As a promising amendment, biochar has excellent characteristics and can be used as a remediation agent for diverse types of soil pollution. Biochar is mostly made from agricultural wastes, forestry wastes, and biosolids (eg, sewage sludge), but not all the biochar has the same performance in the improvement of soil quality. There is a lack of guidelines devoted to the selection of biochar to be used for different types of soil pollution, and this can undermine the remediation efficiency. To shed light on this sensitive issue, this review focus on the following aspects, (i) how feedstocks affect biochar properties, (ii) the effects of biochar on heavy metals and organic pollutants in soil, and (iii) the impact on greenhouse gas emissions from soil. Generally, the biochars produced from crop residue and woody biomass which are composed of lignin, cellulose, and hemicellulose are more suitable for organic pollution remediation and greenhouse gas emission reduction, while biochar with high ash content are more suitable for cationic organic pollutant and heavy metal pollution (manure and sludge, etc.). Additionally, the effect of biochar on soil microorganisms shows that gram-negative bacteria in soil tend to use WB biochar with high lignin content, while biochar from OW (rich in P, K, Mg, and other nutrients) is more able to promote enzyme activity. Finally, our recommendations on feedstocks selection are presented in the form of a flow diagram, which is precisely intended to be used as a support for decisions on the crucial proportioning conditions to be selected for the preparation of biochar having specific properties and to maximize its efficiency in pollution control.

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.

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.

Plastic film mulching and use of wastewaters for irrigation have been common agricultural practices for over half a century in Tunisia, especially in arid regions, resulting in the undesired creation of a pathway for microplastics (MPs) to enter farmland soil. In order to assess the extent and characteristics of soil contamination by MPs in the Moknine province, an area of intensive agricultural practices, 16 farmland soil samples were collected and characterized. The total concentration of targeted MPs was 50–880 items/kg; among them, the most common MPs type being polypropylene (PP), mainly occurring as white/transparent fibers with small size (cross section <0.3 mm). SEM images of MPs surfaces revealed multiple features related to environmental exposure and degradation. ATR-FTIR spectroscopy and pyrolysis-GC/MS analyses enabled the accurate identification of MPs separated from the embedding soil micro- and macro-aggregates. Finally, contamination of the polymeric microparticles with a broad range of metals was found by ICP-MS analysis, suggesting that MPs can be vectors for transporting heavy metals in the soil and indicators of soil contamination as a result of mismanagement of industrial wastewaters.

Organochlorine pesticides in soil samples across urban, suburban, agricultural, and industrial sites were analyzed every year between 2013 and 2016 in South Korea. The study aims to understand the residual status, diminution of occurrence from the South Korean environment, and its risk to humans after three decades of the ban. A general decreasing trend of OCPs has been observed over the years. The OCP concentrations were below the guideline values prescribed for soil pollution. Metabolites like p,p’-DDD and endosulfan sulfate contributed a major portion to the total OCP concentration over the years. The agricultural sites showed higher OCP levels than other site types. Compositional profile and diagnostic ratios suggested that the occurrence of DDT and endosulfan residues were due to historical inputs, but those of HCH and chlordane reflect recent usage in some pockets. The calculated incremental lifetime cancer risk was within the safety limit for all age groups across the genders in the majority of the sites. It is evident that the OCP load on soil is decreasing since the ban on usage. However, regular monitoring with a special focus on metabolites can be an effective control measure to regulate and eliminate the contamination of OCPs.

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0–20 cm) in a 2574 km² agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (−0.26 > r > −0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.

The application of exogenous biodegradation strains in pesticide-polluted soils encounters the challenges of migration and persistence of inoculants. In this study, the degradation characteristics, vertical migration capacity, and microbial ecological risk assessment of an enhanced green fluorescent protein (EGFP)-tagged 2-Methyl-4-chlorophenoxyacetic acid (MCPA)-degrading strain Cupriavidus gilardii T1 (EGFP) were investigated in the laboratory and field soils. The optimum remediation conditions for T1 (EGFP) was characterized in soils. Meanwhile, leaching experiments showed that T1 (EGFP) migrated vertically downwards in soil and contribute to the degradation of MCPA at different depths. After inoculation with T1 (EGFP), a high expression levels of EGFP gene was observed at 28 d in the laboratory soil and at 45 d in the field soil. The degradation rates of MCPA were ≥ 60% in the laboratory soil and ≥ 48% in the field soil, indicating that T1 (EGFP) can efficiently and continuously remove MCPA in both laboratory and field conditions. In addition, the inoculation of T1 (EGFP) not only showed no significant impact on the soil microbial community structure but also can alleviate the negative effects induced by MCPA to some extent. Overall, our findings suggested that T1 (EGFP) strain is an ecologically safe resource for the in situ bioremediation of MCPA-contaminated soils.

Source apportionment can be an effective tool in mitigating soil pollution but its efficacy is often limited by a lack of information on the factors that influence the accumulation of pollutants at a site. In response to this limitation and focusing on a suite of heavy metals identified as priorities for pollution control, the study established a comprehensive pollution control framework using factor identification coupled with spatial agglomeration for agricultural soils in an industrialized part of Zhejiang Province, China. In addition to elucidating the key role of industrial and traffic activities on heavy metal accumulation through implementing a receptor model, specific influencing factors were identified using a random forest model. The distance from the soil sample location to the nearest likely industrial source was the most important factor in determining cadmium and copper concentrations, while distance to the nearest road was more important for lead and zinc pollution. Soil parent materials, pH, organic matter, and clay particle size were the key factors influencing accumulation of arsenic, chromium, and nickel. Spatial auto-correlation between levels of soil metal pollution and industrial agglomeration can enable a more targeted approach to pollution control measures. Overall, the approach and results provide a basis for improved accuracy in source apportionment, and thus improved soil pollution control, at the regional scale.

The Yellow River Delta (YRD) wetland, the most important estuary wetland in eastern China, has an important ecosystem service function. Rapid and intensive development has inevitably led to the accumulation of potentially toxic elements (PTEs) in soils. Therefore, identifying quantitative sources and spatial distributions of PTEs is essential for soil environmental protection in the YRD. A total of 240 topsoil samples (0–20 cm) were collected in the Yellow River Delta Nature Reserve (YRDNR) and analyzed the PTE contents. To avoid the biases of the single receptor model, positive matrix factorization, factor analysis with nonnegative constraints, and maximum likelihood principal component analysis-multivariate curve resolution-alternating least squares were used for source apportionment of soil PTEs. To promote the efficiency of multivariate geostatistical simulation, a minimum/maximum autocorrelation factor-sequential Gaussian simulation was built to map the spatial patterns of PTEs. Three factors were derived by the three receptor models, and their contributions to the source explanation were similar. As, Cr, Cu, Mn, Ni, and Zn originated from natural sources, with contributions of 85.6%–96.4 %. A total of 61.5 % of Hg was associated with atmospheric deposition of coal combustion and wastewater from upstream. Agricultural activities and oil exploitation contributed 33.5 % and 15.9 % of the Cd and Pb concentrations. Spatial distributions of soil PTEs were controlled by sedimentary grain size. A total of 47.2 % of the total study area was identified as hazardous area for Cd, 10.3 % for As, and 5.4 % for Hg. This work is expected to provide references for soil pollution assessment and management of YRDNR.

Heavy metal pollution in soil around abandoned mine sites is one of the most critical environmental issues worldwide. Soil microbes form complex communities and perform ecological functions individually or in cooperation with other organisms to adapt to harsh environments. In this study, we investigated the distribution patterns of bacterial and fungal communities in non-contaminated and heavy metal-contaminated soil of the abandoned Samkwang mine in Korea to explore microbial interaction mechanisms and their modular structures. As expected, the bacterial and fungal community structures showed large differences depending on the degree of heavy metal contamination. The microbial network was divided into three modules based on the levels of heavy metal pollution: heavy metal-tolerant (HM-Tol), heavy metal-mid-tolerant (HM-mTol), and heavy metal-sensitive (HM-Sens) modules. Taxonomically, microbes assigned to Vicinamibacterales, Pedosphaeraceae, Nitrosomonadaceae, and Gemmatimonadales were the major groups constituting the HM-Tol module. Among the detected heavy metals (As, Pb, Cd, Cu, and Zn), copper concentrations played a key role in the formation of the HM-Tol module. In addition, filamentous fungi (Fusarium and Mortierella) showed potential interactions with bacteria (Nitrosomonadaceae) that could contribute to module stability in heavy metal-contaminated areas. Overall, heavy metal contamination was accompanied by distinct microbial communities, which could participate in the bioremediation of heavy metals. Analysis of the microbial interactions among bacteria and fungi in the presence of heavy metals could provide fundamental information for developing bioremediation mechanisms for the recovery of heavy metal-contaminated soil.