Quantification of Central and Eastern China's atmospheric CH_(4) enhancement changes and its contributions based on machine learning approach

作     者：Xinyue Ai Cheng Hu Yanrong Yang Leying Zhang Huili Liu Junqing Zhang Xin Chen Guoqiang Bai Wei Xiao Xinyue Ai;Cheng Hu;Yanrong Yang;Leying Zhang;Huili Liu;Junqing Zhang;Xin Chen;Guoqiang Bai;Wei Xiao

作者机构：College of Biology and the EnvironmentJoint Center for sustainable Forestry in Southern ChinaNanjing Forestry UniversityNanjing 210037China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China Guang’an Vocational&Technical CollegeGuangan 638550China HuaNan Meteorological AdministrationHuanan 154400China 

出 版 物：《Journal of Environmental Sciences》 (环境科学学报（英文版）)

年 卷 期：2024年第138卷第4期

页      面：236-248页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 07[理学] 070602[理学-大气物理学与大气环境] 0903[农学-农业资源与环境] 0706[理学-大气科学] 0901[农学-作物学] 0714[理学-统计学（可授理学、经济学学位）] 0703[理学-化学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science foundation of China(No.42105117) the Natural Science Foundation of Jiangsu Province(No.BK20200802) supported by the National Key R&D Program of China(Nos.2020YFA0607501 and 2019YFA0607202) 

主　　题：TROPOMI Methane column concentrations Anthropogenic sources Random Forest model 

摘      要：Methane is the second largest anthropogenic greenhouse gas,and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and ***,the monitoring of CH_(4) concentration changes and the assessment of underlying driving factors can provide scientific basis for the government’s policy making and *** is the world’s largest emitter of anthropogenic ***,due to the lack of ground-based observation sites,little work has been done on the spatial-temporal variations for the past decades and influencing factors in China,especially for areas with high anthropogenic emissions as Central and Eastern *** to quantify atmospheric CH_(4) enhancements trends and its driving factors in Central and Eastern China,we combined the most up-to-date TROPOMI satellite-based column CH_(4)(xCH_(4))concentration from 2018 to 2022,anthropogenic and natural emissions,and a random forest-based machine learning approach,to simulate atmospheric xCH_(4) enhancements from 2001 to *** results showed that(1)the random forest model was able to accurately establish the relationship between emission sources and xCH_(4) enhancement with a correlation coefficient(R^(2))of 0.89 and a root mean-square error(RMSE)of 11.98 ppb;(2)The xCH_(4) enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018,with a relative change of 3.27%±0.13%;(3)The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH_(4) enhancement,contributing 68.00% and 31.21%,respectively,and the decrease of animal ruminants contributed-6.70% of its enhancement trend.