Integrated risk-resource assessment and automated decision-making for sewage sludge management in megacities: development and implementation of the IRRSS framework.

Authors

Hui Wang, Yufeng Xu, Xiaojie Sun, Bin Dong, Zuxin Xu

Published in

Waste management (New York, N.Y.). Volume 228. Pages 115967. Oct 31, 2026. Epub Oct 31, 2026.

Abstract

Sewage sludge management in megacities faces a critical dilemma: balancing high resource recovery potential against complex ecological risks. This study establishes a novelIntegrated Risk-Resource Scoring System (IRRSS)to navigate this trade-off, using Shanghai as a representative case. We conducted a one-year monitoring campaign across ten wastewater treatment plants (WWTPs), analyzing 77 indicators encompassing heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), antibiotics (ABXs), and nutrients.Spatial analysis coupled with Point-of-Interest (POI) kernel density estimation revealed distinct contamination signatures:industrial-peripheral plants were dominated by heavy metals (Zn, Cr) and persistent organic pollutants, whereas urban-core plants exhibited exceptional antibiotic loads (e.g., Ofloxacin up to 3883 µg/kg) driven by dense medical and residential activities. The proposedIRRSS, powered by Principal Component Analysis (PCA), successfully distilled multidimensional data into four actionable sludge archetypes (e.g., High-Resource-High-Risk). This approach uncovered nuances missed by traditional methods, such as the decoupling of contaminant load from immediate bioavailability. Risk Quotient (RQ)-based ecological risk assessment indicated that Cu and Zn risks persist even after conventional stabilization (composting/anaerobic digestion), necessitating advanced control strategies.A key innovation of this work is the development of a standalone Visual Basic for Applications (VBA)-based automated decision-support tool.Validated against calculations (R² > 0.8), this tool integrates the IRRSS algorithm with regulatory standards to instantly translate raw monitoring data into tailored disposal recommendations. This study provides a transferable, data-driven framework that transforms sludge from a waste burden into a managed resource stream, bridging the gap between complex statistical analysis and practical engineering decision-making.

PMID:
42966273
Bibliographic data and abstract were imported from PubMed on 01 Nov 2026.

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