基于月排放数据的北京3座区级污水处理厂年碳排放特征

以3座北京市某区代表性区级污水处理厂为研究对象，综合《城镇水务系统碳核算与减排路径技术指南》、《污水处理厂低碳运行评价技术规范》和《IPCC 2006 年国家温室气体清单指南 2019 修订版》碳排放核算方法，采用定量统计、相关性分析及敏感性分析等手段对污水处理厂正常运行状态下碳排放核算和影响因素进行特征分析和规律识别，并提出针对性减排路径建议。结果表明：C厂C-TECH工艺的吨水碳排放强度为三厂最低1.35 kg CO₂eq·m⁻³，处于我国较低水平，接近“双碳”目标要求；而B厂改良SBR工艺的单位污染去除碳排放强度较小，其COD碳排放强度为3.16 t CO₂eq·t⁻¹，TN碳排放强度为36.44 t CO₂eq)·t⁻¹，TP碳排放强度为176.69 t CO₂eq·t⁻¹，处于我国中上水平，从效能角度更接近“双碳”目标要求。不同工艺产生的温室气体、进水水质波动及用电消耗导致三厂碳排放强度上存在差异，但各工艺的主排碳因子均为间接碳排放 (A厂、B厂和C厂中分别占73%、59%和59%) ，间接碳排放的主贡献因子为电力消耗 (A厂、B厂和C厂中分别占33%、40%和40%) 。相关性分析发现，各水厂碳排放强度还与N₂O造成的直接碳排放有较大相关性，3座水厂年排放N₂O 2.48×10⁴ t CO₂eq，A厂、B厂和C厂中相关性系数值分别达到0.68、0.87、0.66。敏感性分析表明，整体碳排放强度对电力消耗、N₂O排放和药耗的变化更为敏感，药耗中葡萄糖溶液和多效高分子除磷剂对碳排放强度影响较大。以上结果表明，北京区级污水处理厂应从优化曝气系统、水泵效能等节电措施和调整水厂加药模式等节药措施上实现减排。

Characterization of annual carbon emissions of three district-level wastewater treatment plants in Beijing based on monthly emission data

In order to support the planning and implementation of emission reduction pathways for wastewater treatment plants in the northern region of China, this paper taked three representative district-level wastewater treatment plants in a district of Beijing as the research object, synthesized the Carbon Accounting and Emission Reduction Pathway Technical Guidelines for Urban Water Systems, the Technical Specification for Evaluation of Low-Carbon Operation of Wastewater Treatment Plants, and the carbon emission accounting method of the IPCC 2006 Guidelines for National Greenhouse Gas Inventories, Revised 2019, and adopted the quantitative statistics, correlation analysis, and sensitivity analysis, quantitative statistics, correlation analysis and sensitivity analysis to characterize and identify the patterns of carbon emission accounting and influencing factors under the normal operating state of wastewater treatment plants, and to propose targeted emission reduction paths. The results showed that the ton of water carbon emission intensity of C-TECH process of C plant was the lowest at 1.35 kg CO2eq·m-3 for the three plants, which was at a lower level in China and close to the target requirement of "double-carbon". The carbon emission intensity of the unit pollution removal of B plant's improved SBR process was small, and the COD carbon emission intensity of was 3.16 t CO2eq·t-1, TN carbon emission intensity was 36.44 3.16 t CO2eq·t-1, TP carbon emission intensity was 176.69 3.16 t CO2eq·t-1, which was in the middle and upper level in our country, while it was closer to the target requirement of "double carbon" from the viewpoint of efficiency. The greenhouse gases generated by different processes, fluctuations in water quality and electricity consumption led to differences in the carbon emission intensity of the three plants. Despite the differences in carbon emission intensity of different processes, the main carbon emission factor of each process was indirect carbon emission (73%, 59% and 59% in Plant A, Plant B and Plant C, respectively), and the main contributor of indirect carbon emission was electricity consumption (33%, 40% and 40% in Plant A, Plant B and Plant C, respectively). The correlation analysis found that the carbon emission intensity of each water plant also had a large correlation with the direct carbon emission caused by N2O, and the three water plants emitted 24.8 million t CO2-eq of N2O annually, with correlation coefficients of 0.68, 0.87, and 0.66 in Plants A, B, and C, respectively. The sensitivity analysis showed that the overall carbon emission intensity was more sensitive to the changes in electric power consumption, N2O emission, and drug consumption, and the overall carbon emission intensity was more sensitive to the changes in electric power consumption, N2O emission, and drug consumption. Glucose solution and multi-effect polymer phosphorus remover in drug consumption had a greater impact on carbon emission intensity. The above results indicated that the emission reduction should be achieved by optimizing the efficiency of aeration system and pump and adjusting the dosing mode of water plant for the district-level wastewater treatment plants in Beijing.