Associations between ambient air pollution and hospitalizations for acute exacerbation of chronic obstructive pulmonary disease in Jinhua, 2019. (March 2021) - Du, Wenjun; Zhang, Weidong; Hu, Hongyu; Zhang, Miaoyun; He, Yiling; Li, Zhiming
Abstract: Most cities in China are experiencing severe air pollution due to rapid economic development and accelerated industrialization. Exposure to various air pollutants has been related to acute exacerbations of chronic obstructive pulmonary disease (AECOPD). However, less is known about the spatial association between air pollution and AECOPD hospitalizations in the Yangtze River delta (YRD) economic and industrial region. Jinhua city is located in the YRD region of East China, in the middle of Zhejiang province. For the first time, 1563 AECOPD hospitalization cases in Jinhua during 2019 were enrolled in our analysis. The spatial distribution of six pollutants (SO2, CO, PM10, PM2.5, O3, NO2 ) and the population temporal-spatial specific air pollutant exposure levels were estimated using the ordinary Kriging model through geographic information system (GIS). Global Moran's I was used to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. The Z-scores of residential SO2, CO, PM10, PM2.5, O3, and NO2 levels were 31.88, 42.95, 45.90, 32.29, 52.18, and 34.59, respectively. The concentrations of six monitored pollutants and AECOPD hospitalizations showed statistically significant spatial clustering. A generalized linear model (GLM) using a Poisson distribution with the log-link function was used to construct a core model. After adjusting for potential confounders in the model, residential SO2, NO2 and O3 concentrations were significantly associated with increased AECOPD hospitalizations. Graphical abstract: Image 1 Highlights: Ambient air pollution is correlated with AECOPD hospitalizations spatially in Jinhua city, 2019. SO2, CO, PM10, PM2.5, O3, and NO2 were evaluated. O3 levels are most highly spatially correlated with AECOPD hospitalization. GIS is a powerful tool to analyze the spatial association between air pollution and respiratory illness.