Grant: Alliance Grants
University of British Columbia
$300,633.00
Wireless water quality monitoring for improved drinking water system management
FY 2022-23
Summary
Advancements in information and communication technologies are enabling cities to manage infrastructure and services more intelligently. A promising area of smart city development is improving the capabilities of monitoring and controlling drinking water infrastructure. Delivery of safe and high-quality drinking water is an essential public service. However, there is limited real-time monitoring of the water distribution networks' edges or customer water quality, ultimately where the water is used, and quality matters the most. Water quality in distribution systems has been a pervasive challenge for water systems management, with inadequate monitoring leading to untimely detection of short-term contamination events and water quality deterioration or contamination. This project aims to address challenges with maintaining water quality in distribution systems by developing wireless water quality sensors for monitoring at service connections (i.e. connection to a home or at the customer's point of use). This technology will enable utilities to respond to water quality issues faster and with greater spatial information. Furthermore, monitoring data at customer use points will increase customer assurance in the delivered water quality. The project also looks to leverage large amounts of monitored data to develop AI models that would aid in tighter control of the water treatment processes. The developed models will focus on reducing chlorine dosing to limit the formation of potentially harmful byproducts while ensuring that sufficient chlorine is present to protect public health.
Program
Alliance Grants
Recipient
Location
British Columbia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
570897-2021
Name nom
Peleato, Nicolas NM
Department département
Okanagan - Engineering, School of
Organization id
2
Institution établissement
University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2021
Award amount
300633
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
1500
Research subject group en
Environmental engineering
Research subject group fr
Génie de l'environnement
Research subject en
Environmental engineering
Research subject fr
Génie de l'environnement
Installment
inst:1;2
Partie
2
Nb partie
2
Application title
Wireless water quality monitoring for improved drinking water system management
Application summary
Advancements in information and communication technologies are enabling cities to manage infrastructure and services more intelligently. A promising area of smart city development is improving the capabilities of monitoring and controlling drinking water infrastructure. Delivery of safe and high-quality drinking water is an essential public service. However, there is limited real-time monitoring of the water distribution networks' edges or customer water quality, ultimately where the water is used, and quality matters the most. Water quality in distribution systems has been a pervasive challenge for water systems management, with inadequate monitoring leading to untimely detection of short-term contamination events and water quality deterioration or contamination. This project aims to address challenges with maintaining water quality in distribution systems by developing wireless water quality sensors for monitoring at service connections (i.e. connection to a home or at the customer's point of use). This technology will enable utilities to respond to water quality issues faster and with greater spatial information. Furthermore, monitoring data at customer use points will increase customer assurance in the delivered water quality. The project also looks to leverage large amounts of monitored data to develop AI models that would aid in tighter control of the water treatment processes. The developed models will focus on reducing chlorine dosing to limit the formation of potentially harmful byproducts while ensuring that sufficient chlorine is present to protect public health.