Comparative study of deterministic and probabilistic assessments of microbial risk associated with combined sewer overflows upstream of drinking water intakes

Abbas, 2019, An innovative method for installing a separate sewer system in narrow streets, J. Water Manag. Model. Al Aukidy, 2017, Contributions of combined sewer overflows and treated effluents to the bacterial load released into a coastal area, Sci. Total Environ., 607, 483, 10.1016/j.scitotenv.2017.07.050 Aziz, 2013, Applicability of artificial neural network in hydraulic experiments using a new sewer overflow screening device, Australasian Journal of Water Resources, 17, 77, 10.7158/W12-019.2013.17.1 Birch, 2011, Micropollutants in stormwater runoff and combined sewer overflow in the Copenhagen area, Denmark, Water Sci. Technol., 64, 485, 10.2166/wst.2011.687 Botturi, 2020, Combined sewer overflows: a critical review on best practice and innovative solutions to mitigate impacts on environment and human health, Crit. Rev. Environ. Sci. Technol., 51, 1585, 10.1080/10643389.2020.1757957 Burnet, 2021, Automated targeted sampling of waterborne pathogens and microbial source tracking markers using near-real time monitoring of microbiological water quality, Water., 13, 10.3390/w13152069 Calderon, 2017, Bacterial diversity impacts as a result of combined sewer overflow in a polluted waterway, Glob. J. Environ. Sci. Manag., 3, 437 Cox, 2008, What's wrong with risk matrices?, Risk Anal., 28, 497, 10.1111/j.1539-6924.2008.01030.x Dirckx, 2022, Stochastic determination of combined sewer overflow loads for decision-making purposes and operational follow-up, Water, 14, 10.3390/w14101635 El Ghazouli, 2022, Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems, Water Sci. Technol., 85, 398, 10.2166/wst.2021.511 Elmasry, 2017, Defect based deterioration model for sewer pipelines using Bayesian belief networks, Can. J. Civ. Eng., 44, 675, 10.1139/cjce-2016-0592 Fry, 2017, Evaluation of distributed BMPs in an urban watershed—High resolution modeling for stormwater management, Hydrol. Process., 31, 2700, 10.1002/hyp.11177 Fu, 2019, Development of a scenario-based stormwater management planning support system for reducing combined sewer overflows (CSOs), J. Environ. Manag., 236, 571, 10.1016/j.jenvman.2018.12.089 Gasperi, 2012, Priority pollutants in urban stormwater: part 2 - case of combined sewers, Water Res., 46, 6693, 10.1016/j.watres.2011.09.041 Goulding, 2012, A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways, Water Res., 46, 4933, 10.1016/j.watres.2012.03.044 Grêt-Regamey, 2006, Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS, Nat. Hazards Earth Syst. Sci., 6, 911, 10.5194/nhess-6-911-2006 Grafton, 2017, Risks, resilience, and natural resource management: lessons from selected findings†, Nat. Resour. Model., 30, 91, 10.1111/nrm.12104 Hahn, 2002, Expert system for prioritizing the inspection of sewers: knowledge base formulation and evaluation, J. Water Resour. Plan. Manag., 128, 121, 10.1061/(ASCE)0733-9496(2002)128:2(121) Hajj-Mohamad, 2017, The distribution dynamics and desorption behaviour of mobile pharmaceuticals and caffeine to combined sewer sediments, Water Res., 108, 57, 10.1016/j.watres.2016.10.053 Hansen K.D., Gentry J., Long L., Gentleman R., Falcon S., Hahne F., et al., 2022. Package ‘Rgraphviz’: provides plotting capabilities for R graph objects. Version 2.41.1. Harris, 2017, A Bayesian approach to integrated ecological and human health risk assessment for the South River, Virginia Mercury-Contaminated Site, Risk Anal., 37, 1341, 10.1111/risa.12691 Hojsgaard, 2012, Graphical independence networks with the gRain Package for R, J. Stat. Softw., 46, 1 Jalliffier-Verne, 2016, Cumulative effects of fecal contamination from combined sewer overflows: management for source water protection, J. Environ. Manag., 174, 62, 10.1016/j.jenvman.2016.03.002 Jalliffier-Verne, 2017, Modelling the impacts of global change on concentrations of Escherichia coli in an urban river, Adv. Water Resour., 108, 450, 10.1016/j.advwatres.2016.10.001 Jean, 2021, Optimization of real-time control with green and gray infrastructure design for a cost-effective mitigation of combined sewer overflows, Water Resour. Res., 57, 10.1029/2021WR030282 Kaikkonen, 2020, Bayesian networks in environmental risk assessment: a review, Integr. Environ. Assess. Manag., 17, 62, 10.1002/ieam.4332 Kammoun R., McQuaid N., Bichai F., Goitom E.A., Lessard V., Prévost M., et al., 2023. Risk assessment of drinking water intake contamination from agricultural activities using Bayesian Network (Article submetted). PLOS Water Unpublished results:45. Khosravi-Farmad, 2020, Bayesian decision network-based security risk management framework, J. Netw. Syst. Manag., 28, 1794, 10.1007/s10922-020-09558-5 Locatelli, 2020, Modeling of <i>E. coli</i> distribution for hazard assessment of bathing waters affected by combined sewer overflows, Nat. Hazards Earth Syst. Sci., 20, 1219, 10.5194/nhess-20-1219-2020 Madoux-Humery, 2013, Temporal variability of combined sewer overflow contaminants: evaluation of wastewater micropollutants as tracers of fecal contamination, Water Res., 47, 4370, 10.1016/j.watres.2013.04.030 Madoux-Humery, 2015, Temporal analysis of E. coli, TSS and wastewater micropollutant loads from combined sewer overflows: implications for management, Environ. Sci. Process. Impacts, 17, 965, 10.1039/C5EM00093A Madoux-Humery, 2016, The effects of combined sewer overflow events on riverine sources of drinking water, Water Res., 92, 218, 10.1016/j.watres.2015.12.033 Marcot, 2019, Advances in Bayesian network modelling: integration of modelling technologies, Environ. Model. Softw., 111, 386, 10.1016/j.envsoft.2018.09.016 McCarthy, 2012, Intra-event variability of Escherichia coli and total suspended solids in urban stormwater runoff, Water Res., 46, 6661, 10.1016/j.watres.2012.01.006 McGinnis, 2022, Assessing the risk of acute gastrointestinal illness (AGI) acquired through recreational exposure to combined sewer overflow-impacted waters in Philadelphia: a quantitative microbial risk assessment, Microb. Risk Anal., 20 McQuaid N., Madoux-Humery A.-.S., Touttée J.-.M., Dorner S., Prévost M., 2019. Technical Sheet No. 2: Potential risk assessment of wastewater overflows (DEU) (Fiche Technique Nº2: évaluation du potentiel de risque associé aux débordements d'eaux usées (DEU)). Montreal, Quebec: Polytechnique Montreal. MELCC and MAMROT, 2014. Stormwater management guide: Development Strategies, Design Principles and Best Management Practices for Urban Drainage Systems (Guide de gestion des eaux pluviales: Stratégies d'aménagement, principes de conception et pratiques de gestion optimales pour les réseaux de drainage en milieu urbain). Quebec, Canada. MELCC, 2021. Monitoring of the exploitation of the municipal wastewater treatment plants (OMAEU): Wastewater Treatment Plant and Overflow Works (Suivi d'exploitation des ouvrages municipaux d'assainissement des eaux usées (OMAEU): Station d’épuration et ouvrages de surverse. Quebec, Canada. Mounce, 2014, Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data, Water Sci. Technol., 69, 1326, 10.2166/wst.2014.024 Norsys Software and Corp. Netica, Version 6.09: norsys; 2021 Available from: https://www.norsys.com/netica.html. Notaro, 2014, 139, 12 Olds, 2018, High levels of sewage contamination released from urban areas after storm events: a quantitative survey with sewage specific bacterial indicators, PLoS Med., 15, 10.1371/journal.pmed.1002614 Passerat, 2011, Impact of an intense combined sewer overflow event on the microbiological water quality of the Seine River, Water Res., 45, 893, 10.1016/j.watres.2010.09.024 Peace, 2017, The risk matrix: uncertain results?, Policy Pract. Health Saf., 15, 131, 10.1080/14773996.2017.1348571 Pearl, 1988, 552 Penman, 2020, Bayesian decision network modeling for environmental risk management: a wildfire case study, J. Environ. Manag., 270, 10.1016/j.jenvman.2020.110735 Petrie, 2021, A review of combined sewer overflows as a source of wastewater-derived emerging contaminants in the environment and their management, Environ. Sci. Pollut. Res. Int., 10.1007/s11356-021-14103-1 Phan, 2016, Applications of Bayesian belief networks in water resource management: a systematic review, Environ. Model. Softw., 85, 98, 10.1016/j.envsoft.2016.08.006 Phan, 2019, Applications of Bayesian networks as decision support tools for water resource management under climate change and socio-economic stressors: a critical appraisal, Water., 11, 10.3390/w11122642 Prévost M., Madoux-Humery A.S., Dorner S., 2017. Protection measures for surface water withdrawals for human consumption: protection areas and source vulnerability - literature review (Mesures de protection des prélèvements d'eau de surface effectués à des fins de consommation humaine : aires de protection et vulnérabilité des sources- Revue bibliographique.), p. 101, Polytechnique Montreal, Quebec. 2022 2022, 34, 1 Rosin, 2021, A Committee Evolutionary Neural Network for the Prediction of Combined Sewer Overflows, Water Resources Management, 35, 1273, 10.1007/s11269-021-02780-z Ryu, 2015, Optimal planning of decentralised storage tanks to reduce combined sewer overflow spills using particle swarm optimisation, Urban Water J., 14, 202, 10.1080/1573062X.2015.1086004 Scutari, 2010, Learning Bayesian networks with the bnlearn R package, J. Stat. Softw., 35, 1, 10.18637/jss.v035.i03 Shan, 2019, Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China, Harmful Algae, 83, 14, 10.1016/j.hal.2019.01.005 Sojobi, 2022, Impact of sewer overflow on public health: a comprehensive scientometric analysis and systematic review, Environ. Res., 203, 10.1016/j.envres.2021.111609 Stritih, 2020, An online platform for spatial and iterative modelling with Bayesian Networks, Environ. Model. Softw., 127 Sylvestre, 2020, Can routine monitoring of E. coli fully account for peak event concentrations at drinking water intakes in agricultural and urban rivers?, Water Res., 170, 10.1016/j.watres.2019.115369 Sylvestre, 2021, Using surrogate data to assess risks associated with microbial peak events in source water at drinking water treatment plants, Water Res., 200, 10.1016/j.watres.2021.117296 Taghipour, 2019, Microbial risk associated with CSOs upstream of drinking water sources in a transboundary river using hydrodynamic and water quality modeling, Sci. Total Environ., 683, 547, 10.1016/j.scitotenv.2019.05.130 Taghipour, 2019, Normalized dynamic behavior of combined sewer overflow discharges for source water characterization and management, J. Environ. Manag., 249, 10.1016/j.jenvman.2019.109386 Taghipour, 2019, Microbial risk associated with CSOs upstream of drinking water sources in a transboundary river using hydrodynamic and water quality modeling, Sci. Total Environ., 683, 547, 10.1016/j.scitotenv.2019.05.130 Taghipour M., 2019. Characterization of CSO microbial contamination and their risks to drinking water sources. Montral, Canada: Civil, Geological and Mining Engineering Department, Polytechnique Montreal. Vatanpour, 2015, Can public health risk assessment using risk matrices be misleading?, Int. J. Environ. Res. Public Health, 12, 9575, 10.3390/ijerph120809575 Vijayashanthar, 2018, Modeling fecal indicator bacteria in urban waterways using Artificial Neural Networks, J. Environ. Eng., 144, 10.1061/(ASCE)EE.1943-7870.0001377 Wijesiri, 2018, Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: a Bayesian Network approach, Environ. Pollut., 233, 655, 10.1016/j.envpol.2017.10.076 Wong, 2006, An overview of water sensitive urban design practices in Australia, Water Pract. Technol., 1, 10.2166/wpt.2006018 World Health Organization (WHO), 2016. Protecting surface water for health. Identifying, assessing and managing drinking-water quality risks in surface-water catchments. Geneva. Report No.: ISBN 978 92 4 151055 4. Yu, 2021, Early warning of water quality degradation: a copula-based Bayesian network model for highly efficient water quality risk assessment, J. Environ. Manag., 292, 10.1016/j.jenvman.2021.112749 Zhang, 2018, Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring, J. Hydrol., 556, 409, 10.1016/j.jhydrol.2017.11.018 Zou, 2017, A Bayesian network approach to causation analysis of road accidents using netica, J. Adv. Transp., 2017, 1, 10.1155/2017/2525481