Analyzing the Dispersion of Tire Wear Particles in Urban Runoff through Computational Modeling

Abstract

Tire wear particles (TWPs), generated from vehicle tires by friction, are dangerous but understudied pollutants in urban runoff which induce freshwater mortality at even minute concentrations. These particles accumulate in stormwater systems and release toxic byproducts that imperil aquatic ecosystems. While their toxicology and concentrations in stormwater have been documented, the physical mechanisms driving TWP transport across urban surfaces remain poorly understood. Existing research focuses on emission rates and chemical analysis, leaving a gap in predictive modeling on how TWPs migrate into and through stormwater runoff. To address this, we developed a one-dimensional computational model coupling the advection–diffusion equation for pollutant concentration with a modified Burgers equation for runoff velocity. We implemented a stable Crank–Nicolson finite difference scheme and trained an inverse physics-informed neural network to infer parameters of the partial differential equation system under distinct boundary conditions, enabling future calibration with real-world data. Preliminary results suggest that even modest TWP buildup can reshape velocity fields over time, creating downstream concentration hotspots. Our model captures the two-way feedback between particle accumulation and runoff behavior, offering a data-adaptive, physics-based tool to forecast TWP dispersion under real-time weather conditions. This research serves to inform targeted mitigation strategies in line with United Nations Sustainable Development Goals 6 and 14 on Clean Water and Sanitation and Life Below Water, respectively.