Skip to main content
Log in

Modelling chlorine residuals in drinking water: a review

  • Review
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

World Health Organization’s guidelines on water quality limit concentrations of residual chlorine in drinking water to the range 0.2–5 mg/l. Modelling tends to be applied to understand how chlorine concentrations can be kept within the recommended limits. In this line, we reviewed 105 articles to show advances in modelling of chlorine residuals while focussing on both data-driven statistical models and process-based models. A total of 83 and 17% reviewed articles applied process-based models and statistical models, respectively. The most influential water parameters which were reported for chlorine decay were pH and temperature. For statistical models, modellers reported a wide range of sizes of training, testing, validation sub-samples, and number of neurons in the hidden layers of the network. Thus, the use of novel fitness function to concurrently seek for the most accurate and compact solution was recommended. Most studies applied coefficient of determination (despite its issues such as failure to quantify bias) to evaluate model performance. We recommended revised coefficient of determination and hydrological model skill score to be used as “goodness-of-fits” metrics since they can quantify model’s bias, and capacity to reproduce observed variability. We found that many modellers portrayed a common practice of not providing sufficient information (such as values of parameters) regarding their modelling results. For instance, 47% of the reviewed articles did not expressly specify the order of reaction in their chlorine decay modelling studies. The practice of not reporting sufficient pertinent information can affect reproducibility of results and hinder model improvement which would arise from possible follow-up studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abokifa AA, Yang YJ, Lo CS, Biswas P (2016) Water quality modeling in the dead end sections of drinking water distribution networks. Water Res 89:107–117

    Article  CAS  Google Scholar 

  • Al Heboos S (2017) Effects of water quality characters on chlorine decay in water distribution networks. PhD Thesis, Budapest University of Technology and Economics, p 108

  • Al Heboos S, Licskó I (2015) Influence of water quality characters on kinetics of chlorine bulk decay in water distribution systems. Int J Appl Sci Technol 5(4):64–73

    Google Scholar 

  • Al Heboos S, Licskó I (2017) Application and comparison of two chlorine decay models for predicting bulk chlorine residuals. Periodica Polytech Civ Eng 61:7–13

    Google Scholar 

  • Al-Omari A, Fayyad M, Al-Nimer A (2004) Modelling chlorine residuals at Jabal Amman water supply. J Water Supply Res Technol 53(5):351–358

    Article  CAS  Google Scholar 

  • Al-Omari A, Fayyad M, Jamrah A (2008) Drinking water quality in roof storage tanks in the city of Amman, Jordan. Water Int 33(2):189–201

    Article  Google Scholar 

  • Alsaydalani MOA (2019) Simulation of pressure head and chlorine decay in a water distribution network: a case study. Open Civ Eng J 13:58–68

    Article  Google Scholar 

  • Ammar TA, Abid KY, El-Bindary AA, El-Sonbati AZ (2014) Chlorine dioxide bulk decay prediction in desalinated drinking water. Desalination 352(3):45–51

    Article  CAS  Google Scholar 

  • Angulo F, Urueta E, Valverde G, Paternina O (2017) Cartagena’s water distribution system. Procedia Eng 186:28–35

    Article  Google Scholar 

  • Araya A, Sanchez LD (2018) Residual chlorine behaviour in a distribution network of a small water supply system. J Water Sanit Hyg Dev 8(2):349–358

    Article  Google Scholar 

  • Azad A, Karami H, Farzin S, Mousavi SF, Kisi O (2019) Modelling river quality parameters using modified adaptive neuro fuzzy inference system. Water Sci Eng 12(1):45–54

    Article  Google Scholar 

  • Bensoltane M, Zeghadnia L, Djemili L, Gheid A, Djebbar Y (2018) Enhancement of the free residual chlorine concentration at the ends of the water supply network: case study of Souk Ahras city—Algeria. J Water Land Dev 38(VII–IX):3–9

    Article  Google Scholar 

  • Blokker M, Vreeburg J, Speight V (2014) Residual chlorine in the extremities of the drinking water distribution system: the influence of stochastic water demands. Procedia Eng 70:172–180

    Article  CAS  Google Scholar 

  • Bowden GJ, Nixon JB, Dandy GC, Maier HR, Holmes M (2006) Forecasting chlorine residuals in a water distribution system using a general regression neural network. Math Comput Model 44(5–6):469–484

    Article  Google Scholar 

  • Branz A, Levine M, Lehmann L, Bastable A, Si A, Kadir K, Yates T, Bloom D, Lantagne D (2017) Chlorination of drinking water in emergencies: a review of knowledge to develop recommendations for implementation and research needed. Waterlines 36:4–39

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Chaudhuri A, Hu W (2019) A fast algorithm for computing distance correlation. Comput Stat Data Anal 135:15–24

    Article  Google Scholar 

  • Clark RM (2011) Chlorine fate and transport in drinking water distribution systems: results from experimental and modelling studies. Front Earth Sci 5(4):334–340

    CAS  Google Scholar 

  • Cordoba GAC, Tuhovčák L, Tauš M (2014) Using artificial neural network models to assess water quality in water distribution networks. Procedia Eng 70:399–408

    Article  Google Scholar 

  • Courtis BJ, West JR, Bridgeman J (2009) Temporal and spatial variations in bulk chlorine decay within a water supply system. J Environ Eng 135:147–152

    Article  CAS  Google Scholar 

  • Davis MJ, Janke R, Taxon TN (2018) Mass imbalances in EPANET water-quality simulations. Drink Water Eng Sci 11(1):25–47

    Article  CAS  Google Scholar 

  • Diao K, Sweetapple C, Farmani R, Fu G, Ward S, Butler D (2016) Global resilience analysis of water distribution systems. Water Res 106:383–393

    Article  CAS  Google Scholar 

  • Fisher I, Kastl G, Sathasivan A (2011) Evaluation of suitable chlorine bulk-decay models for water distribution systems. Water Res 45:4896–4908

    Article  CAS  Google Scholar 

  • Fisher I, Kastl G, Sathasivan A (2012) A suitable model of combined effects of temperature and initial condition on chlorine bulk decay in water distribution systems. Water Res 46(10):3293–3303

    Article  CAS  Google Scholar 

  • Garcia D, Puig V, Quevedo J (2020) Prognosis of water quality sensors using advanced data analytics: application to the Barcelona drinking water network. Sensors 20:1342. https://doi.org/10.3390/s20051342

    Article  CAS  Google Scholar 

  • García-Ávila F, Sánchez-Alvarracín C, Cadme-Galabay M, Conchado-Martínez J, García-Mera G, Zhindón-Arévalo C (2020) Relationship between chlorine decay and temperature in the drinking water. MethodsX. https://doi.org/10.1016/j.mex.2020.101002

    Article  Google Scholar 

  • García-Ávila F, Avilés-Añazco A, Ordoñez-Jara J, Guanuchi-Quezada C, del Pino LF (2021) Ramos-Fernández L (2021) Modeling of residual chlorine in a drinking water network in times of pandemic of the SARS-CoV-2 (COVID-19). Sustain Environ Res 31:12. https://doi.org/10.1186/s42834-021-00084-w

    Article  CAS  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76:60–68

    Article  Google Scholar 

  • Georgescu A-M, Georgescu S-C (2012) Chlorine concentration decay in the water distribution system of a town with 50000 inhabitants. UPB Sci Bull Ser D Mech Eng 74(1):103–114

    Google Scholar 

  • Gibbs MS, Morgan N, Maier HR, Dandy GC, Holmes M, Nixon JB (2003) Use of artificial neural networks for modelling chlorine residuals in water distribution systems. In: Modsim 2003: international congress on modelling and simulation. Modelling and Simulation Society of Australia and New Zealand Inc, Townsville, Australia, 14–17 July 2003, pp 789–794

  • Gibson J (2019) Water quality and hydraulic trade-offs in drinking water distribution networks. PhD Thesis, Department of Civil Engineering, University of Toronto, Canada

  • Government of Sudan (2017) Protocols for the chlorination of drinking water for small to medium sized supplies. Federal Ministry of Health and Ministry of Water Resources, Irrigation and Electricity

  • Goyal RP, Patel HM (2014) Analysis of residual chlorine in simple drinking water distribution system with intermittent water supply. Appl Water Sci 5:311–319

    Article  Google Scholar 

  • Grayman W, Kshirsagar S, Rivera-Sustache M, Ginsberg M (2012) An improved water distribution system chlorine decay model using EPANET MSX. J Water Manag Model R245–21:367–376

    Google Scholar 

  • Green DE, Stumpf PK (1946) The mode of action of chlorine. J Am Water Works Assoc 38:1301–1305

    Article  CAS  Google Scholar 

  • Hallam NB, West JR, Forster CF, Powell JC, Spencer I (2002) The decay of chlorine associated with the pipe wall in water distribution systems. Water Res 36(14):3479–3488

    Article  CAS  Google Scholar 

  • Helbling DE and VanBriesen JM (2009) Propagation of chlorine demand signals induced by microbial contaminants in a drinking water distribution system. In: Proceedings of world environ and water research congress 2009: Great Rivers. ASCE. Kansas City, MO, pp 515–524. https://doi.org/10.1061/41036(342)50

  • Jamwal P, Kumar MSM (2016) Effect of flow velocity on chlorine decay in a water distribution network: a pilot study. Curr Sci 111(8):1349–1354

    Article  CAS  Google Scholar 

  • Karadirek IE, Kara S, Muhammetoglu A, Muhammetoglu H, Soyupak S (2015) Management of chlorine dosing rates in urban water distribution networks using online continuous monitoring and modeling. Urban Water J 13(4):345–359

    Article  Google Scholar 

  • Karikari AY, Ampofo JA (2013) Chlorine treatment effectiveness and physico-chemical and bacteriological characteristics of treated water supplies in distribution network of Acrra-Tema metropolis, Ghana. Appl Water Sci 3:535–543

    Article  CAS  Google Scholar 

  • Kim H, Kim S (2017) Evaluation of chlorine decay models under transient conditions in a water distribution system. J Hydroinf 19(4):522–537

    Article  Google Scholar 

  • Kim H, Kim S, Koo J (2014) Prediction of chlorine concentration in various hydraulic conditions for a pilot scale water distribution system. Procedia Eng 70:934–942

    Article  Google Scholar 

  • Kim JD, Yoo DG, Lee SM, Lee HM, Choi YH (2018) Optimizing re-chlorination injection points for water supply networks using harmony search algorithm. Water. https://doi.org/10.3390/w10040547

    Article  Google Scholar 

  • Knox WE, Stumpf PK, Green DE, Auerbach VH (1948) The inhibition of sulfhydryl enzymes as the basis of the bactericidal action of chlorine. J Bacteriol 55:451–458

    Article  CAS  Google Scholar 

  • Kote AS, Wadkar DV (2019) Modeling of chlorine and coagulant dose in a water treatment plant by artificial neural networks. Eng Technol Appl Sci Res 9(3):4176–4181

    Article  Google Scholar 

  • Kulkami V, Awad J, Medlock A, Monis P, Lau M, Drigo B, Leeuwen JV (2018) Field based pilot-scale drinking water distribution system: simulation of long hydraulic retention times and microbiological mediated monochloramine decay. MethodsX 5:684–696

    Article  Google Scholar 

  • Lee DK, In J, Lee S (2015) Standard deviation and standard error of the mean. Korean J Anesthesiol 68(3):220–223. https://doi.org/10.4097/kjae.2015.68.3.220

    Article  Google Scholar 

  • Lee H, Shin GS, Hong S, Choi J, Chun M (2016) Post-chlorination process control based on flow prediction by time series neural network in water treatment plant. Int J Fuzzy Logic Intell Syst 16(3):197–207

    Article  Google Scholar 

  • Lenntech (2021) Disinfectants chlorine. Retrieved via https://www.lenntech.com/processes/disinfection/chemical/disinfectants-chlorine.htm. Accessed 09 July 2021

  • Li Q, Chen Z, Wang H, Yang H, Wen T, Wang S, Hu B, Wang X (2021) Removal of organic compounds by nanoscale zero-valent iron and its composites. Sci Total Environ 792:148546. https://doi.org/10.1016/j.scitotenv.2021.148546

    Article  CAS  Google Scholar 

  • Librantz AFH, dos Santos FCR, Gustavo C (2018) Artificial neural networks to control chlorine dosing in a water treatment plant. Acta Sci Technol 40:e37275. https://doi.org/10.4025/actascitechnol.v40i1.37275

    Article  Google Scholar 

  • Madzivhandila V, Chirwa EMN (2017) Modeling chlorine decay in drinking water distribution systems using aquasim. Chem Eng Trans 57:1111–1116

    Google Scholar 

  • Mahendrarajah R (2014) Chlorine demand analysis in distribution systems using hydraulic models and laboratory tests. In: 77th annual WIOA Victorian water industry operations conference and exhibitions. Bendigo Exhibition Centre, 2–4 Sept 2014

  • Mao Q, Feng J, Wang W, Wang Q, Hu Z, Yuan S (2016) Chlorination of parabens: reaction kinetics and transformation product identification. Environ Sci Pollut Res 23:23081–23091

    Article  CAS  Google Scholar 

  • May RJ, Maier HR, Dandy GC, Nixon JB (2004) Controloriented water quality modelling using artificial neural networks. In: Proceedings on CD-ROM. Enviro’04, Sydney, Australia, 28 May–10 June 2004

  • May RJ, Dandy GC, Maier HR, Nixon JB (2008a) Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ Model Softw 23:1289–1299

    Article  Google Scholar 

  • May RJ, Maier HR, Dandy GC, Fernando TMKG (2008b) Non-linear variable selection for artificial neural networks using partial mutual information. Environ Model Softw 23:1312–1326

    Article  Google Scholar 

  • Mentes A, Galiatsatou P, Spyrou D, Samara A, Stournara P (2020) Hydraulic simulation and analysis of an urban centre’s aqueducts using scenario analysis for network operations: the case of Thessaloniki City in Greece. Water 12:1627. https://doi.org/10.3390/w12061627http://www.mdpi.com/journal/water

  • Mohammed H, Tornyeviadzi HM, Seidu R (2021) Modelling the impact of water temperature, pipe, and hydraulic conditions on water quality in water distribution networks. Water Pract Technol 16(2):387–403

    Article  Google Scholar 

  • Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097

    Article  Google Scholar 

  • Monteiro L, Menaia J, Covas D (2012) The influence of temperature on chlorine bulk-decay rates in drinking water. Presented at the IWA world congress on water, climate and energy. Dublin, Ireland

  • Monteiro L, Figueiredo D, Dias S, Freitas R, Covas D, Menaia J, Coelho ST (2014) Modeling of chlorine decay in drinking water supply systems using EPANET MSX. Procedia Eng 70:1192–1200

    Article  CAS  Google Scholar 

  • Monteiro L, Figueiredo D, Covas D, Menaia J (2017) Integrating water temperature in chlorine decay modelling: a case study. Urban Water J 14(10):1097–1101

    Article  CAS  Google Scholar 

  • Mostafa NG, Matta ME, Halim HA (2013) Simulation of chlorine decay in water distribution networks using EPANET—case study. Civ Environ Res 3:100–116

    Google Scholar 

  • National Research Council (US) Safe Drinking Water Committee (1980). Drinking water and health: volume 2. Washington (DC): National Academies Press (US). 1980. II, The disinfection of drinking water. Available from: https://www.ncbi.nlm.nih.gov/books/NBK234590/. Accessed 09 July 2021

  • Nono D, Odirile PT, Basupi I, Parida BP (2019) Assessment of probable causes of chlorine decay in water distribution systems of Gaborone city, Botswana. Water SA 45(2):190–198

    CAS  Google Scholar 

  • Nouri I (2017) Optimal design and management of chlorination in drinking water networks: a multi-objective approach using genetic algorithms and the Pareto optimality concept. Appl Water Sci 7:3527–3538

    Article  Google Scholar 

  • Oliker N, Ostfeld A (2015a) Network hydraulics inclusion in water quality event detection using multiple sensor stations data. Water Res 80:47–58

    Article  CAS  Google Scholar 

  • Oliker N, Ostfeld A (2015b) Comparison of two multivariate classification models for contamination event detection in water quality time series. J Water Supply Res Technol AQUA 64(5):558–566

    Article  Google Scholar 

  • Oliker N, Ohar Z, Ostfeld A (2016) Spatial event classification using simulated water quality data. Environ Model Softw 77:71–80

    Article  Google Scholar 

  • Onyutha C (2020) From R-squared to coefficient of model accuracy for assessing “goodness-of-fits”. Geosci Model Dev Discuss. https://doi.org/10.5194/gmd-2020-51

    Article  Google Scholar 

  • Onyutha C (2021) A hydrological model skill score and revised R-squared. Hydrol Res. https://doi.org/10.2166/nh.2021.071

    Article  Google Scholar 

  • Ozdemir ON, Ucak A (2002) Simulation of chlorine decay in drinking-water distribution systems. J Environ Eng 128:31–39

    Article  CAS  Google Scholar 

  • Perelman L, Arad J, Housh M, Ostfeld A (2012) Event detection in water distribution systems from multivariate water quality time series. Environ Sci Technol 46:8212–8219

    Article  CAS  Google Scholar 

  • Powell JC, Hallam NB, West JR, Forster CF, Simms J (2000) Factors which control bulk chlorine decay rates. Water Res 34:117–126

    Article  CAS  Google Scholar 

  • Rajasingham A, Harvey B, Taye Y, Kamwaga S, Martinsen A, Sirad M, Aden M, Gallagher K, Handzel T (2020) Improved chlorination and rapid water quality assessment in response to an outbreak of acute watery diarrhea in Somali Region, Ethiopia. J Water Sanit Hyg Dev 10(3):596–602

    Article  Google Scholar 

  • Ramos HM, Loureiro D, Lopes A, Fernandes C, Covas D, Reis LF, Cunha MC (2010) Evaluation of chlorine decay in drinking water systems for different flow conditions: from theory to practice. Water Resour Manag 24:815–835

    Article  Google Scholar 

  • Reichert P (1994) AQUASIM—a tool for simulation and data analysis of aquatic systems. Water Sci Technol 30(2):21–30

    Article  CAS  Google Scholar 

  • Ricca H, Aravinthan V, Mahinthakumar G (2019) Modelling chloramine decay in full-scale drinking water supply systems. Urban Water J 91(5):441–454

    CAS  Google Scholar 

  • Rodriguez MJ, Sérodes JB (1999) Assessing empirical linear and non-linear modelling of residual chlorine in urban drinking water systems. Environ Model Softw 14:93–102

    Article  Google Scholar 

  • Rossman LA (2000) EPANET 2.0 user manual. Water Supply and Water Resources Division, National Risk management Laboratory, USEPA, Cincinnati

  • Rossman LA, Clark RM (1994) Modeling chlorine residuals in drinking-water distribution systems. J Environ Eng 120:803–820

    Article  CAS  Google Scholar 

  • Saidan MN, Rawajfeh K, Nasrallah S, Meric S, Mashal A (2017) Evaluation of factors affecting bulk chlorine decay kinetics for the Zai water supply system in Jordan. Case study. Environ Prot Eng 43(4):223–231

    Google Scholar 

  • Sérodes JB, Rodriguez MJ, Ponton A (2001) Chlorcast(c): a methodology for developing decision-making tools for chlorine disinfection control. Environ Model Softw 16:53–62

    Article  Google Scholar 

  • Soyupak S, Kilic H, Karadirek IE, Muhammetoglu H (2011) On the usage of artificial neural networks in chlorine control applications for water distribution networks with high quality water. J Water Supply Res Technol AQUA 6:51–60

    Article  Google Scholar 

  • Stathakis D (2009) How many hidden layers and nodes? Int J Remote Sens 30(8):2133–2147

    Article  Google Scholar 

  • Stoianov I, Aisopou A (2014) Chlorine decay under steady and unsteady-state hydraulic conditions. Procedia Eng 70:1592–1601

    Article  CAS  Google Scholar 

  • Székely GJ, Rizzo ML, Bakirov NK (2007) Measuring and testing independence by correlation of distances. Ann Stat 35(6):2769–3279

    Article  Google Scholar 

  • Tiruneh AT, Debessai TY, Bwembya GC, Nkambule SJ, Zwane L (2019a) Variable chlorine decay rate modeling of the Matsapha Town water network using EPANET program. J Water Resour Prot 11:37–52

    Article  CAS  Google Scholar 

  • Tiruneh AT, Debessai TY, Bwembya GC, Nkambule SJ (2019b) A mathematical model for variable chlorine decay rates in water distribution systems. Model Simul Eng 2019:1–11. https://doi.org/10.1155/2019/5863905

    Article  Google Scholar 

  • UNSDGs (2015) UN general assembly, transforming our world: the 2030 agenda for sustainable development, 21 October 2015, A/RES/70/1. Available at: https://www.refworld.org/docid/57b6e3e44.html. Accessed 10 Dec 2021

  • Vargas TF, Baía CC, Machado TLdS, Dórea CC, Bastos WR (2021) Decay of free residual chlorine in wells water of Northern Brazil. Water 13(7):992. https://doi.org/10.3390/w13070992

    Article  CAS  Google Scholar 

  • Vasconcelos JJ, GraymanW, Kiene L, Wable O, Biswas P, Bhari A, Rossman L, Clark RM, Goodrich J (1996) Characterization and modeling of chlorine decay in distribution systems. AWWA Research Foundation: American Water Works Association

  • Vuta L, Dumitran GE (2011) Some aspects regarding chlorine decay in water distribution networks. Cluj University Press. Available via http://aerapa.conference.ubbcluj.ro/2011/PDF/Vuta_Dumitran.pdf. Accessed 11 July 2021

  • Wadkar D, Kote A (2017) Prediction of residual chlorine in a Water treatment plant using Generalized regression neural Network. Int J Civ Eng Technol 8(8):1264–1270

    Google Scholar 

  • Wadkar DV, Nangare P, Wagh MP (2021) Evaluation of water treatment plant using Artificial Neural Network (ANN) case study of Pimpri Chinchwad Municipal Corporation (PCMC). Sustain Water Resour Manag 7:52. https://doi.org/10.1007/s40899-021-00532-w

    Article  Google Scholar 

  • Wang H, Harrison KW (2014) Improving efficiency of the Bayesian approach to water distribution contaminant source characterization with support vector regression. J Water Resour Plan Manag 40:3–11

    Article  Google Scholar 

  • World Health Organization (2011) Guidelines for drinking-water quality, 4th edn. Geneva: World Health Organization. Retrieved online via http://apps.who.int/iris/bitstream/10665/44584/1/9789241548151_eng.pdf. Accessed 10 July 2021

  • World Health Organization (2014) Water safety in distribution systems, Geneva-Switzerland: WHO Library Cataloguing-in-Publication Data. Available: http://www.who.int/water_sanitation_health/publications/Water_Safety_in_Distribution_System/en

  • World Health Organization (2017) Principles and practices of drinking-water chlorination: a guide to strengthening chlorination practices in small-to medium sized water supplies. New Delhi: World Health Organization, Regional Office for South-East Asia

  • Wu H, Dorea CC (2020) Towards a predictive model for initial chlorine dose in humanitarian emergencies. Water 12(5):1506. https://doi.org/10.3390/w12051506

    Article  Google Scholar 

  • Yang YJ, Goodrich JA, Clark RM, Li SY (2008) Modeling and testing of reactive contaminant transport in drinking water pipes: chlorine response and implications for online contaminant detection. Water Res 42:1397–1412

    Article  CAS  Google Scholar 

  • Yoo DG, Lee SM, Lee HM, Choi YH, Kim JH (2018) Optimizing re-chlorination injection points for water supply networks using harmony search algorithm. Water 10(5):547. https://doi.org/10.3390/w10050547

    Article  CAS  Google Scholar 

  • Yu S, Pang H, Huang S, Tang H, Wang S, Qiu M, Chen Z, Yang H, Song G, Fu D, Hu B, Wang X (2021) Recent advances in metal-organic framework membranes for water treatment: a review. Sci Total Environ 800:149662. https://doi.org/10.1016/j.scitotenv.2021.149662

    Article  CAS  Google Scholar 

  • Zhang C, Li C, Zheng X, Zhao J, He G, Zhang T (2017) Effect of pipe materials on chlorine decay, trihalomethanes formation, and bacterial communities in pilot-scale water distribution systems. Int J Environ Sci Technol 14:85–94

    Article  CAS  Google Scholar 

  • Zhang S, Wang J, Zhang Y, Ma J, Huang L, Yu S, Chen L, Song G, Qiu M, Wang X (2021) Applications of water-stable metal-organic frameworks in the removal of water pollutants: a review. Environ Pollut 291:118076. https://doi.org/10.1016/j.envpol.2021.118076

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors wish to thank all who assisted in conducting this work.

Funding

This study was financed through an award to the first author in the phase of the third competitive research grant from Kyambogo University, Uganda, under the support from the Government of Uganda.

Author information

Authors and Affiliations

Authors

Contributions

All authors were involved in study conception. CO and JCKT wrote original draft of the review article and revised the manuscript. Manuscript was reviewed and endorsed for publication by all the authors.

Corresponding author

Correspondence to C. Onyutha.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest and no competing financial interests.

Consent for publication

This research did not involve personal information for which consent could have been sought.

Ethical approval

This research did not involve human subjects.

Consent to participate

This research did not involve human subjects.

Additional information

Editorial responsibility: Fatih ŞEN.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Onyutha, C., Kwio-Tamale, J.C. Modelling chlorine residuals in drinking water: a review. Int. J. Environ. Sci. Technol. 19, 11613–11630 (2022). https://doi.org/10.1007/s13762-022-03924-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-022-03924-3

Keywords

Navigation