Occurrence of Arsenic in Ground Water in the Choushui River Alluvial Fan, Taiwan

National Chung Cheng University, Chia-i-hsien, Taiwan, Taiwan
Journal of Environmental Quality (Impact Factor: 2.65). 01/2006; 35(1):68-75. DOI: 10.2134/jeq2005.0129
Source: PubMed


An investigation of shallow ground water quality revealed that high arsenic (As) concentrations were found in both aquifers and aquitards in the southern Choushui River alluvial fan of Taiwan. A total of 655 geological core samples from 13 drilling wells were collected and analyzed. High As contents were found primarily in aquitards, to a maximum of 590 mg/kg. The contents were correlated with the locations of the marine sequences. Additionally, strong correlations among the As concentrations of core samples, the clay, and the geological age of the Holocene transgression were identified. Most of the As in ground water originated from the aquitard of the marine sequence. The high As content in marine formations with high clay contents may be attributable to the bioaccumulation of As in the sea organisms, which accrued and were deposited in the formation. A preliminary geogenic model of the origin of the high As concentration in the shallow sedimentary basin of the Choushui River alluvial fan of Taiwan is proposed.

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Available from: Chen-Wuing Liu,
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    • "Subsurface hydrogeological analyses of approximately 300 m in depth partitioned the alluvial fan deposits into four marine units and four nonmarine units in the distal-fan and mid-fan areas (Taiwan Central Geological Survey (CGS), 1999) (the hydrogeological map shown in Chen et al. (2013)). Nonmarine units ranging from medium sand to gravel with high permeability (hydraulic conductivity (K) of more than 0.4 m/day) comprised aquifers, and marine units ranging from clay to fine sand with low permeability (K of less than 0.001 m/day) served as aquitards (Liu et al., 2006). Aquitards were primarily present in the distal-fan and mid-fan regions, but were absent in the proximal-fan region. "
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    ABSTRACT: Groundwater nitrate-N contamination occurs frequently in agricultural regions, primarily resulting from surface agricultural activities. The focus of this study is to establish groundwater protection zones based on indicator-based geostatistical estimation and aquifer vulnerability of nitrate-N in the Choushui River alluvial fan in Taiwan. The groundwater protection zones are determined by univariate indicator kriging (IK) estimation, aquifer vulnerability assessment using logistic regression (LR), and integration of the IK estimation and aquifer vulnerability using simple IK with local prior means (sIKlpm). First, according to the statistical significance of source, transport, and attenuation factors dominating the occurrence of nitrate-N pollution, a LR model was adopted to evaluate aquifer vulnerability and to characterize occurrence probability of nitrate-N exceeding 0.5 mg/L. Moreover, the probabilities estimated using LR were regarded as local prior means. IK was then used to estimate the actual extent of nitrate-N pollution. The integration of the IK estimation and aquifer vulnerability was obtained using sIKlpm. Finally, groundwater protection zones were probabilistically determined using the three aforementioned methods, and the estimated accuracy of the delineated groundwater protection zones was gauged using a cross-validation procedure based on observed nitrate-N data. The results reveal that the integration of the IK estimation and aquifer vulnerability using sIKlpm is more robust than univariate IK estimation and aquifer vulnerability assessment using LR for establishing groundwater protection zones. Rigorous management practices for fertilizer use should be implemented in orchards situated in the determined groundwater protection zones.
    Journal of Hydrology 02/2015; 523. DOI:10.1016/j.jhydrol.2015.01.077 · 3.05 Impact Factor
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    • "). Liu et al. (2006) indicated over-pumping groundwater induces dissolved oxygen and increases As mobility in water and the relatively high As content has accumulated and been deposited in the marine sequences with fine sediments. "
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    ABSTRACT: Arsenic (As) is an odorless semi-metal that occurs naturally in rock and soil, and As contamination in groundwater resources has become a serious threat to human health. Thus, assessing the spatial and temporal variability of As concentration is highly desirable, particularly in heavily As-contaminated areas. However, various difficulties may be encountered in the regional estimation of As concentration such as cost-intensive field monitoring, scarcity of field data, identification of important factors affecting As, over-fitting or poor estimation accuracy. This study develops a novel systematical dynamic-neural modeling (SDM) for effectively estimating regional As-contaminated water quality by using easily-measured water quality variables. To tackle the difficulties commonly encountered in regional estimation, the SDM comprises of a neural network and four statistical techniques: the Nonlinear Autoregressive with exogenous input (NARX) network, Gamma test, cross-validation, Bayesian regularization method and indicator kriging (IK). For practical application, this study investigated a heavily As-contaminated area in Taiwan. The backpropagation neural network (BPNN) is adopted for comparison purpose. The results demonstrate that the NARX network (Root mean square error (RMSE): 95.11 mu g l(-1) for training; 106.13 mu g l(-1) for validation) outperforms the BPNN (RMSE: 121.54 mu g l(-1) for training; 143.37 mu g l(-1) for validation). The constructed SDM can provide reliable estimation (R-2 > 0.89) of As concentration at ungauged sites based merely on three easily-measured water quality variables (Alk, Ca2+ and pH). In addition, risk maps under the threshold of the WHO drinking water standard (10 mu g l(-1)) are derived by the IK to visually display the spatial and temporal variation of the As concentration in the whole study area at different time spans. The proposed SDM can be practically applied with satisfaction to the regional estimation in study areas of interest and the estimation of missing, hazardous or costly data to facilitate water resources management.
    Journal of Hydrology 08/2013; 499:265-274. DOI:10.1016/j.jhydrol.2013.07.008 · 3.05 Impact Factor
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    • "partitioned the plain deposits into eight overlapping sequences, including four marine sequences and four non-marine sequences, in the distal fan (Taiwan CGS 2002). Non-marine sequences with coarse sediments, ranging from medium sand to gravel with high permeability, are considered to be aquifers, whereas marine sequences with fine sediments, ranging from clay to fine sand with low permeability, are considered aquitards (Liu et al. 2006) (Fig. 2). "
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