A VIKOR-based approach to evaluate river contamination risks caused by wastewater treatment plant discharges.

Wastewater treatment plants (WWTPs) rarely eliminate emerging contaminants from effluents they discharged into waterways, and therefore, represent significant contaminations sources with deleterious environmental risks. This paper presents a VIKOR-based model to assess the contamination risk posed by a cluster of WWTPs. A risk index was defined via building a membership function embodying the performance degrees of WWTPs and risks levels within the framework of fuzzy set theory. The proposed approach was tested using a case study of WWTPs cluster along the Pearl River. Sensitivity analyses were carried out to investigate the robustness of the model. The results confirmed the ability of the proposed approach to reveal the risk level of a given treatment point. Further, the comparison with a TOPSIS scheme as well as sensitivity analysis results substantiate the consistency, accuracy, and reliability of the proposed approach. It is therefore bounds to improve the decentralized management of WWTPs-induced river contamination.

Method for lake eutrophication levels evaluation: TOPSIS-MCS.

Monte Carlo simulation (MCS) is applied in the engineering with great fuzziness and uncertainty. Technique for order preference by similarity to an ideal solution (TOPSIS) method is used to deal with multi-criteria decision-making issue. Membership function is used to determine the membership degree of evaluated index. This paper presents the method for lake eutrophication level evaluation. The developed approach merges MCS method, TOPSIS method and membership function. The evaluated results are consistent with real eutrophication level in Lake Erhai, China. Global sensitivity analysis (GSA) is conducted. Results show that potassium permanganate index (CODMn) displays the highest negative correlation with the evaluated results and Secchi disc (SD) performs the highest positive correlation under different errors in measured data. The novelty of this work are: (1) the application of TOPSIS considers Surface water environmental quality standards and measured data. Besides, the Monte Carlo simulation method is applied to generate a normal distributed dataset to overcome the errors caused by human and equipment in data collection. The approach is utilized in the article, titled "Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels" (Lin et al., 2020) [1].•Developed approach merges TOPSIS and MCS method.•It can increase the reliability of evaluated result.

Assessment and management of lake eutrophication: A case study in Lake Erhai, China.

Some wastewater sources, such as agricultural waste and runoff, and industrial sewage, can degrade water quality. This study summarises the sources and corresponding mechanisms that trigger eutrophication in lakes. Additionally, the trophic status index and water quality index (WQI) which are effective tools for evaluating the degree of eutrophication of lakes, have been discussed. This study also explores the main nutrients (nitrogen and phosphorus) driving transformations in the water body and sediment. Lake Erhai was used as a case study, and it was found to be in a mesotrophic state, with N and P co-limitation before 2006, and only P limitation since 2006. Finally, effective measures to maintain sustainable development in the watershed are proposed, along with a framework for an early warning system adopting the latest technologies (geographic information systems (GIS), remote sensing (RS)) for preventing eutrophication.

Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels.

This study presents an approach for eutrophication evaluation based on the technique for order preference by similarity to an ideal solution (TOPSIS) method and Monte Carlo simulation (MCS). The MCS is employed to produce a normally distributed dataset based on the observed data while the TOPSIS method and membership function are used to evaluate the level of eutrophication. Herein, a eutrophication problem in Lake Erhai is evaluated to check the performance of the proposed approach. The evaluation results were consistent with the real situation when the coefficient P in the membership function is equal to 1. Moreover, the developed approach is able to (i) deal with evaluation items with inherent fuzziness and uncertainties, (ii) improve the reliability of evaluation results via MCS, and (iii) raise the tolerance to errors in measured data. A global sensitivity analysis indicated that the potassium permanganate index (CODMn) and Secchi disc (SD) are the most sensitive factors in the developed approach. Finally, a range for the coefficient P value in the membership function was recommended.