College of Civil Engineering and Architecture, Zhejiang University, China.
College of Civil Engineering and Architecture, Anzhong Building, Zijingang Campus, Zhejiang University, Zhejiang University, A501, , 866 Yuhangtang Rd, Hangzhou 310058, China.
Water Res. 2021 Sep 1;202:117419. doi: 10.1016/j.watres.2021.117419. Epub 2021 Jul 8.
Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.
城市污水管网（SNs）由于人口增长、城市化和气候变化等诸多挑战，面临着越来越多的水质问题。解决这些问题的一个有希望的方法是开发和使用水质模型。近年来，为了便于 SN 管理，已经开发了许多此类模型。鉴于不同水质模型的大量出现及其表现出的潜力，及时评估该领域的最新技术，确定潜在挑战并提出未来的研究方向是适时的。在这篇综述中，根据 2010 年至 2019 年期间发表的 110 篇论文，对模型类型、建模质量参数、建模目的、数据可用性、案例研究类型和模型性能评估进行了批判性分析和讨论。综述确定，经验和动力学模型的应用在解决水质问题方面主导着数据驱动模型的应用。大多数模型都是为使用实验或现场采样数据进行预测和过程理解而开发的。虽然许多模型已经应用于实际问题，但相应的预测精度总体上是中等的，或者在某些情况下是低的，尤其是在处理较大的 SN 时。综述还确定了与 SN 水质建模相关的最常见问题，并在此基础上提出了几个未来的研究方向。这些包括为开发不同的 SN 模型确定适当的数据分辨率，开发混合 SN 模型的必要性和机会，以及提高 SN 模型的可转移性。
