Appraisal of pollution and health risks associated with coal mine contaminated soil using multimodal statistical and Fuzzy-TOPSIS approaches
Sumit Kumar, Sonali Banerjee, Saibal Ghosh, Santanu Majumder, Jajati Mandal, Pankaj Kumar Roy, Pradip Bhattacharyya
Appraisal of pollution and health risks associated with coal mine contaminated soil using multimodal statistical and Fuzzy-TOPSIS approaches
● Farmlands impacted by coal mines, contained heavy metals like Pb and Cr.
● HMs in contaminated soils and rice grains were above the permissible limits.
● Source classification and apportionment were analyzed by SOM and PMF models.
● Fuzzy-TOPSIS showed Ni to be mostly responsible for the toxicity in the rice grain.
● Health risk analysis predicted high carcinogenic risk.
The present study assesses the concentration, probabilistic risk, source classification, and dietary risk arising from heavy metal (HMs) pollution in agricultural soils affected by coal mining in eastern part of India. Analyses of soil and rice plant indicated significantly elevated levels of HMs beyond the permissible limit in the contaminated zones (zone 1: PbSoil: 108.24 ± 72.97, CuSoil: 57.26 ± 23.91, CdSoil: 8.44 ± 2.76, CrSoil: 180.05 ± 46.90, NiSoil: 70.79 ± 25.06 mg/kg; PbGrain: 0.96 ± 0.8, CuGrain: 8.6 ± 5.1, CdGrain: 0.65 ± 0.42, CrGrain: 4.78 ± 1.89, NiGrain: 11.74 ± 4.38 mg/kg. zone 2: PbSoil: 139.56 ± 69.46, CuSoil: 69.89 ± 19.86, CdSoil: 8.95 ± 2.57, CrSoil: 245.46 ± 70.66, NiSoil: 95.46 ± 22.89 mg/kg; PbGrain: 1.27 ± 0.84, CuGrain: 7.9 ± 4.57, CdGrain: 0.76 ± 0.43, CrGrain: 8.6 ± 1.58, NiGrain: 11.50 ± 2.46 mg/kg) compared to the uncontaminated zone (zone 3). Carcinogenic and non-carcinogenic health risks were computed based on the HMs concentration in the soil and rice grain, with Pb, Cr, and Ni identified as posing a high risk to human health. Monte Carlo simulation, the solubility-free ion activity model (FIAM), and severity adjusted margin of exposure (SAMOE) were employed to predict health risk. FIAM hazard quotient (HQ) values for Ni, Cr, Cd, and Pb were > 1, indicating a significant non-carcinogenic risk. SAMOE (risk thermometer) results for contaminated zones ranged from low to moderate risk (CrSAMOE: 0.05, and NiSAMOE: 0.03). Fuzzy-TOPSIS and variable importance plots (from random forest) showed that Ni and Cr were mostly responsible for the toxicity in the rice plant, respectively. A self-organizing map for source classification revealed common origin for the studied HMs with zone 2 exhibiting the highest contamination. The positive matrix factorization model for the source apportionment identified coal mining and transportation as the predominant sources of HMs. Spatial distribution analysis indicated higher contamination near mining sites as compared to distant sampling sites. Consequently, this study will aid environmental scientists and policymakers controlling HM pollution in agricultural soils near coal mines.
Coal mine / Free ion activity model / Monto Carlo Simulation / Pollution and Health risk / Fuzzy-TOPSIS
[1] |
Al osman M, Yang F, Massey I Y. (2019). Exposure routes and health effects of heavy metals on children. Biometals, 32(4): 563–573
CrossRef
Google scholar
|
[2] |
Antoine J M, Fung L A H, Grant C N. (2017). Assessment of the potential health risks associated with the aluminium, arsenic, cadmium and lead content in selected fruits and vegetables grown in Jamaica. Toxicology Reports, 4: 181–187
CrossRef
Google scholar
|
[3] |
Banerjee S, Ghosh S, Jha S, Kumar S, Mondal G, Sarkar D, Datta R, Mukherjee A, Bhattacharyya P. (2023). Assessing pollution and health risks from chromite mine tailings contaminated soils in India by employing synergistic statistical approaches. Science of the Total Environment, 880: 163228
CrossRef
Google scholar
|
[4] |
Chen H, Teng Y, Lu S, Wang Y, Wang J. (2015). Contamination features and health risk of soil heavy metals in China. Science of the Total Environment, 512-513: 143–153
CrossRef
Google scholar
|
[5] |
Chen L, Zhou M, Wang J, Zhang Z, Duan C, Wang X, Zhao S, Bai X, Li Z, Fang L. (2022). A global meta-analysis of heavy metal (loid) s pollution in soils near copper mines: evaluation of pollution level and probabilistic health risks. Science of the Total Environment, 835: 155441
CrossRef
Google scholar
|
[6] |
Chowdhury N R, Das A, Joardar M, De A, Mridha D, Das R, Rahman M M, Roychowdhury T. (2020). Flow of arsenic between rice grain and water: its interaction, accumulation, and distribution in different fractions of cooked rice. Science of the Total Environment, 731: 138937
CrossRef
Google scholar
|
[7] |
CoalIndia (2020). About the company. Available at the website of
|
[8] |
Cortes-Ramirez J, Naish S, Sly P D, Jagals P. (2018). Mortality and morbidity in populations in the vicinity of coal mining: a systematic review. BMC Public Health, 18(1): 721
CrossRef
Google scholar
|
[9] |
Datta S P, Young S D. (2005). Predicting metal uptake and risk to the human food chain from leaf vegetables grown on soils amended by long-term application of sewage sludge. Water, Air, and Soil Pollution, 163(1–4): 119–136
CrossRef
Google scholar
|
[10] |
Fasinu P S, Orisakwe O E. (2013). Heavy metal pollution in sub-Saharan Africa and possible implications in cancer epidemiology. Asian Pacific Journal of Cancer Prevention, 14(6): 3393–3402
CrossRef
Google scholar
|
[11] |
Ghosh S, Banerjee S, Prajapati J, Mandal J, Mukherjee A, Bhattacharyya P. (2023). Pollution and health risk assessment of mine tailings contaminated soils in India from toxic elements with statistical approaches. Chemosphere, 324: 138267
CrossRef
Google scholar
|
[12] |
Ghosh S, Mondal S, Mandal J, Mukherjee A, Bhattacharyya P. (2024). Effect of metal fractions on rice grain metal uptake and biological parameters in mica mines waste contaminated soils. Journal of Environmental Sciences (China), 136: 313–324
CrossRef
Google scholar
|
[13] |
Golui D, Guha Mazumder D N, Sanyal S K, Datta S P, Ray P, Patra P K, Sarkar S, Bhattacharya K. (2017). Safe limit of arsenic in soil in relation to dietary exposure of arsenicosis patients from Malda district, West Bengal: a case study. Ecotoxicology and Environmental Safety, 144: 227–235
CrossRef
Google scholar
|
[14] |
Goumenou M, Tsatsakis A. (2019). Proposing new approaches for the risk characterisation of single chemicals and chemical mixtures: the source related Hazard Quotient (HQS) and Hazard Index (HIS) and the adversity specific Hazard Index (HIA). Toxicology Reports, 6: 632–636
CrossRef
Google scholar
|
[15] |
Hakanson L. (1980). An ecological risk index for aquatic pollution control. A sedimentological approach. Water Research, 14(8): 975–1001
CrossRef
Google scholar
|
[16] |
InternationalAgency for Research on Cancer (1990). Chromium, Nickel, and Welding. Geneva: IARC Monographs on the Evaluation of Carcinogenic Risks to Human/World Health Organization
|
[17] |
IRIS (2019). Integrated risk information system-database, US Environmental Protection Agency.
|
[18] |
Jiang Y, Chao S, Liu J, Yang Y, Chen Y, Zhang A, Cao H. (2017). Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China. Chemosphere, 168: 1658–1668
CrossRef
Google scholar
|
[19] |
Jolly Y N, Rakib M R J, Sakib M S, Shahadat M A, Rahman A, Akter S, Kabir J, Rahman M S, Begum B A, Rahman R, Sulieman A, Tamam N, Khandaker M U, Idris A M. (2022). Impact of industrially affected soil on humans: a soil-human and soil-plant-human exposure assessment. Toxics, 10(7): 347–371
CrossRef
Google scholar
|
[20] |
Kabir M H, Kormoker T, Shammi R S, Tusher T R, Islam M S, Khan R, Omor M Z, Sarker M E, Yeasmin M, Idris A M. (2022). A comprehensive assessment of heavy metal contamination in road dusts along a hectic national highway of Bangladesh: spatial distribution, sources of contamination, ecological and human health risks. Toxin Reviews, 41(3): 860–879
CrossRef
Google scholar
|
[21] |
Khaledian Y, Pereira P, Brevik E C, Pundyte N, Paliulis D. (2017). The influence of organic carbon and pH on heavy metals, potassium, and magnesium levels in Lithuanian Podzols. Land Degradation & Development, 28(1): 345–354
CrossRef
Google scholar
|
[22] |
Kim H S, Kim Y J, Seo Y R. (2015). An overview of carcinogenic heavy metal: molecular toxicity mechanism and prevention. Journal of Cancer Prevention, 20(4): 232–240
CrossRef
Google scholar
|
[23] |
Li K, Cui S, Zhang F, Hough R, Fu Q, Zhang Z, Gao S, An L. (2020). Concentrations, possible sources and health risk of heavy metals in multi-media environment of the Songhua River, China. International Journal of Environmental Research and Public Health, 17(5): 1766–1782
CrossRef
Google scholar
|
[24] |
Liang J, Feng C, Zeng G, Gao X, Zhong M, Li X, Li X, He X, Fang Y. (2017). Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan, China. Environmental Pollution, 225: 681–690
CrossRef
Google scholar
|
[25] |
Lindsay W L, Norvell W. (1978). Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Science Society of America Journal, 42(3): 421–428
CrossRef
Google scholar
|
[26] |
Mandal J, Bakare W A, Rahman M M, Rahman M A, Siddique A B, Oku E, Wood M D, Hutchinson S M, Mondal D. (2022). Varietal differences influence arsenic and lead contamination of rice grown in mining impacted agricultural fields of Zamfara State, Nigeria. Chemosphere, 305: 135339
CrossRef
Google scholar
|
[27] |
Mandal J, Golui D, Datta S P. (2019). Assessing equilibria of organo-arsenic complexes and predicting uptake of arsenic by wheat grain from organic matter amended soils. Chemosphere, 234: 419–426
CrossRef
Google scholar
|
[28] |
Men C, Liu R, Wang Q, Guo L, Miao Y, Shen Z. (2019). Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system. Science of the Total Environment, 652: 27–39
CrossRef
Google scholar
|
[29] |
Ministryof Coal GOI (2018). Coal Reserves in India. Ministry of Coal Government of India.
|
[30] |
Mirecki N, Agic R, Sunic L, Milenkovic L, Ilic Z S. (2015). Transfer factor as indicator of heavy metals content in plants. Fresenius Environmental Bulletin, 24(11c): 4212–4219
|
[31] |
Nakagawa K, Yu Z Q, Berndtsson R, Hosono T. (2020). Temporal characteristics of groundwater chemistry affected by the 2016 Kumamoto earthquake using self-organizing maps. Journal of Hydrology (Amsterdam), 582: 124519
CrossRef
Google scholar
|
[32] |
Núñez O, Fernández-Navarro P, Martín-Méndez I, Bel-Lan A, Locutura J F, López-Abente G. (2016). Arsenic and chromium topsoil levels and cancer mortality in Spain. Environmental Science and Pollution Research International, 23(17): 17664–17675
CrossRef
Google scholar
|
[33] |
Onyedikachi U B, Belonwu D C, Wegwu M O. (2018). Human health risk assessment of heavy metals in soils and commonly consumed food crops from quarry sites located at Isiagwu, Ebonyi State. Analele Universitatii Ovidius Constanta. Seria Chimie, 29(1): 8–24
CrossRef
Google scholar
|
[34] |
PageA L, Miller R H, KeeneyD R (1982). Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties. Madison: American Society of Agronomy
|
[35] |
Park Y S, Céréghino R, Compin A, Lek S. (2003). Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling, 160(3): 265–280
CrossRef
Google scholar
|
[36] |
Raj D, Kumar A, Tripti S K. (2022). Health risk assessment of children exposed to the soil containing potentially toxic elements: a case study from coal mining areas. Metals, 12(11): 1795–1805
CrossRef
Google scholar
|
[37] |
Rezaei Kalantary R R, Barzegar G, Jorfi S. (2022). Monitoring of pesticides in surface water, pesticides removal efficiency in drinking water treatment plant and potential health risk to consumers using Monte Carlo simulation in Behbahan City, Iran. Chemosphere, 286: 131667
CrossRef
Google scholar
|
[38] |
Ruggieri F, Majorani C, Domanico F, Alimonti A. (2017). Mercury in children: current state on exposure through human biomonitoring studies. International Journal of Environmental Research and Public Health, 14(5): 519–546
CrossRef
Google scholar
|
[39] |
Saif-Ud-Din A S, Hussain S, Hussain J, Luqman M, Hussain J, Ali S. (2022). Evaluation of heavy metal contamination in indigenous fruits and associated human health risk: evidence from Fuzzy-TOPSIS approach. Global NEST Journal, 24(3): 435–444
|
[40] |
SandS, Bjerselius R, BuskL, EnerothH, Sanner-F’arnstrand J, LindqvistR (2015). The risk thermometer: a tool for risk comparison. Swedish National Food Agency
|
[41] |
Sengupta S, Bhattacharyya K, Mandal J, Bhattacharya P, Halder S, Pari A. (2021). Deficit irrigation and organic amendments can reduce dietary arsenic risk from rice: introducing machine learning-based prediction models from field data. Agriculture, Ecosystems & Environment, 319: 107516
CrossRef
Google scholar
|
[42] |
Singh K R, Dutta R, Kalamdhad A S, Kumar B. (2019). Information entropy as a tool in surface water quality assessment. Environmental Earth Sciences, 78(1): 15–27
CrossRef
Google scholar
|
[43] |
Tomlinson D L, Wilson J G, Harris C R, Jeffrey D W. (1980). Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgoland Marine Research, 33(1–4): 566–575
CrossRef
Google scholar
|
[44] |
Tong R, Cheng M, Yang X, Yang Y, Shi M. (2019). Exposure levels and health damage assessment of dust in a coal mine of Shanxi Province, China. Process Safety and Environmental Protection, 128: 184–192
CrossRef
Google scholar
|
[45] |
USEPA . (1986). Guidelines for the health risk assessment of chemical mixtures. Federal Register, 51(185): 34014–34025
|
[46] |
USEPA (1989). Risk assessment guidance for superfund. Volume I: Human health evaluation manual (Part A). EPA/540/1–89/002
|
[47] |
Wang L, Rinklebe J, Tack F M, Hou D. (2021). A review of green remediation strategies for heavy metal contaminated soil. Soil Use and Management, 37(4): 936–963
CrossRef
Google scholar
|
[48] |
Wang Z, Xiao J, Wang L, Liang T, Guo Q, Guan Y, Rinklebe J. (2020). Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environmental Pollution, 260: 114065
CrossRef
Google scholar
|
[49] |
WHO (1996). World Health Organization Permissible Limits of Heavy Metals in Soil and Plants
|
[50] |
WHO (2022). Lead Poisoning.
|
[51] |
Xiao X, Zhang J, Wang H, Han X, Ma J, Ma Y, Luan H. (2020). Distribution and health risk assessment of potentially toxic elements in soils around coal industrial areas: a global meta-analysis. Science of the Total Environment, 713: 135292
CrossRef
Google scholar
|
[52] |
Yan G, Mao L, Liu S, Mao Y, Ye H, Huang T, Li F, Chen L. (2018). Enrichment and sources of trace metals in roadside soils in Shanghai, China: a case study of two urban/rural roads. Science of the Total Environment, 631-632: 942–950
CrossRef
Google scholar
|
[53] |
Zakir H M, Quadir Q F, Mollah M Z I. (2021). Human health risk assessment of heavy metals through the consumption of common foodstuffs collected from two divisional cities of Bangladesh. Exposure and Health, 13(2): 253–268
CrossRef
Google scholar
|
[54] |
Zerizghi T, Guo Q, Tian L, Wei R, Zhao C. (2022). An integrated approach to quantify ecological and human health risks of soil heavy metal contamination around coal mining area. Science of the Total Environment, 814: 152653
CrossRef
Google scholar
|
[55] |
Zhang H, Jiang Y, Ding M, Xie Z. (2017). Level, source identification, and risk analysis of heavy metal in surface sediments from river-lake ecosystems in the Poyang Lake, China. Environmental Science and Pollution Research International, 24(27): 21902–21916
CrossRef
Google scholar
|
[56] |
Zhang H B, Luo Y M, Wong M H, Zhao Q G, Zhang G L. (2007). Defining the geochemical baseline: a case of Hong Kong soils. Environmental Geology, 52(5): 843–851
CrossRef
Google scholar
|
[57] |
Zhao F J, Wang P. (2020). Arsenic and cadmium accumulation in rice and mitigation strategies. Plant and Soil, 446(1–2): 1–21
CrossRef
Google scholar
|
/
〈 | 〉 |