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Classification and predictive leaching risk assessment of construction and demolition waste using multivariate
Andrea Bisciotti1, Valentina Brombin1, Yu Song2
1Department of Physics and Earth Science, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy.
Waste Management (New York, N.Y.)
|February 20, 2025
Summary
Construction and demolition waste (CDW) management is improved by new methods that classify ceramic waste using geochemical analysis and machine learning. These approaches predict environmental hazards, aiding sustainable construction practices.
Area of Science:
- Environmental Science
- Geochemistry
- Materials Science
Background:
- Construction and demolition waste (CDW), particularly ceramics, presents significant landfilling and recycling challenges due to potential hazardous element leaching.
- Ceramic materials like bricks, tiles, and porcelain constitute over 70% of CDW, exacerbating environmental concerns.
Purpose of the Study:
- To develop and validate methods for classifying CDW, focusing on ceramic materials, to predict environmental hazards.
- To investigate the leaching behavior of ceramics in mixed environments with concrete.
- To automate CDW classification and hazard prediction using bulk chemical composition.
Main Methods:
- Geochemical analyses and leaching tests (UNI EN 12457-2) on 14 CDW samples from Ferrara, Italy.
- Comparison with a global database of over 150 CDW samples.
- Application of multivariate statistical analysis and machine learning for classification and prediction.
Main Results:
- Established classification of CDW compositions based on bulk chemical data.
- Quantified environmental hazards using contaminant factors (Cf, Cd) and hazardous quotients (HQ, HQm).
- Demonstrated the potential to predict leaching behavior and hazards from initial bulk composition.
Conclusions:
- The proposed methods effectively automate CDW classification and hazard prediction.
- Findings support improved CDW management and promote sustainability in the construction industry.
- Geochemical analysis combined with machine learning offers a robust approach to assessing CDW environmental risks.
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