Statistical-physical method for simulating the transport of microplastic-antibiotic compound pollutants in typical bay area.

Microplastics and antibiotics are emerging pollutants in the environment and have received widespread attention globally. In coastal areas, microplastic and antibiotic pollution is ubiquitous and often overlapping. Microplastic-antibiotic compound pollutants that are formed through adsorption have thus become a major concern. However, modeling knowledge of microplastic transport in coastal areas is still limited, and research on the impact of compound pollutants caused by Polythene (PE)-antibiotics in such settings is in early stages. In this study, using a lattice Boltzmann method (LBM) and temporal Markov method (TMM) under a statistical-physical framework, we simulated pollutant transport and PE-antibiotic compound pollutants in coastal areas. First, a series of models are proposed, including an LBM wave-current coupling model, an LBM antibiotic transport model, an LBM particle-tracking model, a TMM microplastic transport model and the final LBM-TMM hybrid compound pollutant model. Then, the suitability and applicability of the models was validated using experimental data and numerical simulations. Finally, the models were applied to a study area, Laizhou Bay (China). The simulation results demonstrate that adsorption will reduce the concentration of antibiotics in the water environment. Within 44 days, the adsorbed antibiotic carried by PE particles migrate further, and the width of the pollution zone escalates from 234.2 m to 689.0 m.

Uncertainty Reduction and Environmental Justice in Air Pollution Epidemiology: The Importance of Minority Representation.

Ambient air pollution is an increasing threat to society, with rising numbers of adverse outcomes and exposure inequalities worldwide. Reducing uncertainty in health outcomes models and exposure disparity studies is therefore essential to develop policies effective in protecting the most affected places and populations. This study uses the concept of information entropy to study tradeoffs in mortality uncertainty reduction from increasing input data of air pollution versus health outcomes. We study a case scenario for short-term mortality from particulate matter (PM2.5) in North Carolina for 2001-2016, employing a case-crossover design with inputs from an individual-level mortality data set and high-resolution gridded data sets of PM2.5 and weather covariates. We find a significant association between mortality and PM2.5, and the information tradeoffs indicate that a 10% increase in mortality information reduces model uncertainty three times more than increased resolution of the air pollution model from 12 to 1 km. We also find that Non-Hispanic Black (NHB) residents tend to live in relatively more polluted census tracts, and that the mean PM2.5 for NHB cases in the mortality model is significantly higher than that of Non-Hispanic White cases. The distinct distribution of PM2.5 for NHB cases results in a relatively higher information value, and therefore faster uncertainty reduction, for new NHB cases introduced into the mortality model. This newfound influence of exposure disparities in the rate of uncertainty reduction highlights the importance of minority representation in environmental research as a quantitative advantage to produce more confident estimates of the true effects of environmental pollution.

Suspended Materials Affect Particle Size Distribution and Removal of Environmental DNA in Flowing Waters.

Environmental DNA (eDNA) in aquatic systems is a complex mixture that includes dissolved DNA, intracellular DNA, and particle-adsorbed DNA. Information about the various components of eDNA and their relative proportions could be used to discern target organism abundance and location. However, a limited knowledge of eDNA adsorption dynamics and interactions with other materials hinders these applications. To address this gap, we used recirculating stream mesocosms to investigate the impact of suspended materials (fine particulate organic matter, plankton, clay, and titanium dioxide) on the eDNA concentration and particle size distribution (PSD) from two fish species in flowing water. Our findings revealed that eDNA rapidly adsorbs to other materials in the water column, affecting its concentration and PSD. Nonetheless, only particulate organic matter affected eDNA removal rate after 30 h. Moreover, we observed that the removal of larger eDNA components (≥10 µm) was more strongly influenced by physical processes, whereas the removal of smaller eDNA components was driven by biological degradation. This disparity in removal mechanisms between larger and smaller eDNA components could explain changes in eDNA composition over time and space, which have implications for modeling the spatial distribution and abundance of target species and optimizing eDNA detection in high turbidity systems.

Environmental DNA (eDNA) removal rates in streams differ by particle size under varying substrate and light conditions.

The use of environmental DNA (eDNA) as a sampling tool offers insights into the detection of invasive and/or rare aquatic species and enables biodiversity assessment without traditional sampling approaches, which are often labor-intensive. However, our understanding of the environmental factors that impact eDNA removal (i.e., how rapidly eDNA is removed from the water column by the combination of decay and physical removal) in flowing waters is limited. This limitation constrains predictions about the location and density of target organisms after positive detection. To address this question, we spiked Common Carp (Cyprinus carpio) eDNA into recirculating mesocosms (n = 24) under varying light (shaded versus open) and benthic substrate conditions (no substrate, bare substrate, and biofilm-colonized substrate). We then collected water samples from each mesocosm at four time points (40 min, 6 h, 18 h, and 48 h), and sequentially filtered the samples through 10, 1.0, and 0.2 µm filters to quantify removal rates for different eDNA particle sizes under varying light and substrate conditions. Combining all size classes, total eDNA removal rates were higher for mesocosms with biofilm-colonized substrate compared to those with no substrate or bare (i.e., no biofilm) substrate, which is consistent with previous findings linking biofilm colonization with increased eDNA removal and degradation. Additionally, when biofilm was present, light availability increased eDNA removal; eDNA levels fell below detection after 6-18 h for open mesocosms versus 18-48 h for shaded mesocosms. Among size classes, larger particles (>10 µm) were removed faster than small particles (1.0-0.2 µm). These results suggest that changes in the distribution of eDNA size classes over time (e.g., with downstream transport) and with differing environmental conditions could be used to predict the location of target organisms in flowing waters, which will advance the use of eDNA as a tool for species monitoring and management.

Persistence of Antibiotic Resistance Genes Varies with Particle Size and Substrate Conditions in Recirculating Streams.

Antibiotic resistance (AR) determinants are enriched in animal manures, a significant portion of which is land-applied as a soil amendment or as fertilizer, leading to potential AR runoff and microbial pollution in adjacent surface waters. To effectively inform AR monitoring and mitigation efforts, a thorough understanding and description of the persistence and transport of manure-derived AR in flowing waters are needed. We used experimental recirculating mesocosms to assess water-column removal rates of antibiotic resistance genes (ARGs) originating from a cow manure slurry collected from a dairy farm. We quantified the effect of three benthic (i.e., bottom) substrate variations and particle sizes of manure slurry on water column removal rates. Overall, we observed variation in ARG behavior across substrate treatments and particle sizes. For ARGs associated with small particles, removal rates were higher in mesocosms with a substrate. tetW was typically removed at the highest rates across particle size and treatment, followed by ermB and blaTEM. Our data suggests that both substrate character and particle size exert control on the fate and transport of ARGs in surface waters, laying the foundation for future research in this area to establish a predictive framework for AR persistence and fate in flowing waters.

Particle size influences decay rates of environmental DNA in aquatic systems.

Environmental DNA (eDNA) analysis is a powerful tool for remote detection of target organisms. However, obtaining quantitative and longitudinal information from eDNA data is challenging, requiring a deep understanding of eDNA ecology. Notably, if the various size components of eDNA decay at different rates, and we can separate them within a sample, their changing proportions could be used to obtain longitudinal dynamics information on targets. To test this possibility, we conducted an aquatic mesocosm experiment in which we separated fish-derived eDNA components using sequential filtration to evaluate the decay rate and changing proportion of various eDNA particle sizes over time. We then fit four alternative mathematical decay models to the data, building towards a predictive framework to interpret eDNA data from various particle sizes. We found that medium-sized particles (1-10 µm) decayed more slowly than other size classes (i.e., <1 and > 10 µm), and thus made up an increasing proportion of eDNA particles over time. We also observed distinct eDNA particle size distribution (PSD) between our Common carp and Rainbow trout samples, suggesting that target-specific assays are required to determine starting eDNA PSDs. Additionally, we found evidence that different sizes of eDNA particles do not decay independently, with particle size conversion replenishing smaller particles over time. Nonetheless, a parsimonious mathematical model where particle sizes decay independently best explained the data. Given these results, we suggest a framework to discern target distance and abundance with eDNA data by applying sequential filtration, which theoretically has both metabarcoding and single-target applications.

The impact of biofilms and dissolved organic matter on the transport of nanoparticles in field-scale streams.

The fate and transport of nanoparticles (NPs) in streams is critical for understanding their overall environmental impact. Using a unique field-scale stream at the Notre Dame-Linked Experimental Ecosystem Facility, we investigated the impact of biofilms and the presence of dissolved organic matter (DOM) on the transport of titanium dioxide (TiO2) NPs. Experimental breakthrough curves were analyzed using temporal moments and fit using a mobile-immobile model. The presence of biofilms in the stream severely reduced the transport of the TiO2 NPs, but this was mitigated by the presence of DOM. Under minimal biofilm conditions, the presence of DOM increased the mass recovery of TiO2 from 4.2% to 32% for samples taken 50 m downstream. For thriving biofilm conditions only 0.5% of the TiO2 mass was recovered (50 m), but the presence of DOM improved the mass recovery TiO2 to 36%. The model was suitable for predicting early, peak, tail, and truncation time portions of the breakthrough curves, which attests to its ability to capture a range of processes in the mobile and immobile domains of the stream. The model outcomes supported the hypothesis that DOM changed the interaction of NP-biofilm from an irreversible to a reversible process. Collectively, these outcomes stress the importance of considering biogeological complexity when predicting the transport of NPs in streams.

Information entropy tradeoffs for efficient uncertainty reduction in estimates of air pollution mortality.

Implementing effective policy to protect human health from the adverse effects of air pollution, such as premature mortality, requires reducing the uncertainty in health outcomes models. Here we present a novel method to reduce mortality uncertainty by increasing the amount of input data of air pollution and health outcomes, and then quantifying tradeoffs associated with the different data gained. We first present a study of long-term mortality from fine particulate matter (PM2.5) based on simulated data, followed by a real-world application of short-term PM2.5-related mortality in an urban area. We employ information yield curves to identify which variables more effectively reduce mortality uncertainty when increasing information. Our methodology can be used to explore how specific pollution scenarios will impact mortality and thus improve decision-making. The proposed framework is general and can be applied to any real case-scenario where knowledge in pollution, demographics, or health outcomes can be augmented through data acquisition or model improvements to generate more robust risk assessments.

Effects of large-scale heterogeneity and temporally varying hydrologic processes on estimating immobile pore space: A mesoscale-laboratory experimental and numerical modeling investigation.

The advection-dispersion equation (ADE) often fails to predict solute transport, in part due to incomplete mixing in the subsurface, which the development of non-local models has attempted to deal with. One such model is dual-domain mass transfer (DDMT); one parameter that exists within this model type is called immobile porosity. Here, we explore the complexity of estimating immobile porosity under varying flow rates and density dependencies in a large-scale heterogeneous system. Immobile porosity is estimated experimentally and using numerical models in 3-D flow systems, and is defined by domains of comparatively low advective velocity instead of truly immobile regions at the pore scale. Tracer experiments were conducted in a mesoscale 3-D tank system with embedded large impermeable zones and the generated data were analyzed using a numerical model. The impermeable zones were used to explore how large-scale structure and heterogeneity affect parameter estimation of immobile porosity, assuming a dual-porosity model, and resultant characterization of the aquifer system. Spatially and temporally co-located fluid electrical conductivity (σf) and bulk apparent electrical conductivity (σb)-using geophysical methods-were measured to estimate immobile porosity, and numerical modeling (i.e., SEAWAT and R3t) was conducted to explore controls of the immobile zones on the experimentally observed flow and transport. Results showed that density-dependent flow increased the hysteresis between measured fluid and bulk electrical conductivity, resulting in larger interpreted immobile pore-space estimates. Increasing the dispersivity in the model simulations decreased the estimated immobile porosity; flow rate had no impact. Overall, the results of this study highlight the difficulty faced in determining immobile porosity values in field settings, where hydrogeologic processes may vary temporally. Our results also highlight that immobile porosity is an effective parameter in an upscaled model whose physical meaning is not necessarily clear and that may not align with intuitive interpretations of a porosity.

Transient surface hydration impacts biogeography and intercellular interactions of non-motile bacteria.

There are many hydrated surface niches that are neither static nor continuously flowing that are colonized by microbes such as bacteria. Such periodic hydrodynamic regimes are distinct from aquatic systems where microbial dissemination is reasonably predicted by assuming continuous flow or static systems where motile microbes largely control their own fate. Here we show how non-motile bacteria exhibit rapid, dispersive bursts of movement over surfaces using transient confluent hydration from the environment, which we term "surface hydrodispersion" where cells traverse thousands of cell lengths within minutes. The fraction of the population disseminated by surface hydrodispersion is small-on order of 1 cell per million. Thus, surface hydrodispersion can promote isolated distribution of single cells, which is unlike other characterized active and passive surface motilities. We describe this translocation using a continuous time random walk modeling approach and find in computational simulations that transient fluid accumulation, dilution, and gravitational pull are the contributing factors. Surface hydrodispersion, consistent with advection, is unlike simple colony expansion as it dramatically alters spatial relationships, shown here with Staphylococcus aureus, which becomes increasingly virulent when isolated from Corynebacterium striatum Surface hydrodispersion of non-motile bacteria exploiting transient fluid availability and gravity is a mechanism that can result in sporadic and sudden shifts in microbial community behavior. To better understand how this movement can impact biogeography on the millimeter scale, this work describes a system for study of primary factors behind this movement as well as a stochastic model describing this dispersal.Importance: Understanding the dynamics within microbiome communities is a challenge. Knowledge of phylogeny and spatial arrangement has led to increased understanding of numerous polymicrobial communities yet, these snapshots do not convey the dynamics of populations over time. The actual biogeography of any microbiome controls the potential interactions, governing any possible antagonistic or synergistic behavior. Accordingly, a shift in biogeography can enable new behavior. Little is known about the movement mechanisms of "non-motile" microbes. Here we characterize a universal means of movement we term hydrodispersion where non-motile bacteria are transported thousands of cell lengths in minutes. We show that only a small fraction of the population is translocated by hydrodispersion and describe this movement further using a random-walk mathematical model approach in silico We demonstrate the importance of hydrodispersion by showing that Staphylococcus aureus can separate from a coculture inoculation with Corynebacterium striatum thus permitting transition to a more virulent state.

anem: A Simple Web-Based Platform to Build Stakeholder Understanding of Groundwater Behavior.

Groundwater supports essential societal and ecological functions by acting as a reservoir that buffers against natural variability. Increasing water scarcity and climate variability have resulted in more intensive management of groundwater resources, but groundwater often remains difficult to understand and manage. With this in mind, we develop a simple platform that provides a straightforward, web-based user interface applicable to a wide variety of end-user scenarios. Groundwater behavior is modeled using the method of images in a new R package, anem, which serves as the engine for the web platform, anem-app, produced using R Shiny. Both tools allow users to define aquifer properties and pumping wells, view maps of hydraulic head, and simulate particle tracking under steady-state conditions. These tools have the advantage of being platform independent and open source, so that they are freely available to anyone with a web browser and internet connection (anem-app) or computing platform with R installed (anem). We designed both tools to lower the learning curve and up-front costs to building simple groundwater models. The simplicity of the web application allows exploration of groundwater behavior under various conditions, and should be especially valuable in low-budget applications where advanced analysis may not be practical or necessary. Integration with the R language allows for advanced analysis and deeper exploration of groundwater dynamics. In this manuscript, we describe how anem and anem-app are built in the R environment and demonstrate how they might be used by planners or stakeholders.

A review of spatial Markov models for predicting pre-asymptotic and anomalous transport in porous and fractured media.

Heterogeneity across a broad range of scales in geologic porous media often manifests in observations of non-Fickian or anomalous transport. While traditional anomalous transport models can successfully make predictions in certain geological systems, increasing evidence suggests that assumptions relating to independent and identically distributed increments constrain where and when they can be reliably applied. A relatively novel model, the Spatial Markov model (SMM), relaxes the assumption of independence. The SMM belongs to the family of correlated continuous time random walks and has shown promise across a wide range of transport problems relevant to natural porous media. It has been successfully used to model conservative as well as more recently reactive transport in highly complex flows ranging from pore scales to much larger scales of interest in geology and subsurface hydrology. In this review paper we summarize its original development and provide a comprehensive review of its advances and applications as well as lay out a vision for its future development.

Predicting Vertical Concentration Profiles in the Marine Atmospheric Boundary Layer With a Markov Chain Random Walk Model.

In an effort to better represent aerosol transport in mesoscale and global-scale models, large eddy simulations (LES) from the National Center for Atmospheric Research (NCAR) Turbulence with Particles (NTLP) code are used to develop a Markov chain random walk model that predicts aerosol particle profiles in a cloud-free marine atmospheric boundary layer (MABL). The evolution of vertical concentration profiles are simulated for a range of aerosol particle sizes and in a neutral and an unstable boundary layer. For the neutral boundary layer we find, based on the LES statistics and a specific model time step, that there exist significant correlation for particle positions, meaning that particles near the bottom of the boundary are more likely to remain near the bottom of the boundary layer than being abruptly transported to the top, and vice versa. For the unstable boundary layer, a similar time interval exhibits a weaker tendency for an aerosol particle to remain close to its current location compared to the neutral case due to the strong nonlocal convective motions. In the limit of a large time interval, particles have been mixed throughout the MABL and virtually no temporal correlation exists. We leverage this information to parameterize a Markov chain random walk model that accurately predicts the evolution of vertical concentration profiles. The new methodology has significant potential to be applied at the subgrid level for coarser-scale weather and climate models, the utility of which is shown by comparison to airborne field data and global aerosol models.

Study of phase-field lattice Boltzmann models based on the conservative Allen-Cahn equation.

Conservative phase-field (CPF) equations based on the Allen-Cahn model for interface tracking in multiphase flows have become more popular in recent years, especially in the lattice-Boltzmann (LB) community. This is largely due to their simplicity and improved efficiency and accuracy over their Cahn-Hilliard-based counterparts. Additionally, the improved locality of the resulting LB equation (LBE) for the CPF models makes them more ideal candidates for LB simulation of multiphase flows on nonuniform grids, particularly within an adaptive-mesh refinement framework and massively parallel implementation. In this regard, some modifications-intended as improvements-have been made to the original CPF-LBE proposed by Geier et al. [Phys. Rev. E 91, 063309 (2015)PLEEE81539-375510.1103/PhysRevE.91.063309] which require further examination. The goal of the present study is to conduct a comparative investigation into the differences between the original CPF model proposed by Geier et al. [Phys. Rev. E 91, 063309 (2015)PLEEE81539-375510.1103/PhysRevE.91.063309] and the so-called improvements proposed by Ren et al. [Phys. Rev. E 94, 023311 (2016)2470-004510.1103/PhysRevE.94.023311] and Wang et al. [Phys. Rev. E 94, 033304 (2016)2470-004510.1103/PhysRevE.94.033304]. Using the Chapman-Enskog analysis, we provide a detailed derivation of the governing equations in each model and then examine the efficacy of the above-mentioned models for some benchmark problems. Several test cases have been designed to study different configurations ranging from basic yet informative flows to more complex flow fields, and the results are compared with finite-difference simulations. Furthermore, as a development of the previously proposed CPF-LBE model, axisymmetric formulations for the proposed model by Geier et al. [Phys. Rev. E 91, 063309 (2015)PLEEE81539-375510.1103/PhysRevE.91.063309] are derived and presented. Finally, two benchmark problems are designed to compare the proposed axisymmetric model with the analytical solution and previous work. We find that the accuracy of the model for interface tracking is roughly similar for different models at high viscosity ratios, high density ratios, and relatively high Reynolds numbers, while the original CFP-LBE without the additional time-dependent terms outperforms the so-called improved models in terms of efficiency, particularly on distributed parallel machines.

Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments.

Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.

Anionic nanoparticle and microplastic non-exponential distributions from source scale with grain size in environmental granular media.

Nanoparticle and microplastic (colloid) transport behaviors impact strategies for groundwater protection and remediation. Complex colloid transport behaviors of anionic nano- and micro-sized colloids have been previously elucidated via independent experiments in chemically-cleaned and amended granular media with grain sizes in the range of fine to coarse sand (e.g., 200-1000 µm). Such experiments show that under conditions where a repulsive barrier was present in colloid-collector interactions (unfavorable conditions), the distribution of retained colloids down-gradient from their source deviates from the exponential decrease expected from compounded loss across a series of collectors (grains). Previous experiments have not examined the impact of colloid size or granular media grain size on colloid distribution down-gradient from their source, particularly in streambed-equilibrated granular media. To address this gap, a field transport experiment in constructed wetland stream beds to distances up to 20 m were conducted for colloids ranging in size from micro to nano (60 nm-7 µm) in streambed-equilibrated pea gravel and sand (4200 and 420 µm mean grain sizes, respectively). All colloid sizes showed non-exponential (hyper-exponential) distributions from source, over meter scales in pea gravel versus cm scales reported for fine sand. Colloids in the ca. 1 µm size range were most mobile, as expected from mass transfer to surfaces and interaction with nanoscale heterogeneity. The distance over which non-exponential colloid distribution occurred increased with media grain size, which carries implications for the potential mechanism driving non-exponential colloid distribution from source, and for strategies to predict transport.

Climate change and the opportunity cost of conflict.

A growing empirical literature associates climate anomalies with increased risk of violent conflict. This association has been portrayed as a bellwether of future societal instability as the frequency and intensity of extreme weather events are predicted to increase. This paper investigates the theoretical foundation of this claim. A seminal microeconomic model of opportunity costs-a mechanism often thought to drive climate-conflict relationships-is extended by considering realistic changes in the distribution of climate-dependent agricultural income. Results advise caution in using empirical associations between short-run climate anomalies and conflicts to predict the effect of sustained shifts in climate regimes: Although war occurs in bad years, conflict may decrease if agents expect more frequent bad years. Theory suggests a nonmonotonic relation between climate variability and conflict that emerges as agents adapt and adjust their behavior to the new income distribution. We identify 3 measurable statistics of the income distribution that are each unambiguously associated with conflict likelihood. Jointly, these statistics offer a unique signature to distinguish opportunity costs from competing mechanisms that may relate climate anomalies to conflict.

Comprehensive comparison of pore-scale models for multiphase flow in porous media.

Multiphase flows in porous media are important in many natural and industrial processes. Pore-scale models for multiphase flows have seen rapid development in recent years and are becoming increasingly useful as predictive tools in both academic and industrial applications. However, quantitative comparisons between different pore-scale models, and between these models and experimental data, are lacking. Here, we perform an objective comparison of a variety of state-of-the-art pore-scale models, including lattice Boltzmann, stochastic rotation dynamics, volume-of-fluid, level-set, phase-field, and pore-network models. As the basis for this comparison, we use a dataset from recent microfluidic experiments with precisely controlled pore geometry and wettability conditions, which offers an unprecedented benchmarking opportunity. We compare the results of the 14 participating teams both qualitatively and quantitatively using several standard metrics, such as fractal dimension, finger width, and displacement efficiency. We find that no single method excels across all conditions and that thin films and corner flow present substantial modeling and computational challenges.

Transport and instream removal of the Cry1Ab protein from genetically engineered maize is mediated by biofilms in experimental streams.

The majority of maize planted in the US is genetically-engineered to express insecticidal properties, including Cry1Ab protein, which is designed to resist the European maize borer (Ostrinia nubilalis). After crop harvest, these proteins can be leached into adjacent streams from crop detritus left on fields. The environmental fate of Cry1Ab proteins in aquatic habitats is not well known. From June-November, we performed monthly short-term additions of leached Cry1Ab into four experimental streams with varying benthic substrate to estimate Cry1Ab transport and removal. At the start of the experiments, when rocks were bare, we found no evidence of Cry1Ab removal from the water column, but uptake steadily increased as biofilm colonized the stream substrate. Overall, Cry1Ab uptake was strongly predicted by measures of biofilm accumulation, including algal chlorophyll a and percent cover of filamentous algae. Average Cry1Ab uptake velocity (vf = 0.059 ± 0.009 mm s-1) was comparable to previously reported uptake of labile dissolved organic carbon (DOC; mean vf = 0.04 ± 0.008 mm s-1). Although Cry1Ab has been shown to rapidly degrade in stream water, benthic biofilms may decrease the distance proteins are transported in lotic systems. These results emphasize that once the Cry1Ab protein is leached, subsequent detection and transport through agricultural waterways is dependent on the structure and biology of receiving stream ecosystems.