Scholarly Works, Civil and Environmental Engineering

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  • An Econometric Analysis to Explore the Temporal Variability of the Factors Affecting Crash Severity Due to COVID-19
    Alrumaidhi, Mubarak; Rakha, Hesham A. (MDPI, 2024-02-01)
    This study utilizes multilevel ordinal logistic regression (M-OLR), an approach that accounts for spatial heterogeneity, to assess the dynamics of crash severity in Virginia, USA, over the years 2018 to 2023. This period was notably influenced by the COVID-19 pandemic and its associated stay-at-home orders, which significantly altered traffic behaviors and crash severity patterns. This study aims to evaluate the pandemic’s impact on crash severity and examine the consequent changes in driver behaviors. Despite a reduction in total crashes, a worrying increase in the proportion of severe injuries is observed, suggesting that less congested roads during the pandemic led to riskier driving behaviors, notably increased speed violations. This research also highlights heightened risks for vulnerable road users such as pedestrians, cyclists, and motorcyclists, with changes in transportation habits during the pandemic leading to more severe crashes involving these groups. Additionally, this study emphasizes the consistent influence of environmental and roadway features, like weather conditions and traffic signals, in determining crash outcomes. These findings offer vital insights for road safety policymakers and urban planners, indicating the necessity of adaptive road safety strategies in response to changing societal norms and behaviors. The research underscores the critical role of individual behaviors and mental states in traffic safety management and advocates for holistic approaches to ensure road safety in a rapidly evolving post-pandemic landscape.
  • How has COVID-19 impacted customer perceptions and demand for delivery services: An exploratory analysis
    Kaplan, Marcella; Hotle, Susan; Heaslip, Kevin (Elsevier, 2023-02-23)
    The novel coronavirus (COVID-19) pandemic created an environment where nearly all aspects of mobility changed to ensure the health and safety of the public. The Centers for Disease Control and Prevention (CDC) recommended that people quarantine for 14 days if they were potentially exposed to the virus, stay at least six feet apart from others, and stay at home as much as possible. Delivery via third-party restaurant app, grocery, and package delivery quickly became an essential service. This study assesses customer's changes in use and perceived quality of delivery services in Southwest Virginia, via an online stated-preference survey (n = 423). The responses were analyzed using ordered logit and generalized ordered logit models to identify which population segments had changing delivery behavior and perceptions due to the pandemic. Findings include that before the pandemic, only households with an income greater than $100,000 had a significantly higher demand for package delivery services than those making less than $25,000. During the pandemic, all income brackets had a significantly higher demand for package delivery “weekly” than households with less than a $25,000 income, with a 19.50%, 22.54%, and 45.59% greater chance of use for income levels $25,000 to $50,000, $50,000 to $100,000, and over $100,000, respectively. This trend highlights that package delivery became necessary during the pandemic. Respondents who lived within town limits were statistically significantly more likely to use third-party restaurant delivery apps at least once a week before (3.10%), during (9.20%), and after (4.50%) the pandemic compared to those outside town limits. The results also found people who lived within town limits were 7.77% more likely to be satisfied with delivery services in general than those who lived outside town limits. The findings from this paper identify expanding delivery equity gaps within the population and provide recommendations for policymakers and delivery agencies. Some limitations include that low sample size did not allow for fully segmented models and meant that the study should be considered exploratory in nature.
  • Monte Carlo Simulation of Barrier-Island Systems and Tsunami Hazards
    Irish, Jennifer L.; Weiss, Robert; Dura, Tina (Coastal Engineering Research Council, 2023-09-01)
    Robust characterization of the future tsunami hazard is critically important for resilient planning and engineering in coastal communities prone to tsunami inundation. The hazard from earthquake-generated tsunami waves is not only determined by the earthquake's characteristics and distance to the earthquake area, but also by the geomorphology of the nearshore and onshore areas, which can change over time. In coastal hazard assessments, a changing coastal environment is commonly taken into account by increasing the sea-level to projected values (static). However, sea-level changes and other climate-change impacts influence the entire coastal system causing morphological change (dynamic). Here, we present the modeling framework and results initially published in Weiss et al. (2022), which employs within a Monte Carlo framework the barrier island-marsh, lagoon- marsh evolution model of Lorenzo-Trueba and Mariotti (2017) and the tsunami model Geoclaw (e.g., LeVeque et al. 2011). We compare the runup of the same suite of earthquake-generated tsunamis to a barrier system for statically adjusted and dynamically adjusted sea level and bathymetry over the period from 2000 to 2100. We employ Representative Concentration Pathways 2.6 and 8.5 without and with treatment of Antarctic ice-sheet processes (e.g., Kopp et al. 2017) as different sea-level projections.
  • Real-Time Prediction of Alongshore Near-Field Tsunami Runup Distribution From Heterogeneous Earthquake Slip Distribution
    Lee, Jun-Whan; Irish, Jennifer L.; Weiss, Robert (American Geophysical Union, 2023-01-05)
    Real-time tsunami prediction is necessary for tsunami forecasting. Although tsunami forecasting based on a precomputed tsunami simulation database is fast, it is difficult to respond to earthquakes that are not in the database. As the computation speed increases, various alternatives based on physics-based models have been proposed. However, physics-based models still require several minutes to simulate tsunamis and can have numerical stability issues that potentially make them unreliable for use in forecasting—particularly in the case of near-field tsunamis. This paper presents a data-driven model called the tsunami runup response function for finite faults (TRRF-FF) model that can predict alongshore near-field tsunami runup distribution from heterogeneous earthquake slip distribution in less than a second. Once the TRRF-FF model is trained and calibrated based on a discrete set of tsunami simulations, the TRRF-FF model can predict alongshore tsunami runup distribution from any combination of finite fault parameters. The TRRF-FF model treats the leading-order contribution and the residual part of the alongshore tsunami runup distribution separately. The interaction between finite faults is modeled based on the leading-order alongshore tsunami runup distribution. We validated the TRRF-FF modeling approach with more than 200 synthetic tsunami scenarios in eastern Japan. We further explored the performance of the TRRF-FF model by applying it to the 2011 Tohoku (Japan) tsunami event. The results show that the TRRF-FF model is more flexible, occupies much less storage space than a precomputed tsunami simulation database, and is more rapid and reliable than real-time physics-based numerical simulation.
  • Queue Length Estimation and Optimal Vehicle Trajectory Planning Considering Queue Effects at Actuated Traffic Signal Controlled Intersections
    Shafik, Amr; Rakha, Hesham A. (2024-01-08)
    This research explores the implementation and evaluation of a green light optimal speed advisory (GLOSA) system in proximity to actuated traffic signals, considering the inherent uncertainty in switching times. Additionally, the impact of surrounding traffic is considered by optimizing vehicle trajectories with real-time queue estimation, derived from both loop-detector and probe vehicle data. Through comprehensive simulation experiments involving a single vehicle approaching an intersection with queued vehicles, as well as network-level simulations covering various market penetration levels (ranging from 0% to 100%), the fuel savings achieved by the GLOSA system is quantified. The results demonstrate substantial fuel savings of 31.4% and 35.4% when optimizing without and with consideration of the queueing process, respectively, from the perspective of individual vehicles. Furthermore, from a network-wide standpoint, a total fuel saving of 19.7% is observed for both scenarios. In addition, the optimization algorithm with considering the queuing process resulted in less average number of stops per vehicle than the case without considering it. Notably, the integration of the queue estimation yields remarkable improvements in system performance from the perspective of individual vehicles. However, it is found that considering the queue effects does not lead to an overall enhancement in system performance from a network-wide perspective. This research highlights the benefits and underscores the limitations of the GLOSA system when considering surrounding traffic. The incorporation of real-time queueing information for trajectory optimization offers valuable insights for the deployment and advancement of connected vehicle systems in real-world traffic environments.
  • Impacts of Vehicle-to-Everything Enabled Applications: Literature Review of Existing Studies
    Du, Jianhe; Ahn, Kyoungho; Farag, Mohamed; Rakha, Hesham A. (Universal Wiser Publisher, 2023-03-10)
    As communication technology is developing at a rapid pace, connected vehicles (CVs) can potentially enhance vehicle safety while reducing vehicle energy consumption and emissions via data sharing. Many researchers have attempted to quantify the impacts of such CV applications and vehicle-to-everything (V2X) communication, or the instant and accurate communication among vehicles, devices, pedestrians, infrastructure, network, cloud, and grid. Cellular V2X (C-V2X) has gained interest as an efficient method for this data sharing. In releases 14 and 15, C-V2X uses 4G LTE technology, and in release 16, it uses the latest 5G new radio (NR) technology. Among its benefits, C-V2X can function even with no network infrastructure coverage; in addition, C-V2X surpasses older technologies in terms of communication range, latency, and data rates. Highly efficient information interchange in a CV environment can provide timely data to enhance the transportation system's capacity, and it can support applications that improve vehicle safety and minimize negative impacts on the environment. Achieving the full benefits of CVs requires rigorous investigation into the effectiveness, strengths, and weaknesses of different CV applications. It also calls for deeper understanding of the communication protocols, results with different CV market penetration rates (MPRs), CV- and human-driven vehicle interactions, integration of multiple applications, and errors and latencies associated with data communication. This paper includes a review of existing literature on the safety, mobility, and environmental impacts of CV applications; gaps in current CV research; and recommended directions for future research. The results of this paper will help shape future research for CV applications to realize their full potential.
  • Relating Geotechnical Sediment Properties and Low Frequency CHIRP Sonar Measurements
    Jaber, Reem; Stark, Nina; Sarlo, Rodrigo; McNinch, Jesse E.; Massey, Grace (MDPI, 2024-01-08)
    Low frequency acoustic methods are a common tool for seabed stratigraphy mapping. Due to the efficiency in seabed mapping compared to geotechnical methods, estimating geotechnical sediment properties from acoustic surveying is attractive for many applications. In this study, co-located geotechnical and geoacoustic measurements of different seabed sediment types in shallow water environments (<5 m of water depth) are analyzed. Acoustic impedance estimated from sediment properties based on laboratory testing of physical samples is compared to acoustic impedance deduced from CHIRP sonar measurements using an inversion approach. Portable free fall penetrometer measurements provided in situ sediment strength. The results show that acoustic impedance values deduced from acoustic data through inversion fall within a range of ±25% of acoustic impedance estimated from porosity and bulk density. The acoustic measurements reflect variations in shallow sediment properties such as porosity and bulk density (~10 cm below seabed surface), even for very soft sediments (su < 3 kPa) and loose sands (~20% relative density). This is a step towards validating the ability of acoustic methods to capture geotechnical properties in the topmost seabed layers.
  • Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding
    Nourali, Zahra; Shortridge, Julie E.; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L. (MDPI, 2024-01-11)
    Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding.
  • Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transport
    Aredah, Ahmed; Du, Jianhe; Hegazi, Mohamed; List, George; Rakha, Hesham A. (Elsevier, 2024-02-15)
    This study assesses the energy efficiency and environmental implications of six powertrain technologies in the U.S. freight rail network: diesel, biodiesel, diesel-hybrid, biodiesel-hybrid, hydrogen fuel cell, and electric. Utilizing a simulation model, energy consumption at the tank across different demand scenarios and geographical regions is conducted. The study revealed electric powertrains as the standout, slashing energy consumption at the tank by 56% compared to traditional diesel, with the potential for zero CO2 emissions when powered by green energy sources. Biodiesel and biodiesel-hybrid also outperformed conventional diesel, cutting CO2 tank emissions by 6% and 21%, respectively. Diesel-hybrid registered a 16% reduction in both tank energy and diesel consumption, while hydrogen fuel cells demonstrated a 15% energy consumption drop at the tank and zero emissions. Implementing these advanced technologies requires considerable infrastructure investment and adaptation, which is beyond the scope of our analysis. While centered on the U.S. rail network, our findings offer valuable insights for global freight rail systems, underpinning the push for sustainable powertrain transitions.
  • ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization
    Liang, Xiao; Zhang, Jingyi; Kim, Yoonjin; Ho, Josh; Liu, Kevin; Keenum, Ishi M.; Gupta, Suraj; Davis, Benjamin; Hepp, Shannon L.; Zhang, Liqing; Xia, Kang; Knowlton, Katharine F.; Liao, Jingqiu; Vikesland, Peter J.; Pruden, Amy; Heath, Lenwood S. (Frontiers, 2023-09-15)
    Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user’s configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.
  • TUNEOPT: An Evolutionary Reinforcement Learning HVAC Controller For Energy-Comfort Optimization Tuning
    Meimand, Mostafa; Khattar, Vanshaj; Yazdani, Zahra; Jazizadeh, Farrokh; Jin, Ming (ACM, 2023-11-15)
    HVAC systems account for the majority of energy consumption in buildings. Efficient control of HVAC systems can reduce energy consumption and enhance occupants’ comfort. In the existing literature, energy-comfort or cost-comfort co-optimization frameworks commonly involve manual tuning of the balancing coefficient between energy and comfort through parameter tuning by an expert. Nevertheless, achieving the optimal balance between energy usage and occupant comfort remains challenging. This limitation restricts the generalizability of different formulations across various scenarios or testing on different environments. In this paper, we propose an implicit evolutionary Reinforcement Learning (RL) approach to learn and adapt the trade-off parameter of an energy-comfort optimization formulation. We have developed a predictive comfortenergy co-optimization formulation for controlling the setpoint of a building. The RL agent utilizes a novel guidance-induced random search method to learn the energy-comfort trade-off coefficient and guide the optimization formulation. The reward function of the RL model is energy productivity (comfort over energy consumption). To evaluate the feasibility of our proposed approach, we conducted experiments on a real-world testbed - i.e., an apartment unit. Our feasibility study shows that the proposed approach can learn an optimal control parameter and reduce energy consumption by 24.3% while decreasing comfort by only 1% compared to the baseline.
  • Alleviating Water Scarcity by Optimizing Crop Mixes
    Richter, Brian D.; Ao, Yufei; Lamsal, Gambhir; Wei, Dongyang; Amaya, Maria; Marston, Landon T.; Davis, Kyle F. (Nature Portfolio, 2023-11)
    Irrigated agriculture dominates freshwater consumption globally, but crop production and farm revenues suffer when water supplies are insufficient to meet irrigation needs. In the United States, the mismatch between irrigation demand and freshwater availability has been exacerbated in recent decades due to recurrent droughts, climate change and over extraction that dries rivers and depletes aquifers. Yet, there has been no spatially detailed assessment of the potential for shifting to new crop mixes to reduce crop water demands and alleviate water shortage risks. In this study, we combined modelled crop water requirements and detailed agricultural statistics within a national hydrological model to quantify sub-basin-level river depletion, finding high-to-severe levels of irrigation scarcity in 30% of sub-basins in the western United States, with cattle-feed crops—alfalfa and other hay—being the largest water consumers in 57% of the region’s sub-basins. We also assessed recent trends in irrigation water consumption, crop production and revenue generation in six high-profile farming areas and found that in recent decades, water consumption has decreased in four of our study areas—a result of a reduction in the irrigated area and shifts in the production of the most water-consumptive crops—even while farm revenues increased. To examine the opportunities for crop shifting and fallowing to realize further reductions in water consumption, we performed optimizations on realistic scenarios for modifying crop mixes while sustaining or improving net farm profits, finding that additional water savings of 28–57% are possible across our study areas. These findings demonstrate strong opportunities for economic, food security and environmental co-benefits in irrigated agriculture and provide both hope and direction to regions struggling with water scarcity around the world.
  • A rapid micro chamber method to measure SVOC emission and transport model parameters
    Wang, Chunyi; Eichler, Clara M. A.; Bi, Chenyang; Delmaar, Christiaan J. E.; Xu, Ying; Little, John C. (Royal Society of Chemistry, 2023-04-26)
    Assessing exposure to semivolatile organic compounds (SVOCs) that are emitted from consumer products and building materials in indoor environments is critical for reducing the associated health risks. Many modeling approaches have been developed for SVOC exposure assessment indoors, including the DustEx webtool. However, the applicability of these tools depends on the availability of model parameters such as the gas-phase concentration at equilibrium with the source material surface, y(0), and the surface-air partition coefficient, K-s, both of which are typically determined in chamber experiments. In this study, we compared two types of chamber design, a macro chamber, which downscaled the dimensions of a room to a smaller size with roughly the same surface-to-volume ratio, and a micro chamber, which minimized the sink-to-source surface area ratio to shorten the time required to reach steady state. The results show that the two chambers with different sink-to-source surface area ratios yield comparable steady-state gas- and surface-phase concentrations for a range of plasticizers, while the micro chamber required significantly shorter times to reach steady state. Using y(0) and K-s measured with the micro chamber, we conducted indoor exposure assessments for di-n-butyl phthalate (DnBP), di(2-ethylhexyl) phthalate (DEHP) and di(2-ethylhexyl) terephthalate (DEHT) with the updated DustEx webtool. The predicted concentration profiles correspond well with existing measurements and demonstrate the direct applicability of chamber data in exposure assessments.
  • Advances in Morphodynamic Modeling of Coastal Barriers: A Review
    Hoagland, Steven W. H.; Jeffries, Catherine R.; Irish, Jennifer L.; Weiss, Robert; Mandli, Kyle; Vitousek, Sean; Johnson, Catherine M.; Cialone, Mary A. (ASCE, 2023-05-30)
    As scientific understanding of barrier morphodynamics has improved, so has the ability to reproduce observed phenomena and predict future barrier states using mathematical models. To use existing models effectively and improve them, it is important to understand the current state of morphodynamic modeling and the progress that has been made in the field. This manuscript offers a review of the literature regarding advancements in morphodynamic modeling of coastal barrier systems and summarizes current modeling abilities and limitations. Broadly, this review covers both event-scale and long-term morphodynamics. Each of these sections begins with an overview of commonly modeled phenomena and processes, followed by a review of modeling developments. After summarizing the advancements toward the stated modeling goals, we identify research gaps and suggestions for future research under the broad categories of improving our abilities to acquire and access data, furthering our scientific understanding of relevant processes, and advancing our modeling frameworks and approaches.
  • Projecting barrier island storm erosion
    Hoagland, Steven W. H.; Jeffries, Catherine R.; Irish, Jennifer L.; Weiss, Robert; Mandli, Kyle; Vitousek, Sean; Johnson, Catherine M.; Cialone, Mary A. (Center for Coastal Studies, 2023-10-03)
  • Projecting long-term barrier island change
    Hoagland, Steven W. H.; Jeffries, Catherine R.; Irish, Jennifer L.; Weiss, Robert; Mandli, Kyle; Vitousek, Sean; Johnson, Catherine M.; Cialone, Mary A. (Center for Coastal Studies, 2023-10-03)
  • Scale modeling of thermo-structural fire tests
    Gangi, Michael J.; Lattimer, Brian Y.; Case, Scott W. (Wiley, 2023-03)
    Standard methods for fire resistance testing require large-scale assemblies and are typically conducted on specialized furnaces at considerable cost. This research focused on developing a scaling methodology for a reduced-scale fire resistance test that reduces the size of the test article while maintaining the same thermal and structural response exhibited in the large-scale test. The developed scaling methodology incorporates uniform geometric scaling, Fourier number time scaling, and furnace boundary condition matching. The scaling laws were experimentally validated with fire exposure tests on gypsum wallboard samples at three scales (full-scale, 1/2-scale, and 1/6-scale). In the tests, samples were exposed to a full-scale equivalent of 60-min of ASTM E119 fire curve exposure on a reduced-scale horizontal furnace, and the temperature rise through the thickness profile was measured. Models were created to calculate the modified fire curves for the smaller-scale tests. Experimental results show that on the exposed surface, the 1/2-scale absolute temperature was within 1.7% of full-scale, while the 1/6-scale temperature was within 2.5%. While the time-dependent properties of burning and cracking caused visual differences in these gypsum tests, modeling and temperature measurements demonstrated that the test results were thermally similar. The good similarity of temperatures is achievable in fire exposure tests of non-combustible gypsum wallboard down to 1/6-scale.
  • Environmental Stability of Enveloped Viruses Is Impacted by Initial Volume and Evaporation Kinetics of Droplets
    French, Andrea J.; Longest, Alexandra K.; Pan, Jin; Vikesland, Peter J.; Duggal, Nisha K.; Marr, Linsey C.; Lakdawala, Seema S. (American Society for Microbiology, 2023-04)
    Efficient spread of respiratory viruses requires the virus to maintain infectivity in the environment. Environmental stability of viruses can be influenced by many factors, including temperature and humidity. Our study measured the impact of initial droplet volume (50, 5, and 1 mu L) and relative humidity (RH; 40%, 65%, and 85%) on the stability of influenza A virus, bacteriophage Phi6 (a common surrogate for enveloped viruses), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) under a limited set of conditions. Our data suggest that the drying time required for the droplets to reach quasi-equilibrium (i.e., a plateau in mass) varied with RH and initial droplet volume. The macroscale physical characteristics of the droplets at quasi-equilibrium varied with RH but not with the initial droplet volume. Virus decay rates differed between the wet phase, while the droplets were still evaporating, and the dry phase. For Phi6, decay was faster in the wet phase than in the dry phase under most conditions. For H1N1pdm09, decay rates between the two phases were distinct and initial droplet volume had an effect on virus viability within 2 h. Importantly, we observed differences in virus decay characteristics by droplet size and virus. In general, influenza virus and SARS-CoV-2 decayed similarly, whereas Phi6 decayed more rapidly under certain conditions. Overall, this study suggests that virus decay in media is related to the extent of droplet evaporation, which is controlled by RH. Importantly, accurate assessment of transmission risk requires the use of physiologically relevant droplet volumes and careful consideration of the use of surrogates. IMPORTANCE During the COVID-19 pandemic, policy decisions were being driven by virus stability experiments with SARS-CoV-2 in different droplet volumes under various humidity conditions. Our study, the first of its kind, provides a model for the decay of multiple enveloped RNA viruses in cell culture medium deposited in 50-, 5-, and 1-mu L droplets at 40%, 65%, and 85% RH over time. The results of our study indicate that determination of half-lives for emerging pathogens in large droplets may overestimate transmission risk for contaminated surfaces, as observed during the COVID-19 pandemic. Our study implicates the need for the use of physiologically relevant droplet sizes with use of relevant surrogates in addition to what is already known about the importance of physiologically relevant media for risk assessment of future emerging pathogens.
  • Comparison of Cefotaxime-Resistant Escherichia coli and sul1 and intI1 by qPCR for Monitoring of Antibiotic Resistance of Wastewater, Surface Water, and Recycled Water
    Liguori, Krista; Calarco, Jeanette; Maldonado Rivera, Gabriel; Kurowski, Anna; Keenum, Ishi M.; Davis, Benjamin C.; Harwood, Valerie J.; Pruden, Amy (MDPI, 2023-07-29)
    Awareness of the need for surveillance of antimicrobial resistance (AMR) in water environments is growing, but there is uncertainty regarding appropriate monitoring targets. Adapting culture-based fecal indicator monitoring to include antibiotics in the media provides a potentially low-tech and accessible option, while quantitative polymerase chain reaction (qPCR) targeting key genes of interest provides a broad, quantitative measure across the microbial community. The purpose of this study was to compare findings obtained from the culture of cefotaxime-resistant (cefR) Escherichia coli with two qPCR methods for quantification of antibiotic resistance genes across wastewater, recycled water, and surface waters. The culture method was a modification of US EPA Method 1603 for E. coli, in which cefotaxime is included in the medium to capture cefR strains, while qPCR methods quantified sul1 and intI1. A common standard operating procedure for each target was applied to samples collected by six water utilities across the United States and processed by two laboratories. The methods performed consistently, and all three measures reflected the same overarching trends across water types. The qPCR detection of sul1 yielded the widest dynamic range of measurement as an AMR indicator (7-log versus 3.5-log for cefR E. coli), while intI1 was the most frequently detected target (99% versus 96.5% and 50.8% for sul1 and cefR E. coli, respectively). All methods produced comparable measurements between labs (p < 0.05, Kruskal–Wallis). Further study is needed to consider how relevant each measure is to capturing hot spots for the evolution and dissemination of AMR in the environment and as indicators of AMR-associated human health risk.
  • Multispectral Imaging for Identification of High-Water Marks in Postdisaster Flood Reconnaissance
    Gardner, Michael; Nichols, Elliot; Stark, Nina; Lemnitzer, Anne; Frost, David (ASCE, 2023-05)
    Flooding annually causes thousands of fatalities and billions of dollars in damage globally. Predicting future floods has become increasingly challenging due to changing urban environments and land surface conditions. Simultaneously, severe floods are likely to increase due to climate change and associated shifts in rain patterns, resulting into potentially stronger and more consequential flood events. High-water marks represent key information to be collected after flooding for advancing the understanding of flood impacts and the development of mitigation strategies. However, high-water marks often become increasingly difficult to detect with time passing after a flood event due to drying. In addition, access into flooded areas can be complicated by destroyed infrastructure, leading to significant loss of data or risk to personnel entering these recently flooded areas. Here, initial data are presented demonstrating the application of multispectral imagery in rapidly collecting and mapping high-water marks after flooding. The multispectral images were collected 3-4 weeks after the July 14, 2021, western European flood events in the town of Mayschoss, Germany, along the Ahr River. At that time, affected buildings, walls, and soil were exposed to high summer temperatures and solar radiation, as well as dust from surrounding emergency response and repair works. High-water marks were barely visible by eye. Preliminary results showed the high-water mark is significantly enhanced in the blue band (wavelength 443 to 507 nm) and can be modally isolated through linear combination of the blue band and red-edge band (wavelength 705 to 729 nm). The results illustrate the potential to apply this technique in postdisaster reconnaissance to quickly and safely map high-water levels to identify the magnitude and extent of flooding in urban areas.