Predicting the risk of arsenic contaminated groundwater in Shanxi Province, Northern China

Zhang, Qiang; Rodríguez‐Lado, Luis; Johnson, C. Annette; Xue, Hanbin; Shi, Jianbo; Zheng, Qing‐Shui; Sun, Guifan

doi:10.1016/j.envpol.2012.02.020

Cited by 42 publications

(10 citation statements)

References 51 publications

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“…Density of rivers is calculated as the total length of rivers per raster cell (i.e., 1 km × 1 km), and distance to rivers is the shortest distance from the center of each raster cell to rivers. Both variables can influence hydrological processes within aquifers and were previously shown to be strong predictors of elevated As concentrations in shallow groundwater …”

Section: Methodsmentioning

confidence: 99%

“…On the basis of previous investigations, ,,− a total of 15 independent variables, which were considered potentially closely related to groundwater As contamination, were initially compiled for the logistic regression analysis (SI Table S2). Due to differences in resolution and data format, the 15 variables (SI Table S3) were uniformly converted to a raster format at 1 km resolution using ArcGIS (Version 10) and then classified into the following four categories.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Geogenic Arsenic Contamination in Shallow Groundwater of South Louisiana, United States

Yang

Winkel

Johannesson

2014

Environ. Sci. Technol.

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Groundwater contaminated with arsenic (As) threatens the health of more than 140 million people worldwide. Previous studies indicate that geology and sedimentary depositional environments are important factors controlling groundwater As contamination. The Mississippi River delta has broadly similar geology and sedimentary depositional environments to the large deltas in South and Southeast Asia, which are severely affected by geogenic As contamination and therefore may also be vulnerable to groundwater As contamination. In this study, logistic regression is used to develop a probability model based on surface hydrology, soil properties, geology, and sedimentary depositional environments. The model is calibrated using 3286 aggregated and binary-coded groundwater As concentration measurements from Bangladesh and verified using 78 As measurements from south Louisiana. The model's predictions are in good agreement with the known spatial distribution of groundwater As contamination of Bangladesh, and the predictions also indicate high risk of As contamination in shallow groundwater from Holocene sediments of south Louisiana. Furthermore, the model correctly predicted 79% of the existing shallow groundwater As measurements in the study region, indicating good performance of the model in predicting groundwater As contamination in shallow aquifers of south Louisiana.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting Geogenic Arsenic Contamination in Shallow Groundwater of South Louisiana, United States

Yang

Winkel

Johannesson

2014

Environ. Sci. Technol.

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“…Multidisciplinary research techniques provide opportunities in addressing the challenges associated with understanding the links that exist between mining operations and how it affects the environment. These models offer an alternative approach to a better interpretation of data and to understand water quality [13][14][15], while making it possible to assess factors influencing the behavior of an environmental system and offers a valuable tool for managing resources as well as solution to pollution problems.…”

Section: Introductionmentioning

confidence: 99%

Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems

et al. 2022

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This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO42-, total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO3, Mn and total coliform were associated with optimal water pH while SO42-, EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.

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“…Prolonged As exposure from drinking water also increases mortality from cardiovascular disease, especially among smokers (Argos et al, 2010;Chen et al, 2011). The most significant large-scale human exposure to As occurs in the basins of major rivers draining the Himalaya including the Indus (Fatmi et al, 2013), Ganges-Brahmaputra-Meghna (Chakraborti et al, 2013), Mekong (Berg et al, 2007;Buschmann et al, 2008;Phan et al, 2010), Red (Berg et al, 2007), Yangtze (Currell et al, 2011;Gan et al, 2014;He and Charlet, 2013), and Yellow (He and Charlet, 2013;Wang et al, 2012;Zhang et al, 2012), where over 100 million people are estimated to consume drinking water containing > 10 μg As L -1 (the World Health Organization standard) (Ravenscroft et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Reactivity and speciation of mineral-associated arsenic in seasonal and permanent wetlands of the Mekong Delta

Stuckey

Schaefer

Benner

et al. 2015

Geochimica et Cosmochimica Acta

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Millions of people in the deltaic regions of S/SE Asia regularly consume As contaminated groundwater. Arsenic persists within these groundwaters despite being flushed by several pore volumes of recharge, indicative of an in-situ solid phase source. Despite the importance of this solid phase source, the identity and reactivity of the minerals hosting and releasing As in the deltaic aquifers remain poorly constrained. Here we seek to define the chemistry and reactivity of As and its host minerals in aquifer sediments recharged by both seasonally saturated and permanent wetlands on the Mekong Delta of Cambodia. Sediment cores were retrieved to depths of up to 30 m. Selective extractions on near-surface sediments (to depths of 4-6 m) indicate that more than a third (27-50%) of the Fe in the profile is "reducible" (citrate-bicarbonate-dithioniteextractable)-nominally consisting of Fe(III) oxides. The Fe(III) oxide fraction was approximately the same under seasonal wetlands (mean = 39%) compared with sediments from beneath permanent wetlands (mean = 37%). Correspondingly, Fe Kedge extended X-ray absorption fine structure (EXAFS) spectra analysis indicates that the Fe mineral phases are predominantly goethite and hematite (30-50%). Analysis of solid phase As speciation by X-ray absorption near edge structure (XANES) spectroscopy showed arsenate in the shallowest sediments (upper 1 and 4 m of seasonal and permanent wetlands), and arsenite and/or As sulfides as the dominant phases in the deeper sediments. The reducible As fraction ranged from 17-42 % of the total As at both sites. The exception was found in a peat layer at 6 m beneath the seasonal wetland, in which only 1% of the total As was reducible. Arsenian pyrite was the predominant form of As in the peat. Thus, a pool of As hosted by reducible mineral phases persists in sediments below both the seasonal and permanently saturated wetland sites.

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Predicting the risk of arsenic contaminated groundwater in Shanxi Province, Northern China

Cited by 42 publications

References 51 publications

Predicting Geogenic Arsenic Contamination in Shallow Groundwater of South Louisiana, United States

Predicting Geogenic Arsenic Contamination in Shallow Groundwater of South Louisiana, United States

Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems

Reactivity and speciation of mineral-associated arsenic in seasonal and permanent wetlands of the Mekong Delta

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