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Performance assessment of peat-based advanced...
Journal article

Performance assessment of peat-based advanced treatment devices in Six Nations of the Grand River

Abstract

Peat-based biofilters have been used to treat septic tank effluent for decades but few studies investigate their longevity. Similarly, long-term performance evaluations of medium-scale decentralized wastewater treatment systems in Indigenous communities are rare. The community-led study described herein assesses five peat moss biofilters installed in Six Nations of the Grand River. The assessment compares sample data with contemporary performance-based classification standards for advanced treatment devices. The results indicate the peat moss filter media is continuing to provide physical, chemical, and biological treatment beyond its expected lifespan, but also that most systems are underperforming. The findings demonstrate without basic maintenance structural components may degrade faster than peat moss, which impacts system monitoring and operation. Routine inspection and maintenance of advanced treatment devices are necessary to ensure they maintain their performance, necessitating funding for such activities. It was also found the older systems exhibited poor denitrification. Such findings support recent literature asserting that peat-based systems cannot independently provide stable and consistent levels of disinfection and nutrient removal to meet standardized performance thresholds. Nevertheless, for advanced levels of Basic Treatment, peat-based systems may continue to be a viable solution for Six Nations if maintained adequately, especially in areas unsuitable for conventional septic systems.

Authors

Gibson CM; Ayachi S; McBean EA; de Lannoy C-F; Martin-Hill D; Montour M; Lickers S; Works SNP

Journal

Canadian Water Resources Journal / Revue canadienne des ressources hydriques, Vol. ahead-of-print, No. ahead-of-print, pp. 1–24

Publisher

Taylor & Francis

Publication Date

January 18, 2026

DOI

10.1080/07011784.2025.2597934

ISSN

0701-1784

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