Surface waters are important natural resources and widely used for different purpose in human life such as agriculture, industry, municipal services and so on. Using surface water at high rate led to increasing of their pollution and scarcity. This pollution is mainly human made, in some case anthropogenic. Recognizing this problem currently, water pollution source identification and quantification is an active research area. The main objective of this review is to identify different pollution factors of surface water, approaches and methods used by different researchers for identification and quantification this pollution sources. There is different pollution factors surface water such as: heavy metal, micro plastic, nutrients like Nitrogen and phosphorus, waterborne pathogenic microbes, and petroleum hydrocarbons. Different pollution identification and quantification methods were used in different literature based on objectives and scopes of the studies. This include: Inverse Methods, Bayesian Inference, an Innovative Biosensor Network, Differential Evolution (DE) optimization algorithm, Combining Differential Evolution Algorithm (DEA) and Metropolis– Hastings–Markov Chain Monte Carlo (MH–MCMC), Field Observation and Laboratory Analysis, and Multivariate Receptor Model.
Published in | American Journal of Water Science and Engineering (Volume 9, Issue 3) |
DOI | 10.11648/j.ajwse.20230903.11 |
Page(s) | 50-57 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Surface Waters, Pollution Source, Identification and Quantification
[1] | Khoshgou H, Ali S, Salehi A. Using the backward probability method in contaminant source identification with a finite-duration source loading in a river. 2022; 6306–16. |
[2] | Amirabdollahian, M. and Datta, B. (2013) ‘Identification of Contaminant Source Characteristics and Monitoring Network Design in Groundwater Aquifers: An Overview’, Journal of Environmental Protection, 04 (05), pp. 26–41. |
[3] | Zhang, Y. et al. (2009) ‘Water quality assessment and source identification of Daliao river basin using multivariate statistical methods’, pp. 105–121. |
[4] | Yin, L. et al. (2019) ‘Microplastic pollution in surface water of urban lakes in changsha, china’, International Journal of Environmental Research and Public Health, 16 (9). |
[5] | Wada, Y. et al. (2016) ‘Modeling global water use for the 21st century: The Water Futures and Solutions (WFaS) initiative and its approaches’, Geoscientific Model Development, 9 (1), pp. 175–222. |
[6] | Srinivas, R. et al. (2018) ‘Holistic approach for quantification and identification of pollutant sources of a river basin by analyzing the open drains using an advanced multivariate clustering’, Environmental Monitoring and Assessment, 190 (12). |
[7] | Sorensen P. The chronic water shortage in Cape Town and survival strategies. Int J Environ Stud [Internet]. 2017; 74 (4): 515–27. |
[8] | Ercin, A. E. and Hoekstra, A. Y. (2014) ‘Water footprint scenarios for 2050: A global analysis’, Environment International, 64, pp. 71–82. |
[9] | Ismail, A. H. and Abed, G. A. (2013) ‘BOD and DO modeling for Tigris River at Baghdad city portion using QUAL2K model, pp. 257–273. |
[10] | Bagtzoglou, A. C. and Atmadja, J. (2005) ‘Mathematical Methods for Hydrologic Inversion : The Case of Pollution Source Identification Mathematical Methods for Hydrologic Inversion : The Case of Pollution Source Identification’, (November). |
[11] | Barati Moghaddam M, Mazaheri M, Mohammad Vali Samani J. Inverse modeling of contaminant transport for pollution source identification in surface and groundwaters: a review. Groundw Sustain Dev [Internet]. 2021; 15. |
[12] | Singh, R. M. and Gupta, a. (2017) ‘Water Pollution-Sources, Effects and Control Water Pollution-Sources, Effects and Control’, Research gate, 5 (3), pp. 1–17. |
[13] | Liu, C. yang et al. (2018) ‘Trace elements spatial distribution characteristics, risk assessment and potential source identification in surface water from Honghu Lake, China’, Journal of Central South University, 25 (7), pp. 1598–1611. |
[14] | Zhou Q, Yang N, Li Y, Ren B, Ding X, Bian H, et al. Total concentrations and sources of heavy metal pollution in global river and lake water bodies from 1972 to 2017. Glob Ecol Conserv [Internet]. 2020; 22: e00925. |
[15] | Hameed, M. et al. (2020b) ‘Concerns and Threats of Heavy Metals’ Contamination on Aquatic Ecosystem Chapter 1 Concerns and Threats of Heavy Metals ’ Contamination on Aquatic Ecosystem’, (October). |
[16] | Hu X, Zhang Y, Ding Z, Wang T, Lian H, Sun Y, et al. Bioaccessibility and health risk of arsenic and heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Zn and Mn) in TSP and PM2.5 in Nanjing, China. Atmos Environ [Internet]. 2012; 57: 146–52. |
[17] | Bernard, A. (2008) ‘Cadmium & its adverse effects on human health’, Indian Journal of Medical Research, 128 (4), pp. 557–564. |
[18] | Buxton, S. et al. (2019) ‘Concise Review of Nickel Human Health Toxicology and Ecotoxicology’. |
[19] | Roney, N. et al. (2006) ‘ATSDR evaluation of the health effects of zinc and relevance to public health’, pp. 423–493. |
[20] | Scinicariello, F. et al. (2007) ‘Lead and δ -Aminolevulinic Acid Dehydratase Polymorphism : Where Does It Lead ? A Meta-Analysis’, 115 (1), pp. 35–41. |
[21] | Briffa, J., Sinagra, E. and Blundell, R. (2020) ‘Heliyon Heavy metal pollution in the environment and their toxicological effects on humans’, Heliyon, 6 (August), p. e04691. |
[22] | Wu H, Xu C, Wang J, Xiang Y, Ren M, Qie H, et al. Health risk assessment based on source identification of heavy metals: A case study of Beiyun River, China. Ecotoxicol Environ Saf. 2021; 213. |
[23] | Sahoo MM, Swain JB. Modified heavy metal Pollution index (m-HPI) for surface water Quality in river basins, India. Environ Sci Pollut Res. 2020; 27 (13): 15350–64. |
[24] | Shen, L. Q. et al. (2020) ‘Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework’, Scientific Data, 7 (1), pp. 1–11. |
[25] | Lower, T. et al. (2021) ‘Assessment of Heavy Metal Pollution Levels in Sediments and of Ecological Risk by Quality Indices, Applying a Case Study ’: 2021. |
[26] | Yang, H. et al. (2016) ‘Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo’, Stochastic Environmental Research and Risk Assessment, 30 (2), pp. 507–522. |
[27] | Guo, H. Y., Wang, X. R. and Zhu, J. G. (2004) ‘Quantification and index of non-point source pollution in Taihu Lake region with GIS’, pp. 147–156. |
[28] | Jiang C, Yin L, Li Z, Wen X, Luo X, Hu S, et al. Microplastic pollution in the rivers of the Tibet Plateau. Environ Pollut [Internet]. 2019; 249: 91–8. |
[29] | Cózar, A. et al. (2014) ‘Plastic debris in the open ocean’, Proceedings of the National Academy of Sciences of the United States of America, 111 (28), pp. 10239–10244. |
[30] | Aragaw, T. A. (2021) ‘Microplastic pollution in African countries’ water systems: a review on findings, applied methods, characteristics, impacts, and managements’, SN Applied Sciences, 3 (6). |
[31] | Wagner, M. and Lambert, S. (2018) Freshwater Microplastics - The Handbook of Environmental Chemistry 58. |
[32] | Law, K. L. et al. (2020) ‘The United States’ contribution of plastic waste to land and ocean’, Science Advances, 6 (44), pp. 1–8. |
[33] | Zhang, K. et al. (2018) ‘Microplastic pollution in China’s inland water systems: A review of findings, methods, characteristics, effects, and management’, Science of the Total Environment, 630, pp. 1641–1653. |
[34] | Arnone, R. D. and Walling, J. P. (2007) ‘Waterborne pathogens in urban watersheds’, Journal of Water and Health, 5 (1), pp. 149–162. |
[35] | Pandey, P. K. et al. (2014) ‘Contamination of water resources by pathogenic bacteria’, AMB Express, 4 (1), pp. 1–16. |
[36] | Schwarzenbach, R. P. et al. (2010) ‘Global water pollution and human health’, Annual Review of Environment and Resources, 35 (May 2014), pp. 109–136. |
[37] | E. Ite, A. et al. (2018) ‘Petroleum Hydrocarbons Contamination of Surface Water and Groundwater in the Niger Delta Region of Nigeria’, Journal of Environment Pollution and Human Health, 6 (2), pp. 51–61. |
[38] | State, A. (2018) ‘Total Petroleum Hydrocarbon Content in Surface Water and Sediment of Qua-Iboe DOI : https://dx.doi.org/10.4314/jasem.v22i12.14. |
[39] | Filho, P. J. S. et al. (2013) ‘Studies of n-alkanes in the sediments of Colony Z3 (Pelotas - RS - Brazil)’, Brazilian Journal of Aquatic Science and Technology, 17 (1), p. 27. |
[40] | River, U. et al. (2011) ‘Determination of Total Petroleum Hydrocarbons and Heavy Metals in Surface Water and Sediment of’. |
[41] | Wu, H. et al. (2021) ‘Health risk assessment based on source identification of heavy metals: A case study of Beiyun River, China’, Ecotoxicology and Environmental Safety, 213. |
[42] | Tang P, Jiang Q, Mi L. One-vote veto: The threshold effect of environmental pollution in China’s economic promotion tournament. Ecol Econ [Internet]. 2021; 185 (February): 107069. |
[43] | Gurarslan, G. and Karahan, H. (2015) ‘Résoudre les problèmes inverses d’identification de la source de pollution des eaux souterraines au moyen d’un algorithme d’évolution différentielle’, Hydrogeology Journal, 23 (6), pp. 1109–1119. |
[44] | Yang, H. et al. (2021) ‘Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis–Hastings sampling’, Environmental Science and Pollution Research, 28 (47), pp. 67292–67309. |
[45] | Mazaheri M, Mohammad Vali Samani J, Samani HMV. Mathematical Model for Pollution Source Identification in Rivers. Environ Forensics. 2015; 16 (4): 310–21. |
[46] | Naranjo J, Fuad H, Hakim Z, Panchadria PA, Robbi MS, Yulianti Y, et al. 2016; 12 (1): 579–87. |
[47] | Wang Q, Shan E, Zhang B, Teng J, Wu D, Yang X, et al. Microplastic pollution in intertidal sediments along the coastline of China. Environ Pollut. 2020; 263. |
[48] | Zeunert, S. and Meon, G. (2020) ‘Influence of the spatial and temporal monitoring design on the identification of an instantaneous pollutant release in a river’, Advances in Water Resources, 146 (October), p. 103788. |
[49] | Xin, S. Z. X. (2017) ‘Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm’, Applied Water Science, 7 (4), pp. 1955–1963. |
[50] | Ajami, N. K., Duan, Q. and Sorooshian, S. (2007) ‘An integrated hydrologic Bayesian multimodel combination framework : Confronting input, parameter, and model structural uncertainty in hydrologic prediction’, 43, pp. 1–19. |
[51] | Duan, Q. et al. (2007) ‘Multi-model ensemble hydrologic prediction using Bayesian model averaging’, Advances in Water Resources, 30 (5), pp. 1371–1386. |
[52] | Zhu, Y., Chen, Z. and Asif, Z. (2021) ‘Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis’, Environmental Pollution, 285, p. 34380214. |
[53] | Sharifi, S. et al. (2014) ‘Storm Water Pollution Source Identification in Washington, DC, Using Bayesian Chemical Mass Balance Modeling’, Journal of Environmental Engineering, 140 (3). |
[54] | Di Nardo A, Santonastaso GF, Battaglia R, Musmarra D, Tuccinardi FP, Castaldo F, et al. Smart identification system of surface water contamination by an innovative biosensor network. CEMEPE-5th Int Conf Environ Manag Eng Plan Econ. 2015. |
[55] | Funari, R. et al. (2015) ‘Detection of parathion and patulin by quartz-crystal microbalance functionalized by the photonics immobilization technique’, Biosensors and Bioelectronics, 67, pp. 224–229. Gulgundi, M. S. and Shetty, A. (2016) ‘Identification and Apportionment of Pollution Sources to Groundwater Quality’, Environmental Processes, 3 (2), pp. 451–461. |
[56] | Karim, M. A. et al. (2019) ‘Pollution and Source Identification of Halda River Water of Bangladesh Using Field Observation, Laboratory Analysis and GIS Technique’. |
[57] | Yan, C.-A. et al. (2015) ‘Assessment of Water Quality and Identification of Polluted Risky Regions Based on Field Observations & GIS in the Honghe River Watershed, China’, PLOS ONE. Edited by Y. Hong, 10 (3), p. e0119130. |
[58] | Krishna AK, Satyanarayanan M, Govil PK. 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 (1–3): 366–73. |
[59] | Oktaviana. Africa Educ Rev. 2010; 15 (1): 156–79. Availablefrom: http://epa.sagepub.com/content/15/2/129.short%0Ahttp://joi.jlc.jst.go.jp/JST.Journalarchive/materia1994/46.171 |
[60] | Samsudin, M. S. et al. (2017) ‘River water quality assessment using APCS-MLR and statistical process control in Johor River Basin, Malaysia International Journal of Advanced and Applied Sciences River water quality assessment using APCS-MLR and statistical process’. Available at: https://doi.org/10.21833/ijaas.2017.08.013. |
[61] | Herojeet, R. et al. (2017) ‘Quality characterization and pollution source identification of surface water using multivariate statistical techniques, Nalagarh Valley, Himachal Pradesh, India’, Applied Water Science, 7 (5), pp. 2137–2156. |
[62] | Simeonov, V. et al. (2003) ‘Assessment of the surface water quality in Northern Greece’, Water Research, 37 (17), pp. 4119–4124. |
[63] | Practice, W. et al. (2020) ‘Application of Environmetrics tools for geochemistry, water quality assessment and apportionment of pollution sources in Deepor Beel, Assam ’, (April 2021). |
[64] | Gulgundi MS, Shetty A. Identification and Apportionment of Pollution Sources to Groundwater Quality. Environ Process [Internet]. 2016; 3 (2): 451–61. |
[65] | Angello ZA, Tränckner J, Behailu BM. Spatio-Temporal Evaluation and Quantification of Pollutant Source Contribution in Little Akaki River, Ethiopia : Conjunctive Application of Factor Analysis and Multivariate Receptor Model. 2021; (July 2020). |
[66] | Jain, C. K., Singhal, D. C. and Sharma, M. K. (2007) ‘Estimating nutrient loadings using chemical mass balance approach’, pp. 385–396. |
[67] | Duan, W. et al. (2016) ‘Water quality assessment and pollution source identification of the eastern poyang lake basin using multivariate statistical methods’, Sustainability (Switzerland), 8 (2), pp. 1–15. |
[68] | Simeonova, P., Simeonov, V. and Andreev, G. (2006) ‘Water quality study of the Struma river basin, Bulgaria (1989–1998)’, Open Chemistry, 1 (2), pp. 121–136. |
APA Style
Mohammedsalih Kadir Gobana, Alemayehu Haddis, Dessalegn Dadi. (2023). Surface Water Pollution Source Identification and Quantification: Literature Review. American Journal of Water Science and Engineering, 9(3), 50-57. https://doi.org/10.11648/j.ajwse.20230903.11
ACS Style
Mohammedsalih Kadir Gobana; Alemayehu Haddis; Dessalegn Dadi. Surface Water Pollution Source Identification and Quantification: Literature Review. Am. J. Water Sci. Eng. 2023, 9(3), 50-57. doi: 10.11648/j.ajwse.20230903.11
AMA Style
Mohammedsalih Kadir Gobana, Alemayehu Haddis, Dessalegn Dadi. Surface Water Pollution Source Identification and Quantification: Literature Review. Am J Water Sci Eng. 2023;9(3):50-57. doi: 10.11648/j.ajwse.20230903.11
@article{10.11648/j.ajwse.20230903.11, author = {Mohammedsalih Kadir Gobana and Alemayehu Haddis and Dessalegn Dadi}, title = {Surface Water Pollution Source Identification and Quantification: Literature Review}, journal = {American Journal of Water Science and Engineering}, volume = {9}, number = {3}, pages = {50-57}, doi = {10.11648/j.ajwse.20230903.11}, url = {https://doi.org/10.11648/j.ajwse.20230903.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajwse.20230903.11}, abstract = {Surface waters are important natural resources and widely used for different purpose in human life such as agriculture, industry, municipal services and so on. Using surface water at high rate led to increasing of their pollution and scarcity. This pollution is mainly human made, in some case anthropogenic. Recognizing this problem currently, water pollution source identification and quantification is an active research area. The main objective of this review is to identify different pollution factors of surface water, approaches and methods used by different researchers for identification and quantification this pollution sources. There is different pollution factors surface water such as: heavy metal, micro plastic, nutrients like Nitrogen and phosphorus, waterborne pathogenic microbes, and petroleum hydrocarbons. Different pollution identification and quantification methods were used in different literature based on objectives and scopes of the studies. This include: Inverse Methods, Bayesian Inference, an Innovative Biosensor Network, Differential Evolution (DE) optimization algorithm, Combining Differential Evolution Algorithm (DEA) and Metropolis– Hastings–Markov Chain Monte Carlo (MH–MCMC), Field Observation and Laboratory Analysis, and Multivariate Receptor Model.}, year = {2023} }
TY - JOUR T1 - Surface Water Pollution Source Identification and Quantification: Literature Review AU - Mohammedsalih Kadir Gobana AU - Alemayehu Haddis AU - Dessalegn Dadi Y1 - 2023/07/20 PY - 2023 N1 - https://doi.org/10.11648/j.ajwse.20230903.11 DO - 10.11648/j.ajwse.20230903.11 T2 - American Journal of Water Science and Engineering JF - American Journal of Water Science and Engineering JO - American Journal of Water Science and Engineering SP - 50 EP - 57 PB - Science Publishing Group SN - 2575-1875 UR - https://doi.org/10.11648/j.ajwse.20230903.11 AB - Surface waters are important natural resources and widely used for different purpose in human life such as agriculture, industry, municipal services and so on. Using surface water at high rate led to increasing of their pollution and scarcity. This pollution is mainly human made, in some case anthropogenic. Recognizing this problem currently, water pollution source identification and quantification is an active research area. The main objective of this review is to identify different pollution factors of surface water, approaches and methods used by different researchers for identification and quantification this pollution sources. There is different pollution factors surface water such as: heavy metal, micro plastic, nutrients like Nitrogen and phosphorus, waterborne pathogenic microbes, and petroleum hydrocarbons. Different pollution identification and quantification methods were used in different literature based on objectives and scopes of the studies. This include: Inverse Methods, Bayesian Inference, an Innovative Biosensor Network, Differential Evolution (DE) optimization algorithm, Combining Differential Evolution Algorithm (DEA) and Metropolis– Hastings–Markov Chain Monte Carlo (MH–MCMC), Field Observation and Laboratory Analysis, and Multivariate Receptor Model. VL - 9 IS - 3 ER -