Integrated modelling framework for predicting microplastic build-up on urban surfaces.

Urban impervious surfaces are major accumulation zones for microplastics, with deposited urban dust as a key carrier matrix facilitating their retention, redistribution, and transport from terrestrial to aquatic environments. However, processes governing microplastic build-up and the ability to predict its spatial distribution remain poorly understood. This study quantified the key factors influencing microplastic build-up and created a catchment-scale spatial predictive framework. Using dust samples from typical urban land uses in Brisbane, Australia, five influential predictors were identified, including dust loading, mineralogical composition, wind speed, land use, and population and were integrated using principal component analysis, multivariate regression, and spatial response surfaces modelling. Population was defined as the number of residents within a 1 km radius of each sampling site. Site-based observations combined with spatial predictor layers generated a continuous microplastic build-up surface across the study area. The resulting model has strong explanatory and predictive performance (R² = 0.91; cross-validated R² = 0.84). The novelty of this work lies in the integration of dust mineralogy and environmental drivers within a spatially explicit predictive framework, enabling identification of microplastic build-up at unsampled locations. The proposed framework improves understanding of terrestrial microplastic dynamics, providing a practical tool to support targeted monitoring and mitigation.