Revealing the impact of built environment, air pollution and housing price on health inequality: an empirical analysis of Nanjing, China

Abstract

IntroductionResidential segregation have become a common phenomenon in China recently. Socioeconomically disadvantaged residents were more likely to live in communities with higher PM2.5 concentrations and poorer built environment, which may ultimately lead to a higher health risk, further exacerbating health inequalities. However, the reasons for health inequalities under residential segregation remain unclear.MethodsThis study quantified the built environment, air pollution, mortality rate and housing price at 1 km × 1 km grid scale. Moderating effect model, mediating effect model, moderated mediating effect model were used to progressively clarify the relationship between the four.ResultsResults show that, in terms of spatial distribution, the central area has high housing price with good built environment, low PM2.5 concentration and low mortality rate. While the suburban area has low housing price, poor built environment, high PM2.5 concentration and high mortality rate. Additionally, built environment can not only reduce health risks through moderating effect, but also affect health through the mediating effect of PM2.5. There is heterogeneity in moderating effect of built environment in different locations. Housing prices can moderate the effect of built environment on health. This study would offer important reference for urban planning to mitigate the effect of built environment inequalities on health inequalities in China.

Environmental Exposome and Atrial Fibrillation: Emerging Evidence and Future Directions

“…The built environment's impact on health is found to be moderated and mediated by PM 2.5 levels and housing prices, offering crucial insights for mitigating health inequalities through informed urban planning in China. 79 In Africa as well, similar trends are seen-A study addressed the lack of long-term air pollution exposure data in growing sub-Saharan African cities by developing high-resolution spatiotemporal land use regression models to map PM 2.5 and black carbon concentrations in the Greater Accra Metropolitan Area. The 1-year measurement campaign at 146 sites informed the models, revealing that 48% to 69% and 63% to 71% of the variance in PM 2.5 and black carbon concentrations, respectively, could be explained by factors related to road traffic, vegetation, and temporal variables.…”