Computer algorithms might be useful in identifying sources of ground water pollution, according to researchers in Australia and India. Writing in theInternational Journal of Environment and Waste Management, they explain how notoriously difficult it is to trace such pollution.
Ground water is a major and economical source of drinking
water for both urban and rural areas. Although ground water represents a small
percentage of the total water distribution across the globe, it is the largest
available reservoir of freshwater. Available freshwater amounts to less than
one half of 1 percent of all the water on earth. However, the subsurface is
also the principal receptacle for increasing volumes of human and industrial
waste. As global consumption of water is doubling every 20 years, more than
twice the rate of human population growth, the issue of pollution of ground water
is a growing problem.
Ground water pollution occurs from different anthropogenic
sources, such as leakage from underground storage tanks and chemical and waste
depositories, leakage from hazardous waste dump sites, sewers, liquid effluent
and process lagoons, soak pits and accidental discharge, explain Ravi Naidu of
the Centre for Environmental Risk Assessment and Remediation at the University of South Australia, and colleagues.
"Remediation of these contaminated sites requires the optimal
decision-making system so that the remediation is done in a cost-effective and
efficient manner," the researchers say. "Identification of unknown
pollution sources plays an important role in remediation and containment of
contaminant plume in a hazardous site."
They point out that reliable and accurate estimation
of unknown ground water pollution sources remains a challenge because of the
uncertainties involved and the lack of adequate observation data in most cases.
The non-unique nature of the identification results also is an issue in finding
the original source of a pollutant. They have tested the validity of different
optimization algorithms, including a genetic algorithm, an artificial neural
network and simulated annealing and hybrid methods. All of these methods
essentially process available data, including pollutant concentrations and how
these change over time and any monitoring data to home in on a potential
source. The benefit of using such algorithms is that, as more information
becomes available, another iteration will take investigators closer to the
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