ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

Figure 1Loading Img

A Random Forest Model for Daily PM2.5 Personal Exposure Assessment for a Chinese Cohort

  • Yanwen Wang
    Yanwen Wang
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    More by Yanwen Wang
  • Yanjun Du
    Yanjun Du
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    More by Yanjun Du
  • Jianlong Fang
    Jianlong Fang
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
  • Xiaoyan Dong
    Xiaoyan Dong
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    More by Xiaoyan Dong
  • Qiong Wang
    Qiong Wang
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    More by Qiong Wang
  • Jie Ban
    Jie Ban
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    More by Jie Ban
  • Qinghua Sun
    Qinghua Sun
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    More by Qinghua Sun
  • Runmei Ma
    Runmei Ma
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    More by Runmei Ma
  • Wenjing Zhang
    Wenjing Zhang
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
  • Mike Z. He
    Mike Z. He
    Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
    More by Mike Z. He
  • Cong Liu
    Cong Liu
    School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
    More by Cong Liu
  • Yue Niu
    Yue Niu
    School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
    More by Yue Niu
  • Renjie Chen
    Renjie Chen
    School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
    More by Renjie Chen
  • Haidong Kan
    Haidong Kan
    School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
    More by Haidong Kan
  • , and 
  • Tiantian Li*
    Tiantian Li
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
    Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
    *Email: [email protected]
    More by Tiantian Li
Cite this: Environ. Sci. Technol. Lett. 2022, 9, 5, 466–472
Publication Date (Web):April 7, 2022
https://doi.org/10.1021/acs.estlett.1c00970
Copyright © 2022 American Chemical Society

    Article Views

    1058

    Altmetric

    -

    Citations

    LEARN ABOUT THESE METRICS
    Other access options
    Supporting Info (1)»

    Abstract

    Abstract Image

    Errors in air pollution exposure assessment are often considered as a major limitation in epidemiological studies. However, it is difficult to obtain accurate personal level exposure on cohort populations due to the often prohibitive expense. Personal exposure estimation models are used in lieu of direct personal exposure measures but still suffer from issues of availability and accuracy. We aim to establish a personal PM2.5 exposure assessment model for a cohort population and assess its performance by applying our model on cohort subjects. We analyzed data from representative sites selected from the subclinical outcomes of polluted air in China (SCOPA-China) cohort study and established a random forest model for estimating daily PM2.5 personal exposure. We also applied the model among subjects recruited in the project mentioned above within the same area and study period to estimate the reliability of the model. The established model showed a good fit with an R2 of 0.81. The model application results showed similar patterns with empirically measured data. Our pilot study provided a validated and feasible modeling approach for assessing daily personal PM2.5 exposure for large cohort populations. The promising model framework can improve PM2.5 exposure assessment accuracy for future environmental health studies of large populations.

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. You can change your affiliated institution below.

    Supporting Information

    ARTICLE SECTIONS
    Jump To

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.estlett.1c00970.

    • Detailed information about the study subjects and the collection of data (Text S1), model establishment and sensitivity analysis results (Text S2), discussion of the limitations of spatial validation methods (Text S3), study site locations (Figure S1), descriptive statistics of selected variables (Table S1), Bland–Altman analysis (Figure S2), and model sensitivity analysis results (Figures S3–S8 and Table S2) (PDF)

    Terms & Conditions

    Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 4 publications.

    1. Varun Katoch, Alok Kumar, Fahad Imam, Debajit Sarkar, Luke D. Knibbs, Yang Liu, Dilip Ganguly, Sagnik Dey. Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India. Environmental Science & Technology 2023, 57 (48) , 19190-19201. https://doi.org/10.1021/acs.est.3c03355
    2. Yanting Qiu, Yuechen Liu, Zhijun Wu, Fuzhou Wang, Xiangxinyue Meng, Zirui Zhang, Ruiqi Man, Dandan Huang, Hongli Wang, Yaqin Gao, Cheng Huang, Min Hu. Predicting Atmospheric Particle Phase State Using an Explainable Machine Learning Approach Based on Particle Rebound Measurements. Environmental Science & Technology 2023, 57 (40) , 15055-15064. https://doi.org/10.1021/acs.est.3c05284
    3. Zhenglei Li, Yu Chen, Yan Tao, Xiuge Zhao, Danlu Wang, Tong Wei, Yaxuan Hou, Xiaojing Xu. Mapping the personal PM2.5 exposure of China's population using random forest. Science of The Total Environment 2023, 871 , 162090. https://doi.org/10.1016/j.scitotenv.2023.162090
    4. Tianshuai Li, Qingzhu Zhang, Yanbo Peng, Xu Guan, Lei Li, Jiangshan Mu, Xinfeng Wang, Xianwei Yin, Qiao Wang. Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective. Environment International 2023, 173 , 107861. https://doi.org/10.1016/j.envint.2023.107861

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    You’ve supercharged your research process with ACS and Mendeley!

    STEP 1:
    Click to create an ACS ID

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    MENDELEY PAIRING EXPIRED
    Your Mendeley pairing has expired. Please reconnect