Advancing Simultaneous Extraction and Sequential Single-Particle ICP-MS Analysis for Metallic Nanoparticle Mixtures in Plant Tissues.

Engineered nanoparticles (ENPs) have been increasingly used in agricultural operations, leading to an urgent need for robust methods to analyze co-occurring ENPs in plant tissues. In response, this study advanced the simultaneous extraction of coexisting silver, cerium oxide, and copper oxide ENPs in lettuce shoots and roots using macerozyme R-10 and analyzed them by single-particle inductively coupled plasma-mass spectrometry (ICP-MS). Additionally, the standard stock suspensions of the ENPs were stabilized with citrate, and the long-term stability (up to 5 months) was examined for the first time. The method performance results displayed satisfactory accuracies and precisions and achieved low particle concentration and particle size detection limits. Significantly, the oven drying process was proved not to impact the properties of the ENPs; therefore, oven-dried lettuce tissues were used in this study, which markedly expanded the applicability of this method. This robust methodology provides a timely approach to characterize and quantify multiple coexisting ENPs in plants.

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.

The uptake of two organic compounds (RDX and exogenous caffeine) was examined in three crop plants (corn, wheat, and tomato). There have not been any uptake studies on exogenous caffeine and also we do not have good data for the uptake of RDX by these three crop plants. The estimated sap concentration factor from these experiments fills the gap in the data for developing predictive models for uptake of emerging contaminants. A novel rapid freeze­thaw/centrifugation extraction method followed by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was used to analyze the samples.

Phytomanagement of Pb/Zn/Cu tailings using biosolids-biochar or -humus combinations: Enhancement of bioenergy crop production, substrate functionality, and ecosystem services.

The extreme characteristics of mine tailings generally prohibit microbial processes and natural plant growth. Consequently, vast and numerous tailings sites remain barren for decades and highly susceptible to windblown dust and water erosion. Amendment-assisted phytostabilization is a cost-effective and ecologically productive approach to mitigate the potential transport of residual metals. Due to the contrasting and complementary characteristics of biosolids (BS) and biochar (BC), co-application might be more efficient than individually applied. Studies considering BS and BC co-application for multi-metal tailings revegetation are scarce. As tailings revegetation is a multidimensional issue, clearly notable demand exists for a study that provides a comprehensive understanding on the co-application impact on interrelated properties of physicochemical, biological, mineral nitrogen availability, metal immobilization, water-soil interactions, and impacts on plant cultivation and biomass production. This 8-month greenhouse study aimed at investigating the efficacy of co-application strategies targeting BS and carbon-rich amendments (BC or humic substances (HS)) to phytomanage a slightly alkaline Pb/Zn/Cu tailings with bioenergy crops (poplar, willow, and miscanthus). A complementary assessment linking revegetation effectiveness to ecosystem services (ES) provision was also included. Owing to their rich nutrient and organic matter contents, BS had the most pronounced influence on most of the measured properties including physicochemical, enzyme activities, NH4+-N and NO3--N availability, immobilization of Zn, Cu, and Cd, and biomass production. Co-applying with BC exhibited efficient nutrient release and was more effective than BS alone in reducing metal bioavailability and uptake particularly Pb. Poplar and willow exhibited more superior phytostabilization efficiency compared to miscanthus which caused acidification-induced metal mobilization, yet BC and BS co-application was effective in ameliorating this effect. Enhancement of ES and substrate quality index mirrored the positive effect of amendment co-application and plant cultivation. Co-applying HS with BS resulted in improved nutrient cycling while BC enhanced water purification and contamination control services.

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.

High throughput screening of native species for tailings eco-restoration using novel computer visualization for plant phenotyping.

Historical hard-rock mine activities have resulted in nearly half a million mining-impacted sites scattered around the US. Compared to conventional remediation, (aided) phytostabilization is generally cost-effective and ecologically productive approach, particularly for large-scale sites. Native species act to maintain higher local biodiversity, providing a foundation for natural ecological succession. Due to heterogeneity of mine waste, revegetation strategies are inconsistent in approach, and to avoid failure scenarios, greenhouse screening studies can identify candidate plants and amendment strategies before scaling up. This greenhouse study aimed to concurrently screen a variety of native species for their potential to revegetate Cu/Pb/Zn mine tailings and develop a high throughput and non-destructive approach utilizing computer vision and image-based phenotyping technologies to quantify plant responses. A total number of 34 species were screened in this study, which included: 5 trees, 8 grasses, and 21 forbs and legumes. Most of the species tested were Missouri native and prairie species. Plants were non-destructively imaged, and 15 shape and color phenotypic attributes were extracted utilizing computer vision techniques of PlantCV. Compared to reference soil, all species tested were negatively impacted by the tailings' characteristics, with lowest tolerance generally observed in tree species. However, significant improvement in plant growth and tolerance generally observed with biosolids addition with biomass surpassing reference soil for most legumes. Accumulation of Cu, Pb, and Zn was below Domestic Animal Toxicity Limits in most species. Statistically robust differences in species responses were observed using phenotypic data, such as area, height, width, color, and 9 other morphological attributes. Correlations with destructive data indicated that area displayed the greatest positive correlation with biomass and color the greatest negative correlation with shoot metals. Computer visualization greatly increased the phenotypic data and offers a breakthrough in rapid, high throughput data collection to project site-specific phytostabilization strategies to efficiently restore mine-impacted sites.

Green Analysis: Rapid-Throughput Analysis of Volatile Contaminants in Plants by Freeze-Thaw-Equilibration Sample Preparation and SPME-GC-MS Analysis.

Emerging and fugitive contaminants (EFCs) can be introduced into the food chain through plants, particularly crop plants, and have threatened food safety and human health. The method for determination of volatile EFCs in plant tissues remains challenging. A new rapid, simple, precise, and accurate freeze-thaw-equilibration followed by head space (HS)-solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS) analytical method was developed in this study for high-throughput analysis of 1,4-dioxane and 1,2,3-trichloropropane (TCP) in tissues of three representative crop plants, corn, wheat, and tomato. The samples were treated by a freeze-thaw procedure, then equilibrated in a saturated sodium sulfate solution, and analyzed by HS-SPME-GC-MS method. Method detection limits ranged from 0.6 to 16 ng/g. The calibration showed good linearity (R2 > 0.9). Recoveries of spiked analytes in the three plant species ranged from 82.69 to 106.3%. The ability of plant uptake of the compounds from soil has been investigated. As demonstrated in this study, this method is used to measure the concentrations of volatile contaminants in the stems of crop plants. This method should also be applicable for other plant tissues and therefore will contribute significantly to the sight of EFC transport in plants and to assess the potential risks EFCs pose to food safety and human health.

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.

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).

Filtration performances of non-medical materials as candidates for manufacturing facemasks and respirators.

The recent outbreak of the coronavirus disease (COVID-19) is causing a shortage of personal protective equipment (PPE) in different countries around the world. Because the coronavirus can transmit through droplets and aerosols, facemasks and N95 respirators that require complex certification, are urgently needed. Given the situation, the U.S. Centers for Disease Control and Prevention (CDC) recommends that "in settings where facemasks are not available, healthcare personnel might use homemade masks (e.g., bandana, scarf) for the care of patients with COVID-19 as a last resort." Although aerosols and droplets can be removed through the fibers of fabrics through a series of filtration mechanisms, their filtration performances have not been evaluated in detail. Moreover, there are a series of non-medical materials available on the market, such as household air filters, coffee filters, and different types of fabrics, which may be useful when facemasks and respirators are not available. In this study, we comprehensively evaluated the overall and size-dependent filtration performances of non-medical materials. The experiments were conducted under different face velocities to study its influence on size-dependent filtration performances. The flow resistance across these filter materials is measured as an indicator of the breathability of the materials. The results illustrate that multiple layers of household air filters are able to achieve similar filtration efficiencies compared to the N95 material without causing a significant increase in flow resistance. Considering that these air filters may shed micrometer fibers during the cutting and folding processes, it is recommended that these filters should be inserted in multiple layers of fabrics when manufacturing facemasks or respirators.

Quantification of toluene phytoextraction rates and microbial biodegradation functional profiles at a fractured bedrock phytoremediation site.

This field study evaluated the efficacy of a mature hybrid poplar phytoremediation system for the remediation of toluene in a fractured bedrock aquifer site. Phytoextraction activity of the trees and the ecology and biodegradation potential of root-colonizing bacteria that ultimately influence how much toluene is transported from the roots and phytoextracted to the aboveground point of measurement were explored. Peak-season toluene mass removal rates ranging from 313 to 743 µg/day were quantified using passive in planta contaminant sampling techniques and continuous heat dissipation transpiration measurements in tree stems. Root bacterial microbiome structure and biodegradation potential were evaluated via high-throughput sequencing and predictive metagenomic functional modelling of bacterial 16S rRNA genes in roots. Poplar roots were colonized mostly by Proteobacteria, Actinobacteria, and Bacteroidetes. Distinct, more uniform communities were observed in roots associated with trees planted in the toluene source area compared to other areas, with differences apparent at lower taxonomic levels. Significant enrichment of Streptomyces in roots was observed in the source area, implicating that genus as a potentially important poplar endophyte at toluene-impacted sites. Moreover, significantly greater aerobic toluene biodegradation capacity was predicted in these roots compared to other areas using taxonomic functional modelling. Together with passive sampling, the molecular results provided supporting evidence of biodegradation activity in the source area and contextualized the detected phytoextraction patterns. These results support the application of phytoremediation systems for aromatic hydrocarbons in environments with complex geology and demonstrate field-validated monitoring techniques to assess phytoextraction and biodegradation in these systems.

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 Kow, 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.

Green Analysis: High Throughput Analysis of Emerging Pollutants in Plant Sap by Freeze-Thaw-Centrifugal Membrane Filtration Sample Preparation-HPLC-MS/MS Analysis.

Emerging and fugitive contaminants (EFCs) released to our biosphere have caused a legacy and continuing threat to human and ecological health, contaminating air, water, and soil. Polluted media are closely linked to food security through plants, especially agricultural crops. However, measuring EFCs in plant tissues remains difficult, and high-throughput screening is a greater challenge. A novel rapid freeze-thaw/centrifugation extraction followed by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis was developed for high-throughput quantification of 11 EFCs with diverse chemical properties, including estriol, codeine, oxazepam, 2,4-dinitrotoluene, 1,3,5-trinitroperhydro-1,3,5-triazine, bisphenol A, triclosan, caffeine, carbamazepine, lincomycin, and DEET, in three representative crops, corn, tomato, and wheat. The internal aqueous solution, i.e., sap, is liberated via a freeze/thaw cycle, and separated from macromolecules utilizing molecular weight cutoff membrane centrifugal filtration. Detection limits ranged from 0.01 µg L-1 to 2.0 µg L-1. Recoveries of spiked analytes in three species ranged from 83.7% to 109%. Developed methods can rapidly screen EFCs in agriculture crops and can assess pollutant distribution at contaminated sites and gain insight on EFCs transport in plants to assess transmembrane migration in vascular organisms. The findings contribute significantly to environmental research, food security, and human health, as it assesses the first step of potential entry into the food chain, that being transmembrane migration and plant uptake, the primary barrier between polluted waters or soils and our food.

Amendment-assisted revegetation of mine tailings: improvement of tailings quality and biomass production.

Mining activities have left a legacy of metals containing tailings impoundments. After mine closure, reclamation of mine wastes can be achieved by restoration of a vegetation cover. This study investigated the impact of biochar (BC), biosolids (BS), humic substances (HS), and mycorrhizal fungi (MF) for improving mine tailings fertility and hydraulic properties, supporting plant establishment, tailings revegetation, and enabling growth of energy crops. We conducted a pot trial by growing willow, poplar, and miscanthus in Pb/Zn/Cu mine tailings untreated or amended with two rates of amendments (low or high input). Biosolids resulted in the most significant changes in tailings properties, neutralizing pH and increasing organic carbon, nutrient concentrations, cation exchange capacity, water retention, and saturated hydraulic conductivity. The greatest increase in energy crops production was also observed in BS treatments enabling the financial viability of mine reclamation. Although BC resulted in significant improvements in tailings fertility and hydraulic properties, its impact on biomass was less pronounced, most likely due to lower N and P available concentrations. Increases in willow and miscanthus biomass were observed in HS and MF treatments in spite of their lower nutrient content. A pot experiment is underway to assess synergistic effects of combining BS with BC, HS, or MF.

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

Uptake of contaminants from the groundwater is one pathway of interest, and efforts have been made to relate root exposure to transloation throughout the plant, termed the transpiration stream concentration factor (TSCF). This work utilized machine learning techniques and statistcal analysis to improve the understanding of plant uptake and translocation of emerging contaminants. Neural network (NN) was used to develop a reliable model for predicting TSCF using physicochemical properties of compounds. Fuzzy logic was as a technique to examine the simultaneous impact of properties on TSCF, and interactions between compound properties. The significant and effective compound properties were determined using stepwise and forward regression as two widely used statiscal techniques. Clustering was used for detecting the hidden structures in the plant uptake data set. The NN predicted the TSCF with improved accuracy compared to mechanistic models. We also delivered new insight to compound properteis and their importance in transmembrane migration. The sensitivity analysis indicated that log Kow, molecular weight, hydrogen bond donor, and rotatable bonds are the most important properties. The results of fuzzy logic demonstrated that the relationship between molecular weight and log Kow with TSCF are both bell-shape and sigmoidal. The employed clustering algorithms all discovered two major distinct clusters in the data set.

Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants.

Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeO2NPs) are now used in a plethora of industrial products, while cadmium (Cd) is a great environmental concern because of its toxicity to animals and humans. Up to now, the interactions between heavy metals, nanoparticles and plants have not been extensively studied. The main objectives of this study were (i) to determine the synergistic effects of Cd and CeO2NPs on the physiological parameters of Brassica and their accumulation in plant tissues and (ii) to explore the underlying physiological/phenotypical effects that drive these specific changes in plant accumulation using Artificial Neural Network (ANN) as an alternative methodology to modeling and simulating plant uptake of Ce and Cd. The combinations of three cadmium levels (0 [control] and 0.25 and 1 mg/kg of dry soil) and two CeO2NPs concentrations (0 [control] and 500 mg/kg of dry soil) were investigated. The results showed high interactions of co-existing CeO2NPs and Cd on plant uptake of these metal elements and their interactive effects on plant physiology. ANN also identified key physiological factors affecting plant uptake of co-occurring Cd and CeO2NPs. Specifically, the results showed that root fresh weight and the net photosynthesis rate are parameters governing Ce uptake in plant leaves and roots while root fresh weight and Fv/Fm ratio are parameters affecting Cd uptake in leaves and roots. Overall, ANN is a capable approach to model plant uptake of co-occurring CeO2NPs and Cd.

Phytoremediation removal rates of benzene, toluene, and chlorobenzene.

Phytoremediation is a sustainable remedial approach, although performance efficacy is rarely reported. In this study, we assessed a phytoremediation plot treating benzene, toluene, and chlorobenzene. A comparison of the calculated phytoremediation removal rate with estimates of onsite contaminant mass was used to forecast cleanup periods. The investigation demonstrated that substantial microbial degradation was occurring in the subsurface. Estimates of transpiration indicated that the trees planted were removing approximately 240,000 L of water per year. This large quantity of water removal implies substantial removal of contaminant due to large amounts of contaminants in the groundwater; however, these contaminants extensively sorb to the soil, resulting in large quantities of contaminant mass in the subsurface. The total estimate of subsurface contaminant mass was also complicated by the presence of non-aqueous phase liquids (NAPL), additional contaminant masses that were difficult to quantify. These uncertainties of initial contaminant mass at the site result in large uncertainty in the cleanup period, although mean estimates are on the order of decades. Collectively, the model indicates contaminant removal rates on the order of 10-2-100 kg/tree/year. The benefit of the phytoremediation system is relatively sustainable cleanup over the long periods necessary due to the presence of NAPL.

Phytoforensics: Trees as bioindicators of potential indoor exposure via vapor intrusion.

Human exposure to volatile organic compounds (VOCs) via vapor intrusion (VI) is an emerging public health concern with notable detrimental impacts on public health. Phytoforensics, plant sampling to semi-quantitatively delineate subsurface contamination, provides a potential non-invasive screening approach to detect VI potential, and plant sampling is effective and also time- and cost-efficient. Existing VI assessment methods are time- and resource-intensive, invasive, and require access into residential and commercial buildings to drill holes through basement slabs to install sampling ports or require substantial equipment to install groundwater or soil vapor sampling outside the home. Tree-core samples collected in 2 days at the PCE Southeast Contamination Site in York, Nebraska were analyzed for tetrachloroethene (PCE) and results demonstrated positive correlations with groundwater, soil, soil-gas, sub-slab, and indoor-air samples collected over a 2-year period. Because tree-core samples were not collocated with other samples, interpolated surfaces of PCE concentrations were estimated so that comparisons could be made between pairs of data. Results indicate moderate to high correlation with average indoor-air and sub-slab PCE concentrations over long periods of time (months to years) to an interpolated tree-core PCE concentration surface, with Spearman's correlation coefficients (ρ) ranging from 0.31 to 0.53 that are comparable to the pairwise correlation between sub-slab and indoor-air PCE concentrations (ρ = 0.55, n = 89). Strong correlations between soil-gas, sub-slab, and indoor-air PCE concentrations and an interpolated tree-core PCE concentration surface indicate that trees are valid indicators of potential VI and human exposure to subsurface environment pollutants. The rapid and non-invasive nature of tree sampling are notable advantages: even with less than 60 trees in the vicinity of the source area, roughly 12 hours of tree-core sampling with minimal equipment at the PCE Southeast Contamination Site was sufficient to delineate vapor intrusion potential in the study area and offered comparable delineation to traditional sub-slab sampling performed at 140 properties over a period of approximately 2 years.

Contaminant Gradients in Trees: Directional Tree Coring Reveals Boundaries of Soil and Soil-Gas Contamination with Potential Applications in Vapor Intrusion Assessment.

Contaminated sites pose ecological and human-health risks through exposure to contaminated soil and groundwater. Whereas we can readily locate, monitor, and track contaminants in groundwater, it is harder to perform these tasks in the vadose zone. In this study, tree-core samples were collected at a Superfund site to determine if the sample-collection location around a particular tree could reveal the subsurface location, or direction, of soil and soil-gas contaminant plumes. Contaminant-centroid vectors were calculated from tree-core data to reveal contaminant distributions in directional tree samples at a higher resolution, and vectors were correlated with soil-gas characterization collected using conventional methods. Results clearly demonstrated that directional tree coring around tree trunks can indicate gradients in soil and soil-gas contaminant plumes, and the strength of the correlations were directly proportionate to the magnitude of tree-core concentration gradients (spearman's coefficient of -0.61 and -0.55 in soil and tree-core gradients, respectively). Linear regression indicates agreement between the concentration-centroid vectors is significantly affected by in planta and soil concentration gradients and when concentration centroids in soil are closer to trees. Given the existing link between soil-gas and vapor intrusion, this study also indicates that directional tree coring might be applicable in vapor intrusion assessment.

Tree Sampling as a Method to Assess Vapor Intrusion Potential at a Site Characterized by VOC-Contaminated Groundwater and Soil.

Vapor intrusion (VI) by volatile organic compounds (VOCs) in the built environment presents a threat to human health. Traditional VI assessments are often time-, cost-, and labor-intensive; whereas traditional subsurface methods sample a relatively small volume in the subsurface and are difficult to collect within and near structures. Trees could provide a similar subsurface sample where roots act as the "sampler' and are already onsite. Regression models were developed to assess the relation between PCE concentrations in over 500 tree-core samples with PCE concentrations in over 50 groundwater and 1000 soil samples collected from a tetrachloroethylene- (PCE-) contaminated Superfund site and analyzed using gas chromatography. Results indicate that in planta concentrations are significantly and positively related to PCE concentrations in groundwater samples collected at depths less than 20 m (adjusted R2 values greater than 0.80) and in soil samples (adjusted R2 values greater than 0.90). Results indicate that a 30 cm diameter tree characterizes soil concentrations at depths less than 6 m over an area of 700-1600 m2, the volume of a typical basement. These findings indicate that tree sampling may be an appropriate method to detect contamination at shallow depths at sites with VI.

Enhanced Degradation of TCE on a Superfund Site Using Endophyte-Assisted Poplar Tree Phytoremediation.

Trichloroethylene (TCE) is a widespread environmental pollutant common in groundwater plumes associated with industrial manufacturing areas. We had previously isolated and characterized a natural bacterial endophyte, Enterobacter sp. strain PDN3, of poplar trees, that rapidly metabolizes TCE, releasing chloride ion. We now report findings from a successful three-year field trial of endophyte-assisted phytoremediation on the Middlefield-Ellis-Whisman Superfund Study Area TCE plume in the Silicon Valley of California. The inoculated poplar trees exhibited increased growth and reduced TCE phytotoxic effects with a 32% increase in trunk diameter compared to mock-inoculated control poplar trees. The inoculated trees excreted 50% more chloride ion into the rhizosphere, indicative of increased TCE metabolism in planta. Data from tree core analysis of the tree tissues provided further supporting evidence of the enhanced rate of degradation of the chlorinated solvents in the inoculated trees. Test well groundwater analyses demonstrated a marked decrease in concentration of TCE and its derivatives from the tree-associated groundwater plume. The concentration of TCE decreased from 300 µg/L upstream of the planted area to less than 5 µg/L downstream of the planted area. TCE derivatives were similarly removed with cis-1,2-dichloroethene decreasing from 160 µg/L to less than 5 µg/L and trans-1,2-dichloroethene decreasing from 3.1 µg/L to less than 0.5 µg/L downstream of the planted trees. 1,1-dichloroethene and vinyl chloride both decreased from 6.8 and 0.77 µg/L, respectively, to below the reporting limit of 0.5 µg/L providing strong evidence of the ability of the endophytic inoculated trees to effectively remove TCE from affected groundwater. The combination of native pollutant-degrading endophytic bacteria and fast-growing poplar tree systems offers a readily deployable, cost-effective approach for the degradation of TCE, and may help mitigate potential transfer up the food chain, volatilization to the atmosphere, as well as direct phytotoxic impacts to plants used in this type of phytoremediation.