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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Watershed Planning within a Quantitative Scenario Analysis Framework
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Small drinking water systems under spatiotemporal water quality variability: a risk-based performance benchmarking

Ty Bereskie1, Husnain Haider2, Manuel J Rodriguez3

  • 1School of Engineering, Okanagan Campus, University of British Columbia, 1137 Alumni Ave, Kelowna, BC, V1V 1V7, Canada. tyberesk@gmail.com.

Environmental Monitoring and Assessment
|August 25, 2017
PubMed
Summary

A new risk-based framework (R_WQI) offers a more accurate way to benchmark drinking water systems by considering location, season, and failure consequences. This improves performance assessment for small drinking water systems (SDWSs).

Keywords:
Drinking water qualityFuzzy rule-based modelingPerformance assessmentPerformance benchmarkingRisk assessmentSmall drinking water systems

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Area of Science:

  • Environmental Engineering
  • Water Quality Management
  • Risk Assessment

Background:

  • Traditional drinking water system benchmarking relies on binary compliance with regulatory standards, ignoring spatiotemporal variability and the varying consequences of non-compliance.
  • Existing methods fail to account for factors like season and location, leading to inaccurate performance assessments for water quality.
  • Small drinking water systems (SDWSs) require nuanced evaluation due to operational uncertainties and monitoring limitations.

Purpose of the Study:

  • To propose and demonstrate a hierarchical risk-based water quality performance benchmarking framework (R_WQI) for evaluating SDWSs.
  • To quantify the consequences of seasonal and location-specific water quality issues within drinking water systems.
  • To facilitate more efficient decision-making for continuous performance improvement in SDWSs.

Main Methods:

  • Development of a hierarchical risk-based water quality index (R_WQI) framework.
  • Utilizing fuzzy rule-based modeling to handle imprecision in performance measurement and operational uncertainties.
  • Application and comparison of the R_WQI framework using data from 16 SDWSs in Canada against the Canadian Council of Ministers of the Environment WQI.

Main Results:

  • The R_WQI framework provides a more in-depth assessment of water quality compared to traditional methods.
  • Benchmarking using the R_WQI framework is more rational, considering the frequency and consequence of water quality failures.
  • The framework effectively quantifies the impact of spatiotemporal variations on water quality performance.

Conclusions:

  • The proposed R_WQI framework offers a superior approach to benchmarking SDWSs by incorporating risk and spatiotemporal factors.
  • Fuzzy rule-based modeling effectively addresses uncertainties inherent in SDWS operations and water quality monitoring.
  • The R_WQI framework enables more informed decision-making for enhancing the performance and reliability of drinking water systems.