Civil & Environmental Engineering Research Works

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    Elimination of Bloodstream Infections Associated with Candida albicans Biofilm in Intravascular Catheters
    (MDPI, 2015-06-29) Akbari, Freshta; Kjellerup, Birthe Veno
    Intravascular catheters are among the most commonly inserted medical devices and they are known to cause a large number of catheter related bloodstream infections (BSIs). Biofilms are associated with many chronic infections due to the aggregation of microorganisms. One of these organisms is the fungus Candida albicans. It has shown to be one of the leading causes of catheter-related BSIs. The presence of biofilm on intravascular catheters provide increased tolerance against antimicrobial treatments, thus alternative treatment strategies are sought. Traditionally, many strategies, such as application of combined antimicrobials, addition of antifungals, and removal of catheters, have been practiced, but they were not successful in eradicating BSIs. Since these fungal infections can result in significant morbidity, mortality, and increased healthcare cost, other promising preventive strategies, including antimicrobial lock therapy, chelating agents, alcohol, and biofilm disruptors, have been applied. In this review, current success and failure of these new approaches, and a comparison with the previous strategies are discussed in order to understand which preventative treatment is the most effective in controlling the catheter-related BSIs.
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    Model-Based Design and Formal Verification Processes for Automated Waterway System Operations
    (MDPI, 2016-06-07) Petnga, Leonard; Austin, Mark
    Waterway and canal systems are particularly cost effective in the transport of bulk and containerized goods to support global trade. Yet, despite these benefits, they are among the most under-appreciated forms of transportation engineering systems. Looking ahead, the long-term view is not rosy. Failures, delays, incidents and accidents in aging waterway systems are doing little to attract the technical and economic assistance required for modernization and sustainability. In a step toward overcoming these challenges, this paper argues that programs for waterway and canal modernization and sustainability can benefit significantly from system thinking, supported by systems engineering techniques. We propose a multi-level multi-stage methodology for the model-based design, simulation and formal verification of automated waterway system operations. At the front-end of development, semi-formal modeling techniques are employed for the representation of project goals and scenarios, requirements and high-level models of behavior and structure. To assure the accuracy of engineering predictions and the correctness of operations, formal modeling techniques are used for the performance assessment and the formal verification of the correctness of functionality. The essential features of this methodology are highlighted in a case study examination of ship and lock-system behaviors in a two-stage lock system.
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    Compositional Approach to Distributed System Behavior Modeling and Formal Validation of Infrastructure Operations with Finite State Automata: Application to Viewpoint-Driven Verification of Functionality in Waterways
    (MDPI, 2018-01-12) Austin, Mark A.; Johnson, John
    Now that modern infrastructure systems are moving toward an increased use of automation in their day-to-day operations, there is an emerging need for new approaches to the formal analysis and validation of system functionality with respect to correctness of operations. This paper describes a compositional approach to the multi-level behavior modeling and formal validation of large-scale distributed system operations with hierarchies and networks of finite state automata. To avoid the well-known state explosion problem, we develop a new procedure for viewpoint-action-process traceability, thereby allowing parts of a behavior model not relevant to a specific decision to be removed from consideration. Key features of the methodology are illustrated through the development of behavior models and validation procedures for polite conversation between two individuals, and lockset- and system-level concerns for ships traversing a large-scale waterway system.
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    Advancing Scientific Knowledge: Ethical Issues in the Journal Publication Process
    (MDPI, 2017-12-31) McCuen, Richard H.
    The goal of this paper is to assess the journal publication process from value and ethical perspectives. The specific objectives are: (1) To define fundamental values relevant to scientific journal publication; (2) To identify stakeholders involved in professional journals and their value rights and responsibilities; (3) To discuss the steps of the journal publication process where ethical dilemmas arise and the potential influences of such dilemmas on the advancement of knowledge; and (4) To summarize actions that can minimize unethical practices throughout the steps of the publication process. Values such as honesty, efficiency, accountability, and fairness will be discussed. Issues related to the various stakeholders such as self-citation, plagiarism, dual publication, a lack of timeliness, and issues related to authorship will be a primary focus.
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    A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information
    (MDPI, 2018-10-26) Chung, Younshik; Won, Minsu
    Traditionally, traffic safety improvement programs (TSIPs) have been based on the number of crashes at a specific location or their severity. However, the crash datasets used for such programs are obtained from the police and include two limitations: not all crashes are collected by the police (most minor and near-miss crashes are not reported), and the traditional process uses crash data recorded for the past two or three years (meaning most data inevitably include a time lag). To overcome these limitations, this study proposes a new approach for a TSIP based on citizen participation through an online survey that is broadcasted through social media. The method uses the public as sensors of hazardous road information, which means that information can be collected on individual experiences of minor crashes and latent risk factors, such as near misses and traffic conflicts. To demonstrate this approach, a case study was carried out in a small district in the city of Goyang, Korea, which has one of the highest usage rates of social media technologies. The proposed method and a traditional method were both assessed.
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    Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate
    (MDPI, 2019-05-09) Hu, Huiling; Ayyub, Bilal M.
    Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.
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    Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia
    (MDPI, 2019-09-28) Kwon, Yonghwan; Forman, Barton A.; Ahmad, Jawairia A.; Kumar, Sujay V.; Yoon, Yeosang
    This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.
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    Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America
    (MDPI, 2020-09-22) Park, Jongmin; Forman, Barton A.; Reichle, Rolf H.; De Lannoy, Gabrielle; Tarik, Saad B.
    L-band brightness temperature (𝑇𝑏) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in 𝑇𝑏 observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, 𝑇𝑏 estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily 𝑇𝑏 observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed 𝑇𝑏 were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed 𝑇𝑏 with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled 𝑇𝑏 due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.
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    Impact of the Built Environment Measured at Multiple Levels on Nonmotorized Travel Behavior: An Ecological Approach to a Florida Case Study
    (MDPI, 2020-10-24) Mahmoudi, Jina; Zhang, Lei
    Research continues to reveal the benefits of nonmotorized travel modes such as walking and bicycling. Therefore, identification of the factors that nurture these activities is essential in developing sustainable urban planning policies and designs. Among those factors are the built environment characteristics of the place of residence. To date, research on the role of the built environment in nonmotorized travel has focused on neighborhood-level factors. However, people do not stay within their neighborhoods; they live and work at a regional scale and travel to various destinations and distances each day. Nonetheless, little is known about the impact of built environment factors at larger spatial scales on nonmotorized travel behavior. Guided by the principles of the ecological model of behavior, this study investigates the role of the built environment at hierarchical spatial scales in nonmotorized travel behavior. Multilevel Structural Equation Models have been developed to comprehensively examine the complex links between the built environment and individuals’ nonmotorized travel. Findings indicate that built environment factors at multiple spatial scales can influence nonmotorized travel behavior. Thus, to promote walking and bicycling, more effective policies are those that include multilevel built environment and land use interventions and consider the overall physical form of urban areas.
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    Recycled Asphalt Pavement Materials in Transport Pavement Infrastructure: Sustainability Analysis & Metrics
    (MDPI, 2021-07-20) Zhao, Yunpeng; Goulias, Dimitrios; Peterson, Dominique
    Transportation infrastructure is one of the largest consumers of natural materials. To improve the environmental quality and sustainable development of transportation infrastructure, it is important to implement sustainable strategies in pavement construction and rehabilitation. The use of recycled materials is a key element in generating sustainable pavement designs to save natural resources, reduce energy, greenhouse gas emissions, and costs. The objective of this study was to propose a methodology for assessing the environmental and economic life-cycle benefits when using recycled asphalt pavement (RAP) materials in highway projects. Previous studies on life cycle analysis (LCA) using RAP focused on the economics and/or environmental impacts during the material production process. Thus, there is a need to consider sustainability analysis at all stages of construction and rehabilitation during the performance period of pavement structures. This study addresses this need with the proposed methodology. The suggested approach could be potentially implemented in a pavement management system (PMS) so as to introduce sustainability principles in optimizing alternative rehabilitation strategies. The methodology includes various steps for the analysis, starting with condition assessment of the existing highway, identifying alternative structural pavement designs, predicting service life, setting up alternative rehabilitation strategies, and conducting life cycle environmental and economic analysis. To demonstrate the value of the methodology, a comparative parametric study was conducted on two real case study projects representing actual field conditions for primary roads in Maryland. These case studies were used in order to quantify the economic savings and environmental benefits of using different levels of RAP in highway rehabilitation. The results of the analysis indicate that incorporating RAP in pavement rehabilitation can contribute substantially to cost savings and environmental impact reduction (e.g., greenhouse gas emission, energy, water, and hazardous waste). The benefits illustrated in this study are expected to encourage wide adoption of the proposed methodology and the use of recycled materials in highway construction and rehabilitation. The methodology is transferable where similar materials and highway construction techniques are used.
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    Life Cycle Economic and Environmental Impacts of CDW Recycled Aggregates in Roadway Construction and Rehabilitation
    (MDPI, 2021-08-02) Zhao, Yunpeng; Goulias, Dimitrios; Tefa, Luca; Bassani, Marco
    The use of recycled materials in roadway construction and rehabilitation can achieve significant benefits in saving natural resources, reducing energy, greenhouse gas emissions and costs. Construction and demolition waste (CDW) recycled aggregate as an alternative to natural one can enhance sustainability benefits in roadway infrastructure. The objective of this study was to quantitatively assess the life cycle economic and environmental benefits when alternative stabilized-CDW aggregates are used in pavement construction. Comparative analysis was conducted on a pavement project representative of typical construction practices in northern Italy so as to quantify such benefits. The proposed alternative sustainable construction strategies considered CDW aggregates stabilized with both cement and cement kiln dust (CKD) for the base layer of the roadway. The life cycle assessment results indicate that using CDW aggregate stabilized with CKD results in considerable cost savings and environmental benefits due to (i) lower energy consumption and emissions generation during material processing and (ii) reduction in landfill disposal. The benefits illustrated in this analysis should encourage the wider adoption of stabilized CDW aggregate in roadway construction and rehabilitation. In terms of transferability, the analysis approach suggested in this study can be used to assess the economic and environmental benefits of these and other recycled materials in roadway infrastructure elsewhere.
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    Investigating the Impacts of the Political System Components in Iran on the Existing Water Bankruptcy
    (MDPI, 2021-12-10) Ketabchy, Mehdi
    Iran is suffering from a state of water bankruptcy. Several factors have contributed to the current water resources bankruptcy, ranging from anthropogenic impacts, such as an inefficient agricultural sector and aggressive withdrawal of groundwater, to climatological impacts. This paper suggests that water resources mismanagement in Iran should be evaluated beyond the policy-makers decisions, as it recognizes that the bankruptcy has been intensified due to the structural and institutional form of the political system in Iran. This study discusses the roots of the water bankruptcy and identifies four major shortcomings caused by the political system: (1) the absence of public engagement due to the lack of a democratic and decentralized structure; (2) adopting ideological policies in domestic and foreign affairs; (3) conflicts of interest and the multiplicity of governmental policy-makers and sectors; and (4) a state-controlled, resource-dependent economy. Through the development of a generic causal model, this study recommends a systematic transition towards a democratic, decentralized, non-ideological, and economically diverse political governance as the necessary–but not necessarily sufficient–adaptive and sustainable solution for mitigating the impacts of water resources bankruptcy in Iran. The insights highlighted in this paper could be employed to inform water resources decision-makers and political actors in other non-democratic and ideological political structures struggling with a water resources crisis or bankruptcy.
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    Exploring the Spatiotemporal Coverage of Terrestrial Snow Mass Using a Suite of Satellite Constellation Configurations
    (MDPI, 2022-01-28) Wang, Lizhao; Forman, Barton A.; Kim, Edward
    Terrestrial snow is a vital freshwater resource for more than 1 billion people. Remotely-sensed snow observations can be used to retrieve snow mass or integrated into a snow model estimate; however, optimally leveraging remote sensing observations of snow is challenging. One reason is that no single sensor can accurately measure all types of snow because each type of sensor has its own unique limitations. Another reason is that remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations may not meet the requirements of a given snow applications. In order to quantify the feasible availability of remotely-sensed observations across space and time, this study simulates the sensor coverage for a suite of hypothetical snow sensors as a function of different orbital configurations and sensor properties. The information gleaned from this analysis coupled with a dynamic snow binary map is used to evaluate the efficiency of a single sensor (or constellation) to observe terrestrial snow on a global scale. The results show the efficacy achievable by different sensors over different snow types. The combination of different orbital and sensor configurations is explored to requirements of remote sensing missions that have 1-day, 3-day, or 30-day repeat intervals. The simulation results suggest that 1100 km, 550 km, and 200 km are the minimum required swath width for a polar-orbiting sensor to meet snow-related applications demanding a 1-day, 3-day, and 30-day repeat cycles, respectively. The results of this paper provide valuable input for the planning of a future global snow mission.
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    Health Impacts of the Built and Social Environments, and Travel Behavior: The Case of the Sunshine State
    (MDPI, 2022-07-26) Mahmoudi, Jina; Zhang, Lei
    As physical inactivity statistics for the U.S. population show an alarming trend, many health problems have been increasing among Americans in recent decades. Thus, identification of the factors that influence people’s physical activity levels and health outcomes has become ever more essential to promote public health. The built envSFironment is among the main factors that impact individuals’ health outcomes. However, little is known about the health impacts of built environment factors at large geographical scales such as those of the metropolitan area of residence. Further, the health impacts of travel behavior such as telecommuting and teleshopping remain unclear. This study uses an ecological model framework to probe the roles of travel behavior and built as well as social environments at different spatial levels in health. Instrumental variable binary probit models have been developed to examine the complex interlinks between measures of travel behavior, physical activity levels, built and social environment characteristics, and individuals’ health outcomes. Findings indicate that built and social environment factors at different spatial levels, including the metropolitan area, are correlated with individuals’ health outcomes. Additionally, the findings suggest that increased levels of telecommuting and teleshopping within communities may lead to unfavorable health outcomes. The findings shed light on the most promising policy interventions that can promote public health through modifications targeting people’s travel choices as well as the built and social environments within urban areas.
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    Life-Cycle Sustainability Assessment of Using Rock Dust as a Partial Replacement of Fine Aggregate and Cement in Concrete Pavements
    (MDPI, 2022-09-30) Zhao, Yungpeng; Goulias, Dimitrios; Dobiszewska, Magdalena; Modrzyński, Paweł
    The use of recycled materials and industrial by-products in pavement construction and rehabilitation can achieve substantial benefits in saving nature resources and reducing energy consumption as well as greenhouse gas (GHG) emissions. Alternative geological origin rock dust for the partial replacement of fine aggregate and/or cement in Portland cement concrete (PCC) pavements may provide positive environmental and economic benefits. The objective of this study was to quantitatively assess the life-cycle economic and environmental impacts when rock dust is used in PCC pavement roadway construction. Previous studies have primarily focused on the economics and/or environmental impacts during the material production process. Thus, a methodological framework considering all stages (such as material production, transportation, construction, maintenance, rehabilitation and end of life), involved in the life-cycle assessment of concrete pavements is proposed when using recycled materials/by-products. The life-cycle assessment (LCA) was conducted on a pavement project representative of typical construction practices in Poland to quantify such benefits. The alternative sustainable construction strategies considered partially replacing fine aggregate and/or cement with rock dust of basalt origin in PCC pavements. The LCA results indicate that using rock dust to replace 20% FA and 10% cement provided a reduction of 6.5% in cost, 10% in CO2 emissions and 11% in energy consumption. This study also provides significant insights on the specific contribution of material production, construction processes and the transportation of materials to the overall environmental benefits and cost savings. The suggested approach for LCA analysis in pavement construction can be adopted elsewhere for quantifying the sustainability benefits of using alternative recycled materials in roadways.
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    Facility design and worker justice: COVID-19 transmission in meatpacking plants
    (Wiley, 2023-06-17) Lou, Jiehong; Borjigin, Sachraa; Tang, Connie; Saadat, Yalda; Hu, Ming; Niemeier, Deb A.
    Background Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output. Methods Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions. Results Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space. Conclusion Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.
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    COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages
    (Wiley, 2022-11-02) Sun, Qianqian; Zhou, Weiyi; Kabiri, Aliakbar; Darzi, Aref; Hu, Shonghua; Younes, Hannah; Zhang, Lei
    Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: “best performance,” “effort,” and “consistency.” We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.
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    Implementing wastewater surveillance for SARS-CoV-2 on a university campus: Lessons learned
    (Wiley, 2022-10-21) Wartell, Brian A.; Proano, Camila; Bakalian, Lena; Kaya, Devrim; Croft, Kristen; McCreary, Michael; Lichtenstein, Naomi; Miske, Victoria; Arcellana, Patricia; Boyer, Jessica; Van Benschoten, Isabelle; Anderson, Marya; Crabb, Andrea; Gilson, Susan; Gourley, Anthony; Wheeler, Tim; Trest, Brian; Bowman, Glynnis; Kjellerup, Birthe V.
    Wastewater surveillance, also known as wastewater-based epidemiology (WBE), has been successfully used to detect SARS-CoV-2 and other viruses in sewage in many locations in the United States and globally. This includes implementation of the surveillance on college and university campuses. A two-phase study was conducted during the 2020–2021 academic year to test the feasibility of a WBE system on campus and to supplement the clinical COVID-19 testing performed for the student, staff, and faculty body. The primary objective during the Fall 2020 semester was to monitor a large portion of the on-campus population and to obtain an understanding of the spreading of the SARS-CoV-2 virus. The Spring 2021 objective was focused on selected residence halls and groups of residents on campus, as this was more efficient and relevant for an effective follow-up response. Logistical problems and planning oversights initially occurred but were corrected with improved communication and experience. Many lessons were learned, including effective mapping, site planning, communication, personnel organization, and equipment management, and obtained along the way, thereby paving an opportune guide for future planning efforts.
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    Does federal flood hazard mitigation assistance affect community rating system participation?
    (Wiley, 2022-09-13) Frimpong, Eugene; Reilly, Allison C.; Niemeier, Deb
    With the inexorable march of climate change, increased flooding is inevitable. Understanding the feedback between federal flood mitigation policies and the ways in which local governments build flood resilience is a significant gap in the literature. In particular, the effect that federal flood mitigation grants have on the intensity of local flood mitigation is nonexistent. This work measures flood risk mitigation by using the level of participation in FEMA's Community Rating System (CRS). Communities that participate in the CRS and undertake mitigation are awarded points; more points imply a higher level of participation. Since its inception in 1990, CRS communities have received considerably more federal pre-disaster flood mitigation grants compared to non-CRS communities. This study assesses the effect of federal pre-disaster flood mitigation grants on the level of participation in the CRS program. We use data on Hazard Mitigation Assistance programs and CRS participation data between 2010 and 2015. We link these data to flood risk and socioeconomic information. Our results indicate (i) federal pre-disaster flood mitigation grants do not appear to significantly influence the level of CRS participation, (ii) the effect of flood risk and socioeconomic factors on the level of CRS participation are mixed, and (iii) the current level of CRS participation is influenced by the previous level of CRS participation, which is not tied to federal pre-disaster flood mitigation grant. These findings add to the growing discussions on the drivers and barriers of local flood risk mitigation.
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    Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Code
    (2022-07-11) Ghaedi, Hamed; Baroud, Hiba; Ferreira, Celso; Perrucci, Daniel; Reilly, Allison; Reilly, Allison
    A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.