解读和缓解城市排水系统中的动态温室气体排放,使用知识注入的图形神经网络
Wan-Xin Yin1, Ke-Hua Chen2, Jia-Qiang Lv3
1College of the Environment, Liaoning University, Shenyang 110036, China.
Environmental science & technology
|February 12, 2025
概括
本研究引入了一个生态知识注入的图形神经网络 (EcoGNN-GHG) 模型,以准确预测城市排水系统 (UDGS) 的温室气体 (GHG) 排放. 该模型通过准关键的生物途径,成功减少了甲和氧化排放.
科学领域:
- 环境科学 环境科学
- 废水工程 废水工程
- 计算化学计算化学
背景情况:
- 由于复杂的生物生产途径,城市排水系统 (UDGS) 的动态温室气体 (GHG) 排放难以预测和减轻.
- 现有的模型缺乏在波动的环境条件下量化这些途径的具体贡献的准确性.
研究的目的:
- 通过整合生物通路知识,开发一种高度预测的模型来量化UDGS中的甲 (CH4) 和氧化 (N2O) 产量.
- 确定影响温室气体生产的关键生物途径,并指导有针对性的减缓策略.
主要方法:
- 将生物生产路径注入图形神经网络 (GNN) 架构中,创建了生态知识注入的GNN (EcoGNN-GHG) 模型.
- 应用了EcoGNN-GHG模型来评估下水道和废水处理厂 (WWTP) 的CH4和N2O产量.
- 进行了模型解释性分析,以了解路径的贡献,并指导溶解氧 (DO) 控制策略.
主要成果:
- 环保温室气体模型实现了高预测准确度 (下水道中CH4的R2 = 0.96,水下水道中N2O的R2 = 0.82).
- 鉴定了厌氧水解酸化 (对于CH4) 和化-脱化 (对于N2O) 途径的动态贡献.
- 一个有针对性的DO控制策略减少了CH4产量35.50%和N2O产量29.94%.
结论:
- 环保GNN-GHG模型提供了一种可靠且准确的方法,用于预测UDGS中的温室气体排放.
- 量化生物生产途径的贡献使得开发有效的排放控制策略成为可能.
- 有针对性的溶化氧控制有效地减轻了下水道和水电道的温室气体产量.
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