Main Article Content
Abstract
Drinking water sources are regularly polluted by various human activities that cause severe health problem all over the world. In recent years, water quality research has drawn great attention from scientific communities. A lot number of tools and techniques are used for proper water quality analysis, monitoring and assessment.
This paper includes brief information about some of the them namely, physio-chemical water analysis (PCWA), adsorption, metal pollution index (MPI), water quality index (WQI), water quality modelling tools (WQMT) and multivariable statistical models that include five multivariate data mining approaches i.e. cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), multiple linear regression analysis (MLRA), discriminant analysis (DA).
Present paper also explores the interaction between science and technologies and provides basic knowledge of emerging tools and techniques used in water purification.
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References
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References
Iscen C.F., Emiroglu O., Ilhan S., Arslan N., Yilmaz V., and Ahiska S., Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey, Environmental Monitoring and Assessment, 144, 2008, 269-276.
https://doi.org/10.1007/s10661-007-9989-3 DOI: https://doi.org/10.1007/s10661-007-9989-3
PMid:17929181
Mustapha A., Abdu A., Application of principal component analysis & multiple regression models in surface water quality assessment, Journal of Environment and Earth Science, 2 (2), 2012, 16-23.
Wang Q., Li S., Jia P., Qi C., Ding F., A Review of surface water quality models, The ScientificWorld Journal, article ID 231768, 2013, 1-7
https://doi.org/10.1155/2013/231768 DOI: https://doi.org/10.1155/2013/231768
PMid:23853533 PMCid:PMC3703326
Limousin G., Gaudet J.P., Charlet L., Szenknect S., Barthes V., Krimissa M., Sorption isotherms: a review on physical bases, modeling and measurement, Applied Geochemistry 22, 2007, 249-275.
https://doi.org/10.1016/j.apgeochem.2006.09.010 DOI: https://doi.org/10.1016/j.apgeochem.2006.09.010
Allen S.J., Mckay G., Porter J.F., Adsorption isotherm models for basic dye adsorption by peat in single and binary component systems, Journal of Colloid and Interface Science 280, 2004, 322-333.
https://doi.org/10.1016/j.jcis.2004.08.078 DOI: https://doi.org/10.1016/j.jcis.2004.08.078
PMid:15533404
Ncibi M.C., Applicability of some statistical tools to predict optimum adsorption isotherm after linear and non-linear regression analysis, Journal of Hazardous Materials 153, 2008, 207-212
https://doi.org/10.1016/j.jhazmat.2007.08.038 DOI: https://doi.org/10.1016/j.jhazmat.2007.08.038
PMid:17900804
Malek A., Farooq S., Comparison of isotherm models for hydrocarbon adsorption on activated carbon, AIChE J. 42 (11), 1996, 3191-3201.
https://doi.org/10.1002/aic.690421120 DOI: https://doi.org/10.1002/aic.690421120
Dubinin M.M., The potential theory of adsorption of gases and vapors for adsorbents with energetically non-uniform surface, Chemical Reviews 60, 1960, 235-266.
https://doi.org/10.1021/cr60204a006 DOI: https://doi.org/10.1021/cr60204a006
Foo K.Y., Hameed B.H., Insights into the modeling of adsorption isotherm systems, Chemical Engineering Journal 156, 2010, 2-10 and references cited therein.
https://doi.org/10.1016/j.cej.2009.09.013 DOI: https://doi.org/10.1016/j.cej.2009.09.013
Amadi A. N., Yisa J., Ogbonnaya I. C., Dan-Hassan M. A., Jacob J. O., Alkali Y. B., Quality evaluation of river chanchaga using metal pollution index and principal component analysis, Journal of Geography and Geology, 4 (2) 2012, 13-21 and references cited therein.
https://doi.org/10.5539/jgg.v4n2p13 DOI: https://doi.org/10.5539/jgg.v4n2p13
Tamasi, G., Cini, R., Heavy metals in drinking waters from Mount Amiata. Possible risks from arsenic for public health in the province of Siena. Science of the Total Environment, 327, 2004, 41-51.
https://doi.org/10.1016/j.scitotenv.2003.10.011 DOI: https://doi.org/10.1016/j.scitotenv.2003.10.011
PMid:15172570
Reza R., Singh G., Assessment of ground water quality status by using water quality index method in Orissa, India, World Applied Sciences Journal 9 (12), 2010, 1392-1397 and references cited therein.
Hair Jr.J.F.K., Black W.C., Babin B.J., Anderson R.E., Multivariate data analysis. New Jersey: Pearson prentice hall, 2010.
Johnson R.A., Wichern D.W., Applied multivariate statistical analysis, New Jersey: Pearson prentice hall, 2007.
Mazlum N., Özer A., Mazlum S., Interpretation of water quality data by principal components analysis, Turkish Journal of Engineering and Environmental Science, 23, 1999, 19-26.
Kanade S.B., Gaikwad V.B., A multivariate statistical analysis of bore well chemistry data-Nashik and Niphad taluka of Maharashtra, India, Universal Journal of Environmental Research and Technology, 1(2), (2011),pp 193-202.
Kumar M., Padhy P. K., Multivariate statistical techniques and water quality assessment: Discourse and review on some analytical models, Internation Journal of Environmental Sciences, 5 (3), 2014, 607-626 and references cited therein