Scholarly Works, Civil and Environmental Engineering

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Formation of late-generation atmospheric compounds inhibited by rapid deposition
Bi, Chenyang; Isaacman-VanWertz, Gabriel (Nature Portfolio, 2025-02-17)
Reactive organic carbon species are important fuel for atmospheric chemical reactions, including the formation of secondary organic aerosol. However, in parallel to atmospheric oxidation processes, deposition can remove compounds from the atmosphere and impact downstream environments. To understand the impact of deposition on atmospheric oxidation, we present a framework for predicting and visualizing the fate of a molecule on the basis of the physicochemical properties of compounds (Henry’s law constant, vapour pressure and reaction rate constants), which are used to estimate timescales for oxidation and deposition. By implementing our deposition rates in chemical models, we show that deposition substantially suppresses atmospheric reactivity and aerosol formation by removing early-generation products and preventing the formation of large fractions (up to 90%) of downstream, late-generation compounds. Deposition is frequently missing in the laboratory experiments and detailed chemical modelling, which probably biases our understanding of atmospheric composition.
Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals
Eteifa, Seifeldeen; Shafik, Amr; Eldardiry, Hoda; Rakha, Hesham A. (MDPI, 2025-03-07)
Predicting Signal Phase and Timing (SPaT) information and confidence levels is needed to enhance Green Light Optimal Speed Advisory (GLOSA) and/or Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information obtained from traffic signal controllers for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 s. Task two is predicting the exact change time within 20 s. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. Specifically, for the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 s, compared to 1.63 s for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time.
ShipNetSim: An Open-Source Simulator for Real-Time Energy Consumption and Emission Analysis in Large-Scale Maritime Networks
Aredah, Ahmed; Rakha, Hesham A. (MDPI, 2025-03-08)
The imperative of decarbonization in maritime shipping is underscored by the sector’s sizeable contribution to worldwide greenhouse gas emissions. ShipNetSim, an open-source multi-ship simulator created in this study, combines state-of-the-art hydrodynamic modeling, dynamic ship-following techniques, real-time environmental data, and cybersecurity threat simulation to quantify and evaluate marine fuel consumption and CO₂ emissions. ShipNetSim uses well-validated approaches, such as the Holtrop resistance and B-Series propeller analysis with a ship-following model inspired by traffic flow theory, augmented with a novel module simulating cyber threats (e.g., GPS spoofing) to evaluate operational efficiency and resilience. In a case study simulation of the journey of an S175 container vessel from Savannah to Algeciras, the simulator estimated the total fuel consumption to be 478 tons of heavy fuel oil and approximately 1495 tons of CO₂ emissions for a trip of 7 days and 15 h within 13.1% of reported operational estimates. A twelve-month sensitivity analysis revealed a marginal 1.5% range of fuel consumption variation, demonstrating limiting variability for different environmental conditions. ShipNetSim not only yields realistic predictions of energy consumption and emissions but is also demonstrated to be a credible framework for the evaluation of operational scenarios—including speed adjustment, optimized routing, and alternative fuel strategies—that directly contribute to reducing the marine carbon footprint. This capability supports industry stakeholders and policymakers in achieving compliance with global decarbonization targets, such as those established by the International Maritime Organization (IMO).
Deicing Facility Capacity and Delay Estimation Using ASDE-X Data: Chicago O’Hare Simulation Case Study
Alsalous, Osama; Hotle, Susan (Sage, 2023-08-08)
The main 34 airports in the United States reported 1,792,877 aircraft-minutes of departure delay related to ice and snow conditions in 2019 alone. This delay includes deicing aircraft at a centralized deicing facility, increasing the taxi-out phase. In this study, Airport surface detection equipment, Model X (ASDE-X) data at Chicago O’Hare International Airport (ORD) are used to extract deicing times based on aircraft movements within the centralized deicing facility. Airline-related information is parsed as individual lanes of the deicing facility are controlled by the airlines, determining which lanes a flight can use. The extracted deicing time for each aircraft type is then used to develop a simulation model evaluating the efficiency of the centralized deicing facility. Different queueing scenarios are tested for operational performance improvements. Our results show that if all deicing pads were open to all flights, removing airline control, a first come first served (FCFS) strategy with the pads having individual queues could save 25.1% of the aircraft-minutes spent in the deicing system compared with the currently implemented queueing process (baseline scenario). Removing airline control using a FCFS with a combined queue approach reduced aircraft-minutes by 17.8%. Assigning flights to lanes based on their mean deicing time (slow, medium, and fast groups) increased aircraft-minutes by 1.3% on average. This is the first study in the literature to use ASDE-X data from an airport located in the United States to provide data-driven deicing facility capacities and queueing recommendations.
Digital Surface-Enhanced Raman Spectroscopy-Lateral Flow Test Dipstick: Ultrasensitive, Rapid Virus Quantification in Environmental Dust
Wang, Wei; Srivastava, Sonali; Garg, Aditya; Xiao, Chuan; Hawks, Seth; Pan, Jin; Duggal, Nisha; Isaacman-VanWertz, Gabriel; Zhou, Wei; Marr, Linsey C.; Vikesland, Peter J. (American Chemical Society, 2024-03-07)
This study introduces a novel surface-enhanced Raman spectroscopy (SERS)-based lateral flow test (LFT) dipstick that integrates digital analysis for highly sensitive and rapid viral quantification. The SERS-LFT dipsticks, incorporating gold-silver core-shell nanoparticle probes, enable pixel-based digital analysis of large-area SERS scans. Such an approach enables ultralow-level detection of viruses that readily distinguishes positive signals from background noise at the pixel level. The developed digital SERS-LFTs demonstrate limits of detection (LODs) of 180 fg for SARS-CoV-2 spike protein, 120 fg for nucleocapsid protein, and 7 plaque forming units for intact virus, all within <30 min. Importantly, digital SERS-LFT methods maintain their robustness and their LODs in the presence of indoor dust, thus underscoring their potential for accurate and reliable virus diagnosis and quantification in real-world environmental settings.
Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review
Srivastava, Sonali; Wang, Wei; Zhou, Wei; Jin, Ming; Vikesland, Peter J. (American Chemical Society, 2024-11-13)
Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.
DC vs AC Electrokinetics-Driven Nanoplasmonic Raman Monitoring of Charged Analyte Molecules in Ionic Solutions
Xiao, Chuan; Wang, Xin; Zhao, Yuming; Zhang, Hongwei; Song, Junyeob; Vikesland, Peter J.; Qiao, Rui; Zhou, Wei (American Chemical Society, 2024-08-31)
Electrokinetic surface-enhanced Raman spectroscopy (EK-SERS) is an emerging high-order analytical technique that combines the plasmonic sensitivity of SERS with the electrode interfacial molecular control of electrokinetics. However, previous EK-SERS works primarily focused on non-Faradaic direct current (DC) operation, limiting the understanding of the underlying mechanisms. Additionally, developing reliable EK-SERS devices with electrically connected plasmonic hotspots remains challenging for achieving high sensitivity and reproducibility in EK-SERS measurements. In this study, we investigated the use of two-tier nanolaminate nano-optoelectrode arrays (NL-NOEAs) for DC and alternating current (AC) EK-SERS measurements of charged analyte molecules in ionic solutions. The NL-NOEAs consist of Au/Ag/Au multilayered plasmonic nanostructures on conductive nanocomposite nanopillar arrays (NC-NPAs). We demonstrate that the NL-NOEAs exhibit high SERS enhancement factors (EFs) of ∼106 and can be used to modulate the concentration and orientation of Rhodamine 6G (R6G) molecules at the electrode surface by applying DC and AC voltages. We also performed numerical simulations to investigate the ion and R6G dynamics near the electrode surface under DC and AC voltage modulation. Our results show that AC EK-SERS can provide additional insights into the dynamics of molecular transport and adsorption processes compared to DC EK-SERS. This study demonstrates the potential of NL-NOEAs for developing high-performance EK-SERS sensors for a wide range of applications.
Simulating the emergence of institutions that reverse freshwater salinization: An agent-based modeling approach
Armstrong, Kingston; Zhong, Yinman; Bhide, Shantanu V.; Grant, Stanley B.; Birkland, Thomas; Berglund, Emily Zechman (Elsevier, 2024-12-01)
Salt concentration in global freshwater supplies has increased steadily, leading to the Freshwater Salinization Syndrome (FSS). To curb the FSS, stakeholders can self-organize to develop institutions, or a set of rules that limit salt emissions. This research develops an agent-based modeling framework to explore how institutions reverse the FSS. Property owners are represented as agents that apply rules of behavior to apply salt to deice pavement in response to winter weather, vote on institutions, and comply with or defect from institutions. Salt enters the soil-groundwater system through infiltration, which is modeled using a transit time distribution approach. Results demonstrate that stable institutions lead to positive economic outcomes for stakeholders, based on their ability to apply salt during winter events and access high-quality drinking water. Simulations are analyzed to explore institutions, or limits to the application of salt, that emerge based on the interactions of stakeholders as they agree on salt application limits, the intensity of monitoring for defectors, and sanctions. Institutions that emerge effectively limit the concentration of salt in drinking water. The emergence of stable institutions low rates of innovation among stakeholders, and the concentration of salt in groundwater exceeds standards due to high rates of defection among stakeholders. This research demonstrates how self-organized institutions can lead to sustainable application strategies that reverse the FSS.
Toward a Universal Model of Hyporheic Exchange and Nutrient Cycling in Streams
Monofy, Ahmed; Grant, Stanley B.; Boano, Fulvio; Rippy, Megan A.; Gomez-Velez, Jesus D.; Kaushal, Sujay S.; Hotchkiss, Erin R.; Shelton, Sydney (American Geophysical Union, 2024-11-12)
In this paper we demonstrate that several ubiquitous hyporheic exchange mechanisms can be represented simply as a one-dimensional diffusion process, where the diffusivity decays exponentially with depth into the streambed. Based on a meta-analysis of 106 previously published laboratory measurements of hyporheic exchange (capturing a range of bed morphologies, hydraulic conditions, streambed properties, and experimental approaches) we find that the reference diffusivity and mixing length-scale are functions of the permeability Reynolds Number and Schmidt Number. These dimensionless numbers, in turn, can be estimated for a particular stream from the median grain size of the streambed and the stream's depth, slope, and temperature. Application of these results to a seminal study of nitrate removal in 72 headwater streams across the United States, reveals: (a) streams draining urban and agricultural landscapes have a diminished capacity for in-stream and in-bed mixing along with smaller subsurface storage zones compared to streams draining reference landscapes; (b) under steady-state conditions nitrate uptake in the streambed is primarily biologically controlled; and (c) median reaction timescales for nitrate removal in the hyporheic zone are (Formula presented.) 0.5 and 20 hr for uptake by assimilation and denitrification, respectively. While further research is needed, the simplicity and extensibility of the framework described here should facilitate cross-disciplinary discussions and inform reach-scale studies of pollutant fate and transport and their scale-up to watersheds and beyond.
The impact of deicer and anti-icer use on plant communities in stormwater detention basins: Characterizing salt stress and phytoremediation potential
Long, Samuel; Rippy, Megan A.; Krauss, Lauren M.; Stacey, Melissa; Fausey, Kaitlin (2025-01-25)
We present the results of a 1-year study that quantified salt levels in stormwater, soils, and plant tissues from 14 stormwater detention basins across Northern VA in an above-average snow year. We characterize (1) the level of salt stress plants experience, (2) the extent to which current plant communities feature salt tolerant species, and (3) the capacity of these species to phytoremediate soils and reduce the impacts of deicer and anti-icer use. Our results suggest that detention basin vegetation experience a range of salt stress levels that depend on drainage area type (roads: moderate to high > parking lots: low to moderate > pervious areas: none). Established thresholds for salt sensitive vegetation (Na+, Cl+, electrical conductivity, sodium adsorption ratio, exchangeable sodium percentage) were exceeded at least twice in stormwater or soils from all systems draining roads and half of systems draining parking lots. Winter exceedances were most common, but saline conditions did persist into the growing season, particularly at sites draining roads. Two hundred fifty-five plant species were identified across all detention basins, including 48 natives capable of tolerating elevated salt levels (electrical conductivity ≥2 dS/m). Within-tissue concentrations of sodium and chloride ions were highest in Typha (latifolia and angustifolia) (11.1 mg Na+/g; 30 mg Cl−/g), making it our top phytoremediation candidate. Scaling these concentrations up, we estimate that a standard-size highway detention basin (2000–3000 m2) with 100 % cattail cover can phytoremediate up to 100 kg of Na+ and 200 kg of Cl− per year. Uptake at this level is not sufficient to offset winter salt application, constituting only 5–6 % of basin inputs. This suggests that phytoremediation should not be considered a standalone solution to basin salinization, although it could be one approach of many in a broader salt management strategy.
Atmospheric Deposition of Microplastics in South Central Appalachia in the United States
Elnahas, Adam; Gray, Austin; Lee, Jennie; AlAmiri, Noora; Pokhrel, Nishan; Allen, Steve; Foroutan, Hosein (American Chemical Society, 2024-12-26)
Due to the increased prevalence of plastic pollution globally, atmospheric deposition of microplastics (MPs) is a significant issue that needs to be better understood to identify potential consequences for human health. This study is the first to quantify and characterize atmospheric MP deposition in the Eastern United States. Passive sampling was conducted at two locations within the Eastern United States, specifically in remote South Central Appalachia, from March to September 2023. Each location had five sampling periods, with collections over a 21 day period. Samples were processed to remove biological material, and the presence of MPs was confirmed using Raman spectroscopy to match particles based on polymer similarity. The relative average atmospheric MP deposition in South Central Appalachia was determined to be 68 MPs m^-2 d^-1. Most verified MPs were fibers, and the most abundant polymer type identified was poly(ethylene terephthalate) PETE. This study's average MP deposition rate is qualitatively comparable to rates reported in other studies that employed a similar methodology in a similar landscape. Scaling up our measured deposition rate to all of South Central Appalachia, an area of over 94,000 km² and home to five million people, suggests a yearly MP deposition of approximately 321 metric tonnes. Our study highlights the prevalence of MP deposition in the Eastern United States, providing baseline data for future work to further assess routes of MP introduction.
Establishing performance criteria for evaluating watershed-scale sediment and nutrient models at fine temporal scales
Pandit, Aayush; Hogan, Sarah; Mahoney, David T.; Ford, William I.; Fox, James F.; Wellen, Christopher; Husic, Admin (Pergamon-Elsevier, 2025-01-18)
Watershed water quality models are mathematical tools used to simulate processes related to water, sediment, and nutrients. These models provide a framework that can be used to inform decision-making and the allocation of resources for watershed management. Therefore, it is critical to answer the question “when is a model good enough?” Established performance evaluation criteria, or thresholds for what is considered a ‘good’ model, provide common benchmarks against which model performance can be compared. Since the publication of prior meta-analyses on this topic, developments in the last decade necessitate further investigation, such as the advancement in high performance computing, the proliferation of aquatic sensors, and the development of machine learning algorithms. We surveyed the literature for quantitative model performance measures, including the Nash-Sutcliffe efficiency (NSE), with a particular focus on process-based models operating at fine temporal scales as their performance evaluation criteria are presently underdeveloped. The synthesis dataset was used to assess the influence of temporal resolution (sub-daily, daily, and monthly), calibration duration (< 3 years, 3 to 8 years, and > 8 years), and constituent target units (concentration, load, and yield) on model performance. The synthesis dataset includes 229 model applications, from which we use bootstrapping and personal modeling experience to establish sub-daily and daily performance evaluation criteria for flow, sediment, total nutrient, and dissolved nutrient models. For daily model evaluation, the NSE for sediment, total nutrient, and dissolved nutrient models should exceed 0.45, 0.30, and 0.35, respectively, for ‘satisfactory’ performance. Model performance generally improved when transitioning from short (< 3 years) to medium (3 to 8 years) calibration durations, but no additional gain was observed with longer (> 8 years) calibration. Dissolved nutrient models calibrated to load (e.g., kg/s) out-performed those calibrated to concentration (e.g., mg/L), whereas selection of target units was not significant for sediment and total nutrient models. We recommend the use of concentration rather than load as a water quality modeling target, as load may be biased by strong flow model performance whereas concentration provides a flow-independent measure of performance. Although the performance criteria developed herein are based on process-based models, they may be useful in assessing machine learning model performance. We demonstrate one such assessment on a recent deep learning model of daily nitrate prediction across the United States. The guidance presented here is intended to be used alongside, rather than to replace, the experience and modeling judgement of engineers and scientist who work to maintain our collective water resources.
Analyzing the Efficacy of Water Treatment Disinfectants as Vector Control: The Larvicidal Effects of Silver Nitrate, Copper Sulfate Pentahydrate, and Sodium Hypochlorite on Juvenile Aedes aegypti
Turner, Sydney S.; Smith, James A.; Howle, Sophie L.; Hancock, Patrick I.; Brett, Karin; Davis, Julia; Bruno, Lorin M.; Cecchetti, Victoria; Ford, Clay (MDPI, 2025-01-26)
For communities without access to uninterrupted, piped water, household water storage (HWS) practices can lead to adverse public health outcomes caused by water degradation and mosquito proliferation. With over 700,000 deaths caused by vector-borne diseases annually, the objective of this study was to determine whether water disinfectants, at concentrations deemed safe for human consumption and beneficial for water treatment, are effective in reducing the emergence of adult mosquitoes that transmit disease. Laboratory bioassays, designed to resemble the context of treating HWS containers, were conducted to assess the larvicidal effects of chemicals at concentrations below regulatory limits for drinking water: silver (20, 40, 80 μg/L Ag), copper (300, 600, 1200 μg/L Cu), and chlorine (500, 1000, 2000 ug/L free chlorine). The water disinfectants demonstrated the ability to significantly reduce the population of juvenile Ae. aegypti. Sodium hypochlorite was found to be the most effective in decreasing the survival rate of late first instar larvae, while silver nitrate exhibited the highest effectiveness in inhibiting the emergence of late third instar larvae. Ultimately, this study highlights the potential of an integrated approach to Water, Sanitation, and Health (WASH) solutions with vector control management.
Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data
Shafik, Amr K.; Rakha, Hesham A. (MDPI, 2025-01-30)
This paper introduces a two-stage adaptive Kalman filter algorithm to estimate and predict traffic states required for real-time traffic signal control. Leveraging probe vehicle trajectory and upstream detector data, turning movement (TM) counts in the vicinity of signalized intersections are estimated in the first stage, while the upstream approach density and queue sizes are estimated in the second stage. The proposed approach is evaluated using drone-collected and simulated data from a four-legged signalized intersection in Orlando, Florida. The performance of the two-stage approach is quantified relative to the baseline estimation without a Kalman filter. The results show that the Kalman filter is effective in enhancing traffic state estimates at various market penetration levels, where the filter both improves the estimation accuracy over the baseline case and provides reliable state predictions. In the first stage, the standard deviation (SD) in TM estimates improves by up to 50% compared to the estimates provided by the sole use of probe vehicle headings. The proposed approach also provides predictions with a minimal SD of 92.8 veh/h at a 5% level of market penetration. In the second stage, the proposed queue size estimation method results in an enhancement to the queue size estimation of up to 32.8% compared to the estimates obtained from the baseline approach. In addition, the estimated traffic density is enhanced by up to 18.5%. The proposed two-stage approach demonstrates the capability of providing reliable turning movement predictions across varying levels of market penetration. This highlights the readiness of this approach for practical application in real-time traffic signal control systems.
Adaptive modification of antiviral defense systems in microbial community under Cr-induced stress
Huang, Dan; Liao, Jingqiu; Balcazar, Jose L.; Ye, Mao; Wu, Ruonan; Wang, Dongsheng; Alvarez, Pedro J. J.; Yu, Pingfeng (BioMed Central, 2025-01-31)
Background: The prokaryotic antiviral defense systems are crucial for mediating prokaryote-virus interactions that influence microbiome functioning and evolutionary dynamics. Despite the prevalence and significance of prokaryotic antiviral defense systems, their responses to abiotic stress and ecological consequences remain poorly understood in soil ecosystems. We established microcosm systems with varying concentrations of hexavalent chromium (Cr(VI)) to investigate the adaptive modifications of prokaryotic antiviral defense systems under abiotic stress. Results: Utilizing hybrid metagenomic assembly with long-read and short-read sequencing, we discovered that antiviral defense systems were more diverse and prevalent in heavily polluted soils, which was corroborated by meta-analyses of public datasets from various heavy metal-contaminated sites. As the Cr(VI) concentration increased, prokaryotes with defense systems favoring prokaryote-virus mutualism gradually supplanted those with defense systems incurring high adaptive costs. Additionally, as Cr(VI) concentrations increased, enriched antiviral defense systems exhibited synchronization with microbial heavy metal resistance genes. Furthermore, the proportion of antiviral defense systems carried by mobile genetic elements (MGEs), including plasmids and viruses, increased by approximately 43% and 39%, respectively, with rising Cr concentrations. This trend is conducive to strengthening the dissemination and sharing of defense resources within microbial communities. Conclusions: Overall, our study reveals the adaptive modification of prokaryotic antiviral defense systems in soil ecosystems under abiotic stress, as well as their positive contributions to establishing prokaryote-virus mutualism and the evolution of microbial heavy metal resistance. These findings advance our understanding of microbial adaptation in stressful environments and may inspire novel approaches for microbiome manipulation and bioremediation.
Causal inference to scope environmental impact assessment of renewable energy projects and test competing mental models of decarbonization
Gazar, Amir M.; Borsuk, Mark E.; Calder, Ryan S. D. (IOP Publishing, 2024-11-25)
Environmental impact assessment (EIA), life cycle analysis (LCA), and cost benefit analysis (CBA) embed crucial but subjective judgments over the extent of system boundaries and the range of impacts to consider as causally connected to an intervention, decision, or technology of interest. EIA is increasingly the site of legal, political, and social challenges to renewable energy projects proposed by utilities, developers, and governments, which, cumulatively, are slowing decarbonization. Environmental advocates in the United States have claimed that new electrical interties with Canada increase development of Canadian hydroelectric resources, leading to environmental and health impacts associated with new reservoirs. Assertions of such second-order impacts of two recently proposed 9.5 TWh yr−1 transborder transmission projects played a role in their cancellation. We recast these debates as conflicting mental models of decarbonization, in which values, beliefs, and interests lead different parties to hypothesize causal connections between interrelated processes (in this case, generation, transmission, and associated impacts). We demonstrate via Bayesian network modeling that development of Canadian hydroelectric resources is stimulated by price signals and domestic demand rather than increased export capacity per se. However, hydropower exports are increasingly arranged via long-term power purchase agreements that may promote new generation in a way that is not easily modeled with publicly available data. We demonstrate the utility of causal inference for structured analysis of sociotechnical systems featuring phenomena that are not easily modeled mechanistically. In the setting of decarbonization, such analysis can fill a gap in available energy systems models that focus on long-term optimum portfolios and do not generally represent questions of incremental causality of interest to stakeholders at the local level. More broadly, these tools can increase the evidentiary support required for consequentialist (as opposed to attributional) LCA and CBA, for example, in calculating indirect emissions of renewable energy projects.
Establishing flood thresholds for sea level rise impact communication
Mahmoudi, Sadaf; Moftakhari, Hamed; Munoz, David F.; Sweet, William; Moradkhani, Hamid (Nature Portfolio, 2024-05-18)
Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States’ coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.
Optimization of Zn Leaching Recovery from Tire Rubber and High-Purity ZnO Production
Li, Shiyu; Tran, Thien Q.; Ji, Bin; Brand, Alexander S.; Zhang, Wencai (Springer, 2024-12-18)
Waste tire rubber is regarded as a potential source for Zn recovery and recycling. In this study, the occurrence of modes of Zn was first characterized by an electron probe microanalyzer (EPMA), and the result indicated both ZnO and ZnS were present in the tire rubber. The Zn leaching recovery was optimized by response surface methodology, and temperature was identified as the most significant variable. The highest recovery of over 98% was obtained at 90 °C for 400 min when using 2.0 mol/L HNO3 as the lixiviant. After that, the Zn-containing leach liquor was subjected to solvent extraction for further separation and purification using bis(2,4,4-trimethylpentyl) phosphinic acid (Cyanex 272) and 2-ethylhexylphosphonic mono-2-ethylhexyl (PC88A) as extractants. Various parameters, such as equilibrium pH, extractant concentration, and organic-to-aqueous (O/A) ratio, were investigated to maximize the Zn extraction while minimizing the contamination of impurities. The result indicated that 0.1 mol/L Cyanex 272 exhibited a higher separation factor for Zn over major impurities compared to PC88A under the same conditions. To produce the high-purity ZnO, the Zn-loaded organic phase was subjected to stripping tests, and over 92% of Zn was stripped out with trace amounts of impurities. Finally, the pH value of the stripped solution was increased to precipitate Zn, and a final ZnO product with a purity of over 99% was obtained. This study provided a reference for waste tire rubber management and utilization.
Monitoring Wind and Particle Concentrations Near Freshwater and Marine Harmful Algal Blooms (HABs)
Bilyeu, Landon; Gonzalez-Rocha, Javier; Hanlon, Regina; AlAmiri, Noora; Foroutan, Hosein; Alading, Kun; Ross, Shane D.; Schmale, David G. III (Royal Society of Chemistry, 2023-10-05)
Harmful algal blooms (HABs) are a threat to aquatic ecosystems worldwide. New information is needed about the environmental conditions associated with the aerosolization and transport of HAB cells and their associated toxins. This information is critical to help inform our understanding of potential exposures. We used a ground-based sensor package to monitor weather, measure airborne particles, and collect air samples on the shore of a freshwater HAB (bloom of predominantly Rhaphidiopsis, Lake Anna, Virginia) and a marine HAB (bloom of Karenia brevis, Gulf Coast, Florida). Each sensor package contained a sonic anemometer, impinger, and optical particle counter. A drone was used to measure vertical profiles of windspeed and wind direction at the shore and above the freshwater HAB. At the Florida sites, airborne particle number concentrations (cm−3) increased throughout the day and the wind direction (offshore versus onshore) was strongly associated with these particle number concentrations (cm−3). Offshore wind sources had particle number concentrations (cm−3) 3 to 4 times higher than those of onshore wind sources. A predictive model, trained on a random set of weather and particle number concentrations (cm−3) collected over the same time period, was able to predict airborne particle number concentrations (cm−3) with an R squared value of 0.581 for the freshwater HAB in Virginia and an R squared value of 0.804 for the marine HAB in Florida. The drone-based vertical profiles of the wind velocity showed differences in wind speed and direction at different altitudes, highlighting the need for wind measurements at multiple heights to capture environmental conditions driving the atmospheric transport of aerosolized HAB toxins. A surface flux equation was used to determine the rate of aerosol production at the beach sites based on the measured particle number concentrations (cm−3) and weather conditions. Additional work is needed to better understand the short-term fate and transport of aerosolized cyanobacterial cells and toxins and how this is influenced by local weather conditions.
The impact of deicer and anti-icer use on plant communities in stormwater detention basins: Characterizing salt stress and phytoremediation potential
Long, S.; Rippy, Megan A.; Krauss, Lauren M.; Stacey, M.; Fausey, K. (Elsevier, 2025-01-15)
We present the results of a 1-year study that quantified salt levels in stormwater, soils, and plant tissues from 14 stormwater detention basins across Northern VA in an above-average snow year. We characterize (1) the level of salt stress plants experience, (2) the extent to which current plant communities feature salt tolerant species, and (3) the capacity of these species to phytoremediate soils and reduce the impacts of deicer and anti-icer use. Our results suggest that detention basin vegetation experience a range of salt stress levels that depend on drainage area type (roads: moderate to high > parking lots: low to moderate > pervious areas: none). Established thresholds for salt sensitive vegetation (Na⁺, Cl⁺, electrical conductivity, sodium adsorption ratio, exchangeable sodium percentage) were exceeded at least twice in stormwater or soils from all systems draining roads and half of systems draining parking lots. Winter exceedances were most common, but saline conditions did persist into the growing season, particularly at sites draining roads. Two hundred fifty-five plant species were identified across all detention basins, including 48 natives capable of tolerating elevated salt levels (electrical conductivity ≥2 dS/m). Within-tissue concentrations of sodium and chloride ions were highest in Typha (latifolia and angustifolia) (11.1 mg Na⁺/g; 30 mg Cl⁻ /g), making it our top phytoremediation candidate. Scaling these concentrations up, we estimate that a standard-size highway detention basin (2000–3000 m²) with 100 % cattail cover can phytoremediate up to 100 kg of Na⁺ and 200 kg of Cl⁻ per year. Uptake at this level is not sufficient to offset winter salt application, constituting only 5–6 % of basin inputs. This suggests that phytoremediation should not be considered a standalone solution to basin salinization, although it could be one approach of many in a broader salt management strategy.

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