Atmospheric CH4 and Its Isotopic Composition (δ13C) in Urban Environment in the Example of Moscow, Russia

Abstract

Measurements of near-surface methane (CH4) mixing ratio and its stable isotope 13C were carried out from January 2018 to December 2020 at the A.M. Obukhov Institute of Atmospheric Physics (IAP) research site in the center of Moscow city. The data show moderate interannual variations in monthly mean CH4 with maximum values being observed predominantly in winter (2.05–2.10 ppmv on average). The most δ13C depleted CH4 (up to −56‰) is observed in summer and autumn following seasonal decrease in traffic load in the city. The highest CH4 concentrations (>2.2 ppmv) were likely to be caused by air transport from the E–SE sector where potentially large microbial CH4 sources are located (landfills and water treatment plants, Moscow River). Keeling plots of these episodes in different seasons of 2018–2020 showed δ13C isotopic signatures of about −58–−59‰ for the spring–autumn period and −67‰ for winter. A good correlation was observed between CH4 and other pollutants: CO2, CO, and benzene in daytime (10:00–19:00) hours (R > 0.7). Contribution of urban methane emissions due to vehicle exhausts (∆[CH4]auto) and microbial activity (∆[CH4]micro+) along with regional baseline mixing ratios of CH4 ([CH4]base) and CO ([CO]base) were estimated from the linear orthogonal regression analyses of the measured daytime mixing ratios. A significant role of microbial methane in the formation of CH4 maximums in Moscow was revealed. Contributions of the upwind continental CH4 and CO sources to the measured species levels were estimated through comparison with the Mace Head site data representative for the Northern Hemisphere baseline air. The study provides, for the first time, important insights into the long- and short-term variations of CH4 levels in Moscow in connection to the local (urban) emissions and long-range transport from upwind continental sources. The results will contribute to elaboration of a default emission inventory in air quality modeling and help to identify the areas for targeted mitigation efforts.