Mathematical models of chlorine demand in surface waters: A systematic review
DOI:
https://doi.org/10.21754/tecnia.v34i1.1635Keywords:
Modeling, Tool, Chlorine demand, Calibration, Validation, QualityAbstract
The mathematical model of water quality is a simplified representation of reality and constitutes a support tool to know a priori the changes in chemical or biological quality that occur throughout the river basin and useful for decision-making. decisions of the authorities in the management of water resources and the administration of water service providers. The objective of the article is a systematic review of the models that have been developed for the prediction of chlorine demand in water. From the review, seven models developed for the estimation of chlorine demand have been identified, which shows that it is a line of research to be explored. These models were built based on experimentation in the laboratory, in the Plant and data of previous studies, accompanied by tests of the estimate of the predictive capacity of the model, with the exception of some cases. This review is a contribution to the knowledge of the demand for chlorine, being of interest to the companies that provide services and actors in the management of water resources to have predictive tools on natural and anthropic phenomena that occur, interact and generate pollution in a hydrographic basin.
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References
A. Ziemińska-Stolarska, “Review of Mathematical Models of Water Quality,” Ecological Chemistry and Engineering, vol. 19, no. 2, pp. 1-15, 2012, doi: 10.2478/v10216-011-0015-x.
M. T. Ejigu, “Overview of water quality modeling,” Cogent Engineering, vol. 8, no. 1, p. 1891711, 2021, doi:10.1080/23311916.2021.1891711.
A. Rizzoli et al., “Chapter seven integrated modelling frameworks for environmental assessment and decision support,” Developments in integrated environmental assessment, vol. 3, pp. 1021-118, 2008, doi: 10.1016/S1574-101X(08)00607-8.
Y. Wang, et al., “Analysis and prevention of Urban River pollution,” Journal of Physics: Conference Series, vol. 1549, no. 2, p. 022056, 2020, doi: 10.1088/1742-6596/1549/2/022056.
A. K. Dwivedi, “Researches in water pollution: A review,” International Research Journal of Natural and Applied Sciences, vol. 4, no. 1, pp. 118-142, 2017, doi: 10.13140/RG.2.2.12094.08002.
J. Escobar, La contaminación de los ríos y sus efectos en las áreas costeras y el mar. CEPAL, CEPAL, 2002. [En línea]. Disponible: https://repositorio.cepal.org/server/api/core/bitstreams/db3b12df-ae24-4302-97ca-94db2b0d738c/content
Y. Montoya-Moreno, y J. Naranjo-Cardona, “Efectos asociados al cambio del cauce del río San Lorenzo, El Carmen de Viboral-Antioquia,” Revista Politécnica, vol. 16, no. 32, pp. 120-128, 2020, doi: 10.33571/rpolitec.v16n32a11.
T. Maavara, et al., “Global perturbation of organic carbon cycling by river damming,” Nature communications, vol. 8, no. 15347, 2017, doi:10.1038/ncomms15347.
Health Canada, “Guidance on Natural Organic Matter in Drinking Water,” Public Health Agency of Canada, Otawa, 2020. [En línea]. Disponible: https://publications.gc.ca/site/eng/9.883173/publication.html
G. Aiken y E. Cotsaris, “Soil and hydrology: their effect on NOM,” Journal‐American Water Works Association, vol. 87, no. 1, pp. 36-45, 1995, doi: 10.1002/j.1551-8833.1995.tb06299.x.
J. Jiménez-Antillón, et al. , “Remoción de materia organica natural por tratamiento convencional de agua en un río tropical,” Revista Tecnología en Marcha, vol. 35, no. 2, pp. 48-60, 2022, doi: 10.18845/tm.v35i2.5547.
R. D. Letterman, Water Quality and Treatmen. Fifth Edition, McGraw-Hill, Inc., 1999.
J. A. Valencia, Teoría y Práctica de la desinfección del agua Tomo 1, McGRAW-HILL, 2000. [En línea]. Disponible: https://www.academia.edu/49010823/Teoria_y_Practica_de_la_Purificacion_del_H2O_Tomo_1_Arboleda_Valencia
D. Nuevo, La cloracion del agua en tratamientos de aguas residuales, 2020. [En línea]. Available: https://www.tecpa.es/cloracion-tratamiento-aguas/
J.A. Valencia, Teoría y práctica de la purificación del agua potable. Tomo 2, McGRAW-HILL, 2000. [En línea]. Disponible: https://www.academia.edu/49010824/Teoria_y_Practica_de_la_Purificacion_del_H2O_Tomo_2_Arboleda_Valencia
H. Galal-Gorchev, “Chlorine in Water Desinfection,” Pure and Applied chemistry, vol. 68, no. 9, pp. 1731-1735, 1996, doi: 10.1351/pac199668091731
D. Taras y M. Feben, “Chlorine Demand Constants of Detroit's Water Supply,” American Water Works Association, vol. 42, no. 5, p. 453–461, 1950, doi: 10.1002/j.1551-8833.1950.tb16367.x
G. B. Luilo y S.E. Cabaniss, “Quantitative structure− property relationship for predicting chlorine demand by organic molecules,” Environmental science & technology, vol. 44, no. 7, pp. 2503-2508, 2010, doi: 10.1021/es903164d
E. L. Katz, “The stability of turbidity in raw water and its relationship to chlorine demand,” Journal‐American Water Works Association, vol. 78, no. 2, pp. 72-75, 1986, doi: 10.1002/j.1551-8833.1986.tb05697.x
W. Quinggai et al., “A review of surface water quaiity models,” The Scientific World Journal, pp. 1-7, 2013, doi: 10.1155/2013/231768
N. Sumita y B. Kaur, “Water quality models: A review,” Int. J. Res. Granthaalayah, vol. 5, no. 1, pp. 395-398, 2017, doi:10.29121/granthaalayah.v5.i1.2017.1914
Ministerio de Ambiente y Desarrollo Sostenible, Guía Nacional de Modelación del Recurso Hídrico para aguas superficiales continentales, Bogota, Colombia, 2018.
J. C. Refsgaard, “Terminology, modelling protocol and classification of hydrological model codes,” Distributed hydrological modelling, pp. 17-39, 1996, doi: 10.1007/978-94-009-0257-2_2
N. Khandan, Modeling Tools for Environmental Engineers and Scientists (1st ed), CRC Press., 2001, doi: 10.1201/9781420003390
G.T. Orlob, Mathematical modeling of water quality: Streams, lakes and reservoirs (Vol. 12), John Wiley & Sons, 1983.
J. C. Souza Inácio Gonçalves y M. G. Giorgetti, “Mathematical model for the simulation of water quality in rivers using the VENSIM PLE® software,” Journal of Urban and Environmental Engineering, vol. 7, no. 1, pp. 48-63, 2013, doi: 10.4090/juee.2013.v7n1.48-63
G. Marusic, “A study on the mathematical modeling of water quality in" river-type" aquatic systems,” WSEAS Transactions on Fluid Mechanics, vol. 8, 2012. [En línea]. Disponible: https://wseas.com/journals/articles.php?id=5992
J. Pazmiño, Fundamentos de Modelizaciòn de la Calidad del Agua, COMPAS, 2020.
M. J. Page et al., “Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas,” Revista española de cardiología, vol. 74, no. 9, pp. 790-799, 2021, doi: 10.1016/j.recesp.2021.06.016
E. Crocetti, “Systematic reviews with meta-analysis: Why, when, and how?,” Emerging Adulthood, vol. 4, no. 1, pp. 3-18, 2016, doi: 10.1177/2167696815617076
J. Sánchez-Meca, “Cómo realizar una revisión sistemática y un meta-análisis,” Aula abierta, vol. 38, no. 2, pp. 53-64, 2010. [En línea]. Disponible: https://reunido.uniovi.es/index.php/AA/issue/view/1037/140
V. J. Escrig. et al, “Metaanálisis: una forma básica de entender e interpretar su evidencia,” Revista de senologia y Patologia Mamaria, vol. 34, no. 1, pp. 44-51, 2021, doi: 10.1016/j.senol.2020.05.007
M. Molina, “Aspectos metodológicos del metaanálisis,” Rev. Pediatrica Atención Primaria, vol. 20, no. 79, pp. 227-292, 2018. [En línea]. Disponible: https://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1139-76322018000300020
C. Fau y S. Nabzo, “Metaanálisis: bases conceptuales, análisis e interpretación estadística,” Revista Mexicana de Oftalmología, vol. 94, no. 6, pp. 260-273, 2020, doi: 10.24875/rmo.m20000134.
F.J. García, “Desarrollo de estados de la cuestión robustos: Revisiones Sistemáticas de Literatura,” Education in the knowledge society: EKS, vol. 23, 2022, doi: 10.14201/eks.28600
R. A. Benavent, et al., “El factor de impacto: un polémico indicador de calidad científica,” Rev Esp Econ Salud, vol. 3, no. 5, 2004. [En línea]. Disponible: https://www.researchgate.net/publication/290164174
R. Repiso, “Factores que Influyen en la frecuencia de citación de un artículo,” Escuela de Autores, 2020, doi: /10.3916/escuela-de-autores-145
M. E. Szretter, “Apunte de regresión lineal,” Buenos Aires, 2017. [En línea]. Disponible: https://mate.dm.uba.ar/~meszre/apunte_regresion_lineal_szretter.pdf
D. Rodriguez, “Diferencia entre R2 y R2 ajustado en modelos de regresión,” 8 marzo 2024. [En línea]. Disponible: https://www.analyticslane.com/2024/03/08/diferencia-entre-r2-y-r2-ajustado-en-modelos-de-regresion/
M. Levine y M. Berenson, Estadística Básica en Administración, México: Prentice Hall Hispanoamericana, 1996.
J.S. Milton y J. O. Tsokos, Estadística para Biología y Ciencias de la Salud, Interamericana Mc Graw-Hill, 1991.
L. Yee et al., “Dissolved organic matter and its impact on the chlorine demand of treated water,” The Malaysian Journal of Analytical Sciences, vol. 10, no. 2, pp. 243-250, 2006. [En línea]. Disponible: https://www.semanticscholar.org/paper/DISSOLVED-ORGANIC-MATTER-AND-ITS-IMPACT-ON-THE-OF-Yee-Abdullah/64cdad53344c397ce898a69601fc319f96008d85
A. Imai et al., “Fractionation and characterization of dissolved organic matter in a shallow eutrophic lake, its inflowing rivers, and other organic matter sources.,” Water Research, vol. 35, no. 17, pp. 4019-4028, 2001, doi: 10.1016/S0043-1354(01)00139-7
F. Worrall, et al., “The importance of sewage effluent discharge in the export of dissolved organic carbon from UK rivers,” Hydrological Processes, vol. 33, no. 13, pp. 1851-1864, 2019, doi: 10.1002/hyp.13442
M.E. Abdullahi y B.I. Abdulkarim, “Development of Mathematical Model for determining the quantity of chlorine required for water treatment,” Journal of Applied Sciences Research, vol. 6, no. 8, pp. 1002-1007, 2010. [En línea]. Disponible: https://www.researchgate.net/publication/236612115
C. T. Butterfield et al., “Influence of pH and temperature on the survival of coliforms and enteric pathogens when exposed to free chlorine,” Public Health Reports (1896-1970), vol. 58, no. 57, pp. 1837-1866, 1943, doi: 10.2307/4584715.
L. B. y. E. Allen, “Some Factors affecting the Bactericidal Action of Chlorine,” Proceedings of the Society for Applied Bacteriology, vol. 15, no. 1, pp. 155-165, 1952, doi: 10.1111/j.1365-2672.1952.tb00018.x
M.P. Abdullah et al, “The study of interrelationship between raw water quality parameters, chlorine demand and the formation of disinfection by-products,” Physics and Chemistry of the Earth, vol. 34, pp. 806-811, 2009, doi: 10.1016/j.pce.2009.06.014
World Health Organization, Guidelines for drinking-water quality: incorporating the first and second addenda, World Health Organization, 2022.
G. Zhu et al., “Multi-linear regression model for chlorine consumption by waters,” Environmental Engineering Research, vol. 26, no. 4, 2021, doi:10.4491/eer.2020.402
R. M. Fuentes et al., “Caracterización de la materia orgánica disuelta en agua subterránea del Valle de Toluca mediante espectrofotometría de fluorescencia 3D,” Revista internacional de contaminación ambiental, vol. 31, no. 3, pp. 253-264, 2015. [En línea]. Disponible: http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-49992015000300005&lng=es&nrm=iso
T. F. Marhaba, “Fluorescence technique for rapid identification of DOM fractions”, Journal of Environmental Engineering, vol. 126, no. 2, pp. 145-152, 2000, doi: 10.1061/(ASCE)0733-9372(2000)126:2(145)
A.Goffin et al., “An environmentally friendly surrogate method for measuring the soluble chemical oxygen demand in wastewater: use of three-dimensional excitation and emission matrix fluorescence spectros,” Environmental monitoring and assessment, vol. 191, no. 421, pp. 1-8, 2019, doi: 10.1007/s10661-019-7570-5
A. Goffin et al., “Towards a better control of the wastewater treatment process: excitation-emission matrix fluorescence spectroscopy of dissolved organic matter as a predictive tool of soluble BOD 5 in influents of six Parisian wastewater treatment plants,” Environmental Science and Pollution Research, vol. 25, pp. 8765-8776, 2018, doi: 10.1007/s11356-018-1205-1
W. Chu et al., “Formation and speciation of nine haloacetamides, an emerging class of nitrogenous DBPs, during chlorination or chloramination,” Journal of Hazardous Materials, vol. 260, pp. 806-812, 2013, doi: 10.1016/j.jhazmat.2013.06.044
H. Cooper et al., The handbook of research synthesis and meta-analysis, Russell Sage Foundation, 2019.
M. Borenstein et al., Introduction to Metal-Analysis, WILEY, 2009, doi: 10.1002/9780470743386
J. D. Leongómez, “Meta-análisis de correlaciones y meta-regresión en R: Guía práctica,” MetaArXiv, 2023, doi: 10.31222/osf.io/yaxd4
J.E. Fernandez-Chinguel et al., “Aspectos básicos sobre la lectura de revisiones sistemáticas y la interpretación de meta-análisis,” Acta Médica Peruana, vol. 36, no. 2, pp. 157-169, 2019, doi: 10.35663/amp.2019.362.818
H. Oliveros, “La heterogeneidad en los metaanálisis, ¿es nuestra mejor aliada?,” Revista Colombiana de Anestesiología, vol. 43, no. 3, pp. 176-178, 2015, doi: 10.1016/j.rca.2015.05.002
K. S Khan et al., “Revisiones sistemáticas en cinco pasos: IV. Cómo sintetizar los resultados,” Medicina de Familia. SEMERGEN, vol. 48, no. 8, 2022, doi: 10.1016/j.semerg.2022.02.006
C. W. Guerra et al., “Criterios para la selección de modelos estadísticos en la investigación científica,” Revista Cubana de Ciencia Agrícola, vol. 37, no. 1, pp. 3-10, 2003. [En línea]. Disponible: http://www.redalyc.org/articulo.oa?id=193018072001
R. Christensen, “General prediction theory and the role of R2. Unpublished manuscript,” 2007. [En línea]. Disponible: https://www.math.unm.edu/~fletcher/JPG/rsq.pdf
J. Bolster y C. Tellinghuisen, “Using R2 to compare least-squares fit models: When it must fail,” Chemometrics and intelligent laboratory systems, vol. 105, no. 2, pp. 220-222, 2011, doi: 10.1016/j.chemolab.2011.01.004
T. O. Kvålseth, “Cautionary note about R2,” The American Statistician, vol. 39, no. 4, pp. 279-285, 1995, doi: 10.1080/00031305.1985.10479448
D. F. y. M. Taras, “Studies on Chlorine Demand Constants,” American Water Works Association, vol. 43, no. 11, pp. 922-931, 1951, doi: 10.1002/j.1551-8833.1951.tb19057.x
P. Durán, “Los datos perdidos en estudios de investigación ¿son realmente datos perdidos?,” Archivos argentinos de pediatría, vol. 103, no. 6, pp. 566-568, 2005. [En línea]. Disponible: https://www.sap.org.ar/docs/archivos/2005/arch05_6/566.pdf
A. Nouri et al., “Effect of temperature on pH, turbidity, and residual free chlorine in Sanandaj Water Distribution Network, Iran,” Journal of advances in environmental health research, vol. 3, no. 3, pp. 188-195, 2015, doi: 10.22102/jaehr.2015.40202
J. De la Horra, Modelos Matemáticos para Ciencias Experimentales, Madrid: Diaz de Santos, 2018.
J. Li, “Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?,” PloS one, vol. 12, no. 8, p. e0183250, 2017, doi: 10.1371/journal.pone.0183250
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