Mechanistic insights into the spatial distribution and predictability limits of sewage sludge constituents

Recent changes in European sewage sludge disposal address organic contaminant concerns, but risk continental-scale resource depletion. As the first methodology of its kind, a prediction model for central European sewage sludge compositions is developed from 730 data points, enabling resource quantification coupled with geographic distribution assessment. Limited sewage sludge constituent knowledge stems from undisclosed treatments, variable influent properties, and data confidentiality. These limitations were addressed by a review comprising 1908 literature sources and supplementary datasets. Thirty physicochemical parameters were quantified, and 7 factors were developed to contextualize UWWTPs within surficial-geological, pedological, and technological frameworks for data analysis. Linear regression (LR), random forest (RF), and XGBoost (XGB) were compared using robustness-enhanced preprocessing (IQR + Huber; ∼80% data retained), with performance evaluated against a mean baseline and transferability assessed via leave-one-country-out (LOCO) validation. Models outperform baselines for selected trace elements (SnO₂, CoO, CdO, Cu₂O, PbO, Sb₂O₃), and Sulfur, while most organic and mineral constituents remain near baseline, indicating dominant process control. LOCO results demonstrate European-scale transferability for selected constituents (CoO, CdO, Cu₂O, K₂O, P₂O₅, ash content). Surface-data-driven models cannot resolve point sources; however, removing influential outliers improves model stability and the reliability of large-scale resource recovery estimates, particularly when combining linear and non-linear approaches. Despite limitations due to missing plant-specific process data, this framework provides a scalable proxy for regional assessments. Future improvements should integrate broader compositional datasets and detailed operational variables to better link geographic drivers with treatment processes and enable more reliable identification of material streams within European sewage sludges.