High Arsenic Risk Distribution Prediction of Groundwater in the Hetao Basin by Random Forest Modeling

FU Yu; CAO Wen-geng; ZHANG Juan-juan

doi:10.15898/j.cnki.11-2131/td.202108170099

Rock and Mineral Analysis > 2021 > 40(6): 860-870. > DOI: 10.15898/j.cnki.11-2131/td.202108170099

FU Yu, CAO Wen-geng, ZHANG Juan-juan. High Arsenic Risk Distribution Prediction of Groundwater in the Hetao Basin by Random Forest Modeling[J]. Rock and Mineral Analysis, 2021, 40(6): 860-870. DOI: 10.15898/j.cnki.11-2131/td.202108170099

Citation:

PDF (13398 KB)

High Arsenic Risk Distribution Prediction of Groundwater in the Hetao Basin by Random Forest Modeling

1.
North China University of Water Resources and Electric Power, Zhengzhou 450046, China
2.
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
3.
The 6th Geological Team, Hebei Bureau of Geology and Mineral Resources, Shijiazhuang 050085, China

More Information

Received Date: August 16, 2021
Revised Date: September 07, 2021
Accepted Date: September 20, 2021
Published Date: November 27, 2021

Abstract

HIGHLIGHTS
(1) A random forest model was established to identify the probability distribution of high arsenic areas in different seasons driven by climate factors.

(2) Climate variables (precipitation and drought index) were significantly correlated with arsenic accumulation in aquifers.

(3) High arsenic risk areas in groundwater were concentrated in the depositional center of the Hetao Basin, and the area of high arsenic risk areas in winter was smaller than that in summer.

ABSTRACT

BACKGROUNDArsenic pollution is a serious problem in shallow groundwater in the Hetao Basin, and has seriously affected the health of residents. The research on the distribution of high arsenic shallow groundwater in the Hetao Basin is limited by the sampling time and sample number.
OBJECTIVESTo obtain a comprehensive understanding of the risk distribution characteristics and important influencing factors of high arsenic groundwater in different seasons in the region.
METHODSBased on 506 shallow groundwater samples and 9 surface environmental parameters as prediction variables, a random forest model was established to evaluate the importance of prediction variables and the impact of important variables on high arsenic groundwater. Taking the climate factors as the dynamic prediction variables, the probability distribution of high arsenic groundwater in different seasons was identified and thematic maps of risk areas were made.
RESULTSThe results showed that the arsenic content of 506 groundwater samples ranged from 0.05 to 916.7μg/L with an overshoot rate (>10μg/L) of 50%. Groundwater arsenic risk areas were mainly distributed in the depositional center of the Hetao Basin, but the area of groundwater arsenic risk areas decreased by 1907km² in winter, accounting for 14.14% of the total area. Precipitation and drought index, influence of drainage and irrigation channels, potential evapotranspiration and temperature were the most important indexes affecting the high arsenic groundwater in this area.
CONCLUSIONSIn the Hetao Basin, climate variables (precipitation and drought index) are significantly correlated with arsenic accumulation in the aquifer, which controls the seasonal variation of groundwater with high arsenic content in the depositional center of the Hetao Basin.
- groundwater,
- arsenic pollution,
- Hetao Basin,
- random forest,
- seasonal variation,
- risk distribution

FullText(HTML)

References (40)

References

Polya D A, Middleton D R. Arsenic in drinking water: Sources & human exposure[M]//Bhattacharya P, Polya D A, Draganovic D. Best practice guide on the control of arsenic in drinking water (The 1st edition). London: International Water Association Publishing, 2017.

Tong J, Guo H, Wei C. Arsenic contamination of the soil-wheat system irrigated with high arsenic groundwater in the Hetao Basin, Inner Mongolia, China[J]. Science of the Total Environment, 2014, 496: 479-487. doi: 10.1016/j.scitotenv.2014.07.073

杨文蕾, 沈亚婷. 水稻对砷吸收的机理及控制砷吸收的农艺途径研究进展[J]. 岩矿测试, 2020, 39(4): 475-492. doi: 10.15898/j.cnki.11-2131/td.202004160052

Yang W L, Shen Y T. A review of research progress on the absorption mechanism of arsenic and agronomic pathways to control arsenic absorption[J]. Rock and Mineral Analysis, 2020, 39(4): 475-492. doi: 10.15898/j.cnki.11-2131/td.202004160052

Wang Y, Pi K, Fendorf S, et al. Sedimentogenesis and hydrobiogeochemistry of high arsenic Late Pleistocene-Holocene aquifer systems[J]. Earth-Science Reviews, 2019, 189: 79-98. doi: 10.1016/j.earscirev.2017.10.007

Podgorski J, Berg M. Global threat of arsenic in ground-water[J]. Science, 2020, 368: 845-850. doi: 10.1126/science.aba1510

Jia Y, Guo H, Jiang Y, et al. Hydrogeochemical zonation and its implication for arsenic mobilization in deep groundwaters near alluvial fans in the Hetao Basin, Inner Mongolia[J]. Journal of Hydrology, 2014, 518(Part C): 410-420. http://www.sciencedirect.com/science/article/pii/S0022169414001012

曹文庚, 董秋瑶, 谭俊, 等. 河套盆地晚更新世以来黄河改道对高砷地下水分布的控制机制[J]. 南水北调与水利科技, 2021, 19(1): 140-150. https://www.cnki.com.cn/Article/CJFDTOTAL-NSBD202101014.htm

Cao W G, Dong Q Y, Tan J, et al. The mechanism of Yellow River diversion in controlling high arsenic groundwater distribution since the Late Pleistocene[J]. South-to-North Water Transfers and Water Science & Technology, 2021, 19(1): 140-150. https://www.cnki.com.cn/Article/CJFDTOTAL-NSBD202101014.htm

高存荣, 刘文波, 冯翠娥, 等. 干旱半干旱地区高砷地下水形成机理研究: 以中国内蒙古河套平原为例[J]. 地学前缘, 2014, 21(4): 13-29. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY201404003.htm

Gao C R, Liu W B, Feng C E, et al. Study on the formation mechanism of high arsenic groundwater in arid and semi-arid areas: Taking Hetao Plain in Inner Mongolia as an example[J]. Earth Science Frontier, 2014, 21(4): 13-29. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY201404003.htm

Cao W G, Guo H M, Zhang Y L, et al. Controls of paleochannels on groundwater arsenic distribution in shallow aquifers of alluvial plain in the Hetao Basin, China[J]. Science of the Total Environment, 2018, 613-614: 958-968. doi: 10.1016/j.scitotenv.2017.09.182

Guo H M, Li X M, Xiu W, et al. Controls of organic matter bioreactivity on arsenic mobility in shallow aquifers of the Hetao Basin, P.R. China[J]. Journal of Hydrology, 2019, 571: 448-459. doi: 10.1016/j.jhydrol.2019.01.076

李媛. 内蒙古河套盆地高砷含水系统的微生物特征及生物地球化学效应[D]. 北京: 中国地质大学(北京), 2016.

Li Y. Microbial characteristics and biogeochemical effects of high arsenic aquifer system in Hetao Basin, Inner Mongolia[D]. Beijing: China University of Geosciences (Beijing), 2016.

Shen M M, Guo H M, Jia Y F, et al. Partitioning and reactivity of iron oxide minerals in aquifer sediments hosting high arsenic groundwater from the Hetao Basin, P.R. China[J]. Applied Geochemtry, 2018, 89: 190-201. doi: 10.1016/j.apgeochem.2017.12.008

Dietrich S, Bea S A, Weinzettel P, et al. Occurrence and distribution of arsenic in the sediments of a carbonate-rich unsaturated zone[J]. Environmental Earth Sciences, 2016, 75(2): 1-14. http://www.ihlla.org.ar/sites/default/files/integrantes/CVs/cv_sabea_0.pdf

高存荣. 河套平原地下水砷污染机理的探讨[J]. 中国地质灾害与防治学报, 1999(2): 25-32. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH902.003.htm

Gao C R. Research on the mechanism of arsenic pollution in groundwater in the Hetao Plain, Inner Mongolia, China[J]. The Chinese Journal of Geological Hazard and Control, 1999(2): 25-32. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH902.003.htm

Guo H M, Li Y, Zhao K, et al. Removal of arsenite from water by synthetic siderite: Behaviors and mechanisms[J]. Journal of Hazardous Materials, 2011, 186(2-3): 1847-1854. doi: 10.1016/j.jhazmat.2010.12.078

Zhang Z, Guo H, Zhao W, et al. Influences of groundwater extraction on flow dynamics and arsenic levels in the western Hetao Basin, Inner Mongolia, China[J]. Hydrogeology Journal, 2018, 26(5): 1499-1512. doi: 10.1007/s10040-018-1763-9

Ayotte J D, Nolan B T, Nucklos J R, et al. Modeling the probability of arsenic in groundwater in New England as a tool for exposure assessment[J]. Environmental Science & Technology, 2006, 40: 3578-3585. http://webarchive.library.unt.edu/eot2008/20081108061849/http:/vt.water.usgs.gov/Publications/2006/es051972f.pdf

Rodríguez-Lado L, Sun G, Berg M, et al. Groundwater arsenic contamination throughout China[J]. Science, 2013, 341: 866-868. doi: 10.1126/science.1237484

Wu R H, Joel P, Michael B, et al. Geostatistical model of the spatial distribution of arsenic in groundwater in Gujarat State, India[J]. Environmental Geochemistry and Health, 2021, 43: 2649-2664. doi: 10.1007/s10653-020-00655-7

Bretzler A, Lalanne F, Nikiema J, et al. Groundwater arsenic contamination in Burkina Faso, West Africa: Predicting and verifying regions at risk[J]. Science of the Total Environment, 2017, 584-585: 958-970. doi: 10.1016/j.scitotenv.2017.01.147

苏彩红, 向娜, 陈广义, 等. 基于人工蜂群算法BP神经网络的水质评价模型[J]. 环境工程学报, 2012, 6(2): 699-704. https://www.cnki.com.cn/Article/CJFDTOTAL-HJJZ201202065.htm

Su C H, Xiang N, Chen G Y, et al. Water quality evaluation model based on artificial bee colony algorithm and BP neural network[J]. Journal of Environmental Engineering, 2012, 6(2): 699-704. https://www.cnki.com.cn/Article/CJFDTOTAL-HJJZ201202065.htm

He Z B, Wen X H, Liu H, et al. A comparative study of artificial neural networks, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semi-arid mountain region[J]. Journal of Hydrology, 2014, 509: 379-386. doi: 10.1016/j.jhydrol.2013.11.054

Podgorski J E, Labhasetwar P, Saha D, et al. Prediction modeling and mapping of groundwater fluoride contamination throughout India[J]. Environmental Science and Technology, 2018, 52(17): 9889-9898. doi: 10.1021/acs.est.8b01679

Iverson L R, Prasad A M, Matthews S N, et al. Estimating potential habitat for 134 eastern US tree species under six climate scenarios[J]. Forest Ecology and Management, 2008, 254: 390-406. doi: 10.1016/j.foreco.2007.07.023

邓娅敏. 河套盆地西部高砷地下水系统中的地球化学过程研究[D]. 武汉: 中国地质大学(武汉), 2008.

Deng Y M. Study on geochemical process in high arsenic groundwater system in western Hetao Basin[D]. Wuhan: China University of Geosciences (Wuhan), 2008.

袁溶潇. 内蒙古河套盆地含水层沉积物可溶性组分与可溶性砷的分布规律研究[D]. 北京: 中国地质大学(北京), 2017.

Yuan R X. Soluble components of sediments and their relation with soluble arsenic in aquifers from the Hetao Basin, Inner Mongolia[D]. Beijing: China University of Geosciences (Beijing), 2017.

刘文波. 河套平原地下水化学特征研究[D]. 北京: 中国地质大学(北京), 2015.

Liu W B. Chemical characteristics of groundwater in Hetao Plain[D]. Beijing: China University of Geosciences (Beijing), 2015.

何薪. 河套平原农业灌溉影响下地下水中砷迁移富集规律研究[D]. 武汉: 中国地质大学(武汉), 2010.

He X. Study on the migration and enrichment law of arsenic in groundwater under the influence of agricultural irrigation in Hetao Plain[D]. Wuhan: China University of Geosciences (Wuhan), 2010.

Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324

Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874. doi: 10.1016/j.patrec.2005.10.010

Bindal S, Singh C K. Predicting groundwater arsenic contamination: Regions at risk in highest populated state of India[J]. Water Research, 2019, 159: 65-76. doi: 10.1016/j.watres.2019.04.054

Podgorski J E, Eqani S A M A S, Khanam T, et al. Extensive arsenic contamination in high-pH unconfined aquifers in the Indus Valley[J]. Science Advances, 2017, 3: 1-10. http://d3a5ak6v9sb99l.cloudfront.net/content/advances/3/8/e1700935.full.pdf

Erickson M L, Elliott S M, Christenson, et al. Predicting geogenic arsenic in drinking water wells in glacial aquifers, North-Central USA: Accounting for depth-dependent features[J]. Water Resources Research, 2018, 54(12): 172-187, 10. http://www.onacademic.com/detail/journal_1000041601093199_2916.html

Zhang Q, Rodríguez-Lado L, Johnson C A, et al. Predicting the risk of arsenic contaminated groundwater in Shanxi Province, northern China[J]. Environmental Pollution, 2012, 165: 118-123. doi: 10.1016/j.envpol.2012.02.020

Michael V S, Samantha C Y, Shawn G B, et al. Aquifer arsenic cycling induced by seasonal hydrologic changes within the Yangtze River Basin[J]. Environmental Science & Technology, 2016, 50(7): 3521-3529.

Yadav I C, Devi N L, Singh S. Spatial and temporal variation in arsenic in the groundwater of upstream of Ganges River Basin, Nepal[J]. Environmental Earth Science, 2015, 73: 1265-1279. doi: 10.1007/s12665-014-3480-6

Alarcón-Herrera M T, Bundschuh J, Nath B, et al. Co-occurrence of arsenic and fluoride in groundwater of semi-arid regions in Latin America: Genesis, mobility and remediation[J]. Journal of Hazardous Materials, 2013, 262: 960-969. doi: 10.1016/j.jhazmat.2012.08.005

Joel P, Wu R H, Biswajit C, et al. Groundwater arsenic distribution in India by machine learning geospatial modeling[J]. Environmental Research and Public Health, 2020, 17: 7119-7135. doi: 10.3390/ijerph17197119

张巧凤, 刘桂香, 于红博, 等. 基于MOD16A2的锡林郭勒草原近14年的蒸散发时空动态[J]. 草地学报, 2016, 24(2): 286-293. https://www.cnki.com.cn/Article/CJFDTOTAL-CDXU201602007.htm

Zhang Q F, Liu G X, Yu H B, et al. Temporal and spatial dynamics of evapotranspiration in Xilingole grassland in recent 14 years based on MOD16A2[J]. Journal of Grassland, 2016, 24(2): 286-293. https://www.cnki.com.cn/Article/CJFDTOTAL-CDXU201602007.htm

闫俊杰, 吕光辉, 徐海量, 等. 2000-2014年塔里木河干流的植被覆盖与蒸散发时空变化及其关系[J]. 水土保持通报, 2018, 38(3): 248-255. https://www.cnki.com.cn/Article/CJFDTOTAL-STTB201803040.htm

Yan J J, Lv G H, Xu H L, et al. Temporal and spatial changes of vegetation cover and evapotranspiration in the main stream of Tarim River from 2000 to 2014 and their relationship[J]. Bulletin of Water and Soil Conservation, 2018, 38(3): 248-255. https://www.cnki.com.cn/Article/CJFDTOTAL-STTB201803040.htm

Supplements (0)

Cited By