A probabilistic approach to the quantification of methane generation in sewer networks.

Quantifying greenhouse gas (GHG) emissions from the conveyance of wastewater is an essential part of emissions reduction as it can identify areas of high emissions that can be targeted for mitigative action. In this study, a Monte Carlo algorithm that employs a reach-based methane generation sub-model was developed and applied to a full-scale municipal sewer system in Ontario, Canada. The algorithm employed eight categories of random variables including sewage temperature, slope, and coefficients described in the sewer reach model.