Null-Space Monte Carlo Particle Backtracking to Identify Groundwater Tetrachloroethylene Sources

Abstract

Groundwater in urban areas around the globe happens to be contaminated by toxic substances. Among the various sources of contamination, industries determine the heaviest impacts when toxic compounds are released in the underground, mainly through leaking tanks or pipelines. Some contaminants (typically chlorinated hydrocarbons) tend to persist within the underground and are hard to biodegrade. As a result, substances that leaked decades ago are still impacting groundwater. Milano and its hinterland (Functional Urban Area) are a good example of an area that has been hosting for over a century, industries of all dimensions, many of them contributing chlorinated hydrocarbons in the underground. While the position of the biggest industrial facilities is well known, many smaller sources are hard to identify in many cases where direct surveys haven’t been undertaken. Furthermore, the overlapping effects of big, small, known and unknown sources on groundwater contamination makes it hard to identify the contribution of each. In order to identify the contribution of several Point Sources responsible of Tetrachloroethylene contamination in Public Water Supply Wells, a numerical model (MODFLOW-2005) has been implemented and calibrated (PEST) in the Northwestern portion of Milano Functional Urban Area. In contaminant transport modeling, the deterministic approach is still favored over the stochastic because of the simplicity of its application. Nevertheless, the latter is considered by the authors as the suitable dealing with problems characterized by high uncertainty, such as hydrogeological parameters distribution. Adopting a Null-Space Monte Carlo analysis, 400 different sets of hydraulic conductivity fields have been randomly generated, of which only 336 have been selected using an objective function threshold. Subsequently, a particle back-tracking has been performed for each of the accepted hydraulic conductivity fields, by placing particles in a contaminated well. The number of particle passages is considered as being proportional to the contribution of each unknown Point Source to the Tetrachloroethylene contamination identified in the target well. The study provides a methodology to help Public Authorities to locate the most probable than not area responsible to the Tetrachloroethylene contamination detected in groundwater and to focus environmental investigations in specific sectors of Milano city.