Keywords
gis, bos, urban groundwater, groundwater pollution, probabilistic risk modelling
Start Date
1-7-2008 12:00 AM
Abstract
Urban groundwater continues to be at risk of pollution by organic chemicals and microbiological contaminants despite its potential economic and ecological value. Assessing these risks is hampered by the large number of potential sources and the general lack of detailed site data, and so there is a likelihood of poor decisions being made which would spoil the overall quality of life. However, we have shown that it is possible to build a GIS-based risk analysis tool. The tool, called the Borehole Optimisation System or BOS, has been validated against a variety of field datasets, and has been shown to make reasonable predictions of risks – that is within 2 orders of magnitude. Better predictions are made when there is a multiplicity of potential sources within a borehole catchment. There are a range of types of risk predictions that can be made with BOS, including analysis of a single site, mapping of risk over a city, and generic risk analysis without a site specific component. These risk analyses are probabilistic to take account of the uncertainties and poor characterisation of the environment. The information can be presented in a simple enough way to support decision-making and help to enhance the quality of life by targeting resources in an efficient manner.
Estimating the Pollution Risks to Urban Groundwater from Industry and Sewers
Urban groundwater continues to be at risk of pollution by organic chemicals and microbiological contaminants despite its potential economic and ecological value. Assessing these risks is hampered by the large number of potential sources and the general lack of detailed site data, and so there is a likelihood of poor decisions being made which would spoil the overall quality of life. However, we have shown that it is possible to build a GIS-based risk analysis tool. The tool, called the Borehole Optimisation System or BOS, has been validated against a variety of field datasets, and has been shown to make reasonable predictions of risks – that is within 2 orders of magnitude. Better predictions are made when there is a multiplicity of potential sources within a borehole catchment. There are a range of types of risk predictions that can be made with BOS, including analysis of a single site, mapping of risk over a city, and generic risk analysis without a site specific component. These risk analyses are probabilistic to take account of the uncertainties and poor characterisation of the environment. The information can be presented in a simple enough way to support decision-making and help to enhance the quality of life by targeting resources in an efficient manner.
