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.
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.
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•Hydrologic conditions influence the detection of silver carp eDNA in the Chicago Area Waterways System in the absence of adult silver carp.•Positive eDNA detections are more probable during periods of low flow, making the interpretation of eDNA results a function of hydrologic conditions.•A beta-binomial model out-performs a binomial model for modeling detection probability.
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.
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•Hypothetical framework on policy support for adaptation is tested.•Contextual flooding risks significantly impact risk perceptions.•Contextual flooding risks does not impact policy support for adaptation.•Risk perception mediates the contextual risks on support for adaptation.
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.
Improved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and ...tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Niño–Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision‐making processes.
Multicategory reliability diagram for the calibrated rainfall forecast in the early wet season driven by the output of six climate models (separated into two plots for readability).
•A Bayesian Network links storm damage and human behavioral response.•Highly variable event outcomes stipulate that mitigation regret is likely.•Decision maker use of climate forecasts can reduce the ...probability of regret.•Climate studies on future sea level and storm frequency have value, if utilized.
A Bayesian network model is developed to explore the interaction between physical and social processes that influence mitigation decisions and outcomes for extreme events. The network includes statistical relationships for event occurrence and magnitude; uncertainty in the parameters of these models; a high degree of variability in the sequence of events that occurs in any given time interval, and the possibility of long-term trends in the frequency, magnitude and impact of events. The model is applied to coastal storm surge events in the New York City (NYC) area. A 50 cm increase in sea level is predicted to approximately double the expected cumulative damage over a 40-year period. A 20% increment in storm frequency yields a further predicted increase of about 18% in the cumulative damage. The uncertainties in long-term trends associated with climate change may be reduced by scientific studies. However the value of this information is affected both by study accuracy and the extent of its trust, acceptance and utilization by decision makers. Implications of this are assessed in the model, showing that the probability of regret is notably reduced when climate study results are used to support mitigation decisions. This is demonstrated even when the studies have relatively low accuracy, moreso when they exhibit good or perfect accuracy. Based on model insights and limitations, further research needs are identified to better understand extreme event risk perception and management in coupled human-environmental systems.
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.
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.
The U.S. Department of Energy has estimated that if the United States is to generate 20% of its electricity from wind, over 50 GW will be required from shallow offshore turbines. Hurricanes are a ...potential risk to these turbines. Turbine tower buckling has been observed in typhoons, but no offshore wind turbines have yet been built in the United States. We present a probabilistic model to estimate the number of turbines that would be destroyed by hurricanes in an offshore wind farm. We apply this model to estimate the risk to offshore wind farms in four representative locations in the Atlantic and Gulf Coastal waters of the United States. In the most vulnerable areas now being actively considered by developers, nearly half the turbines in a farm are likely to be destroyed in a 20-y period. Reasonable mitigation measures—increasing the design reference wind load, ensuring that the nacelle can be turned into rapidly changing winds, and building most wind plants in the areas with lower risk—can greatly enhance the probability that offshore wind can help to meet the United States' electricity needs.
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.
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