University of Waterloo, Department of Civil and Environmental Engineering, 200 University Avenue W, Waterloo, Ontario, N2L 3G1, Canada.
University of Waterloo, Department of Civil and Environmental Engineering, 200 University Avenue W, Waterloo, Ontario, N2L 3G1, Canada.
J Environ Manage. 2022 Oct 15;320:115775. doi: 10.1016/j.jenvman.2022.115775. Epub 2022 Aug 4.
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. Using best estimates for the employed distributions and algorithm design choices, it was estimated that 2.1-3.0 g CH/m (of total wastewater conveyed) is generated in the sewer system. Gravity reaches contributed 1.3-2.2 g CH/m, and force main reaches contributed 0.6-0.9 g CH/m, or 30% of total sewer-generated methane despite contributing only 4.4% of total network length. The results suggest that addressing force main methane generation (such as employing chemical addition) could reduce a large fraction of sewer-generated methane while only requiring action on a small fraction of sewer reaches which is consistent with literature. Extending the results from this study to all sewage generated in Canada indicates that anthropogenic emissions from the wastewater sector are increased by 28-40% if sewer-generated methane is included in the assessment. After testing alternative distributions and model designs, it was determined that replacing the fullness and temperature distributions with constant (no distribution) average conditions yielded identical results to that of the base case assessment, suggesting that these random variables can be excluded from future modelling exercises. It was also observed that treating model coefficients as random variables resulted in a significant increase in the standard deviation of estimates, indicating that much of the uncertainty in the results is due to the uncertainty associated with the model coefficients. The results were sensitive to the temperature correction coefficient in the methane generation model and the Manning's n used in flow calculations; indicating that dedicating resources to accurately characterize these values will increase model accuracy.
量化污水输送过程中的温室气体（GHG）排放是减排的重要组成部分，因为它可以确定高排放区域，以便采取缓解措施。在这项研究中，开发了一种基于河段的甲烷生成子模型的蒙特卡罗算法，并将其应用于加拿大安大略省的一个全规模市政污水系统。该算法采用了包括污水温度、坡度和污水管段模型中描述的系数在内的 8 类随机变量。使用所采用分布的最佳估计值和算法设计选择，估计在污水系统中会产生 2.1-3.0 g CH/m（输送的总污水量）。重力污水管段贡献了 1.3-2.2 g CH/m，压力污水管段贡献了 0.6-0.9 g CH/m，占总污水产生甲烷的 30%，尽管仅占总管网长度的 4.4%。结果表明，解决压力污水管段甲烷生成问题（例如采用化学添加剂）可以减少很大一部分污水产生的甲烷，而仅需对一小部分污水管段采取行动，这与文献一致。将本研究的结果扩展到加拿大所有污水表明，如果将污水产生的甲烷纳入评估范围，则废水部门的人为排放将增加 28-40%。在测试了替代分布和模型设计之后，确定用恒定（无分布）平均条件替代饱和度和温度分布会产生与基本案例评估相同的结果，这表明这些随机变量可以从未来的建模练习中排除。还观察到，将模型系数视为随机变量会显著增加估计值的标准差，这表明结果的大部分不确定性是由于与模型系数相关的不确定性。结果对甲烷生成模型中的温度校正系数和流量计算中使用的曼宁 n 敏感；这表明专门投入资源来准确描述这些值将提高模型的准确性。
