Robust Statistical Approaches for Stratified Data of Municipal Solid Waste Composition: A Case Study of the Czech Republic

Accurate information on waste composition is essential for strategic planning in waste management and developing environmental technologies. However, detailed analyses of individual waste containers are both time- and cost-intensive, resulting in a limited number of available samples. Therefore, it is crucial to apply statistical methods that enable reliable estimation of average waste composition and its variability, while accounting for territorial differences. This study presents a statistical approach based on territorial stratification, aggregating data from individual waste container analyses to higher geographic units. The methodology was applied in a case study conducted in the Czech Republic, where 19.4 tons of mixed municipal waste (MMW) were manually analyzed in selected representative municipalities. The method considers regional heterogeneity, monitors the precision of partial estimates, and supports reliable aggregation across stratified regions. Three alternative approaches for constructing interval estimates of individual waste components are presented. Each interval estimate addresses variability from the random selection of waste containers and the selection of strata representatives at multiple levels. The proposed statistical framework is particularly suited to situations where the number of samples is small, a common scenario in waste composition analysis. The approach provides a practical tool for generating statistically sound insights under limited data conditions. The main fractions of MMW identified in the Czech Republic were as follows: paper 6.7%, plastic 7.3%, glass 3.6%, bio-waste 28.4%, metal 2.1%, and textile 3.0%. The methodology is transferable to other regions with similar waste management systems.