环境科学
热点(地质)
大气科学
大气(单位)
环境化学
地理
化学
气象学
地质学
地球物理学
作者
Rui Wang,Han Zhang,Wei Zhang,Xunhua Zheng,Klaus Butterbach‐Bahl,Siqi Li,Shenghui Han
标识
DOI:10.1016/j.envpol.2020.114770
摘要
Polluted urban river systems might be a strong source of atmospheric methane (CH4) and nitrous oxide (N2O), but so far only a few urban river systems have been quantified with regard to their source strength for greenhouse gases (GHGs). In this study, we measured loads of dissolved inorganic nitrogen and organic carbon, dissolved oxygen (DO) concentrations, and fluxes of CH4 and N2O from an urban river in Beijing, China during the course of an entire year. Fluxes calculated using the floating chamber approach or via the diffusion method with measurements of river water GHG concentrations showed comparable temporal variations. However, the flux magnitude based on the diffusion method was found to strongly depend on the underlying parameterization of the gas transfer velocity. In view of the large differences while applying different methodologies to estimate surface water GHG fluxes further studies are still needed to prove and eventually quantify the systematic errors which are likely caused by either the chamber technique or the approaches of individual diffusion models. For both the floating chamber and the diffusion-based flux estimates, strong seasonal variations in CH4 and N2O fluxes from the river surface were observed, with fluxes ranging from 3 to 8374 μg C m-2 h-1 for CH4 and 1-3986 μg N m-2 h-1 for N2O. The CH4 fluxes were strongly negatively correlated with the DO concentration (P < 0.01). The highest N2O fluxes were observed at times with low CH4 fluxes (i.e., in spring and autumn). Annual CH4 and N2O fluxes totaled 19.3-79.4 and 17.4-44.8 kg C (N) ha-1 yr-1, respectively. These high fluxes are in agreement with estimates from the few other studies carried out for urban river systems to date and indicate that urban polluted river systems are a significant regional source of atmospheric GHGs.
科研通智能强力驱动
Strongly Powered by AbleSci AI