A fundamental model for calculating interfacial adsorption of complex ionic and nonionic PFAS mixtures in the presence of mixed salts.

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants that have been used extensively as firefighting agents and in a wide range of commercial applications around the world. As many of the most-common PFAS components are surfactants, they readily accumulate at interfaces, a process that can govern their environmental fate. There are thousands of PFAS compounds, and they have nearly always been used as mixtures, so it is common to find many different PFAS components present together in the environment. Furthermore, the interfacial behavior of ionic PFAS can be strongly influenced by the presence of salts, with adsorption dependent on both the composition and concentration of salts present. Any predictions of PFAS interfacial behavior made without considering both the mixed nature of PFAS present, as well as the composition of the salts present, have the potential to be off by orders of magnitude. To date, models capable of making predictions of PFAS interfacial adsorption when both mixed PFAS and mixed salts are present have not been presented. The work described here addresses this need by extending a mass-action model developed previously by the authors to allow predictions in cases where complex combinations of mixed PFAS and mixed salts are present. Predictions of PFAS interfacial affinity for a range of PFAS mixture conditions and ionic strengths are verified using experimentally-measured surface tension data. The new model provides physically-realistic prediction of interfacial adsorption of a wide range of PFAS mixtures over a wide range of salt concentrations and compositions. The model is capable of predicting interfacial adsorption of ionic/nonionic PFAS mixtures in the presence of salts, and can also make predictions when there is competitive adsorption between different PFAS components, a common case in PFAS source zones where high concentrations of multiple components are present and in foam fractionation reactors.

Calculating PFAS interfacial adsorption as a function of salt concentration using model parameters determined from chemical structure.

Per- and polyfluoroalkyl substances (PFAS) are widely-detected environmental contaminants known to concentrate at surfaces and interfaces. Many of the most commonly-detected PFAS function as ionic surfactants under environmental conditions. The interfacial behaviors of ionic surfactants, including PFAS, are strongly dependent on salt concentration and composition, with interfacial affinity potentially varying by orders of magnitude for the same compound under different conditions. The work described here presents a tool for predicting the salt-dependent adsorption of PFAS compounds based entirely on chemical structure, something of great value for predicting the real-world environmental behavior of many of the large numbers of PFAS compounds for which experimental data are not available. The approach combines two different previously-developed models, one a mass-action model designed to predict the effects of salts on interfacial adsorption of ionic PFAS (the UNSW-OU salt model), and the second a group-contribution model designed to predict interfacial adsorption of PFAS in the absence of salt based on chemical structure. The challenge of combining the two models comes from the fact that both are based on different isotherms. The salt model can produce sigmoidal isotherms under salt-limited conditions (an isotherm shape that is supported by experimental evidence), while the group-contribution model can generate Langmuir parameters from calculations based on chemical structure. Equations were derived to determine salt model isotherm parameters from Langmuir parameters (either from the group-contribution model or experimental sources) by matching surface tension curves in the vicinity of the concentration of highest second derivative. Refined group-contribution model parameters were determined based on data from an additional 40 surface tension curves to allow improved structure-based predictions for important classes of PFAS that were not sufficiently well-represented in the original model. The resulting equations provide a tool allowing quantitative predictions of PFAS behavior under realistic environmental conditions for compounds for which little or no experimental data are available.

Predicting the impact of salt mixtures on the air-water interfacial behavior of PFAS.

Salts are known to have strong impacts on environmental behavior of per- and polyfluoroalkyl substances (PFAS) including air-water interfacial adsorption. Multivalent salts impact interfacial adsorption to a greater extent than monovalent salts. Models to make a priori predictions of PFAS interfacial adsorption in the presence of multiple salts with different ionic charges are needed given the need to predict PFAS environmental fate. This study further develops a mass-action model to predict the interfacial behavior of PFAS as a function of both salt valency and concentration. The model is validated using surface tension data for a series of monovalent and divalent salt mixtures over a wide range of ionic strengths (i.e., from no added salt to 0.5 M) as well as comparison to data from literature. This model highlights the disproportionate impact of multivalent salts on interfacial adsorption and the practical utility of the model for predicting interfacial adsorption in the presence of multiple monovalent and multivalent inorganic salts. Results suggest that failure to account for divalent salt, even when concentrations are much smaller than monovalent salt, under most environmentally relevant aqueous phase conditions will result in significant underpredictions of PFAS interfacial adsorption. Simple examples of PFAS distribution in a range of salt conditions in the vadose zone and in aerated-water treatment reactors highlight the predictive utility of the model.

A new framework for modeling the effect of salt on interfacial adsorption of PFAS in environmental systems.

Per- and polyfluoroalkyl substances (PFAS) are surface active contaminants of great environmental concern, due to their widespread historical use and their environmental persistence. Salts are known to have a profound influence on the interfacial behaviors of all ionic surfactants, including some of the most commonly detected PFAS. This work describes a new mass-action model for predicting the interfacial behavior of surfactants as a function of salt concentration. The three-parameter model is fit to interfacial tension data over a range of salt concentrations, and is then able to predict interfacial adsorption isotherms for the entire range from no added salt, up to 0.5 M added salt. The phenomenological nature of the model means that it is likely to provide more robust predictions for new systems and conditions than some of the existing empirical approaches, and the minimal number of adjustable parameters ensures that unique calibrations are possible with limited data. The model is found to be consistent with experimental data, and is bracketed by experimental values at low PFAS concentrations. Of particular interest, the model predicts the existence of sigmoidal adsorption isotherms at low salt concentrations, a deviation from isotherms calculated the commonly-used Szyszkowski equation; the observation is supported by a maximum in measured interfacial adsorption coefficient calculated from low-concentration surface tension measurements. Because adsorption affinities can vary by orders of magnitude with changing salt concentration, the ability to predict the effects of salt on adsorption is of critical importance for quantitative prediction of PFAS behavior in the environment.

Predicting the relationship between PFAS component signatures in water and non-water phases through mathematical transformation: Application to machine learning classification.

Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, as a result of decades of use across a range of applications. While PFAS contamination often enters the environment in the aqueous phase, PFAS is regularly detected in a range of different phases, including soils, sediments and biota. Although PFAS at a given site may originate from the same sources, the compositions observed in different phases are nearly always different, a fact that can complicate source allocation efforts. This paper presents a quantitative method for prediction of the relative composition of PFAS in different phases for components for which differences in behavior are primarily driven by hydrophobicity. The derived equations suggest that under these conditions, the relative compositions in different phases in contact with water should be independent of overall affinity for the phase, and as such should be the same for all non-water phases. This result is illustrated with data from individual samples, as well as from site-wide evaluations for a range of different phases. The results of the work provide a useful tool to reconcile PFAS composition differences in different phases, and provide a baseline for recognizing cases where hydrophobicity is not the primary driver of differences in distribution between phases. Furthermore, the results may be useful in forensic applications for classification of PFAS across different phases. The use of the resulting equations to transform water data to train a supervised learning algorithm for forensic analysis of PFAS in non-water phases is illustrated.

Source allocation of per- and polyfluoroalkyl substances (PFAS) with supervised machine learning: Classification performance and the role of feature selection in an expanded dataset.

This work explores the use of supervised machine learning as a tool for identifying the source of per- and polyfluorinated alkyl substances (PFAS) in water samples on the basis of the detected component concentrations. Specifically, the work focuses on distinguishing between PFAS used in aqueous film forming foam (AFFF) fire suppression applications, and PFAS from other sources. The fact that many sites contaminated with legacy PFOS-based AFFF formulations are dominated by perfluorinated sulfonates can make it tempting to naïvely classify samples dominated by perfluorinated sulfonates as being of AFFF origin. However, a large fraction of samples do not follow this pattern, including some of the most important cases, such as legacy PFOS-based AFFF far from its source. Although PFAS composition can vary substantially at a site as a result of mobility differences between components and other factors, the hypothesis driving the work is that compositional patterns created in the environment can be recognized across different sites by machine learning, and used for source allocation. This work builds on earlier preliminary work by the authors based on a small dataset. This work is based on a much larger 8040-sample dataset, and explores different preprocessing approaches, as well as how feature selection impacts classification performance. The results of this work strongly support the idea that supervised machine learning based on composition can identify patterns that can be used to distinguish PFAS sources. The results provide new insights into selection of classifiers and features for source identification based on PFAS sample composition.

A group-contribution model for predicting the physicochemical behavior of PFAS components for understanding environmental fate.

The factors controlling per- and polyfluoroalkyl substances (PFAS) environmental fate remains the subject of considerable debate and study. As surfactants, PFAS readily partition to interfaces, a property that controls their transport and fate. A group contribution model is developed to predict the extent to which PFAS partitions to the air-water interface. Langmuir adsorption and Szyszkowski equation parameters were fitted to literature air-water surface tension data for a range of PFAS and conventional hydrocarbon surfactants. This approach enabled the prediction of the impact of the hydrophilic head group, and other molecular components, on PFAS interfacial partitioning in instances when PFAS data are unavailable but analogous hydrocarbon surfactant data are available. The model was extended to predict a range of parameters (i.e., solubility, critical micelle concentration (CMC), KD, Koc and Kow) that are used to predict PFAS environmental fate, including long-range PFAS transport and in multimedia models. Model predictions were consistent with laboratory and field derived parameters reported in the literature. Additionally, the proposed model can predict the impact of pH and speciation on the extent of PFAS interfacial partitioning, a potentially important feature for understanding the behaviors of some ionizable PFAS, such as fluorinated carboxylic acids. The proposed model provides a conceptually straightforward method to predict a wide range of environmental fate parameters for a wide range of PFAS. As such, the model is a powerful tool that can be used to determine parameters needed to predict PFAS environmental fate.

Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples.

Environmental contamination by per- and polyfluoroalkyl substances (PFAS) is widespread, because of both their decades of use, and their persistence in the environment. These factors can make identification of the source of contamination in samples a challenge, because in many cases contamination may originate from decades ago, or from a number of candidate sources. Forensic source allocation is important for delineating plumes, and may also be able to provide insights into environmental behaviors of specific PFAS components. This paper describes work conducted to explore the use of supervised machine learning classifiers for allocating the source of PFAS contamination based on patterns identified in component concentrations. A dataset containing PFAS component concentrations in 1197 environmental water samples was assembled based on data from sites from around the world. The dataset was split evenly into training and test datasets, and the 598-sample training dataset was used to train four machine learning classifiers, including three conventional machine learning classifiers (Extra Trees, Support-Vector Machines, K-Neighbors), and one multilayer perceptron feedforward deep neural network. Of the methods tested, the deep neural network and Extra Trees exhibited particularly high performance at classification of samples from a range of sources. The fact that the methods function on completely different principles and yet provide similar predictions supports the hypothesis that patterns exist in PFAS water sample data that can allow forensic source allocation. The results of the work support the idea that supervised machine learning may have substantial promise as a tool for forensic source allocation.

The effect of evaporation-induced flow at the pore scale on nanoparticle transport and deposition in drying unsaturated porous media.

An understanding of nanoparticle interactions with solid surfaces in unsaturated porous media is important both for understanding nanoparticle fate in the environment, and for the design of environmental applications that depend on the delivery of nanoparticles. While surface-chemical interactions can have a strong influence on nanomaterial attachment to surfaces in environmental porous media, the hydraulics of water flow can also play an important role in attachment. In the unsaturated zone, naturally-occurring evaporation is a major source of water flow. The purpose of this work was to examine how evaporation-induced water flow at the pore scale impacts the transport and deposition of negatively-charged sulfate-modified polystyrene nanoparticles. Evaporation experiments were conducted by initially saturating small clusters of sand grains with a suspension of nanoparticles. Confocal microscopy was then used to track the changing water surface profile, as well as to track the transport and deposition of nanoparticles in the grain clusters. Confocal data showed that nanoparticles tended to deposit on sand grains near the receding air-water interface, an expected behavior. This process led to attachment on grain surfaces as they were exposed by the receding interface. Evaporation was found to produce complex flow patterns with temporally-changing flow directions at the pore scale. A finite difference model developed to explore the link between evaporation and water flow in pore spaces was able to duplicate many of the observed phenomena. Simulations suggest that distinct differences in deposition mechanisms should be expected for porous media undergoing evaporation compared with porous media experiencing drainage.

A boosted decision tree approach to shadow detection in scanning electron microscope (SEM) images for machine vision applications.

Scanning electron microscopy is important across a wide range of machine vision applications, and the ability to detect shadows in images could provide an important tool for evaluating attributes of the surfaces being imaged, such as the presence of defects or particulate impurities. One example where the presence of shadows can be important is in the reconstruction of elevation maps from stereo-pair scanning electron microscopy (SEM) images. Shadows can both interfere with determination of matching points for stereoscopic calculations, and confuse shape-from-shading algorithms which rely on pixel intensity gradients to calculate surface slope, leading to inaccurate reconstructions. This paper describes a machine learning method for identifying locations in SEM images impacted by shadows, based on a training set of photographic images. The method could be useful as a means of identifying parts of images likely to suffer from reconstruction artifacts in shape-from-shading-based reconstructions, or as a tool for automated defect identification. The method uses a boosted decision tree machine learning approach to identify shadows based on the features of images. The method is illustrated with four different natural surfaces exhibiting a range of different types of shadow features, and an example is used to illustrate how the method can identify regions likely to be impacted by shadows in reconstructions.

The effect of nanoparticles on soil and rhizosphere bacteria and plant growth in lettuce seedlings.

Nanomaterials are increasingly being considered for use in agricultural applications, where they have been suggested for a range of uses including fertilizer and pesticide applications. Among nanomaterial applications, agricultural use has a particularly high likelihood of introducing significant quantities of nanomaterials to the environment. The focus of this work was on conducting preliminary experiments examining how nanomaterials might influence rhizosphere bacteria, and in turn influence plant growth. For this work, buttercrunch lettuce seeds were grown in the presence of suspensions of three different nanoparticles. Two of the studied nanomaterials, amine-modified polystyrene nanospheres and titanium dioxide nanoparticles, caused significant decreases in both rhizosphere bacterial counts and plant root and stem growth. In contrast, sulfate-modified polystyrene nanospheres actually increased rhizosphere bacterial counts, but had no significant impact on growth. Only the amine-modified polystyrene nanospheres were found to attach to root surfaces, suggesting that nanomaterial attachment to root surfaces is not a requirement for hindered plant growth. It was hypothesized that attachment of amine-modified polystyrene and TiO2 nanomaterials to bacteria themselves could be changing the bacteria surface properties, and ultimately reducing bacterial affinity for root surfaces.

The impact of antibacterial handsoap constituents on the dynamics of triclosan dissolution from dry sand.

Triclosan has been widely used as an antibacterial agent in consumer and industrial products, and large quantities continue to be discharged to natural waters annually. The focus of this work was on studying the dynamics of triclosan dissolution following evaporative drying. Warm weather can cause the water in intermittent streams or the unsaturated zone to evaporate, causing nonvolatile compounds to form solid precipitates. Because dissolution of precipitates is a relatively slow process, the dynamics of dissolution following evaporation may play an important role in controlling the release of contaminants to the environment. The specific purpose of the work was to explore the effects of surfactant co-contaminants from an industrial antibiotic handsoap on the dissolution dynamics of triclosan. The work used a fiber optic-based optical cell to conduct stirred-batch dissolution experiments for sands coated with different mass loadings of triclosan. Results show that the presence of surfactants from the hand soap not only increase the apparent equilibrium solubility, but also increase the rate of approach to equilibrium. A model describing the dissolution process was developed, and was found to be consistent with experimental data. Results of the work suggest that even small solubility enhancement by surfactant co-contaminants may have a significant impact on dissolution dynamics. Because waters containing significant quantities of triclosan are also among those most likely to contain surfactant co-contaminants, it is likely that the release of triclosan to the environment following evaporation may be faster in many cases than would be predicted from experiments based on pure triclosan.

Remobilization Dynamics of Caffeine, Ciprofloxacin, and Propranolol following Evaporation-Induced Immobilization in Porous Media.

Changing weather conditions can cause cycles of wetting and drying in the unsaturated zone. When porewater evaporates, any nonvolatile solutes present in the pores will be driven to adsorb and ultimately precipitate on solid surfaces. When media are subsequently resaturated through rainfall infiltration, the remobilization of solutes likely depends on both the hydraulics of resaturation and the dynamics of dissolution processes. The focus of this work was to study the dynamics of remobilization of three different emerging contaminants (caffeine, ciprofloxacin, and propranolol) and two model compounds (fluorescein and sulforhodamine B) from porous media following evaporation of porewater. Remobilization column experiments were conducted to study this phenomenon and were evaluated using a finite difference model developed to simulate the adsorption-desorption dynamics during resaturation and elution. Results indicate that dissolution dynamics become increasingly important with increasing adsorption affinity for solid surfaces. Trends in observed elution behavior are not well-predicted from chemical properties, such as solubility. One of the most significant observations of the work is the presence of spikes in elution concentrations well above initial porewater concentration, resulting from the hydraulics of the resaturation process. The effect is most significant in highly mobile compounds that exhibit low adsorption affinity for solid surfaces.

A hybrid 3D SEM reconstruction method optimized for complex geologic material surfaces.

Reconstruction methods are widely used to extract three-dimensional information from scanning electron microscope (SEM) images. This paper presents a new hybrid reconstruction method that combines stereoscopic reconstruction with shape-from-shading calculations to generate highly-detailed elevation maps from SEM image pairs. The method makes use of an imaged glass sphere to determine the quantitative relationship between observed intensity and angles between the beam and surface normal, and the detector and surface normal. Two specific equations are derived to make use of image intensity information in creating the final elevation map. The equations are used together, one making use of intensities in the two images, the other making use of intensities within a single image. The method is specifically designed for SEM images captured with a single secondary electron detector, and is optimized to capture maximum detail from complex natural surfaces. The method is illustrated with a complex structured abrasive material, and a rough natural sand grain. Results show that the method is capable of capturing details such as angular surface features, varying surface roughness, and surface striations.

Transport and retention of TiO2 and polystyrene nanoparticles during drainage from tall heterogeneous layered columns.

Recent developments in nanotechnology have seen an increase in the use of manufactured nanomaterials. Although their unique physicochemical properties are desirable for many products and applications, concern continues to exist about their environmental fate and potential to cause risk to human and ecological health. The purpose of this work was to examine one aspect of nanomaterial environmental fate: transport and retention in the unsaturated zone during drainage. The work made use of tall segmented columns packed with layers of two different porous media, one medium sand and one fine sand. The use of tall columns allowed drainage experiments to be conducted where the water table remained within the height of the column, permitting control of final saturation profiles without the need for capillary barrier membranes which can potentially complicate analyses. Experiments were conducted with titanium dioxide (TiO2) and polystyrene nanomaterials. For the strongly negatively-charged polystyrene nanomaterials, little retention was observed under the conditions studied. For the TiO2 nanomaterials, results of the work suggest that while saturated fine sand layers may retain more nanomaterials than saturated coarse sand layers, significantly greater retention is possible in unsaturated media. Furthermore, unsaturated medium sand layers exhibited significantly greater retention than adjacent saturated fine sand layers when present at low saturations high above the water table. Retention by unsaturated media were found to correlate strongly with elevation. Free drainage experiments including both primary and secondary drainages in homogeneous columns showed evidence of redistribution during imbibition and secondary drainage, but still showed substantial unsaturated retention of TiO2 nanoparticles high in the column, despite re-saturation with- and drainage of nanoparticle-free water.

Transport and retention of fullerene (nC60) nanoparticles in unsaturated porous media: effects of solution chemistry and solid phase coating.

The retention and release of aqueous aggregates of fullerene nanoparticles (nC(60)) were studied under dynamic unsaturated conditions. Porous media containing nC(60) were taken through multiple drainage/imbibition (drying/wetting) cycles to explore the effects of solution conditions and solid surface modification on transport and ultimate fate in unsaturated porous media. In experiments conducted with NaCl as the background electrolyte, the retention of nC(60) during drainage was found to be negligibly small over a wide range of ionic strengths (I=0.2 to I=6 mM), significantly lower than the retention of titanium dioxide nanoparticles studied previously under similar conditions. In contrast, experiments conducted with CaCl(2) as the background electrolyte found that retention of nC(60) during drainage was significant at higher ionic strengths, particularly at the highest ionic strength studied (I=6 mM). Experiments examining the influence of dissolved natural organic matter on nC(60) retention in unsaturated media found no measurable impact on the transport. The effects of solid surface modification were examined by creating coatings that modified surface hydrophobicity and charge. Experiments found that a hydrophobic coating had no measurable impact on nC(60) retention, when compared with retention by unmodified media. In contrast, a porous medium with surfaces that were both hydrophobic and positively-charged retained 5-10 times more nC(60) during drainage than an unmodified porous medium. This result suggests that electrostatic interactions play a more important role than hydrophobic interactions in the transport and fate of nC(60) in the unsaturated zone. For all conditions where retention was observed, experiments found very little release or retained nC(60) after subsequent flushing with water, suggesting that once retained, the environmental mobility of nC(60) may be extremely limited.

Retention and release of TiO2 nanoparticles in unsaturated porous media during dynamic saturation change.

The retention and release of TiO(2) nanoparticles in porous media (packed glass beads) were studied under transient unsaturated conditions as the media were taken through multiple drainage/imbibition (drying/wetting) cycles at three different pH values. The focus of the work was to better understand the role of changing water table levels and rainfall infiltration events on the ultimate mobility of TiO(2) nanoparticles. Results indicate that retention during saturated transport varied considerably, from very strong retention at pH 5 (likely due to electrostatic interactions), to no measurable retention at pH 10. During primary drainage, additional retention (i.e., beyond what was retained during initial saturation) was observed at all pH values. During subsequent imbibition/drainage cycles where nanoparticle-free water was imbibed into the porous medium prior to drainage, the mass of retained TiO(2) remained nearly constant at all three pH values. Final imbibition/drainage and subsequent flushing, both using solution conditions adjusted to favor high mobility, showed very little additional nanoparticle release. These results indicate that the release of TiO(2) nanoparticles following retention by either saturated or unsaturated packed glass beads was difficult to achieve, regardless of the likely initial mechanisms of retention, even when solution conditions were changed to those that should favor high mobility.

Model stream channel testing of a UV-transparent polymer-based passive sampler for ultra-low-cost water screening applications.

Passive samplers are increasingly being considered for analyses of waters for screening applications, to monitor for the presence of unwanted chemical compounds. Passive samplers typically work by accumulating and concentrating chemicals from the surrounding water over time, allowing analyses to identify temporally short concentration surges that might be missed by water grab samples, and potentially reducing analysis and sample handling costs, allowing a greater number of sites to be monitored. The work described here tests a recently-developed passive sampling device which was designed to provide an ultra-low-cost screening method for organic chemicals in waters. The device was originally designed for detection of endocrine disrupting chemicals, but has the advantage that it is capable of simultaneously detecting a wide range of other aqueous organic contaminants as well. The device is based on a UV-transparent polymer which is used both to concentrate dissolved chemicals, and as an optical cell for absorbance detection and full-spectrum deconvolution to identify compounds. This paper describes the results of a test of the device conducted at the US EPA Experimental Stream Facility in Milford, Ohio. The test examined detection of triclosan and 4-nonylphenol in model stream channels using two different deployment methods. Results indicate that deployment method can significantly impact measured results due to differences in mass transfer. Passive samplers deployed in vials with permeable membrane septa showed no detection of either compound, likely due to lack of water motion in the vials. In contrast, passive samplers deployed directly in the flow were able to track concentrations of both compounds, and respond to temporal changes in concentration. The results of the work highlight the importance of using internal spiking standards (performance reference compounds) to avoid false non-detection results in passive sampler applications.

A UV-transparent passive concentrator/spectrum deconvolution method for simultaneous detection of endocrine disrupting chemicals (EDCs) and related contaminants in natural waters.

Suspected endocrine disrupting chemicals (EDCs) have been widely detected in the environment, and are a source of increasing concern. One of the major challenges in assessing the risk associated with EDCs in the environment is that their environmental concentrations are typically extremely low - on the order of ngL(-1) to microgL(-1) - making them difficult to quantify without extensive pre-concentration procedures. Further complicating their detection is the fact that they are present in mixtures, sometimes with tens to hundreds of other compounds (pharmaceuticals, personal care products, detergents, natural organic matter). The objective of the work described here was to develop a method for rapid monitoring and detection of EDCs at trace concentrations in natural waters. The method makes use of a UV-transparent polymer-based concentrator to be used as a passive sampling device. The UV-transparent polymer-based concentrator serves both as a solid phase extraction medium to concentrate EDCs for analysis and exclude many compounds likely to interfere with detection (fines, macromolecules such as organic matter, ionic surfactants), and as an analytical optical cell, allowing rapid EDC quantification without labor-intensive pre-concentration procedures. A full-spectrum deconvolution technique is used to determine EDC concentrations from measured UV absorbance spectra in the polymer. Experiments were conducted to measure partitioning rate behavior and partition coefficients between the selected polymer (a functional polydimethylsiloxane) and water for seven compounds known or suspected of being endocrine disruptors: estrone, progesterone, estradiol, 2,6-di-tert-butyl-1,4-benzoquinone, phenanthrene, triclosan, and 4-nonylphenol. The method was tested for its ability to detect and quantify individual compounds in mixtures containing up to six components. Results show the method to have selectivity suitable for rapid screening applications at many sites where multiple compounds are present.