Modeled Pathways and Fluxes of PCB Dechlorination by Redox Potentials.

Dechlorination is one of the main processes for the natural degradation of polychlorinated biphenyls (PCBs) in an anaerobic environment. However, PCB dechlorination pathways and products vary with PCB congeners, types of functional dechlorinating bacteria, and environmental conditions. The present study develops a novel model for determining dechlorination pathways and fluxes by tracking redox potential variability, transforming the complex dechlorination process into a stepwise sequence. The redox potential is calculated via the Gibbs free energy of formation, PCB concentrations in reactants and products, and environmental conditions. Thus, the continuous change in the PCB congener composition can be tracked during dechlorination processes. The new model is assessed against four measurements from several published studies on PCB dechlorination. The simulation errors in all four measurements are calculated between 2.67 and 35.1% under minimum (n = 0) and maximum (n = 34) numbers of co-eluters, respectively. The dechlorination fluxes for para-dechlorination pathways dominate PCB dechlorination in all measurements. Furthermore, the model also considers multiple-step dechlorination pathways containing intermediate PCB congeners absent in both the reactants and the products. The present study indicates that redox potential might be an appropriate indicator for predicting PCB dechlorination pathways and fluxes even without prior knowledge of the functional dechlorinating bacteria.

A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems.

Sustainability challenges, such as solid waste management, are usually scientifically complex and data scarce, which makes them not amenable to science-based analytical forms or data-intensive learning paradigms. Deep integration between data science and sustainability science in highly complementary manners offers new opportunities for tackling these conundrums. This study develops a novel hybrid neural network (HNN) model that imposes the holistic decision-making context of solid waste management systems (SWMS) on a traditional neural network (NN) architecture. Equipped with adaptable hybridization designs of hand-crafted model structure, constrained or predetermined parameters, and a customized loss function, the HNN model is capable of learning various technical, economic, and social aspects of SWMS from a small and heterogeneous data set. In comparison, the versatile HNN model not only outperforms traditional NN models in convergence rates, which leads to a 22% lower mean testing error of 0.20, but also offers superior interpretability. The HNN model is capable of generating insights into the enabling factors, policy interventions, and driving forces of SWMS, laying a solid foundation for data-driven decision making.

Assessment of Lead in Drinking Water from Multiple Drinking Water Sampling Programs for a Midsize City.

Following an exceedance of the lead action level for drinking water in 2016, the Pittsburgh Water and Sewer Authority (PWSA) undertook two sampling programs: the required biannual Lead and Copper Rule (LCR) compliance testing and a home sampling program based on customer requests. The LCR sampling results, at locations expected to be elevated when corrosion is not well controlled, had higher concentrations than customer-requested homes, with 90th percentile values for the LCR sites exceeding the action level through 2019 (except for June 2018). Customer-requested concentrations showed greater variability, with the median lead concentration for customer-requested samples below detection for each year of sampling, suggesting only some homes show elevated lead when corrosion control is not fully effective. Corrosion control adjustments brought the utility back into compliance in 2020 (LCR 90th percentile of 5.1 ppb in June 2020); customer-requested sampling after the addition of orthophosphate indicated below detection levels for 59% of samples. Monte Carlo simulations indicate LCR samples do not all represent high lead risk sites, and the application of corrosion control more significantly affects higher lead concentration sites. Broader water quality sampling provides information about specific homes but is not well suited to assessing the efficacy of corrosion control efforts by utilities.

Forecast of the U.S. Copper Demand: a Framework Based on Scenario Analysis and Stock Dynamics.

In a world of finite metallic minerals, demand forecasting is crucial for managing the stocks and flows of these critical resources. Previous studies have projected copper supply and demand at the global level and the regional level of EU and China. However, no comprehensive study exists for the U.S., which has displayed unique copper consumption and dematerialization trends. In this study, we adapted the stock dynamics approach to forecast the U.S. copper in-use stock (IUS), consumption, and end-of-life (EOL) flows from 2016 to 2070 under various U.S.-specific scenarios. Assuming different socio-technological development trajectories, our model results are consistent with a stabilization range of 215-260 kg/person for the IUS. This is projected along with steady growth in the annual copper consumption and EOL copper generation driven mainly by the growing U.S. population. This stabilization trend of per capita IUS indicates that future copper consumption will largely recuperate IUS losses, allowing 34-39% of future demand to be met potentially by recycling 43% of domestic EOL copper. Despite the recent trends of "dematerialization", adaptive policies still need to be designed for enhancing the EOL recovery, especially in light of a potential transitioning to a "green technology" future with increased electrification dictating higher copper demand.

A Decision-Centered Method to Evaluate Natural Hazards Decision Aids by Interdisciplinary Research Teams.

There is a growing number of decision aids made available to the general public by those working on hazard and disaster management. When based on high-quality scientific studies across disciplines and designed to provide a high level of usability and trust, decision aids become more likely to improve the quality of hazard risk management and response decisions. Interdisciplinary teams have a vital role to play in this process, ensuring the scientific validity and effectiveness of a decision aid across the physical science, social science, and engineering dimensions of hazard awareness, option identification, and the decisions made by individuals and communities. Often, these aids are not evaluated before being widely distributed, which could improve their impact, due to a lack of dedicated resources and guidance on how to systematically do so. In this Perspective, we present a decision-centered method for evaluating the impact of hazard decision aids on decisionmaker preferences and choice during the design and development phase, drawing from the social and behavioral sciences and a value of information framework to inform the content, complexity, format, and overall evaluation of the decision aid. The first step involves quantifying the added value of the information contained in the decision aid. The second involves identifying the extent to which the decision aid is usable. Our method can be applied to a variety of hazards and disasters, and will allow interdisciplinary teams to more effectively evaluate the extent to which an aid can inform and improve decision making.

Using Compliance Data to Understand Uncertainty in Drinking Water Lead Levels in Southwestern Pennsylvania.

The historical use of lead in potable water plumbing systems has caused significant public health challenges. The Lead and Copper Rule requires utilities to take action if the 90th percentile lead concentration exceeds the action level (AL) of 15 ppb. Assessment of the AL is based on a sample of homes representing a relatively small fraction of connections. Due to the intentional nonrepresentative sampling approach, the full set of conditions influencing lead concentrations in a large distribution system may be poorly characterized. Further, there is uncertainty in assessing statistical parameters such as the 90th percentile concentration. This work demonstrates methods to compute the uncertainty in the 90th percentile statistic and assesses the associated effect on compliance outcomes. The method is demonstrated on four utilities in southwest Pennsylvania (referred to as A, B, C, and D). For Utility A, evaluation of the 90th percentile showed an increase over time in observed and estimated values and the value's uncertainty. This type of change in the uncertainty might have served as an early warning of the exceedance that followed. This could have triggered more timely review of operational changes in order to avoid the effects of noncompliance on utility costs and consumer confidence.

A probabilistic model for designing and assessing the performance of eDNA sampling protocols.

Environmental DNA (eDNA) sampling, the detection of species-specific genetic material in water samples, is an emerging tool for monitoring aquatic invasive species. Optimizing eDNA sampling protocols can be challenging because there is imperfect understanding of how each step of the protocol influences its sensitivity. This paper develops a probabilistic model that characterizes each step of an eDNA sampling protocol to evaluate the protocol's overall detection sensitivity for one sample. The model is then applied to analyse how changes over time made to the eDNA sampling protocol to detect bighead (BH) and silver carp (SC) eDNA have influenced its sensitivity, and hence interpretation of the results. The model shows that changes to the protocol have caused the sensitivity of the protocol to fluctuate. A more efficient extraction method in 2013, new species-specific markers with a qPCR assay in 2014, and a more efficient capture method in 2015 have improved the sensitivity, while switching to a larger elution volume in 2013 and a smaller sample volume in 2015 have reduced the sensitivity. Overall, the sensitivity of the current protocol is higher for BH eDNA detection and SC eDNA detection compared to the original protocol used from 2009 to 2012. The paper shows how this model of eDNA sampling can be used to evaluate the effect of proposed changes in an eDNA sampling and analysis protocol on the sensitivity of that protocol to help researchers optimize their design.

Bayesian network-based framework for exposure-response study design and interpretation.

Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.

A population-based simultaneous fugacity model design for polychlorinated biphenyls (PCBs) transport in an aquatic system.

A population-based bioaccumulation fugacity model is designed to simulate the continuous and dynamic transport of polychlorinated bisphenols (PCBs) in an aquatic environment. The extended model is developed based on a previous fugacity model by Campfens and Mackay. The new model identifies each biotic species as a populated compartment and constructs all the exchange routes between organisms and the environment based on known biological processes. The population-based design could assist to uncover the impacts of organism activities on PCB fate and transport in the ecosystem. The new model utilizes the PCB loading as inputs and calculates the PCB distribution in each biotic and environmental compartment simultaneously.

Modeling the impact of biota on polychlorinated biphenyls (PCBs) fate and transport in Lake Ontario using a population-based multi-compartment fugacity approach.

Organisms have long been treated as receptors in exposure studies of polychlorinated biphenyls (PCBs) and other persistent organic pollutants (POPs). The influences of environmental pollution on organisms are well recognized. However, the impact of biota on PCB transport in an environmental system has not been considered in sufficient detail. In this study, a population-based multi-compartment fugacity model is developed by reconfiguring the organisms as populated compartments and reconstructing all the exchange processes between the organism compartments and environmental compartments, especially the previously ignored feedback routes from biota to the environment. We evaluate the model performance by simulating the PCB concentration distribution in Lake Ontario using published loading records. The lake system is divided into three environment compartments (air, water, and sediment) and several organism groups according to the dominant local biotic species. The comparison indicates that the simulated results are well-matched by a list of published field measurements from different years. We identify a new process, called Facilitated Biotic Intermedia Transport (FBIT), to describe the enhanced pollution transport that occurs between environmental media and organisms. As the hydrophobicity of PCB congener increases, the organism population exerts greater influence on PCB mass flows. In a high biomass scenario, the model simulation indicates significant FBIT effects and biotic storage effects with hydrophobic PCB congeners, which also lead to significant shifts in systemic contaminant exchange rates between organisms and the environment.

Making sense of the noise: The effect of hydrology on silver carp eDNA detection in the Chicago area waterway system.

Environmental DNA (eDNA) sampling is an emerging tool for monitoring the spread of aquatic invasive species. One confounding factor when interpreting eDNA sampling evidence is that eDNA can be present in the water in the absence of living target organisms, originating from excreta, dead tissue, boats, or sewage effluent, etc. In the Chicago Area Waterway System (CAWS), electric fish dispersal barriers were built to prevent non-native Asian carp species from invading Lake Michigan, and yet Asian carp eDNA has been detected above the barriers sporadically since 2009. In this paper the influence of stream flow characteristics in the CAWS on the probability of invasive Asian carp eDNA detection in the CAWS from 2009 to 2012 was examined. In the CAWS, the direction of stream flow is mostly away from Lake Michigan, though there are infrequent reversals in flow direction towards Lake Michigan during dry spells. We find that the flow reversal volume into the Lake has a statistically significant positive relationship with eDNA detection probability, while other covariates, like gage height, precipitation, season, water temperature, dissolved oxygen concentration, pH and chlorophyll concentration do not. This suggests that stream flow direction is highly influential on eDNA detection in the CAWS and should be considered when interpreting eDNA evidence. We also find that the beta-binomial regression model provides a stronger fit for eDNA detection probability compared to a binomial regression model. This paper provides a statistical modeling framework for interpreting eDNA sampling evidence and for evaluating covariates influencing eDNA detection.

A sequential model to link contextual risk, perception and public support for flood adaptation policy.

The economic damage from coastal flooding has dramatically increased over the past several decades, owing to rapid development in shoreline areas and possible effects of climate change. To respond to these trends, it is imperative for policy makers to understand individuals' support for flood adaptation policy. Using original survey data for all coastal counties of the United States Gulf Coast merged with contextual data on flood risk, this study investigates coastal residents' support for two adaptation policy measures: incentives for relocation and funding for educational programs on emergency planning and evacuation. Specifically, this study explores the interactive relationships among contextual flood risks, perceived flood risks and policy support for flood adaptation, with the effects of social-demographic variables being controlled. Age, gender, race and partisanship are found to significantly affect individuals' policy support for both adaptation measures. The contextual flooding risks, indicated by distance from the coast, maximum wind speed and peak height of storm surge associated with the last hurricane landfall, and percentage of high-risk flood zone per county, are shown to impact one's perceptions of risk, which in turn influence one's support for both policy measures. The key finding -risk perception mediates the impact of contextual risk conditions on public support for flood management policies - highlights the need to ensure that the public is well informed by the latest scientific, engineering and economic knowledge. To achieve this, more information on current and future flood risks and options available for mitigation as well as risk communication tools are needed.

Estimating the effect of multiple environmental stressors on coral bleaching and mortality.

Coral cover has been declining in recent decades due to increased temperatures and environmental stressors. However, the extent to which different stressors contribute both individually and in concert to bleaching and mortality is still very uncertain. We develop and use a novel regression approach, using non-linear parametric models that control for unobserved time invariant effects to estimate the effects on coral bleaching and mortality due to temperature, solar radiation, depth, hurricanes and anthropogenic stressors using historical data from a large bleaching event in 2005 across the Caribbean. Two separate models are created, one to predict coral bleaching, and the other to predict near-term mortality. A large ensemble of supporting data is assembled to control for omitted variable bias and improve fit, and a significant improvement in fit is observed from univariate linear regression based on temperature alone. The results suggest that climate stressors (temperature and radiation) far outweighed direct anthropogenic stressors (using distance from shore and nearby human population density as a proxy for such stressors) in driving coral health outcomes during the 2005 event. Indeed, temperature was found to play a role ~4 times greater in both the bleaching and mortality response than population density across their observed ranges. The empirical models tested in this study have large advantages over ordinary-least squares-they offer unbiased estimates for censored data, correct for spatial correlation, and are capable of handling more complex relationships between dependent and independent variables. The models offer a framework for preparing for future warming events and climate change; guiding monitoring and attribution of other bleaching and mortality events regionally and around the globe; and informing adaptive management and conservation efforts.

Impacts of PCB analytical interpretation uncertainties on dechlorination assessment and remedial decisions.

Laboratory analyses of polychlorinated biphenyls (PCBs) often do not quantitate the 209 individual PCB congeners, thereby requiring analyst interpretation to determine individual congener concentrations. Error introduced during this interpretation is subsequently propagated to calculated surrogate variables, such as the number of chlorines per biphenyl (CPB), and the molar dechlorination product ratio (MDPR), which are used to assess the extent of dechlorination and inform remedial decisions. The present work applies a Monte Carlo (MC) analysis to assess current methods for quantitating co-eluting congeners and the errors that could occur in individual congeners and derived CPB and MDPR estimates. Synthetic chromatograms, which were created using two alternative methods (random assignment and assignment based on relative proportions in Aroclors) for assigning mass to co-eluting congeners, were compared to their fully-quantitated counterparts. The percent error introduced in total PCB (∑PCB) concentration ranges from approximately -60% to +50%. Similarly, the errors associated with CPB and MDPR estimates range from approximately -20% to +20% and -120% to +30%, respectively. Uncertainties introduced during congener analysis and propagated to surrogate variables can thus be substantial, and should be considered in assessments of the extent of dechlorination and associated remedial decisions.

Quantifying the benefit of wellbore leakage potential estimates for prioritizing long-term MVA well sampling at a CO2 storage site.

This work uses probabilistic methods to simulate a hypothetical geologic CO2 storage site in a depleted oil and gas field, where the large number of legacy wells would make it cost-prohibitive to sample all wells for all measurements as part of the postinjection site care. Deep well leakage potential scores were assigned to the wells using a random subsample of 100 wells from a detailed study of 826 legacy wells that penetrate the basal Cambrian formation on the U.S. side of the U.S./Canadian border. Analytical solutions and Monte Carlo simulations were used to quantify the statistical power of selecting a leaking well. Power curves were developed as a function of (1) the number of leaking wells within the Area of Review; (2) the sampling design (random or judgmental, choosing first the wells with the highest deep leakage potential scores); (3) the number of wells included in the monitoring sampling plan; and (4) the relationship between a well's leakage potential score and its relative probability of leakage. Cases where the deep well leakage potential scores are fully or partially informative of the relative leakage probability are compared to a noninformative base case in which leakage is equiprobable across all wells in the Area of Review. The results show that accurate prior knowledge about the probability of well leakage adds measurable value to the ability to detect a leaking well during the monitoring program, and that the loss in detection ability due to imperfect knowledge of the leakage probability can be quantified. This work underscores the importance of a data-driven, risk-based monitoring program that incorporates uncertainty quantification into long-term monitoring sampling plans at geologic CO2 storage sites.

Correlation of the physicochemical properties of natural organic matter samples from different sources to their effects on gold nanoparticle aggregation in monovalent electrolyte.

Engineered nanoparticles (NPs) released into natural environments will interact with natural organic matter (NOM) or humic substances, which will change their fate and transport behavior. Quantitative predictions of the effects of NOM are difficult because of its heterogeneity and variability. Here, the effects of six types of NOM and molecular weight fractions of each on the aggregation of citrate-stabilized gold NPs are investigated. Correlations of NP aggregation rates with electrophoretic mobility and the molecular weight distribution and chemical attributes of NOM (including UV absorptivity or aromaticity, functional group content, and fluorescence) are assessed. In general, the >100 kg/mol components provide better stability than lower molecular weight components for each type of NOM, and they contribute to the stabilizing effect of the unfractionated NOM even in small proportions. In many cases, unfractionated NOM provided better stability than its separated components, indicating a synergistic effect between the high and low molecular weight fractions for NP stabilization. Weight-averaged molecular weight was the best single explanatory variable for NP aggregation rates across all NOM types and molecular weight fractions. NP aggregation showed poorer correlation with UV absorptivity, but the exponential slope of the UV-vis absorbance spectrum was a better surrogate for molecular weight. Functional group data (including reduced sulfur and total nitrogen content) were explored as possible secondary parameters to explain the strong stabilizing effect of a low molecular weight Pony Lake fulvic acid sample to the gold NPs. These results can inform future correlations and measurement requirements to predict NP attachment in the presence of NOM.

Risks and risk governance in unconventional shale gas development.

A broad assessment is provided of the current state of knowledge regarding the risks associated with shale gas development and their governance. For the principal domains of risk, we identify observed and potential hazards and promising mitigation options to address them, characterizing current knowledge and research needs. Important unresolved research questions are identified for each area of risk; however, certain domains exhibit especially acute deficits of knowledge and attention, including integrated studies of public health, ecosystems, air quality, socioeconomic impacts on communities, and climate change. For these, current research and analysis are insufficient to either confirm or preclude important impacts. The rapidly evolving landscape of shale gas governance in the U.S. is also assessed, noting challenges and opportunities associated with the current decentralized (state-focused) system of regulation. We briefly review emerging approaches to shale gas governance in other nations, and consider new governance initiatives and options in the U.S. involving voluntary industry certification, comprehensive development plans, financial instruments, and possible future federal roles. In order to encompass the multiple relevant disciplines, address the complexities of the evolving shale gas system and reduce the many key uncertainties needed for improved management, a coordinated multiagency federal research effort will need to be implemented.

When can scientific studies promote consensus among conflicting stakeholders?

While scientific studies may help conflicting stakeholders come to agreement on a best management option or policy, often they do not. We review the factors affecting trust in the efficacy and objectivity of scientific studies in an analytical-deliberative process where conflict is present, and show how they may be incorporated in an extension to the traditional Bayesian decision model. The extended framework considers stakeholders who differ in their prior beliefs regarding the probability of possible outcomes (in particular, whether a proposed technology is hazardous), differ in their valuations of these outcomes, and differ in their assessment of the ability of a proposed study to resolve the uncertainty in the outcomes and their hazards--as measured by their perceived false positive and false negative rates for the study. The Bayesian model predicts stakeholder-specific preposterior probabilities of consensus, as well as pathways for increasing these probabilities, providing important insights into the value of scientific information in an analytic-deliberative decision process where agreement is sought. It also helps to identify the interactions among perceived risk and benefit allocations, scientific beliefs, and trust in proposed scientific studies when determining whether a consensus can be achieved. The article provides examples to illustrate the method, including an adaptation of a recent decision analysis for managing the health risks of electromagnetic fields from high voltage transmission lines.

Spatial stochastic modeling of sedimentary formations to assess CO2 storage potential.

Carbon capture and sequestration (CCS) is a technology that provides a near-term solution to reduce anthropogenic CO2 emissions to the atmosphere and reduce our impact on the climate system. Assessments of carbon sequestration resources that have been made for North America using existing methodologies likely underestimate uncertainty and variability in the reservoir parameters. This paper describes a geostatistical model developed to estimate the CO2 storage resource in sedimentary formations. The proposed stochastic model accounts for the spatial distribution of reservoir properties and is implemented in a case study of the Oriskany Formation of the Appalachian sedimentary basin. Results indicate that the CO2 storage resource for the Pennsylvania part of the Oriskany Formation has substantial spatial variation due to heterogeneity of formation properties and basin geology leading to significant uncertainty in the storage assessment. The Oriskany Formation sequestration resource estimate in Pennsylvania calculated with the effective efficiency factor, E=5%, ranges from 0.15 to 1.01 gigatonnes (Gt) with a mean value of 0.52 Gt of CO2 (E=5%). The methodology is generalizable to other sedimentary formations in which site-specific trend analyses and statistical models are developed to estimate the CO2 sequestration storage capacity and its uncertainty. More precise CO2 storage resource estimates will provide better recommendations for government and industry leaders and inform their decisions on which greenhouse gas mitigation measures are best fit for their regions.