The groundwater quality is essential for high quality of life and social development. Thus, the importance and necessity of the accurate and rigorous requirements for contaminated groundwater assessment has increasingly attracted engineers' and researchers' attentions. In order to improve the precision and robustness of the groundwater quality evaluation of domestic waste landfills, based on the variable fuzzy set (VFS) pair and the optimized N.L. Nemerow index, we develop an optimized fuzzy set pair (OFSP) model for groundwater quality assessment. Then, we devise the OFSP model by five key elements of optimized synthesis operator "C", relative difference[Formula: see text], connection degree"u", optimized N.L. Nemerow index "P," and pollution load ratio "J", which can achieve the reasonable groundwater quality assessment model, the stable groundwater quality evaluation process, and the convincing evaluation results. Finally, a case study on groundwater quality assessment of various domestic landfills in China is conducted to explore the comprehensive impacts of domestic landfills in different regions and types on groundwater pollution from multiple perspectives, and demonstrate the effectiveness of the proposed OFSP model. The groundwater quality assessment results of various domestic landfills indicate that the pollution level of groundwater under unregulated domestic landfills in eastern and southern China is the worst. Based on the assessment results of groundwater quality, we compare the groundwater quality levels obtained by various mainstream methods. In line with precision (0.985), correlation (0.934), robustness (0.953), and rationality (0.946), our designed OFSP model has the best performance. In addition, according to the indexes of discrimination (0.217) and versatility (0.837), the designed OFSP model also has a good ability. Results of experiments well prove that the proposed OFSP model could play a good performance on groundwater quality evaluation in domestic landfills, compared with other mainstream models.

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http://dx.doi.org/10.1007/s11356-021-12521-9DOI Listing

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