Yunquan Zhang1, Jiaying Fang2, Feiyue Mao3, Zan Ding4, Qianqian Xiang5, Wei Wang6. 1. Department of Epidemiology and Biostatistics, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, China; Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, 430065, China. Electronic address: YunquanZhang@wust.edu.cn. 2. Medical Department, Huadu District People's Hospital, Southern Medical University, Guangzhou, 510800, China. 3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430071, China; State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430071, China. 4. The Institute of Metabolic Diseases, Baoan Central Hospital of Shenzhen, The Fifth Affiliated Hospital of Shenzhen University, Shenzhen, 518102, China. 5. Hubei Provincial Center for Disease Control and Prevention, Wuhan, 430079, China. 6. School of Geosciences and Info-Physics, Central South University, Changsha, China.
Abstract
BACKGROUND: Ambient PM2.5 has been identified as the top leading cause of risk-attributable deaths worldwide, particularly in China. Evidence suggested that PM1 contributed the most majority of PM2.5 concentrations in Chinese cities. However, epidemiologic knowledge to date is of wide lack regarding PM1-associated health effects. METHODS: We collected daily records of all-cause emergency department visits (EDVs) and ground measurements of ambient air pollutants and meteorological factors in Guangzhou and Shenzhen, China, 2015-2016. Case-crossover design and conditional logistic regression models were used to comparatively assess the short-term effects of ambient PM1, PM2.5, and PM10 on EDVs. Stratified analyses by gender, age and season were performed to identify vulnerable groups and periods. RESULTS: PM1, PM2.5 and PM10 were all significantly associated with increased EDVs in both cities. Population risks for EDVs increased by 2.2% [95% confidence interval, 1.8 to 2.6] in Guangzhou and 1.7% [1.0 to 2.4] in Shenzhen, for a 10 μg/m3 rise in PM1 at lag 0-1 days and lag 0-4 days, respectively. Relatively lower risks were found to be associated with PM2.5 and PM10. PM-EDVs associations exhibited no gender differences, but varied across age groups. Compared with adults and the elderly, children under 14 years-of-age suffered higher PM-induced risks. Results from both cities suggested greatly significant effect modification by season, with consistently stronger PM-EDVs associations during cold months. CONCLUSIONS: Our study added comparative evidence for increased EDVs risks associated with short-term exposures to ambient PM1, PM2.5 and PM10. Besides, PM-associated effects were significantly stronger among children and during cold months.
BACKGROUND: Ambient PM2.5 has been identified as the top leading cause of risk-attributable deaths worldwide, particularly in China. Evidence suggested that PM1 contributed the most majority of PM2.5 concentrations in Chinese cities. However, epidemiologic knowledge to date is of wide lack regarding PM1-associated health effects. METHODS: We collected daily records of all-cause emergency department visits (EDVs) and ground measurements of ambient air pollutants and meteorological factors in Guangzhou and Shenzhen, China, 2015-2016. Case-crossover design and conditional logistic regression models were used to comparatively assess the short-term effects of ambient PM1, PM2.5, and PM10 on EDVs. Stratified analyses by gender, age and season were performed to identify vulnerable groups and periods. RESULTS:PM1, PM2.5 and PM10 were all significantly associated with increased EDVs in both cities. Population risks for EDVs increased by 2.2% [95% confidence interval, 1.8 to 2.6] in Guangzhou and 1.7% [1.0 to 2.4] in Shenzhen, for a 10 μg/m3 rise in PM1 at lag 0-1 days and lag 0-4 days, respectively. Relatively lower risks were found to be associated with PM2.5 and PM10. PM-EDVs associations exhibited no gender differences, but varied across age groups. Compared with adults and the elderly, children under 14 years-of-age suffered higher PM-induced risks. Results from both cities suggested greatly significant effect modification by season, with consistently stronger PM-EDVs associations during cold months. CONCLUSIONS: Our study added comparative evidence for increased EDVs risks associated with short-term exposures to ambient PM1, PM2.5 and PM10. Besides, PM-associated effects were significantly stronger among children and during cold months.