Spatio-Temporal Analysis and Health Risk Assessment of Heavy Metals in Water from the Fuhe River, South China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Sampling and Analysis
2.3. Data Processing
2.3.1. Statistical Methods
2.3.2. Human Health Risk Assessment (HHRA)
3. Results and Discussion
3.1. Distribution Characteristics of pH, EC, and Heavy Metals in River Water
3.2. Influencing Factors of Heavy Metals in River Water
3.3. Human Health Risk Assessment
4. Conclusions
- 1.
- From the upper to lower reaches of the Fuhe River, the pH value of river water changed from between 7.23 and 7.97 in the dry season and from between 5.82 and 7.81 in the rainy season, with a significant decreasing trend along river flow. The EC value of river water increased gradually along river flow in both the dry and rainy seasons. The spatial variations of pH and EC values along river flow were closely related to industrial, agricultural, and urban activities. Especially in the rainy season, the pH decrease and the EC increase in river water in the lower reaches of the Fu River watershed were directly related to frequent human activities.
- 2.
- The spatial dispersions and variabilities of heavy metals were large in river water. The average concentrations of heavy metals in the dry and rainy seasons were Pb > Zn > Cr > Cu > Cd in the dry season and Cu > Pb > Cr > Zn > Cd in the rainy season. Except for Pb, the concentration of other heavy metals in the Fuhe River watershed was low, which was superior to the Chinese drinking standard values. The sources of heavy metals seemed to be relatively complex. Overall, Cu and Zn were controlled by natural weathering and human activities, and Pb, Cr, and Cd were mainly from human activities.
- 3.
- In the dry and rainy seasons, the HQ values of most heavy metals by direct ingestion and dermal absorption were less than 1. Only in the dry season, especially in the middle and lower reaches of the river, the HQ values of Pb related to adults and children by direct ingestion at most sampling points were greater than 1, indicating significant non-carcinogenic risks for adults and children. In addition, the minimum CRingestion of Cr in river water was greater than 10−4 in the dry and rainy seasons. In the rainy season, the CRingestion value of Cd was >10−4 at 16%–28% of the sampling points, and most of them occurred in the lower reach of the river. Therefore, significant cancer risks related to Cr and Cd existed for both adults and children. It is important and imperative to seriously control the inputs of various materials from industries, agricultures, and residential living into river water.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ullah, R.; Muhammad, S.; Jadoon, I.A.K. Potentially harmful elements contamination in water and sediment: Evaluation for risk assessment and provenance in the northern Sulaiman fold belt, Baluchistan, Pakistan. Microchem. J. 2019, 147, 1155–1162. [Google Scholar] [CrossRef]
- Gleick, P.H.; Singh, A.; Shi, H. Threats to the World’s Freshwater Resources; Pacific Institute for Studies in Development, Environment, and Security: Oakland, CA, USA, 2001. [Google Scholar]
- Ghotbizadeh, M.; Cuss, C.W.; Grant-Weaver, I.; Markov, A.; Noernberg, T.; Ulrich, A.; Shotyk, W. Spatiotemporal variations of total and dissolved trace elements and their distributions amongst major colloidal forms along and across the lower Athabasca River. J. Hydrol.-Reg. Stud. 2022, 40, 101029. [Google Scholar] [CrossRef]
- USEPA. Regulated Drinking Water Contaminants. Online Database 2015. Available online: http://www.epa.gov/dwstandardsregulations#Disinfectants (accessed on 5 March 2022).
- Karbassi, A.R.; Monavari, S.M.; Bidhendi, G.R.N.; Nouri, J.; Nematpour, K. Metal pollution assessment of sediment and water in the Shur River. Environ. Monit. Assess. 2008, 147, 107–116. [Google Scholar] [CrossRef] [PubMed]
- Qu, S.Y.; Wu, W.H.; Nel, W.; Ji, J.F. The behavior of metals/metalloids during natural weathering: A systematic study of the mono-lithological watersheds in the upper Pearl River Basin, China. Sci. Total Environ. 2020, 108, 134572. [Google Scholar] [CrossRef] [PubMed]
- Li, S.Y.; Zhang, Q.F. Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques. J. Hazard. Mater. 2010, 176, 579–588. [Google Scholar] [CrossRef]
- Zeng, X.X.; Liu, Y.G.; You, S.H.; Zeng, G.M.; Tan, X.F.; Hu, X.J.; Hu, X.; Huang, L.; Li, F. Spatial distribution, health risk assessment and statistical source identification of the trace elements in surface water from the Xiangjiang River, China. Environ. Sci. Pollut. Res. 2015, 22, 9400–9412. [Google Scholar] [CrossRef]
- Gholizadeh, M.H.; Melesse, A.M.; Reddi, L. Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florid. Sci. Total Environ. 2016, 556–557, 1552–1567. [Google Scholar] [CrossRef]
- Fernandes, A.C.P.; Fernandes, L.F.S.; Cortes, R.M.V.; Pacheco, F.A.L. The role of landscape configuration, season, and distance from contaminant sources on the degradation of stream water quality in urban catchments. Water 2019, 11, 2025. [Google Scholar] [CrossRef]
- Wu, H.; Qu, S.Y.; Nel, W.; Ji, J.F. The impact of natural weathering and mining on heavy metal accumulation in the karst areas of the Pearl River Basin, China. Sci. Total Environ. 2020, 734, 139480. [Google Scholar] [CrossRef]
- Yan, F.; Liu, C.L.; Wei, B. Evaluation of heavy metal pollution in the sediment of Poyang Lake based on stochastic geo-accumulation model (SGM). Sci. Total Environ. 2019, 659, 1–6. [Google Scholar] [CrossRef]
- Li, R.; Tang, X.Q.; Guo, W.J.; Lin, L.; Zhao, L.Y.; Hu, Y.; Liu, M. Spatiotemporal distribution dynamics of heavy metals in water, sediment, and zoobenthos in mainstream sections of the middle and lower Changjiang River. Sci. Total Environ. 2020, 714, 136779. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Lin, K.X.; Liu, X.S. Distribution and pollution risk assessment of heavy metals in the surface sediment of the intertidal zones of the Yellow River Estuary, China. Mar. Pollut. Bull. 2022, 174, 113286. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.M.; Shao, D.G.; Westerhoff, P. Wastewater discharge impact on drinking water sources along the Yangtze River (China). Sci. Total Environ. 2017, 599–600, 1399–1407. [Google Scholar] [CrossRef] [PubMed]
- Zeng, Y.S.; Bi, C.J.; Jia, J.P.; Deng, L.; Chen, Z.L. Impact of intensive land use on heavy metal concentrations and ecological risks in an urbanized river network of Shanghai. Ecol. Indic. 2020, 116, 106501. [Google Scholar] [CrossRef]
- Sun, Z.H.; Xie, X.D.; Wang, P.; Hu, Y.N.; Cheng, H.F. Heavy metal pollution caused by small-scale metal ore mining activities: A case study from a polymetallic mine in South China. Sci. Total Environ. 2018, 639, 217–227. [Google Scholar] [CrossRef]
- Chen, J.H.; Zhang, Y.h.; Shen, W.; Gao, B.; Zhang, W.M.; Shi, H.; Chen, J.H.; Tang, Y.H. Distribution characteristics and source analysis of heavy metals in Nanchang section of the Fuhe River. China Environ. Sci. 2020, 40, 299–307, (In Chinese with English abstract). [Google Scholar] [CrossRef]
- Gao, B.; Wei, Z.Y.; Xu, W.D. The present situation of water pollution and sustainable development strategy of the Fuhe River. China Rural Water Hydropower 2005, 11, 49–50, (In Chinese with English abstract). [Google Scholar] [CrossRef]
- Ye, J.H.; Yu, T.; Xu, Z.T.; Liu, H.Y.; Cai, Q.R. Distribution and probabilistic integrated ecological risk assessment of heavy metals in the surface water of Poyang Lake, China. Chin. J. Anal. Chem. 2021, 49, 29–34. [Google Scholar] [CrossRef]
- Li, J.H.; Zheng, F.W.; Wu, C.; Huang, C.; Tang, C.; Peng, Y.W.; Xu, C.X.; Yang, H.Q. Hydrochemical characteristics of river in a representative silicate rock region under natural weathering and anthropogenic activities—A case study of the Fuhe River in the middle and lower reaches of the Yangtze River. Earth Environ. 2021, 49, 347–357, (In Chinese with English abstract). [Google Scholar] [CrossRef]
- Wang, Z.C.; Yang, Y.; Chen, G.; Wu, J.F.; Wu, J.C. Variation of lake-river-aquifer interactions induced by human activity and climatic condition in poyang lake basin, China. J. Hydrol. 2021, 595, 126058. [Google Scholar] [CrossRef]
- Yao, J.; Zhang, Q.; Li, Y.L.; Li, M.F. Hydrological evidence and causes of seasonal low water levels in a large river-lake system: Poyang Lake, China. Hydrol. Res. 2016, 47, 24–39. [Google Scholar] [CrossRef]
- Tao, S.Y.; Zhang, X.; Pan, G.Y.; Xu, J.; Zeng, Z.Y. Moisture source identification based on the seasonal isotope variation of precipitation in the Poyang Lake Wetland, China. J. Hydrol. Reg. Stud. 2021, 37, 100892. [Google Scholar] [CrossRef]
- Chu, X.D.; Wang, H.; Zheng, F.W.; Huang, C.; Xu, C.X.; Wu, D.S. Spatial distribution characteristics and sources of nutrients and heavy metals in the Xiujiang River of Poyang Lake Basin in the dry season. Water 2021, 13, 1654. [Google Scholar] [CrossRef]
- Chu, X.D.; Wu, D.S.; Wang, H.; Zheng, F.W.; Huang, C.; Hu, L. Spatial distribution characteristics and risk assessment of nutrient elements and heavy metals in the Ganjiang River Basin. Water 2021, 13, 3367. [Google Scholar] [CrossRef]
- Mendiguchía, C.; Moreno, C.; García-Vargas, M. Evaluation of natural and anthropogenic influences on the Guadalquivir River (Spain) by dissolved heavy metals and nutrients. Chemosphere 2007, 69, 1509–1517. [Google Scholar] [CrossRef] [PubMed]
- Wu, Z.S.; Ma, T.T.; Lai, X.J.; Li, K.Y. Concentration, distribution, and assessment of dissolved heavy metals in rivers of Lake Chaohu Basin, China. J. Environ. Manag. 2021, 300, 113744. [Google Scholar] [CrossRef] [PubMed]
- Qiao, J.; Zhu, Y.; Jia, X.; Shao, M.A.; Niu, X.Q.; Liu, J.Y. Distributions of arsenic and other heavy metals, and health risk assessments for groundwater in the Guanzhong Plain region of China. Environ. Res. 2019, 181, 108957. [Google Scholar] [CrossRef]
- Abdi, H.; Williams, L.J. Principal component analysis: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2010, 2, 433–459. [Google Scholar] [CrossRef]
- Chen, K.; Sun, L.; Tang, J. Hydrochemical differences between river water and groundwater in Suzhou, Northern Anhui Province, China. Open Geosci. 2020, 12, 1421–1429. [Google Scholar] [CrossRef]
- USEPA (U.S. Environmental Protection Agency). United States Environmental Protection Agency, Risk Assessment Guidance for Superfund, Volume I: Human Health Evaluation Manual (Part A); EPA/540/1-89/002; Interim Final, Office of Emergency and Remedial Response: Washington, DC, USA, 1989. [Google Scholar]
- China MEP. Exposure Factors Handbook of Chinese Population; China Environmental Science Press: Beijing, China, 2013; (In Chinese with English abstract). [Google Scholar]
- USEPA. Risk Assessment Guidance for Superfund Vol. 1 Human Health Evaluation Manual, Part E, Supplemental Guidance from Dermal Risk Assessment; Office of Emergency and Remedial Response: Washington, DC, USA, 2004. [Google Scholar]
- IRAC (International Agency for Research on Cancer). Agents Classified by the IARC Monographs; 20111-102; IRAC: Lyon, France, 2011. [Google Scholar]
- USEPA. Supplemental Guidance for Developing Soil Screening Levels for Superfund Sites, Appendix D-Dispersion Factors Calculations; United States Environmental Protection Agency: Washington, DC, USA, 2002. [Google Scholar]
- USEPA. Exposure Factors Handbook; United States Environmental Protection Agency: Washington, DC, USA, 2011. Available online: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252 (accessed on 5 March 2022).
- Wu, B.; Zhao, D.Y.; Jia, H.Y.; Zhang, Y.; Zhang, X.X.; Cheng, S.P. Preliminary risk assessment of trace metal pollution in surface water from Yangtze River in Nanjing section, China. Bull. Environ. Contam. Toxicol. 2009, 82, 405–409. [Google Scholar] [CrossRef]
- USEPA. Risk Assessment Guidance for Superfund: Human Health Evaluation Manual Supplemental Guidance ‘Standard Default Exposure Factors; OSWER Directive 9285.6-03; Office of Emergency and Remedial Response: Washington, DC, USA, 1991. [Google Scholar]
- Xiao, J.; Wang, L.Q.; Deng, L.; Jin, Z.D. Characteristics, sources, water quality and health risk assessment of trace elements in river water and well water in the Chinese Loess Plateau. Sci. Total Environ. 2019, 650, 2004–2012. [Google Scholar] [CrossRef]
- Chetelat, B.; Liu, C.Q.; Zhao, Z.Q.; Wang, Q.L.; Li, S.L.; Li, J.; Wang, B.L. Geochemistry of the dissolved load of the Changjiang Basin rivers: Anthropogenic impacts and chemical weathering. Geochim. Cosmochim. Acta 2008, 72, 4254–4277. [Google Scholar] [CrossRef]
- Hu, C.H.; Zhou, P.; Huang, P.; Du, J.; Zhou, W.B. Behavior characteristics of dissolved heavy metals and health risks assessment from Poyang Lake Basin, China. J. Agro-Environ. Sci. 2012, 31, 1009–1014, (In Chinese with English abstract). [Google Scholar]
- Shu, W.; Wang, P.; Xiao, H.Y.; Liu, Z.J.; Zhao, J.; Yu, X.F. Hydrochemical characteristics and influencing factors in the Le’ an River, Poyang Lake Basin. Resour. Environ. Yangtze Basin 2019, 28, 681–690. [Google Scholar] [CrossRef]
- Gaillardet, J.; Viers, J.; Dupré, B. Trace elements in river waters. Treatise Geochem. 2003, 5, 225–272. [Google Scholar] [CrossRef]
- WHO. Guidelines for Drinking Water Quality, 4th ed.; Incorporating the first addendum: Geneva, Switzerland, 2017. [Google Scholar]
- China, M.O.H. Standard for Drinking Water Quality; Ministry of Health of China: Beijing, China, 2006; (In Chinese with English abstract). [Google Scholar]
- Wang, X.N.; Cui, L.; Li, J.; Zhang, C.; Gao, X.Y.; Fan, B. Water quality criteria for the protection of human health of 15 toxic metals and their human risk in surface water, China. Environ. Pollut. 2021, 276, 116628. [Google Scholar] [CrossRef] [PubMed]
- Zhang, C.; Zhang, D.; Duan, H.Z.; Zhao, Z.Q.; Zhang, J.W.; Huang, X.Y.; Ma, B.J.; Zhen, D.S. Combining metal and sulfate isotopes measurements to identify different anthropogenic impacts on dissolved heavy metals levels in river water. Chemosphere 2023, 310, 136747. [Google Scholar] [CrossRef] [PubMed]
- Wu, W.H.; Qu, S.Y.; Nel, W.; Ji, J.F. The influence of natural weathering on the behavior of heavy metals in small basaltic watersheds: A comparative study from different regions in China. Chemosphere 2021, 262, 127897. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Jiang, Y.H.; Wang, M.; Wang, P.; Shi, G.X.; Ding, M.J. Spatial characterization, risk assessment, and statistical source identification of the dissolved trace elements in the Ganjiang River—Feeding tributary of the Poyang Lake, China. Environ. Sci. Pollut. Res. 2016, 24, 2890–2903. [Google Scholar] [CrossRef]
- Varol, M. Dissolved heavy metal concentrations of the Kralkz, Dicle and Batman dam reservoirs in the Tigris River basin, Turkey. Chemosphere 2013, 93, 954–962. [Google Scholar] [CrossRef]
- Gao, L.; Chen, J.Y.; Tang, C.Y.; Ke, Z.T.; Zhu, A.P. Distribution, migration and potential risk of heavy metals in the Shima River catchment area, South China. Environ. Sci.-Proc. Imp. 2015, 10, 1769–1782. [Google Scholar] [CrossRef] [PubMed]
- Dević, G.; Sakan, S.; Đorđević, D. Assessment of the environmental significance of nutrients and heavy metal pollution in the river network of Serbia. Environ. Sci. Pollut. R. 2016, 23, 282–297. [Google Scholar] [CrossRef] [PubMed]
- Liu, C.W.; Lin, K.H.; Kuo, Y.M. Application of factor analysis in the assessment of ground water quality in a blackfoot disease area in Taiwan. Sci. Total Environ. 2003, 313, 77–89. [Google Scholar] [CrossRef] [PubMed]
- Islam, M.S.; Ahmed, M.K.; Raknuzzaman, M.; Habibullah-Al-Mamun, M.; KamrulIslam, M. Heavy metal pollution in surface water and sediment: A preliminary assessment of an urban river in a developing country. Ecol. Indic. 2015, 48, 282–291. [Google Scholar] [CrossRef]
- Eslami, H.; Esmaeili, A.; Razaeian, M.; Salari, M.; Hosseini, A.N.; Mobini, M.; Baranid, A. Potentially toxic metal concentration, spatial distribution, and health risk assessment in drinking groundwater resources of southeast iran. Geosci. Front. 2022, 13, 101276. [Google Scholar] [CrossRef]
- Kullar, S.S.; Shao, K.; Surette, C.; Foucher, D.; Mergler, D.; Cormier, P.; Bellinger, D.C.; Barbeau, B.; Sauvé, S.; Bouchard, M.F. A benchmark concentration analysis for manganese in drinking water and IQ deficits in children. Environ. Int. 2019, 130, 104889. [Google Scholar] [CrossRef]
- Sarkar, B. Heavy Metals in the Environment; CRC Express Inc.: Plaines, FL, USA, 2009. [Google Scholar] [CrossRef]
- Jaishankar, M.; Tseten, T.; Anbalagan, N.; Mathew, B.B.; Beeregowda, K.N. Toxicity, mechanism and health effects of some heavy metals. Interdiscipl. Toxicol. 2014, 7, 60–72. [Google Scholar] [CrossRef]
- Yuan, X.Y.; Zhang, L.J.; Li, J.Z.; Wang, C.; Ji, J.F. Sediment properties and heavy metal pollution assessment in the river, estuary and lake environments of a fluvial plain, China. Catena 2014, 119, 52–60. [Google Scholar] [CrossRef]
- Ibrahim, L.A.; El-Sesy, M.E.; ElSayed, E.E.; Zelenakova, M.; Hlinkova, M.; Mohamed, E.S.; Abu-Hashim, M. Simultaneous Removal of Metal Ions from Wastewater by a Greener Approach. Simultaneous Removal of Metal Ions from Wastewater by a Greener Approach. Water 2022, 14, 4049. [Google Scholar] [CrossRef]
- Rehman, H.; Munir, M.; Fatima, K.; Shahab, S.; Ali, B.; Al-Saeed, F.A.; Abbas, A.M.; Zaman, Q. Heavy Metals, Pesticide, Plasticizers Contamination and Risk Analysis of Drinking Water Quality in the Newly Developed Housing Societies of Gujranwala, Pakistan. Water 2022, 14, 3787. [Google Scholar] [CrossRef]
Parameter | Abbreviation | Unit | Mean Value | |
---|---|---|---|---|
Adult | Child | |||
Ingestion rate | IR | L·day−1 | 2.65 a | 0.78 b |
Exposure frequency | EF | days·year−1 | 365 b | 365 b |
Exposure duration | ED | year | 70 c | 6 c |
Skin-surface area | SA | cm2 | 16,000 a | 5700 d |
Adherence factor | AF | cm·h−1 | 0.07 d | 0.07 d |
Dermal absorption factor | ABS d | 0.03 d | 0.03 d | |
Exposure time | ET | h·day−1 | 0.6 e | 0.6 e |
Conversion factor | CF | L·cm−3 | 10−6 c | 10−6 c |
Body weight | BW | Kg | 60.5 a | 15 f |
Average time | AT | day | ED × 365 | ED × 365 |
Rivers | Period | pH | EC | Cu | Zn | Pb | Cd | Cr | Reference |
---|---|---|---|---|---|---|---|---|---|
Fuhe River, China | Dry season | 7.64 | 59.8 | 3.12 | 3.60 | 34.94 | 0.051 | 3.66 | This study |
Rainy season | 7.14 | 58.48 | 17.64 | 1.43 | 8.76 | 0.286 | 2.09 | This study | |
Ganjiang River, China | Dry season | - | - | 1.98 | 4.32 | 1.42 | - | 1.71 | [42] |
Rainy season | - | - | 4.81 | 18.16 | 5.36 | - | 4.52 | [42] | |
Xiuhe River, China | Dry season | - | - | 6.15 | 5.81 | 2.71 | - | 2.53 | [42] |
Rainy season | - | - | 4.65 | 18.53 | 6.45 | - | 6.15 | [42] | |
Xinjiang River, China | Dry season | - | - | 5.81 | 30.05 | 1.89 | - | 4.55 | [42] |
Rainy season | - | - | 8.82 | 17.89 | 5.63 | - | 4.51 | [42] | |
Leanhe River, China | Dry season | 7.32 | 259.93 | 5.11 | 25.21 | 1.71 | 0.53 | 1.37 | [43] |
Rainy season | 7.19 | 191.07 | 2.19 | 6.99 | 0.35 | 0.07 | 0.99 | [43] | |
Yellow River, China | April | 8.64 | 153 | 5.07 | 6.63 | 0.25 | 0.03 | 5.13 | [40] |
Yangtze River, China | April | 2.86 | 5.40 | 4.69 | 0.96 | - | [12] | ||
World average | 1.48 | 0.60 | 0.08 | 0.08 | 0.7 | [44] | |||
WHO a | 6.5–8.2 | - | 2000 | 3000 | 10 | 3 | 50 | [45] | |
China MOH b | 6–9 | - | 1000 | 1000 | 50 | 5 | 50 | [46] | |
EQS c | I | 10 | 50 | 10 | 1 | 10 | [47] | ||
EQS c | II | 1000 | 1000 | 10 | 5 | 50 | [47] | ||
EQS c | III | 1000 | 1000 | 50 | 5 | 50 | [47] | ||
EQS c | IV | 1000 | 2000 | 50 | 5 | 50 | [47] | ||
EQS c | V | 1000 | 2000 | 100 | 10 | 100 | [47] |
Variables | Dry Season | Rainy Season | |||||
---|---|---|---|---|---|---|---|
PC1 | PC2 | PC3 | PC4 | PC1 | PC2 | PC3 | |
pH | 0.50 | 0.31 | 0.58 | −0.34 | −0.46 | −0.25 | 0.62 |
EC | 0.88 | 0.01 | 0.01 | −0.16 | 0.79 | 0.14 | 0.03 |
Zn | −0.01 | 0.00 | 0.01 | 0.94 | 0.82 | 0.24 | 0.13 |
Cu | 0.71 | −0.15 | −0.35 | 0.25 | 0.94 | −0.06 | −0.03 |
Cd | −0.15 | −0.16 | 0.85 | 0.02 | 0.14 | 0.89 | 0.24 |
Pb | 0.02 | 0.90 | 0.02 | 0.17 | 0.08 | 0.93 | −0.18 |
Cr | −0.20 | 0.79 | −0.20 | −0.30 | −0. 29 | −0.17 | −0.71 |
Eigenvalue | 1.75 | 1.60 | 1.29 | 1.02 | 2.9 | 1.45 | 1.0 |
% of variance | 24.94 | 22.73 | 18.37 | 14.52 | 40.87 | 20.68 | 14.11 |
Cumulative % | 24.94 | 47.67 | 66.04 | 80.63 | 40.87 | 61.55 | 75.66 |
Dry Season | pH | EC | Zn | Cu | Cd | Pb | Cr | |
---|---|---|---|---|---|---|---|---|
Rainy Season | ||||||||
pH | 0.45 * | −0.25 | −0.03 | 0.18 | 0.24 | 0.09 | ||
EC | −0.26 | −0.12 | 0.41 * | −0.09 | 0.05 | −0.1 | ||
Zn | −0.23 | 0.55 * | 0.07 | 0.02 | 0.05 | −0.22 | ||
Cu | −0.38 | 0.64 ** | 0.74 ** | −0.16 | −0.02 | −0.16 | ||
Cd | −0.06 | 0.28 | 0.39 | 0.09 | −0.08 | −0.15 | ||
Pb | −0.34 | 0.19 | 0.25 | 0.06 | 0.69 ** | 0.49 * | ||
Cr | 0.08 | −0.16 | −0.21 | −0.21 | −0.22 | −0.09 |
Variables | Dry Season | Rainy Season | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
C1 (n = 5) | C2 (n = 13) | C3 (n = 7) | C1 (n = 18) | C2 (n = 7) | ||||||
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | |
pH | 7.69 b | 0.07 | 7.75 c | 0.12 | 7.39 bc | 0.14 | 7.18 | 0.36 | 7.02 | 0.69 |
EC | 52.20 | 11.17 | 71.6 c | 25.28 | 43.29 c | 6.40 | 49.1 | 7.64 | 82.6 | 20.4 |
Zn | 2.14 | 0.21 | 3.26 | 2.12 | 5.27 | 6.00 | 1.24 | 0.28 | 1.90 | 0.21 |
Cu | 2.12 | 1.66 | 3.23 | 1.89 | 3.89 | 3.77 | 13.9 | 4.74 | 27.1 | 7.36 |
Cd | 0.017 | 0.017 | 0.082 | 0.18 | 0.020 | 0.022 | 0.24 | 0.08 | 0.41 | 0.21 |
Pb | 50.72 ab | 5.98 | 34.99 a | 15.06 | 23.57 b | 9.13 | 8.10 | 2.82 | 10.5 | 7.24 |
Cr | 11.12 ab | 3.22 | 1.81 a | 1.20 | 1.78 b | 1.91 | 2.58 | 2.29 | 0.84 | 0.08 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Guo, X.; Xiao, Y.; Zhao, L.; Yang, T.; Tang, C.; Luo, W.; Huang, C.; Zheng, F. Spatio-Temporal Analysis and Health Risk Assessment of Heavy Metals in Water from the Fuhe River, South China. Water 2023, 15, 641. https://doi.org/10.3390/w15040641
Guo X, Xiao Y, Zhao L, Yang T, Tang C, Luo W, Huang C, Zheng F. Spatio-Temporal Analysis and Health Risk Assessment of Heavy Metals in Water from the Fuhe River, South China. Water. 2023; 15(4):641. https://doi.org/10.3390/w15040641
Chicago/Turabian StyleGuo, Xiaojuan, Yilong Xiao, Lanzhi Zhao, Tao Yang, Chun Tang, Wei Luo, Cheng Huang, and Fangwen Zheng. 2023. "Spatio-Temporal Analysis and Health Risk Assessment of Heavy Metals in Water from the Fuhe River, South China" Water 15, no. 4: 641. https://doi.org/10.3390/w15040641
APA StyleGuo, X., Xiao, Y., Zhao, L., Yang, T., Tang, C., Luo, W., Huang, C., & Zheng, F. (2023). Spatio-Temporal Analysis and Health Risk Assessment of Heavy Metals in Water from the Fuhe River, South China. Water, 15(4), 641. https://doi.org/10.3390/w15040641