High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city

Yiyi Wang; Lei Huang; Conghong Huang; Jianlin Hu; Meng Wang

doi:10.1016/j.envint.2023.107752

High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city

Environ Int. 2023 Feb:172:107752. doi: 10.1016/j.envint.2023.107752. Epub 2023 Jan 13.

Authors

Yiyi Wang¹, Lei Huang², Conghong Huang³, Jianlin Hu⁴, Meng Wang⁵

Affiliations

¹ Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China; State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
² State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
³ College of Land Management, Nanjing Agricultural University, Nanjing 210095, China; National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing 210095, China; Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY 14214, USA.
⁴ Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China. Electronic address: jianlinhu@nuist.edu.cn.
⁵ Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY 14214, USA; RENEW Institute, University at Buffalo, Buffalo, NY, USA; Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA. Electronic address: mwang54@buffalo.edu.

PMID: 36709673
DOI: 10.1016/j.envint.2023.107752

Abstract

The Air Quality Index (AQI), which jointly accounts for levels of criteria air pollutants relative to their guidelines, is largely reported at the city level. Little is known about the spatial patterns of the AQI in terms of the magnitude, temporal variability, and predominant air pollutant contributions at the hyperlocal scale within a city. To fill this research gap, we developed spatiotemporal models for each criteria air pollutant based on an advanced geostatistical framework and estimated daily AQI levels at 100-meter resolution in a metropolitan city in 2019. The model prediction ability (cross-validation, CV, Coefficient of determination, R², and root mean square error, RMSE) ranged from 0.43 and 1.86 µg/m³ for sulfur dioxide (SO₂) to 0.92 and 6.25 µg/m³ for fine particulate matter (PM_2.5) across the six air pollutants, leading to good performance in the subsequent AQI estimations (CV R² = 0.86, RMSE = 10.05). The AQI varies substantially over space at a fine scale and differs from the distributions of individual air pollutants. The unhealthy air quality (AQI > 100 over 75 days) spatial pattern was dominated by excessive ground-level ozone exposure in a large area. Our research provides a useful tool for accurately estimating AQI spatiotemporal variations for population health studies.

Keywords: Air quality index; Criteria air pollutants; High resolution; Spatiotemporal model.

Publication types

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

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Cities
Environmental Monitoring
Ozone*
Particulate Matter / analysis

Substances

Air Pollutants
Particulate Matter
Ozone