A 2O 工艺城市再生水厂减污降碳协同效益评估与绿色低碳运行策略

王敬烨 ,  张紫檀 ,  李翰文 ,  刘存辉 ,  王振北 ,  贾韫翰 ,  齐飞 ,  李群

北京师范大学学报(自然科学版) ›› 2026, Vol. 62 ›› Issue (3) : 389 -401.

PDF (1998KB)
北京师范大学学报(自然科学版) ›› 2026, Vol. 62 ›› Issue (3) : 389 -401. DOI: 10.12202/j.0476-0301.2025207
专栏:绿色流域(主持人:许继军 王红瑞)

A 2O 工艺城市再生水厂减污降碳协同效益评估与绿色低碳运行策略

作者信息 +

Synergistic benefit assessment of reductions in pollution and in CO 2 emission and green low-carbon operation strategies for urban recycled water plant with A 2O process

Author information +
文章历史 +
PDF (2045K)

摘要

以 A2O 工艺再生水厂为研究对象,构建减污降碳协同评价体系,开展情景分析,深入揭示典型城市再生水厂减污降碳协同效益及碳减排潜力,提出绿色低碳运行策略. 研究结果表明:该再生水厂在减污维度中 COD、BOD5、SS、NH3-N、TP 污染物削减率均>95%,出水满足 DB11/890-2012《城镇污水处理厂水污染物排放标准》,减污指标评分达优秀;在降碳维度,基准情景下平均降碳得分为 77.2 分,通过实施污泥资源化、太阳能光伏、水源热泵等技术及综合优化,平均得分提升至 92.0 分,增幅达 19.2%;在协同效益方面,基准情景减污降碳综合得分为 87.7 分,技术集成后综合得分达 95.1 分的优秀水平;在综合优化情景下,再生水厂绿色低碳运行综合得分较基准情景提高 8.4%,年际碳排放强度降至 0.297 kg·m-3,COD 去除率每提升 1%,污水体积碳排放强度下降 0.8%. 研究表明,资源回收与清洁能源利用技术是提升再生水厂减污降碳协同效益的核心路径,为再生水厂融入“双碳”管理提供高效技术支撑.

Abstract

To explore the synergistic benefits of reductions both in pollution and in carbon emission, and carbon emission reduction potential of urban recycled water plants, a recycled water plant with A 2O process at a capacity of 600 000 m 3·d -1 was studied. A synergistic evaluation system for reductions in pollution and in carbon emission was established. A baseline scenario (BAU, business as usual), technology optimization scenarios (hypothesis 1, hypothesis 2), was set up. Five sub-scenarios ( S1- S5) of reduction in carbon emission, and a comprehensive scenario ( S6) were all studied. Quantitative analysis was done combining monitoring data and simulation models. For reduction in pollution, the removal rates of COD, BOD 5, SS, NH 3-N, and TP all reached over 95% (with TN removal rate ranging from 64% to 78%), with the effluent meeting Discharge Standard of Water Pollutants for Municipal Wastewater Treatment Plants (DB11/ 890-2012). The pollution reduction index score reached an excellent level. For reduction in carbon emissions, average carbon emission reduction score under the baseline scenario was 77.2 (Grade C). The scenario implementing technologies such as sludge resource utilization, solar photovoltaic, and water-source heat pump increased the average carbon emission reduction score to 92.0 (with a growth rate of 19.2%). Comprehensive emission reduction scenario S6 raised the carbon emission reduction index score from baseline 77.2 to 92.0, with an increase of 19.2%. For synergistic benefits, comprehensive score of pollution reduction and carbon emission reduction under the baseline scenario was 87.7 (Grade B), and after technology integration, the comprehensive score reached 95.1 (excellent level). In scenario S6, the comprehensive score of green low-carbon operation of the recycled water plant was 8.4% higher than that under BAU, the inter-annual carbon emission intensity decreased to 0.297 kg·m -3, and the carbon emission intensity per unit of wastewater decreased by 0.8%. This study indicates that resource recoveries and clean energy utilization technologies are core to improving the synergistic benefits of pollution reduction and carbon emission reduction in recycled water plants. This provides efficient technical support for recycled water plants to integrate into “dual-carbon” management.

关键词

A 2O 工艺 / 再生水厂 / 绿色低碳运行 / 减污降碳协同 / 碳减排策略 / 情景分析

Key words

A 2O process / recycled water plant / green low-carbon operation / synergy of pollution reduction and carbon emission reduction / carbon emission reduction strategy / scenario analysis

引用本文

引用格式 ▾
王敬烨,张紫檀,李翰文,刘存辉,王振北,贾韫翰,齐飞,李群. A 2O 工艺城市再生水厂减污降碳协同效益评估与绿色低碳运行策略[J]. 北京师范大学学报(自然科学版), 2026, 62(3): 389-401 DOI:10.12202/j.0476-0301.2025207

登录浏览全文

4963

注册一个新账户 忘记密码

参考文献

[1]

段诗昊, 贾志昇, 张明慧, . 中国绿色流域政策制度管理体系研究[J]. 北京师范大学学报(自然科学版), 2025, 61(5): 680.

[2]

中华人民共和国住房和城乡建设部 . 中国城乡建设统计年鉴2022[M]. 北京: 中国统计出版社, 2022.

[3]

Ramírez-Melgarejo M, Reyes-Figueroa A D, Gassó-Domingo S, et al. Analysis of empirical methods for the quantification of N2O emissions in wastewater treatment plants: comparison of emission results obtained from the IPCC Tier 1 methodology and the methodologies that integrate operational data [J]. Science of the Total Environment, 2020, 747: 141288.

[4]

Maktabifard M, Al-Hazmi H E, Szulc P, et al. Net-zero carbon condition in wastewater treatment plants: a systematic review of mitigation strategies and challenges[J]. Renewable and Sustainable Energy Reviews, 2023, 185: 113638.

[5]

齐浩然, 尹明山, 唐兆国, . 城镇污水处理厂节能减碳实现路径与技术探讨[J]. 净水技术, 2023, 42(10): 16.

[6]

王红瑞, 刘艺欣, 张力, . “双碳”目标下流域生态城市绿色发展与高质量发展实施路径[J]. 水资源保护, 2024, 40(1): 16.

[7]

Zhang Z T, Qi F, Liu Y, et al. Comprehensive assessment, intelligent prediction, and precise mitigation strategies for greenhouse gas emissions in full-scale wastewater treatment plants[J]. Environmental Research, 2025, 270: 121052.

[8]

胡香, 陈孔明, 李涛 . 城镇污水处理厂低碳运行评价指标体系的构建及应用[J]. 工业用水与废水, 2023, 54(2): 39.

[9]

中华环保联合会. 废水处理减污降碳协同评估指南: T/ACEF 144-2024[S]. 北京: 中华环保联合会, 2024.

[10]

Angelidaki I, Treu L, Tsapekos P, et al. Biogas upgrading and utilization: current status and perspectives[J]. Biotechnology Advances, 2018, 36(2): 452.

[11]

乔翅嵩, 顾登海, 卢广亮, . 城镇生活污水处理厂污泥资源化利用研究进展[J]. 工业水处理, 2025, 45(7): 1.

[12]

李翔, 朱灿耀 . 新能源在污水处理厂中的应用[J]. 给水排水, 2023, 59(增刊2): 472.

[13]

Yang M J, Peng M, Wu D, et al. Greenhouse gas emissions from wastewater treatment plants in China: historical emissions and future mitigation potentials[J]. Resources, Conservation and Recycling, 2023, 190: 106794.

[14]

Wei L L, Zhu F Y, Li Q Y, et al. Development, current state and future trends of sludge management in China: based on exploratory data and CO2-equivalent emissions analysis [J]. Environment International, 2020, 144: 106093.

[15]

Yang X Z, Wei J Y, Ye G J, et al. The correlations among wastewater internal energy, energy consumption and energy recovery/production potentials in wastewater treatment plant: an assessment of the energy balance[J]. Science of the Total Environment, 2020, 714: 136655.

[16]

Liu R X, Huang R Y, Shen Z H, et al. Optimizing the recovery pathway of a net-zero energy wastewater treatment model by balancing energy recovery and eco-efficiency[J]. Applied Energy, 2021, 298: 117157.

[17]

Duan H R, Zhao Y F, Koch K, et al. Insights into nitrous oxide mitigation strategies in wastewater treatment and challenges for wider implementation[J]. Environmental Science & Technology, 2021, 55(11): 7208.

[18]

Lee Y Y, Choi H, Cho K S . Effects of carbon source, C/N ratio, nitrate, temperature, and pH on N2O emission and functional denitrifying genes during heterotrophic denitrification [J]. Journal of Environmental Science and Health, Part A: Toxic/Hazardous Substances and Environmental Engineering, 2019, 54(1): 16.

[19]

北京市质量技术监督局 . 城镇污水处理厂水污染物排放标准: DB11/890-2012[S]. 北京: 中国环境科学出版社, 2012.

[20]

何秋杭, 陈奕彤, 乔金岩, . 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[J]. 环境工程学报, 2023, 17(9): 2827.

[21]

梁言, 杨景然, 陈阳, . 福建某污水厂碳排放核算及低碳运行分析[J]. 净水技术, 2025, 44(4): 108.

[22]

刘善军, 马雪研, 刘雪洁, . 济南市某污水处理厂碳排放评估与分析[J]. 环境污染与防治, 2023, 45(12): 1732.

[23]

张涵, 王吉苹, 孔子轩, . 厦门市污水处理厂碳排放核算及碳减排途径探讨[J]. 厦门理工学院学报, 2025, 33(3): 81.

[24]

李书钺, 邓艾欣, 马念, . 重庆某污水处理厂碳排放核算及减碳路径探究[J]. 净水技术, 2025, 44(6): 116.

[25]

郑晓英, 胡天星, 邓雄成, . 典型A2O工艺城镇污水处理厂碳排放核算、影响因素及碳减排路径研究 [J]. 环境工程, 2026, 44(3): 177.

[26]

Li K L, Duan H R, Liu L F, et al. An integrated first principal and deep learning approach for modeling nitrous oxide emissions from wastewater treatment plants[J]. Environmental Science & Technology, 2022, 56(4): 2816.

[27]

何道清, 何涛, 邓勇 . 太阳能光伏发电系统原理与应用技术[M]. 2版. 北京: 化学工业出版社, 2023.

[28]

Nourani V, Elkiran G, Abba S I . Wastewater treatment plant performance analysis using artificial intelligence: an ensemble approach[J]. Water Science and Technology, 2018, 78(10): 2064.

[29]

吴昌永 . A2O工艺脱氮除磷及其优化控制的研究 [D]. 哈尔滨: 哈尔滨工业大学, 2010.

[30]

刘耕源, 郭丽思, 陈钰, . 城市碳中和措施的边际减排成本分析: 以北京市为例[J]. 北京师范大学学报(自然科学版), 2023, 59(2): 249.

[31]

中国城镇供水排水协会组织 . 城镇水务系统碳核算与减排路径技术指南[M]. 北京: 中国建筑工业出版社, 2022.

[32]

Tong Y D, Liao X W, He Y Y, et al. Mitigating greenhouse gas emissions from municipal wastewater treatment in China[J]. Environmental Science and Ecotechnology, 2024, 20: 100341.

[33]

Asadi M, Mcphedran K . Estimation of greenhouse gas and odour emissions from a cold region municipal biological nutrient removal wastewater treatment plant[J]. Journal of Environmental Management, 2021, 281: 111864.

[34]

洪思扬, 王红瑞, 程涛 . 广东省水与能源利用效率与部门使用特征分析[J]. 北京师范大学学报(自然科学版), 2022, 58(1): 107.

基金资助

国家重点研发计划资助项目(2023YFC3205600)

AI Summary AI Mindmap
PDF (1998KB)

0

访问

0

被引

详细

导航
相关文章

AI思维导图

/