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.

Oral bioaccessibility (BAc) is a surrogate for the bioavailability (BAv) of a broad range of substances, reflecting the value that the approach offers for assessing oral exposure and risk. BAc is generally considered to have been validated as a proxy for oral BAv for the important soil contaminants Pb, Cd, and As. Here, using literature data for Ni BAc and BAv, we confirmed that Ni BAc (gastric only, with HCl mimicking stomach conditions) is a conservative measure of BAv for the oral exposure pathway. Measured oral BAv of Ni in soil was shown to be 50–100 times less than the simplest oral BAc estimates (%BAv = 0.012(%BAc) - 0.023 (r = 0.701, 95%CI [0.456, 0.847], n = 30)) in rats, demonstrating a significant conservatism for exposure assessment. The relationship between the oral BAv and BAc of nickel sulfate hexahydrate (NSHH) was comparable to that of soil, with measured oral BAv of NSHH (1.94%) being a small fraction of NSHH gastric BAc (91.1%). BAc and BAv reflect the underlying Ni speciation of the sample, with the bioaccessible leaching limits being represented by the highly soluble Ni salts and the poorly soluble Ni monoxide, and the environmental (e.g. soil properties) or gastric (e.g. food present) conditions. BAc has potential utility for chemical classification purposes because pure Ni substances can be grouped by %BAc values(using standardized methodologies for the relevant exposure routes), these groupings reflecting the underlying chemistry and speciation of the samples of substances tested here, with 0.008% %BAc for alloys (SS304, SS316, Inconel, Monel), <1% in green NiO and Ni metal massives, 0.9–23.6% for Ni powders, 9.8–22.7% for Ni sulfides, 26.3–29.6% for black oxidic Ni, and 82–91% for the soluble Ni salts. Oral BAc provides realistic yet conservative estimates of BAv for the hazard classification and risk assessment of Ni substances.

Inputs of polycyclic aromatic hydrocarbons (PAHs) of regulatory interest from diffuse atmospheric sources within urban areas frequently elevate local soil concentrations to levels requiring remediation despite the lack of in-situ contamination. This research sought to determine the distribution and potential health effects of aerially deposited PAHs in soil within the urban core of metropolitan Milwaukee, Wisconsin, U.S.A. as part of a soil regulatory standards reevaluation. Park areas (n = 27) identified as undisturbed for 80+ years, containing no fill material, and receiving only atmospheric deposition were selected for composite surface and 92 cm core soil sample collection (n = 295). Samples were analyzed for the 16 USEPA priority PAHs, 1- and 2- methylnapthalene and ancillary soil properties. Soil core and ancillary data confirm lack of site disturbance. PAH diagnostic ratios and homologue summations indicate that diffuse multiple point source emissions contribute equally to PAH deposition throughout the area. Benzo(a)pyrene (BaP) and dibenz(a,h)anthracene mean concentrations exceed health-based clean up levels. Risk assessment shows only a worst-case exposure scenario (BaP at the 95% upper confidence limit) increasing cancer risk (1.67 × 10⁻⁶) over current regulatory thresholds (1.0 × 10⁻⁶). Health quotients show potential health risks from fluoranthene and pyrene for daily park users and from BaP for all others. Mean soil PAH values are similar to New Orleans, but lower than Chicago, Boston, and London reflecting industrial history and site selection protocols. The soil PAH results presented here for sites selected for non-manipulated soils combined with an almost 100-year uninterrupted atmospheric exposure effectively show the maximum potential PAH values that can be found at any given undisturbed location within the Milwaukee urban core due solely to atmospheric deposition.

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.

Phthalate pollution in soil and vegetables in plastic agricultural greenhouses has attracted wide concern. Investigating airborne phthalates in this environment can improve understanding of air-soil or air-vegetable phthalate migration. However, studies of phthalates in plastic agricultural greenhouse air are rare. To fill this gap, 25 gas-phase and 23 particle-phase samples were collected from 12 typical plastic greenhouses in Shaanxi. 16 types of phthalates were measured by a gas chromatography-mass spectrometry system (GC-MS) to analyse their pollution features and variations. Results showed that in the air of the plastic greenhouses, the median concentration of the sum of sixteen type phthalates (∑₁₆ phthalates) was 5305 ng m⁻³, with 5th-95th value of 1214–9616 ng m⁻³. Phthalates in gas-phase samples were over 100 times higher than the levels in particle-phase samples. Air phthalate concentrations in the plastic greenhouses were higher than those in the control groups (P < 0.05). Air bis (2-ethylhexyl) phthalate (DEHP) and di-n-butyl phthalate (DnBP) accounted for 66.9% and 29.3% of total ∑₁₆ phthalate concentrations, respectively. Air phthalate concentrations in the plastic greenhouses in winter were 1.1–5.3 times higher than the levels in summer respectively (P < 0.05). Gas-particle partition coefficients (KP) values of DEHP in summer (median of 1.52 × 10⁻⁴ m³ μg⁻¹) were higher than KP values of DnBP in summer (0.60 × 10⁻⁴ m³ μg⁻¹). Log-transformed KP values of DnBP and DEHP were linear correlated to the reciprocal of air temperatures, respectively (P < 0.05).

Converting biomass waste into biochar by slow pyrolysis with subsequent soil amendment is a prospective approach with multiple environmental benefits including soil contamination remediation, soil amelioration and carbon sequestration. This study selected cow manure as precursor to produce biochar under 300 °C, 400 °C, 500 °C and 600 °C, and a remarkable promotion of carbon (C) retention in biochar by incorporation of exogenous Ca was achieved at all investigated pyrolysis temperatures. The C retention was elevated from 49.2 to 68.3% of pristine biochars to 66.1–79.7% of Ca-composite biochars. It was interesting that extent of this improvement increased gradually with rising of pyrolysis temperature, i.e., doping Ca in biomass promoted pyrolytic C retention in biochar by 16.6%, 23.4%, 29.1% and 31.1% for 300 °C, 400 °C, 500 °C and 600 °C, respectively. Thermogravimetric-mass spectrometer (TG-MS) and X-ray photoelectron spectroscopy (XPS) showed that Ca catalyzed thermal-chemical reactions and simultaneously suppressed the release of small organic molecular substances (C₂–C₇) via physical blocking (CaO, CaCO₃, and CaClOH) and chemical bonding (CO and OC–O). The catalyzation mainly occurred at 200–400 °C, while the suppression was more prominent at higher temperatures. Raman spectra and 2D FTIR analysis on biochar microstructure showed that presence of Ca had negative influence on carbon aromatization and thus weakened biochar's stability, while increasing pyrolysis temperature enhanced the stability of carbon structure. Finally, with integrating “C retention” during pyrolysis and “C stability” in biochar, the maximum C sequestration (56.3%) was achieved at 600 °C with the participation of Ca. The study highlights the importance of both Ca and pyrolysis temperature in enhancing biochar's capacity of sequestrating C.

Excessive copper (Cu) in contaminated soil and groundwater has attracted continuous attentions due to the bioaccumulation and durability. In this study, the feasibility of remediation of heavy metal pollution in soil and groundwater was investigated using hydroxyapatite/calcium silicate hydrate (HAP/C–S–H) recovered from phosphorus-rich wastewater in farmland. The results show that the pH has a strong effect on copper removal from Cu-contaminated groundwater but the impact of ion strength on the removal is weak. In general, high pH and low ion strength give better results in copper removal. Kinetic and isotherm data from the study fit well with Pseudo-second-order kinetic model and Langmuir isotherm model, respectively. The maximum adsorption capacity of HAP/C–S–H (138 mg/g) was higher than that of C–S–H (90.3 mg/g) when pH value, temperature, and ionic strength were 5, 308 K, and 0.01 M, respectively. Thermodynamics results indicate that Cu removal is a spontaneous and endothermic process. X-ray diffraction (XRD) results show that the mechanism of copper removal involves physical adsorption, chemical precipitation and ion exchange. For the remediation of Cu-contaminated soil, 76.3% of leachable copper was immobilized by HAP/C–S–H after 28 d. Acid soluble Cu, the main contributor to biotoxicity, decreased significantly while reducible and residual Cu increased. After immobilization, the acid neutralization capacity of the soil increased and the dissolution of copper was substantially reduced in near-neutral pH. It can be concluded that HAP/C–S–H is an effective, low-cost and eco-friendly reagent for in-situ remediation of heavy metal polluted soil and groundwater.

Soil samples from a contaminated site in Sweden were analyzed to identify the presence of 78 polycyclic aromatic compounds (PACs) using gas chromatography coupled with mass spectrometry (GC-MS). The target analysis revealed large contributions not only from polycyclic aromatic hydrocarbons (PAHs), but also from alkylated- and oxygenated-PAHs (alkyl- and oxy-PAHs, respectively), and N-heterocyclics (NPACs). PAC profiles indicated primarily pyrogenic sources, although contribution of petrogenic sources was also observed in one sample as indicated by a high ratio of alkylated naphthalene compared to naphthalene. The aryl hydrocarbon receptor (AhR)-activity of the soil extracts was assessed using the H4IIe-pGudluc 1.1 cells bioassay. When compared with the calculated total AhR-activity of the PACs in the target list, 35–97% of the observed bioassay activity could be explained by 62 PACs with relative potency factors (REPs). The samples were further screened using GC coupled with Orbitrap™ high resolution MS (GC-HRMS) to investigate the presence of other PACs that could potentially contribute to the AhR-activity of the extracts. 114 unique candidate compounds were tentatively identified and divided into four groups based on their AhR-activity and environmental occurrence. Twelve substances satisfied all the criteria, and these compounds are suggested to be included in regular screening in future studies, although their identities were not confirmed by standards in this study. High unexplained bio-TEQ fractions in three of the samples may be explained by tentatively identified compounds (n = 35) with high potential of being toxic. This study demonstrates the benefit of combining targeted and non-targeted chemical analysis with bioassay analysis to assess the diversity and effects of PACs at contaminated sites. The applied prioritization strategy revealed a number of tentatively identified compounds, which likely contributed to the overall bioactivity of the soil extracts.