| The atmospheric methane(CH4)concentration has increased by 150%compared to pre-industrial levels.CH4 has a relatively short survival time in the atmosphere(9±2 years)and has a strong warming potential.Therefore,clarifying the sources of CH4,an important greenhouse gas,and the corresponding emission intensity is an important part of climate change research.However,the traditional inventory results have high uncertainty,especially at the regional scale.The accurate estimation of CH4 emissions at the regional scale is key to formulating reasonable emission reduction measures.In this paper,the Yangtze River Delta region(YRD),where anthropogenic and natural CH4 sources are alternately distributed,densely populated,and frequent industrial activities are happened is selected.A"bottom-up"method(i.e.,inventory algorithm)and two"top-down"methods(i.e.,observation combined with simulation methods)were used to estimate natural and anthropogenic CH4emissions in the YRD region.In this study,the average anthropogenic CH4 emissions in the YRD region from 2012 to2015 were estimated to be 5.66 Tg yr-1 based on provincial statistical data and corresponding regional emission factors by using inventory algorithm.In order to improve estimation of anthropogenic CH4 emissions,atmospheric method was used to estimate anthropogenic CH4emissions in the Yangtze River Delta by combining observed atmospheric CH4 and CO2concentration with CO2 emissions estimated by inventory algorithm.The concentration data used here are annual winter observations from May 2012 to April 2017 in the suburbs of Wuxi,Jiangsu Province.In order to distinguish and quantify CH4 emissions from natural and anthropogenic sources,a second"top-down"approach which is atmospheric transport model combined with inversion method was used to estimate the CH4 emissions in the YRD.The WRF-STILT(Weather Research and Forecasting Model-Stochastic Time Inverted Lagrangian Transport model)was chosen as the atmospheric transport model,and it was combined with the spatial distribution of prior emissions to simulate the enhancement of CH4 concentration of the observation station contributed by various emission sources.By minimizing the difference between the simulated concentration values and the high-precision atmospheric CH4 concentration observations through Bayesian inverse modeling approach,the scaling factors from inventory optimization were obtained and further applied in evaluating the prior emission inventory and obtaining the posteriori anthropogenic and natural CH4 emissions that were closer to the real monthly emissions in the YRD region.The atmospheric CH4concentration observation platform was a 70 m tall tower in rural Chuzhou,Anhui province,and the observation period was from December 2017 to December 2018.The main research results are as follows:(1)The total anthropogenic CH4 emissions in the YRD based on provincial and national statistical data differ greatly,and the characteristics of inter-annual variation are not consistent.Anthropogenic CH4 emissions in the YRD region in 2018 were estimated to be between 8.16and 10.08 Tg according to the Emissions Database for Global Atmospheric Research(EDGAR v432&EDGAR v50),which is based on national statistical data.According to the spatial distribution of national statistical data,the CH4 emission hotspots in the YRD mainly concentrated in the southern Jiangsu province,Shanghai,northern Zhejiang Province and the Coastal areas of The Yangtze River in Anhui Province.From the source apportionment of the two statistical data sources,the main anthropogenic source of CH4 in the YRD region is agricultural soil(AGS,4.01 Tg),followed by fuel extraction(PRO,1.97 Tg).CH4 emissions from natural sources were 0.87±0.33 Tg in the YRD in 2018.(2)The anthropogenic CH4 emissions estimated by atmospheric method in the YRD region were 20-70%higher than those estimated by inventory algorithm based on provincial data.The results obtained by atmospheric method showed that the estimated average anthropogenic CH4 emissions in the YRD region from 2012 to 2015 were 4.37±0.61 Tg yr-1.In order to avoid the interference of biological sources and sinks of CO2,the study period was focused on winter.Therefore,the estimated results do not include CH4 emissions generated by rice cultivation and natural emission sources such as wetlands.In addition,the difference between two methods may be caused by the underestimation of emission factors of landfill,ruminant fermentation and transportation used in the inventory algorithm,thus these emission sources were underestimated.(3)Optimizing prior emissions from different sources on a monthly scale based on observational combined model approach will improve CH4 emission estimation and source apportionment on a regional scale.The results showed that when EDGAR v432 and EDGAR v50 were used as prior anthropogenic emissions,the posterior anthropogenic CH4 emissions in the YRD region in 2018 were 10.68±1.63 Tg and 10.07±1.67 Tg,respectively.CH4emissions from rice cultivation(AGS)were 4.58 Tg(EDGAR v432)and 5.21 Tg(EDGAR v50),accounting for 39%and 47%of the total emissions.In 2018,the posterior natural CH4emissions in the YRD region were 1.21 Tg and 1.06 Tg,accounting for 10.1%(EDGAR v432)and 9.5%(EDGAR v50)of the total CH4 emissions in the Yangtze River Delta,respectively.The seasonal variation of atmospheric CH4 concentration in the YRD region was mainly driven by rice cultivation,followed by natural sources.In prior anthropogenic emission inventories(EDGAR v432 and EDGAR v50),CH4 emissions from AGS were underestimated all over the year,especially during the growing season.This study attempted to distinguish and estimate anthropogenic and natural CH4emissions at regional scale,and has realized the transition from the total annual estimate to month,from just focusing on anthropogenic sources to differentiating the anthropogenic and natural sources.The uncertainty of anthropogenic CH4emission at the regional scale was reduced to some extent by evaluating and optimizing the emission inventory.The methods and results in this study can provide data and methodological support for research of CH4emission estimation at the regional scale and provide scientific reference for the realization of"emission peak"and"carbon neutral"goals of China. |
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