Spatial variability and determinants of atmospheric methane concentrations in the metropolitan city of Shanghai, China

摘要

Methane, CH4, is a powerful greenhouse gas released to the atmosphere by both anthropogenic and natural sources. Urban areas play an important role in greenhouse gas emissions, yet few efforts have been made to investigate CH4 concentration and its spatial variability in cities. In this paper, a case study to monitor the spatial pattern and determinants of near-surface urban CH4 concentration was conducted in Shanghai, the commercial and financial center of mainland China. A total of 173 sampling points were collected to examine the atmospheric CH4 concentration across different land use types and urban-rural gradients at a height of 2 m above the ground in April and May 2014. The contributions of potential influencing factors, including four indicators of human activities (e.g., distance to natural gas pipelines [Dist_gas], distance to sewage plants (Dist_sew), population density [Den_pop), density of road network [Den-road]) and two indicators of natural conditions (i.e., planetary boundary layer height [BLH] and land surface temperature [LST]), in determining spatial CH4 concentrations were investigated using Lindeman-Merenda-Gold (LMG) metric. The results indicated that the average CH4 concentration in the morning for the region was 1,897 +/- 92 parts per billion by volume (ppbv), with significant spatial heterogeneity. Atmospheric CH4 had negative correlation with the distance to urban center except the influence of obvious emission sources, such as petrochemical plants etc. The six selected influencing factors could explain 76.3% of the CH4 spatial variability. Population density was the dominant factor for about 27.2%, while the distance to the natural gas pipelines, planetary boundary layer height and distance to the sewage plants were about 14.4%, 14.2% and 12.6%, respectively. The finding in this study might help to build a better understanding of the mechanics of CH4 variations and evaluation of the potentially influencing factors.