Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.1.1. Study Area
2.1.2. Data Description
2.2. Methodology
2.2.1. Principal Component Analysis (PCA)
2.2.2. Absolute Principal Component Analysis
2.2.3. Multivariate Linear Regression
2.2.4. Positive Matrix Factorization (PMF)
3. Results
3.1. Variations in Water Quality Indicators
3.2. PCA Results
3.3. Source Identification
3.4. Source Pollutant Contributions
3.5. Comparison of the Results
4. Discussion
5. Conclusions
- (1)
- The observed water quality analysis results showed that the water quality of Pukou was the worst among the eight monitored sections, and the maximum values of EC, COD, NH3-N, TN, and TP occurred in Pukou. In addition to TN, the average values of the other water quality indicators were above Class II. The variation characteristics of daily NH3-N and TN were consistent. The Nemerow index was the largest for Pukou. Most sections had no significant annual change trends, except for a significant decrease in Xinguan.
- (2)
- The results of the correlation analysis demonstrated that EC and COD, COD and NH3-N, NH3-N and TP, and TN and TP may have similar pollution sources. The sources of pH and FC were significantly different from other indicators.
- (3)
- For APCS-MLR, three pollution sources were defined. For EC, COD, and NH3-N, the major pollution sources were urban nonpoint sources and rural domestic pollution, accounting for 50.4%, 66.2%, and 55.7%, respectively. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). The major pollution sources of pH, DO, TP, and FC were unidentified factors, which represented 73.9%, 54.1%, 46.5%, and 89.1%, respectively, of the total pollution.
- (4)
- For the PMF model, five pollution sources were defined. pH and DO were affected by meteorological factors. NH3-N and TP were influenced mainly by agricultural nonpoint source pollution, and the contribution rates were 84.2% and 55.3%, respectively. Atmospheric deposition was the main pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution, accounting for 62.9% and 38.5%, respectively.
- (5)
- Both models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The remaining 40% of the APCS-MLRs were unidentified. According to the PMF model, the remaining 42% were associated with atmospheric deposition (27%) and livestock and poultry breeding pollution (15%).
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | pH | EC | DO | COD | NH3-N | TN | TP | FC |
---|---|---|---|---|---|---|---|---|
Unit | - | μS/cm | mg/L | mg/L | mg/L | mg/L | mg/L | MPN/L |
Hengjiang | 7.9 ± 0.6 | 235 ± 101 | 9.1 ± 1.7 | 2.1 ± 0.7 | 0.18 ± 0.14 | 1.54 ± 0.58 | 0.053 ± 0.029 | 4665 ± 3585 |
Shuaishui | 7.9 ± 0.5 | 67 ± 33 | 9.1 ± 1.5 | 1.4 ± 0.5 | 0.11 ± 0.06 | 0.99 ± 0.29 | 0.035 ± 0.022 | 4087 ± 3267 |
Xinguan | 7.8 ± 0.5 | 257 ± 77 | 8.3 ± 1.7 | 2.0 ± 0.6 | 0.21 ± 0.15 | 1.81 ± 1.05 | 0.055 ± 0.029 | 4060 ± 3824 |
Huangkou | 7.6 ± 0.5 | 147 ± 71 | 9.1 ± 1.5 | 1.9 ± 0.7 | 0.25 ± 0.18 | 1.45 ± 0.50 | 0.057 ± 0.024 | 5955 ± 3374 |
Huangdun | 7.7 ± 0.5 | 152 ± 62 | 9.7 ± 2.0 | 2.1 ± 0.9 | 0.26 ± 0.23 | 1.62 ± 0.54 | 0.074 ± 0.034 | 5280 ± 3632 |
Pukou | 7.7 ± 0.5 | 345 ± 210 | 7.8 ± 1.8 | 2.6 ± 0.8 | 0.41 ± 0.28 | 2.24 ± 0.81 | 0.106 ± 0.038 | 6342 ± 3339 |
Kengkou | 7.7 ± 0.5 | 203 ± 107 | 8.2 ± 1.6 | 2.3 ± 0.8 | 0.25 ± 0.19 | 0.08 ± 0.13 | 0.031 ± 0.015 | 4287 ± 3559 |
Jiekou | 7.8 ± 0.5 | 146 ± 44 | 8.9 ± 1.5 | 1.8 ± 0.5 | 0.02 ± 0.03 | 1.59 ± 0.26 | 0.058 ± 0.009 | 2538 ± 2987 |
Class I | 6~9 | - | ≤7.5 | ≤2 | ≤0.15 | ≤0.2 | ≤0.02 | ≤200 |
Class II | - | ≤6 | ≤4 | ≤0.5 | ≤0.5 | ≤0.1 | ≤2000 | |
Class III | - | ≤5 | ≤6 | ≤1 | ≤1 | ≤0.2 | ≤10,000 | |
Class IV | - | ≤3 | ≤10 | ≤1.5 | ≤1.5 | ≤0.3 | ≤20,000 | |
Class V | - | ≤2 | ≤15 | ≤2 | ≤2 | ≤0.4 | ≤40,000 |
Component | Initial Eigenvalue | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | ||||||
---|---|---|---|---|---|---|---|---|---|
Total | Variance (%) | Cumulative (%) | Total | Variance (%) | Cumulative (%) | Total | Variance (%) | Cumulative (%) | |
1 | 2.377 | 29.719 | 29.719 | 2.377 | 29.719 | 29.719 | 2.022 | 28.276 | 25.276 |
2 | 1.429 | 17.858 | 47.576 | 1.429 | 17.858 | 47.576 | 1.477 | 18.459 | 43.736 |
3 | 1.067 | 13.334 | 60.910 | 1.067 | 13.334 | 60.910 | 1.374 | 17.175 | 60.910 |
Items | APCS-MLR | PMF | ||
---|---|---|---|---|
O/S | R2 | O/S | R2 | |
pH | 1 | 0.41 | 0.89 | 0.75 |
EC | 1 | 0.61 | 1.17 | 0.76 |
DO | 1 | 0.67 | 1.17 | 0.53 |
COD | 1 | 0.69 | 1.05 | 0.69 |
NH3-N | 1 | 0.60 | 1.16 | 0.60 |
TN | 1 | 0.65 | 1.008 | 0.82 |
TP | 0.997 | 0.67 | 0.96 | 0.75 |
FC | 1 | 0.57 | 0.998 | 0.93 |
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Wang, A.; Wang, J.; Luan, B.; Wang, S.; Yang, D.; Wei, Z. Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China. Water 2024, 16, 1356. https://doi.org/10.3390/w16101356
Wang A, Wang J, Luan B, Wang S, Yang D, Wei Z. Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China. Water. 2024; 16(10):1356. https://doi.org/10.3390/w16101356
Chicago/Turabian StyleWang, Ai, Jiangyu Wang, Benjie Luan, Siru Wang, Dawen Yang, and Zipeng Wei. 2024. "Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China" Water 16, no. 10: 1356. https://doi.org/10.3390/w16101356
APA StyleWang, A., Wang, J., Luan, B., Wang, S., Yang, D., & Wei, Z. (2024). Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China. Water, 16(10), 1356. https://doi.org/10.3390/w16101356