Abstract
High-arsenic (As) groundwater was first discovered in the Yanchi region, Northwest China, which is an arid or semiarid area, and the groundwater quality seriously affects the health of local residents. A comprehensive understanding of the spatiotemporal distribution characteristics, water quality, and health risk of high-As groundwater is indispensable for the sustainable utilization of groundwater sources and resident health. Seventy-nine groundwater samples were collected from different aquifers and seasons. The hazard quotient (HQ) and carcinogenic risk (CR) of As for adults and children were assessed. Moreover, the effects of groundwater sampling site and seasonal change on As concentration were investigated. Then, the random forest method was used to evaluate the importance of the indicators and the influence of these important indicators on groundwater classification. Thirty-three percent of the groundwater samples had HQ values > 1, and the CR values of all groundwater > 1.00 × 10−6 for children, representing a serious health risk. Twenty-one percent of the groundwater samples had health risk for adult. High-As groundwater is present at depths less than 60 m, and groundwater As concentrations are slightly affected by seasonal changes. The random forest shows that the most important indicators that affect groundwater quality are Na, TDS, TH, and F, and the least important is As. Furthermore, the optimal set of indicators contained all four of the most important indicators obtained by the random forest model, which achieved a classification accuracy of 88.21% for groundwater quality.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmad SA, Gulzar A, Rehman HU, Soomro ZA, Hussain M, Rehman M, Qadir MA (2013) Study of arsenic in drinking water of Distric Kasur Pakistan. World Appl Sci J 24(5): 634–640. https://doi.org/10.58129/idosi.wasj.2013.24.05.13228
Anawar HM, Akai J, Sakugawa H (2004) Mobilization of arsenic from subsurface sediments by effect of bicarbonate ions in groundwater. Chemosphere 54(6):753–762. https://doi.org/10.1016/j.chemosphere.2003.08.030
Azizullah A, Khattak MNK, Richter P, Hader DP (2011) Water pollution in Pakistan and its impact on public health-a review. Environ Int 37(2):479–497. https://doi.org/10.1016/j.envint.2010.10.007
Baudron P, Alonso-Sarría F, García-Aróstegui JL, Cánovas-García F, Martínez-Vicente D, Moreno-Brotóns J (2013) Identifying the origin of groundwater samples in a multi-layer aquifer system with random forest classification. J Hydrol 499:303–315. https://doi.org/10.1016/j.jhydrol.2013.07.009
Bindal S, Singh CK (2019) Predicting groundwater arsenic contamination: regions at risk in highest populated state of India. Water Res 159:65–76. https://doi.org/10.1016/j.watres.2019.04.054
Birkle P, Alvarado IST (2010) Water-rock interaction XIII. CRC Press, Boca Raton
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Chappells H, Parker L, Fernandez CV, Conrad C, Drage JO, Toole G, Campbell N, Dummer TJ (2014) Arsenic in private drinking water wells: an assessment of jurisdictional regulations and guidelines for risk remediation in North America. J Water Health. 12(3):372–392. https://doi.org/10.2166/wh.2014.054
Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JL (2007) Random forests for classification in ecology. Ecology 88(11):2783–2792. https://doi.org/10.1890/07-0539.1
Duan L, Wang W, Zhou L, Cheng Z (2016) The formation of shallow fresh groundwater in the north of Yanchi County, Ningxia, China: main influencing factors and mechanism. Environ Earth Sci 75(6):461. https://doi.org/10.1007/s12665-016-5333-y
Farooqi A (2015) Arsenic and Fluoride contamination: a Pakistan perspective. Springer, New York
Ghimire B, Rogan J, Miller J (2010) Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sens Lett 1(1):45–54. https://doi.org/10.1080/01431160903252327
Guo HM, Zhang Y, Xing LN, Jia YF (2012) Spatial variation in arsenic and fluoride concentrations of shallow groundwater from the town of Shahai in the Hetao basin, Inner Mongolia. Appl Geochem 27(11):2187–2196. https://doi.org/10.1016/j.apgeochem.2012.01.016
Guo HM, Zhang D, Wen DG, Wu Y, Ni P, Jiang YX, Guo Q, Li FL, Zheng H, Zhou YZ (2014) Arsenic mobilization in aquifers of the southwest Songnen basin, P.R. China: evidences from chemical and isotopic characteristics. Sci Total Environ 490:590–602. https://doi.org/10.1016/j.scitotenv.2014.05.050
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd ed. Springer, p 745, ISBN: 978-0-387-84857-0
He S, Wu J (2019) Hydrogeochemical characteristics, groundwater quality and health risks from hexavalent chromium and nitrate in groundwater of Huanhe Formation in Wuqi County, northwest China. Expo Health 11(2):125–137. https://doi.org/10.1007/s12403-018-0289-7
He X, Wu J, He S (2019) Hydrochemical characteristics and quality evaluation of groundwater in terms of health risks in Luohe aquifer in Wuqi County of the Chinese Loess Plateau, northwest China. Hum Ecol Risk Assess 25(1–2):32–51. https://doi.org/10.1080/10807039.2018.1531693
Lee YJ, Park C, Lee ML (2018) Identification of a contaminant source location in a river system using random forest models. Water 10(4):391. https://doi.org/10.3390/w10040391
Liaw A, Wiener M (2002) Classification and regression by random Forest. R News. 2(3):18–22
Li P, Qian H (2011) Human health risk assessment for chemical pollutants in drinking water source in Shizuishan City, Northwest China. Iran J Environ Health Sci Eng 8(1):41–48
Li P, Wu J, Qian H (2014) Hydrogeochemistry and quality assessment of shallow groundwater in the southern part of the Yellow River alluvial plain (Zhongwei section), Northwest China. Earth Sci Res J 18(1), 27–38. https://doi.org/10.15446/esrj.v18n1.34048
Li P, Li X, Meng X, Li M, Zhang Y (2016) Appraising groundwater quality and health risks from contamination in a semiarid region of northwest China. Expo Health. 8(3):361–379. https://doi.org/10.1007/s12403-016-0205-y
Li P, Qian H, Wu J (2018) Conjunctive use of groundwater and surface water to reduce soil salinization in the Yinchuan Plain, North-West China. Int J Water Resour Dev 34(3):337–353. https://doi.org/10.1080/07900627.2018.1443059
Li P, He X, Guo W (2019a) Spatial groundwater quality and potential health risks due to nitrate ingestion through drinking water: a case study in Yan’an City on the Loess Plateau of northwest China. Hum Ecol Risk Assess 25(1–2):11–31. https://doi.org/10.1080/10807039.2018.1553612
Li P, He X, Li Y, Xiang G (2019b) Occurrence and health implication of fluoride in groundwater of loess aquifer in the Chinese loess plateau: a case study of Tongchuan Northwest China. Expo Health 11(2):95–107. https://doi.org/10.1007/s12403-018-0278-x
McArthur J, Banerjee D, Sengupta S, Ravenscroft P, Klump S, Sarkar A, Disch B, Kipfer R (2010) Migration of As, and 3H/3He ages, in groundwater from West Bengal: implications for monitoring. Water Res 44(4):4171–4185. https://doi.org/10.1016/j.watres.2010.05.010
Memon M, Soomro MS, Akhtar MS, Memon KS (2011) Drinking water quality assessment in Southern Sindh (Pakistan). Environ Monit Assess. 177(1–4):39–50. https://doi.org/10.1007/s10661-010-1616-z
Ministry of Environmental Protection of the P.R. China (2014) Technical guidelines for risk assessment of contaminated sites, (HJ 25.3–2014). China Environmental Science Press, Beijing (in Chinese)
Ministry of Land and Resources of the P.R. China (2015) Standard for groundwater quality (DZ/T 0290–2015). National standard for geological exploration of mineral resources (in Chinese)
Muhammad S, Shah MT, Khan S (2010) Arsenic health risk assessment in drinking water and source apportionment using multivariate statistical techniques in Kohistan region, northern Pakistan. Food Chem Toxicol. 48(10):0278–2864. https://doi.org/10.1016/j.fct.2010.07.018
Naghibi SA, Ahmadi K, Daneshi A (2017) Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resour Manag 31(9):2761–2775. https://doi.org/10.1007/s11269-017-1660-3
Niazi NK, Bibi I, Shahid M, Ok YS, Burton ED, Wang H, Shaheen SM, Rinklebe J, Lüttge A (2018) Arsenic removal by perilla leaf biochar in aqueous solutions and groundwater: an integrated spectroscopic and microscopic examination. Environ Pollut 232:31–41. https://doi.org/10.1016/j.envpol.2017.09.051
Nickson RT, Mcarthur JM, Shrestha B, Kyaw-Myint TO, Lowry D (2004) Arsenic and other drinking water quality issues, Muzaffargarh District, Pakistan. Appl Geochem 20(1):55–68. https://doi.org/10.1016/j.apgeochem.2004.06.004
Nicolli HB, Bundschuh J, García JW, Falcón CM, Jean JS (2010) Sources and controls for the mobility of arsenic in oxidizing groundwaters from loess-type sediments in arid/semi-arid dry climates-evidence from the Chaco-Pampean plain (Argentina). Water Res 44(19):5589–5604. https://doi.org/10.1016/j.watres.2010.09.029
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O (2012) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2):181–199. https://doi.org/10.1007/s10021-005-0054-1
Qian C, Wu X, Mu WP, Fu RZ, Zhu G, Wang ZR, Wang DD (2016) Hydrogeochemical characterization and suitability assessment of groundwater in an agro-pastoral area, Ordos Basin NW China. Environ Earth Sci. 75(20):1356. https://doi.org/10.1007/s12665-016-6123-2
Radfard MM, Yunesian M, Nodehi RN, Biglari H, Nazmara S, Hadi M (2018) Drinking water quality and arsenic health risk assessment in Sistan and Baluchestan, Southeastern Province, Iran. Hum Ecol Risk Assess 25(4):949–965. https://doi.org/10.1080/10807039.2018.1458210
Rasool A, Farooqi A, Xiao T, Masood S, Kamran MA (2016) Elevated levels of arsenic and trace metals in drinking water of Tehsil Mailsi, Punjab Pakistan. J Geochem Explor 169:89–99. https://doi.org/10.1016/j.gexplo.2016.07.013
Ravindra K, Mor S (2019) Distribution and health risk assessment of arsenic and selected heavy metals in Groundwater of Chandigarh India. Environ Pollut 250:820–830. https://doi.org/10.1016/j.envpol.2019.03.080
Sahoo S, Russo TA, Elliott J, Foster I (2017) Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resour Res 53(5):3878–3895. https://doi.org/10.1002/2016WR019933
Sanaullah M, Mehmood Q, Ahmad SR, Rehman HU (2016) Arsenic contamination trends of abandoned river banks: a case study at the left bank of river Ravi, Lahore. Int J Econ Environ Geol 6(1):21–24
Savarimuthu X, Hira-Smith MM, Yuan Y, von Ehrenstein OS, Das S, Ghosh N, Mazumder DG, Smith AH (2006) Seasonal variation of arsenic concentrations in tubewells in West Bengal, India. J Health Popul Nutr 24(3):277–281
Shahid M, Khalid M, Dumat C, Khalid S, Niazi NK, Imran M, Bibi I, Ahmad I, Hammad HM, Tabassum RA (2018) Arsenic level and risk assessment of groundwater in Vehari, Punjab Province, Pakistan. Expo Health. 10(4):229–239. https://doi.org/10.1007/s12403-017-0257-7
Shakoor MB, Bibi I, Niazi NK, Shahid M, Nawaz MF, Farooqi A, Naidu R, Rahman MM, Murtaza G, Lüttge A (2018) The evaluation of arsenic contamination potential, speciation and hydrogeochemical behaviour in aquifers of Punjab, Pakistan. Chemosphere 199:737–746. https://doi.org/10.1016/j.chemosphere.2018.02.002
Sharifi R, Moore F, Keshavarzi B, Badiei S (2018) Assessment of health risks of arsenic exposure via consumption of crops. Water Qual Expos Health 10(2):129–143. https://doi.org/10.1007/s12403-017-0250-1
Sultana J, Farooqi A, Ali U (2014) Arsenic concentration variability, health risk assessment, and source identification using multivariate analysis in selected villages of public water system, Lahore Pakistan. Environ Monit Assess 186(2):1241–1251. https://doi.org/10.1007/s10661-013-3453-3
Tesoriero AJ, Gronberg JA, Juckem PF, Miller MP, Austin BP (2017) Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification. Water Resour Res 53(8):7316–7331. https://doi.org/10.1002/2016WR020197
Toor I, Tahir S (2009) Study of arsenic concentration levels in Pakistani drinking water. Pol J Environ Stud 18(5):907–912
Wang YX, Shvartsev SL, Su CL (2009) Genesis of arsenic/fluoride-enriched soda water: a case study at Datong Northern China. Appl Geochem 24(4):641–649. https://doi.org/10.1016/j.apgeochem.2008.12.015
Wang WL, Wu QY, Wang C, He T, Hu HY (2015) Health risk assessment of phthalate esters (paes) in drinking water sources of china. Environ Sci Pollut Res 22(5):3620–3630. https://doi.org/10.1007/s11356-014-3615-z
Wang Z, Guo HM, Xiu W, Wang J, Shen MM (2018) High arsenic groundwater in the Guide basin, northwestern China: distribution and genesis mechanisms. Sci Total Environ 640–641:194–206. https://doi.org/10.1016/j.scitotenv.2018.05.255
Wu J, Sun Z (2016) Evaluation of shallow groundwater contamination and associated human health risk in an alluvial plain impacted by agricultural and industrial activities, mid-west China. Expo Health 8(3):311–329. https://doi.org/10.1007/s12403-015-0170-x
Wu C, Wu X, Zhu G, Qian C, Mu WP, Zhang YZ (2018a) Influence of a power plant in Ezhou city on the groundwater environment in the nearby area. Environ Earth Sci 77(13):503. https://doi.org/10.1007/s12665-018-7674-1
Wu C, Wu X, Qian C, Zhu G (2018b) Hydrogeochemistry and groundwater quality assessment of high fluoride levels in the Yanchi endorheic region, northwest China. Appl Geochem 98:404–417. https://doi.org/10.1016/j.apgeochem.2018.10.016
Wu J, Zhou H, He S, Zhang Y (2019) Comprehensive understanding of groundwater quality for domestic and agricultural purposes in terms of health risks in a coal mine area of the Ordos basin, north of the Chinese Loess Plateau. Environ Earth Sci 78(15):446. https://doi.org/10.1007/s12665-019-8471-1
Yan BW, Chen L (2013) Coincidence probability of precipitation for the middle route of south-to-north water transfer project in China. J Hydrol 499(9):19–26. https://doi.org/10.1016/j.jhydrol.2013.06.040
Yang J, Griffiths J, Zammit C (2019) National classification of surface–groundwater interaction using random forest machine learning technique. River Res Appl 35(7):932–943. https://doi.org/10.1002/rra.3449
Zubair A, Khan A, Khan M (2014) Incidence and allocation of arsenic in the coastal area of Sindh, Pakistan. World Appl Sci J 32(5), 945–951. https://doi.org/10.58129/idosi.wasj.2014.32.05.34
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 41572227) and the National Key Research and Development Program of China (No. 2018YFC0406404).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, C., Fang, C., Wu, X. et al. Health-Risk Assessment of Arsenic and Groundwater Quality Classification Using Random Forest in the Yanchi Region of Northwest China. Expo Health 12, 761–774 (2020). https://doi.org/10.1007/s12403-019-00335-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12403-019-00335-7