Abstract
Ozone (O3) significantly impacts air quality. Despite reductions in PM2.5 since the 2013 Clean Air Act, the level of the O3 concentration has continued to rise in China, underscoring the need for targeted pollution control measures. This study examined the seasonal and spatial variations of pollutants and meteorological variables in a major industrial city in Eastern China. Three widely used approaches, including the ozone formation potential (OFP) calculation, an observation-based model (OBM), and a random forest algorithm, were employed to investigate O3 formation in urban environments. Results show that oxygenated volatile organic compounds were the most significant contributors to summer urban O3, whereas their impact was significantly reduced during the winter. Each O3 formation evaluation model provided unique insights, with OFP offering rapid estimates, the OBM revealing detailed chemistry, and random forest capturing nonlinear interactions. However, the study also identified limitations in these models. OFP failed to account for seasonal variations in the level of O3 formation, and the random forest model struggled to distinguish causal relationships from correlations. These findings highlight the need for caution when relying on a single model and underscore the importance of integrating multiple methods to gain an accurate understanding of urban O3 formation dynamics, which is crucial to developing effective control strategies. © 2025 American Chemical Society.
| Original language | English |
|---|---|
| Pages (from-to) | 297–304 |
| Journal | Environmental Science and Technology Letters |
| Volume | 12 |
| Issue number | 3 |
| Online published | 12 Feb 2025 |
| DOIs | |
| Publication status | Published - 11 Mar 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
Research Keywords
- observation-based model
- Ozone
- ozone formation potential
- random forest
- volatile organic compounds
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