"MODELING THE DISPERSION OF MICROPLASTIC PARTICLE CONCENTRATION IN THE ATMOSPHERE FOR WASTE MANAGEMENT FACILITIES"

This study analyzes the features of constructing models for microplastic dispersion around waste management facilities. The specific characteristics of microplastic dispersion models have been identified, highlighting their dependence on the complex influence of atmospheric conditions, environmental aerodynamics, and particle properties (density, shape, and size). Traditional advection-diffusion models require adaptation to account for gravitational settling and microplastic particle coagulation. Using the example of a waste shredding plant, models of airborne microplastic particle dispersion were developed for four seasons. The finite difference method was employed for model construction. The reliability of numerical models depends on the accuracy of input data, particularly meteorological parameters (wind direction, wind speed, air humidity, and precipitation), which significantly influence microplastic particle trajectories. Spatial pollution concentration measurements were used for verification, and kriging was applied to construct spatial concentration maps. Thus, diffusion equations are a powerful tool for assessing environmental pollution around waste management facilities. They offer flexibility in forecasting, consider various natural and anthropogenic factors, and enable data integration into GIS systems. However, challenges such as the difficulty of obtaining accurate data, model limitations, and high computational demands require further refinement of approaches and the use of more precise numerical methods. The development of numerical models for microplastic dispersion around waste management facilities necessitates integrating multifactor approaches, including seasonal variability, topographic influence, and the chemical interactions of microplastics with other atmospheric pollutants. The obtained results indicate the feasibility of using interval statistical models for verifying numerical calculations of microplastic dispersion. Future research may focus on developing adaptive methods for model refinement, incorporating turbulence effects and local meteorological conditions.