Université de Paris, Institut de physique du globe de Paris, CNRS, F-75005 Paris, France; Water Treatment and Management Consultancy, B.V., 2289 ED Rijswijk, the Netherlands.
IHE Delft, Institute for Water Education, 2611 AX Delft, the Netherlands; Water Treatment and Management Consultancy, B.V., 2289 ED Rijswijk, the Netherlands.
Sci Total Environ. 2021 Mar 15;760:143334. doi: 10.1016/j.scitotenv.2020.143334. Epub 2020 Nov 4.
There is an increasing focus on research related to the removal of emerging organic contaminants (EOCs) from wastewater by using constructed wetlands (CWs). However, research is lacking on translating the available scientific evidence into decision support tools. In this paper, a novel decision tree framework is developed and demonstrated. The proposed framework consists of five steps: (1) generate a list of EOCs by the analysis of the wastewater; (2) select the best type of CW for each of the selected EOCs; (3) select a final type of CW for the removal of the selected EOCs; (4) identify detailed design and operational features of the proposed CW such as, depth, area, plants, support matrix, hydraulic loading rate, organic loading rate, and hydraulic retention time; and (5) assess the expected removal efficiency of EOCs in the selected CW. A novel decision support tool, named as DTFT-CW, was developed to generate data and information for the application of the proposed decision tree framework. DTFT-CW (given as a supplementary material) was developed using Microsoft Excel 2016 to support decisions on the design, operation, and performance of CWs for the removal of 59 EOCs (33 pharmaceuticals-PhCs, 15 personal care products-PCPs, and 11 steroidal hormones-SHs). The paper demonstrates the usefulness of the developed decision-making tools by considering 19 EOCs (13 PhCs, one PCPs, and five SHs) as an example, which pose high environmental risk and are on the European Union watch list (six of the 19 EOCs). An integrated design of HCW (combining vertical flow CW, horizontal flow CW-HFCW, and free water surface CW) is recommended for the treatment of multiple EOCs instead of a single type of CW such as HFCW that is most widely used in practice. The proposed tools could be useful for decision makers such as policy makers, design engineers, and researchers.
人们越来越关注利用人工湿地（CWs）从废水中去除新兴有机污染物（EOCs）的相关研究。然而，将现有科学证据转化为决策支持工具的研究还很缺乏。在本文中，开发并演示了一种新的决策树框架。该框架由五个步骤组成：（1）通过对废水进行分析，生成 EOCs 清单；（2）为每个选定的 EOC 选择最佳类型的 CW；（3）为去除选定的 EOC 选择最终类型的 CW；（4）确定拟议 CW 的详细设计和操作特性，例如深度、面积、植物、支撑基质、水力负荷率、有机负荷率和水力停留时间；（5）评估选定 CW 中 EOCs 的预期去除效率。开发了一种新的决策支持工具，命名为 DTFT-CW，用于生成数据和信息，以应用提出的决策树框架。DTFT-CW（作为补充材料提供）是使用 Microsoft Excel 2016 开发的，用于支持有关设计、操作和 CW 性能的决策，以去除 59 种 EOCs（33 种药物-PhCs、15 种个人护理产品-PCPs 和 11 种甾体激素-SHs）。通过考虑 19 种 EOCs（13 种 PhCs、1 种 PCPs 和 5 种 SHs）作为示例，展示了开发的决策工具的有用性，这些 EOCs 具有很高的环境风险，并且在欧盟的观察名单上（19 种 EOCs 中有 6 种）。建议采用 HCW（垂直流 CW、水平流 CW-HFCW 和自由水面 CW 的组合）的综合设计来处理多种 EOCs，而不是实际中最广泛使用的单一类型的 CW，如 HFCW。该工具对于决策者（如政策制定者、设计工程师和研究人员）可能很有用。
