Prediction of water pollution using mathematical simulation techniques
Water quality modeling involves water quality based data using mathematical simulation techniques. Water quality modeling helps people understand the eminence of water quality issues and models provide evidence for policy makers to make decisions in order to properly mitigate water.[1] Water quality modeling also helps determine correlations to constituent sources and water quality along with identifying information gaps.[2] Due to the increase in freshwater usage among people, water quality modeling is especially relevant[3] both in a local level and global level. In order to understand and predict the changes over time in water scarcity, climate change, and the economic factor of water resources,[1] water quality models would need sufficient data by including water bodies from both local and global levels.
A typical water quality model consists of a collection of formulations representing physical mechanisms that determine position and momentum of pollutants in a water body.[4] Models are available for individual components of the hydrological system such as surface runoff;[5] there also exist basin wide models addressing hydrologic transport and for ocean and estuarine applications. Often finite difference methods are used to analyze these phenomena, and, almost always, large complex computer models are required.[6]
^ abTang, Ting; Strokal, Maryna; van Vliet, Michelle T.H.; Seuntjens, Piet; Burek, Peter; Kroeze, Carolien; Langan, Simon; Wada, Yoshihide (February 2019). "Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide". Current Opinion in Environmental Sustainability. 36: 39–48. Bibcode:2019COES...36...39T. doi:10.1016/j.cosust.2018.10.004. hdl:10067/1586430151162165141.
^Preston, S.D. "SPARROW MODELING—Enhancing Understanding of the Nation's Water Quality". USGS – via US Dep of Interior.
^Bozorg-Haddad, Omid; Soleimani, Shima; Loáiciga, Hugo A. (July 2017). "Modeling Water-Quality Parameters Using Genetic Algorithm–Least Squares Support Vector Regression and Genetic Programming". Journal of Environmental Engineering. 143 (7): 04017021. doi:10.1061/(ASCE)EE.1943-7870.0001217. ISSN 0733-9372.
^Zhang, Wanshun; Wang, Yan; Peng, Hong; Li, Yiting; Tang, Jushan; Wu, K. Benjamin (February 2010). "A Coupled Water Quantity–Quality Model for Water Allocation Analysis". Water Resources Management. 24 (3): 485–511. doi:10.1007/s11269-009-9456-8. ISSN 0920-4741. S2CID 153922326.
^Vallet, B.; Muschalla, D.; Lessard, P.; Vanrolleghem, P.A. (2014-04-03). "A new dynamic water quality model for stormwater basins as a tool for urban runoff management: Concept and validation". Urban Water Journal. 11 (3): 211–220. doi:10.1080/1573062X.2013.775313. ISSN 1573-062X. S2CID 111045671.
^Liu, Yaoze; Li, Sisi; Wallace, Carlington W.; Chaubey, Indrajeet; Flanagan, Dennis C.; Theller, Lawrence O.; Engel, Bernard A. (September 2017). "Comparison of Computer Models for Estimating Hydrology and Water Quality in an Agricultural Watershed". Water Resources Management. 31 (11): 3641–3665. doi:10.1007/s11269-017-1691-9. ISSN 0920-4741. S2CID 158035959.
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