Characterization of Plant Accumulation of Pharmaceuticals from Soils with Their Concentration in Soil Pore Water.

Predicting plant uptake of pharmaceuticals from soils is very challenging because many pharmaceuticals are ionizable compounds, which experience highly variable sorption/desorption and transformation processes in soils. This study aimed to elucidate how the equilibrium between sorbed and dissolved phases influences radish uptake of 15 pharmaceuticals from three soils with different properties. After 30 days of uptake, the accumulation of acetaminophen, carbamazepine, lamotrigine, carbadox, trimethoprim, and triclosan in radish ranked as Riddles > Capac > Spinks soil. In contrast, radish accumulation of caffeine, lincomycin, monensin, tylosin, sulfadiazine, and sulfamethoxazole exhibited the opposite order of Riddles < Capac < Spinks soil. Oxytetracycline and estrone demonstrated similar accumulation in radish grown in the three soils. Accumulation of pharmaceuticals in radish demonstrated no apparent relation with their concentration in soils. However, we identified strong positive correlation between pharmaceutical accumulation in radish and their corresponding concentration in soil pore water. These results reveal that pharmaceutical in soil pore water is the dominant fraction bioavailable to plant uptake. Relatively constant root concentration factors (RCFs) on the basis of pharmaceutical concentration in soil pore water, compared to the highly variable RCFs derived from soils, suggest that pore water-based RCF is superior for describing pharmaceutical accumulation in plants grown in soils. We recommend that pharmaceuticals in soil pore water should be evaluated and included in modeling their uptake by plants.

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.

Metabolomic Approaches to Studying the Response to Drought Stress in Corn (Zea mays) Cobs.

Metabolomics is a technique that allows for the evaluation of the entire extractable chemical profile of a plant, for example, using high-resolution mass spectrometry (HRMS) and can be used to evaluate plant stress responses, such as those due to drought. Metabolomic analysis is dependent upon the efficiency of the extraction protocol. Currently, there are two common extraction procedures widely used in metabolomic experiments, those that extract from plant tissue processed in liquid nitrogen or extraction from lyophilised plant tissues. Here, we evaluated the two using non-targeted metabolomics to show that lyophilisation can stabilise the maize (Zea mays) extractable metabolome, increasing throughput and efficiency of extraction as compared to the more traditional processing in liquid nitrogen. Then, we applied the lyophilisation approach to explore the effect of drought upon the maize metabolome in a non-targeted HRMS metabolomics approach. Metabolomics revealed differences in the mature maize metabolome having undergone three drought conditions imposed at two critical development stages (three-leaf stage and grain-fill stage); moreover, this difference was observed across two tissue types (kernel and inner cob/pith). It was shown that under ideal conditions, the biochemical make-up of the tissue types is different. However, under stress conditions, the stress response dominates the metabolic profile. Drought-related metabolites known from other plant systems have been identified and metabolomics has revealed potential novel drought-stress indicators in our maize system.

An Automated Methodology for Non-targeted Compositional Analysis of Small Molecules in High Complexity Environmental Matrices Using Coupled Ultra Performance Liquid Chromatography Orbitrap Mass Spectrometry.

The life-critical matrices of air and water are among the most complex chemical mixtures that are ever encountered. Ultrahigh-resolution mass spectrometers, such as the Orbitrap, provide unprecedented analytical capabilities to probe the molecular composition of such matrices, but the extraction of non-targeted chemical information is impractical to perform via manual data processing. Automated non-targeted tools rapidly extract the chemical information of all detected compounds within a sample dataset. However, these methods have not been exploited in the environmental sciences. Here, we provide an automated and (for the first time) rigorously tested methodology for the non-targeted compositional analysis of environmental matrices using coupled liquid chromatography-mass spectrometric data. First, the robustness and reproducibility was tested using authentic standards, evaluating performance as a function of concentration, ionization potential, and sample complexity. The method was then used for the compositional analysis of particulate matter and surface waters collected from worldwide locations. The method detected >9600 compounds in the individual environmental samples, arising from critical pollutant sources, including carcinogenic industrial chemicals, pesticides, and pharmaceuticals among others. This methodology offers considerable advances in the environmental sciences, providing a more complete assessment of sample compositions while significantly increasing throughput.

Detection, occurrence, and fate of emerging contaminants in agricultural environments (2019).

A review of 82 papers published in 2018 is presented. The topics ranged from detailed descriptions of analytical methods, to fate and occurrence studies, to ecological effects and sampling techniques for a wide variety of emerging contaminants likely to occur in agricultural environments. New methods and studies on veterinary pharmaceuticals, microplastics, and engineered nanomaterials in agricultural environments continue to expand our knowledge base on the occurrence and potential impacts of these compounds. This review is divided into the following sections: Introduction, Analytical Methods, Fate and Occurrence, Pharmaceutical Metabolites, Anthelmintics, Microplastics, and Engineered Nanomaterials. PRACTITIONER POINTS: New research describes innovative new techniques for emerging contaminant detection in agricultural settings. Newer classes of contaminants include human and veterinary pharmaceuticals. Research in microplastics and nanomaterials shows that these also occur in agricultural environments and will likely be topics of future work.