distinct exposure profiles. Conclusions: Geospatial analysis and explainable AI demonstrate that AD prevalence is driven by interacting environmental toxicants and socioeconomic disadvantages. This “exposome” perspective highlights that mitigating environmental hazards and systemic inequities is critical for reducing the neurodegenerative disease burden.