Maryland Department of Natural Resources

Garrett County Ground-Water-Quality Data Acquisition

Project Details

Key Results

An ArcGIS database of ground-water-quality data, with associated ArcMap project, was constructed for Garrett County, Maryland. Data sources included the Garrett County Health Department PatTrac database and radon records, the U.S. Geological Survey National Water Information System, the Maryland Department of the Environment Public Water Supply database, and the Maryland Geological Survey methane database. Data were obtained from more than 2,200 sites (including both wells and springs). Four parameters—arsenic, chloride, manganese and radon—were selected for a more focused review and analysis by both geographic information system (GIS) and statistical methods. These and other constituents were examined with respect to regulatory levels.


Ground-water-quality data for Garrett County has been acquired over the years by different agencies and organizations for different purposes, and is located in different databases and formats. The potential development of natural gas resources from the Marcellus Shale underscores the importance of having water-quality data available to users in a centralized database. In 2012, the Maryland Geological Survey (MGS) worked with Garrett County Health Department (GCHD) personnel to assemble a limited data set involving arsenic, iron, and manganese. MGS personnel worked with GCHD to identify and improve well location information, reviewed and edited the Health Department data, and ultimately converted it to ArcGIS coverages that can be used by GCHD and other ArcGIS users. More ground-water-quality data are available in County, State, and other databases that could be processed in a similar fashion, including chloride, conductivity, sulfate, dissolved solids, and radon. Once the data are in digital format, they could be used by the County for a variety of purposes, including evaluation of spatial trends in ground-water-quality data, correlation with geology, land use, and other factors, and identification of gaps in data.