Solid waste treatment and resourceization critically depend on waste characterization. Heavy metals and critical raw materials are found as trace elements in solid waste dumps, and their reliable quantification plays a critical role for decision risk regarding effective waste management. The reliable quantification of trace elements is a very challenging issue. Hence, in this study, a new conservative approach for data analysis in screening for trace elements in waste dumps is presented. We propose a theoretical model for statistical data interpretation to overcome the drawbacks of conventional approaches based on unproven hypotheses, such as binomial, Poisson, or Gaussian distributions of the particles carrying the analyte. Our model addresses concentration values close to the limit of quantification (LOQ) of an analytical method. This model fills the gap of data analysis when a set of analytical results are uniformly distributed. Our approach deals with results reported as being lower than the LOQ. The model was applied on XRFS results from studies carried out on tailings to emphasize the differences among classic, robust, and conservative data analyses. Classical analyses overestimate the concentration values and underestimate the associated uncertainties increasing the decision risk. This study demonstrates that a conservative approach is mandatory when screening for trace elements if the concentration values are uniformly distributed. The proposed model can be applied to any solid waste dump, regardless of the analytical method used for trace element screening.

Solid waste treatment and resourceization critically depend on waste characterization. Heavy metals and critical raw materials are found as trace elements in solid waste dumps. Their reliable quantification plays a critical role for decision risk regarding effective waste management. Reliable quantification of trace elements is a very difficult issue. Hence, the paper addresses a new conservative approach for data analysis in screening for trace element in waste dumps. We propose a theoretical model for statistical data interpretation to overcome the drawbacks of the conventional approaches that are based on unproved hypotheses, like binomial, Poisson or Gaussian distributions of the particles carrying the analyte. Our model addresses concentration values close to the limit of quantification (LOQ) of an analytical method. The model fills the gap of data analysis in case where a set of laboratory outcomes are uniform distributed. Our approach cope with results reported as lower than LOQ. The model was applied on XRFS results carried on tailings to emphasize the differences among classic, robust and conservative data analyses. Classical analyses overestimate the concentration values and sub-evaluate the associated uncertainties which enhances the decision risk. The paper demonstrates that conservative approach is mandatory in case of screening for trace elements if concentration values are uniform distributed. The model can be applied to any solid waste dump, regardless the analytical method used for trace element screening.