Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Methodology
2.2.1. Potential Risk Assessment Index System for ANPSP
2.2.2. Weights of Potential Risk Assessment Indicators
2.2.3. Calculation of Potential Risk Index and Risk Classification of ANPSP
2.3. Data Sources
2.4. Verification of Risk Identification Results
2.4.1. Model Verification
2.4.2. Verification Method
3. Results
3.1. Spatial Characteristics of Potential Risk Identification Indicators for ANPSP
3.2. Identification of Potential Risks for ANPSP
3.3. Verification of Identification Using CIES
4. Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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First-Class Indicators | Weights | Secondary Indicators | Weights |
---|---|---|---|
Hydrometeorological indicators | 0.5396 | Annual precipitation | 0.2662 |
Coefficient of dissolved nonpoint source pollutants entering the river | 0.1677 | ||
Coefficient of adsorbed nonpoint source pollutants entering the river | 0.1057 | ||
Soil topography Vegetation indicators | 0.1634 | Annual vegetation coverage | 0.0267 |
Slope | 0.0485 | ||
Soil erodibility factor | 0.0882 | ||
Economic indicators | 0.2970 | Apparent balance of nitrogen in farmland | 0.1980 |
Apparent balance of phosphorus in farmland | 0.0990 |
Indicators | Indicator Classification | |||
---|---|---|---|---|
Level 1 | Level 2 | Level 3 | Level 4 | |
Assignment: 1 | Assignment: 2 | Assignment: 3 | Assignment: 4 | |
AP | ≤400 | 400–500 | 500–700 | >700 |
CR | ≤0.018 | 0.018–0.055 | 0.055–0.130 | >0.130 |
SDR | ≤0.018 | 0.018–0.13 | 0.13–0.28 | >0.28 |
AVC | >60 | 45–60 | 30–45 | ≤30 |
Slope | ≤8 | 8–15 | 15–25 | >25 |
K | ≤0.010 | 0.010–0.023 | 0.023–0.027 | >0.027 |
FANB | ≤0 | 0–15 | 15–40 | >40 |
FAPB | ≤0 | 0–5 | 5–15 | >15 |
Data Type | Data Information | Data Source | Use |
---|---|---|---|
Remote sensing data | NDVI Data of MOD13A2 (1-km resolution vegetation index 16a synthetic product) | https://ladsweb.nascom.nasa.gov/search accessed on 27 October 2023 | Used for vegetation coverage inversion |
Precipitation data | Annual precipitation data | http://data.cma.cn accessed on 27 October 2023 | Used for interpolation of annual precipitation and calculation of R factor of rainfall erosivity |
Hydrological data | Annual runoff and sediment content data | Hydrological Yearbook and Bulletin of River Sediment in China | Used for calculating the river entry coefficient of dissolved and adsorbed nonpoint source pollutants |
Elevation data | ASTER Global DEM Data/30-m Resolution | https://wist.echo.nasa.gov/api/ accessed on 27 October 2023 | Used for slope and slope length calculation |
Soil data | Soil organic carbon content and soil mechanical composition | http://globalchange.bnu.edu.cn/research/soilw accessed on 27 October 2023 | Used for calculating soil erodibility factor K |
Statistical data | county population, livestock and poultry breeding, and cultivated land area, Statistical data of the quantity of chemical fertilizer application, crop yield, and sown area. | http://tongji.cnki.net/kns55/Navi/NaviDefault.aspx accessed on 27 October 2023 | Used for estimating the apparent balance of nitrogen and phosphorus in farmland |
Level (DN) | Level 1 (1) | Level 1 (2) | Level 1 (3) | Level 1 (4) |
---|---|---|---|---|
Level 1 (1) | 0 | 1 | 2 | 3 |
Level 2 (2) | −1 | 0 | 1 | 2 |
Level 3 (3) | −2 | −1 | 0 | 1 |
Level 4 (4) | −3 | −2 | −1 | 0 |
DN Values | Deviation | Description |
---|---|---|
0 | No deviation | There is no overshoot between risk levels. |
−1 or 1 | Low deviation | Risk levels have crossed one level. |
−2 or 2 | Medium deviation | Risk levels have crossed two levels. |
−3 or 3 | Slightly higher deviation | Risk levels have crossed three levels. |
Identification Method | Risk Grade | Area (km2) | Area Proportion (%) |
---|---|---|---|
CIES | No risk | 17,215.83 | 81.14 |
Low risk | 1544.51 | 7.28 | |
Medium risk | 1408.23 | 6.64 | |
High risk | 1049.30 | 4.95 | |
DPeRS model | No risk | 16,346.36 | 77.04 |
Low risk | 370.57 | 1.75 | |
Medium risk | 589.70 | 2.78 | |
High risk | 3911.24 | 18.43 |
Deviation Value | Deviation | Area Value (km2) | Area Ratio (%) |
---|---|---|---|
0 | No deviation | 14,515.80 | 68.41 |
1 or −1 | Low deviation | 1798.51 | 8.48 |
2 or −2 | Medium deviation | 1915.11 | 9.03 |
3 or −3 | Slightly higher deviation | 2988.44 | 14.08 |
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Yang, J.; Wang, X.; Li, X.; Tian, Z.; Zou, G.; Du, L.; Guo, X. Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province. Sustainability 2023, 15, 16324. https://doi.org/10.3390/su152316324
Yang J, Wang X, Li X, Tian Z, Zou G, Du L, Guo X. Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province. Sustainability. 2023; 15(23):16324. https://doi.org/10.3390/su152316324
Chicago/Turabian StyleYang, Jinfeng, Xuelei Wang, Xinrong Li, Zhuang Tian, Guoyuan Zou, Lianfeng Du, and Xuan Guo. 2023. "Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province" Sustainability 15, no. 23: 16324. https://doi.org/10.3390/su152316324