Jing Shang1, Yuanxun Zhang2, James J Schauer3, Jingyu Tian4, Jinxi Hua4, Tingting Han5, Dongqing Fang6, Jianxiong An7. 1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; College of Life Science, University of Chinese Academy of Sciences, Beijing, China. 2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen, China; Institute of Bishan Eco-Environment, Bishan, Chongqing, China. Electronic address: yxzhang@ucas.ac.cn. 3. Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, USA. 4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China. 5. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China; Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089, China. 6. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Meteorological Observation Center, China Meteoological Administration, Beijing, China. 7. Department of Anesthesiology, Pain Medicine and Critical Care Medicine, Aviation General Hospital of China Medical University, Beijing, China.
Abstract
BACKGROUND: A large number of research studies have explored the health effects of exposure to atmospheric particulate matter. However, limited quantitative evidence has linked specific sources of personal PM2.5 directly to adverse health effects. This study was conducted in order to examine the association between airway inflammation and personal exposure to PM2.5 mass, components, and sources among two healthy cohorts living in both urban and rural areas of Beijing, China. METHODS: We conducted a follow-up study during the summer of 2016 and the winter of 2016/2017 among 92 students and 43 guards. 24-h personal and ambient exposure to PM2.5 and fractional exhaled nitric oxide (FeNO) were measured at least twice for each participant. Chemical components of 385 personal PM2.5 exposure samples were analyzed, and pollution sources were resolved by a positive matrix factorization (PMF) receptor model. We have constructed linear mixed effect models to evaluate the association between ambient/personal PM2.5 mass, chemical constituents, and source specific PM2.5 with FeNO after controlling for temperature, relative humidity, sites, season, and potential individual confounders. RESULTS: Interquartile range (IQR) increase in household heating sources was associated with increased FeNO (2.72%; 95% CI = 1.26-4.17%) across two sites. IQR increase in roadway transport was associated with increased FeNO (9.84%; 95% CI = 2.69-17%) in urban areas; IQR increase in Secondary inorganic sources and Industrial/Combustion sources were associated with increased FeNO (7.96%; 95% CI = 1.47-14.4%% and 7.85%; 95% CI = 0.0676-15.6%, respectively) in rural areas. Personal exposure to EC, OC, and some trace elements (Se, Pb, Bi, Cs) were also estimated to be significantly associated with the increase of FeNO. In addition, there was no significant difference (P > 0.05) between the effects of ambient and personal PM2.5 mass. CONCLUSIONS: Although personal PM2.5 mass was not significantly associated with the health effects, airway inflammation can be linked to source-resolved exposures.
BACKGROUND: A large number of research studies have explored the health effects of exposure to atmospheric particulate matter. However, limited quantitative evidence has linked specific sources of personal PM2.5 directly to adverse health effects. This study was conducted in order to examine the association between airway inflammation and personal exposure to PM2.5 mass, components, and sources among two healthy cohorts living in both urban and rural areas of Beijing, China. METHODS: We conducted a follow-up study during the summer of 2016 and the winter of 2016/2017 among 92 students and 43 guards. 24-h personal and ambient exposure to PM2.5 and fractional exhaled nitric oxide (FeNO) were measured at least twice for each participant. Chemical components of 385 personal PM2.5 exposure samples were analyzed, and pollution sources were resolved by a positive matrix factorization (PMF) receptor model. We have constructed linear mixed effect models to evaluate the association between ambient/personal PM2.5 mass, chemical constituents, and source specific PM2.5 with FeNO after controlling for temperature, relative humidity, sites, season, and potential individual confounders. RESULTS: Interquartile range (IQR) increase in household heating sources was associated with increased FeNO (2.72%; 95% CI = 1.26-4.17%) across two sites. IQR increase in roadway transport was associated with increased FeNO (9.84%; 95% CI = 2.69-17%) in urban areas; IQR increase in Secondary inorganic sources and Industrial/Combustion sources were associated with increased FeNO (7.96%; 95% CI = 1.47-14.4%% and 7.85%; 95% CI = 0.0676-15.6%, respectively) in rural areas. Personal exposure to EC, OC, and some trace elements (Se, Pb, Bi, Cs) were also estimated to be significantly associated with the increase of FeNO. In addition, there was no significant difference (P > 0.05) between the effects of ambient and personal PM2.5 mass. CONCLUSIONS: Although personal PM2.5 mass was not significantly associated with the health effects, airway inflammation can be linked to source-resolved exposures.
Authors: Nan Zhang; Chunmei Geng; Jia Xu; Liwen Zhang; Penghui Li; Jinbao Han; Shuang Gao; Xinhua Wang; Wen Yang; Zhipeng Bai; Wenge Zhang; Bin Han Journal: Int J Environ Res Public Health Date: 2022-04-07 Impact factor: 4.614
Authors: Khairul Nizam Mohd Isa; Juliana Jalaludin; Saliza Mohd Elias; Norlen Mohamed; Jamal Hisham Hashim; Zailina Hashim Journal: Int J Environ Res Public Health Date: 2022-04-11 Impact factor: 4.614