Mastering waste characterisation: common practices and field techniques for overcoming challenges

Waste characterisation data is fundamental for effective solid waste management, yet practitioners often face significant field and methodological challenges, including inconsistent protocols and limited data comparability. Drawing on over five years of field experience, established industry guidelines, technical reports, and academic literature, this paper provides a comprehensive field-test framework for waste characterisation, detailing common practices for conducting methodologically sound audits. The developed framework outlines steps for waste characterisation planning and preparation, sampling design and data collection including proper material categorisation, and data analysis. This framework delivers measurable improvements in three critical areas: enhanced data reliability through standardised categorisation, reduced operational errors via pre- and post-fieldwork checklists, and prioritised challenge-mitigation strategies. Furthermore, the paper provides a systematic approach to methodology selection, matching the distinct needs of municipal planners, consultants, and researchers with appropriate methods based on their institutional contexts and resource constraints. This guide also includes a comparative analysis of established methodologies and emerging AI-driven approaches/techniques, helping practitioners select the most suitable approach for their study objectives, available resources, and waste streams. By implementing this framework, practitioners can produce more comparable, defensible data to support evidence-based waste infrastructure planning, policy development, and diversion strategies.