Seasonal and Spatial Variations of Heavy Metals in Two Typical Chinese Rivers: Concentrations, Environmental Risks, and Possible Sources
Abstract
:1. Introduction
2. Methods and Materials
2.1. Study Area
2.2. Sampling
2.3. Analytical Methods and Quality Control
2.4. Risk Assessment
2.5. Source Analysis
3. Results and Discussion
3.1. Heavy Metal Concentrations
3.1.1. Average Heavy Metal Concentrations in the Rivers
3.1.2. Seasonal and Spatial Variations in Metal Concentrations
Rivers | Seasons | C | Ba | Cd | Cr | Cu | Fe | Mn | Ni | Pb | V | Zn |
---|---|---|---|---|---|---|---|---|---|---|---|---|
T.R. | DS | Range | 72–92 | 1–5 | 7–12 | 10–34 | 194–472 | 15–130 | 13–19 | 40–63 | 5–7 | 39–114 |
Avg.± SD | 80 ± 10 | 3 ± 2 | 9 ± 2 | 16 ± 10 | 334 ± 108 | 97 ± 49 | 16 ± 2 | 57 ± 9 | 6 ± 1 | 64 ± 31 | ||
ER | Range | 140–170 | 1–5 | 13–19 | 20–88 | 202–636 | 98–410 | 13–29 | 30–188 | 7–10 | 120–240 | |
Avg.± SD | 158 ± 13 | 3 ± 2 | 17 ± 3 | 43 ± 26 | 423 ± 166 | 266 ± 119 | 24 ± 7 | 108 ± 71 | 9 ± 1 | 190 ± 47 | ||
LR | Range | 70–80 | 0 | 0 | 13–22 | 96–300 | 70–210 | 2–6 | 7–9 | 15–20 | 25–53 | |
Avg.± SD | 78 ± 4 | 0 | 0 | 17 ± 4 | 192 ± 79 | 132 ± 58 | 4 ± 2 | 8 ± 1 | 17 ± 2 | 40 ± 11 | ||
Avg. | -- | 105 | 2 | 9 | 25 | 316 | 165 | 15 | 57 | 11 | 98 | |
W.R. | DS | Range | 33–290 | 3–6 | 3–10 | 7–24 | 96–384 | 37–190 | 12–37 | 58–80 | 3–12 | 14–72 |
Avg.± SD | 98 ± 95 | 4 ± 1 | 8 ± 3 | 17 ± 7 | 253 ± 100 | 150 ± 59 | 29 ± 10 | 71 ± 9 | 9 ± 3 | 53 ± 22 | ||
ER | Range | 110–310 | 4–12 | 9–31 | 24–87 | 384–1536 | 100–430 | 24–67 | 100–198 | 9–24 | 80–540 | |
Avg.± SD | 190 ± 83 | 8 ± 3 | 22 ± 7 | 50 ± 21 | 963 ± 425 | 295 ±114 | 52 ± 15 | 151 ± 39 | 19 ± 6 | 257 ± 152 | ||
LR | Range | 80–120 | 0 | 0 | 26–60 | 250–504 | 170–210 | 9–15 | 9–14 | 10–23 | 46–93 | |
Avg.± SD | 95 ± 15 | 0 | 0 | 38 ± 12 | 376 ± 86 | 182 ± 16 | 12 ± 2 | 11 ± 2 | 18 ± 5 | 59 ± 18 | ||
Avg. | -- | 128 | 4 | 10 | 35 | 530 | 209 | 31 | 77 | 150 | 123 |
Water Function | Rivers/lakes | Ba | Cd | Cr | Cu | Fe | Mn | Ni | Pb | V | Zn | References |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Source of drinking water | Taipu River, China | 105 | 2 | 9 | 25 | 316 | 165 | 15 | 57 | 11 | 98 | this study |
Taihu Lake | -- | -- | -- | 4 | 34 | 41 | -- | 17 | -- | 0 | [32] | |
Rawal Lake, Pakistan (summer) | -- | 6 | 9 | 10 | 93 | 4 | -- | 162 | -- | 14 | [34] | |
Rawal Lake, Pakistan (winter) | -- | 25 | 97 | 17 | 76 | 13 | -- | 223 | -- | 22 | [34] | |
Rivers in Ghana | 30 | -- | 0.52 | 2.65 | -- | 682 | -- | 0.85 | -- | 138 | [33] | |
Yangtze River in Nanjing Section, China | 37 | 5 | 21 | 11 | 240 | 5 | 13 | 55 | 10 | 9 | [36] | |
Industry | Wusong River, China | 128 | 4 | 10 | 35 | 530 | 209 | 31 | 77 | 150 | 123 | this study |
Lambro River, Italy | -- | 4.8 | 66 | 134 | -- | -- | -- | 138.8 | -- | 0 | [38] | |
Rivers in Latvia | -- | 0.02 | 0.56 | -- | 3 | 0.34 | 0.1 | -- | 3.35 | [39] | ||
Ruda River, Polish | -- | <3 | <5 | 5–22 | 470–9610 | 179–1760 | 8–10 | 30–140 | 41–122 | -- | [28] | |
DilDeresi (stream), Turkey | 1200 | 7 | 30 | 31 | 1310 | -- | -- | 80 | -- | 220 | [40] | |
Patancheru industrial area, India | 78 | -- | 18 | -- | 162 | 73 | 26 | 2 | 99 | -- | [37] | |
Hindon River, India | -- | 12 | 124 | -- | 692 | 617 | -- | 276 | -- | 110 | [41] | |
WHO drinking water guideline | -- | 3 | 50 | 2000 | 300 | 100 | -- | 10 | -- | 3000 | [30] | |
USEPA drinking water standards | -- | 5 | 100 | 1300 | 300 | 50 | -- | 15 | -- | 5000 | [31] |
3.2. Risk Assessment
3.2.1. Comprehensive Risk Indices for the Metal Pollution
Rivers | Sites | Dry Season | Early Rainy Season | Late Rainy Season | Seasonal Averagely | ||||
---|---|---|---|---|---|---|---|---|---|
CRI | Risk Level | CRI | Risk Level | CRI | Risk Level | CRI | Risk Level | ||
T.R. | T1 | 27.48 | low | 33.73 | low | 6.97 | low | 22.73 | low |
T2 | 26.37 | low | 91.37 | moderate | 5.78 | low | 41.17 | low | |
T3 | 51.39 | low | 79.72 | moderate | 8.18 | low | 46.43 | low | |
T4 | 66.59 | moderate | 98.59 | moderate | 6.53 | low | 57.24 | low | |
T5 | 53.47 | low | 31.36 | low | 6.21 | low | 30.35 | low | |
Avg. | 45.06 | low | 66.95 | moderate | 6.73 | low | 39.58 | low | |
W.R. | W1 | 40.26 | low | 67.69 | moderate | 14.36 | low | 40.77 | low |
W2 | 60.88 | moderate | 104.62 | moderate | 10.41 | low | 58.64 | low | |
W3 | 74.91 | moderate | 142.97 | high | 13.77 | low | 77.22 | moderate | |
W4 | 53.05 | low | 123.32 | high | 11.75 | low | 62.71 | moderate | |
W5 | 74.29 | moderate | 136.90 | high | 12.38 | low | 74.53 | moderate | |
W6 | 83.28 | moderate | 193.13 | high | 19.78 | low | 98.73 | moderate | |
Avg. | 64.44 | moderate | 128.11 | high | 13.74 | low | 68.76 | moderate |
3.2.2. Risks Posed by the Metals Analyzed
3.3. Source Apportionment
3.3.1. Possible Sources in Each of the Three Seasons
Items | Dry Season | Early Rainy Season | Late Rainy Season | |||||
---|---|---|---|---|---|---|---|---|
DPC1 | DPC2 | DPC3 | EPC1 | EPC2 | ERPC3 | LPC1 | LPC2 | |
Cumulative Contribution | ||||||||
Metals | 43.199 | 67.483 | 79.042 | 71.162 | 81.318 | 89.630 | 60.778 | 79.827 |
Loading Values | ||||||||
Ba | 0.413 | −0.014 | 0.686 | 0.770 | −0.234 | 0.562 | 0.857 | −0.123 |
Cd | 0.588 | −0.696 | −0.169 | 0.884 | 0.024 | −0.319 | -- | -- |
Cr | 0.665 | 0.608 | −0.247 | 0.965 | −0.044 | 0.095 | -- | -- |
Cu | 0.644 | 0.262 | −0.538 | 0.673 | 0.576 | 0.121 | 0.899 | −0.313 |
Fe | 0.115 | 0.922 | 0.107 | 0.903 | −0.279 | −0.010 | 0.866 | 0.461 |
Mn | 0.900 | −0.265 | −0.179 | 0.837 | 0.357 | 0.090 | 0.750 | 0.416 |
Ni | 0.900 | −0.225 | 0.052 | 0.890 | −0.229 | −0.310 | 0.893 | 0.206 |
Pb | 0.656 | −0.031 | 0.495 | 0.779 | 0.416 | 0.325 | 0.897 | −0.252 |
V | 0.900 | −0.002 | 0.090 | 0.864 | −0.442 | −0.202 | -0.150 | 0.931 |
Zn | 0.273 | 0.730 | 0.088 | 0.836 | 0.035 | 0.374 | 0.618 | −0.230 |
Factors’ Coefficients | ||||||||
T1 | −1.823 | 2.587 | 0.694 | −3.740 | −0.702 | 0.615 | −2.836 | −0.564 |
T2 | −1.552 | 2.257 | 0.708 | −0.459 | 2.030 | 0.413 | −1.611 | −0.602 |
T3 | 0.304 | 1.531 | −1.718 | −1.120 | 0.583 | 0.251 | −0.773 | 1.168 |
T4 | −0.928 | −1.437 | −0.127 | −0.279 | 1.182 | −0.001 | −1.793 | −0.070 |
T5 | −1.476 | −0.814 | −0.816 | −2.293 | −0.643 | 1.207 | −2.875 | −0.991 |
W1 | −3.780 | −2.427 | −0.200 | −3.174 | −0.345 | −0.794 | 1.339 | −1.208 |
W2 | 0.590 | 0.034 | 0.953 | 2.378 | −1.749 | 0.593 | 1.459 | 0.324 |
W3 | 2.695 | −0.015 | −0.347 | 1.501 | −0.195 | −1.286 | 0.512 | 1.274 |
W4 | 1.603 | 0.040 | −0.430 | 0.052 | −0.138 | −0.769 | 0.259 | 0.873 |
W5 | 2.594 | −0.986 | 2.076 | 1.820 | −0.257 | −1.346 | 2.240 | 1.927 |
W6 | 1.773 | −0.769 | −1.193 | 5.314 | 0.233 | 1.118 | 4.079 | −2.131 |
3.3.2. Dominant Sources in Different Samples
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Lin, K.C.; Lee, Y.L.; Chen, C.Y. Metal toxicity to Chlorella pyrenoidosa assessed by a short-term continuous test. J. Hazard. Mater. 2007, 142, 236–241. [Google Scholar] [PubMed]
- Atli, G.; Canli, M. Responses of metallothionein and reduced glutathione in a freshwater fish Oreochromis niloticus following metal exposures. Environ. Toxicol. Phar. 2008, 25, 33–38. [Google Scholar] [CrossRef]
- Manyin, T.; Rowe, C.L. Bioenergetic effects of aqueous copper and cadmium on the grass shrimp, Palaemonetes pugio. Comp. Biochem. Phys. 2009, 150, 65–71. [Google Scholar]
- Perianez, R. Environmental modelling in the Gulf of Cadiz: Heavy metal distributions in water and sediments. Sci. Total. Environ. 2009, 407, 3392–3406. [Google Scholar] [CrossRef] [PubMed]
- Huang, Y.L.; Zhu, W.B.; Le, M.H.; Lu, X.X. Temporal and spatial variations of heavy metals in urban riverine sediment: An example of Shenzhen River, Pearl River Delta, China. Quatern. Int. 2012, 282, 145–151. [Google Scholar] [CrossRef]
- Kumar, R.N.; Solanki, R.; Kumar, J.I.N. Seasonal variation in heavy metal contamination in water and sediments of river Sabarmati and Kharicut canal at Ahmedabad, Gujarat. Environ. Monit. Assess. 2013, 185, 359–368. [Google Scholar] [CrossRef] [PubMed]
- Li, S.Y.; Zhang, Q.F. Risk assessment and seasonal variations of dissolved trace elements and heavy metals in the Upper Han River, China. J. Hazard. Mater. 2010, 181, 1051–1058. [Google Scholar] [CrossRef] [PubMed]
- Matache, M.L.; David, I.G.; Matache, M.; Ropota, M. Seasonal variation in trace metals concentrations in the Ialomita River, Romania. Environ. Monit. Assess. 2009, 153, 273–279. [Google Scholar] [CrossRef] [PubMed]
- Lenoble, V.; Omanovic, D.; Garnier, C.; Mounier, S.; Donlagic, N.; Le Poupon, C.; Pizeta, I. Distribution and chemical speciation of arsenic and heavy metals in highly contaminated waters used for health care purposes (Srebrenica, Bosnia and Herzegovina). Sci. Total Environ. 2013, 443, 420–428. [Google Scholar] [CrossRef] [PubMed]
- Zhou, D.M.; Chen, H.M.; Zheng, C.R. Heavy metals in water bodies purified by suspended substrate of rivers. J. Environ. Sci. China 2002, 14, 44–48. (In Chinese) [Google Scholar] [PubMed]
- Woitke, P.; Wellmitz, J.; Helm, D.; Kube, P.; Lepom, P.; Litheraty, P. Analysis and assessment of heavy metal pollution in suspended solids and sediments of the river Danube. Chemosphere 2003, 51, 633–642. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.Z.; Zhang, J. Analysis on the mineral compositions of suspended matter from Rhone and some main Chinese rivers. Oceanologia et Limnologia Sinica 1994, 25, 319–327. [Google Scholar]
- Chen, J.; Wang, Z. The potential hazards of metal pollution in the water body: evaluation on the basis of sedimentology. Environ. Tech. 1989, 9, 16–25. [Google Scholar]
- Nguyen, H.L.; Leermakers, M.; Osan, J.; Torok, S.; Baeyens, W. Heavy metals in Lake Balaton: Water column, suspended matter, sediment and biota. Sci. Total Environ. 2005, 340, 213–230. [Google Scholar] [CrossRef] [PubMed]
- Bilotta, G.S.; Brazier, R.E. Understanding the influence of suspended solids on water quality and aquatic biota. Water Res. 2008, 42, 2849–2861. [Google Scholar] [CrossRef] [PubMed]
- Hakanson, L. An ecological risk index for aquatic pollution-control-a sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
- Jiao, W.; Lu, S.Y.; Li, G.D.; Jin, X.C.; Yu, H.; Cai, M.M. Heavy metal pollution of main inflow and outflow rivers around the Taihu Lake and assessment of its potential ecological risk. Chin. J. Appl. Environ. Biol. 2010, 16, 577–580. (In Chinese) [Google Scholar]
- The National Census of Pollution Sources. 2007. Available online: http://cpsc.mep.gov.cn/ (accessed on 13 November 2014).
- U.S. Environmental Protection Agency (USEPA). The Acid Digestion of Aqueous Samples and Extracts for Total Metals for Analysis by ICP Spectroscopy (EPA method 3010); USEPA: Washington, DC, USA, 1992.
- Zhang, Y.; Guo, C.S.; Xu, J.; Tian, Y.Z.; Shi, G.L.; Feng, Y.C. Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models. Water Res. 2012, 46, 3065–3073. [Google Scholar] [CrossRef] [PubMed]
- Van der Aa, M.; Bijlsma, L.; Emke, E.; Dijkman, E.; van Nuijs, A.L.N.; van de Ven, B.; Hernandez, F.; Versteegh, A.; de Voogt, P. Risk assessment for drugs of abuse in the Dutch watercycle. Water Res. 2013, 47, 1848–1857. [Google Scholar]
- Yi, Y.J.; Yang, Z.F.; Zhang, S.H. Ecological risk assessment of heavy metals in sediment and human health risk assessment of heavy metals in fishes in the middle and lower reaches of the Yangtze River basin. Environ. Pollut. 2011, 159, 2575–2585. [Google Scholar] [CrossRef] [PubMed]
- Biesinger, K.E.; Christensen, G.M. Effects of various metals on survival, growth, reproduction, and metabolism of daphnia magna. J. Fisheries Res. Board Can. 1972, 29, 1691–1700. [Google Scholar]
- Astel, A.; Tsakouski, S.; Barbieri, P.; Simeonov, V. Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Res. 2007, 41, 4566–4578. [Google Scholar] [CrossRef] [PubMed]
- Helena, B.; Pardo, R.; Vega, M.; Barrado, E.; Fernandez, J.M.; Fernandez, L. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Res. 2000, 34, 807–816. [Google Scholar] [CrossRef]
- Olsen, R.L.; Chappell, R.W.; Loftis, J.C. Water quality sample collection, data treatment and results presentation for principal components analysis—Literature review and Illinois River watershed case study. Water Res. 2012, 46, 3110–3122. [Google Scholar] [CrossRef] [PubMed]
- Ouyang, Y. Evaluation of river water quality monitoring stations by principal component analysis. Water Res. 2005, 39, 2621–2635. [Google Scholar] [CrossRef] [PubMed]
- Loska, K.; Wiechula, D. Application of principal component analysis for the estimation of source of heavy metal contamination in surface sediments from the Rybnik Reservoir. Chemosphere 2003, 51, 723–733. [Google Scholar] [CrossRef] [PubMed]
- Guo, R.C.; He, X.Y. Spatial variations and ecological risk assessment of heavy metals in surface sediments on the upper reaches of Hun River, Northeast China. Environ. Earth Sci. 2013, 70, 1083–1090. [Google Scholar] [CrossRef]
- World Health Organization. Guidelines for Drinking-Water Quality, Recommendations Incorporating 1st and 2nd Addenda, 13th ed.; World Health Organization: Geneva, Switzerland, 2008. [Google Scholar]
- U.S. Environmental Protection Agency (USEPA). 2012 Edition of the Drinking Water Standards and Health Advisories; EPA 822-S-12-001; Office of Water, U.S. Environmental Protection Agency: Washington, DC, USA, 2012.
- Zhang, Z.P.; Sun, X.J.; Chi, Q.Q.; Zhu, G.W. Contents of colloidal trace metals in Taihu Lake in Spring. Environ. Chem. 2007, 26, 232–235. [Google Scholar]
- Asante, K.A.; Agusa, T.; Subramanian, A.; Ansa-Asare, O.D.; Biney, C.A.; Tanabe, S. Contamination status of arsenic and other trace elements in drinking water and residents from Tarkwa, a historic mining township in Ghana. Chemosphere 2007, 66, 1513–1522. [Google Scholar] [PubMed]
- Iqbal, J.; Shah, M.H.; Akhter, G. Characterization, source apportionment and health risk assessment of trace metals in freshwater Rawal Lake, Pakistan. J. Geochem. Explor. 2013, 125, 94–101. [Google Scholar] [CrossRef]
- The Council of Experts on Environmental Affairs (FRG). Environmental Problems of the River Rhine; Kohlhammer: Stuttgart, Germany, 1976; pp. 258–259. [Google Scholar]
- 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] [PubMed]
- Krishna, A.K.; Satyanarayanan, M.; Govil, P.K. Assessment of heavy metal pollution in water using multivariate statistical techniques in an industrial area: A case study from Patancheru, Medak District, Andhra Pradesh, India. J. Hazard. Mater. 2009, 167, 366–373. [Google Scholar] [CrossRef] [PubMed]
- Pettine, M.; Bianchi, M.; Martinotti, W.; Muntau, H.; Renoldi, M.; Tartari, G. Contribution of the Lambro River to the total pollutant transport in the Po watershed (Italy). Sci. Total Environ. 1996, 192, 275–297. [Google Scholar] [CrossRef]
- Klavinš, M.; Briede, A.; Rodinov, V.; Kokorite, I.; Parele, E.; Klavina, I. Heavy metals in rivers of Latvia. Sci. Total Environ. 2000, 262, 175–183. [Google Scholar]
- Pekey, H.; Karakaş, D.; Bakogˇlu, M. Source apportionment of trace metals in surface waters of a polluted stream using multivariate statistical analyses. Mar. Pollut. Bull. 2004, 49, 809–818. [Google Scholar] [CrossRef] [PubMed]
- Suthar, S.; Nema, A.K.; Chabukdhara, M.; Gupta, S.K. Assessment of metals in water and sediments of Hindon River, India: Impact of industrial and urban discharges. J. Hazard. Mater. 2009, 171, 1088–1095. [Google Scholar] [CrossRef] [PubMed]
- Semenzin, E.; Temminghoff, E.J.M.; Marcomini, A. Improving ecological risk assessment by including bioavailability into species sensitivity distributions: An example for plants exposed to nickel in soil. Environ. Pollut. 2007, 148, 642–647. [Google Scholar] [CrossRef] [PubMed]
- Liu, C.W.; Lin, K.H.; Kuo, Y.M. Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci. Total Environ. 2003, 313, 77–89. [Google Scholar] [CrossRef] [PubMed]
- Yu, R.L.; Hu, G.R.; Yuan, X. Development in research on pollution source of heavy metals from atmospheric dust-recognition and analysis. Earth Environ. 2009, 37, 73–79. [Google Scholar]
- Li, Y.; Yu, Z.M.; Song, X.X. Application of principal component analysis for the estimation of source of heavy metal contamination in Marine Sediments. Environ. Sci. 2006, 27, 137–142. [Google Scholar]
- Mutia, T.M.; Virani, M.Z.; Moturi, W.N.; Muyela, B.; Mavura, W.J.; Lalah, J.O. Copper, lead and cadmium concentrations in surface water, sediment and fish, C. Carpio, samples from Lake Naivasha: Effect of recent anthropogenic activities. Environ. Earth Sci. 2012, 67, 1121–1130. [Google Scholar] [CrossRef]
- Guidance of Industrial Structure Adjustment In China. Available online: http://www.gov.cn/flfg/2011-04/26/content_1852729.htm. (accessed on 13 November 2014).
- Cong, Z.; Kang, S.C.; Zhang, Y.L.; Li, X.D. Atmospheric wet deposition of trace elements to central Tibetan Plateau. Appl. Geochem. 2010, 25, 1415–1421. [Google Scholar] [CrossRef]
- Onat, B.; Sahin, U.A.; Bayat, C. Assessment of particulate matter in the urban atmosphere: Size distribution, metal composition and source characterization using principal component analysis. J. Environ. Monitor. 2012, 14, 1400–1409. [Google Scholar] [CrossRef]
- Yang, J.J.; Liu, C.C.; Chen, W.H.; Yuan, C.S.; Lin, C. Assessing the altitude effect on distributions of volatile organic compounds from different sources by principal component analysis. J. Environ. Monitor. 2013, 15, 972–985. [Google Scholar]
© 2014 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 license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yao, H.; Qian, X.; Gao, H.; Wang, Y.; Xia, B. Seasonal and Spatial Variations of Heavy Metals in Two Typical Chinese Rivers: Concentrations, Environmental Risks, and Possible Sources. Int. J. Environ. Res. Public Health 2014, 11, 11860-11878. https://doi.org/10.3390/ijerph111111860
Yao H, Qian X, Gao H, Wang Y, Xia B. Seasonal and Spatial Variations of Heavy Metals in Two Typical Chinese Rivers: Concentrations, Environmental Risks, and Possible Sources. International Journal of Environmental Research and Public Health. 2014; 11(11):11860-11878. https://doi.org/10.3390/ijerph111111860
Chicago/Turabian StyleYao, Hong, Xin Qian, Hailong Gao, Yulei Wang, and Bisheng Xia. 2014. "Seasonal and Spatial Variations of Heavy Metals in Two Typical Chinese Rivers: Concentrations, Environmental Risks, and Possible Sources" International Journal of Environmental Research and Public Health 11, no. 11: 11860-11878. https://doi.org/10.3390/ijerph111111860