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This paper optimized the traditional comprehensive water quality identification index model（CWQI）and established two improved models: the improved comprehensive water quality identification index model based on game theory（ICWQIG）, which considered both subjective weight and objective weight, and the improved comprehensive water quality identification index model with phased period combination weights（ICWQIP）, which incorporated the variation of weight time. Taking Qionghai Lake as a case, the water quality monitoring data of 11 sampling sites in different hydrological periods from 2020 to 2023 were selected to evaluate the water quality of Qionghai Lake using ICWQIG and ICWQIP models, which could verify the scientific validity of the improved comprehensive water quality identification index method. The results show that, compared with the traditional CWQI model, the improved ICWQIG and ICWQIP models both take into account the factor of TP exceedance. The evaluation results could better reflect the actual water quality of the study area, and sensitively identify more pollution risk areas and severely polluted water bodies.ICWQIP used phased weights instead of uniform weights, compensating the effect of environmental factors such as such as precipitation on the weight of water quality. This could better identify the key environmental variables affecting water quality in different periods, leading to more accurate and reasonable results. At the same time, based on the improved comprehensive water quality identification index method, it was found that the water quality of Qionghai Lake in 2023 was worse than that in 2020~2022. The pollution degree of the northwestern lake area was higher than that in the eastern and southern lake area. The improved ICWQIG and ICWQIP models showed better rationality in the water quality assessment of the Qionghai Lake, providing theoretical support for the refined management of the lake’s ecological environment, and offering important reference value for water quality assessment of other similar water bodies.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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