Polycyclic aromatic hydrocarbons (PAHs), abundant in mixed contaminant sites, often coexist with heavy metals. The fate and remediation of PAHs depend heavily on the sorption and desorption behavior of these contaminants. The sorption behavior can in turn be highly affected by certain soil components and properties, such as soil organic matter (SOM) and the presence of heavy metals. Through review of the literature focused on research from 2006 to 2018, this paper discusses interactions, challenges, influencing factors and potential synergies in sorption/desorption of mixed PAHs and heavy metal contamination of soil. The presence of either natural organic matter or heavy metals can enhance the sorption capability of fine soil, retarding the PAHs in the solid matrix. The co-existence of SOM and heavy metals has been reported to have synergistic effect on PAHs sorption. Enhanced and surfactant desorption of PAHs are also affected by the presence of both SOM and metals. Remediation techniques for PAHs removal from soil, such as soil washing, soil flushing and electrokinetics, can be affected by the presence of SOM and heavy metals. More detailed studies on the simultaneous effects of soil components and properties on the sorption/desorption of PAHs are needed to enhance the effectiveness of PAHs remediation technologies.

Corresponding author. fax: +49 381 498 2159. E-mail address: bernd.lennartz@uni-rostock.de (B. Lennartz). | International audience | This work was initiated to study the effects of climate induced soil water status variations which can reach extreme values under natural conditions on the sorption process of hydrophobic organic compounds. Based on the classical slurry batch methodology an approach is developed that allows the fast and careful complete drying of soil suspensions (microwave technique). Classical adsorption experiments were followed by three desorption steps with and without drying cycles. Drying and re-wetting enhanced the sorption-desorption hysteresis and Freundlich adsorption coefficients increased from 5.9 to 16 and 5.2 to 21 over three drying cycles for diuron and terbuthylazine respectively. Assuming the validity of a dual stage adsorption process, model evaluation suggests that drying is as a shrinking-like process leading to conformational changes of the dominant sorbent (soil organic matter) which restrict the intra-micro-particle diffusion. Rewetting only leads to a partial recovery of the diffusional pore space.

Corresponding author. fax: +49 381 498 2159. E-mail address: bernd.lennartz@uni-rostock.de (B. Lennartz). | International audience | This work was initiated to study the effects of climate induced soil water status variations which can reach extreme values under natural conditions on the sorption process of hydrophobic organic compounds. Based on the classical slurry batch methodology an approach is developed that allows the fast and careful complete drying of soil suspensions (microwave technique). Classical adsorption experiments were followed by three desorption steps with and without drying cycles. Drying and re-wetting enhanced the sorption-desorption hysteresis and Freundlich adsorption coefficients increased from 5.9 to 16 and 5.2 to 21 over three drying cycles for diuron and terbuthylazine respectively. Assuming the validity of a dual stage adsorption process, model evaluation suggests that drying is as a shrinking-like process leading to conformational changes of the dominant sorbent (soil organic matter) which restrict the intra-micro-particle diffusion. Rewetting only leads to a partial recovery of the diffusional pore space.

Riverine carbon (C) composition and export are closely related to changes in the coastal environment and climate. Excessive C inputs from rivers to seas and their subsequent decomposition could result in harmful algal blooms and ecosystem degradation in the coastal sea. In this study, we explored the C transportation and composition in the 24 major rivers of the Bohai Sea (BS) Rim based on the investigation of dissolved organic carbon (DOC), carbon stable isotopes (δ¹³CDOC) and chromophoric dissolved organic matter (CDOM). The results showed that the riverine DOC concentrations were high (10.6 ± 6.04 mg/L) in the BS Rim compared with the DOC levels in the main rivers in Eastern China (4.98 ± 2.45 mg/L). The δ¹³CDOC ranged from −28.29‰ to −25.32‰ in the rivers of the BS Rim, suggesting that the DOC mainly originated from riverine plankton, soil organic matter mainly induced by C3 plants, and sewage. The excitation-emission matrix fluorescence spectroscopy of the CDOM indicated that a soluble, microbial by product-like material accounted for the largest proportion (approximately 40%) of CDOM in these rivers and that CDOM mainly originated from autochthonous riverine sources with high protein-like components. The rivers in the BS Rim transported approximately 0.55 Tg C of DOC to the BS each year, with more than 70% of reactive C based on the CDOM composition. The DOC yields in terms of unit drainage area transported from the small rivers to the BS were higher compared to those of the larger rivers in the world, which indicated that the small rivers in the Bohai Rim could be an important source of the C in the BS. This study would enrich our understanding of environmental evolution in coastal areas with numerous small rivers.

Because of a shortage of water resources, sewage irrigation has become a popular management tool for farmland soil in arid areas of China; however, this has led to the accumulation of polycyclic aromatic hydrocarbons (PAHs) in soil. Soil is an important component of ecosystems, and nitrogen is an important nutrient required for plant growth. Nitrogen input can alter the physical, chemical, and biological processes in soil and thus lead to changes in soil organic matter and organic pollutants. However, whether these changes affect the accumulation of PAHs and whether such accumulation differs in the low-density fraction (LF) and high-density fraction (HF) of soil remains unclear. In this study, the response of PAHs in soil to nitrogen input (0, 100, 200, and 300 kg N ha⁻¹ yr⁻¹, respectively), including differences in LF and HF, were investigated through field experiments in a typical sewage-irrigated area. The results showed that nitrogen input could increase the concentrations of PAHs in soil from (7.6 ± 1.1) × 10³ to (10.4 ± 0.6) × 10³ μg kg⁻¹ and lead to striking differences between the LF ((5.06 ± 0.75) × 10³ to (1.89 ± 0.18) × 10³ μg kg⁻¹) and HF ((2.54 ± 0.36) × 10³ to (8.54 ± 0.44) × 10³ μg kg⁻¹). Given the significant increase in global nitrogen input, our findings have implications for the optimization and management of agricultural activities in sewage irrigation areas, such as soil investigation before fertilization, the use of soil improvers, and the improvement of soil planting measures.

Phytoextraction using hyperaccumulator, Pteris vittata, to extract arsenic (As) from soil has been applied to large areas to achieve an As removal rate of 18% per year. However, remarkable difference among different studies and field practices has led to difficulties in the standardization of phytoextraction technology. In this study, data on As concentration in P. vittata and related environmental conditions were collected through literature search. A conceptual framework was proposed to guide the improvement of phytoextraction efficiency in the field. The following influencing factors of As concentration in this hyperaccumulator were identified: total As concentration in soil, soil available As, organic matter in soil, total potassium (K) concentration in soil, and annual rainfall. The geodetection results show that the main factors that affect As concentration in P. vittata include soil organic matter (q = 0.75), soil available As (q = 0.67), total K (q = 0.54), and rainfall (q = 0.42). The predictive models of As concentration in P. vittata were established separately for greenhouse and field conditions through multivariate linear stepwise regression method. Under greenhouse condition, soil available As was the most important influencing factor and could explain 41.4% of As concentration in P. vittata. Two dominant factors were detected in the field: soil available As concentration and average annual rainfall. The combination of these two factors gave better prediction results with R² = 0.762. The establishment of the model might help predict phytoextraction efficiency and contribute to technological standardization. The strategies that were used to promote As removal from soil by P. vittata were summarized and analyzed. Intercropping with suitable plants or a combination of different measures (e.g., phosphate fertilizer and water retention) was recommended in practice to increase As concentration in P. vittata.

The on-going and extensive use of neonicotinoids occur in orchards. However, it is still unknown whether and how orchard management affects soil properties, especially the contents and structure of soil organic matter during orchard development, and their further influences on neonicotinoid persistence. Here, surface soil samples were collected from the citrus orchards with different cultivation ages (1, 10, 14, and 20 years), and their physicochemical properties were determined. Changes in the chemical structure of soil organic matter (SOM) were furtherly examined using solid-state CP/TOSS ¹³C NMR. Then, the sorption isotherms of imidacloprid in these soils were investigated. The sorption coefficient (Kd) of imidacloprid at Cₑ of 0.05 mg/L in the orchard soils increased by 19.4–23.3%, along a 20-year chronosequence of cultivation, which should be mainly ascribed to the increase of SOM. However, the organic carbon-normalized sorption coefficient (Kₒc, sorption per unit mass of OM) of imidacloprid declined with increasing cultivation ages. Moreover, the polar and aliphatic domains of SOM had a significantly positive relation to the Kₒc of imidacloprid, suggesting its key role in governing imidacloprid sorption. The results highlighted that reasonable management measures could be adopted to control the occurrence and fate of neonicotinoids in soils, mainly by affecting the content and quality of SOM.

A pot experiment was carried out on brown soil polluted by dibutyl phthalate (DBP) and di-(2-ethylhexyl) phthalate (DEHP) to investigate the effects of biochar (BC) derived from corn straw and Fe–Mn oxide modified biochar composites (FMBC) on the bioavailability of DBP and DEHP, as well as ecosystem responses in rhizosphere soil after wheat ripening. The results indicate that the application of BC and FMBC significantly increases soil organic matter, pH, available nitrogen (AN), Olsen phosphorus, and available potassium (AK); reduces the bioavailability of DBP and DEHP; enhances the activities of dehydrogenase, urease, protease, β-glucosidase, and polyphenol oxidase; and decreases acid phosphatase activity. No changes in richness and diversity, which were measured by Illumina MiSeq sequencing, were observed following BC and FMBC application. The bacterial community structure and composition varied with DBP/DEHP concentrations and BC/FMBC additions in a nonsystematic way and no significant trends were observed. In addition, FMBC exhibited better performance in increasing soil properties and decreasing the bioavailability of DBP and DEHP compared with BC. Hence, the FMBC amendment may be a promising way of developing sustainable agricultural environmental management.

High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.

Widespread soil contamination threatens living standards and weakens global efforts towards the Sustainable Development Goals (SDGs). Detailed soil mapping is needed to guide effective countermeasures and sustainable remediation operations. Here, we review visible and infrared reflectance spectroscopy (VIRS) based detection methods in combination with machine learning. To date, proximal, airborne and spaceborne carrier devices have been employed for soil contamination detection, allowing large areas to be covered at low cost and with minimal secondary environmental impact. In this way, soil contaminants can be monitored remotely, either directly or through correlation with soil components (e.g. Fe-oxides, soil organic matter, clay minerals). Observed vegetation reflectance spectra has also been proven an effective indicator for mapping soil pollution. Calibration models based on machine learning are used to interpret spectral data and predict soil contamination levels. The algorithms used for this include partial least squares regression, neural networks, and random forest. The processes underlying each of these approaches are outlined in this review. Finally, current challenges and future research directions are explored and discussed.