Spatial Distribution of Pollutants and Risk Assessment of Heavy Metals in Farmland Groundwater around a Traditional Industrial Park—A Case Study of Shifang City, Southwestern China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Groundwater Sample Collection and Analysis
2.3. Methods of Risk Assessment
2.4. Statistics and Analyses
3. Results and Discussion
3.1. Descriptive Statistics of Contaminants
CODMn | NH3-N | TP | TH | SO42− | Cl− | Zn | Ni | Cr | Cu | Mn | Pb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Minimum (mg/L) | 2.378 | 0.360 | 0.033 | 320.95 | 59.8 | 25.80 | 0.371 | 0.004 | 0.007 | 0.143 | 0.042 | 0.004 |
Maximum (mg/L) | 8.890 | 1.550 | 0.120 | 617.68 | 137.0 | 67.60 | 0.959 | 0.182 | 0.048 | 0.988 | 0.091 | 0.01 |
Mean (mg/L) | 5.225 | 0.785 | 0.065 | 487.405 | 92.969 | 43.523 | 0.62 | 0.047 | 0.024 | 0.511 | 0.067 | 0.007 |
Standard deviation | 2.158 | 0.360 | 0.023 | 87.671 | 20.331 | 12.109 | 0.164 | 0.053 | 0.011 | 0.227 | 0.014 | 0.002 |
Coefficient of variation | 0.413 | 0.459 | 0.360 | 0.18 | 0.219 | 0.278 | 0.265 | 1.124 | 0.461 | 0.445 | 0.212 | 0.25 |
Skewness | 0.372 | 0.799 | 0.551 | −0.153 | 0.323 | 0.423 | 0.371 | 1.376 | 0.635 | 0.251 | 0.26 | −0.366 |
Kurtosis | −1.341 | −0.496 | −0.447 | −1.238 | −0.411 | −0.836 | −0.867 | 0.733 | −0.22 | −0.799 | −0.919 | −1.246 |
K-S test | 0.019 | 0.019 | 0.200 | 0.065 | 0.200 | 0.200 | 0.2 | 0 | 0.2 | 0.2 | 0.2 | 0.045 |
Background values (mg/L) | 3.0 | 0.02 | 0.156 | 263.0 | 45.0 | 25.0 | 0.05 | 0.02 | 0.004 | 0.05 | 0.01 | 0.01 |
Groundwater quality standards [47] (mg/L) | 3.0 | 0.50 | 0.200 | 450.0 | 250.0 | 250.0 | 1 | 0.02 | 0.05 | 1 | 0.1 | 0.01 |
3.2. Frequency Distribution of Contaminants
3.3. Correlation Analysis of Pollutants in Groundwater
3.4. Spatial Distribution Characteristics of Groundwater Contaminants
3.4.1. Spatial Distribution of Pollutant Concentrations
3.4.2. Kriging Interpolation Distribution of Contaminants
3.5. Evaluation of Comprehensive Potential Ecological Risks of Heavy Metals
3.6. Discussion on Applicability
4. Conclusions
- The distributions of CODMn and NH3-N in the study area showed poor continuity of variation and high spatial differentiation, mainly due to intensive anthropogenic activities, such as industrial discharges. TH, SO42−, and Cl− were more or less uniformly affected by external factors, had relatively insignificant spatial differentiation, and may have similar sources within the study area. Co-precipitation or adsorption of these metal elements by oxides or hydroxides of Cr is less frequent and may be explained by external sources of pollution.
- A spatial distribution variation map of six heavy metals in groundwater obtained by ordinary kriging interpolation showed that the high concentrations of Cu, Mn, and Pb are mainly concentrated in the farmland area south of the industrial park. Due to the groundwater recharge from the Shiting River in the north, the heavy metals in the industrial park area migrate to the southern part after infiltration, explaining the low concentrations in other areas.
- Based on the potential ecological pollution index, the degree of heavy metal pollution decreased in the following order: Cu > Zn > Cr > Ni > Mn > Pb. Only Cu had a moderate potential ecological risk, mainly due to its larger ecological risk coefficient. The average value of the integrated potential ecological RI of the six heavy metals was 97.27. Overall, the traditional industrial park poses a slight ecological risk to the shallow groundwater of agricultural areas.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Potential Ecological Risks | Low | Moderate | High | Very High | Extremely High |
---|---|---|---|---|---|
<40 | 40–79 | 80–159 | 160–320 | >320 | |
RI | <150 | 150–299 | 300–599 | 600–1200 | >1200 |
CODMn | NH3-N | TP | TH | SO42− | Cl− | Zn | Ni | Cr | Cu | Mn | Pb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
CODMn | 1 | 0.168 | 0.254 | 0.242 | −0.049 | −0.033 | −0.054 | 0.117 | −0.128 | 0.077 | 0.436 * | 0.184 |
NH3-N | 1 | 0.553 ** | 0.134 | 0.627 ** | 0.276 | 0.193 | 0.507 ** | 0.356 | 0.295 | 0.282 | 0.618 ** | |
TP | 1 | 0.043 | 0.170 | 0.031 | 0.053 | 0.347 | 0.108 | 0.097 | 0.191 | 0.355 | ||
TH | 1 | 0.105 | −0.168 | 0.126 | 0.158 | −0.024 | −0.075 | 0.218 | 0.276 | |||
SO42− | 1 | 0.557 ** | 0.350 | 0.430 * | 0.577 ** | 0.472 * | 0.331 | 0.629 ** | ||||
Cl− | 1 | 0.488 * | 0.464 * | 0.661 ** | 0.679 ** | 0.356 | 0.546 ** | |||||
Zn | 1 | 0.473 * | 0.378 | 0.587 ** | 0.243 | 0.592 ** | ||||||
Ni | 1 | 0.290 | 0.264 | 0.424 * | 0.495 * | |||||||
Cr | 1 | 0.640 ** | 0.240 | 0.489 * | ||||||||
Cu | 1 | 0.476 * | 0.631 ** | |||||||||
Mn | 1 | 0.443 * | ||||||||||
Pb | 1 |
Evaluation Index | Minimum | Maximum | Mean | Standard Deviation | Coefficient of Variation | Average Level of Contamination |
---|---|---|---|---|---|---|
CODMn | 0.793 | 2.963 | 1.742 | 0.719 | 0.413 | Low |
NH3-N | 0.720 | 3.100 | 1.570 | 0.720 | 0.459 | Low |
TP | 0.165 | 0.600 | 0.323 | 0.116 | 0.360 | Un-pollution |
TH | 0.710 | 1.370 | 1.083 | 0.195 | 0.180 | Low |
SO42− | 0.239 | 0.548 | 0.372 | 0.081 | 0.219 | Un-pollution |
Cl− | 0.103 | 0.270 | 0.174 | 0.048 | 0.278 | Un-pollution |
Zn | 0.371 | 0.959 | 0.620 | 0.164 | 0.265 | Un-pollution |
Ni | 0.200 | 9.100 | 2.344 | 2.634 | 1.124 | Moderate |
Cr | 0.140 | 0.960 | 0.475 | 0.219 | 0.461 | Un-pollution |
Cu | 0.143 | 0.988 | 0.511 | 0.227 | 0.445 | Un-pollution |
Mn | 0.417 | 0.913 | 0.666 | 0.141 | 0.212 | Un-pollution |
Pb | 0.389 | 0.960 | 0.709 | 0.177 | 0.249 | Un-pollution |
Single-Factor Potential Ecological Pollution Index of Heavy Metals | RI | ||||||
---|---|---|---|---|---|---|---|
Zn | Ni | Cr | Cu | Mn | Pb | ||
Minimum | 7.42 | 1.00 | 3.50 | 14.30 | 4.17 | 1.95 | 39.44 |
Maximum | 19.17 | 45.50 | 24.00 | 98.80 | 9.13 | 4.80 | 151.20 |
Mean | 12.40 | 11.72 | 11.89 | 51.06 | 6.66 | 3.55 | 97.27 |
Standard deviation | 3.28 | 13.17 | 5.48 | 22.71 | 1.41 | 0.89 | 36.50 |
Coefficient of variation | 0.27 | 1.12 | 0.46 | 0.45 | 0.21 | 0.25 | 0.38 |
Potential ecological risk grades | Low | Low | Low | Moderate | Low | Low | Low |
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Zhang, Y.; Yu, Y.; An, G.; Huang, T.; Huang, J. Spatial Distribution of Pollutants and Risk Assessment of Heavy Metals in Farmland Groundwater around a Traditional Industrial Park—A Case Study of Shifang City, Southwestern China. Sustainability 2023, 15, 14903. https://doi.org/10.3390/su152014903
Zhang Y, Yu Y, An G, Huang T, Huang J. Spatial Distribution of Pollutants and Risk Assessment of Heavy Metals in Farmland Groundwater around a Traditional Industrial Park—A Case Study of Shifang City, Southwestern China. Sustainability. 2023; 15(20):14903. https://doi.org/10.3390/su152014903
Chicago/Turabian StyleZhang, Yibo, Yue Yu, Guanping An, Tao Huang, and Junhan Huang. 2023. "Spatial Distribution of Pollutants and Risk Assessment of Heavy Metals in Farmland Groundwater around a Traditional Industrial Park—A Case Study of Shifang City, Southwestern China" Sustainability 15, no. 20: 14903. https://doi.org/10.3390/su152014903