A simple contaminant fate and transport modelling tool for management and risk assessment of groundwater pollution from contaminated sites

Abstract

Contaminated sites pose a significant threat to groundwater resources. The resources that can be allocated by water regulators for site investigation and cleanup are limited compared to the large number of contaminated sites. Numerical transport models of individual sites require large amounts of data and are labor intensive to set up, and thus they are likely to be too expensive to be useful in the management of thousands of contaminated sites. Therefore, simple tools based on analytical solutions of contaminant transport models are widely used to assess (at an early stage) whether a site might pose a threat to groundwater. We present a tool consisting of five different models, representing common geological settings, contaminant pathways, and transport processes. The tool employs a simplified approach for preliminary, conservative, fast and inexpensive estimation of the contamination levels of aquifers. This is useful for risk assessment applications or to select and prioritize the sites, which should be targeted for further investigation. The tool is based on steady-state semi-analytical models simulating different contaminant transport scenarios from the source to downstream groundwater, and includes both unsaturated and saturated transport processes. The models combine existing analytical solutions from the literature for vertical (from the source to the top of the aquifer) and horizontal (within the aquifer) transport. The effect of net recharge causing a downward migration and an increase of vertical dispersion and dilution of the plume is also considered. Finally, we illustrate the application of the tool for a preliminary assessment of two contaminated sites in Denmark and compare the model results with field data. The comparison shows that a first preliminary assessment with conservative, and often non-site specific parameter selection, is qualitatively consistent with broad trends in observations and provides a conservative estimate of contamination.

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

“…Accordingly, the consumption of water polluted through nitrate can be connected to health problems, for example, cancers in adults via drinking water and skin contact [ 13 , 14 ]. For this purpose, groundwater-pollution predicting could assist managers of water resources and environmental protection in their probes to hamper groundwater pollution and to enhance its quality [ 15 , 16 , 17 ].…”

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods

“…Nitrate pollution of groundwater can have severe impacts on human health, issues such as cancer (e.g., esophageal, lymphatic, and gastric cancers) and methemoglobinemia in infants and pregnant women (Suthar et al, 2009;Panagopoulos et al, 2011;Sajil et al, 2014), and on the environment, such as causing ecological disruption and eutrophication throughout the hydrological system (Neshat et al, 2015;Wheeler et al, 2015;Stelzer and Scott, 2018). Groundwater-pollution modelling can aid managers of water resources and environmental protection in their quests to prevent groundwater pollution and to improve its quality (Almasri, 2008;Takizawa, 2008;Locatelli et al, 2019).…”

Hydrochemical characterization of shallow and deep groundwater in Basement Complex areas of southern Kebbi State, Sokoto Basin, Nigeria

“…However, groundwater has been studied using different techniques: geothermal techniques (Nwankwo 2015;Nwankwo and Shehu 2015;Olatunji and Musa 2013;Toyin et al 2016), isotope techniques (Adelana et al 2003;Geyh and Wirth 1980;Fillion et al 2014Fillion et al , 2018Selck et al 2018;Kattan 2018;Alemayehu et al 2019;Besser et al 2019), multivariate statistics (Mondal et al 2010;Azhar et al 2015;Yidana et al 2018), univariate statistics (Marghade et al 2010;Selvakumar et al 2017;Wali et al 2018a, b), modeling (Ebrahim et al 2019;Locatelli et al 2019), and chemical indices (Marghade et al 2010;Panaskar et al 2016;Wagh et al 2016). Results obtained from these studies showed groundwater quality is influenced by both the natural geogenic processes and anthropogenic activities.…”