Abstract
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.
Highlights
Groundwater quality monitoring for the Lanyang Plain conducted by the Environmental Protection Bureau (EPB) of Yilan County [1,2,3] has clearly identified that the arsenic (As) content in some monitoring wells exceeds the World Health Organization’s (WHO) permissible drinking water threshold of 10 μg/L [4]
The results show that PC-artificial neural network (ANN) yielded a superior outcome with a significant performance improvement due to the Nash–Sutcliffe model efficiency coefficient (NSE)
The groundwater monitoring data used in this study were collected from 921 household wells located in Aquifer 1, as shown in Figure 2, during the period from 1997 to 1999 by the Environmental Protection Bureau (EPB) of the Yilan County
Summary
Groundwater accounts for a substantial portion of the freshwater supply in the Lanyang Plain, Taiwan. To resolve the problem of a lack of reservoirs for the storage of seasonal rainfall and the poor quality of the surface water, area residents are heavily reliant upon the groundwater for agricultural irrigation, aquaculture, domestic and drinking purposes. Groundwater quality monitoring for the Lanyang Plain conducted by the Environmental Protection Bureau (EPB) of Yilan County [1,2,3] has clearly identified that the arsenic (As) content in some monitoring wells exceeds the World Health Organization’s (WHO) permissible drinking water threshold of 10 μg/L [4].
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