Jian-Ming Wang

Bio: Jian-Ming Wang is an academic researcher. The author has contributed to research in topics: Yield (engineering) & Fluoride. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Effect of soil contamination with fluorine on the yield and content of nitrogen forms in the biomass of crops

Abstract: The research was based on a pot experiment, in which the response of eight species of crops to soil contamination with fluorine was investigated. In parallel, some inactivating substances were tested in terms of their potential use for the neutralization of the harmful influence of fluorine on plants. The response of crops to soil contamination with fluorine was assessed according to the volume of biomass produced by aerial organs and roots as well as their content of N-total, N-protein, and N-NO3-. The following crops were tested: maize, yellow lupine, winter oilseed rape, spring triticale, narrow-leaf lupine, black radish, phacelia, and lucerne. In most cases, soil pollution with fluorine stimulated the volume of biomass produced by the plants. The exceptions included grain and straw of spring triticale, maize roots, and aerial parts of lucerne, where the volume of harvested biomass was smaller in treatments with fluorine-polluted soil. Among the eight plant species, lucerne was most sensitive to the pollution despite smaller doses of fluorine in treatments with this plant. The other species were more tolerant to elevated concentrations of fluorine in soil. In most of the tested plants, the analyzed organs contained more total nitrogen, especially aerial organs and roots of black radish, grain and straw of spring triticale, and aerial biomass of lucerne. A decrease in the total nitrogen content due to soil contamination with fluorine was detected only in the aerial mass of yellow lupine. With respect to protein nitrogen, its increase in response to fluorine as a soil pollutant was found in grain of spring triticale and roots of black radish, whereas the aerial biomass of winter oilseed rape contained less of this nutrient. Among the analyzed neutralizing substances, lime most effectively alleviated the negative effect of soil pollution with fluorine. The second most effective substance was loam, while charcoal was the least effective in this respect. Our results showed the effect of soil contamination with fluorine on the yield and chemical composition of fluorine depended on the species and organ of a tested plant, on the rate of the xenobotic element and on the substance added to soil in order to neutralize fluorine.

Machine learning methods for estimation the indicators of phosphogypsum influence in soil

Abstract: The full understanding of the effect of mineral waste-based fertilizer in soil is still unrelieved, because of the extreme complex chemical composition and plethora of their action pathways. The purposes of this paper is to quantify the input of PG into the soil ecosystem process, considering the direct effects of PG as a whole on soil environment using of a plethora of chemical, toxicological, and biological tests. Greenhouse experiment includes different PG doses (0, 1%, 3%, 7.5%, 15%, 25%, and 40%) and two-time collection points after treatments—7 and 28 days. For each treatment and each time collection point, we measure (i) soil pH, bioavailable (H20 and NH4COOH-extractable) element content (S, P, K, Na, Mg, Ca, Fe, Zn, Sr, Ba, F); (ii) soil enzyme activities—dehydrogenase, urease, acid phosphatase, FDA; (iii) soil CO2 respiration activity with and without glucose addition; (iv) Eisenia fetida, Sinapis alba, and Avena sativa responses. Finally, we combine the ordinary chemical, toxicology, and biological measuring of soil properties with state-of-the-art mathematical analysis, namely (i) support vector machines (used for prediction), (ii) mutual information test (variable importance tasks), (iii) t-SNE and LLE algorithms (used for unsupervised classification). The results show similarity between the 0%, 1%, and 3% PG treatments in all collection times based on the toxicological and biological properties. Beyond 7.5% PG, some biological test was significantly inhibited in response to trace element stress. Among all tested parameters, soil urease activities, soil respiration activities after glucose addition, S. alba root lengths, and E. fetida survival rates show sensitivity to PG addition. Furthermore, the machine learning algorithms revealed that only several elements (mobile and water-soluble forms of Ca, Ba, Sr, S, and Na; water-soluble F) could be responsible to elevated soil toxicity for those indicators. SVR models were able to predict soil biological and ecotoxicity properties, and increasing numbers of randomly selected training examples from 50 to 90% of initial experimental data significantly improved model performance. At this study, we demonstrate benefits of unsupervised machine learning methods for investigating toxicity of man-made substances in soil that can be further applied to risk assessments of various toxins, which are of significant interest to environmental protection.