Mosses ML: Machine-Learning-Enhanced Biomonitoring of Emerging Contaminants Using <i>Hylocomium splendens</i>: An Integrated Approach Linking Atmospheric Deposition, Trace Metals, and Predictive Risk Assessment.

Abstract

Atmospheric deposition of emerging contaminants, including toxic trace elements, remains a critical environmental and public health concern. Moss biomonitoring offers a sensitive and cost-effective tool for assessing airborne pollutants, yet traditional analyses rely on descriptive statistics and lack predictive and mechanistic insights. Here, we introduce Mosses ML, a machine-learning-enhanced framework that integrates moss biomonitoring with bulk and dry deposition measurements to improve detection, interpretation, and risk assessment of atmospheric contaminants. Using <i>Hylocomium splendens</i> transplants exposed for 90 days across industrial, urban, and rural sites in Upper Silesia (Poland), we combined trace element accumulation (Cd, Pb, Zn, Ni, Cr, Fe), relative accumulation factors (RAFs), PCA-derived gradients, and site-level metadata with Random Forest and Gradient Boosting models. ML algorithms achieved high predictive performance (R² up to 0.91), accurately estimating moss metal concentrations from deposition metrics and environmental variables. SHAP feature-importance analysis identified dry deposition load and co-occurring metal signals as the dominant predictors of contamination, confirming the primary role of particulate emissions in shaping moss chemistry. Compared with classical threshold-based classification, the ML approach improved high-risk site identification by 24-38%. Mosses ML combines biologically meaningful indicators with modern computational tools, strengthening the role of mosses as early-warning systems for atmospheric pollution. The framework is broadly applicable to bryophyte biomonitoring and supports regulatory decision-making for emerging contaminants.