Dismantling and recycling e-waste has been recognized as a potential emission source of rare earth elements (REEs). However, the presence of REEs in typical regional soils has yet to be studied. Given the potential health implications of such soil contamination, it is vital to study the characteristics, spatial distribution, and pollution level of REEs caused by e-waste dismantling as well as determine the influencing mechanism. This study focused on Guiyu Town as an example site, which is a typical e-waste dismantling base. From the site, 39 topsoil samples of different types were collected according to grid distribution points. Soil profiles were also collected in the dismantling and non-dismantling areas. The REE characteristic parameters showed that the REE distribution was abnormal and was affected by multiple factors. The results of the integrated pollution index showed that approximately 61.5% of soil samples were considered to be lightly polluted. Spatial distribution and correlation analysis showed that hot spots of REE-polluted soil coincided with known, main pollution sources. Moreover, there was a significant negative correlation (p ≤0.05) between the REE concentration and the distance from the pollution source. E-waste disassembly and recycling greatly affect the physical and chemical properties of the surrounding soil as well as downward migration areas. In the disassembly area, REE accumulated more easily in the surface layer (0–20 cm). Geographical detector results showed that distance factor was the main contribution factor for both light rare earth elements (LREE) and heavy rare earth element (HREE) (q = 34.59% and 53.33%, respectively). REE distribution in soil was nonlinear enhanced by different factors. Taken together, these results showed that e-waste disassembling and recycling not only directly affected the spatial distribution of REEs, but that their distribution was also affected by land use type and soil properties.

The most prominent source of Cu contamination in soils is metal mining and processing, partly since the Middle Age. However, coal mining and combustion can also cause (some) Cu contamination. We studied the distribution of Cu concentrations and isotope ratios in soils of the Huaibei coal mining area. The contribution of the coal mining and combustion to total Cu concentrations in soil was determined with a two-end-member mixing model based on the distinct δ⁶⁵Cu values of the Cu emitted from coal mining and combustion and in native soil. The mean Cu concentration of 75 mg kg⁻¹ exceeded the local soil background value (round to 22.13 mg kg⁻¹). The similar δ⁶⁵Cu value of grass near the coal mining and combustion operation as in gangue and flying ash indicated a superficial Cu contamination. Mining input was the dominant source of Cu in the contaminated soils, contributing up to 95% and on average 72% of the total Cu in the topsoils. The mining-derived Cu was leached to a depth of 65 cm, where still 29% of the Cu could be attributed to the mining emissions. Grasses showed lower δ⁶⁵Cu values than the topsoils, because of the preferential uptake of light Cu isotopes. However, the Δ⁶⁵Cugᵣₐₛₛ₋ₛₒᵢₗ was lower in the contaminated than the uncontaminated area because of superficial adsorption of isotopically heavy Cu from the mining emissions. Overall, in this study the distinct δ⁶⁵Cu values of the mining-derived Cu emissions and the native soil allowed for the quantification of the mining-derived Cu and had already reached the subsoil and contaminated the grass by superficial adsorption in only 60 years of mining operation.

Soil heavy metal contamination has increasingly become a serious environmental issue globally, nearing crisis proportions. There is an urgent need to find environmentally friendly materials to remediate heavy-metal contaminated soils. With the continuing maturation of research on using biochar (BC) for the remediation of contaminated soil, nano-biochar (nano-BC), which is an important fraction of BC, has gradually attracted increasing attention. Compared with BC, nano-BC has unique and useful properties for soil remediation, including a high specific surface area and hydrodynamic dispersivity. The efficacy of nano-BC for immobilization of non-degradable heavy-metal contaminants in soil systems, however, is strongly affected by plant rhizosphere processes, and there is very little known about the role that nano-BC play in these processes. The rhizosphere represents a dynamically complex soil environment, which, although having a small thickness, drives potentially large materials fluxes into and out of plants, notably agricultural foodstuffs, via large diffusive gradients. This article provides a critical review of over 140 peer-reviewed papers regarding nano-BC-rhizosphere interactions and the implications for the remediation of heavy-metal contaminated soils. We conclude that, when using nano-BC to remediate heavy metal-contaminated soil, the relationship between nano-BC and rhizosphere needs to be considered. Moreover, the challenges to extending our knowledge regarding the environmental risk of using nano-BC for remediation, as well as further research needs, are identified.

Soil contamination with multiple heavy metals poses threats to human health and ecosystem functioning. Using the Nemerow pollution index, which considers the effects of multiple heavy metals, we compared the diversity and composition of bacteria, fungi and protists and their potential interactions in response to a multi-metal contamination gradient. Multi-metal contamination significantly altered the community composition of bacteria, fungi and protists, and the degree of alteration increased with increasing severity of contamination. The alpha-diversity of bacteria, fungi and protists significantly decreased with increasing contamination level. The dominant generalists, found in all soil samples, were Gammaproteobacteria, Chloroflexi and Bacillus sp, whereas the dominant specialists were Anaerolineaceae, Entoloma sp. and Sandonidae_X sp. The relative abundances of generalists were positively correlated, whereas those of specialists were negatively correlated, with the Nemerow pollution index. In addition, the complexity of the microbial co-occurrence network increased with increasing contamination level. Generalists, rather than specialists, were the keystones in the microbial co-occurrence network and played a crucial role in adaptation to multi-metal contamination through enhanced potential interactions within the entire microbiome. Our results provide insights into the ecological effects of multi-metal contamination on the soil microbiome and will help to develop bio-remediation technologies for contaminated soils.

With the ever-increasing demand for lithium (Li) for portable energy storage devices, there is a global concern associated with environmental contamination of Li, via the production, use, and disposal of Li-containing products, including mobile phones and mood-stabilizing drugs. While geogenic Li is sparingly soluble, Li added to soil is one of the most mobile cations in soil, which can leach to groundwater and reach surface water through runoff. Lithium is readily taken up by plants and has relatively high plant accumulation coefficient, albeit the underlying mechanisms have not been well described. Therefore, soil contamination with Li could reach the food chain due to its mobility in surface- and ground-waters and uptake into plants. High environmental Li levels adversely affect the health of humans, animals, and plants. Lithium toxicity can be considerably managed through various remediation approaches such as immobilization using clay-like amendments and/or chelate-enhanced phytoremediation. This review integrates fundamental aspects of Li distribution and behaviour in terrestrial and aquatic environments in an effort to efficiently remediate Li-contaminated ecosystems. As research to date has not provided a clear picture of how the increased production and disposal of Li-based products adversely impact human and ecosystem health, there is an urgent need for further studies on this field.

The use of mercury in small-scale gold mining is globally the largest anthropogenic source of mercury in the environment. In countries like Ghana, where small-scale gold mining is a highly important economic sector, the activity is also expected to cause local pollution. This study is based on a hypothesis that the mining activity in Ghana is causing more widespread soil pollution also outside active mining sites, and that the main part of regional differences in soil concentrations of mercury might come from pollution. Little systematic and dependable data has been collected to assess the extent of mercury contamination of soils in areas outside active mining areas. The regional aspect of mercury pollution from mining has not been studied in Ghana or other countries with a large small-scale gold mining sector. Systematic collection of soil samples on a 25 × 25 km² net covering the entire country was carried out to ensure the representativeness of data and to allow calculation of spatial trends. The soil concentrations found in one-third of the country, where most intensive mining takes place, are three times higher than concentrations in the rest of the country. This difference cannot be explained by sources of natural variation in mercury concentrations but can be explained by decades of atmospheric deposition. It is therefore likely that the mining activity has caused a more widespread increase in soil concentrations, also outside active mining sites. The mercury concentrations found are on average 0.024 mg kg⁻¹, which is low compared to published studies from other countries and regions and estimated world averages. All measured concentrations are well below soil quality criteria for human health. The build-up of soil concentrations in the mining area is still problematic because mercury is a hazardous substance in the environment.

This experiment was conducted to evaluate the ecotoxicity of typical explosives and their mechanisms in the soil microenvironment. Here, TNT (trinitrotoluene), RDX (cyclotrimethylene trinitramine), and HMX (cyclotetramethylene tetranitramine) were used to simulate the soil pollution of single explosives and their combination. The changes in soil enzyme activity and microbial community structure and function were analyzed in soil, and the effects of explosives exposure on the soil metabolic spectrum were revealed by non-targeted metabonomics. TNT, RDX, and HMX exposure significantly inhibited soil microbial respiration and urease and dehydrogenase activities. Explosives treatment reduced the diversity and richness of the soil microbial community structure, and the microorganisms able to degrade explosives began to occupy the soil niche, with the Sphingomonadaceae, Actinobacteria, and Gammaproteobacteria showing significantly increased relative abundances. Non-targeted metabonomics analysis showed that the main soil differential metabolites under explosives stress were lipids and lipid-like molecules, organic acids and derivatives, with the phosphotransferase system (PTS) pathway the most enriched pathway. The metabolic pathways for carbohydrates, lipids, and amino acids in soil were specifically inhibited. Therefore, residues of TNT, RDX, and HMX in the soil could inhibit soil metabolic processes and change the structure of the soil microbial community.

Earthworms and arbuscular mycorrhizal fungi (AMF) act synergistically in the rhizosphere and may increase host plant tolerance to Cd. However, mechanisms by which earthworm-AMF-plant partnerships counteract Cd phytotoxicity are unknown. Thus, we evaluated individual and interactive effects of these soil organisms on photosynthesis, antioxidant capacity, and essential nutrient uptake by Solanum nigrum, as well as on soil quality following Cd exposure (0–120 mg kg⁻¹). Decreases in biomass and photosynthetic activity, as well as nutrient imbalances were observed in Cd-stressed plants; however, the addition of AMF and earthworms reversed these effects. Cd exposure increased superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities, whereas inoculation with Rhizophagus intraradices decreased those. Soil enzymatic activity decreased by 15–60% with increasing Cd concentrations. However, Cd-mediated toxicity was partially reversed by soil organisms. Earthworms and AMF ameliorated soil quality based on soil enzyme activity. At 120 mg kg⁻¹ Cd, the urease, catalase, and acid phosphatase activities were 1.6-, 1.4-, and 1.2-fold higher, respectively, in soils co-incubated with earthworms and AMF than in uninoculated soil. Cd inhibited shoot Fe and Ca phytoaccumulation, whereas AMF and earthworms normalized the status of essential elements in plants. Cd detoxification by earthworm-AMF-S. nigrum symbiosis was manifested by increases in plant biomass accumulation (22–117%), chlorophyll content (17–63%), antioxidant levels (SOD 10–18%, POD 9–25%, total polyphenols 17–22%, flavonoids 15–29%, and glutathione 7–61%). It also ameliorated the photosynthetic capacity, and macro- and micronutrient statuses of plants; markedly reduced the levels of malondialdehyde (20–27%), superoxide anion (29–36%), and hydrogen peroxide (19–30%); and upregulated the transcription level of FeSOD. Thus, the combined action of earthworms and AMF feasibly enhances metal tolerance of hyperaccumulating plants and improves the quality of polluted soil.

Mapping soil contamination enables the delineation of areas where protection measures are needed. Traditional soil sampling on a grid pattern followed by chemical analysis and geostatistical interpolation methods (GIMs), such as Kriging interpolation, can be costly, slow and not well-suited to highly heterogeneous soil environments. Here we propose a novel method to map soil contamination by combining high-resolution aerial imaging (HRAI) with machine learning algorithms. To support model establishment and validation, 1068 soil samples were collected from an arsenic (As) contaminated area in Zhongxiang, Hubei province, China. The average arsenic concentration was 39.88 mg/kg (SD = 213.70 mg/kg), with individual sample points determined as low risk (66.9%), medium risk (29.4%), or high risk (3.7%), respectively. Then, identified features were extracted from a HRAI image of the study area. Four machine learning algorithms were developed to predict As risk levels, including (i) support vector machine (SVM), (ii) multi-layer perceptron (MLP), (iii) random forest (RF), and (iii) extreme random forest (ERF). Among these, we found that the ERF algorithm performed best overall and that its prediction performance was generally better than that of traditional Kriging interpolation. The accuracy of ERF in test area 1 reached 0.87, performing better than RF (0.81), MLP (0.78) and SVM (0.77). The F1-score of ERF for discerning high-risk points in test area 1 was as high as 0.8. The complexity of the distribution of points with different risk levels was a decisive factor in model prediction ability. Identified features in the study area associated with fertilizer factories had the most important contribution to the ERF model. This study demonstrates that HRAI combined with machine learning has good potential to predict As soil risk levels.