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.

A Web-based Spatial Decision Support System of Wastewater Surveillance for COVID-19 Monitoring: A Case Study of a University Campus

“…Crimi et al (2019) investigated the identification of priority regions in Bradford, UK for freight lorry parking within a web-based SDSS environment. Lan et al (2020) applied web-based SDSS that guides the monitoring and sharing of water quality information of private wells in Gaston County, NC, USA. Spatial interpolation algorithms were used in Lan et al’s work to generate the spatially continuous distribution of water quality that will inform residents or governments for potential water contamination.…”

“…A variety of applications such as environmental monitoring, natural resources, public health, transportation, and land use and land cover change have built SDSS to address complex decision problems within spatially explicit contexts (Delmelle et al, 2014;Keenan & Jankowski, 2019;Sugumaran & Degroote, 2010). In particular, driven heavily by Internet technologies and cyberinfrastructure (NSF, 2007), web-based SDSS has received much attention over the past few years (Lan et al, 2020;Lee et al, 2017;Tayyebi et al, 2016). While a growing body of the literature has highlighted the power of web-based SDSS, the applications of web-based SDSS for the resolution of complex spatiotemporal decision problems in general and small-scale wastewater surveillance for COVID-19 monitoring, in particular, remain scant.…”

Analysis of quasi-periodic space-time non-separable processes to support decision-making in medical monitoring systems

“…Let us do the same manipulations with init data taken for Ukraine on January 1 st , 2021 and form the learning set by model provided in this DSS. Evaluation or error (8) showing a deviation of simulated process from real one is…”

“…The statistics report of January 1 st , 2021 was taken as init state vector and passed to system (4-7) input. Based on formed learning set let us compare this model with real process and calculate error (8) Thus, a spatial-time non-separable process formation method which uses linearization showed the worst result in prospective of simulation quality. A better result was shown by separable model.…”

“…It leads to DSS developers to have problems with promptness of learning set accumulation because of restrictions of existing. It significantly affects the reliability of the decisions made [7,8], [9,10], [11].…”