Talk A Python algorithm for Contaminated Land Risk Assessment.

Abstract

We present a python algorithm implemented in the framework of a decision support system (DSS) to help decision makers find the best redevelopment scenarios for contaminated sites. Support is given to the identification and integrated assessment of potential site development options. The assessment includes the estimation of costs for clean up of contaminated soil and groundwater to mitigate risks to human health. The decision support system is composed of several modules, including the so-called conflict analysis module, which is built around an algorithm implemented in Python. The algorithm evaluates conflicts between required environmental quality to implement future land use scenarios and subsurface contamination in two alternative ways: either on the basis of land-use specific remediation targets (i.e. regulative threshold values) or by means of a risk analysis employing analytical functions to quantify exposure to contaminants via diverse pathways like water ingestion or soil dermal contact.
The Python algorithm rely on GDAL to read input data in the form of GIS layers (ESRI shapefiles and ASCII raster grids) and produce intermediate output shape files and ASCII raster files. NumPy is used to process the inputs, and aid in the process of producing the intermediates outputs. Regular text processing to parse configuration files and other text inputs is done, in order to produce a final MAP file (UMN MapServer syntax), and then call MapServer which parses the MAP file and produces to produce the final cartographic images. These images are maps showing the desired layout of land uses and overlay of conflict areas.
Previously, these algorithms were implemented in Visual Basic, and ran only on systems with the commercial ArcGIS software pre-installed. Using Python and GDAL, allows the distribution of the MMT package without the requirement to have an expensive commercial software pre-installed.
We intend to present the algorithm and its implementation, together with simple usage scenarios and the results obtained for a chosen contaminated land site in Germany.

AUTHORS:
Dr. Maximilian Morio(a),Dr. Michael Finkel (a), Dr. Sebastian Sch├Ądler (a), M. Sc. Oz Nahum (b,*),
(a) Center for Applied Geosciences, University of Tuebingen, Germany
(b) Science+Computing AG, Tuebingen, Germany.
* Correspondent Author.