A deeper look at plant uptake of environmental contaminants using intelligent approaches

Mechanistic and data-driven perspectives on plant uptake of organic pollutants

Establishing reliable predictive models for plant uptake of organic pollutants is crucial for environmental risk assessment and guiding phytoremediation efforts. This study compiled an expanded dataset of plant cuticle-water partition coefficients (Kcw), a useful indicator for plant uptake, for 371 data points of 148 unique compounds and various plant species. Quantum/computational chemistry software and tools were utilized to compute various molecular descriptors, aiming to comprehensively characterize the properties and structures of each compound. Three types of models were developed to predict Kcw: a mechanism-driven pp-LFER model, a data-driven machine learning model, and an integrated mechanism-data-driven model. The mechanism-data-driven GBRT-ppLFER model exhibited superior performance, achieving RMSEtrain = 0.133 and RMSEtest = 0.301 while maintaining interpretability. The Shapley Additive Explanation analysis indicated that pp-LFER parameters, ESPI, FwRadicalmax, ExtFP607, and RDF70s are the key factors influencing plant uptake in the GBRT-ppLFER model. Overall, pp-LFER parameter, ESPI, and ExtFP607 show positive effects, while the remaining factors exhibit negative effects. Partial dependency analysis further indicated that plant uptake is not solely determined by individual factors but rather by the combined interactions of multiple factors. Specifically, compounds with ppLFER parameter >4, ESPI > -25.5, 0.098 < FwRadicalmax <0.132, and 2 < RFD70s < 3, are generally more readily taken up by plants. Besides, the predicted Kcw values from the GBRT-ppLFER model were effectively employed to estimate the plant-water partition coefficients and bioconcentration factors across different plant species and growth media (water, sand, and soil), achieving an outstanding performance with an RMSE of 0.497. This study provides effective tools for assessing plant uptake of organic pollutants and deepens our understanding of plant-environment-compound interactions.

Absorption and translocation of selected pharmaceuticals in Pistia stratiotes: Spatial distribution analysis using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Phytoremediation can eliminate pharmaceuticals from aquatic environments through absorption; however, understanding of absorption and transport processes in plants remains limited. In this study, a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-MSI) method was developed to explore the absorption and translocation mechanisms of seven common pharmaceuticals in Pistia stratiotes. Results showed that 2,3-dicyanohydroquinone, an infrequently used matrix, exhibited outstanding performance in MALDI-MSI analysis, producing the highest signal intensity for four of the seven pharmaceuticals. Region of Interest (ROI) analysis revealed that charge speciation of pharmaceuticals significantly influenced their ability to enter vascular bundle. Neutral and positively charged pharmaceuticals easily entered vascular bundle, while negatively charged pharmaceuticals faced difficulty. ROI results for neutral and negatively charged pharmaceuticals exhibited positive correlation with their transfer factor values, indicating that their translocation ability from root to shoot was related to their capacity to enter vascular bundle. However, no correlation was observed for positively charged pharmaceuticals, suggesting that these compounds, upon entering vascular bundle, encountered difficulties in upward translocation through the xylem. This study introduces an innovative approach and offers novel insights into the retention and migration of pharmaceuticals in plant tissues, aiming to enhance the understanding of pharmaceutical accumulation in plants. ENVIRONMENTAL IMPLICATION: Pharmaceuticals in aquatic environment can inflict detrimental effects on both human health and ecosystem. Phytoremediation can remove pharmaceuticals from aquatic environments through absorption. However, our understanding of absorption and transportation of pharmaceuticals in plants remains limited. This study developed a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-MSI) method for pharmaceuticals in plant roots, and to explore the absorption and translocation mechanisms of pharmaceuticals. The study offers direct evidence of differences in accumulation behavior of pharmaceuticals in plants, providing valuable insights for targeted and effective strategies in using plants for remediating the aquatic ecosystem from pharmaceuticals.

Benzotriazole Ultraviolet Stabilizers (BUVSs) as Potential Protein Kinase Antagonists in Rice

The ubiquitous occurrence of benzotriazole ultraviolet stabilizers (BUVSs) in the environment and organisms has warned of their potential ecological and health risks. Studies showed that some BUVSs exerted immune and chronic toxicities to animals by disturbing signaling transduction, yet limited research has investigated the toxic effects on crop plants and the underlying mechanisms of signaling regulation. Herein, a laboratory-controlled hydroponic experiment was conducted on rice to explore the phytotoxicity of BUVSs by integrating conventional biochemical experiments, transcriptomic analysis, competitive sorption assays, and computational studies. The results showed that BUVSs inhibited the growth of rice by 6.30-20.4% by excessively opening the leaf stomas, resulting in increased transpiration. BUVSs interrupted the transduction of abscisic acid (ABA) signal through competitively binding to Ca2+-dependent protein kinase (CDPK), weakening the CDPK phosphorylation and further inhibiting the downstream signaling. As structural analogues of ATP, BUVSs acted as potential ABA signaling antagonists, leading to physiological dysfunction in mediating stomatal closure under stresses. This is the first comprehensive study elucidating the effects of BUVSs on the function of key proteins and the associated signaling transduction in plants and providing insightful information for the risk evaluation and control of BUVSs.

Development, Evaluation, and Application of Machine Learning Models for Accurate Prediction of Root Uptake of Per- and Polyfluoroalkyl Substances

Machine learning (ML) models were developed for understanding the root uptake of per- and polyfluoroalkyl substances (PFASs) under complex PFAS-crop-soil interactions. Three hundred root concentration factor (RCF) data points and 26 features associated with PFAS structures, crop properties, soil properties, and cultivation conditions were used for the model development. The optimal ML model, obtained by stratified sampling, Bayesian optimization, and 5-fold cross-validation, was explained by permutation feature importance, individual conditional expectation plot, and 3D interaction plot. The results showed that soil organic carbon contents, pH, chemical logP, soil PFAS concentration, root protein contents, and exposure time greatly affected the root uptake of PFASs with 0.43, 0.25, 0.10, 0.05, 0.05, and 0.05 of relative importance, respectively. Furthermore, these factors presented the key threshold ranges in favor of the PFAS uptake. Carbon-chain length was identified as the critical molecular structure affecting root uptake of PFASs with 0.12 of relative importance, based on the extended connectivity fingerprints. A user-friendly model was established with symbolic regression for accurately predicting RCF values of the PFASs (including branched PFAS isomerides). The present study provides a novel approach for profound insight into the uptake of PFASs by crops under complex PFAS-crop-soil interactions, aiming to ensure food safety and human health.

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.

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.

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.

Mapping Plant Bioaccumulation Potentials of Pesticides from Soil Using Satellite-Based Canopy Transpiration Rates

The transpiration rate is an important factor that determines the bioaccumulation potential of pesticides from soil and can present a spatiotemporal pattern. In the present study, we proposed a satellite-based approach to map the bioaccumulation potential of pesticides from soil using the Global Land Evaporation Amsterdam Model (GLEAM). In the proposed model, the spatiotemporal variable (i.e., plant transpiration rate) was separately analyzed from the plant- and chemical-specific variables. The simulated bioaccumulation factors (BAFs; steady-state concentration ratios between plants and soil) of atrazine and lindane for the United States indicated that the proposed model can better predict the spatiotemporal pattern of bioaccumulation potentials of pesticides from soil than a previous weather-based model. The proposed approach using GLEAM's satellite data avoids the overestimation of plant transpiration rate in regions with a dry and warm climate. The comparison of BAFs between the proposed and weather-based models indicated that the satellite-based simulation was consistent with the weather-based simulation for most states and was more effective for the southwest region. Furthermore, plant- and chemical-specific variables were simulated for over 700 pesticides, which could be multiplied by satellite-based canopy transpiration rates to map the bioaccumulation potentials of chemicals from soil. Further evaluation of plant-specific variables, partitioning behaviors of ionizable compounds, and multiple uptake routes (e.g., airborne residue deposition) will aid in the evaluation of the spatiotemporal patterns of pesticide BAFs in plants in future research. Environ Toxicol Chem 2023;42:117-129. © 2022 SETAC.

Influence of land use changes on the remaining available aquifer storage (RAAS): A case study of the Taoerhe alluvial-proluvial fan

Groundwater resources are important water sources for people living in arid-semiarid China. To solve the problem of continuously declining groundwater levels, groundwater artificial recharge has been widely conducted by using available aquifers. However, the effects of land use changes on the available aquifer storage, especially on the remaining available aquifer storage (RAAS), have not been fully explored. Here, we quantitatively evaluated the effects of land use changes on the RAAS, exemplifying the Taoerhe alluvial-proluvial fan. Independent component analysis (ICA) is used to determine precipitation- and groundwater extraction-affected RAASs, and regression equations are established for land use type areas and precipitation- and groundwater extraction-affected RAASs through stepwise regression and all-subsets regression. An integrated model combining the future land use simulation (FLUS) model and Markov-chain model is established to predict three land use change scenarios in 2036, and the impacts of land use changes on the precipitation- and groundwater extraction-affected RAASs are evaluated. The results show that land use changes were generally active from 2000 to 2018; during this time, the RAAS showed a fluctuating upward trend. Rational land use changes are critical to the RAAS. In the 2036 baseline scenario, the precipitation-affected RAAS is the smallest and the groundwater extraction-affected RAAS is the largest among the three scenarios, contrary to the economic development scenario results. The woodland conservation scenario shows that the groundwater level can be maintained at a stable level with appropriate woodland protection measures to ensure the stability of the RAAS, providing the most promising results for groundwater development and utilization in the study area. These results temporally quantify the effects of land use changes on the precipitation- and groundwater extraction-affected RAASs and provide a reference for developing artificial recharge schemes in arid-semiarid regions and studying the effects of land use changes on available aquifer storages.

Assessing plant uptake of organic contaminants by food crops tomato, wheat, and corn through sap concentration factor

This study investigated uptake of two organic compounds including hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and exogenous caffeine by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aestivum L.). The plants were grown in a growth chamber under recommended conditions and then were exposed to these compounds for 19 days. The uptake of the compounds was measured by sap concentration factor. The plant samples (stem transpiration stream) and solution in the exposure media were taken and analyzed by high performance liquid chromatography-tandem mass spectrometry. The plant stem samples were analyzed after a freeze-thaw centrifugation process. The average sap concentration factor for the RDX by tomato, wheat, and corn was 0.71, 0.67, and 0.65. The average sap concentration factor for the exogenous caffeine by tomato, wheat, and corn was 0.72, 0.50, and 0.34. These relatively high sap concentration factor values were expected as available predictive models offer high sap concentration factor values for moderately hydrophobic and hydrophilic compounds. The generated sap concentration factor values for the RDX and exogenous caffeine are important for improving the accuracy of previously developed machine learning models predicting the uptake and translocation of emerging contaminants.

Rapid plant uptake of isothiazolinone biocides and formation of metabolites by hydroponic Arabidopsis

Isothiazolinones biocides are water-soluble, low molecular weight, nitrogenous compounds widely used to prevent microbial growth in a variety of applications including personal care products and building façade materials. Because isothiazolinones from buildings wash off and enter stormwater, interactions with terrestrial plants may represent an important part of the environmental fate of these compounds (e.g., in green stormwater infrastructure). Using the model plant Arabidopsis thaliana grown hydroponically, we observed rapid (≥99% within 24 hours), plant-driven removal of four commonly used isothiazolinones: benzisothiazolinone (BIT), chloromethylisothiazolinone, methylisothiazolinone, and octylisothiazolinone. No significant differences in uptake rate occurred between the four compounds; therefore, BIT was used for further detailed investigation. BIT uptake by Arabidopsis was concentration-dependent in a manner that implicates transporter-mediated substrate inhibition. BIT uptake was also minimally impacted by multiple BIT spikes, suggesting constituently active uptake. BIT plant uptake rate was robust, unaffected by multiple inhibitors. We investigated plant metabolism as a relevant removal process. Proposed major metabolites that significantly increased in the BIT-exposure treatment compared to the control included: endogenous plant compounds nicotinic acid (confirmed with a reference standard) and phenylthioacetohydroximic acid, a possible amino acid-BIT conjugate, and two accurate masses of interest. Two of the compounds (phenylthioacetohydroximic acid and TP 470) were also present in increased amounts in the hydroponic medium after BIT exposure, possibly via plant excretion. Upregulation of endogenous plant compounds is environmentally significant because it demonstrates that BIT impacts plant biology. The rapid plant-driven isothiazolinone removal observed here indicates that plant-isothiazolinone processes could be relevant to the environmental fate of these stormwater compounds.

Contaminants of emerging concerns in recycled water: Fate and risks in agroecosystems

Recycled water (RW) has been increasingly recognized as a valuable source of water for alleviating the global water crisis. When RW is used for agricultural irrigation, many contaminants of emerging concern (CECs) are introduced into the agroecosystem. The ubiquity of CECs in field soil, combined with the toxic, carcinogenic, or endocrine-disrupting nature of some CECs, raises significant concerns over their potential risks to the environment and human health. Understanding such risks and delineating the fate processes of CECs in the water-soil-plant continuum contributes to the safe reuse of RW in agriculture. This review summarizes recent findings and provides an overview of CECs in the water-soil-plant continuum, including their occurrence in RW and irrigated soil, fate processes in agricultural soil, offsite transport including runoff and leaching, and plant uptake, metabolism, and accumulation. The potential ecological and human health risks of CECs are also discussed. Studies to date have shown limited accumulation of CECs in irrigated soils and plants, which may be attributed to multiple attenuation processes in the rhizosphere and plant, suggesting minimal health risks from RW-fed food crops. However, our collective understanding of CECs is rather limited and knowledge of their offsite movement and plant accumulation is particularly scarce for field conditions. Given a large number of CECs and their occurrence at trace levels, it is urgent to develop strategies to prioritize CECs so that future research efforts are focused on CECs with elevated risks for offsite contamination or plant accumulation. Irrigating specific crops such as feed crops and fruit trees may be a viable option to further minimize potential plant accumulation under field conditions. To promote the beneficial reuse of RW in agriculture, it is essential to understand the human health and ecological risks imposed by CEC mixtures and metabolites.

Influences of elevated O3 and CO2 on Cd distribution in different Festuca arundinacea tissues

It is necessary to reveal the responses of the biomass production and metal accumulation capacity of different plants to the variations of atmospheric conditions and soil metals, with the acceleration of urbanization and industrialization. In the present study, a series of experiments were designed to study the individual and interactive influences of O₃ and CO₂ fumigation on the biomass yield, variation in different leaf types, distribution of cadmium (Cd) in various tissues, and phytoremediation efficiency of Festuca arundinacea using open top chambers. The results found that an elevated O₃ content of 80 ppb, a potential O₃ content predicted for 2050, decreased the total dry mass of F. arundinacea and increased the proportion of falling leaf tissues of the species significantly. Under the same ambient CO₂ levels, O₃ fumigation increased the Cd concentrations in the roots and the fresh, mature, senescent, and dead leaf tissues by 27.8%, 133.3%, 94.4%, 125.3%, and 48.6%, respectively. An elevated CO₂ content (550 ppm) promoted the biomass yield of F. arundinacea, particularly in the falling leaf tissues. The results of the combined O₃ and CO₂ treatment showed that CO₂ fumigation alleviated the negative effects of O₃ on plant growth and increased the accumulation capacity in different plant tissues. Significantly more Cd was accumulated in senescent and dead leaves under the synergistic action of CO₂ and O₃, suggesting that the phytoremediation effect on F. arundinacea using the falling leaves harvesting method could be improved under the future atmospheric environment of high CO₂ and O₃ levels.

Impacts of root pruning intensity and direction on the phytoremediation of moderately Cd-polluted soil by Celosia argentea

Root pruning can impact the physiological functions of various plants, which influence phytoremediation. A series of root pruning treatments with different combinations of direction (two-side pruning and four-side pruning) and intensity (10, 25, and 33% pruning) were performed on Celosia argentea L. All two-side pruning treatments, regardless of intensity, decreased the dry biomass of the C. argentea roots at the end of the experiment relative to that of the control. However, the two-side-10% and two-side-25% pruning treatments stimulated the growth rate of the plant leaves significantly by 58.6 and 41.4%, respectively, relative to that of the control, and even offset the weight loss of the plant roots. Contrastingly, the two-side-33% pruning treatment reduced the biomass yield of leaves by 24.1%. For the four-side pruning treatments, the low intensity increased the dry weight of both the plant roots and leaves, while both decreased under high-intensity root pruning. The dry weight, Cd content, pigment level, and photosynthetic efficiency in the four-side-10% treatment were higher than those in the other treatments during the experiment. This study indicates that root pruning with a suitable combination of direction and intensity can positively influence the Cd removal ability of C. argentea.

Pharmaceutical and transformation products during unplanned water reuse: Insights into natural attenuation, plant uptake and human health impact under field conditions

In urban and periurban areas, agricultural soils are often irrigated with surface water containing a complex mixture of contaminants due to wastewater treatment plant (WWTP) effluent discharges. The unplanned water reuse of these resources for crop irrigation can represent a pathway for contaminant propagation and a potential health risk due to their introduction in the food chain. The aim of this study is to provide data about the magnitude of attenuation processes and plant uptake, allowing for a reliable assessment of contaminant transfer among compartments and of the human health risk derived from unplanned water reuse activities. Target compounds are 25 pharmaceuticals, including transformation products (TPs). The field site is an agricultural parcel where maize is irrigated by a gravity-fed surface system supplied by the Jarama river, a water course strongly impacted by WWTP effluents. Throughout the 3-month irrigation period, irrigation water and water infiltrating through the vadose zone were sampled. The agricultural soil was collected before and after the irrigation campaign, and maize was sampled before harvesting. All selected compounds are detected in irrigation water (up to 12,867 ng L^-1). Metformin, two metamizole TPs and valsartan occur with the highest concentrations. For most pharmaceuticals, results demonstrate a high natural attenuation during soil infiltration (>60%). However, leached concentrations of some compounds can be still at concern level (>400 ng L^-1). A persistent behavior is observed for carbamazepine, carbamazepine epoxide and sulfamethoxazole. Pharmaceutical soil contents are in the order of ng g^-1 and positively ionized compounds accumulate more effectively. Results also indicate the presence of a constant pool of drugs in soils. Only neutral and cationic pharmaceuticals are taken up in maize tissues, mainly in the roots. There is an insignificant threat to human health derived from maize consumption however, additional toxicity tests are recommended for 4AAA and acetaminophen.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Prediction of Plant Uptake and Translocation of Engineered Metallic Nanoparticles by Machine Learning

Machine learning was applied to predict the plant uptake and transport of engineered nanoparticles (ENPs). A back propagation neural network (BPNN) was used to predict the root concentration factor (RCF) and translocation factor (TF) of ENPs from their essential physicochemical properties (e.g., composition and size) and key external factors (e.g., exposure time and plant species). The relative importance of input variables was determined by sensitivity analysis, and gene-expression programming (GEP) was used to generate predictive equations. The BPNN model satisfactorily predicted the RCF and TF in both hydroponic and soil systems, with an R² higher than 0.8 for all simulations. Inclusion of the initial ENP concentration as an input variable further improved the accuracy of the BPNN for soil systems. Sensitivity analysis indicated that the composition of ENPs (e.g., metals vs metal oxides) is a major factor affecting RCF and TF values in a hydroponic system. However, the soil organic matter and clay contents are more dominant in a soil system. The GEP model (R² = 0.8088 and 0.8959 for RCF and TF values) generated more accurate predictive equations than the conventional regression model (R² = 0.5549 and 0.6664 for RCF and TF values) in a hydroponic system, which could guide the sustainable design of ENPs for agricultural applications.

Fate and transport of per- and polyfluoroalkyl substances (PFASs) in the vadose zone

Per- and polyfluoroalkyl substances (PFASs) are a heterogeneous group of persistent organic pollutants that have been detected in various environmental compartments around the globe. Emerging research has revealed the preferential accumulation of PFASs in shallow soil horizons, particularly at sites impacted by firefighting activities, agricultural applications, and atmospheric deposition. Once in the vadose zone, PFASs can sorb to soil, accumulate at interfaces, become volatilized, be taken up in biota, or leach to the underlying aquifer. At the same time, polyfluorinated precursor species may transform into highly recalcitrant perfluoroalkyl acids, changing their chemical identity and thus transport behavior along the way. In this review, we critically discuss the current state of the knowledge and aim to interconnect the complex processes that control the fate and transport of PFASs in the vadose zone. Furthermore, we identify key challenges and future research needs. Consequently, this review may serve as an interdisciplinary guide for the risk assessment and management of PFAS-contaminated sites.

Uptake, accumulation and impact of antiretroviral and antiviral pharmaceutical compounds in lettuce

While the contamination of agroecosystems with pharmaceutical compounds has been reported, the fate of these compounds, particularly uptake into plants remains unclear. This lack of environmental fate data is evident for a critical class of pharmaceuticals, the antivirals and antiretrovirals (ARVDs). Thus, this study evaluated the root uptake of the antiretroviral compounds nevirapine, lamivudine and efavirenz, and the antiviral compound oseltamivir in lettuce. The lettuce was hydroponically grown in a nutrient solution containing the four ARVD pharmaceutical mixture in the 1-100 μg L-1 concentration range. The measured bioaccumulation showed that efavirenz and lamivudine accumulated to the highest and lowest degree, at concentrations of 3463 ng g-1 and 691 ng g-1 respectively. The translocation factor between the root and leaf for nevirapine was greater than 1. The highest concentration of the pharmaceutical mixture had a physiological impact on the lettuce. Potential toxicity was evidenced by a statistically significant 34% (p = 0.04) mean reduction in root and leaf biomass in the 100 μg L-1 ARVD mix exposed lettuce, compared with the controls. This study advances knowledge of the fate of ARVDs in agroecosystems, in particular, plant root - ARVD interaction and the resulting potentially toxic effects on plants.

Mass balance of metals during the phytoremediation process using Noccaea caerulescens: a pot study

There are two widely used methods to estimate the time taken for phytoremediation for the removal of the target pollutants, i.e., using the data of metal uptake by the harvested parts of the selected plant or using the decrement in average element content between the beginning and end of the remediation. The latter not only depends on sampling points but is also determined by sampling time because even if the soil is initially perfectly homogenized, plant growth itself heterogenizes the soil as time goes by. In this study, phytoremediation was tested on one homogenized soil obtained from various soil samples taken within an e-waste dismantling and recycling site, and the remediation time for different points of bulk and rhizosphere soil was estimated using the two methods. Phytoremediation efficiency, as assessed by the change in soil metal concentrations over 100 days, widely varied depending on which of the six soil compartments of the pot was sampled, and the standard deviations of Cd, Zn, Pb, and Cu increased as the experiment proceeded, indicating the inaccuracy of this method. When applied to rhizosphere soil, this method led to a large overestimation of phytoremediation efficiency for Cd and Zn, which was 81- and 77-fold that was obtained by measuring the actual amount of metals taken up by Noccaea caerulescens. The significant difference between the two methods indicated that the blended soil became heterogeneous during the phytoremediation process because the species extracted metals from different soil parts, manifested by the variation in the metal content. The gap between these two estimation methods decreased when the soil was mixed thoroughly at the end of the experiment. This work shows that calculating the metal decontamination efficiency based on the measurement of the actual amount of metal taken by the plant is more robust than estimating it based on the evolution of soil metal concentration over time. In addition, our study reveals that using N. caerulescens may not be appropriate in Pb- or Cu-polluted soil, since this species mobilized these metals but did not extract them.

Investigating plant uptake of organic contaminants through transpiration stream concentration factor and neural network models

Uptake of seven organic contaminants including bisphenol A, estriol, 2,4-dinitrotoluene, N,N-diethyl-meta-toluamide (DEET), carbamazepine, acetaminophen, and lincomycin by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aestivum L.) was measured. The plants were grown in a growth chamber under recommended conditions and dosed by these chemicals for 19 days. The plant samples (stem transpiration stream) and solution in the exposure media were taken to measure transpiration stream concentration factor (TSCF). The plant samples were analyzed by a freeze-thaw centrifugation technique followed by high performance liquid chromatography-tandem mass spectrometry detection. Measured average TSCF values were used to test a neural network (NN) model previously developed for predicting plant uptake based on physicochemical properties. The results indicated that moderately hydrophobic compounds including carbamazepine and lincomycin have average TSCF values of 0.43 and 0.79, respectively. The average uptake of DEET, estriol, acetaminophen, and bisphenol A was also measured as 0.34, 0.29, 0.22, and 0.1, respectively. The 2,4-dinitrotoluene was not detected in the stem transpiration stream and it was shown to degrade in the root zone. Based on these results together with plant physiology measurements, we concluded that physicochemical properties of the chemicals did predict uptake, however, the role of other factors should be considered in the prediction of TSCF. While NN model could predict TSCF based on physicochemical properties with acceptable accuracies (mean squared error less than 0.25), the results for 2,4-dinitrotoluene and other compounds confirm the needs for considering other parameters related to both chemicals (stability) and plant species (role of lipids, lignin, and cellulose).

Spatiotemporal pattern models for bioaccumulation of pesticides in herbivores: An approximation theory for North American white-tailed deer

Dietary exposure is a major cause of pesticide bioaccumulation in herbivores. However, various types of natural conditions affect the structure of the complicated herbivores' diets, making it difficult to assess their exposure to pesticides. In this study, to evaluate the role of pesticides in the terrestrial food web, a dynamic hybrid dietary model was developed for North American white-tailed deer (or whitetails), which integrates different plant types and the digestibility of deer's foods. Moreover, an equivalent season approach was introduced to generalize the pesticide intake rate geographically. The results indicate that the soil-to-whitetail (meat) bioaccumulation factor (BAF) values in summer are significantly higher than those of other seasonal periods, owing to the high food availability and digestibility. Pesticides with low octanol/water partition coefficients have a high computed soil-to-plant BAF, but a low plant-to-whitetail (meat) BAF, because the transpiration process dominates the bioaccumulation process in plants. Lipid absorption plays a more important role in herbivores and lowers the biomagnification ratio (a smaller amount of pesticides flows to the next level of the food chain). According to the equivalent season approach, geographic locations with warmer climates facilitate pesticide bioaccumulation at a higher level of the terrestrial food web.

Uptake, translocation and metabolism of di-n-butyl phthalate in alfalfa (Medicago sativa)

Uptake and metabolism by plants are important biotransformation processes of organic pollutants in ecosystems. However, very limited information is currently available on the metabolism of phthalic acid esters (PAEs) in plants. In this study, alfalfa, highly efficient in phytoremediation of PAE contaminated soil, was chosen as the model to understand the fate of di-n-butyl phthalate (DnBP) in remediation plant. The results of hydroponic experiments indicated that DnBP accumulated mainly in alfalfa roots and adsorption to root epidermis might be the primary uptake mechanism. A large proportion of DnBP was subjected to apparent metabolism. De-esterification could be specified to be the predominant metabolism pathway. Mono-n-butyl phthalate (MnBP) and phthalic acid (PA) were detected as DnBP metabolites in all alfalfa roots and shoots throughout the entire exposure period. Around >90% of MnBP were distributed in cell soluble components and organelles, and MnBP gradually transferred from organelles and cell walls to soluble components as the exposure time extended. Similar to MnBP, PA located mainly in soluble components and organelles as well, while no PA existed in alfalfa cell walls. Exposure to DnBP ultimately resulted in the coexistence of DnBP and MnBP for a long term in interior plants, raising concerns on their combined potential toxicity on plant health or even ecosystem.

The influence of light combination on the physicochemical characteristics and enzymatic activity of soil with multi-metal pollution in phytoremediation

Light irradiation with suitable quality and intensity could influence the success of phytoremediation by improving the biomass yield of plants. However, mechanisms involved in this influence on the contaminant accumulation and translocation ability of plants have rarely been studied. Five light combinations with different red (R) and blue (B) ratios (0, 10, 50, 75 and 100 % blue) at the same intensity (220 μmol m^-2 s^-1) were used to assist phytoremediation using Noccaea caerulescens, and the change in physicochemical characteristics and enzymatic activities of soils after phytoremediation were evaluated. Compared with the control, the light combinations and monochromic blue light significantly increased the activities of soil ureases, invertases, and phosphatases, whereas monochromic red light strongly inhibited the activities of these enzymes, because different light irradiations altered the formation and excretion of carbohydrates from plants for soil microorganism consumption. Plants under B₅₀R₅₀ treatment accumulated the highest concentrations of metals, but their chlorophyll concentrations and lipid peroxidation were similar to those other species with lower metal concentrations. Hence, light with a proper blue/red ratio can simultaneously improve the physicochemical characteristics and enzymatic activities of soils, increase the metal uptake capacity and oxidation resistance of plants, and reduce the leaching risk during phytoremediation processes.

Modeling and optimizing the removal of cadmium by Sinapis alba L. from contaminated soil via Response Surface Methodology and Artificial Neural Networks during assisted phytoremediation with sewage sludge

The study was aimed to model and optimize the removal of cadmium from contaminated post-industrial soil via Sinapis alba L. by comparing two modeling approaches: Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). The experimental design was done using the Box-Behnken Design method. In the RSM model, the quadratic model was shown to predict the closest results in comparison to our experimental data. For ANN approach, a two-layer Feed-Forward Back-Propagation Neural Network model was designed. The results showed that sewage sludge supplementation increased the efficiency of the Sinapis alba plant in removing Cd from the soil. After 28 days of exposure, the removal rate varied from 10.96% without any supplementation to 65.9% after supplementation with the highest possible (law allowed) dose of sewage sludge. The comparison proved that the prediction capability of the ANN model was much higher than that of the RSM model (adjusted R-square: 0.98, standard error of the Cd prediction removal: 0.85 ± 0.02). Thus, the ANN model could be used for the prediction of heavy metal removal during assisted phytoremediation with sewage sludge. Moreover, such approach could also be used to determinate the dose of sewage sludge that will ensure highest process efficiency.

Lipophilicity matters - A new look at experimental plant uptake data from literature

Peer-reviewed Transpiration Stream Concentration Factor (TSCF) values were analysed to elucidate whether pH-induced changes in lipophilicity can explain some of the variability in reported TSCF and whether a potential relationship between lipophilicity and TSCF can be described by a simple mathematical model. The data set for this investigation combined TSCF values of 42 non-ionisable and ionisable compounds from hydroponic tests with intact plants and publicly available lipophilicity data for the tested compounds. The data set was not homogenous in terms of molecular weight of the tested compounds, plant species used for testing and experimental conditions, but a strong effect of one of these factors on variation in reported TSCF was not detected. Variation in TSCF was high for the same or similar predicted octanol/water partitioning coefficient (log P) but could be reduced by considering octanol/water distribution coefficients (log D) instead. The TSCF data set was split into a training and a test data set in order to identify and test a best-fit model describing the relationship between log D and TSCF. Comparing different types of models (linear, sigmoidal, Gaussian), the Gaussian model fitted to the training data set after removal of two outliers was identified as best-fit model based on visual assessment and fit statistics (RMSE = 0.20, NSE = 0.57, R = 0.75 (p < 0.001)). The 95% confidence interval around the best-fit model contained about 70% of data points in the training set and the test set, respectively. In conclusion, compound lipophilicity expressed as log D is a more appropriate descriptor of uptake by plant roots and subsequent translocation than log P when ionisable compounds are considered. Furthermore, findings in this study suggest that a relationship exists between log D and TSCF for uptake tests with intact plants which can be described by a simple bell-shaped Gaussian model.

Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log K_ow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.

Pharmaceutical and Personal Care Products: From Wastewater Treatment into Agro-Food Systems

Irrigation with treated wastewater (TWW) and application of biosolids introduce numerous pharmaceutical and personal care products (PPCPs) into agro-food systems. While the use of TWW and biosolids has many societal benefits, introduction of PPCPs in production agriculture poses potential food safety and human health risks. A comprehensive risk assessment and management scheme of PPCPs in agro-food systems is limited by multiple factors, not least the sheer number of investigated compounds and their diverse structures. Here we follow the fate of PPCPs in the water-soil-produce continuum by considering processes and variables that influence PPCP transfer and accumulation. By analyzing the steps in the soil-plant-human diet nexus, we propose a tiered framework as a path forward to prioritize PPCPs that could have a high potential for plant accumulation and thus pose greatest risk. This article examines research progress to date and current research challenges, highlighting the potential value of leveraging existing knowledge from decades of research on other chemicals such as pesticides. A process-driven scheme is outlined to derive a short list that may be used to refocus our future research efforts on PPCPs and other analogous emerging contaminants in agro-food systems.