Projected Aerosol Changes Driven by Emissions and Climate Change Using a Machine Learning Method

Li, Huimin; Yang, Yang; Wang, Hailong; Wang, Pinya; Yue, Xu; Liao, Hong

doi:10.1021/acs.est.1c04380

Cited by 35 publications

(18 citation statements)

References 65 publications

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“…In this study, a random forest (RF) model is used to predict O 3 concentrations, similar to our previous studies (H. Li et al, , 2022, with input data of assimilated O 3 concentrations in China that combine observations and results from GEOS-Chem model simulations, GEOS-Chem simulated O 3 concentrations outside of China, MERRA-2 meteorological variables, O 3 precursor emissions, land cover (LC), the normalized difference vegetation index (NDVI), topography (TOPO), population density (POP), the month of the year (MOY), and the geographic location of each model grid as spatiotemporal information. Details of the datasets are summarized in Table 1.…”

Section: Predicting O 3 Using a Machine Learning Methodsmentioning

confidence: 98%

“…However, SSP3-7.0 is not a good representative scenario for both air quality and climate in Asia. The emissions of greenhouse gases (GHGs) and air pollutants over East Asia in SSP3-7.0 are assumed to significantly increase in the near future and remain at high levels in the middle of the 21st century, compared to SSPs (Li et al, 2022), while the emissions of air pollutants have been cut by a lot since the 2010s in the real world (Wang et al, 2021). The GHGs and pollutant emissions are very likely to continually decline in the future, related to the carbon neutrality commitment (Cheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data

Yang

Jin

et al. 2023

Atmos. Chem. Phys.

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Abstract. Ozone (O3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O3 pollution, near-surface O3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multi-source data. The ML model is trained with combined O3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O3 concentrations are projected to increase by 5 %–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and the Gangetic Plains under the high-forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O3 increases are primarily owing to the favorable meteorological conditions for O3 photochemical formation in most Asian regions. We also find that the summertime O3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of a climate change penalty on Asian O3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.

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Section: Predicting O 3 Using a Machine Learning Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data

Yang

Jin

et al. 2023

Atmos. Chem. Phys.

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“…The model simulations in 2013 and 2017 with a 1-year spin-up use the same aerosol and precursor gas emissions as used in CAM6, and the results are interpolated to the same resolution as in CAM6. The details of the GEOS-Chem model simulations can be found in H. Li et al (2022) and . Note that the GEOS-Chem model presents a strong decrease in O 3 concentrations in the upper troposphere between 2013 and 2017 which is mainly attributed to the varying meteorological fields between 2013 and 2017.…”

Section: Methodsmentioning

confidence: 99%

Fast climate responses to emission reductions in aerosol and ozone precursors in China during 2013–2017

et al. 2022

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Abstract. China has been implementing a sequence of policies for clean air since the year 2013, and the aerosol pollution has been substantially improved, but ozone (O3)-related issues arose. Here, fast climate responses to emission reductions in aerosol and O3 precursors over China during 2013–2017 are investigated using the Community Earth System Model version 2 (CESM2). The overall decreases in aerosols produced an anomalous warming of 0.09 ± 0.10∘C in eastern China (22–40∘ N, 110–122.5∘ E), which is further intensified by the increase in O3 in the lower troposphere, resulting in an enhanced warming of 0.16 ± 0.15 ∘C in eastern China. Reductions in industrial emissions contributed the most to the aerosol-induced warming, while emission reductions from residential sector induced a cooling effect due to a substantial decrease in light-absorbing black carbon aerosols. This implies that switching the residential sector to cleaner energy is more effective to achieve climate and health benefits in China.

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“…Here In this study, a random forest (RF) model is used to predict O3 concentrations, similar to our previous studies (Li et al, 2021(Li et al, , 2022, with input data of assimilated O3 concentrations that combine observations and results…”

Section: Data Assimilationmentioning

confidence: 99%

“…The emissions of greenhouse gases (GHGs) and air pollutants over East Asia in SSP3-7.0 are assumed to significantly increase in the near future and keep at high levels in the middle of the 21 st century among all SSPs (Li et al, 2022), while the emissions of air pollutants have been cut by a lot since 2010s in the real world (Wang et al, 2021). The GHGs and pollutant emissions are very likely to continually decline in the future related to the carbon neutrality commitment (Cheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multisource data

Yang

Jin

et al. 2022

Preprint

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Abstract. Ozone (O3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O3 pollution, near-surface O3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multisource data. The ML model is trained with combined O3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O3 concentrations are projected to increase by 5–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and Gangetic Plains under the high forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O3 increases are primarily owing to the favorable meteorological conditions for O3 photochemical formation in most Asian regions. We also find that the summertime O3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of climate change penalty on Asian O3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.

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Projected Aerosol Changes Driven by Emissions and Climate Change Using a Machine Learning Method

Cited by 35 publications

References 65 publications

Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data

Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data

Fast climate responses to emission reductions in aerosol and ozone precursors in China during 2013–2017

Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multisource data

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