Macro-environment oscillation notably up risk of water quality degradation: A case study in Shanxi Reservoir, China
Water quality deterioration in reservoirs threats human drinking water safety. It can be influenced by the macro-processes of meteorology, hydrology, and pollution. However, the mechanism of macro-environment affect water quality risk still is unclear. Water quality is a complex system which should...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24015231 |
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| Summary: | Water quality deterioration in reservoirs threats human drinking water safety. It can be influenced by the macro-processes of meteorology, hydrology, and pollution. However, the mechanism of macro-environment affect water quality risk still is unclear. Water quality is a complex system which should be able to adapt to small fluctuation of macro-environment, but turn into deterioration under significant macro-environment oscillation. Shanxi Reservoir in Zhejiang, China, a large reservoir with a total storage capacity of 1.82 billion m3 was used as the study area to test this hypothesis. Monthly macro-environment data from 2014 to 2022 (sample size = 108), including 10 factors of meteorology, hydrology, and pollution, was used to create a Macro-Environment Index (MEI) by Principal Component Analysis. And the water quality data (sample size = 2914) was used to develop a Water Quality Index (WQI) based on Mahalanobis Distance. Bayes methods were used to analyze the relationship between monthly MEI changes (|ΔMEI|) and WQI variations (ΔWQI). Results showed that macro-environment change weakly (|ΔMEI|<0.4) with the overall probability of 41.7 %, strongly (|ΔMEI|≥0.4) with 58.3 %, and acutely (|ΔMEI|≥1) with 26.7 %. While water quality has a 51.7 % chance of water quality deterioration (ΔWQI > 0), significantly increasing (P < 0.01) to 63.6 % when |ΔMEI|≥0.4, and significantly reducing (P < 0.01) to 36.7 % when |ΔMEI|<0.4. |ΔMEI|≥1 leads to a significant jump (P < 0.01) in rapid water quality deterioration (ΔWQI ≥ 0.15) risk from 31.7 % to 62.5 %. Based on Bayesian Inference, we successfully used |ΔMEI| as the independent variable for predicting the probability of water quality deterioration. These findings underscore the importance of monitoring macro-environment changes to assess reservoir water quality risks. |
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| ISSN: | 1470-160X |