A risk-based approach for developing standards for irrigation with reclaimed water.
Mads Troldborg1, Dominic Duckett1, Richard Allan1
1The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, United Kingdom.
Water Research
|October 7, 2017
Summary
Quantitative risk assessment (QRA) models human health risks from reclaimed water irrigation. Results inform treatment needs and water quality standards, with fuzzy set theory addressing input uncertainties for safer irrigation practices.
Area of Science:
- Environmental Science
- Public Health
- Risk Assessment
Background:
- Reclaimed water use in irrigation presents potential human health risks from microbial and chemical contaminants.
- Establishing safe reclaimed water quality standards requires robust risk assessment methodologies.
Purpose of the Study:
- To develop a generalized quantitative risk assessment (QRA) model for evaluating human health risks associated with reclaimed water irrigation.
- To determine acceptable concentration levels of hazards in reclaimed water effluent to inform treatment requirements and quality standards.
Main Methods:
- A backward calculation approach was used, starting from acceptable risk levels (10⁻⁴ annual infection risk for pathogens, reference doses for chemicals).
- Fuzzy set theory (possibilistic approach) was applied to handle uncertainties in numerous input parameters.
- Two scenarios (amenity irrigation, food crop irrigation) and six example hazards (norovirus, Cryptosporidium, cadmium, lead, PCB118, naphthalene) were analyzed using UK-specific data.
Main Results:
- Amenity irrigation risks were generally low, but UV disinfection may be needed for norovirus and Cryptosporidium.
- Stricter standards are needed for hazards with strong soil binding or higher toxicity/infectivity.
- For food crop irrigation, pathogen ingestion via crop surface contamination was dominant; organic compounds and heavy metals via plant uptake posed risks.
Conclusions:
- The QRA provides a framework for setting reclaimed water quality standards, highlighting the need for scenario-specific risk management.
- Uncertainty in QRA predictions necessitates careful consideration in standard setting and suggests areas for future data collection to reduce uncertainty.
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