Nontarget Chemical Composition of Surface Waters May Reflect Ecosystem Processes More than Discrete Source Contributions.

We investigated environmental, landscape, and microbial factors that could structure the spatiotemporal variability in the nontarget chemical composition of four riverine systems in the Oregon Coast Range, USA. We hypothesized that the nontarget chemical composition in river water would be structured by broad-scale landscape gradients in each watershed. Instead, only a weak relationship existed between the nontarget chemical composition and land cover gradients. Overall, the effects of microbial communities and environmental variables on chemical composition were nearly twice as large as those of the landscape, and much of the influence of environmental variables on the chemical composition was mediated through the microbial community (i.e., environment affects microbes, which affect chemicals). Therefore, we found little evidence to support our hypothesis that chemical spatiotemporal variability was related to broad-scale landscape gradients. Instead, we found qualitative and quantitative evidence to suggest that chemical spatiotemporal variability of these rivers is controlled by changes in microbial and seasonal hydrologic processes. While the contributions of discrete chemical sources are undeniable, water chemistry is undoubtedly impacted by broad-scale continuous sources. Our results suggest that diagnostic chemical signatures can be developed to monitor ecosystem processes, which are otherwise challenging or impossible to study with existing off-the-shelf sensors.

Target and Suspect Screening Integrated with Machine Learning to Discover Per- and Polyfluoroalkyl Substance Source Fingerprints.

This study elucidates per- and polyfluoroalkyl substance (PFAS) fingerprints for specific PFAS source types. Ninety-two samples were collected from aqueous film-forming foam impacted groundwater (AFFF-GW), landfill leachate, biosolids leachate, municipal wastewater treatment plant effluent (WWTP), and wastewater effluent from the pulp and paper and power generation industries. High-resolution mass spectrometry operated with electrospray ionization in negative mode was used to quantify up to 50 target PFASs and screen and semi-quantify up to 2,266 suspect PFASs in each sample. Machine learning classifiers were used to identify PFASs that were diagnostic of each source type. Four C5-C7 perfluoroalkyl acids and one suspect PFAS (trihydrogen-substituted fluoroethernonanoic acid) were diagnostic of AFFF-GW. Two target PFASs (5:3 and 6:2 fluorotelomer carboxylic acids) and two suspect PFASs (4:2 fluorotelomer-thia-acetic acid and N-methylperfluoropropane sulfonamido acetic acid) were diagnostic of landfill leachate. Biosolids leachates were best classified along with landfill leachates and N-methyl and N-ethyl perfluorooctane sulfonamido acetic acid assisted in that classification. WWTP, pulp and paper, and power generation samples contained few target PFASs, but fipronil (a fluorinated insecticide) was diagnostic of WWTP samples. Our results provide PFAS fingerprints for known sources and identify target and suspect PFASs that can be used for source allocation.

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data.

A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical features─colloquially termed a chemical fingerprint─that can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92-100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.

Environmental Source Tracking of Per- and Polyfluoroalkyl Substances within a Forensic Context: Current and Future Techniques.

The source tracking of per- and polyfluoroalkyl substances (PFASs) is a new and increasingly necessary subfield within environmental forensics. We define PFAS source tracking as the accurate characterization and differentiation of multiple sources contributing to PFAS contamination in the environment. PFAS source tracking should employ analytical measurements, multivariate analyses, and an understanding of PFAS fate and transport within the framework of a conceptual site model. Converging lines of evidence used to differentiate PFAS sources include: identification of PFASs strongly associated with unique sources; the ratios of PFAS homologues, classes, and isomers at a contaminated site; and a site's hydrogeochemical conditions. As the field of PFAS source tracking progresses, the development of new PFAS analytical standards and the wider availability of high-resolution mass spectral data will enhance currently available analytical capabilities. In addition, multivariate computational tools, including unsupervised (i.e., exploratory) and supervised (i.e., predictive) machine learning techniques, may lead to novel insights that define a targeted list of PFASs that will be useful for environmental PFAS source tracking. In this Perspective, we identify the current tools available and principal developments necessary to enable greater confidence in environmental source tracking to identify and apportion PFAS sources.

Selenium deficiency risk predicted to increase under future climate change.

Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

Marine Primary Productivity as a Potential Indirect Source of Selenium and Other Trace Elements in Atmospheric Deposition.

Atmospheric processes play an important role in the supply of the trace element selenium (Se) as well as other essential trace elements to terrestrial environments, mainly via wet deposition. Here we investigate whether the marine biosphere can be identified as a source of Se and of other trace elements in precipitation samples. We used artificial neural network (ANN) modeling and other statistical methods to analyze relationships between a high-resolution atmospheric deposition chemistry time series (March 2007-January 2009) from Plynlimon (UK) and exposure of air masses to marine chlorophyll a and to other source proxies. Using ANN sensitivity analyses, we found that higher air mass exposure to marine productivity leads to higher concentrations of dissolved organic carbon (DOC) in rainfall. Furthermore, marine productivity was found to be an important but indirect factor in controlling Se as well as vanadium (V), cobalt (Co), nickel (Ni), zinc (Zn), and aluminum (Al) concentrations in atmospheric deposition, likely via scavenging by organic compounds derived from marine organisms. Marine organisms may thus play an indirect but important role in the delivery of trace elements to terrestrial environments and food chains.

Trenbolone acetate metabolite transport in rangelands and irrigated pasture: observations and conceptual approaches for agro-ecosystems.

To assess the relative ecological risks of trenbolone acetate (TBA) use in agro-ecosystems, we evaluated the spatiotemporal dynamics of TBA metabolite transport during irrigation and rainfall events. Within a pasture, TBA-implanted heifers (40 mg TBA, 8 mg estradiol) were briefly penned (24 h) at high stocking densities (500 animal units (AU)/ha), prior to irrigation. Irrigation runoff concentrations of 17α-trenbolone (17α-TBOH) 0.3 m downslope were 11 ng/L in the wetting front, but quickly decreased to ∼0.5 ng/L, suggesting mass transfer limitations to transport. At 3 and 30 m downslope, efficient attenuation of 17α-TBOH concentrations is best explained by infiltration and surface partitioning. At plot scales, transport through vegetated filter strips resulted in <0.5-7 ng/L 17α-TBOH concentrations in rainfall-induced runoff with partial subsequent attenuation. Thus, even under intense grazing scenarios, TBA-metabolite transport potential is expected to be low in rangelands, with ecological risks primarily arising from uncontrolled animal access to receiving waters. However, 17α-TBOH concentrations in initial runoff were predicted to exceed threshold levels (i.e., no observed adverse effect levels [NOAELs]) for manure concentrations exceeding 2.0 ng/g-dw, which occurs throughout most of the implant life. For comparison, estrone and 17ß-estradiol were modeled and are likely capable of exceeding NOAELs by a factor of ∼2-5 in irrigation runoff, suggesting that both endogenous and exogenous steroids contribute to endocrine disruption potential in agro-ecosystems.

Mass balance approaches to characterizing the leaching potential of trenbolone acetate metabolites in agro-ecosystems.

Several studies have documented the occurrence and fate of trenbolone acetate (TBA) metabolites in soil and water. However, considerable uncertainty still exists with respect to TBA risk in agro-ecosystems because limited data are available to quantify excretion, transformation, and leaching processes. To address these uncertainties, we used experimental mesocosms and a mass balance approach to estimate the TBA metabolite leaching potential from manure excreted by implanted (40 mg TBA, 8 mg 17ß-estradiol) beef cattle. Manure sample analysis indicates that over 113 days, a maximum of 9.3% (3,200 µg/animal unit [AU]) of the implant dose was excreted as 17α-trenbolone (17α-TBOH), and <1% was excreted as 17ß-trenbolone (65 µg/AU) or trendione (3 µg/AU). While most (>97%) of the total excreted mass of 17α-TBOH transforms to uncharacterized products, 0.3-0.6% (100-220 µg/AU) of the implant dose accumulates on land surfaces and is available for subsequent transport. During rainfall or irrigation events, a maximum of 0.005-0.06% (1.6-22 µg/AU 17α-TBOH) or 0.005-0.012% (1.8-4 µg/AU 17α-TBOH) of the dose leached into runoff, respectively. Leaching potentials peak at 5-30 days postimplantation, suggesting that targeted timing of implantation and irrigation could minimize steroid leaching during rainfall and irrigation events.

Genomic fingerprints of the world's soil ecosystems.

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes-more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.IMPORTANCEWe address a critical gap in our understanding of soil microorganisms and their functions, which have a profound impact on our environment. We analyzed 1,512 global soils with advanced analytics to create detailed genetic profiles (fingerprints) of soil microbiomes. Our work reveals novel patterns in how microorganisms are distributed across different soil environments. For instance, we discovered shifts in microbial potential to acquire nutrients in relation to soil acidity, as well as changes in stress responses and potential greenhouse gas emissions linked to soil structure. We also identified soils with putative high activity that had unique genomic characteristics surrounding resource acquisition, plant-microbe interactions, and fungal activity. Finally, we observed that closely related microorganisms tend to respond in similar ways to changes in their surroundings. Our work is a significant step toward comprehending the intricate world of soil microorganisms and its role in the global climate.

Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attributed to increases in aridity. Given its international importance in agriculture, reductions of essential elements, including S and Se, in European soils could have important impacts on nutrition and human health. Our objectives were to model current soil S and Se levels in Europe and predict concentration changes for the 21st century. We interrogated four machine-learning (ML) techniques, but after critical evaluation, only outputs for linear support vector regression (Lin-SVR) models for S and Se and the multilayer perceptron model (MLP) for Se were consistent with known mechanisms reported in literature. Other models exhibited overfitting even when differences in training and testing performance were low or non-existent. Furthermore, our results highlight that similarly performing models based on RMSE or R2 can lead to drastically different predictions and conclusions, thus highlighting the need to interrogate machine learning models and to ensure they are consistent with known mechanisms reported in the literature. Both elements exhibited similar spatial patterns with predicted gains in Scandinavia versus losses in the central and Mediterranean regions of Europe, respectively, by the end of the 21st century for an extreme climate scenario. The median change was -5.5% for S (Lin-SVR) and -3.5% (MLP) and -4.0% (Lin-SVR) for Se. For both elements, modeled losses were driven by decreases in soil organic carbon, S and Se atmospheric deposition, and gains were driven by increases in evapotranspiration.

Resolved genomes of wastewater ESBL-producing Escherichia coli and metagenomic analysis of source wastewater samples.

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli pose a serious threat to human health because of their resistance to the most commonly prescribed antibiotics: penicillins and cephalosporins. In this study, we provide a genomic and metagenomic context for the determinant beta-lactam resistance genes of ESBL-positive E. coli isolated from various wastewater treatment utilities in Oregon, USA. Class A beta-lactamase genes on chromosomes (blaCTX-M, blaTEM) were clustered with antibiotic resistance genes associated with other classes of antibiotics (sulfonamides and aminoglycosides) along with insertional elements. ESBL genes such as blaCTX-M, blaTEM, and blaSHV were also detected on conjugable plasmids of IncF and IncI incompatibility types. One novel IncF plasmid (pSHV2A_ESBLF) was identified, which carried a multidrug resistance genotype (blaSHV-2A, aadA22, aac3, aph6, tetA, and sul1) in addition to a mer (mercury resistance) operon, colicin, and aerobactin genes. Shotgun metagenomic analysis of the ESBL-producing E. coli-originating wastewater samples showed the presence of class A beta-lactamases; however, the ESBL genes identified in the E. coli genomes were below the detection limits. Other ESBL-associated genes (i.e., blaOXA.11, blaFOX.7, and blaGES.17) were identified in the wastewater samples, and their occurrences were correlated with the core microbial genera (e.g., Paraprevotella). In the E. coli genomes and wastewater samples, tetracycline, aminoglycoside, and beta-lactam resistance determinants frequently co-occurred. The combination of whole-genome and metagenomic analysis provides a holistic description of ESBL-producing organisms and genes in wastewater systems.IMPORTANCEUsing a hybrid sequencing and assembly strategy (short- and long-read sequencing), we identified the distribution of ARGs and virulence factors harbored on plasmids and chromosomes. We further characterized plasmids' incompatibility types and the co-occurrences of ARGs and virulence factors on plasmids and chromosomes. We investigated the transferability of plasmid-mediated beta-lactams via conjugation. Finally, using shotgun metagenomic analysis of the ESBL-producing Escherichia coli-originated wastewater samples, we described the microbial community, the resistome composition, and the potential associations with plasmid-mediated beta-lactam genes and other ARGs.

Identification and environmental implications of photo-transformation products of trenbolone acetate metabolites.

Despite the widespread use of the anabolic androgen trenbolone acetate (TBA) in animal agriculture, evidence demonstrating the occurrence of TBA metabolites such as 17ß-trenbolone (17ß-TBOH), 17α-trenbolone (17α-TBOH), and trendione (TBO) is relatively scarce, potentially due to rapid transformation processes such as direct photolysis. Therefore, we investigated the phototransformation of TBA metabolites and associated ecological implications by characterizing the photoproducts arising from the direct photolysis of 17ß-TBOH, 17α-TBOH, and TBO and their associated ecotoxicity. LC-HRMS/MS analysis identified a range of hydroxylated products that were no longer photoactive, with primary photoproducts consisting of monohydroxy species and presumptive diastereomers. Also observed were higher-order hydroxylated products probably formed via subsequent reaction of primary photoproducts. NMR analysis confirmed the formation of 12,17-dihydroxy-estra-5(10),9(11),dien-3-one (12-hydroxy-TBOH; 2.2 mg), 10,12,17-trihydroxy-estra-4,9(11),dien-3-one (10,12-dihydroxy-TBOH; 0.7 mg), and a ring-opened 11,12-dialdehyde oxidation product (TBOH-11,12-dialdehyde; 1.0 mg) after irradiation of ∼14 mg of 17ß-trenbolone. Though unconfirmed by NMR, our data suggest that the formation of additional isomeric products may occur, likely due to the reactivity of the unique 4,9,11 conjugated triene structure of trenbolone. In vivo exposure studies employing Japanese medaka (Oryzias latipes) indicate that low concentrations of 17α-TBOH photoproduct mixtures can alter ovarian follicular development, and photoproducts alter whole-body 17ß-estradiol levels. Therefore, direct photolysis yields photoproducts with strong structural similarity to parent steroids, and these photoproducts still retain enough biological activity to elicit observable changes to endocrine function at trace concentrations. These data indicate that environmental transformation processes do not necessarily reduce steroid hormone ecotoxicity.

Arsenic in Bangladeshi soils related to physiographic region, paddy management, and mirco- and macro-elemental status.

While the impact of arsenic in irrigated agriculture has become a major environmental concern in Bangladesh, to date there is still a limited understanding of arsenic in Bangladeshi paddy soils at a landscape level. A soil survey was conducted across ten different physiographic regions of Bangladesh, which encompassed six types of geomorphology (Bil, Brahmaputra floodplain, Ganges floodplain, Meghna floodplain, Karatoya-Bangali floodplain and Pleistocene terrace). A total of 1209 paddy soils and 235 matched non-paddy soils were collected. The source of irrigation water (groundwater and surface water) was also recorded. The concentrations of arsenic and sixteen other elements were determined in the soil samples. The concentration of arsenic was higher in paddy soils compared to non-paddy soils, with soils irrigated with groundwater being higher in arsenic than those irrigated with surface water. There was a clear difference between the Holocene floodplains and the Pleistocene terraces, with Holocene floodplain soils being higher in arsenic and other elements. The results suggest that arsenic is most likely associated with less well weathered/leached soils, suggesting it is either due to the geological newness of Holocene sediments or differences between the sources of sediments, which gives rise to the arsenic problems in Bangladeshi soils.

Selenium cycling across soil-plant-atmosphere interfaces: a critical review.

Selenium (Se) is an essential element for humans and animals, which occurs ubiquitously in the environment. It is present in trace amounts in both organic and inorganic forms in marine and freshwater systems, soils, biomass and in the atmosphere. Low Se levels in certain terrestrial environments have resulted in Se deficiency in humans, while elevated Se levels in waters and soils can be toxic and result in the death of aquatic wildlife and other animals. Human dietary Se intake is largely governed by Se concentrations in plants, which are controlled by root uptake of Se as a function of soil Se concentrations, speciation and bioavailability. In addition, plants and microorganisms can biomethylate Se, which can result in a loss of Se to the atmosphere. The mobilization of Se across soil-plant-atmosphere interfaces is thus of crucial importance for human Se status. This review gives an overview of current knowledge on Se cycling with a specific focus on soil-plant-atmosphere interfaces. Sources, speciation and mobility of Se in soils and plants will be discussed as well as Se hyperaccumulation by plants, biofortification and biomethylation. Future research on Se cycling in the environment is essential to minimize the adverse health effects associated with unsafe environmental Se levels.

Surface and subsurface attenuation of trenbolone acetate metabolites and manure-derived constituents in irrigation runoff on agro-ecosystems.

Although studies have evaluated the ecotoxicity and fate of trenbolone acetate (TBA) metabolites, namely 17α-trenbolone (17α-TBOH), 17ß-trenbolone (17ß-TBOH), and trendione (TBO), their environmental transport processes remain poorly characterized with little information available to guide agricultural runoff management. Therefore, we evaluated TBA metabolite transport in representative agricultural systems with concurrent assessment of other manure-derived constituents. Leachate generated using manure from TBA-implanted cattle was applied to a subsurface infiltration plot (4 m) and surface vegetative filter strips (VFSs; 3, 4, and 5 m). In the subsurface experiment, 17α-TBOH leachate concentrations were 36 ng L(-1) but decreased to 12 ng L(-1) in initial subsurface discharge. Over 75 minutes, concentrations linearly increased to 23 ng L(-1) (C/Co = 0.32-0.64). In surface experiments (n = 4), 17α-TBOH leachate concentrations ranged from 11-150 ng L(-1), remained nearly constant with time, but were attenuated by ∼70-90% after VFS treatment with no statistical dependence on the VFS length. While attenuation clearly occurred, the observations of a highly mobile fraction of all constituents in both surface runoff and subsurface discharge suggest that these treatment strategies may not always be capable of achieving threshold discharge concentrations. To attain no observed adverse effect levels (NOAELs) in receiving waters, concurrent assessment of leachate concentrations and available dilution capacities can be used to guide target treatment performance levels for runoff management. Dilution is usually necessary to achieve NOAELs, and receiving waters with less than 70-100 fold dilution capacity are at the highest risk for steroidal endocrine disruption.

Product-to-parent reversion of trenbolone: unrecognized risks for endocrine disruption.

Trenbolone acetate (TBA) is a high-value steroidal growth promoter often administered to beef cattle, whose metabolites are potent endocrine-disrupting compounds. We performed laboratory and field phototransformation experiments to assess the fate of TBA metabolites and their photoproducts. Unexpectedly, we observed that the rapid photohydration of TBA metabolites is reversible under conditions representative of those in surface waters (pH 7, 25°C). This product-to-parent reversion mechanism results in diurnal cycling and substantial regeneration of TBA metabolites at rates that are strongly temperature- and pH-dependent. Photoproducts can also react to produce structural analogs of TBA metabolites. These reactions also occur in structurally similar steroids, including human pharmaceuticals, which suggests that predictive fate models and regulatory risk assessment paradigms must account for transformation products of high-risk environmental contaminants such as endocrine-disrupting steroids.