Climate change and population settlement patterns are altering the severity and spatial dimensions of flooding. Despite associational evidence linking flood exposure to population health in the United States, few studies have used counterfactual strategies to address confounding or examined how sociospatial determinations of risk, such as floodplain delineation, affect well-being. Using the case of Hurricane Harvey, I leverage novel, repeated cross-sectional health survey data from Houston immediately predisaster (N = 2,540) and six to nine months postdisaster (N = 2,798), linked to local flood inundation and floodplain data. Difference-in-differences models show that the probability of psychological distress and fair/poor health increased significantly in the flooded treatment group, with mixed evidence on unhealthy mental health days and no change in unhealthy physical health days. Triple-difference estimators further reveal buffered mental health adversity for those in flooded areas with high floodplain areal coverage relative to little or no floodplains. Descriptive analyses of mechanisms suggest that floodplain coverage did not differentiate individual-level disaster exposure but increased the likelihood of disaster preparedness and evacuation. This article offers insights into the climate-health nexus empirically by using a causal framework to improve credibility and conceptually by demonstrating how an underexamined dimension of vulnerability-sociospatial risk determinations-can stratify population health.
In recent years, the impact of urban form evolution on atmospheric pollution has become increasingly prominent. However, previous studies have rarely examined the combined influence of urban spatial forms and human perception on air pollution, while excluding emissions from natural sources. To address this gap, our study investigates the spatiotemporal dynamics of the relationship between anthropogenic PM2.5 pollution and urban form in China from 2000 to 2019. Using the Geographically and Temporally Weighted Regression (GTWR) model, we analyze the spatial heterogeneity of the impact of urban form on PM2.5 pollution. Our findings reveal that anthropogenic PM2.5 concentrations in China exhibited an initial increase, followed by a decline after 2013. In heavily polluted regions, such as the Beijing-Tianjin-Hebei area, annual average concentrations in most areas exceeded 60 μg/m3, with southern Hebei exceeding 100 μg/m3. The northern, southwestern, and Yangtze River Economic Belt regions had relatively lower concentrations, but still ranged between 20 and 60 μg/m3. Increasing urban compactness, reducing urban sprawl, and enhancing the complexity of urban form were found to contribute to lower anthropogenic PM2.5 levels in most cities. Additionally, climate conditions characterized by high precipitation and temperature, along with urban form patterns featuring high density, cohesion, and controlled expansion, were associated with reduced anthropogenic PM2.5 concentrations. In contrast, high humidity, dense populations, a thriving secondary sector, heavy traffic flow, and large, complex urban forms were likely to exacerbate anthropogenic PM2.5 pollution. These findings provide scientific insights for coordinated strategies to control atmospheric pollution in Chinese cities.