A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

Mechanisms of adsorption and functionalization of biochar for pesticides: A review

Agricultural production relies heavily on pesticides. However, factors like inefficient application, pesticide resistance, and environmental conditions reduce their effective utilization in agriculture. Subsequently, pesticides transfer into the soil, adversely affecting its physicochemical properties, microbial populations, and enzyme activities. Different pesticides interacting can lead to combined toxicity, posing risks to non-target organisms, biodiversity, and organism-environment interactions. Pesticide exposure may cause both acute and chronic effects on human health. Biochar, with its high specific surface area and porosity, offers numerous adsorption sites. Its stability, eco-friendliness, and superior adsorption capabilities render it an excellent choice. As a versatile material, biochar finds use in agriculture, environmental management, industry, energy, and medicine. Added to soil, biochar helps absorb or degrade pesticides in contaminated areas, enhancing soil microbial activity. Current research primarily focuses on biochar produced via direct pyrolysis for pesticide adsorption. Studies on functionalized biochar for this purpose are relatively scarce. This review examines biochar's pesticide absorption properties, its characteristics, formation mechanisms, environmental impact, and delves into adsorption mechanisms, functionalization methods, and their prospects and limitations.

Calculation of Lipophilicity of Organophosphate Pesticides Using Density Functional Theory

Higher lipophilicity facilitates the passage of a substance across lipid cell membranes, the blood–brain barrier and protein binding, and may also indicate its toxicity. We proposed eight methods for predicting the lipophilicity of the 22 most commonly used organophosphate pesticides. In this work, to determine the lipophilicity and thermodynamic parameters of the solvation of pesticides, we used methods of density functional theory with various basis sets, as well as modern Grimm methods. The prediction models were evaluated and compared against eight performance statistics, as well as time and RAM used in the calculation. The results show that the PBE-SVP method provided the best of the proposed predictive capabilities. In addition, this method consumes relatively less CPU and RAM resources. These methods make it possible to reliably predict the ability of pesticide molecules to penetrate cell membranes and have a negative effect on cells and the organism as a whole.