Heavy metals are naturally occurring elements, but their various applications have led to their wide circulation in the environment, raising concerns over their latent effects on the environment and human health. Their toxicity depends on numerous factors, including chemical species, concentration of heavy metal ions, environmental factors, etc. Experimental studies on the single or cumulative effects of heavy metals on plants are complex, time consuming and difficult to conduct. An alternative is mathematical modeling, which can include different factors into an integrated system and can predict plant and environmental behavior under multiple stressors. This paper presents a mathematical model that simulates the dependence of temperature, concentration of Zn in the soil and the subsequent bioaccumulation in lettuce (Lactuca sativa L.); respectively, the reaction of lettuce to Zn contamination. The main results consist of three mathematical models, based on systems of ordinary differential equations and checking their predictions with available experimental data. The models are applied to predict an optimal harvest time of lettuce with low concentration of Zn, in identifying the availability of the analyzed species to phytoremediation operations and the possibility of maneuvering certain control factors to reduce or increase the intensity of the bioaccumulation process.

The aim of the study was to identify new mathematical models and strategies that can characterize the behavior of pollutants accumulating in the soil over time, considering the special characteristics of these chemicals that cannot be degraded or destroyed easily. The paper proposes a statistical model for assessing the accumulation of Zn in the lettuce (Lactuca sativa L.), based on three indicators that characterize the development of lettuce plants over time. The experimental data can be used to obtain interpolated variations of the mass increase functions and to determine several functions that express the time dependence of heavy metal accumulation in the plant. The resulting interpolation functions have multiple applications, being useful in generating predictions for plant growth parameters when they are grown in contaminated environments, determining whether pollutant concentrations may be hazardous for human health, and may be used to verify and validate dynamic mathematical contamination models.

The current study focuses on analyzing the impact of integrating an optical sorter in a seed-separation technological flow, in terms of increasing the quality of the maize seeds appropriate for sowing. The study showed that there are situations when the use of optical separation may result in a number of difficulties in removing a variable rate of good seeds from the raw mass, which can bring economic disadvantages. The identified issue encouraged the development of several flow assessment approaches in order to determine the problem’s essence and to develop the best strategy for action. The key finding was that the evaluated optical sorting equipment cannot eliminate impurities without also removing good seeds, resulting in every 1% increase in impurity level and a rate of 0.70% of the good seeds lost. Therefore, farmers must carefully consider the scenarios where integrating optical sorting into their technological flow is a suitable option, considering the input material quality, the selling price of the product, and the risk of missing an important quantity of high-quality seeds. The working method described may be of significant importance to other farmers who intend to choose the components of grain-cleaning processes effectively.

Contamination of agricultural soils with heavy metals, such as zinc (Zn), copper (Cu) and lead (Pb), is a major problem for food safety and environmental sustainability. The present study aimed to evaluate the efficiency of phytoremediation induced with Brassica juncea (Indian mustard) and ethylenediaminetetraacetic acid (EDTA) in reducing the content of heavy metals in contaminated soils. The experiment was carried out in a greenhouse, using soil polluted with Zn, Cu and Pb, to which different treatments were applied, using: the biological method (Indian mustard only), the chemical method (EDTA in three concentrations: 0.5–1.0–2.0 mmol·kg⁻¹) and the mixed method (Indian mustard and EDTA in three concentrations: 0.5–1.0–2.0 mmol·kg⁻¹). The determinations included the analysis of the residual metal content by atomic absorption spectroscopy, as well as the evaluation of the physiological parameters of the plants (biomass, chlorophyll content in leaves, humidity, height). The results of unifactorial and bifactorial ANOVA revealed highly significant differences (p < 0.001) between the treatments and the types of metals, confirming the synergistic interaction between the chelation and phytoextraction processes. The combined treatments Indian mustard and EDTA in concentrations of 1.0 mmol·kg⁻¹ and 2.0 mmol·kg⁻¹, ensured the highest decontamination efficiency, with reductions of 51.5% for Zn, 36.3% for Pb and 27.5% for Cu. In conclusion, the mixed method represents a viable, ecological and reproducible strategy for the remediation of soils contaminated with heavy metals.

Heavy metals in contaminated soils have benefited from a considerable attention due to the possible risks for the human body. The current study has investigated the accumulation and transfer coefficient for three heavy metals (Cu, Pb, Zn) found in the contaminated soil with three concentrations (c1=1.5%, c2=3.0%, c3=4.5%, c4=6.0%), obtained by mixing the three metals, in the tomato fruit. The highest accumulation in the tomato fruits was recorded for zinc, then copper and the smallest for lead, for all four concentrations used. The transfer coefficient decreases as the concentration of heavy metals increases, so that for high heavy metals concentrations, the values of the transfer coefficient are very low, and for small heavy metals concentrations in the soil, the values for the transfer coefficient are higher. The assessment of accumulation and transfer of heavy metals in the fruits of tomatoes grown in the contaminated soil has concluded that all concentrations of the copper, lead and zinc mix have shown a low risk for human consumption.

This study aims to optimize and assess the quality of the sorting process into homogeneous size classes of dried and chopped medicinal plants, by obtaining multivariate regression functions of polytropic and polynomial forms. Assessment of sorting quality was carried out by calculating the average coefficient of separation. The influence of several important factors (material feed rate on the sieve, sieve dimensions, sieve inclination angle, sieve oscillation frequencies) on the sorting process was followed. Research was carried out on dried nettle herb (Urtica dioica) using a plant sorter with plane sieves, which allowed for modifying some constructive and functional parameters, making it possible to obtain optimal values. The results showed that the dry nettle herb chopped in bulk at 4 mm, with a moisture of 11.45%, was optimally sorted (index of average separation coefficient, 0.922) if the following parameters were met: drive mechanism speed n = 1000 rpm; sieve inclination angle α = 12.08°; material-specific flow q = 4 kg/dm·h; recommended sieve length L = 1.4 m. It was observed that at high rates, the average coefficient of separation decreased with the decrease in the sieve drive mechanism speed, and when the inclination angle of the sieve decreased, the average coefficient of separation increased. The maximum average deviation of the average separation coefficient was 5.5% for the polytropic function. The new advanced processing technologies of medicinal plants involve the short-term production of quality-finished products, thus supporting the processors of medicinal plants and the consumers of phytotherapeutic products with beneficial effects for health.