Microplastics are recognized as a factor of global change contaminating many environmental compartments. Agricultural soils are very likely to receive microplastic contamination and are of particular concern due to their role in food production. Microplastic fibers have already been shown to be able to affect soil properties, but their effect on different soil types is poorly understood. Moreover, limited information is available on how the presence of this pollutant can affect soil water erosion processes, which are extremely important issues in many environments. In the light of this, we performed two experiments (carried out on a microscale) to investigate how the presence of polyester microplastic fibers affects soil physical and hydrological parameters and processes such as aggregate formation and soil erosion in three different agricultural soil types (a Vertisol, an Entisol, and an Alfisol). Our data show that the effects of polyester microplastic fibers on soil physical parameters and erosion are strongly dependent on soil type. We found that microplastic fiber contamination can affect soil bulk density, capacitive indicators of soil physical quality, and decrease the formation of new aggregates (labile in the incubation period applied in our experiments) but did not affect their stability in water. However, we found that polyester microplastic fibers reduced soil loss and sediment concentration, especially in the most erodible soils. In this paper, we provide some hypotheses, but certainly future data are still needed to confirm or disprove our hypotheses. Overall, our results highlight the importance of broadly exploring soil properties, such as texture, mineralogy, organic carbon content, etc., to better understand how the various soil types respond to microplastic fiber contamination.

The extent (determined by the repellency indices RI and RI_c) and persistence (determined by the water drop penetration time, WDPT) of soil water repellency (SWR) induced by pines were assessed in vastly different geographic regions. The actual SWR characteristics were estimated in situ in clay loam soil at Ciavolo, Italy (CiF), sandy soil at Culbin, United Kingdom (CuF), silty clay soil at Javea, Spain (JaF), and sandy soil at Sekule, Slovakia (SeF). For Culbin soil, the potential SWR characteristics were also determined after oven-drying at 60°C (CuD). For two of the three pine species considered, strong (Pinus pinaster at CiF) and severe (Pinus sylvestris at CuD and SeF) SWR conditions were observed. Pinus halepensis trees induced slight SWR at JaF site. RI and RIc increased in the order: JaF < CuF < CiF < CuD < SeF, reflecting nearly the same order of WDPT increase. A lognormal distribution fitted well to histograms of RI_c data from CuF and JaF, whereas CiF, CuD and SeF had multimodal distributions. RI correlated closely with WDPT, which was used to develop a classification of RI that showed a robust statistical agreement with WDPT classification according to three different versions of Kappa coefficient.

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρ_b), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρ_b, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike’s information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes.