The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.

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Abstract:This study characterized the redox conditions in arsenic-affected groundwater aquifers of the Lanyang plain, Taiwan. Discriminant analysis was adopted to delineate three redox zones (oxidative, transitional and reductive zones) in different aquifers and yielded 92Ð3% correctness on groundwater quality data. Arsenic is mainly distributed in the reductive zone, and arsenic distribution in the shallow aquifer is mainly affected by surface activities. According to PHREEQC modelling results, possible mechanisms for arsenic release to groundwater in Lanyang plain are explored. Arsenic released to groundwater in the oxidative zone (zone 1) is primarily caused by the oxidations of arsenic-bearing pyrite minerals, and arsenate is the predominant species. While the reductive dissolution of Fe-oxides are responsible for the high arsenic concentration found in the transitional and reductive zones (zones 2 and 3), arsenite is the predominant species. The reduction potential of groundwater rises as the depths and zones increase. Some sulphates may be reduced to form sulphide ions, which then react with arsenic to form arseno-sulphide deposits (such as realgar, orpiment) and then slightly lower groundwater arsenic concentrations. A conceptual diagram which summarized the possible release processes of arsenic in different redox zones along groundwater flow in Lanyang plain is postulated. Arsenic-bearing pyrite and arsenopyrite (FeAsS) are oxidized as they are exposed to the infiltrated oxygenated rainwater, releasing soluble arsenate Fe(II) and SO 4 2 into zone 1. The dissolution of arsenic-rich Fe-oxides due to the onset of reducing conditions in zones 2 and 3 is responsible for the mobility of arsenic and likely to be the primary mechanism of arsenic release to groundwater in the Lanyang plain

Spatial distributions of groundwater quality are commonly heterogeneous, varying with depths and locations, which is important in assessing the health and ecological risks. Owing to time and cost constraints, it is not practical or economical to measure arsenic everywhere. A predictive model is necessary to estimate the distribution of a specific pollutant in groundwater. This study developed a logistic regression (LR) model to predict the residential well water quality in the Lanyang plain. Six hydrochemical parameters, pH, NO3- -N, NO2- -N, NH+ -N, Fe, and Mn, and a regional variable (binary type) were used to evaluate the probability of arsenic concentrations exceeding 10 microg/L in groundwater. The developed parsimonious LR model indicates that four parameters in the Lanyang plain aquifer, (pH, NH4+, Fe(aq), and a component to account for regional heterogeneity) can accurately predict probability of arsenic concentration > or =1 microg/Lin groundwater. These parameters provide an explanation for release of arsenic by reductive dissolution of As-rich FeOOH in NH4+ containing groundwater. A comparison of LR and indicator kriging (IK) show similar results in modeling the distributions of arsenic. LR can be applied to assess the probability of groundwater arsenic at sampled sites without arsenic concentration data apriori. However, arsenic sampling is still needed and required in arsenic-assessment stages in other areas, and the need for long-term monitoring and maintenance is not precluded.