School of Environment, Tsinghua University, Beijing, 100084, China; Environmental Simulation and Pollution Control State Key Joint Laboratory, School of Environment, Tsinghua University, Beijing 100084, China.
School of Environment, Tsinghua University, Beijing, 100084, China.
Water Res. 2017 Apr 1;112:195-207. doi: 10.1016/j.watres.2017.01.026. Epub 2017 Jan 18.
In the context of sustainable development, there has been an increasing requirement for an eco-efficiency assessment of wastewater treatment plants (WWTPs). Data envelopment analysis (DEA), a technique that is widely applied for relative efficiency assessment, is used in combination with the tolerances approach to handle WWTPs' multiple inputs and outputs as well as their uncertainty. The economic cost, energy consumption, contaminant removal, and global warming effect during the treatment processes are integrated to interpret the eco-efficiency of WWTPs. A total of 736 sample plants from across China are assessed, and large sensitivities to variations in inputs and outputs are observed for most samples, with only three WWTPs identified as being stably efficient. Size of plant, overcapacity, climate type, and influent characteristics are proven to have a significant influence on both the mean efficiency and performance sensitivity of WWTPs, while no clear relationships were found between eco-efficiency and technology under the framework of uncertainty analysis. The incorporation of uncertainty quantification and environmental impact consideration has improved the liability and applicability of the assessment.
在可持续发展的背景下，人们对污水处理厂（WWTP）的生态效率评估提出了更高的要求。数据包络分析（DEA）是一种广泛应用于相对效率评估的技术，与容差方法相结合，可用于处理 WWTP 的多输入和多输出以及它们的不确定性。在处理过程中，将经济成本、能源消耗、污染物去除和全球变暖效应进行综合，以解释 WWTP 的生态效率。对来自中国各地的 736 个样本厂进行了评估，大多数样本对投入和产出的变化都表现出较大的敏感性，只有三个 WWTP 被确定为稳定有效。研究表明，工厂规模、产能过剩、气候类型和进水特性对 WWTP 的平均效率和性能敏感性都有显著影响，而在不确定性分析框架下，生态效率与技术之间没有明显的关系。不确定性量化和环境影响考虑的纳入提高了评估的责任性和适用性。
