Gelman Site 1,4-Dioxane Groundwater Contamination Plume Modeling and Forecasting

Abstract

Groundwater systems are intrinsically heterogeneous with dynamic spatio-temporal patterns, which cause significant challenges in quantifying and mapping their complex processes. However, accurate forecasting of regional groundwater contamination is commonly needed to identify its spatio-temporal dynamics, which helps the public anticipate the timing and severity of potential groundwater quality issues and could serve as an early warning system. This study focuses on modeling a plume of 1,4-dioxane originating from the Gelman site beneath the city of Ann Arbor, Michigan. It proposes a novel methodology that accounts for the spatially and temporally irregular and uncertain nature of groundwater contamination data to analyze historical trends in dioxane concentration and predict its transport:

• A random forest interpolation model was deployed to fill or extend gaps in fragmented time series across all monitoring wells;
• The Mann-Kendall test was applied to evaluate trends in dioxane concentrations at various wells;
• An automated time series machine learning (AutoTS) package was used to forecast future concentrations; and
• An R-based Shiny web application was designed to allow visualization and quantification of dioxane contamination analytical data.

This research introduces a novel framework for filling spatial and temporal data sampling gaps in groundwater contamination to offer an effective and promising way to predict future plume concentration and spatial distribution.