This repository contains python scripts related to the following paper (open access):
L.J.S. Halloran and D. Hunkeler (2020) "Controls on the persistence of aqueous-phase groundwater contaminants in the presence of reactive back-diffusion." Science of the Total Environment 722, 137749. https://doi.org/10.1016/j.scitotenv.2020.137749
These scripts process and visualise data exported from numerical models. In the contaminant transport models 5 parameters are varied. The script calculates and visualises maximum normalised concentrations, time (or n pore volumes) for attenuation, and plume extent. Constructed parameters (η, Π1 and Π2) are also calculated and various quantities are plotted against them. All plots are exported to a time-stamped pdf file in the subfolder out/.
You need a python distribution (e.g., anaconda). The following standard python packages are needed: pandas, matplotlib, numpy, datetime, scipy.
Three scenarios are tested. Each scenario has a different script. For Scenario A (aquifer between aquitards, contaminant source removed after fixed time) and Scenario B (aquitard between aquifers, contaminant source removed after fixed time), the following are defined by the user in the script:
dropFactor: The attenuation factor as a multiple of the maximum normalised concentration (i.e., <1) below which the normalised concentration must fall to determine n_v,att.cutOffC: A cut-off for normalised concentration below which results are ignored. Should be >1E-5 as this is the approximate precision of the numerical models.pointNumber: to select the observation well for analysis. 0 is the well at 20m, 1 is the well at 40 m, etc. For Scenario B, the wells below the aquitard layer are numbered 5-9, moving left-to-right. In the article it ispointNumber=4that is presented.
For Scenario C (aquifer between aquitards, constant contaminant source), only cutOffC and pointNumber need to be defined.
Users are encouraged to use and modify these scripts however they wish (e.g., for visualisations of discrete data that is a function of 4 or 5 variables). I ask only that the above paper is cited in any published works.
