Successful outcome of phytostabilization in Cr(VI) contaminated soils amended with alkalizing additives

This study analysed the effect of three alkalizing soil amendments (limestone, dolomite chalcedonite) on aided phytostabilization with Festuca rubra L. depending on the hexavalent chromium (Cr(VI)) level in contaminated soil. Four different levels of Cr(VI) were added to the soil (0, 50, 100 and 150 mg/kg). The Cr contents in the plant roots and above-ground parts and the soil (total and extracted Cr by 0.01 M CaCl2) were determined with flame atomic absorption spectrometry. The phytotoxicity of the soil was also determined. Soil amended with chalcedonite significantly increased F. rubra biomass. Chalcedonite and limestone favored a considerable accumulation of Cr in the roots. The application of dolomite and limestone to soil contaminated with Cr(VI) contributed to a significant increase in pH values and was found to be the most effective in reducing total Cr and CaCl2-extracted Cr contents from the soil. F. rubra in combination with a chalcedonite amendment appears to be a promising solution for phytostabilization of Cr(VI)-contaminated areas. The use of this model can contribute to reducing human exposure to Cr(VI) and its associated health risks. © 2020 by the authors.Ministerstwo Nauki i Szkolnictwa Wyższego: MNiS

Ash from gasification of poultry feathers for heavy metal immobilization under assisted phytostabilization in soils

The carried-out experiment aimed to assess the influence of ash derived from the thermochemical conversion of feathers (AGF) as a soil amendment, and Dactylis glomerata L. as a test plant in aided phytostabilization of soil strongly contaminated by Cu, Cd, Pb and Zn. The influence of AHG on the chemical properties of soil (pH as well as total and CaCl2-extracted heavy metals) as well as the plant yield and concentration of heavy metals in the roots and shoots. The applied soil amendment influenced an increase in the pH values of soil (by 0.4 units) and a reduction in CaCl2-extractable forms of Zn (25%), Cu (23%), Cd (20%) and Pb (12%), as well as total forms of Cu (35%), Zn (35%), Pb (20%) and Cd (17%) in the soil. The plant yield of the shoots of Dactylis glomerata L. following the application of AGF was 31% higher when compared to the control series. The roots of the tested plant in the AGF series contained higher values of the analyzed heavy metals in relation to the shoots, which was especially visible in the case of Pb (more than twice as high) and Cd (37%)

Unlocking the Saponite Potential in Aided Phytostabilisation of Multi-Metal-Contaminated Soils

Human activities have significantly impacted the environment, resulting in a need to restore degraded areas through various remediation techniques. This study aimed to evaluate the effectiveness of saponite in the aided phytostabilisation technique for heavy-metal-contaminated soil. The research was conducted on soil from a post-industrial site characterised by high metal content (Cu, Ni, Cd, Pb, Zn, and Cr) surpassing the established regulatory limits. Saponite was added to the contaminated soil at a ratio of 3% (w/w). The experiment was performed using Lolium perenne L. and Festuca rubra L. due to their adaptability to harsh soil conditions and rapid growth. The results demonstrated that saponite application significantly increased soil pH, which is beneficial for phytostabilisation of heavy metals. Saponite has been found to selectively enhance Ni accumulation in roots while not affecting Pb accumulation in above-ground parts, implying that saponite can effectively regulate heavy metal accumulation in plant biomass. Furthermore, saponite has been observed to significantly decrease soil Cd, Zn, and Cr levels with no impact on Cu, Ni, and Pb levels. Overall, saponite shows promise as an effective and scalable solution for large-scale phytostabilisation projects, contributing to the restoration of degraded soils and the protection of environmental and human health

Machine learning analysis of PM1 impact on visibility with comprehensive sensitivity evaluation of concentration, composition, and meteorological factors

Abstract This study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster. The multi-layer perceptron methods developed demonstrate high accuracy in predicting visibility, with R values of 0.90 for Warsaw and an RMSE of 1.52 km for Zabrze. Key findings reveal that air temperature and relative humidity significantly impact visibility, alongside PM1 concentration and specific heavy metals such as Rb, Vi, and Cd in Warsaw and Cr, Vi, and Mo in Zabrze. Cluster analysis underscores the localized and complex nature of visibility determinants, highlighting the substantial but previously underappreciated role of heavy metals. Integrating the k-means clustering and GSA methods emerges as a powerful tool for unravelling complex mechanisms of chemical compound changes in particulate matter and air, significantly influencing visibility development