Pollution Characteristics and Risk Prediction of Endocrine Disruptors in Lakes of Wuhan
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Sample Collection
2.2. Sample Preparation
2.3. Analysis of EDCs by HPLC
2.4. Measurement of Water Quality Parameters
2.5. Ecological Risk Assessment
2.6. Statistical Analysis
3. Results and Discussion
3.1. Variance of EDC Content in 12 Lakes
3.2. Spatial Distribution of EDCs in 12 Lakes of Wuhan
3.3. Correlations between EDCs and Water Quality Parameters
3.4. Ecological Risk Assessment
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Area | EDCs | Concentration (ng/L) | References |
---|---|---|---|
Vietnam, Sai Gon and Dong Nai river basin | Nonylphenol | 5.9–235 | [33] |
Southwest Germany, river Rhine | 17α-EE2 | <1.5 | [34] |
South Africa, south of Johannesburg | E1 | 0.90–4.43 | [35] |
Morocco, Bouregreg River | Nonylphenol | 11–200 | [36] |
Morocco, Bouregreg river | E1 | 5–277 | [36] |
Morocco, Bouregreg river | E2 | 21–200 | [36] |
Brazil, five full-scale wastewater treatment plants | 17β-E2 | ND–776 | [37] |
Brazil, Sinos River basin | BPA | ND–517 | [38] |
China, Lhasa River Basin | E1 | ND–3.9 | [39] |
China, Lhasa River Basin | BPA | ND–433 | [39] |
China, Honghu Lake | 17α-EE2 | ND–33.28 | [40] |
China, 38 wastewater treatment plants | β-E2 | ND–62.92 | [41] |
China, Chaobai watershed | E3 | ND–23.51 | [42] |
China, Jiulong river and estuary | E3 | ND–118 | [43] |
Dan-shui River | E3 | ND–73.5 | [44] |
China, three rivers in Tianjin | 17α-EE2 | 1.55–24.40 | [45] |
China, Taihu Lake | BPA | ND–112 | [46] |
China, Taihu Lake | 4-nonylphenol | ND–324 | [46] |
China, Xiangshui River and Heng River | 17β-boldenone | ND–0.91 | [47] |
China, Yangtze River (Nanjing section) | BPA | 1.7–563 | [48] |
JLL | HJL | ZSL | ||||
---|---|---|---|---|---|---|
Terrain Category | Class Size | Proportion | Class Size | Proportion | Class Size | Proportion |
road | 0.18 | 4.45 | 7.67 | 6.17 | 3.58 | 5.67 |
farmland | 1.77 | 44.86 | 9.32 | 7.50 | 9.76 | 15.44 |
river | 0.05 | 1.23 | 1.04 | 0.84 | 2.09 | 3.30 |
lake | 0.36 | 9.14 | 38.26 | 30.80 | 7.36 | 11.65 |
built-up area | 0.12 | 3.06 | 46.23 | 37.21 | 19.96 | 31.58 |
pit-pond | 1.26 | 32.01 | 1.10 | 0.89 | 1.93 | 3.05 |
woodland | 0.19 | 4.78 | 10.63 | 8.56 | 7.92 | 12.52 |
paddy field | 0.02 | 0.40 | 3.38 | 2.72 | 4.59 | 7.26 |
garden plot | 0.00 | 0.06 | 6.57 | 5.29 | 6.03 | 9.53 |
population | 2019 | 106,087 | 29,824 |
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Zhang, Y.; Cao, J.; Ke, T.; Tao, Y.; Wu, W.; Wang, P.; Zhou, M.; Chen, L. Pollution Characteristics and Risk Prediction of Endocrine Disruptors in Lakes of Wuhan. Toxics 2022, 10, 93. https://doi.org/10.3390/toxics10020093
Zhang Y, Cao J, Ke T, Tao Y, Wu W, Wang P, Zhou M, Chen L. Pollution Characteristics and Risk Prediction of Endocrine Disruptors in Lakes of Wuhan. Toxics. 2022; 10(2):93. https://doi.org/10.3390/toxics10020093
Chicago/Turabian StyleZhang, Yurui, Jun Cao, Tan Ke, Yue Tao, Wanyin Wu, Panpan Wang, Min Zhou, and Lanzhou Chen. 2022. "Pollution Characteristics and Risk Prediction of Endocrine Disruptors in Lakes of Wuhan" Toxics 10, no. 2: 93. https://doi.org/10.3390/toxics10020093
APA StyleZhang, Y., Cao, J., Ke, T., Tao, Y., Wu, W., Wang, P., Zhou, M., & Chen, L. (2022). Pollution Characteristics and Risk Prediction of Endocrine Disruptors in Lakes of Wuhan. Toxics, 10(2), 93. https://doi.org/10.3390/toxics10020093