Quantitative Analysis of the Driving Factors of Water Quality Variations in the Minjiang River in Southwestern China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.2.1. Pollutant Loads
2.2.2. Water Quality Data
2.2.3. Meteorological Data
2.3. Wavelet Analysis
2.4. Machine Learning Models
2.5. Shapley Analysis
2.6. Statistical Index
3. Results and Discussion
3.1. Decomposition Results of Water Quality Indices through the Wavelet Analysis
3.2. The Quantified Impacts of Driving Factors on Water Quality Indices of the MRB
3.3. Seasonal Patterns of Quantified Impacts of Driving Factors on Water Quality
4. Conclusions
- The long-term trend signals of both the CODMn and NH3-N concentrations showed an increasing trend followed by a decreasing trend, in which the CODMn concentration increased and decreased by roughly the same magnitude, while the NH3-N concentration decreased more. This indicated that within the study period, the deterioration trend of water quality in the Minjiang River had been effectively controlled and significantly improved. The periodic signals of the CODMn concentrations exhibited a greater amplitude of fluctuation compared to the NH3-N concentrations, implying that the meteorological periodic drivers may have more pronounced influences on the CODMn concentrations.
- Four machine learning algorithms were used to construct relationships between the driving factors and water quality indices of the MRB. The ensembles of trees approach demonstrated the best performances for both CODMn and NH3-N concentrations (R2 = 0.3648–0.9998).
- For the monitored raw data, the meteorological factors were the dominant factors affecting the variations in CODMn concentrations at the outlet of the MRB (accounting for 64.13%), while the anthropogenic factors were the major factors affecting the NH3-N concentrations (accounting for 58.88%). In terms of the long-term trend signals, anthropogenic factors were the uncontroversial controlling factors, with quantified impacts of 98.38% on the CODMn concentrations and 98.18% on the NH3-N concentrations. For periodic signals, the meteorological factors had higher impact values, with a 68.89% impact on the CODMn concentrations and a 63.94% impact on the NH3-N concentrations.
- The quantified impacts of the driving factors on the water quality of the Minjiang River had seasonal patterns. The meteorological factors demonstrated higher impacts during the flood season with high temperatures (July to September) and the dry season with low temperatures (December to February) compared to other seasons, indicating that the high temperature, low temperature, and precipitation events can significantly alter the biogeochemical processes in the MRB, further affecting the water quality.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Water Quality Indices | Methods of Measurements | Unit |
---|---|---|
CODMn | Potassium permanganate oxidation-ORP potentiometric titration method | mg/L |
NH3-N | Salicylic acid spectrophotometry | mg/L |
Station Name | Latitude (Degree) | Longitude (Degree) | Elevation (m) |
---|---|---|---|
Seda | 32.28 | 100.33 | 3896 |
Maerkang | 31.90 | 102.23 | 2666 |
Songpan | 32.67 | 103.60 | 2883 |
Wenjiang | 30.75 | 103.87 | 541.0 |
Yaan | 29.98 | 103.00 | 629.0 |
Kangding | 30.05 | 101.97 | 2617 |
Emeishan | 29.52 | 103.33 | 3049 |
Yibin | 28.80 | 104.60 | 342.0 |
Year | Agricultural Emissions | Industrial Emissions | Urban Living Emissions | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Chemical Fertilizers | Nitrogenous Fertilizers | Phosphate Fertilizers | Compound Fertilizers | Wastewater | COD | NH3-N | Wastewater | COD | NH3-N | |
2016 | 249.0 | 121.9 | 48.90 | 60.20 | 5.079 × 104 | 5.000 | 0.3200 | 3.017 × 105 | 62.20 | 7.700 |
2017 | 242.0 | 117.0 | 47.10 | 60.20 | 4.662 × 104 | 4.200 | 0.1700 | 3.217 × 105 | 66.40 | 7.500 |
2018 | 235.2 | 112.1 | 45.40 | 60.30 | 4.346 × 104 | 4.100 | 0.1900 | 3.417 × 105 | 70.50 | 7.400 |
2019 | 222.8 | 103.5 | 41.40 | 62.10 | 4.662 × 104 | 3.900 | 0.1700 | 3.617 × 105 | 74.70 | 7.200 |
2020 | 210.8 | 90.70 | 38.00 | 67.00 | 4.374 × 104 | 2.600 | 0.1300 | 3.817 × 105 | 78.80 | 7.100 |
Model | CODMn | NH3-N | ||||
---|---|---|---|---|---|---|
Monitored Data | Trend Data | Periodic Data | Monitored Data | Trend Data | Periodic Data | |
Ensembles of trees | 0.6378 | 0.9997 | 0.6685 | 0.3648 | 0.9998 | 0.3659 |
Regression trees | 0.5386 | 0.9995 | 0.5157 | 0.2052 | 0.9997 | 0.2204 |
Neural networks | 0.5525 | 0.9979 | 0.5554 | 0.2272 | 0.9946 | 0.1226 |
Support vector machines | 0.6436 | 0.9915 | 0.6494 | 0.2454 | 0.9950 | 0.3097 |
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Liu, C.; Hu, Y.; Sun, F.; Ma, L.; Wang, W.; Luo, B.; Wang, Y.; Zhang, H. Quantitative Analysis of the Driving Factors of Water Quality Variations in the Minjiang River in Southwestern China. Water 2023, 15, 3299. https://doi.org/10.3390/w15183299
Liu C, Hu Y, Sun F, Ma L, Wang W, Luo B, Wang Y, Zhang H. Quantitative Analysis of the Driving Factors of Water Quality Variations in the Minjiang River in Southwestern China. Water. 2023; 15(18):3299. https://doi.org/10.3390/w15183299
Chicago/Turabian StyleLiu, Chuankun, Yue Hu, Fuhong Sun, Liya Ma, Wei Wang, Bin Luo, Yang Wang, and Hongming Zhang. 2023. "Quantitative Analysis of the Driving Factors of Water Quality Variations in the Minjiang River in Southwestern China" Water 15, no. 18: 3299. https://doi.org/10.3390/w15183299
APA StyleLiu, C., Hu, Y., Sun, F., Ma, L., Wang, W., Luo, B., Wang, Y., & Zhang, H. (2023). Quantitative Analysis of the Driving Factors of Water Quality Variations in the Minjiang River in Southwestern China. Water, 15(18), 3299. https://doi.org/10.3390/w15183299