Metadata of the sewer methane emission study integrated with microbial information

The sewer system, despite being a significant source of methane emissions, has often been overlooked in current greenhouse gas inventories due to the limited availability of quantitative data. Direct monitoring in sewers can be expensive or biased due to access limitation and internal heterogeneity of sewer networks. Fortunately, since methane is almost exclusively biogenic in sewers, we demonstrate in this study the methanogenic potential can be estimated using known sewer microbiome data. By combining data mining techniques and bioinformatics databases, we developed the first data-driven method to analyze methanogenic potentials using a dataset containing 633 observations of 53 variables obtained from literature mining. The methanogenic potential in the sewer sediment was around 250-870 % higher than in the wet biofilm on the pipe and sewage water. Additionally, <i>k</i>-means clustering and principal component analysis linked higher methane emission rates (9.72 ± 51.3 kg_{CO2 eq} m^-3 d^-1) with smaller pipe size, higher water level, and higher potentials of sulfate reduction in the wetted pipe biofilm. These findings exhibit the possibility of connecting microbiome data with biogenic greenhouse gas, further offering insights into new approaches for understanding greenhouse gas emissions from understudied sources.