Necessity of personal sampling for exposure assessment on specific constituents of PM2.5: Results of a panel study in Shanghai, China

Xiaoning Lei; Renjie Chen; Cuicui Wang; Jingjin Shi; Zhuohui Zhao; Weihua Li; Jovine Bachwenkizi; Wenzhen Ge; Li Sun; Shanqun Li; Jing Cai; Haidong Kan

doi:10.1016/j.envint.2020.105786

Necessity of personal sampling for exposure assessment on specific constituents of PM_2.5: Results of a panel study in Shanghai, China

Environ Int. 2020 Aug:141:105786. doi: 10.1016/j.envint.2020.105786. Epub 2020 May 17.

Authors

Xiaoning Lei¹, Renjie Chen¹, Cuicui Wang¹, Jingjin Shi¹, Zhuohui Zhao¹, Weihua Li², Jovine Bachwenkizi¹, Wenzhen Ge³, Li Sun⁴, Shanqun Li⁵, Jing Cai⁶, Haidong Kan⁷

Affiliations

¹ School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China.
² Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Research, Institute of Reproduction and Development, Fudan University, Shanghai, China.
³ Regeneron Pharmaceuticals Inc., NY 10591, USA.
⁴ School of the Environment, Nanjing University, Nanjing 210023, China.
⁵ Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
⁶ School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China. Electronic address: jingcai@fudan.edu.cn.
⁷ School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Research, Institute of Reproduction and Development, Fudan University, Shanghai, China. Electronic address: kanh@fudan.edu.cn.

PMID: 32428842
DOI: 10.1016/j.envint.2020.105786

Abstract

Many epidemiological studies have evaluated the health risks of ambient fine particulate matter (PM_2.5). However, few studies have investigated the potential exposure misclassification caused by using ambient PM_2.5 concentrations as proxy for individual exposure to PM_2.5 in regions with high-level of air pollution. This study aimed to compare the differences between personal and ambient PM_2.5 constituent concentrations, and to predict the personal exposure of sixteen PM_2.5 constituents. We collected 141 72-h personal exposure filter samples from a panel of 36 healthy non-smoking college students in Shanghai, China. We then used the liner mixed effects models to predict personal constituent-specific exposure using ambient observations and several possible influencing factors including time-activity patterns, temporal variables, and meteorological conditions. The final model of each component was further evaluated by determination coefficient (R²) and root mean square error (RMSE) from leave-one-out-cross-validation (LOOCV). We observed ambient concentrations were higher than personal concentrations for all PM_2.5 components except for Mn, Fe, Ca, and V. Especially, ambient NH₄⁺, As, and NO₃^- concentrations were 3.65, 5.65 and 7.33-fold higher than their corresponding personal concentrations, respectively. The ambient level was the strongest predictor of their corresponding personal PM_2.5 components with the highest marginal R² (R_M²: 0.081 ~ 0.901), meteorological conditions (R_M²: 0.000 ~ 0.357), time-activity pattern (R_M²: 0.000 ~ 0.083) and temporal indicators (R_M²: 0.031 ~ 0.562) were also important predictors. Our final models predicted at least 50% of the variance of all personal PM_2.5 constituents and even over 90% for K, Pb, and SO₄^2-. LOOCV analysis showed that R² and RMSE ranged from 0.251 to 0.907 and 0.000 to 0.092 μg/m³, respectively. Our results showed that ambient concentration of most PM_2.5 constituents along with time-activity patterns, temporal variables, and meteorological conditions, could adequately predict personal exposure concentration. Prediction models of individual PM_2.5 constituent may help to improve the accuracy of exposure measurement in future epidemiological studies.

Keywords: Ambient monitoring; Influencing factors; PM(2.5) components; Panel study; Personal exposure.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
China
Environmental Exposure / analysis
Environmental Monitoring
Humans
Particulate Matter / analysis

Substances

Air Pollutants
Particulate Matter