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

Methane is the second largest anthropogenic greenhouse gas,and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks.Therefore,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 evaluation.China is the world’s largest emitter of anthropogenic methane.However,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 China.Here 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 2018.The 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.