Multi-pollutant approach to model contaminants flow in surface and groundwater: a review

Pollution of surface and groundwater is largely caused by anthropogenic activities and the natural geogenic processes. Most of the contaminants in surface and groundwater have a common origin. The aim of this review is to highlight the importance of multi-approach modeling of pollutants which is req...

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Bibliographic Details
Main Authors: Wali, Saadu Umar, Alias, Noraliani
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Online Access:http://eprints.utm.my/id/eprint/90642/1/SaaduUmarWali2020_MultiPollutantApproachtoModelContaminants.pdf
http://eprints.utm.my/id/eprint/90642/
http://dx.doi.org/10.1088/1757-899X/884/1/012030
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Summary:Pollution of surface and groundwater is largely caused by anthropogenic activities and the natural geogenic processes. Most of the contaminants in surface and groundwater have a common origin. The aim of this review is to highlight the importance of multi-approach modeling of pollutants which is required for various reasons, owing to the availability of different types and sources of water pollutants. We attempted a systematic review to assess the current progress in modeling water pollution using multi-approach methods. Results showed that (9) out of the eleven (11) chosen studies have applied some forms of multi-approach modeling methods to examine pollutants in surface and groundwater. Results also suggest that there is an increased concern on understanding how pollutants are transported from sources to surface water and how impurities are transported to groundwater aquifers by infiltering surface flows. A major limitation of water quality models is that models assumed a uniform environmental setting and can simulate contaminants only in the gas and aqueous states. The rationality of contaminant modeling using multi-pollutant approaches is mostly problematical to validate because suitable field data is wanting for comparison. Therefore, the model output must be scrutinized within the context of the uncertainty of the model inputs, data limitations and consistently essential application of established standards from the literature.