Geospatial Analysis of Groundwater Quality in Southern Ontario

Morwick G360 PROJECT TEAM:  Dr. Emmanuelle Arnaud, Dr. James Longstaffe & Nazia Nawrin (MSc Candidate)

Current vulnerability mapping associated with management of groundwater quality focuses on depth to the water table and travel time inferred from grain size. However, this does not take into consideration unit geometry, which affects connectivity and fate of contaminants. The shallow subsurface geology (overburden) is influenced by previous glaciations, which played a significant role in shaping the current physiography and subsurface of southern Ontario. Geospatial analysis of water quality and physiography will help to determine the distribution of susceptible water wells and its relationship to the subsurface geology, which in turn can help inform well vulnerability and water quality management at the regional scale.

Groundwater geochemical data are often treated as water quality data when compared against regulatory standards. The Ontario Geological Survey (OGS) conducted a 7-year sampling program from 2007-2014 resulting in a comprehensive dataset to characterize the ambient groundwater geochemistry for both bedrock and overburden wells across southern Ontario. The data from 553 overburden wells are the main focus of the current project. The aim of this study is to analyze the geospatial distribution of specific geochemical constituents and investigate whether certain physiography have a significant effect on the shallow groundwater quality across southern Ontario. In conjunction with this Ambient Groundwater Geochemical (AGG) dataset, the OGS physiographic map and 3D mapping of surficial deposits in the Brantford-Woodstock area of southern Ontario will help to determine the factors related to geology. Based on a preliminary geospatial analysis, 8 out of almost 80 geochemical constituents, (e.g. Nitrate, Chloride, Fluoride, Uranium) and 8 physiographic features were selected for this study. Geographically weighted regression, which is an analysis of spatially varying relationships, is performed in ArcGIS by including the concentration of geochemical constituents and physiographic unit as variables. This analysis has shown which constituents are weakly or moderately related to physiography at the regional scale.

Geospatial distribution map of Nitrate across southern Ontario along with physiographic features.
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