Dr. Zhiyong Hu

This chapter assesses the association between adult stroke mortality prevalence rate and long-term exposure to PM2.5 (ambient air pollution of particulate matter with an aerodynamic diameter of 2.5 m or less) adjusting for unhealthy lifestyles and other health conditions. Health data, based on 2017 or 2016 model-based small area estimates of chronic disease, was obtained from the “500 Cities Project” 2019 release. PM2.5 data for the year 2016 was acquired from the “The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), V4.03 (1998-2019)” datasets. Average PM2.5 was calculated for each city using a GIS zonal statistics function. First, classic ordinary least (OLS) square regression modeling (univariate linear regression, and a multivariate linear regression model fitted using a generalized linear model via penalized maximum likelihood). However, Moran’s I test found significant positive spatial autocorrelation with stroke data, PM2.5, and the residues of the OLS multilinear regression model, which makes the OLS modeling unreliable. Therefore, two spatial regression models (spatial lag and spatial error) were further run to account for the spatial dependence. Both models have successfully explained away spatial dependency. The spatial error model has much better goodness-of-fit than the spatial lag model as indicated by the higher log-likelihood, lower Akaike info criterion (AIC), and lower Schwartz criterion. The spatial error model found that long-term exposure to ambient PM2.5 may increase the risk of stroke and that increasing physical activity, reducing smoking and body weight, enough sleep, and controlling diseases such as blood pressure, coronary heart disease, diabetes, and cholesterol, may mitigate the effect.

Geospatial technologies have been widely used in environmental health research, including air pollution and human health. This chapter demonstrates the potential of integrating satellite air quality measurement with ground-based PM2.5 data to explore health effects of fine particulate air pollution. This study assesses the association of estimated PM2.5 concentration with chronic coronary heart disease (CCHD) mortality. Years 2003 and 2004 daily MODIS (Moderate Resolution Imaging Spectrometer) Level 2 AOD images were collated with US EPA PM2.5 data covering the conterminous USA. Pearson's correlation analysis and geographically weighted regression (GWR) found that the relationship between PM2.5 and AOD is not spatially consistent across the conterminous states. GWR predicts well in the east and poorly in the west. The GWR model was used to derive a PM2.5 grid surface for the eastern US (RMSE = 1.67 mu g/m(3)). A Bayesian hierarchical model found that areas with higher values of PM2.5 show high rates of CCHD mortality: beta(PM2.5) = 0.802, posterior 95% Bayesian credible interval (CI) = (0.386, 1.225). Aerosol remote sensing and GIS spatial analyses and modelling could help fill pervasive data gaps in ground-based air quality monitoring that impede efforts to study air pollution and protect public health.