Huimin Li, Yang Yang, Hang Su, Hailong Wang, Pinya Wang, Hong Liao
{"title":"基于过程的可解释机器学习方法预测的碳中和未来气候变化对中国臭氧污染的影响","authors":"Huimin Li, Yang Yang, Hang Su, Hailong Wang, Pinya Wang, Hong Liao","doi":"10.1029/2024GL109520","DOIUrl":null,"url":null,"abstract":"<p>Ozone (O<sub>3</sub>) pollution is a severe air quality issue in China, posing a threat to human health and ecosystems. The climate change will affect O<sub>3</sub> levels by directly changing physical and chemical processes of O<sub>3</sub> and indirectly changing natural emissions of O<sub>3</sub> precursors. In this study, near-surface O<sub>3</sub> concentrations in China in 2030 and 2060 are predicted using the process-based interpretable Extreme Gradient Boosting (XGBoost) model integrated with multi-source data. The results show that the climate-driven O<sub>3</sub> levels over eastern China are projected to decrease by more than 0.4 ppb in 2060 under the carbon neutral scenario (SSP1-1.9) compared with the high emission scenario (SSP5-8.5). Among this reduction, 80% is attributed to the changes in physical and chemical processes of O<sub>3</sub> related to a cooler climate, while the remaining 20% is attributed to the reduced biogenic isoprene emissions.</p>","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024GL109520","citationCount":"0","resultStr":"{\"title\":\"Ozone Pollution in China Affected by Climate Change in a Carbon Neutral Future as Predicted by a Process-Based Interpretable Machine Learning Method\",\"authors\":\"Huimin Li, Yang Yang, Hang Su, Hailong Wang, Pinya Wang, Hong Liao\",\"doi\":\"10.1029/2024GL109520\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Ozone (O<sub>3</sub>) pollution is a severe air quality issue in China, posing a threat to human health and ecosystems. The climate change will affect O<sub>3</sub> levels by directly changing physical and chemical processes of O<sub>3</sub> and indirectly changing natural emissions of O<sub>3</sub> precursors. In this study, near-surface O<sub>3</sub> concentrations in China in 2030 and 2060 are predicted using the process-based interpretable Extreme Gradient Boosting (XGBoost) model integrated with multi-source data. The results show that the climate-driven O<sub>3</sub> levels over eastern China are projected to decrease by more than 0.4 ppb in 2060 under the carbon neutral scenario (SSP1-1.9) compared with the high emission scenario (SSP5-8.5). Among this reduction, 80% is attributed to the changes in physical and chemical processes of O<sub>3</sub> related to a cooler climate, while the remaining 20% is attributed to the reduced biogenic isoprene emissions.</p>\",\"PeriodicalId\":12523,\"journal\":{\"name\":\"Geophysical Research Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024GL109520\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geophysical Research Letters\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2024GL109520\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geophysical Research Letters","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024GL109520","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Ozone Pollution in China Affected by Climate Change in a Carbon Neutral Future as Predicted by a Process-Based Interpretable Machine Learning Method
Ozone (O3) pollution is a severe air quality issue in China, posing a threat to human health and ecosystems. The climate change will affect O3 levels by directly changing physical and chemical processes of O3 and indirectly changing natural emissions of O3 precursors. In this study, near-surface O3 concentrations in China in 2030 and 2060 are predicted using the process-based interpretable Extreme Gradient Boosting (XGBoost) model integrated with multi-source data. The results show that the climate-driven O3 levels over eastern China are projected to decrease by more than 0.4 ppb in 2060 under the carbon neutral scenario (SSP1-1.9) compared with the high emission scenario (SSP5-8.5). Among this reduction, 80% is attributed to the changes in physical and chemical processes of O3 related to a cooler climate, while the remaining 20% is attributed to the reduced biogenic isoprene emissions.
期刊介绍:
Geophysical Research Letters (GRL) publishes high-impact, innovative, and timely research on major scientific advances in all the major geoscience disciplines. Papers are communications-length articles and should have broad and immediate implications in their discipline or across the geosciences. GRLmaintains the fastest turn-around of all high-impact publications in the geosciences and works closely with authors to ensure broad visibility of top papers.