A web-based spatial decision support system for monitoring the risk of water contamination in private wells

ABSTRACT

Long-term exposure to contaminated water can cause health effects, such as cancer. Accurate spatial prediction of inorganic compounds (e.g. arsenic) and pathogens in groundwater is critical for water supply management. Ideally, environmental health agencies would have access to an early warning system to alert well owners of risks of such contamination. The estimation and dissemination of these risks can be facilitated by the combination of Geographic Information Systems and spatial analysis capabilities – i.e., spatial decision support system (SDSS). However, the use of SDSS, especially web-based SDSS, is rare for spatially explicit studies of drinking water quality of private wells. In this study, we introduce the interactive Well Water Risk Estimation(iWWRE), a web-based SDSS to facilitate the monitoring of water contamination in private wells across Gaston County, North Carolina (US). Our system implements geoprocessing web services and generates dynamic spatial analysis results based on a database of private wells. Environmental health scientists using our system can conduct fine-grained spatial interpolation on 1) a particular type of contaminant such as arsenic, 2) on various subsets through a temporal query. Visuals consist of an estimation map, cross validation information, Kriging variance and contour lines that delineate areas with maximum contaminant levels (MCL), as set by the US Environmental Protection Agency (EPA). Our web-based SDSS was developed jointly with environmental health specialists who found it particularly critical for the monitoring of local contamination trends, and a useful tool to reach out to private well users in highly elevated contaminated areas.

KEYWORDS:

• Geoprocessing services
• health
• interpolation
• private well
• risk
• spatial decision support system
• water contamination

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://mndatamaps.web.health.state.mn.us/interactive/wells.html and https://mnwellindex.web.health.state.mn.us/

2. https://www.nj.gov/dep/dsr/pwta/

3. https://www.arcgis.com/apps/MapSeries/index.html?appid=c0d7daea497049c1a686d07dab7106e5

4. https://dnrmaps.wi.gov/H5/?viewer = Water_Use_Viewer

5. Residents may decide to test their water multiple times, especially following the installation of remediation services, like reserve osmosis.

6. OK does not reproduce either the histogram or the spatial variability as given by the variogram function (Yamamoto 2005).

7. When no time range is provided, the default is to incorporate data covering the entire time period under consideration.

8. The average distance to the k^th closest neighbours can be estimated in CrimeStat (Levine 2006) for instance, and are provided in Appendix Table A1.

9. These parameters were defined following careful analysis of the empirical variogram and can be found in Appendix Table A2.

10. ‘Clusters’ here denote areas where the interpolated arsenic values are above the EPA MCL

11. The running time is calculated from the time the user selects data to the time the routines have been executed.

Additional information

Funding

This work was supported by the National Center for Environmental Health [CDC-RFA-EH15-1507].