Emission characteristics and influencing factors of greenhouse gas emissions from urban rivers in the plain river network region of China

Abstract Climate change has emerged as a global issue, and the emission levels of greenhouse gases (GHGs) significantly influence global climate change. The patterns and controls of GHG emissions in urban rivers remain unclear. GHG fluxes in different types of urban rivers in Changzhou City, China, were calculated via the floating static chamber method and boundary layer equation method. Ultraviolet-visible (UV–vis) absorption spectroscopy and three-dimensional excitation–emission matrix fluorescence spectroscopy (3D-EEM) were employed to explore the sources and characteristics of the dissolved organic matter (DOM) in the rivers. The physical and chemical indicators of the rivers and sediment were monitored on-site and analyzed in the laboratory. Additionally, the species and quantity of bacteria in the sediment were determined. Spearman correlation analysis was used to identify the key factors influencing GHG emissions. The results showed (1) that the intensity of sunlight had an impact on the activity of pseudomonas , and thus affected N 2 O flux and that (2) the CO 2 flux measured by the two above-mentioned methods significantly differed and were negatively correlated (p < 0.05), possibly because low wind speed influences the robustness of the boundary layer model. Therefore, using only the boundary layer equation method cannot accurately measure the GHG emissions of rivers in urban areas with low wind speeds. (3) The CO 2 flux exhibited a strong positive correlation with both total phosphorus and ammonia nitrogen in the water, as indicated by a Spearman correlation coefficient with a significance level of p < 0.01. Thus, pollution control and input control play crucial roles in reducing GHG emissions. (4) DOM in the urban rivers was derived mainly from autochthonous sources, which are protein-like substances related to the metabolism of phytoplankton. Studies indicate that GHG emissions are negatively correlated with autochthonous parameters, suggesting that reduced human interference leads to lower GHG emissions.