Improved prediction of the bioconcentration factors of organic contaminants from soils into plant/crop roots by related physicochemical parameters

Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots

To predict the behavior of aromatic contaminants (ACs) in complex soil-plant systems, this study developed machine learning (ML) models to estimate the root concentration factor (RCF) of both traditional (e.g., polycyclic aromatic hydrocarbons, polychlorinated biphenyls) and emerging ACs (e.g., phthalate acid esters, aryl organophosphate esters). Four ML algorithms were employed, trained on a unified RCF dataset comprising 878 data points, covering 6 features of soil-plant cultivation systems and 98 molecular descriptors of 55 chemicals, including 29 emerging ACs. The gradient-boosted regression tree (GBRT) model demonstrated strong predictive performance, with a coefficient of determination (R2) of 0.75, a mean absolute error (MAE) of 0.11, and a root mean square error (RMSE) of 0.22, as validated by five-fold cross-validation. Multiple explanatory analyses highlighted the significance of soil organic matter (SOM), plant protein and lipid content, exposure time, and molecular descriptors related to electronegativity distribution pattern (GATS8e) and double-ring structure (fr_bicyclic). An increase in SOM was found to decrease the overall RCF, while other variables showed strong correlations within specific ranges. This GBRT model provides an important tool for assessing the environmental behaviors of ACs in soil-plant systems, thereby supporting further investigations into their ecological and human exposure risks.

Ecotoxicological effects of triclosan on Lemna minor: bioconcentration, growth inhibition and oxidative stress

Triclosan (TCS), an emerging pollutant, is a notable contributor to adverse impacts on aquatic organisms due to its widespread use during COVID-19 and hydrophobic properties. There is extensive documented literature on TCS toxicity in commercially important fish species; however, studies on aquatic plants remain limited. In this prelude, the present study aims to evaluate the effect of TCS on Lemna minor, a commercially important aquatic plant species for 7 days. The results showed dose-dependent significant alterations in growth, pigments and stress enzymes of L. minor at varied concentrations of TCS (1 to 8 mg L-1). Median inhibitory concentration (IC50) was found to be 4.813 mg L-1. Total chlorophyll and carotenoid levels decreased 73.11 and 81.83%, respectively after 7 days of TCS exposure. A significant increase in catalase and superoxide dismutase activity was observed in TCS exposed groups as compared to the control. Bioconcentration factor was found to be in the range of 5.855 to 37.129 signifying TCS ability to accumulate and transfer through the food chain. Scanning electron microscopy (SEM) analysis showed deformation in the cell surface and alteration of stroma morphology of TCS exposed groups. Furthermore, the Fourier transform infrared spectroscopy (FTIR) study also revealed that higher concentrations of TCS could cause alteration in the functional groups in the plant. This study demonstrates that TCS negatively impacts the growth and metabolism of primary producers, offering crucial insights into its interactions with aquatic plants and establishing baseline information essential for crafting effective mitigation strategies for TCS contamination in aquatic environments.

Assessment of Global Antibiotic Exposure Risk for Crops: Incorporating Soil Adsorption via Machine Learning

The overuse and misuse of antibiotics could significantly increase their accumulation in soils. Consequently, antibiotics possibly enter food chain through crop uptake, posing a threat to global food security. Assessing the exposure risks of antibiotics for crops is crucial for addressing this global issue. In this study, we assessed global antibiotic exposure risk for crops, incorporating a machine learning adsorption model based on 4893 data sets from nine antibiotics. The optimized machine learning adsorption model, using the eXtreme Gradient Boosting algorithm and the class-specific modeling strategy, demonstrated relatively good performance. Notably, we introduced unsaturated soil conditions and considered spatiotemporal variations in soil moisture and temperature for the first time in such a risk assessment. Global distributions of antibiotic exposure risk for crops were predicted for March, June, September, and December. The results indicate that soil moisture significantly influences the exposure risk assessment. Relatively high exposure risk for crops was observed during months with colder local temperatures: generally June for the Southern Hemisphere and December for the Northern Hemisphere. The resulting map highlights high-risk agricultural regions, including southern Canada, western Russia, and southern Australia.

From Water to Water: Insight into the Translocation of Pesticides from Plant Rhizosphere Solution to Leaf Guttation and the Associated Ecological Risks

Plant guttation is an important source of water/nutrients for many beneficial insects, while the presence of pesticides in guttation has been considered as a new exposure route for nontarget insects. This study aimed to elucidate how 15 diverse pesticides are translocated from growth media to guttation by maize plants through a hydroponic experiment. All pesticides were effectively translocated from the growth solution to maize guttation and reached a steady state within 5 days. The strong positive correlation (R2 = 0.43-0.84) between the concentrations of pesticides in guttation and in xylem sap demonstrated that xylem sap was a major source of pesticides in guttation. The relationship between the bioaccumulation of pesticides in guttation (BCFguttation) and the chemical Kow was split into two distinct patterns: for pesticides with log Kow > 3, we identified a good negative linear correlation between log BCFguttation and log Kow (R2 = 0.71); however, for pesticides with log Kow < 3, all data fall close to a horizontal line of BCFguttation ≅ 1, indicating that hydrophilic pesticides can easily pass through the plants from rhizosphere solution to leaf guttation and reach saturation status. Besides, after feeding with pesticide-contaminated guttation, the mortality of honeybees was significantly impacted, even at very low levels (e.g., ∑600 μg/L with a mortality of 93%). Our results provide essential information for predicting the contamination of plant guttation with pesticides and associated ecological risks.

Congener-specific uptake and accumulation of bisphenols in edible plants: Binding to prediction of bioaccumulation by attention mechanism multi-layer perceptron machine learning model

Plant accumulation of phenolic contaminants from agricultural soils can cause human health risks via the food chain. However, experimental and predictive information for plant uptake and accumulation of bisphenol congeners is lacking. In this study, the uptake, translocation, and accumulation of five bisphenols (BPs) in carrot and lettuce plants were investigated through hydroponic culture (duration of 168 h) and soil culture (duration of 42 days) systems. The results suggested a higher bioconcentration factor (BCF) of bisphenol AF (BPAF) in plants than that of the other four BPs. A positive correlation was found between the log BCF and the log Kow of BPs (R2carrot = 0.987, R2lettuce = 0.801, P < 0.05), while the log (translocation factor) exhibited a negative correlation with the log Kow (R2carrot = 0.957, R2lettuce = 0.960, P < 0.05). The results of molecular docking revealed that the lower binding energy of BPAF with glycosyltransferase, glutathione S-transferase, and cytochrome P450 (-4.34, -4.05, and -3.52 kcal/mol) would be responsible for its higher accumulation in plants. Based on the experimental data, an attention mechanism multi-layer perceptron (AM-MLP) model was developed to predict the BCF of eight untested BPs by machine learning, suggesting the relatively high BCF of bisphenol BP, bisphenol PH, and bisphenol TMC (BCFcarrot = 1.37, 1.50, 1.03; BCFlettuce = 1.02, 0.98, 0.67). The prediction of BCF for ever-increasing varieties of BPs by machine learning would reduce repetitive experimental tests and save resources, providing scientific guidance for the production and application of BPs from the perspective of priority pollutants.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

Dynamic modeling of pesticide residue in proso millet under multiple application situations

Proso millet (Panicum miliaceum L.) is a cereal crop with potential resistance to drought and heat stress, making it a promising alternative crop for regions with hot and dry climates. Because of its importance, it is crucial to investigate pesticide residues in proso millet and assess their potential risks to the environment and human health to protect it from insects or pathogens. This study aimed to develop a model for predicting pesticide residues in proso millet using dynamiCROP. The field trials consisted of four plots, with each plot containing three replicates of 10 m2. The applications of pesticides were conducted two or three times for each pesticide. The residual concentrations of the pesticides in the millet grains were quantitatively analyzed using gas and liquid chromatography-tandem mass spectrometry. The dynamiCROP simulation model, which calculates the residual kinetics of pesticides in plant-environment systems, was employed for predicting pesticide residues in proso millet. Crop-specific, environment-specific, and pesticide-specific parameters were utilized to optimize the model. Half-lives of pesticides in grain of proso millet, which were needed to input for dynamiCROP, were estimated using a modified first-order equation. Proso millet-specific parameters were obtained from previous studies. The accuracy of the dynamiCROP model was assessed using statistical criteria, including the coefficient of correlation (R), coefficient of determination (R2), mean absolute error (MAE), relative root mean square error (RRMSE), and root mean square logarithmic error (RMSLE). The model was then validated using additional field trial data, which showed that it could accurately predict pesticide residues in proso millet grain under different environmental conditions. The results demonstrated the accuracy of the model in predicting pesticide residues in proso millet after multiple applications.

Functional Group Properties and Position Drive Differences in Xenobiotic Plant Uptake Rates, but Metabolism Shares a Similar Pathway

Plant uptake of xenobiotic compounds is crucial for phytoremediation (including green stormwater infrastructure) and exposure potential during crop irrigation with recycled water. Experimentally determining the plant uptake for every relevant chemical is impractical; therefore, illuminating the role of specific functional groups on the uptake of trace organic contaminants is needed to enhance predictive power. We used benzimidazole derivatives to probe the impact of functional group electrostatic properties and position on plant uptake and metabolism using the hydroponic model plant Arabidopsis thaliana. The greatest plant uptake rates occurred with an electron-withdrawing functional group at the 2 position; however, uptake was still observed with an electron-donating group. An electron-donating group at the 1 position significantly slowed uptake for both benzimidazole- and benzotriazole-based molecules used in this study, indicating possible steric effects. For unsubstituted benzimidazole and benzotriazole structures, the additional heterocyclic nitrogen in benzotriazole increased plant uptake rates compared to benzimidazole. Analysis of quantitative structure-activity relationship parameters for the studied compounds implicates energy-related molecular descriptors as uptake drivers. Despite significantly varied uptake rates, compounds with different functional groups yielded shared metabolites, including an impact on endogenous glutathione production. Although the topic is complex and influenced by multiple factors in the field, this study provides insights into the impact of functional groups on plant uptake, with implications for environmental fate and consumer exposure.

Insights into the Fate of the Novel Pesticide Vanisulfane from Animal Manure in Plant-Soil Systems: Assisted by Carbon-14 Labeling

Pesticide use can result in plant residues, which can be ingested by livestock consuming plant-derived feed and appear in manure. When this manure is applied as a fertilizer, pesticides can contaminate plant-soil systems. Few studies have focused on pesticide infiltration from applying pesticide-contaminated manure to land. In this study, the fate of pesticide vanisulfane from chicken manure was studied in radish-soil and cabbage-soil systems assisted by carbon-14 labeling. Vanisulfane and its metabolites mostly appeared as bound residues (BRs) after introduction, and BR release was found at 35 d. Notably, manure contaminated with vanisulfane and its metabolites exhibited higher plant accumulation and phytotoxicity than manure contaminated with only the parent. Four metabolites were identified, and germination toxicity assays illustrated that a metabolite with an aldehyde structure induced phytotoxicity. This study provides valuable information on pesticide contamination from manure and emphasizes the importance of considering pesticide metabolites when assessing environmental risks.

Fate of contaminants of emerging concern in the reclaimed wastewater-soil-plant continuum

Reclaimed wastewater irrigation, a common agricultural practice in water-scarce regions, chronically exposes the agricultural environment to a wide range of contaminants of emerging concern (CECs) including pharmaceuticals and personal care products. Here we provide new data and insights into the processes governing the translocation of CECs in the irrigation water-soil-plant continuum based on a comprehensive dataset from 445 commercial fields irrigated with reclaimed wastewater. We report on CEC exposures in irrigation water, soils, and edible produce (leafy greens, carrots, potatoes, bananas, tomatoes, avocados, and citrus fruits). Our data show that CEC concentrations in irrigation water and their physiochemical properties (mainly charge and lipophilicity) are the main factors governing their translocation and accumulation in the soil-plant continuum. CECs exhibiting the highest detection frequency in plants (lamotrigine, venlafaxine, and carbamazepine) showed a reduction in their leaf accumulation factor with increasing soil organic matter content. The higher soil organic matter likely reduced the available CEC concentration in the soil solution due to soil-CEC interactions, leading to reduced uptake. Interestingly, the concentration of carbamazepine in the leaves showed a saturation-like trend when plotted against its concentration in the soils. This probably resulted from steady-state conditions when uptake equals in-planta decomposition. Our data indicate that due to continuous reclaimed wastewater irrigation, the soil acts as a sink for CECs. CECs in the soil reservoir can be desorbed into the soil solution during the rainy season and be taken up by rain-fed crops.

Predicting crop root concentration factors of organic contaminants with machine learning models

Accurate prediction of uptake and accumulation of organic contaminants by crops from soils is essential to assessing human exposure via the food chain. However, traditional empirical or mechanistic models frequently show variable performance due to complex interactions among contaminants, soils, and plants. Thus, in this study different machine learning algorithms were compared and applied to predict root concentration factors (RCFs) based on a dataset comprising 57 chemicals and 11 crops, followed by comparison with a traditional linear regression model as the benchmark. The RCF patterns and predictions were investigated by unsupervised t-distributed stochastic neighbor embedding and four supervised machine learning models including Random Forest, Gradient Boosting Regression Tree, Fully Connected Neural Network, and Supporting Vector Regression based on 15 property descriptors. The Fully Connected Neural Network demonstrated superior prediction performance for RCFs (R² =0.79, mean absolute error [MAE] = 0.22) over other machine learning models (R² =0.68-0.76, MAE = 0.23-0.26). All four machine learning models performed better than the traditional linear regression model (R² =0.62, MAE = 0.29). Four key property descriptors were identified in predicting RCFs. Specifically, increasing root lipid content and decreasing soil organic matter content increased RCFs, while increasing excess molar refractivity and molecular volume of contaminants decreased RCFs. These results show that machine learning models can improve prediction accuracy by learning nonlinear relationships between RCFs and properties of contaminants, soils, and plants.

Life cycle exposure of plants considerably affects root uptake of PCBs: Role of growth strategies and dissolved/particulate organic carbon variability

Plant roots can accumulate organic chemicals, including PCBs, and this could be relevant in spreading chemicals through the food chain. To estimate such uptake, several equations are available in the literature, mostly developed in lab conditions, to obtain the root concentration factor (RCF). Here, a long-term (18 months) greenhouse experiment, using an aged, contaminated soil, was performed to reproduce root uptake in field-like conditions and to account for the ecological variability of exposure during the entire life cycle. Specific growth strategies (i.e., annual vs. perennial), root development (e.g., timing of root production and decaying), and soil parameters (e.g., dissolved organic carbon (DOC), and the particulate organic carbon (POC)) may interfere with the uptake of contaminants into the roots of plants. In this study, we investigate the effects of these factors on the RCF, obtained for 79 PCBs. New predictive equations were calculated for 5 different plants species at four different growth times (from few months to 1.5 years) and stages (growing vs maturity). The relationships highlighted a species-specific and time-dependent accumulation of PCB in plants roots, with higher RCFs in summer than in fall for some species, and the relevant influence of DOC and POC in affecting root uptake.

Residue kinetics of neonicotinoids and abamectin in pistachio nuts under field conditions: model selection, effects of multiple sprayings, and risk assessment

Pistachio is an economically valuable crop, and Iran is among the biggest producers, exporters, and consumers of this product in the world. During the growing season, pistachios are subjected to multiple sprayings with various pesticides, which result in the accumulation of their residues in nuts. These residues have raised concerns regarding consumers' health. In this research, uptake and dissipation kinetics of insecticides imidacloprid (IMI), thiacloprid (THI), thiamethoxam (THX), and abamectin (ABA) were investigated in pistachio nuts. Field experiments were conducted in a pistachio orchard. Pistachio trees were sprayed with the recommended dose of each insecticide formulation and water as the control. Samplings were performed for up to 49 days. Based on the results, pesticides uptake and dissipation kinetics were best fitted to first-order exponential growth (FOEG) and single first-order kinetic (SFOK) models, respectively. Variations in pesticides uptake/dissipation rates were mostly related to their water solubility, pK_a, and log K_ow. THX showed a higher uptake rate (0.16 ± 0.04) compared to IMI (0.10 ± 0.01) and THI (0.06 ± 0.01). The fastest dissipation rates were observed for IMI (0.04 ± 0.002 day^-1) and THX (0.03 ± 0.001 day^-1), while the slowest belonged to THI (0.02 ± 0.003 day^-1). ABA residues were below the quantification limit (LOQ) throughout the experiment. Based on FOEG and SFOK model predictions, multiple sprayings with THI and THX resulted in final concentrations exceeding the maximum residue limit (MRL). Hazard quotients for all pesticides were <1, indicating no risk to humans via consumption of the pistachio nut.

Direct Prediction of Bioaccumulation of Organic Contaminants in Plant Roots from Soils with Machine Learning Models Based on Molecular Structures

Root concentration factor (RCF) is an important characterization parameter to describe accumulation of organic contaminants in plants from soils in life cycle impact assessment (LCIA) and phytoremediation potential assessment. However, building robust predictive models remains challenging due to the complex interactions among chemical-soil-plant root systems. Here we developed end-to-end machine learning models to devolve the complex molecular structure relationship with RCF by training on a unified RCF data set with 341 data points covering 72 chemicals. We demonstrate the efficacy of the proposed gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) by predicting RCF values and achieved prediction performance with R-squared of 0.77 and mean absolute error (MAE) of 0.22 using 5-fold cross validation. In addition, our results reveal nonlinear relationships among properties of chemical, soil, and plant. Further in-depth analyses identify the key chemical topological substructures (e.g., -O, -Cl, aromatic rings and large conjugated π systems) related to RCF. Stemming from its simplicity and universality, the GBRT-ECFP model provides a valuable tool for LCIA and other environmental assessments to better characterize chemical risks to human health and ecosystems.

Translocation versus ion trapping in the root uptake of 2,4-dichlorophenol by wheat seedlings

Understanding of the plant uptake of ionizable organic compounds is critical to the evaluation of crop contamination, plant protection, and phytoremediation. This study investigated the time-dependent uptake of 2,4-dichlorophenol (DCP) by intact wheat seedling roots and subsequent translocation to shoots at pH 5.0 and 8.0. Sorption of DCP by cut roots and shoots at these two pHs was conducted to provide the uptake limits and the Donnan charge. For comparison, sorption was also conducted for 1,3-dichlorobenzene (DCB), a nonionizable compound having a structure similar to that of DCP. The DCB sorption isotherms were linear and independent of pH, yielding a consistent log K_lip of 3.56 with both roots and shoots, reflective of the essential dominant role of lipids in plant partition uptake. Whereas the DCP sorption also showed a linear isotherm at pH 5.0 with log K_lip = 2.88, the sorption at pH 8.0 was nonlinear with a concave downward shape, especially at low concentrations. With live wheat seedlings, the DCB uptake by roots and the DCB translocation to shoots rapidly approached a steady state, showing no obvious pH effect. On the DCP uptake by live plants, there was a rapid attainment of a steady state in roots at pH 5.0 coupled with a retarded transport to shoots due presumably to the polarity of DCP. At pH 8.0, the root uptake of DCP was comparatively slower and the translocation to shoots was completely inhibited due presumably to DCP ionization. At high pH, DCP was supposedly accumulated in an ionized form in root cells via an ion-trapping mechanism.

Uptake kinetics and accumulation of pesticides in wheat (Triticum aestivum L.): Impact of chemical and plant properties

Plant uptake is an important process in determining the transfer of pesticides through a food chain. Understanding how crops take up and translocate pesticides is critical in developing powerful models to predict pesticide accumulation in agricultural produce and potential human exposure. Herein, wheat was selected as a model plant species to investigate the uptake and distribution of eleven widely used pesticides in a hydroponic system as a function of time for 144 h. The time-dependent uptake kinetics of these pesticides were fitted with a first-order 1-compartment kinetic model. During 144 h, flusilazole and difenoconazole, with relative high log K_ow (3.87 and 4.36, respectively), displayed higher root uptake rate constants (k). To clarify the role of root lipid content (f_lip) in plant accumulation of pesticides, we conducted a lipid normalization meta-analysis using data from this and previous studies, and found that the f_lip value was an important factor in predicting the root concentration factor (RCF) of pesticides. An improved correlation was observed between log RCF and log f_lipK_ow (R² = 0.748, N = 26, P < 0.001), compared with the correlation between log RCF and log K_ow (R² = 0.686, N = 26, P < 0.001). Furthermore, the hydrophilic pesticides (e.g. log K_ow < 2) were found to reach partition equilibrium faster than lipophilic pesticides (e.g. log K_ow > 3) during the uptake process. The quasi-equilibrium factor (α_pt) was inversely related to log K_ow (R² = 0.773, N = 11, P < 0.001) suggesting a hydrophobicity-regulated uptake equilibrium. Findings from this study could facilitate crop-uptake model optimization.

Chemical factors affecting uptake and translocation of six pesticides in soil by maize (Zea mays L.)

Uptake of residual pesticides in a soil by a certain crop plant may be governed by their physicochemical properties. Uptake and translocation of pesticides (imidacloprid, acetamiprid, tricyclazole, azoxystrobin, tebuconazole and difenoconazole) with the octanol/water partition coefficient (log K_ow) ranging from 0.57 to 4.36 were investigated in soil with maize as a model plant. The results show that all tested pesticides in soil were uptaken by maize with accumulation amount of 27.73, 17.75, 18.96, 12.56, 10.66 and 2.13 μg for imidacloprid, acetamiprid, tricyclazole, azoxystrobin, tebuconazole and difenoconazole at 14 d, respectively. The accumulation amount was negatively correlated with adsorption coefficients and positively correlated with pesticide concentration in in situ pore water (C_IPW). Root bioconcentration factor varied widely from 0.61 for imidacloprid to 974.64 for difenoconazole was positively correlated with log K_ow and molecular weight but negatively with water solubility. Conversely, translocation factor varied from 0 for difenoconazole to 1.64 for imidacloprid was negatively correlated with log K_ow but positively with water solubility. It determined that uptake, accumulation and translocation of the pesticides in soil by maize are governed by their physicochemical properties, especially log K_ow. C_IPW is an appropriate candidate to evaluate the accumulation of pesticides in maize from soil.

Biochar applications combined with paddy-upland rotation cropping systems benefit the safe use of PAH-contaminated soils: From risk assessment to microbial ecology

This study aimed to establish a method allowing the safe use of polycyclic aromatic hydrocarbon (PAH)-contaminated soils through the combination of biochar applications and different cropping systems. The impact of biochar applications under different cropping systems on the human health risks of PAHs and soil microbiology was elucidated. The residual PAHs were the lowest in rhizosphere soils amended with 2% corn straw-derived biochar pyrolyzed at 300 °C (CB300) under the paddy-upland rotation cropping (PURC) system. Human health risks resulting from the ingestion of PAH-contaminated carrot roots / rice grains under the PURC system were significantly lower than those under continuous upland cropping systems. The greatest diversity, richness and network complexity of soil microbial communities occurred under the PURC system combined with the 2% CB300 treatment. Soil microbial functions associated with soil health and PAH biodegradation were enhanced under this strategy, while the pathogen group was inhibited. Primarily owing to its high sorption capacity, bamboo-derived biochar pyrolyzed at 700 °C realized in the reduction of PAHs, but weakly influenced shifts in soil microbial communities. Overall, the combination of PURC systems and low-temperature-pyrolyzed nutrient-rich biochar could efficiently reduce the human health risks of PAHs and improve soil microbial ecology in agricultural fields.

Comparing root concentration factors of antibiotics for lettuce (Lactuca sativa) measured in rhizosphere and bulk soils

Plant uptake of antibiotics raises serious food safety concerns. Measurements and predictions of antibiotic uptake by plants are often based on root concentration factors (RCF) determined using antibiotic concentrations in bulk soil (RCFbs) rather than in rhizosphere soil (RCFrs) where root uptake actually occurs. This study investigated the fate and transport of nine antibiotics in the continuum of bulk soil, rhizosphere soil, roots and shoots of lettuce (Lactuca sativa) under soil-surface irrigation. Antibiotic concentrations in the lettuce shoots remained unchanged during 25-35 days after seedling transplantation. Compared with the RCFrs values, the RCFbs values were significantly greater for ciprofloxacin, lincomycin, oxytetracycline, sulfamethoxazole, and tetracycline (p < 0.05), similar for trimethoprim and tylosin, but significantly lower for monensin (p < 0.05). Ciprofloxacin, trimethoprim, and tylosin had the lowest translocation factors (TF) ranging between 0.03 and 0.05, suggesting their limited upward transport to the lettuce shoots. Oxytetracycline, monensin, and sulfamethoxazole had intermediate TF values of 0.36-0.64, whereas lincomycin had the highest TF value of 1.46. This study showed significant differences between RCFbs and RCFrs values, suggesting the need to reassess the utility of RCFbs in predicting the antibiotic root uptake in diverse soil-plant systems.

How microbial community composition, sorption and simultaneous application of six pharmaceuticals affect their dissipation in soils

Pharmaceuticals may enter soils due to the application of treated wastewater or biosolids. Their leakage from soils towards the groundwater, and their uptake by plants is largely controlled by sorption and degradation of those compounds in soils. Standard laboratory batch degradation and sorption experiments were performed using soil samples obtained from the top horizons of seven different soil types and 6 pharmaceuticals (carbamazepine, irbesartan, fexofenadine, clindamycin and sulfamethoxazole), which were applied either as single-solute solutions or as mixtures (not for sorption). The highest dissipation half-lives were observed for citalopram (average DT_50,S for a single compound of 152 ± 53.5 days) followed by carbamazepine (106.0 ± 17.5 days), irbesartan (24.4 ± 3.5 days), fexofenadine (23.5 ± 20.9 days), clindamycin (10.8 ± 4.2 days) and sulfamethoxazole (9.6 ± 2.0 days). The simultaneous application of all compounds increased the half-lives (DT_50,M) of all compounds (particularly carbamazepine, citalopram, fexofenadine and irbesartan), which is likely explained by the negative impact of antibiotics (sulfamethoxazole and clindamycin) on soil microbial community. However, this trend was not consistent in all soils. In several cases, the DT_50,S values were even higher than the DT_50,M values. Principal component analyses showed that while knowledge of basic soil properties determines grouping of soils according sorption behavior, knowledge of the microbial community structure could be used to group soils according to the dissipation behavior of tested compounds in these soils. The derived multiple linear regression models for estimating dissipation half-lives (DT_50,S) for citalopram, clindamycin, fexofenadine, irbesartan and sulfamethoxazole always included at least one microbial factor (either amount of phosphorus in microbial biomass or microbial biomarkers derived from phospholipid fatty acids) that deceased half-lives (i.e., enhanced dissipations). Equations for citalopram, clindamycin, fexofenadine and sulfamethoxazole included the Freundlich sorption coefficient, which likely increased half-lives (i.e., prolonged dissipations).

Impact of biochar amendment on the uptake, fate and bioavailability of pharmaceuticals in soil-radish systems

Crops grown in soils receiving wastewaters, biosolids, or manures can accumulate pharmaceuticals in edible parts, raising concerns over potential human exposure to multiple pharmaceuticals. Nonetheless, viable mitigation options for minimizing plant uptake of pharmaceuticals are limited. This study evaluated how biochar amendment could influence the uptake of 15 pharmaceuticals by radish (Raphanus sativus) grown in a sandy loam at two amendment rates (0.1 and 1% w/w). Comparing with that in the unamended soil, the accumulation of acetaminophen, carbamazepine, sulfadiazine, sulfamethoxazole, lamotrigine, carbadox, trimethoprim, oxytetracycline, tylosin, estrone, and triclosan in radish grown in the soil amended with 1.0% of biochar was significantly decreased by 33.3-83.0%. However, the concentration of lincomycin in radish was increased by 36.7-48.2% in the soil amended with 1% biochar. While the soil amended with 1.0% of biochar had increased sorption of all 15 pharmaceuticals, the persistence of 7 pharmaceuticals in the soil were prolonged, including caffeine, sulfadiazine, sulfamethoxazole, lincomycin, estrone, 17 β-estradiol and triclosan. The reduced plant uptake of pharmaceuticals was mainly due to their lowered concentrations in pore water by the presence of biochar. Overall, the estimated daily intake data suggest that biochar amendment could potentially decrease total human exposure to a mixture of pharmaceuticals.

Uptake, translocation and accumulation of the fungicide benzene kresoxim-methyl in Chinese flowering cabbage (Brassica campastris var. parachinensis) and water spinach (Ipomoea aquatica)

Benzene kresoxim-methyl (BKM) is an important methoxyacrylate-based strobilurin fungicide widely used against various phytopathogenic fungi in crops. Uptake, translocation and accumulation of BKM in vegetables remain unknown. This study was designed to investigate uptake, translocation, and accumulation of ¹⁴C-BKM and/or its potential metabolites in Chinese flowering cabbage and water spinach. ¹⁴C-BKM can be gradually taken up to reach a maximum of 44.4% of the applied amount by Chinese flowering cabbage and 34.6% by water spinach at 32 d after application. The ¹⁴CO₂ fractions released from the hydroponic plant system reached 37.8% for cabbage and 45.8% for water spinach, respectively. Concentrations of ¹⁴C in leaves, stems and roots all gradually increased as vegetables growing, with relative 44.9% (cabbage) and 26.8% (water spinach) of translocated from roots to edible leaves. In addition, ¹⁴C in leaves was mainly accumulated in the bottom leaves, which was visualized by quantitative radioautographic imaging. The bioconcentration factor of ¹⁴C ranged from 7.1 to 38.2 mL g^-1 for the cabbage and from 8.6 to 24.6 mL g^-1 for the water spinach. The translocation factor of BKM ranged from 0.10 to 2.04 for the cabbage and 0.10-0.46 for the water spinach throughout the whole cultivation period, indicating that the cabbage is easier to translocate BKM from roots to leaves and stems than water spinach. In addition, the daily human exposure values of BKM in both vegetables were much lower than the limited dose of 0.15 mg day^-1. The results help assess potential accumulation of BMK in vegetables and potential risk.

Subcellular distribution governing accumulation and translocation of pesticides in wheat (Triticum aestivum L.)

Root uptake, translocation, and subcellular distribution of six pesticides (dinotefuran, thiamethoxam, imidacloprid, imazethapyr, propiconazole, and chlorpyrifos) with K_ow ranging from -0.549 to 4.7 were investigated in wheat to study transportation and accumulation of pesticides. The root bioconcentration factor (RCF) of pesticides decreased with water solubility (R² = 0.6121) and increased with hydrophobicity (when the pH-adjusted log K_ow > 2, R² = 0.925), respectively. The translocation of neutral pesticides from roots to shoots increased positively with water solubility (R² > 0.6484) but decreased with hydrophobicity (R² > 0.8039). The subcellular fraction concentration factor (SFCF) increased linearly with hydrophobicity of the tested pesticides (R² > 0.958). The log RCF was positively correlated with log SFCF in root cell walls (R² = 0.9894) and organelles (R² = 0.9786). Transportation of the pesticides from roots to stems and stems to leaves was adversely affected by the log SFCF of cell walls and organelles of roots (R² > 0.7997) and stems (R² > 0.6666), respectively. Hydrophobicity-dependent SFCF is a factor governing accumulation of pesticides in roots after uptake and their subsequent upward translocation.

Structural equation modeling of PAHs in surrounding environmental media and field yellow carrot in vegetable bases from Northern China: In comparison with field cabbage

During a harvest period, a set of field samples, including ambient air (gaseous and particulate phases), dust fall, surface soil and peel-surrounding soil, and yellow carrot tissues (leaf, peel, and core), were collected in a vegetable bases near a large coking manufacturer in Shanxi Province, Northern China. Based on the determinations of the concentrations and compositions of 15 USEPA priority polycyclic aromatic hydrocarbons (PAHs), the statistical results determined by a factor analysis (FA), combined with the isomeric ratios of paired species and the local emission inventory, indicated that coal combustion and vehicular exhaust served as the main emission sources of PAHs in the local environment and in yellow carrot tissues and that the coking industry was a secondary source. In terms of the transport pathways of PAHs in the surrounding media and yellow carrot tissues, the simulation results of a structural equation model (SEM) showed that the PAHs in ambient air were closely associated with those in dust fall, and these in turn had a positive correlation with the PAHs in surface soil, due to air-soil exchange. Furthermore, the PAHs in yellow carrot leaf were mainly derived from those in dust fall via leaf surface absorption, while peel uptake played a dominant role in the accumulation of PAHs in the edible core of yellow carrot. This was different from the case of cabbage, which was characterized by the prevailing contribution from leaf surface absorption. The current study supplied additional evidence to explore the transport pathways of PAHs from environmental media to tissues of different vegetables (leafy vegetables and root vegetables). CAPSULE: A combination of structural equation modeling with factor analysis was employed to quantitatively identify the dominant transport pathways of PAHs among multiple surrounding media and the different tissues of yellow carrot.

Immobilization of Potentially Toxic Elements (PTE) by Mineral-Based Amendments: Remediation of Contaminated Soils in Post-Industrial Sites

In many post-industrial sites, the high contents and high mobility of different potentially toxic elements (PTEs) make the soils unsuitable for effective management and use. Therefore, immobilization of PTE seems to be the best remediation option for such areas. In the present study, soil samples were collected in post-industrial areas in Northeastern Poland. The analyzed soil was characterized by especially high contents of Cd (22 mg·kg−1), Pb (13 540 mg·kg−1), and Zn (8433 mg·kg−1). Yellow lupine (Lupinus luteus L.) and two types of mineral-based amendments were used to determine their combined remediation effect on PTE immobilization. A greenhouse pot experiment was conducted to evaluate the influence of chalcedonite and halloysite on plant growth, chlorophyll a fluorescence, the leaf greenness index (SPAD), PTE uptake, and the physicochemical properties and toxicity of soil. The application of chalcedonite resulted in the greatest increase in soil pH, whereas halloysite contributed to the greatest reduction in the contents of Ni, Pb, Zn, and Cr in soil, compared with the control treatment. The addition of halloysite significantly increased plant biomass. The application of mineral-based amendments increased the ratio of variable fluorescence to maximum chlorophyll fluorescence (Fv/Fm) in yellow lupine leaves. The leaf greenness index was highest in plants growing in soil amended with chalcedonite. The results of this study suggest that mineral-based amendments combined with yellow lupine could potentially be used for aided phytostabilization of multi-PTE contaminated soil in a post-industrial area.

Concentrations and uptake pathways of polychlorinated biphenyls from soil to grass

Field coupled samples in soil and grass were collected to determine the concentrations and identify the uptake pathways of PCBs into the grass at a pasture from Scotland, UK. Concentrations of indicator PCBs (∑₇PCBs) in soils ranged from 0.20 to 0.88 ng g^-1 dw (dry weight), with a mean of 0.33 ng g^-1 dw, and in grass ranged from 0.20 to 2.14 ng g^-1 dw, with a mean of 0.48 ng g^-1 dw. The comprehensive factors of low concentrations and detection rate (PCB28: 18.8%; PCB52: 37.5%) of PCBs in soil, as well as continuously declined air concentrations of PCBs in the UK since the 1990s suggested that the secondary emission from the soil is becoming the supplied source of PCBs to air and grass. The significant correlations between bioconcentration factor (BCF) values and the log K_OW (R = -0.850, p < 0.05) and log K_OA (R = -0.860, p < 0.05) of indicator PCB congeners were found in the present study, indicating that these two parameters are likely to affect the bioaccumulation and uptake of grass. A generic one-compartment model was employed to identify uptake pathways of grass and evaluate the uptake amounts for PCBs. This suggested that the most important pathway for uptake of PCBs by grass was at the aerial part, and the difference of PCBs concentrations between leaves and roots was about four orders of magnitude. Removing and risk transfer of PCBs or other organic pollutants by grass need to be investigated and assessed further.