Groundwater arsenic contamination risk prediction using GIS and classification tree method

Hossain, Md. Moqbul; Piantanakulchai, Mongkut

doi:10.1016/j.enggeo.2013.01.007

Cited by 33 publications

(14 citation statements)

References 24 publications

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“…High concentration of arsenic in groundwater is reported in different countries like India, Bangladesh, Pakistan, China, Nepal, Vietnam, Cambodia, Taiwan, Chile, Mexico and the USA (Hossain and Piantanakulchai 2013). Groundwater is the main source of drinking water in these areas/countries, especially in rural areas.…”

Section: Introductionmentioning

confidence: 99%

“…However, India, Bangladesh, China, and Nepal maintain the pre-1993 WHO Guidelines value-50 μg/L (Appelo 2006). It is well accepted that the occurrence of arsenic in the environment is ubiquitous, but its release in the groundwater is governed by various processes, most of which are poorly understood Hossain and Piantanakulchai 2013). Causes of arsenic contamination in groundwater are attributed to mainly three probable processes:…”

Section: Introductionmentioning

confidence: 99%

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Applications of Remote Sensing, Geographic Information System and Geostatistics in the Study of Arsenic Contamination in Groundwater

Ghosh

Parial

2014

Recent Trends in Modelling of Environmental Contaminants

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The arsenic-related groundwater problem is threatening huge areas in different countries like India, Bangladesh, China, Chile, Mexico etc. Arsenic contamination in ground water affects human health through its direct and indirect consumption. In order to combat this problem, an in-depth understanding of the background process of arsenic contamination is needed. Remote Sensing and Geographic Information System (GIS) offer a suitable solution for understanding and mapping the extent of the arsenic-infected areas. Various thematic maps on geomorphology, landuse/landcover, hydrology etc. can be prepared by interpreting remote sensing data, i.e. satellite imagery of the concerned area. These thematic maps can be further utilized to explore and discover relationships between that particular theme and the distribution of arsenic. Digital elevation models prepared using photogrammetric techniques utilizing Remote Sensing data may help in understanding the nature of distribution of arsenic-contaminated areas. GIS helps capturing, retrieving and analysing geospatial data. It recreates a real-world scenario and helps in analysing and creating new relevant information. In arsenic affected areas, people are exposed to arsenic contamination through its direct consumption with drinking water as well as through consumption of crops contaminated due to irrigation with arsenic rich groundwater. Risk and vulnerability assessment of such population can be done using GIS. Web-based GIS offers a unique opportunity of crowdsourcing and development of participatory public GIS. It can help in generation and analysis of distribution and assessment of impact of arsenic contamination and evolving mitigation strategy. Geostatistical methods help in interpolating the degree of arsenic contamination in unsampled locations as well as modelling uncertainty about unknown values. Initially experimental semivariograms are constructed and are fitted with suitable models like spherical, Gaussian etc. Various techniques of kriging are employed to interpolate level of arsenic contamination in unsampled 198 A. R. Ghosh and K. Parial locations using parameters obtained from variography. Zonation of arsenic contamination levels helps in the assessment of risk and vulnerability of residents of arsenic-contaminated areas and the development of efficient mitigation strategies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of Remote Sensing, Geographic Information System and Geostatistics in the Study of Arsenic Contamination in Groundwater

Ghosh

Parial

2014

Recent Trends in Modelling of Environmental Contaminants

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“…While the use of geostatistical modeling can be used with success for generating prediction maps, these techniques suffer from limitations including data availability, data quality, and local scale arsenic mobilization variations due to complex groundwater flow paths (Ayotte et al, 2011;Bretzler et al, 2017a). As outlined by Frederick et al (2016), generally when exploring potential relationships between elevated arsenic and various explanatory variables two primary modeling strategies can be used: logistic regression (e.g., Ayotte et al, 2003;Winkel et al, 2008;Gross and Low, 2013;Bretzler et al, 2017b), or decision tree analysis (Hossain and Piantanakulchai, 2013;Tesoriero et al, 2017). However, in this present study, an alternative approach to these modeling techniques, previously applied successfully by McGrory et al (2017) which used similar predictor variables in addition to detailed aqueous geochemistry data that can potentially explain the spatial distribution of arsenic in relation to groundwater controls (e.g., bedrock geology) was utilized.…”

Section: Introductionmentioning

confidence: 99%

Arsenic in Groundwater in South West Ireland: Occurrence, Controls, and Hydrochemistry

McGrory

Alvarez‐Iglesias

Bargary

et al. 2018

Front. Environ. Sci.

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Globally numerous regions have been identified with elevated arsenic within groundwater which can result in potential adverse health risks. In Ireland, a previous national-scale research assessment of groundwater identified isolated clusters of elevated arsenic and indicated that lithology was a major controlling factor on arsenic in groundwater. Complementary comparisons of national-scale and regional-scale groundwater assessments of arsenic are lacking in Europe when compared to other global regions. The aims of this study were to demonstrate the value of a regional-scale groundwater hydrochemistry dataset with an existing national-scale approach, describe anomalies that can become the focus of attention for public health and economic reasons, and to provide a wider context for arsenic in groundwater within Ireland and Europe. Regional-scale data using 470 locations comprising 1,493 analyses using several hydrochemical parameters (arsenic, pH, conductivity, iron, manganese, sodium, potassium, calcium, magnesium, and total hardness) in south west Ireland were integrated with geological, hydrogeological, and land use datasets. Statistical analysis was performed using a combination of methods including score tests of geological groups using an empirical cumulative distribution function plot in addition to spatial analysis. Results revealed that hydrochemical parameters exhibited different spatial clusters, which was generally associated with lithology. Arsenic was elevated in sandstone derived bedrock. Weak correlation of arsenic with other hydrochemical parameters were observed and redox-sensitive elements like manganese and iron showed a greater diversity in spatial occurrence. This study has shown that the variation of hydrochemical parameters are controlled by regional geology. Finally, the paper focuses on anomalies identified by concentrations of individual ions or statistical associations in the context of, for example, historical mineral exploration and mining in the area and also discusses whether groundwater chemistry sampling on this scale can assist in future mineral exploration, as well as guiding the future development of high quality public and private water supplies.

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“…The effect of As pollution in drinking water in blocks of Murshidabad district, is assessed using GIS technology, revealed that life expectancy at birth and at other age groups was badly affected (Samadder 2010). GIS technique with a combination of classification tree method is applied to prepare an As contamination risk map for the groundwater As contamination risk prediction (Hossain and Piantanakulchai 2013). Although the anthropogenic source of As contamination is increasingly becoming important yet much of As problem in Bangladesh and West Bengal is due to aquifer geology (Mahimairaja et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Assessing spatial variations of groundwater arsenic with surface elevation, slope and water-table using geospatial techniques in Ballia district, India

Khan

Ali

Khan

et al. 2016

Model. Earth Syst. Environ.

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The present study describes spatial variation of arsenic (As) in groundwater with respect to surface elevation and slope using SRTM data. In total, 34 water samples were collected from shallow aquifers covering all 17 blocks of Ballia district in the pre and post-monsoon season during 2011. Atomic absorption spectrophotometer (AAS) was used for arsenic testing. Inverse Distance Weighted (IDW) model was applied for interpolation of As concentrations. The statistical method was applied to assess the spatial variations amongst the variables. The result showed that the highest arsenic concentrations were found at low surface elevation, slopes and water table. The results also show the inverse relationship among these variables. The relationships between groundwater arsenic, slopes and water table were non-linear. These variables had negative corrections with As in groundwater. The study showed that more than 90 % areas contain arsenic above the permissible limit of 10 ppb in both pre-and post monsoon seasons.

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Groundwater arsenic contamination risk prediction using GIS and classification tree method

Cited by 33 publications

References 24 publications

Applications of Remote Sensing, Geographic Information System and Geostatistics in the Study of Arsenic Contamination in Groundwater

Applications of Remote Sensing, Geographic Information System and Geostatistics in the Study of Arsenic Contamination in Groundwater

Arsenic in Groundwater in South West Ireland: Occurrence, Controls, and Hydrochemistry

Assessing spatial variations of groundwater arsenic with surface elevation, slope and water-table using geospatial techniques in Ballia district, India

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