在污水处理厂(WWTP)中减少氧化亚氮(N2O)的排放已成为适应气候变化的不可逆转的趋势。监测N2O排放在理解和减缓N2O排放方面起着至关重要的作用。本文对N2O的直接和间接监测方法进行了综合评述。技术,优势,局限性,并讨论了各种方法的适用场景。我们得出的结论是,浮室技术适用于捕获和解释实时N2O排放的时空变化,由于其长期的原位监测能力和较高的数据采集频率。监测持续时间,location,应强调频率,以保证数据的准确性和可比性。当需要国家规模或区域规模的污水处理厂的历史N2O排放帐户不明确时,默认计算排放因子(EF)是有效的。使用特定于过程的EF有利于促进主要集中于低排放过程升级的缓解途径。机器学习模型在N2O排放的预测中表现出示例性性能。将机械模型与机器学习模型集成可以提高其解释能力并提高其预测精度。养分去除和N2O缓解策略的协同作用的实施需要校准和验证多路径机制模型,由长期持续的直接监测活动支持。
Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive
review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.