Understanding the characteristics of O_(3)precursor contributions over multiple years is crucial for designing effective O_(3)control strategies over the Pearl River Delta(PRD)region of China.In this study,a deep lear...Understanding the characteristics of O_(3)precursor contributions over multiple years is crucial for designing effective O_(3)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_(3)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_(3)to emission controls,maintaining validity across non-training periods.Our study revealed that O_(3)generation was sensitive to volatile organic compounds(VOC)in the core PRD in 2019,with nitrogen oxides(NO_(x))-limited regimes emerging in most major cities in 2020 and 2021.Further investigation into source contributions showed that in our model domain,O_(3)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_(3)production in Guangzhou and Shenzhen.However,NO_(x)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_(x)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.展开更多
基金supported by the National Key R&D Program of China(Nos.2023YFC3708505,2023YFC3708503,and 2023YFE0121300)the High-End Foreign Expert Recruitment Program,China(No.G2023163014L)。
文摘Understanding the characteristics of O_(3)precursor contributions over multiple years is crucial for designing effective O_(3)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_(3)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_(3)to emission controls,maintaining validity across non-training periods.Our study revealed that O_(3)generation was sensitive to volatile organic compounds(VOC)in the core PRD in 2019,with nitrogen oxides(NO_(x))-limited regimes emerging in most major cities in 2020 and 2021.Further investigation into source contributions showed that in our model domain,O_(3)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_(3)production in Guangzhou and Shenzhen.However,NO_(x)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_(x)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.
