A new peer-reviewed article titled “Numerical Modeling of the Concentration of Microplastics in Lakes and Rivers in Kazakhstan” has been published as a result of a collaborative effort between the Department of Ecology, Life Safety and Environmental Protection at Abay Myrzakhmetov Kokshetau University (Kazakhstan) and the Instituto de Ingeniería del Agua y del Medio Ambiente (IIAMA) at the Universitat Politècnica de València (UPV), Spain. This research represents a significant advancement in the application of mathematical modeling to assess microplastic (MP) pollution in freshwater ecosystems.
The study focuses on three lakes—Kopa, Zerendinskoye, and Borovoe—and the Yesil River. Through the application of differential equations, the researchers modeled the spatial distribution and seasonal variation of MP concentrations in both sediments and surface waters. Field data were collected during three distinct seasons (spring, summer, and autumn), providing a comprehensive basis for model calibration and validation.
Key Findings:
- MP concentrations exhibit an exponential decay pattern with increasing distance from the shoreline, with the highest values consistently observed nearshore.
- A decay coefficient of k = 0.09 was derived through sensitivity analysis, optimizing the model’s representation of concentration gradients.
- Regression analysis was used to calibrate the model, enabling the determination of best-fit parameters for distance–concentration relationships.
- Model performance was quantitatively assessed using statistical metrics such as Root Mean Square Error (RMSE) and the coefficient of determination (R²), demonstrating strong agreement between simulated and observed values.
This research constitutes one of the first detailed numerical modeling efforts addressing MP pollution in Kazakhstan’s freshwater environments. The results underscore the significance of spatial and seasonal dynamics in the distribution of microplastics and reinforce the need for robust monitoring frameworks.
Scientific and Policy Implications:
The integration of modeling with empirical data offers valuable insights into the behavior of MPs under varying hydrological and climatic conditions. These findings contribute to the broader objectives of environmental risk assessment and support the development of targeted strategies for mitigating plastic pollution in inland water bodies.
This work exemplifies the LESLIE project’s emphasis on interdisciplinary collaboration and capacity building in environmental modeling, offering a scalable approach for similar assessments in other regions of Central Asia and beyond.
The article is freely available and can be downloaded from the following link: https://www.researchgate.net/publication/390842539_Numerical_Modeling_of_the_Concentration_of_Microplastics_in_Lakes_and_Rivers_in_Kazakhstan
