Comprehensive assessment of the impact of land use and hydrogeological properties on the groundwater quality in Taiwan using factor and cluster analyses.

This study aims at making a comprehensive assessment of the impact of land use and the hydrogeological properties on groundwater quality. First, factor analysis (FA) is applied to reveal the main pollutant sources and hydrogeological processes controlling the groundwater quality. FA identifies the four most important factors. Factor 1 (seawater salinization) is characterized by a medium loading of land use type of aquaculture. It is recognized that the high scores for factor 1 in coastal areas are due to over-pumping from aquafarms. Focused land use management is required to prevent saline-water intrusion in coastal aquifers. Factor 3 (nitrate pollution) shows high correlations with the land use type of fruit farming and the gravel thickness in unsaturated layers. High scores for factor 3 are also found in the proximal area of the Chuoshui River Alluvial Fan and the northeastern mountain area in the Pingtung Plain. Fruit farmers should be educated to reduce the application of fertilizers and promote the organic fruit farming. The impacts of land use and the hydrogeological properties on both Factor 2 (arsenic enrichment) and Factor 4 (reductive dissolution of Fe2+ and Mn2+) are negligible. Second, cluster analysis (CA) is performed on computed scores of the four main factors to separates 123 monitoring wells into cluster 1 (low polluted zone), cluster 2 (nitrate polluted zone) and cluster 3 (hybrid polluted zone). The results obtained from CA provide practical applications such as reduce agrichemical use in the areas of cluster 2 and enforce intensive monitoring in the prioritizing areas of cluster 3. This study successively uses the FA and CA to extract the meaningful information present by geographical visualization of scores for 4 main factors and 3 distinct clusters zones. The results are essential for formulating sound groundwater resource and land use management policies to ensure groundwater sustainability.

A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan's Lanyang Plain.

Groundwater resources are abundant and widely used in Taiwan's Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization's standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R2) for the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average root mean square errors (RMSE) are 0.49 and 0.54, respectively. Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.

Modified Mixed Lagrangian-Eulerian Method Based on Numerical Framework of MT3DMS on Cauchy Boundary.

MT3DMS, a modular three-dimensional multispecies transport model, has long been a popular model in the groundwater field for simulating solute transport in the saturated zone. However, the method of characteristics (MOC), modified MOC (MMOC), and hybrid MOC (HMOC) included in MT3DMS did not treat Cauchy boundary conditions in a straightforward or rigorous manner, from a mathematical point of view. The MOC, MMOC, and HMOC regard the Cauchy boundary as a source condition. For the source, MOC, MMOC, and HMOC calculate the Lagrangian concentration by setting it equal to the cell concentration at an old time level. However, the above calculation is an approximate method because it does not involve backward tracking in MMOC and HMOC or allow performing forward tracking at the source cell in MOC. To circumvent this problem, a new scheme is proposed that avoids direct calculation of the Lagrangian concentration on the Cauchy boundary. The proposed method combines the numerical formulations of two different schemes, the finite element method (FEM) and the Eulerian-Lagrangian method (ELM), into one global matrix equation. This study demonstrates the limitation of all MT3DMS schemes, including MOC, MMOC, HMOC, and a third-order total-variation-diminishing (TVD) scheme under Cauchy boundary conditions. By contrast, the proposed method always shows good agreement with the exact solution, regardless of the flow conditions. Finally, the successful application of the proposed method sheds light on the possible flexibility and capability of the MT3DMS to deal with the mass transport problems of all flow regimes.

Practical Implementation of New Particle Tracking Method to the Real Field of Groundwater Flow and Transport.

In articles published in 2009 and 2010, Suk and Yeh reported the development of an accurate and efficient particle tracking algorithm for simulating a path line under complicated unsteady flow conditions, using a range of elements within finite elements in multidimensions. Here two examples, an aquifer storage and recovery (ASR) example and a landfill leachate migration example, are examined to enhance the practical implementation of the proposed particle tracking method, known as Suk's method, to a real field of groundwater flow and transport. Results obtained by Suk's method are compared with those obtained by Pollock's method. Suk's method produces superior tracking accuracy, which suggests that Suk's method can describe more accurately various advection-dominated transport problems in a real field than existing popular particle tracking methods, such as Pollock's method. To illustrate the wide and practical applicability of Suk's method to random-walk particle tracking (RWPT), the original RWPT has been modified to incorporate Suk's method. Performance of the modified RWPT using Suk's method is compared with the original RWPT scheme by examining the concentration distributions obtained by the modified RWPT and the original RWPT under complicated transient flow systems.