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Minimizing the impacts of contaminant intrusion in small water distribution networks through booster chlorination optimization

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Abstract

Contaminant intrusion in a water distribution network (DN) has three basic pre-conditions: source of contaminant (e.g., leaky sewer), a pathway (e.g., water main leaks), and a driving force (e.g., negative pressure). The impact of intrusion can be catastrophic if residual disinfectant (chlorine) is not present. To avoid microbiological water quality failure, higher levels of secondary chlorination doses can be a possible solution, but they can produce disinfectant by-products which lead to taste and odour complaints. This study presents a methodology to identify potential intrusion points in a DN and optimize booster chlorination based on trade-offs among microbiological risk, chemical risk and life-cycle cost for booster chlorination. A point-scoring scheme was developed to identify the potential intrusion points within a DN. It utilized factors such as pollutant source (e.g., sewer characteristics), pollution pathway (water main diameter, length, age, and surrounding soil properties, etc.), consequence of contamination (e.g., population, and land use), and operational factors (e.g., water pressure) integrated through a geographical information system using advanced ArcMap 10 operations. The contaminant intrusion was modelled for E. Coli O156: H7 (a microbiological indicator) using the EPANET-MSX programmer’s toolkit. The quantitative microbial risk assessment and chemical (human health) risk assessment frameworks were adapted to estimate risk potentials. Booster chlorination locations and dosages were selected using a multi-objective genetic algorithm. The methodology was illustrated through a case study on a portion of a municipal DN.

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Abbreviations

A:

Agricultural

AC:

Asbestos cement

AHP:

Analytic hierarchy process

BIF:

Bromide incorporation factor

C:

Commercial

CD:

Comprehensive development

CHBr2Cl:

Dibromochloromethane

CHBr3 :

Bromoform

CHBrCl2 :

Bromodichloromethane

CHCl3 :

Chloroform

CI:

Cast iron

CIPRA:

Cast Iron Pipe Research Association

CONC:

Concrete

COP:

Copper

ChRP:

Chemical risk potential

DA:

Dissemination area

DBPs:

Disinfectant by-products

DFI:

Driving force index

DI:

Ductile iron

DIPRA:

Ductile Iron Pipe Research Association

DN:

Distribution network

FRC:

Free residual chlorine

GA:

Genetic algorithm

GIS:

Geographical information system

GWT:

Ground water table

HAAs:

Haloacetic acids

HD:

Health District

HDPE:

High density polyethylene

I:

Industrial

IRIS:

Integrated risk information system

IRP:

Intrusion risk potential

LCC:

Life-cycle cost

LTESWTR:

Long term enhanced surface water treatment rule

LUCI:

Land use consequence index

LUW:

Land use weight

MOGA:

Multi objective genetic algorithm

MRP:

Microbial risk potential

P/W:

Public and Institutional

PD:

Population density

PDCI:

Population density consequence index

PSI:

Pollution source index

PVC:

Poly vinyl chloride

QMRA:

Quantitative microbial risk assessment

RfD:

Reference dose

RR:

Rural residential

RU/RM:

Urban residential

SCI:

Soil corrosivity index

SCI-C:

Soil corrosivity index for cementitious pipes

SCI-M:

Soil corrosivity index for metallic pipes

SCI-P:

Soil corrosivity index for plastic pipes

SF:

Slope factor

SFI:

Structural failure index

STEEL:

Steel

SWTR:

Surface water treatment rule

TTHM:

Total trihalomethane

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Acknowledgements

The authors thankfully acknowledge the financial support of the Natural Sciences and Engineering Research Council (NSERC). The authors also thank the city of Kelowna for providing their valuable source data. Thanks to NSERC for providing the Alexander Graham Bell Canada Graduate Scholarship (CGSD2) to the first author. The project was also partially funded by the National Plan for Science, Technology and Innovation (MAARIFAH)—King Abdulaziz City for Science and Technology—through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM)—the Kingdom of Saudi Arabia, Award Number (WAT-2390-04). Finally, thanks to Mr. Aaron Janzen from the Government of Alberta for providing guideline to estimate costs for booster chlorination.

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Authors and Affiliations

  1. School of Engineering, University of British Columbia (Okanagan Campus), Kelowna, Canada

    Nilufar Islam & Rehan Sadiq

  2. École supérieure d’aménagement du territoire et développement régional, Université Laval, Quebec, Canada

    Manuel J. Rodriguez

  3. College of Applied and Supporting Studies, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia

    Ashraf Farahat

  4. Department of Physics, Faculty of Science, Alexandria University, Alexandria, Egypt

    Ashraf Farahat

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  1. Nilufar Islam
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  2. Manuel J. Rodriguez
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  3. Ashraf Farahat
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  4. Rehan Sadiq
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Correspondence to Rehan Sadiq.

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Islam, N., Rodriguez, M.J., Farahat, A. et al. Minimizing the impacts of contaminant intrusion in small water distribution networks through booster chlorination optimization. Stoch Environ Res Risk Assess 31, 1759–1775 (2017). https://doi.org/10.1007/s00477-017-1440-x

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