A Systematic Approach to Mitigate Ozone Pollution in Northern Taiwan: Evidence from De-pollutant Analysis

Thi-Thuy-Nghiem Nguyen; Manisha Mishra; Laddawan Noynoo; Thi-Cuc Le; Guan-Yu Lin; Tsai-Yin Lin; Racha Dejchanchaiwong; Perapong Tekasakul; Chuen-Jinn Tsai

doi:10.1016/j.envpol.2025.126752

A Systematic Approach to Mitigate Ozone Pollution in Northern Taiwan: Evidence from De-pollutant Analysis

Environ Pollut. 2025 Jun 30:126752. doi: 10.1016/j.envpol.2025.126752. Online ahead of print.

Authors

Thi-Thuy-Nghiem Nguyen¹, Manisha Mishra², Laddawan Noynoo³, Thi-Cuc Le⁴, Guan-Yu Lin⁵, Tsai-Yin Lin⁶, Racha Dejchanchaiwong⁷, Perapong Tekasakul³, Chuen-Jinn Tsai⁸

Affiliations

¹ Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
² School of Climate Change and Sustainability, Azim Premji University, Bengaluru, India.
³ Department of Chemical Engineering, Prince of Songkla University, Songkhla, Thailand; Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, Thailand.
⁴ Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Marine Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
⁵ Department of Environmental Science and Engineering, Tunghai University, Taichung, Taiwan. Electronic address: samlin@thu.edu.tw.
⁶ Industrial Technology Research Institute, Hsinchu, Taiwan.
⁷ Department of Chemical Engineering, Prince of Songkla University, Songkhla, Thailand; Department of Mechanical and Mechatronics Engineering, Prince of Songkla University, Songkhla, Thailand.
⁸ Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; International College of Semiconductor Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. Electronic address: cjtsai@nycu.edu.tw.

PMID: 40602635
DOI: 10.1016/j.envpol.2025.126752

Abstract

Strict air pollution control regulations in Taiwan have led to a gradual decrease in PM_2.5 and most gaseous pollutants from 2012 to 2022, except for ozone (O₃). With annual average concentrations frequently surpassing the Taiwan Ambient Air Quality Standards (AAQS) of 60 ppb (95^th percentile of daily maximum 8-hour averages), O₃ remains a major air quality concern in northern Taiwan. The present study applied machine learning (ML) models, including positive matrix factorization-eXtreme Gradient Boost-SHapely Additive Explanation (PMF-XGB-SHAP), on three years of hourly data to investigate the influence of meteorological parameters, emission sources and other pollutants on O₃ formation at an urban site in Taipei. Then, novel de-pollutant models were developed by controlling the anthropogenic emission factors in the model to quantify the impact of reduction on ambient O₃ levels, and de-weather was applied to assess the impact of meteorological parameters. Findings showed that meteorology contributed 46.7-54.8% and 44.9-54.0% to daytime and nighttime O₃ levels, respectively, with relative humidity (RH) and boundary layer height (BLH) as dominant influencing factor. Among pollutants, NO_X displayed a consistent negative association, while PM_2.5 showed a positive relationship with daytime O₃ levels. The association between vehicular VOCs and O₃ varied across years, reflecting changes in traffic patterns. Furthermore, de-pollutant analysis demonstrated that simultaneous 50% reductions in CO, SO₂, and VOCs from industrial emissions could lower O₃ concentrations by 13.4-22.6% during pollution episode days. By providing a quantitative, source-specific pathway for precursor control, the de-pollutant modelling approach establishes a framework for air quality management in other regions grappling with complex, multi-source ozone pollution.

Keywords: De-pollutant Models; Emission Reduction; Ground-level Ozone; Northern Taiwan; Volatile Organic Compounds (VOCs) sources.