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<p>Understanding the characteristics of O<sub>3</sub> precursor contributions over multiple years is crucial for designing effective O<sub>3</sub> control strategies over the Pearl River Delta (PRD) region of China. In this study, a deep learning-based response surface model (DeepRSM) was developed and applied over the PRD (DeepRSM-PRD) to identify and quantify the main features of O<sub>3</sub> regimes and regional contributions in the core PRD over multiple years (2019–2021). The Out-of-Sample (OOS) validation results indicated that DeepRSM-PRD effectively predicted the nonlinear response of O<sub>3</sub> to emission controls, maintaining validity across non-training periods. Our study revealed that O<sub>3</sub> generation was sensitive to volatile organic compounds (VOC) in the core PRD in 2019, with nitrogen oxides (NO<sub><i>x</i></sub>)-limited regimes emerging in most major cities in 2020 and 2021. Further investigation into source contributions showed that in our model domain, O<sub>3</sub> formation in central cities of the PRD was primarily driven by local contributions and was susceptible to influence from nearby cities. With small emission reductions, VOC contributions predominantly drive O<sub>3</sub> production in Guangzhou and Shenzhen. However, NO<sub><i>x</i></sub> emissions were identified as the primary contributors in all central city receptors when anthropogenic emissions were removed, sharing 59.5%–69.3% in 2019, 64.4%–72.3% in 2020, and 62.75%–73.2% in 2021. Our results highlight the need for a high focus on NO<sub><i>x</i></sub> emissions control in the core PRD. In addition, for Guangzhou and Shenzhen, VOC reduction also plays a crucial role in the initial stages of modest emission reductions.</p>

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Quantifying multi-year ozone precursor contributions over the Pearl River Delta Region of China using a deep learning-based response surface model

  • 期刊论文

  • 发表日期：

    2025年6月27日

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  • Junlin Peng,
  • Carey Jang,
  • Yun Zhu,
  • Jia Xing,
  • Shuxiao Wang,
  • Bin Zhao,
  • Shicheng Long,
  • Jinying Li,
  • Qipeng Wen,
  • Xuehao Yan

摘要

Understanding the characteristics of O3 precursor contributions over multiple years is crucial for designing effective O3 control strategies over the Pearl River Delta (PRD) region of China. In this study, a deep learning-based response surface model (DeepRSM) was developed and applied over the PRD (DeepRSM-PRD) to identify and quantify the main features of O3 regimes and regional contributions in the core PRD over multiple years (2019–2021). The Out-of-Sample (OOS) validation results indicated that DeepRSM-PRD effectively predicted the nonlinear response of O3 to emission controls, maintaining validity across non-training periods. Our study revealed that O3 generation was sensitive to volatile organic compounds (VOC) in the core PRD in 2019, with nitrogen oxides (NOx)-limited regimes emerging in most major cities in 2020 and 2021. Further investigation into source contributions showed that in our model domain, O3 formation in central cities of the PRD was primarily driven by local contributions and was susceptible to influence from nearby cities. With small emission reductions, VOC contributions predominantly drive O3 production in Guangzhou and Shenzhen. However, NOx emissions were identified as the primary contributors in all central city receptors when anthropogenic emissions were removed, sharing 59.5%–69.3% in 2019, 64.4%–72.3% in 2020, and 62.75%–73.2% in 2021. Our results highlight the need for a high focus on NOx emissions control in the core PRD. In addition, for Guangzhou and Shenzhen, VOC reduction also plays a crucial role in the initial stages of modest emission reductions.