Applicability of machine learning models for the assessment of long-term pollutant leaching from solid waste materials.

Column leaching tests are a common approach for evaluating the leaching behavior of contaminated soil and waste materials, which are often reused for various construction purposes. Standardized up-flow column leaching tests typically require about 7 days of laboratory work to evaluate long-term leaching behavior accurately. To reduce testing time, we developed linear and ensemble models based on parametric and non-parametric Machine Learning (ML) techniques. These models predict leachate concentrations of relevant chemical compounds at different Liquid-to-Solid ratios (LS) based on measurements at lower LS values. The ML models were trained using 82 column leaching test samples for Construction and Demolition Waste materials collected in Germany during the last two decades. R-Squared values measuring models' performance are as follows: Sulfate = 0.94, Vanadium = 0.97, Chromium = 0.82, Copper = 0.92, group of 15 (US-EPA) PAHs = 0.98 (values averaged over predictive models for LS 2 and 4). Sensitivity analysis utilizing the Shapley Additive Explanation value indicates that in addition to the concentrations of the considered compound at LS<=1, electrical conductivity and pH are the most critical features of each model, while concentrations of other compounds also play a minor role.