Assessment of groundwater processes using censored data analysis incorporating non-detect chemical, physical, and...

In Europe most environmental based water quality research has focused on both nutrient and microbial contamination which can arise from agricultural processes and inadequate wastewater treatment. Recent work in Ireland has linked the presence of arsenic in groundwater at elevated concentrations at national and subnational scales with bedrock lithology serving as a strong predictor variable. Groundwater data was collected as part of an environmental impact assessment for a road construction project and this resulting groundwater geochemistry dataset was used in this present study to assess the geochemical controls of arsenic in natural waters in addition to biological and nutrient contamination. Physiochemical parameters, trace elements, nutrients, organics, and microbiological parameters were collected for every quarter for four years (2004-2008) in 67 wells. Due to differing sampling procedures and limitations in the data, only one quarter (November 2005) was used to understand groundwater geochemistry in greater detail. Multivariate statistical techniques were used to overcome the presence of non-detect data. This is an important consideration as while methods exist for chemical data, methods incorporating biological data are limited. Elevated levels of nitrate in groundwater may arise from the runoff of septic tanks and/or agricultural practices in the area. Both pesticides and polycyclic aromatic hydrocarbons were not detected in any wells signifying no anthropogenic contamination inputs. However, fuel products such as methyl tert-butyl ether were detected and potentially illustrate point source contamination, these were detected in only one well. Geochemical data indicate that elevated arsenic concentrations are present within alkali-oxic groundwaters through the desorption from Fe and Mn oxyhydroxides, i.e. alkali desorption. This study examines of the geochemistry of arsenic in groundwater in Ireland at a local scale. In addition, the multivariate methods used in this study were able to fully integrate both chemical and biological censored data, which may be applied in other regions with similar data.