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.

Environment pollution with heavy metals, can be a cause of the industrialization activities and technological processes, and has become an important issue. Soil contamination due to natural or anthropogenic causes (such as mining, smelting, warfare and military training, electronic industries, fossil fuel consumption, waste disposal, agrochemical use and irrigation) is a major environmental hazard. Various remediation techniques have been highlighted to clean or restore soils contaminated with heavy metals such physical, chemical or biological. Phytoremediation is a relatively new approach to removing contaminants from the environmental. It may be defined as the use of plants to remove, destroy or sequester hazardous substances from environmental. This paper is a review of removal of heavy metals from a contaminated soil using phytoremediation.

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.

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.

Plants need certain conditions that represent their living environment. When the living environment provides the conditions required by the plant, it will grow and develop properly. The growth and development of plants involve environmental factors, which represent those constituent elements of the natural environment, which actively intervene in plants’ life. The present work shows the characteristics of an agricultural soil, contaminated with heavy metals (Cu, Pb and Zn) in different concentrations, which has been divided into pots, in which were thereafter planted vegetable seedlings (tomatoes, cucumbers, parsley, spinach, carrots, radishes). During the plants’ growing time, the temperature and humidity of the air inside the greenhouse, as well as the humidity and pH of the soil, were monitored. The growth and the development of the plants under certain conditions were also tracked, until the end of the growing period. The results of monitoring the plants’ growth and development are important in assessing the impact of the contamination over the soil and the plants.