Search

Batch and transport experiments were used to investigate the remediation of loamy sand soil contaminated with Cr(VI) using zero-valent iron nanoparticles (nZVI) stabilized by carboxymethylcellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L−1, mainly because of the increased total surface area at a larger particle concentration. Batch experiments showed that the removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5% at pH 5 and 7, respectively. The two-site sorption model described (r2 = 0.96–0.98) the transport of Cr(VI) in soil quite well as compared to the uniform transport model (r2 = 0.81–0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and a shorter time to reach maximum relative concentration at lower pH. The methodology presented in this study, combining column experiment and modeling transport using the Hydrus-1D model, successfully assessed the removal of Cr(VI) from polluted soils, offering innovative, cost-effective, and environmentally friendly remediation methodologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R2) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg−1 for Cr, 20,963.84 ± 4447.83 mg kg−1 for Fe, 1.46 ± 0.42 mg kg−1 for Cd, and 48.71 ± 11.88 mg kg−1 for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination. [ABSTRACT FROM AUTHOR]

Mineralogical analysis and laboratory-based leaching tests coupled with speciation modeling were undertaken to quantify the potential for short-term acid generation and the release of trace elements from soils heavily contaminated with mine waste at Rio Tinto. Three different waste materials were considered as case studies: roasted pyrite, copper slags, and leached sulfide ores. The results showed elevated values of net acid generation (up to 663 mmol H+/kg), the major pools being potential sulfidic acidity and acidity retained in jarosite. Remarkable contents of As and toxic heavy metals were found especially in the slag-contaminated soil. Copper, Zn, and Pb were the most abundant metals in the acid leach solutions resulting from mine soil-water interaction, with peak values of 55.6 mg L−1, 2.77 mg L−1, and 2.62 mg L−1, respectively. Despite the high total contents of trace elements occurring in soil, the mobile fraction was limited to maximum release values of 12.60% for Cd and 10.27% for Cu, according to the test leaching. Speciation calculations indicated that free metal ions (M2+) and sulfate species (MSO40) accounted for most of the dissolved load. Acid soil drainage is a secondary source of acid and heavy metals in the mine site and, therefore, an effective land reclamation program should ensure that acidity and metal mobility are reduced to environmentally sustainable levels. [ABSTRACT FROM AUTHOR]

Soil contamination with organic contaminants and various heavy metals has become a global environmental concern. Biochar application for the remediation of polluted soils may render a novel solution to soil contamination issues. However, the complexity of the decontaminating mechanisms and the real environment significantly influences the preparation and large-scale application of biochar for soil ramification. This review paper highlights the utilization of biochar in immobilizing and eliminating the heavy metals and organic pollutants from contaminated soils and factors affecting the remediation efficacy of biochar. Furthermore, the risks related to biochar application in unpolluted agricultural soils are also debated. Biochar production conditions (pyrolysis temperature, feedstock type, and residence time) and the application rate greatly influence the biochar performance in remediating the contaminated soils. Biochars prepared at high temperatures (800 °C) contained more porosity and specific surface area, thus offering more adsorption potential. The redox and electrostatic adsorption contributed more to the adsorption of oxyanions, whereas ion exchange, complexation, and precipitation were mainly involved in the adsorption of cations. Volatile organic compounds (VOCs), dioxins, and polycyclic aromatic hydrocarbons (PAHs) produced during biochar pyrolysis induce negative impacts on soil alga, microbes, and plants. A careful selection of unpolluted feedstock and its compatibility with carbonization technology having suitable operating conditions is essential to avoid these impurities. It would help to prepare a specific biochar with desired features to target a particular pollutant at a specific site. This review provided explicit knowledge for developing a cost-effective, environment-friendly specific biochar, which could be used to decontaminate targeted polluted soils at a large scale. Furthermore, future study directions are also described to ensure a sustainable and safe application of biochar as a soil improver for the reclamation of polluted soils. [ABSTRACT FROM AUTHOR]

Landscape parks are protected areas, attractive to live close to and relax in. In parks, economic and agricultural activities are allowed to a limited extent. The high interest in these areas is the cause of unfavorable changes, including environmental contamination. This paper presents the results of soil quality research in Wzniesienia Łódzkie Landscape Park (Poland). The analyses were performed in 2008, before the construction of the highway in the park began, and after its completion in 2016. The contents of Zn, Cu, Pb, Cd and Ni were determined by flame atomic absorption spectrometry (FAAS). The descriptive statistics, principal component analysis (PCA), cluster analysis (CA), and geographic information system (GIS) were used to assess the impact of different sources on the content of metal in the soil. Over the period of 8 years, there has been an increase in pH and the level of metals, especially nickel. The changes in the metal content result from the different land use, especially abandonment of agricultural activity and emissions related to the construction of the A1 highway. [ABSTRACT FROM AUTHOR]

The issue of soil contamination is one of the most important subjects that interests decision-makers all over the world. It is also related to soil fertility and food security. The soils adjacent to the drains in Egypt suffer from increasing concentration of heavy metals, which negatively affects soil and crop quality. Precise spatial distribution maps of heavy metals are an essential key to mitigating the negative impacts on the ecosystem. Sixty random soil locations adjacent to the El-Moheet drainage were chosen on the west side of the Nile River, El-Minia governorate, Egypt. Six heavy metals (Cr, Co, Cu, Cd, Pb, and Zn) were selected to generate their spatial pattern maps using ordinary Kriging (OK). Principal component analysis (PCA) and contamination factors (CF) were applied to evaluate soil contamination levels in the study area. The results showed that the Gaussiang model was a high fit for soil pH, and Pb, the Exponential model was fit for EC, Stable model was fit for OC, Co, Cu, and Cd. In addition, the Spherical model was fit for both Cr and Zn. The MSE values were close to zero in all selected metals, while the values of RMSSE were close to one. The results showed that the soil heavy metal concentrations were grouped into two clusters using PCA. Furthermore, three contamination degrees were obtained (moderate, considerable, and very high), with about 70.7% of the study area characterized by considerable heavy metals concentration, where the average heavy metals concentration (mg kg−1) in this degree was 91.23 ± 19.5, 29.44 ± 5.2, 53.83 ± 10.2, 1.12 ± 0.3, 36.04 ± 18.0, and 101.29 ± 35 for Cr, Co, Cu, Cd, Pb, and Zn, respectively. The current results reflect the mismanagement and use of low-quality water for irrigation in the study area, which increased the toxic element concentration in soil surface layers. In the end, the results of spatial distribution maps of pollutants and their degrees could support decision-makers as a basis for developing appropriate mitigation plans for heavy metals. [ABSTRACT FROM AUTHOR]