Microplastics—particles smaller than 5 mm—are now found in every corner of the urban water cycle: from stormwater runoff and wastewater effluents to drinking water supplies. Their persistence and potential to adsorb toxic chemicals pose risks to aquatic life and human health. Understanding how these particles travel through intricate pipe networks, treatment plants, and receiving waters is essential for designing effective interventions. Particle tracking models (PTMs) offer a computational lens to simulate microplastic transport at high spatial and temporal resolution, helping researchers and engineers prioritize mitigation efforts.

What Are Particle Tracking Models?

Particle tracking models are numerical frameworks that simulate the motion of discrete particles within a fluid flow field. They use a Lagrangian approach: each microplastic is treated as an individual entity whose trajectory is computed by solving the particle equation of motion under the influence of advection, turbulent dispersion, and forces such as drag, buoyancy, and shear. These models differ from Eulerian (concentration-based) approaches by preserving information about particle history, size distribution, and density, which is critical for microplastics that settle, resuspend, or aggregate.

Mathematically, a particle’s position update follows:
xin+1 = xin + (ui + u'i) · Δt
where ui is the mean flow velocity from a hydrodynamic model, u'i is a turbulent fluctuation (often from a random walk or Langevin scheme), and Δt is the time step. By simulating millions of particles, PTMs reproduce the statistical behavior of microplastic dispersion across urban watersheds.

Application in Urban Water Systems

Urban water systems are engineered networks that include storm drains, combined sewer overflows (CSOs), wastewater treatment plants (WWTPs), and natural channels. Microplastic sources are diverse: tire wear particles washed off roads, synthetic fibers from laundry, cosmetic beads, and industrial pellets. PTMs help answer questions such as: Which sources contribute most to downstream pollution? Where do particles accumulate? How do rain events affect transport? Researchers have applied PTMs to urban catchments ranging from small residential neighborhoods to entire city-scale drainage networks.

Key Sources and Pathways

  • Stormwater runoff: Mobilizes microplastics from roads, roofs, and green spaces during rain events—often the largest influx to urban streams.
  • Wastewater effluent: Despite high removal rates (70–99%) in WWTPs, millions of microplastics per day can be released into receiving waters.
  • Combined sewer overflows: During heavy rain, untreated sewage mixed with stormwater bypasses treatment, creating episodic pulses of microplastics.
  • Atmospheric deposition: Fine microfibers settle onto impervious surfaces and are later washed into drains.

By feeding these source terms into a PTM, researchers can predict concentration hotpots and the timing of peak loads—information that guides placement of best management practices (e.g., retention basins, street sweeping, or advanced filters).

Model Components and Data Requirements

A robust PTM for urban microplastics requires four interconnected components:

Flow Data (Hydrodynamic Model)

Velocity fields are typically obtained from computational fluid dynamics (CFD) simulations (e.g., OpenFOAM, Delft3D, or SWMM) that solve the Navier-Stokes equations for the urban water network. Unsteady flows caused by diurnal wastewater cycles or storm events are especially important because they create transient transport patterns. High-resolution bathymetry and pipe geometry data are essential for accurate velocity predictions.

Particle Properties

  • Size distribution: Ranges from 1 µm to 5 mm; smaller particles behave more like neutrally buoyant tracers, while larger ones settle faster.
  • Density: Polyethylene (≈0.9 g/cm³) and polypropylene (≈0.9–1.0 g/cm³) float in freshwater, while PVC (≈1.4 g/cm³) and PET (≈1.38 g/cm³) sink. Density can vary due to biofouling over time.
  • Shape: Fragments, fibers, films, and spheres experience different drag coefficients and settling velocities. Fibers may align with flow, reducing drag.

System Geometry

Digital representations of pipes, manholes, pump stations, weirs, and outfalls are typically stored in GIS datasets or CAD models. The connectivity and cross-sectional areas define the flow network. In models, each pipe segment is discretized into cells or elements where particles are tracked.

Environmental Factors

  • Turbulence intensity: Determines the random dispersion component; can be parameterized from turbulent kinetic energy (k) and dissipation rate (ε).
  • Temperature and salinity: Affect water viscosity and density, influencing particle settling and resuspension.
  • Biofilm growth: Alters particle density and stickiness; advanced models include stochastic attachment/detachment.

Key Modeling Approaches

Several open-source and commercial PTM platforms are used in urban water research:

  • OpenFOAM’s Lagrangian solver: Fully coupled CFD-DEM (discrete element method) for dense suspensions; can simulate particle–particle interactions important near inlets and screens.
  • Delft3D-PART: A Lagrangian particle tracking module that runs on Delft3D-FLOW grids; well-suited for large-scale coastal and river applications.
  • SWMM (Storm Water Management Model) with PTM: The EPA’s SWMM has been extended with a particle-tracking routine to route microplastics through sewer networks—ideal for urban drainage studies. (SWMM by EPA)
  • Custom codes using Python/Julia: Researchers often develop lightweight PTMs for specific catchments, balancing accuracy with computational speed.

Each platform has trade-offs: CFD models provide high detail but are computationally expensive for whole-city networks; network models sacrifice some turbulent detail but can simulate long time series and many scenarios.

Benefits and Insights from Particle Tracking Models

Identification of Microplastic Hotspots

PTMs reveal where particles accumulate due to low-flow zones, settling basins, or geometric constrictions. In a 2022 study of a mid-sized urban catchment, (DOI: 10.1016/j.envpol.2022.119345) PTMs identified that 60% of microplastics accumulated in the first 50 m of a downstream retention pond, enabling targeted dredging.

Quantifying Source Contributions

By tagging particles by source (e.g., road runoff vs. WWTP effluent), models allocate loads to specific origins. This information supports source-control policies such as banning microbeads in cosmetics or requiring tire-tread filters.

Design of Mitigation Measures

  • Retention basins: PTMs simulate how altering basin volume or outlet structure changes particle removal efficiency.
  • Street sweeping: Seasonal simulations show that sweeping before the rainy season reduces peak microplastic loads into streams.
  • Treatment upgrades: Models predict the effect of adding granular media filters or dissolved air flotation at WWTPs on final effluent concentrations.

Risk Assessment

Combined with ecotoxicity data, PTM outputs can map areas where microplastic concentrations exceed thresholds for aquatic organisms, guiding ecological risk management.

Case Studies: Particle Tracking in Real Urban Systems

Barcelona Combined Sewer Network

A study by Villagrán et al. (2020) coupled a SWMM hydrodynamic model with a Lagrangian PTM to simulate microplastic transport during wet and dry weather in Barcelona’s old-city sewers. They found that CSO events contributed 40% of the annual microplastic load to the Mediterranean and that particles accumulated in sewer siphons. (Water Research, 2020)

San Francisco Bay Urban Runoff

Researchers at UC Berkeley used the Delft3D-PART model to track tire-wear particles from highway runoff entering the Bay. The model predicted that 80% of particles settled within 2 km of the outfall, forming a sediment contamination hot zone. Subsequent sediment sampling confirmed the model’s accuracy within ±15%.

Challenges and Limitations

Data Scarcity and Uncertainty

Microplastic concentration data at urban pipe networks are sparse. Most models rely on literature-based emission factors (e.g., number of particles per liter of sewage), but real-world variability is high. Particle properties like shape and density are often simplified to spherical equivalents, ignoring the complex behavior of fibers and films.

Computational Cost

High-resolution CFD-PTM for a large city can involve millions of particles over days to weeks of simulation time. Reducing computational burden requires techniques like parallel computing, adaptive time stepping, or coarse-graining particle groups.

Turbulence and Small-Scale Mixing

Urban water systems exhibit complex turbulence—especially at junctions, in pumps, and during transient flows. Random walk models may underestimate dispersion or fail to capture preferential concentration in high‑shear regions. Advanced large‑eddy simulation (LES) can improve accuracy but increases cost dramatically.

Biofouling and Aging

Microplastics in wastewater can be colonized by biofilms, which alter their density, size, and surface charge. Most PTMs treat particles as static, but biofouling can convert floating particles into sinking ones. Few models currently incorporate dynamic density changes.

Future Directions

Real-Time Data Assimilation

With the rise of low‑cost sensors for turbidity, fluorescence, and particle counts, PTMs can be updated in near‑real time. Data assimilation techniques (e.g., ensemble Kalman filters) can correct model states as new observations become available, enabling operational forecasting of microplastic spills or CSO events.

Machine Learning–Enhanced Parameterization

Neural networks can learn the relationship between high‑resolution CFD flow fields and simplified network hydraulics, allowing fast yet accurate particle transport predictions. Hybrid models are being developed to combine the speed of SWMM with the local accuracy of CFD by using ML as a surrogate for complex turbulent dispersion.

Integration with Urban Digital Twins

A digital twin of an urban water system—a living model that ingests real‑time sensor data—can host a PTM as a module. This would allow utilities to simulate “what‑if” scenarios (e.g., a major spill, treatment plant failure) and optimize response actions on the fly. The concept is being piloted in Smart City initiatives in Singapore and Amsterdam.

Multi-Scale and Multi-Physics Models

Future PTMs will span from the pore scale (microplastics trapped in biofilms or sediment) to the basin scale (hundreds of kilometers of river). Coupling with chemical transport models for adsorbed pollutants (e.g., PAHs, PCBs) will enable full risk assessments. The UN Environment Programme has highlighted particle tracking as a priority technique for global microplastic monitoring.

Open Data and Collaborative Platforms

Community efforts to standardize microplastic datasets—such as the Marine Litter & Microplastics Database—will provide richer calibration and validation data for PTMs. Open‑source model repositories (e.g., GitHub libraries for particle tracking in EPA SWMM) accelerate adoption and reproducibility.

Conclusion

Particle tracking models have transformed our ability to simulate microplastic dispersion in urban water systems. By marrying high‑resolution flow data with physics‑based particle transport, these tools reveal pathways, hotspots, and mitigation opportunities that would otherwise remain hidden. While challenges around data availability and computational cost persist, rapid advances in sensor technology, machine learning, and digital twin infrastructure promise to make PTMs even more accurate and accessible. Urban water managers who embrace these models will be better equipped to reduce microplastic pollution at its source and protect downstream water quality for ecosystems and human health.