Under varying flow rate conditions, the transport and retention of polydisperse microplastics (MPls), with an average particle size of 16 ± 6 µm, were investigated in saturated porous media. First-order reversible and irreversible kinetic sorption models were used to describe the sorption kinetics. Sensitivity analyses provided insight into the effects of each sorption parameter. Both numerical modeling and experimental measurements were utilized to evaluate the retention rates of sand filters. The influence of flow rate on sorption was reflected in variations in the distribution coefficient (K_d), the mass transfer coefficient (β), and the irreversible sorption rate (K₁). Lower flow rates were associated with higher K_d and β values, indicating increased sorption and reduced mass transfer rates. An increase in K_d resulted in a more gradual sorption process, with a decrease in peak concentration, whereas changes in β had a comparatively smaller impact on sorption rate and peak concentration. Lower K₁ values were linked to higher peak concentrations and decreased retention efficiency. Numerical modeling revealed retention rates of 28 ± 1% at a flow rate of 31 mL min⁻¹ and 17 ± 1% at 65 mL min⁻¹. The introduction of MPls into saturated sand environments modifies the transport dynamics within the medium. Consequently, these alterations affect the hydrological characteristics of porous media, impacting groundwater quality and agricultural output. The mean absolute error (MAE) of 6% between the modeled and observed retention rates indicated a high level of accuracy. This study underscores the importance of examining retention efficiency and the accuracy of numerical models in understanding MPl transport in porous media.

This study concerns the transport and retention of polydisperse micron-sized (16 ± 6 µm) of microplastics (MPls) in porous media under varying flow rate conditions. Sorption kinetics were modeled using first-order reversible and irreversible kinetic sorption models, with sensitivity analyses providing insights into each sorption parameter's effect. Both numerical modeling and experimental measurements were employed to assess sand filter retention rates. The impact of flow rate on sorption reveals variations in distribution coefficient (Kd), mass transfer coefficient (β), and irreversible sorption rate (K1). Lower flow rates are correlated with higher Kd and β values, indicating an increase in sorption and diminished mass transfer rates. The findings revealed that an increase in Kd resulted in a more gradual sorption process with a decrease in peak concentration, whereas changes in β influenced the rate of sorption and peak concentration to a lesser extent compared to Kd. Lower K1 values are associated with higher peak concentrations and decreased retention efficiency. Retention rates were evaluated by a numerical model and found as 28 ± 1% at a flow rate of 31 ml min⁻¹ and 17 ± 1% at 65 ml min⁻¹. The introduction of MPls into soil environments has been noted to modify transport dynamics into soil. As a result, these alterations effects hydrological characteristics of soil, thereby impacting quality of groundwater and agricultural output. The mean absolute error (MAE) of 6% between modeled and observed retention rates suggests minor discrepancies. This study highlights the importance of examining retention efficiency and the accuracy of numerical models in porous media during MPl transport.

Extreme wildfire events are increasingly shaping Mediterranean fire regimes under the combined influence of climatic variability, vegetation stress, and growing anthropogenic pressure, posing critical risks to ecosystem stability and landscape resilience. Understanding the spatial determinants of wildfire susceptibility is therefore essential for advancing evidence-based fire risk assessment in fire-prone Mediterranean environments. This ongoing doctoral research investigates the environmental controls of wildfire occurrence in Muğla Province (Türkiye) through the integration of multi-source remote sensing data, geospatial analysis, and interpretable machine learning techniques. Burned-area reference data for 2021–2024 were derived from the MODIS MCD64A1 product within the Google Earth Engine environment and represented using a binary burned/non-burned classification. Predictor variables include ERA5-Land meteorological indicators, SRTM-derived topographic parameters, MODIS-based NDVI vegetation condition, and WorldPop population density as a proxy for human exposure, harmonized at a common 1 km spatial resolution. A Random Forest model was implemented to examine wildfire susceptibility patterns with emphasis on model interpretability and variable contribution rather than predictive optimization. Preliminary results indicate that vegetation condition, wind-related dynamics, and population density are dominant contributors to wildfire occurrence, reflecting coupled ecological vulnerability and human influence. Extreme fire conditions observed during 2021 are intentionally reserved for subsequent validation and stress-testing analyses. The proposed framework provides a transparent and transferable methodological basis for analyzing extreme wildfire susceptibility in Mediterranean landscapes and supports future development of interpretable, data-driven wildfire risk assessment approaches.