Ming-Shing Ho1, Ming-Yeng Lin1, Chih-Da Wu2,3, Jung-Der Wang4, Li-Hao Young5, Hui-Tsung Hsu5, Bing-Fang Hwang5, Perng-Jy Tsai This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
2 Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
3 Innovation and Development Center of Sustainable Agriculture, National Chung-Hsing University, Taichung, Taiwan
4 Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
5 Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan

Received: December 11, 2023
Revised: January 15, 2024
Accepted: January 17, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.230313  

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Cite this article:

Ho, M.S., Lin, M.Y., Wu, C.D., Wang, J.D., Young, L.H., Hsu, H.T., Hwang, B.F., Tsai, P.J. (2024). An Integrated Approach for Conducting long-term PM2.5 Exposure and Health Impact Assessments for Residents of a City-scale. Aerosol Air Qual. Res. 24, 230313. https://doi.org/10.4209/aaqr.230313


  • AQMS data is good for characterizing temporal PM2.5 variations of the rooftop level.
  • h_LUR data can characterize spatiotemporal PM2.5 variations of the rooftop level.
  • MMS data can characterize spatiotemporal PM2.5 variations of the ground level.
  • Long-term ground level PM2.5 data can assess residents’ exposure and health impact.


This study aims to show the benefits of integrating the mobile monitoring system (MMS), hybrid land use regression (h_LUR), and air quality monitoring station (AQMS) data in both conducting long-term PM2.5 exposure assessment (EA) and health impact assessment (HIA) for residents in a city-scale. A city installed with a government-operated AQMS was selected. A 1-year PM2.5 dataset was collected from AQMS (AQMS1yr, reflecting the temporal variation of the rooftop level), and was served as a basis for characterizing the spatiotemporal heterogeneity of the rooftop level (h_LUR1yr) using the h_LUR model. A 1-year dataset was simultaneously collected from an established MMS for characterizing the spatiotemporal heterogeneity of the ground level (MMS1yr). A ground-level PM2.5 concentration predictive model was established by relating hourly MMS1yr to h_LUR1yr data and significant environmental covariables using the multivariate linear regression analysis. To establish long-term exposure datasets, 9-year AQMS data (AQMS9yr) were collected, and h_LUR9yr and MMS9yr were established through the application of the h_LUR and the obtained predictive model, respectively. Results show MMS1yr (24–26 µg m–3) > h_LUR1yr (17–19 µg m–3) > AQMS1yr (13–15 µg m–3). An R2 = 0.61 was obtained for the established ground predictive model. PM2.5 concentrations consistently decrease by year for MMS9yr (29–17 µg m–3), h_LUR9yr (25–12 µg m–3), and AQMS9yr (22–11 µg m–3), respectively. The result MMS9yr > h_LUR9yr > AQMS9yr indicates both the use of h_LUR9yr and AQMS9yr would result in underestimating residents’ exposures. By reference to the results obtained from MMS9yr, using AQMS9yr and h_LUR9yr would respectively lead to the underestimation of the attributed fraction (AFs) ~21%–36% and 18%–26% for the 5 disease burdens, including ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and lower respiratory tract infection. The above results clearly indicate the importance of using the integrating approach on conducting EA and HIA.

Keywords: PM2.5, Air quality monitoring station, Hybrid land use regression, Mobile Monitor-ing system, Predictive model, Health impact assessment

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