Soil Sorption Coefficients Research Articles

Visible Near-Infrared Spectroscopy and Pedotransfer Function Well Predict Soil Sorption Coefficient of Glyphosate

The soil sorption coefficient (Kd) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of Kd is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis–NIRS) as an alternative method for glyphosate Kd estimation at a country scale and compare its accuracy against pedotransfer function (PTF). A total of 439 soils with a wide range of Kd values (37–2409 L kg−1) were collected from Denmark (DK) and southwest Greenland (GR). Two modeling scenarios were considered to predict Kd: a combined model developed on DK and GR samples and individual models developed on either DK or GR samples. Partial least squares regression (PLSR) and artificial neural network (ANN) techniques were applied to develop vis–NIRS models. Results from the best technique were validated using a prediction set and compared with PTF for each scenario. The PTFs were built with soil texture, OC, pH, Feox, and Pox. The ratio of performance to interquartile distance (RPIQ) was 1.88, 1.70, and 1.50 for the combined (ANN), DK (ANN), and GR (PLSR) validation models, respectively. vis–NIRS obtained higher predictive ability for Kd than PTFs for the combined dataset, whereas PTF resulted in slightly better estimations of Kd on the DK and GR samples. However, the differences in prediction accuracy between vis–NIRS and PTF were statistically insignificant. Considering the multiple advantages of vis–NIRS, e.g., being rapid and non-destructive, it can provide a faster and easier alternative to PTF for estimating glyphosate Kd.

The parametric uncertainty estimation of water and nitrogen transport simulation in a paddy field experiment using HYDRUS‐1D

AbstractThe HYDRUS model is an efficient technical means to study the process of complex nitrogen (N) transport in farmland. Nonetheless, spatial variability in soil water and N parameters has been observed at the field scale, leading to uncertainties in the HYDRUS model. In this study, the HYDRUS‐1D model was corrected and verified by the measured data. Generalized likelihood uncertainty estimation (GLUE) was used to estimate the uncertainty and sensitivity of the model parameters. The differences in simulated and measured data were expressed in terms of root mean square error, normalized root mean square error and coefficient of determination, and the model calibration and verification indicated that the result was reliable. The soil saturated water content, dispersion and sorption coefficient were well identified, with values ranging from 0.42 to 0.45 cm3/cm3, 0.26 to 0.29 cm and 0.141 to 0.158 L/mg, respectively. The denitrification rate and saturated hydraulic conductivity greatly influenced the uncertainty in the model results. Considering the uncertainty of the parameters, the predicted leaching of N accumulation from 0 to 100 cm depth ranged from 9.5 to 12.7 kg/ha. The HYDRUS‐1D model can simulate water and N transport in paddy fields. The prediction interval can be narrowed by considering the uncertainty of the model parameters.

Pesticide extraction from soil into runoff under a rainfall simulator

Context Runoff estimation is an important aspect of pesticide environmental behaviour and is the major loss pathway to the environment. Aims To improve understanding of pesticide runoff. Methods Data from three rainfall simulator studies was used. Twelve pesticides were studied ranged from tightly sorbed (DDE, soil sorption coefficient (KD) ~15 000 L kg−1) to weakly sorbed (dimethoate, KD < 30). Key results Event runoff pesticide concentrations were closely related to soil concentrations (0–25 mm depth). The ratio of runoff to soil concentration (the runoff extraction ratio, ERO), was similar for pesticides with a wide range of sorption and across the three soils: runoff concentration (μg L−1) = 28 × soil concentration (mg kg−1). ERO decreased with time after spraying, presumably due to lower concentrations in the top few mm of soil. Conclusions This model provides improved or similar estimates of pesticide runoff than previous models. Similar ERO values between sites was probably due to similar hydrology (high rainfall intensity, surface sealing, moist subsoils) and erosion, and because the same masses of soil and water are involved in mixing. Reduction in runoff concentrations by leaching was not influential, because infiltration was small and soil sorption too high. Implications Conditions studied apply during summer storms on most cotton and grain land on clay soils in the northern grain and cotton lands in eastern Australia. The model should be applicable under these conditions.

Including the bioconcentration of pesticide metabolites in plant uptake modeling.

Although several models of pesticide uptake into plants are available, there are few modeling studies on the bioconcentration of metabolites in plants. Ignoring metabolites in plant uptake models can result in an underestimation of the parent compound's overall impacts on human health associated with pesticide residues in harvested food crops. To address this limitation, we offer a metabolite-based plant uptake model to predict the bioconcentration of the parent compound and its metabolites in plants. We used the uptake of glyphosate and its major metabolite (aminomethylphosphonic acid, AMPA) into potato as an example. The analysis of variability revealed that soil properties (affecting the soil sorption coefficient), dissipation half-life in soil, and metabolic half-life in the potato had a significant impact on the simulated AMPA concentration in the potato, indicating that regional variability could be generated in the plant bioconcentration process of metabolites. The proposed model was further compared using the non-metabolite model. The findings of the comparison suggested that the non-metabolite model, which is integrated with the AMPA bioconcentration process, can predict the AMPA concentration in the potato similarly to the proposed model. In conclusion, we provide insight into the bioconcentration process of metabolites in tuber plants from a modeling viewpoint, with some crucial model inputs, such as biotransformation and metabolic rate constants, requiring confirmation in future studies. The modeling demonstration emphasizes that it is relevant to consider bioaccumulation of metabolites, which can propagate further into increased overall residues of harmful compounds, especially in cases where metabolites have higher toxicity effect potency than their respective parent compounds.

The Sulfoximine Insecticide Sulfoxaflor and Its Photodegradate Demonstrate Acute Toxicity to the Nontarget Invertebrate Species Daphnia magna.

The environmental fate and persistence of sulfoxaflor is of significant interest given the potential for the insecticide to impact nontarget organisms, particularly pollinating and aquatic species. In the present study we examine the potential for sulfoxaflor, a new sulfoximine insecticide, to undergo degradation and transformation in sediments and the aquatic environment. Following application of the active substance as a foliar spray or seed coating, sulfoxaflor can be found in the soil at a mass percentage of up to 61% of the total applied concentration. The low soil sorption coefficient (KD ) of sulfoxaflor of 0.103 signifies the potential for sulfoxaflor to undergo transport into nearby surface waters via groundwater run-off. In soils and sediments sulfoxaflor produces a sulfoxaflor-urea analog with a varying half-life of 5.0 to 8.5 d depending on the sediment type. Once in surface waters, sulfoxaflor can undergo photolysis to a sulfoxaflor alcohol derivative with a half-life of 35 h. The photodegradate demonstrates reduced aquatic toxicity to Daphnia magna, but the photolytic half-life may be too long to mitigate the acute toxicity of the parent substance sulfoxaflor to D. magna, which was found to have a 48-h median effect concentration of 361 µg/L. Environ Toxicol Chem 2021;40:2156-2164. © 2021 SETAC.

Soil sorption characteristics of benzobicyclon hydrolysate and estimated leaching risk in soils used for rice production

Environmental contextThe behaviour of herbicides in the environment is largely determined by the partitioning of the compounds between soil solids and soil solution. We determined that the rice herbicide-metabolite benzobicyclon hydrolysate partitions more into soil solution, and does so increasingly as pH increases. These results indicate that benzobicyclon hydrolysate is a risk for leaching in much of the rice-producing area in the US mid-South. AbstractBenzobicyclon hydrolysate (BH) is the major metabolite and active molecule in the pro-herbicide benzobicyclon (BZB), which is pending registration for use in US mid-Southern rice (Oryza sativa L.) production. The current study objectives were to (i) determine BH soil sorption coefficients; (ii) quantify relationships among BH sorption and soil properties; and (iii) estimate leaching potential using calculated retardation factors (RFs). Sorption coefficients for 10 representative Arkansas rice-production soils were determined by batch-equilibration experiments. Soil sorption (KD=0.25–44.3mLg−1), soil organic carbon partitioning (KOC=28.2–7480mLg−1), and soil organic matter partitioning (KOM=17.9–2580mLg−1) coefficients were negatively correlated with soil pH (r=−0.93 – −0.94). Clay and silt were significant secondary regression parameters, accounting for up to 93% of the variation in KD in combination with pH. Clay and silt effects on sorption coefficients increased when regression analyses excluded the lowest pH soil. Soil sorption coefficients were greater in soils with clay ≥27%, which may be a useful parameter for informing herbicide-use rates. Using the calculated RF’s, the estimated depth of leaching over the growing season exceeded the assumed 15-cm plough layer depth in eight of the 10 soils, and only two of the 10 soils had an estimated time to plough layer breakthrough less than the typical six-month growing season (April–September) under average water flux conditions. The results suggest that BH leaching below the plough layer is a potential risk for much of the rice-producing area in the US mid-South.

QSPR modelling of the soil sorption coefficient from training sets of different sizes

ABSTRACTQuantitative structure–property relationship (QSPR) modelling has been used in many scientific fields. This approach has been extensively applied in environmental research to predict physicochemical properties of compounds with potential environmental impact. The soil sorption coefficient is an important parameter for the evaluation of environmental risks, and it helps to determine the final fate of substances in the environment. In the last few years, different QSPR models have been developed for the determination of the sorption coefficient. In this study, several QSPR models were generated and evaluated for the prediction of log Koc from the relationship with log P. These models were obtained from an extensive and diverse training set (n = 639) and from subsets of this initial set (i.e. halves, fourths and eighths). The aim of this study was to investigate whether the size of the training set affects the statistical quality of the obtained models. Furthermore, statistical equivalence was verified between the models obtained from smaller sets and the model obtained from the total training set. The results confirmed the equivalence between the models, thus indicating the possibility of using smaller training sets without compromising the statistical quality and predictive capability, as long as most chemical classes in the test set are represented in the training set.

Leaching of Five Pesticides of Contrasting Mobility through Frozen and Unfrozen Soil

Core Ideas Significantly more pesticides leached from frozen than from unfrozen soil columns. Rapid breakthrough of pesticides indicated preferential flow in frozen soil. A strong negative correlation between Kf and leaching was observed. The effect of sorption properties is weaker in the presence of macropore flow. Macropore flow may be less important for highly mobile or highly sorbed pesticides. Field and laboratory studies show increased leaching of pesticides through macropores in frozen soil. Fast macropore flow has been shown to reduce the influence of pesticide properties on leaching, but data on these processes are scarce. The objective of this study was to investigate the effect of soil freezing and thawing on transport of pesticides with a range of soil sorption coefficients (Kf). To do this we conducted a soil column study to quantify the transport of bromide and five pesticides (2‐methyl‐4‐chlorophenoxyacetic acid, clomazone, boscalid, propiconazole, and diflufenican). Intact topsoil and subsoil columns from two agricultural soils (silt and loam) in southeastern Norway were used in this experiment, and pesticides were applied to the soil surface in all columns. Half the columns were then frozen (−3°C), and the other half were left unfrozen (4°C). Columns were subjected to repeated irrigation events where 25 mm of rainwater was applied during 5 h at each event. Irrigations were followed by 14‐d periods of freezing or refrigeration. Percolate was collected and analyzed for pesticides and bromide. Pesticide leaching was up to five orders of magnitude larger from frozen than unfrozen columns. Early breakthrough (<<1 pore volume) of high concentrations was observed for pesticides in frozen columns, indicating that leaching was dominated by preferential flow. The rank order in pesticide leaching observed in this study corresponded to the rank order of mean Kf values for the pesticides, and the results suggest that sorption plays a role in determining leaching losses even in frozen soil.

Comparison of soil sorption parameters of pesticides measured by batch and centrifugation methods using an andosol.

We compared the soil sorption coefficient (K d) measured by batch and centrifugation methods using a Japanese andosol and ten pesticides. Although the K d values measured by both methods increased with time, those obtained via the batch method tended to be higher during the test period. The difference in K d values between the two methods affected pesticide concentrations estimated in the soil solution, and the results estimated using K d values obtained via the batch method underestimated the observed trends.

QSPR modeling of the logKow and logKoc of polymethoxylated, polyhydroxylated diphenyl ethers and methoxylated-, hydroxylated-polychlorinated diphenyl ethers

In the present study, the structural parameters of 209 types of polymethoxylated diphenyl ethers (PMeODEs), 209 types of polyhydroxylated diphenyl ethers (PHODEs), seven types of methoxylated-polychlorinated diphenyl ethers (MeO-PCDEs) and seven types of hydroxylated-polychlorinated diphenyl ethers (HO-PCDEs) were calculated using the Gaussian 09 program at the B3LYP/6-311G** level. Using structural and positional parameters as descriptors, quantitative structure-property relationships (QSPR) models for the prediction of n-octanol/water partition coefficient (logKow) and soil sorption coefficient normalized to organic carbon (logKoc) were established and verified. The position parameters N2(6), N3(5) and N4 were the main positional factors influencing logKow and logKoc of PMeODEs and PHODEs. The molecular polarizability α was entered into the QSPR models of the logKow and logKoc of PMeODEs, PHODEs and MeO/HO-PCDEs, indicating that the molecular volume could influence the two environment-related properties of DEs significantly. All of the established QSPR models showed good goodness-of-fit, robustness, and predictive ability. The two models for all of the tested DEs are slightly inferior compared with the models for only a class of compounds. In addition, application domain analysis indicated that the models reliably predicted the logKow and logKoc of the mon- to hexa-DEs.

Statistical equivalence of prediction models of the soil sorption coefficient obtained using different log P algorithms

The soil sorption coefficient normalized to the organic carbon content (Koc) is a physicochemical parameter used in environmental risk assessments and in determining the final fate of chemicals released into the environment. Several models for predicting this parameter have been proposed based on the relationship between log Koc and log P. The difficulty and cost of obtaining experimental log P values led to the development of algorithms to calculate these values, some of which are free to use. However, quantitative structure-property relationship (QSPR) studies did not detail how or why a particular algorithm was chosen. In this study, we evaluated several free algorithms for calculating log P in the modeling of log Koc, using a broad and diverse set of compounds (n = 639) that included several chemical classes. In addition, we propose the adoption of a simple test to verify if there is statistical equivalence between models obtained using different data sets. Our results showed that the ALOGPs, KOWWIN and XLOGP3 algorithms generated the best models for modeling Koc, and these models are statistically equivalent. This finding shows that it is possible to use the different algorithms without compromising statistical quality and predictive capacity.

Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds.

We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors.

Predicting Soil Sorption Coefficients of Phenanthrene Using a Neural Network Model

Background: Sorption coefficient modeling is an effective technique for investigating fate and behavior of environmental pollutants. As a polycyclic aromatic hydrocarbon (PAH), Phenanthrene is an important organic pollutant, mainly due to its health risks for humankind. Objectives: To offer an alternative for laborious and high- priced experimental measurements, this study aimed to introduce an accurate artificial intelligence-based model, using minimum input data, to predict soil sorption coefficients (Koc and Kd) of phenanthrene. Materials and Methods: The required data were derived from previous studies carried out on soil samples taken from an under pasture paddock at Flaxley agriculture centre, mount lofty ranges, South Australia (Ahangar et al., 2008). An eight-fold cross-validation technique was also used to choose the best performance model and to obtain more authentic and precise results. Results: Multilayer perceptron (MLP) artificial neural network (ANN) model with a 1-6-1 structure was chosen which explained 97% and 95% of Kd and Koc variances, respectively. The only input data was soil organic carbon content. Conclusions: Based on this study, the ANN method is a promising alternative for conventional methods in modeling and estimating sorption coefficients in relation to soil organic carbon.

Theoretical investigation of congener-specific soil sorption of polychlorinated biphenyls by DFT computation and potent QSAR analyses

Few studies have been conducted to understand well the underlying soil sorptive mechanism due to the limited experimental determination for the enormous number of polychlorinated biphenyl (PCB) congeners. The objective of this paper was to obtain further insights into the soil sorption behavior of PCBs with exploration of the sorptive mechanism at the molecular level for sorption affinity, which could be anticipated to help explore the migration fates and assess the bioavailability of PCBs in soil. Soil sorption coefficients of 52 kinds of PCB congeners were collected in this paper. The geometries of PCBs were fully optimized within Gaussian 03 suite of programs, and 27 molecular descriptors which describe the electronic and thermodynamic properties were finally determined with optimized structures after optimization. The quantitative structure–activity relationships (QSARs) for predicting the soil sorption of PCBs were developed by the combination of density functional theory (DFT) computation and partial least squares analyses, which maximized the correlation between the DFT-calculated properties and soil sorption of PCBs. The QSAR was critically validated with better performance in sensitivity, robustness, interpretation and prediction, and specific description of the applicability domain. For the successfully developed QSAR, R ,cum(adj) 2 and Q cum 2 were respectively recorded as 0.922 and 0.896, and R EXT 2 and Q EXT 2 were respectively recorded as 0.905 and 0.914, which demonstrated the stability and predictability of the model. The molecular electronegativity of PCBs by DFT was significantly indicative of a positive correlation with sorption potency, while polarizability had a negative correlation with it. QSAR analyses also revealed the favorable structural requirement of more chlorination at meta/para sites for soil sorption. It was implied that the soil sorption should be largely ascribed to the electrostatic interaction between PCBs and soil organic matter. Nevertheless, the thermodynamic stability and hydrophobicity related to the molecular entropy increment of PCBs were also beneficial to enhancing soil sorption. QSAR analyses particularly indicated the strong dependence of variation of soil sorption on molecular electronic properties, such as electronegativity and polarizability, which suggested the predominance of electrostatic interaction with soil organic matter. Meta/para chlorination was illustrated as a preferable structural requirement for soil sorption. In addition, the thermodynamic stability and hydrophobicity driven by entropy increment were also effective for soil sorption. These results contributed to predict the migration and fate of PCBs in soil system.

Effect of synthetic clay and biochar addition on dissipation and enantioselectivity of tebuconazole and metalaxyl in an agricultural soil: Laboratory and field experiments

Laboratory and field experiments were conducted to assess how the addition of oleate-modified hydrotalcite (clay) and biochar (BC) to an agricultural soil affected the sorption, leaching, persistence, and enantiomeric composition of soil residues of two chiral fungicides, tebuconazole and metalaxyl. Laboratory experiments showed that the sorption of both fungicides ranked as follows: unamended soil<BC-amended soil<clay-amended soil. The addition of clay at a rate of 1% increased metalaxyl soil sorption coefficient (Kd) from 0.34 to 3.14Lkg−1 and that of tebuconazole from 2.4 to 47.4Lkg−1. In our experimental set-up, field plots were either unamended or amended with clay (2tha−1) or BC (4tha−1), and subsequently treated with a mixture of tebuconazole and metalaxyl at 3 and 6kgha−1, respectively. The leaching, persistence, and enantiomer composition of fungicides residues were monitored by sampling at different soil depths (0–5, 5–10, 10–20cm) for 98days. No significant changes in the scarce mobility and long persistence of tebuconazole upon amending the soil with clay or BC were observed. In contrast, sorption to clay and BC particles reduced the leaching and degradation of metalaxyl and the clay increased its persistence in the topsoil compared to the unamended soil. The enantioselective analysis of tebuconazole and metalaxyl soil residues indicated that tebuconazole remained mostly racemic along the experiment, whereas for metalaxyl the concentration of S-enantiomer was greater than the concentration of R-enantiomer, more so at longer experimental times and deeper horizons. Nevertheless, for the top 0–5cm soil layer metalaxyl remained more racemic in clay- and BC-amended soil than in unamended soil. Our results show that addition of amendments with high sorptive capacities can be beneficial in reducing leaching and degradation losses of chiral pesticide enantiomers from the topsoil, and that sorption by the amendments can influence the final enantiomeric composition of pesticide residues.

Effects of γ-irradiation of original and organic matter-amended soils on the sorption of triclosan and diuron from aqueous solutions

Soil γ-irradiation is a well-known method of inhibiting microbial activity in studies of the soil sorption of organic compounds. However, few studies have addressed the possible effect of γ-irradiation on the sorptive ability of soils enriched with different types of organic matter (OM). The objective of this study was to probe the effect of soil γ-irradiation on organic compound–soil interactions in two different situations representing adding OM to soils through land disposal of (a) OM-rich sewage sludge-originating biosolids and (b) olive mill wastewater (OMW). Both situations describe frequent environmental and agricultural scenarios. Comparisons of aqueous sorption on cobalt-60 γ-irradiated and non-irradiated soil sorbents were carried out for (a) triclosan (in a series of three soils and their lab-incubated mixtures with three different types of biosolids), and (b) the pesticide diuron (in two different untreated and OMW-affected soils). In each case, sodium azide was used as a biocide. Soil γ-irradiation affected the sorption of organic compounds by a factor generally not exceeding 2–3. Specifically, for triclosan, the sorbed concentration ratio between irradiated and non-irradiated soils when averaged over all the soil samples was 0.94. No significant effects of γ-irradiation on soil organic carbon or total nitrogen contents were observed. The effect of γ-irradiation on a soil sorbent may be less important when a rough estimate of a soil sorption coefficient of an organic compound is needed. However, it may need to be taken into account in mechanistic sorption studies, specifically, when the shape of sorption isotherms is of interest.

A quantitative risk ranking model to evaluate emerging organic contaminants in biosolid amended land and potential transport to drinking water

ABSTRACTA quantitative risk ranking model was developed for human exposure to emerging contaminants (EC) following treated municipal sewage sludge (“biosolids”) application to Irish agricultural land. The model encompasses the predicted environmental concentration (PEC) in soil, surface runoff, groundwater, and subsequent drinking water ingestion by humans. Human exposure and subsequent risk was estimated for 16 organic contaminants using a Monte Carlo simulation approach. Nonylphenols ranked the highest across three environmental compartments: concentration in soil (PECsoil), runoff (PECrunoff), and groundwater (PECgroundwater), which had mean values of 5.69 mg/kg, 1.15 × 10−2 µg/l, and 2.22 × 10−1 µg/l, respectively. Human health risk was estimated using the LC50 (chemical intake toxicity ratio, (RR)) as a toxicity endpoint combined with PECrunoff and PECgroundwater. NP ranked highest for LC50 combined with PECrunoff and PECgroundwater (mean RR values 1.10 × 10−4 and 2.40 × 10−3, respectively). The model highlighted triclocarban and triclosan as ECs requiring further investigation. A sensitivity analysis revealed that soil sorption coefficient and soil organic carbon were the most important parameters that affected model variance (correlation coefficient –0.89 and –0.30, respectively), highlighting the significance of contaminant and soil properties in influencing risk assessments. This model can help to prioritize emerging contaminants of concern requiring vigilance in environmental compartments.

Dissolution, sorption, and phytoremediation of IMX-101 explosive formulation constituents: 2,4-dinitroanisole (DNAN), 3-nitro-1,2,4-triazol-5-one (NTO), and nitroguanidine

The insensitive munition, IMX-101 approved for use in the USA, contains 2,4-dinitroanisole (DNAN), 3-nitro-1,2,4-triazol-5-one (NTO), and nitroguanidine (NQ) and is designed to be less sensitive to shock and sympathetic detonation. Given the estimated future use of IMX-101, an understanding of IMX-101 constituent attenuation mechanisms on testing and training ranges is needed. Studies were conducted to determine (1) the rates of IMX-101 fragment dissolution during simulated rainfall, (2) DNAN and NTO soil sorption coefficients, (3) ability of grasses to germinate in and phytoremediate IMX-101 contaminated soil, and (4) effect of the addition of IMX-101 degrading enrichment cultures on phytoremediation. The IMX-101 particles were found to dissolve slowly under simulated rainfall conditions with NQ and NTO dissolving first, leaving DNAN crystals. DNAN and NTO sorption to soils fit Freundlich isotherms and limited desorption was observed. DNAN and NQ were shown to be taken up into the roots and shoots of a mixture of big bluestem grass (Andropogon gerardii), Nash Indiangrass (Sorghastrum nutans), and switchgrass (Panicum virgatum) during phytoremediation of soils contaminated with up to 50mgkg−1 IMX-101. Complete degradation of IMX-101 to below detection limits occurred over 225 days. The addition of an IMX-101 degrading enrichment culture to the treatments significantly increased the root and shoot mass.

Phenanthrene Sorption on Biochar-Amended Soils: Application Rate, Aging, and Physicochemical Properties of Soil

Biochar, a by-product resulting from the pyrolysis of biomass, is considered to be an anthropogenic carbonaceous sorbent. Despite a worldwide increase in the application of biochar on agricultural fields to improve crop productivity over the past few decades, there have been few studies on their influences on the sorption of environmental contaminants. In a field-based study at two experimental sites in Denmark, we investigated the effect of birch wood-derived biochar (Skogans kol) on the sorption of phenanthrene in soils with different properties. The soil sorption coefficient, Kd (L kg−1), of phenanthrene was measured on sandy loam and loamy sand soils which have received from zero up to 100 t ha−1 of biochar. Results show that birch wood biochar had a higher Kd compared to soils. Furthermore, the application of birch wood biochar enhanced the sorption of phenanthrene in agricultural soils, and the enhancement effect increased with an increasing biochar application rate. Aging, repeated application, and higher clay content suppressed the biochar enhancement effect on the sorption of phenanthrene. Phenanthrene Kd was found to be strongly and positively correlated with both total and non-complexed organic carbon, while negatively correlated with clay content. The results also revealed that biochar–mineral interactions play an important role in the sorption of phenanthrene in biochar-amended soil.

Predicting Soil Sorption Coefficients of an Environmental Pollutant Herbicide (Diuron) Using a Neural Network Model

Background: A wide range of groundwater and soil pollutions - due to diuron herbicide - have resulted in intensive studies on their effects and transport in the environment. Modeling of sorption coefficients is an effective technique to investigate the effects and behavior of environmental pollutants such as diuron. Objectives: The purpose of the current study was to present an exact model with minimum required inputs, to predict the soil sorption coefficients (K d ) and the soil organic carbon sorption coefficients (K oc ) of diuron, in order to eliminate the need for time-consuming and costly laboratory experiments. Intelligent models based on artificial neural networks (ANNs) were used to achieve this objective. Materials and Methods: Data of this study were driven from the sorption studies, carried out on soils from a paddock under pasture at Flaxley Agriculture Centre, Mount Lofty Ranges, South Australia. Results: The multilayer perceptron (MLP) artificial neural networks (ANN) model with 1-6-1 layout, predicted nearly 98% of the variance of K d as well as 94% of the K oc variations with soil organic carbon content. Conclusions: Results showed that ANN is a powerful tool for predicting sorption coefficients using soil organic carbon content variations.