Analysis of vehicle-related non-exhaust PM₁₀ and emerging pollutants in Beijing with machine-learning

With the growth of electric vehicles, non-exhaust PM₁₀ is becoming the dominant contributor to vehicle-related particulate pollution and a source of emerging pollutants. This study evaluated four machine-learning algorithms and selected Random Forest (RF) to estimate road traffic flow. By coupling RF with the MOVES model, we developed an RF-MOVES model to quantify the emissions of non-exhaust PM₁₀, heavy metals, and microplastics, and assessed the characteristics of the emissions under three electrification scenarios. Research shows that temporal variations in tire-road wear PM₁₀ (TRWPM₁₀) and tire brake PM₁₀ (TBPM₁₀) are attributed to travel behavior and road conditions, while spatial heterogeneity reflects road-network structure and vehicle-type distribution. Vehicle electrification increased the proportions of TRWPM₁₀ and TBPM₁₀ to total vehicle-related PM₁₀ due to reduced exhaust PM₁₀. The fractions of heavy metals and microplastics in non-exhaust PM₁₀ increased by over 4% and 9%, respectively, indicating a growing potential for environmental contamination. Furthermore, increasing regenerative braking reduces non-exhaust PM₁₀ and heavy metal emissions, while its effects on microplastic mitigation remain limited. This study provides a model for calculating high-resolution non-exhaust PM₁₀. Our results highlight the potential environmental contamination risks of vehicle-related non-exhaust PM₁₀ and offer insights for managing non-exhaust PM₁₀, heavy metals and microplastics in future electrification scenarios.