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The current characterization of flood exposure is largely based on residential location of populations; however, location of residence only partially captures the extent to which populations are exposed to flood hazards. An important, yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. In this study, we analyzed large-scale, high-resolution location-intelligence data collected from anonymous mobile phone users to characterize human mobility patterns and the resulting flood exposure in coastal counties of the United States. We developed the metric of mobility-based exposure based on dwell time in places located in the 100-year floodplain. The results of examining the extent of mobility-based flood exposure across demographic groups reveal significant disparities across race, income, and education level groups. The results show that Black and Asian, economically disadvantaged, and undereducated populations in US coastal cities are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to that of residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among different socio-demographic groups. The results highlight that urban flood risk assessments should not only focus on the level of flood exposure to residences, but also should consider mobility-based exposure to better learn the disparities in flood exposure among social groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people's life activities and enable a better characterization of disparity in populations' exposure to flood hazards beyond their place of residence. The findings of this study have important implications for urban planners, flood managers, and city officials in terms of accounting for mobility-based flood exposure in flood risk management plans and actions. [ABSTRACT FROM AUTHOR]

Despite recognition of the influence of socio-spatial networks on disaster recovery, limited data-driven evidence exists regarding the nature of this relationship. To address this knowledge gap, this study investigated the duration of the human mobility recovery process and its relationship with hazard-exposure heterophily (as a measure for the resourcefulness of social ties) and various other socio-demographic characteristics. We applied the concept of hazard-exposure heterophily to the possibility of resource sharing, through social-spatial networks, between communities with different hazard exposures. We used human movement patterns to measure recovery duration. To understand the disparities during recovery, we examined the correlations between recovery duration and the social demographic characteristics of race and income caused by Hurricane Harvey which made landfall in Harris County, Texas, on August 25, 2017. We discerned high/low hazard-exposure heterophily through the use of a threshold-classification method, and we got four clusters according to the flood-inundation percentage and social connectedness values. Then we applied correlation analysis to analyze the relations between hazard-exposure heterophily/income and recovery weeks among different income groups. The results revealed that hazard-exposure heterophily considerably accelerates human mobility recovery in low-income areas, as the hazard-exposure heterophily allows for the increased exchange of knowledge and resources between members of diverse groups, ultimately accelerating the recovery process. The results of this study could benefit local agents to better allocate recovery resources. [ABSTRACT FROM AUTHOR]

• A quantitative approach to examine policy preference conflicts among diverse stakeholders across multiple plans. • Hazard mitigation plan incorporated the most overall stakeholder policy preferences among the four examined plans. • The regional transportation plan incorporated the fewest overall stakeholder policy preferences. • The hazard mitigation plan and the regional conservation plan had the highest level of policy consistency. • The hazard mitigation plan and regional transportation plan had the lowest level of policy consistency. The objective of this paper was to create and test a methodological framework for examining the extent to which various plans captured diverse stakeholder policy preferences related to resilience planning and management of interdependent urban systems. Policy preferences represent what were important for stakeholders and determine the priorities of stakeholders in resilience planning of urban systems. Stakeholders in different urban sectors may have conflicts of policy preferences in the resilience planning process. A comprehensive understanding of the extent to which plans incorporated and reflected policy preferences of different stakeholders would greatly improve the quality of resilience planning. Hence, we proposed a plan evaluation framework to examine the extent to which various plans captured diverse stakeholder policy preferences in resilience planning of interdependent infrastructure systems. We showed the application of the proposed framework in the evaluation of four plans affecting flood resilience planning in the Houston area. The proposed tool could help identify conflicted stakeholder policy preferences in planning and enable evaluation of policy consistency in networks of plans. [ABSTRACT FROM AUTHOR]

• The preparation of Au/reduced graphene oxide nanocomposite through a one-pot method. • The fabrication of an Au/RGO modified carbon paste elecrode. • Electrochemical determination of agnuside by differential pulse voltammetry technique. • A novel and sensitive technique with good merit figures for agnuside determination in real samples. Agnuside despite its good properties for the body, can be harmful to the kidneys in extra concentrations. So, its measurement is important. For the first time, we developed a simple, rapid and inexpensive method for the investigation of agnuside electrochemical behavior by designing a gold/reduced graphene oxide/carbon paste electrode (Au/RGO/CPE). The Au/RGO nanocomposite was synthesized through a one-pot in situ reduction of graphene oxide (GO) and HAuCl 4 in the presence of sodium citrate and the obtained product was studied by Fourier transform infrared (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The anodic oxidation of agnuside has been carried out at a modified carbon paste electrode in 0.1 M phosphate buffer solution (pH 6.0) using sensitive differential pulse voltammetry (DPV). Several parameters such as the type and pH of supporting electrolyte and scan rates were optimized. This study reported a good linearity in the range of 0.050–250.0 nM and a low detection limit of 0.016 nM. The relative standard deviations (RSDs) for 10.0 nM and 100.0 nM of agnuside were obtained 3.3% and 1.2%, respectively. The sensor was successfully applied to determine agnuside in Vitex agnus castus fruit and urine sample. [ABSTRACT FROM AUTHOR]

Abstract In the present study, for the first time, we develop a sensitive voltammetric technique for the determination of pemetrexed using Pd/carbon nanofibers/methyl (trioctyl)ammonium bis(trifluoromethylsulfonyl)imide ([M3OA]+[NTF2]−) ionic liquid modified carbon paste electrode in which Nafion was utilized as binder (CPE/Pd/CNF/[M3OA]+[NTF2]−/Nafion). Composite nanofibers were synthesized by electrospinning method and characterized by SEM, TEM, XRD and FT-IR and the synthesized ionic liquid was also investigated by FT-IR and UV–Vis. Square wave voltammetry (SWV) measurements were performed to investigate the electrochemical oxidation of pemetrexed using the proposed electrode in 0.1 M phosphate buffer solution at pH 6.0. Several parameters such as pH amount, scan rate and the type of supporting electrolyte were studied for pemetrexed oxidation. In addition, selectivity, repeatability, reproducibility and stability were also evaluated. Under optimized conditions, a linear concentration was detected in the range of 1.00–35.0 nM with a detection limit of 0.33 nM by SWV. The proposed sensor was successfully applied for the quantification of pemetrexed in cancerous human plasma, pharmaceutical and health urine samples. Graphical abstract Unlabelled Image Highlights • Fabrication of Pd/carbon nanofibers by electrospinning method • Modification of carbon paste electrode by Pd/CNF/[M3OA]+[NTF2]−. • Electrochemical pemetrexed determination using SWV technique • The developed sensor being successfully applied for the determination of pemetrexed in real samples [ABSTRACT FROM AUTHOR]

Urban flooding disrupts traffic networks, affecting mobility and disrupting residents' access. Flooding events are predicted to increase due to climate change; therefore, understanding traffic network's flood-caused disruption is critical to improving emergency planning and city resilience. This study reveals the anatomy of perturbed traffic networks by leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis. We evaluate travel times between every pairwise junction in the city and assess higher-order network geometry changes in the network to determine flood impacts. The findings show network-wide persistent increased travel times could last for weeks after the flood water has receded, even after modest flood failure. A modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show that distant trips would experience a greater percentage increase in travel time. Also, the extent of the increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. The findings of this study provide an important novel understanding of floods' impacts on the functioning of traffic networks in terms of travel time and traffic network geometry. • High-order network analysis and reveals anatomy of perturbed traffic networks. • Network-wide persistent travel time increase can last for weeks. • Modest flooding causes significant temporal expansion of network. • Distant trips experience greater percentage increase in travel time. • Flood impacts extend beyond inundated areas. [ABSTRACT FROM AUTHOR]

The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the potential of crowdsourced data (such as user-generated flood reports) for flash flood mapping and situational awareness, little attention has been paid to data imbalance issues that could introduce biases in data and assessment. To address this gap, in this study, we examine biases present in crowdsourced reports to identify data imbalance with a goal of improving disaster situational awareness. Three biases are examined: sample bias, spatial bias, and demographic bias. To examine these biases, we analyzed reported flooding from 3-1-1 reports (which is a citizen hotline allowing the community to report problems such as flooding) and Waze reports (which is a GPS navigation app that allows drivers to report flooded roads) with respect to FEMA damage data collected in the aftermaths of Tropical Storm Imelda in Harris County, Texas, in 2019 and Hurricane Ida in New York City in 2021. First, sample bias is assessed by expanding the flood-related categories in 3-1-1 reports. Integrating other flooding related topics into the Global Moran's I and Local Indicator of Spatial Association (LISA) revealed more communities that were impacted by floods. To examine spatial bias, we perform the LISA and BI-LISA tests on the data sets—FEMA damage, 3-1-1 reports, and Waze reports—at the census tract level and census block group level. By looking at two geographical aggregations, we found that the larger spatial aggregations, census tracts, show less data imbalance in the results. Through a regression analysis, we found that 3-1-1 reports and Waze reports have data imbalance limitations in areas where minority populations and single parent households reside. The findings of this study advance understanding of data imbalance and biases in crowdsourced datasets that are growingly used for disaster situational awareness. Through addressing data imbalance issues, researchers and practitioners can proactively mitigate biases in crowdsourced data and prevent biased and inequitable decisions and actions. • By expanding the flood-related categories in 3-1-1 reports, the research identified more communities that were impacted by flooding, thus mitigating sample bias. • Spatial analysis techniques are preformed on 3-1-1 reports and Waze reports at the census tract and census block group level to identify which geographical aggregation causes a reduction of spatial data to mitigate spatial bias. • Regression models are preformed on 3-1-1 and Waze reports and found that social demographic traits such as minority status are not represented on crowdsourced platforms. • The application of the proposed analytical framework is demonstrated in two study areas: Harris County during Tropical Storm Imelda in 2019 and New York during Hurricane Ida in 2021. • The proposed framework provides insights on the types of biases that exists within crowdsourced data and how to identify biases. • The proposed method can help cities and flood plain managers identify biases within crowdsourced data to create an equitable flood recovery process. [ABSTRACT FROM AUTHOR]

The major problems of the semiconductor nanomaterials in the photocatalytic applications for degradation of organic contaminants are fast recombination of photogenerated electron-hole pairs and low visible light harvesting. Therefore, we tried to resolve these drawbacks by coupling LaFeO 3 nanoparticles with Bi 2 WO 6 nanoflakes and constructing of binary Z-scheme heterojunction through the facile solvothermal synthesis approach. The photocatalytic activity of the LaFeO 3 /Bi 2 WO 6 heterojunction was studied through degradation of tetracycline (TC) upon visible light irradiation. The effects of experimental parameters, including LaFeO 3 /Bi 2 WO 6 dosage, initial concentration of TC, visible light irradiation time, and solution pH on the photocatalyst performance of binary nanocomposite were analyzed by response surface methodology. The results of analysis of variance confirmed that these parameters have a significant effect on the TC degradation efficiency. The value of the first-order kinetic rate constant for removal of TC using LaFeO 3 /Bi 2 WO 6 was calculated 0.1291 min−1, which was 20 and 10 times higher than pure LaFeO 3 and Bi 2 WO 6 , respectively. Besides, LaFeO 3 /Bi 2 WO 6 was shown the excellent photocatalytic stability after 4 cycles. Furthermore, the photocatalytic pathway for photodegradation of TC by binary LaFeO 3 /Bi 2 WO 6 nanocomposite was proposed according to the trapping experiments of active species and the results were signify that the h+ and . OH are the predominant reactive species for the photodegradation of TC. Overall, we believe that current novel research opens a promising route for the preparation of highly efficient LaFeO 3 /Bi 2 WO 6 photocatalyst and its usage for wastewater treatment. [ABSTRACT FROM AUTHOR]

The ability to measure and track the speed and trajectory of a community's post-disaster recovery is essential to inform resource allocation and prioritization. The current survey-based approaches to examining community recovery, however, have significant lags and put the burden of data collection on affected people. Also, the existing literature lacks quantitative measures for important milestones to inform the assessment of recovery trajectory. Recognizing these gaps, this study uses location-based data related to visitation patterns and credit card transactions to specify critical recovery milestones related to population activity recovery. Using data from 2017 Hurricane Harvey in Harris County (Texas), the study specifies four critical post-disaster recovery milestones and calculates quantitative measurements of the length of time between the end of a hazard event and when the spatial areas (census tracts) reached these milestones based on fluctuations in visits to essential and non-essential facilities, and essential and non-essential credit card transactions. Accordingly, an integrated recovery metric is created for an overall measurement of each spatial area's recovery progression. Exploratory statistical analyses were conducted to examine whether variations in community recovery progression in achieving the critical milestones is correlated to its flood status, socioeconomic characteristics, and demographic composition. Finally, the extent of spatial inequality is examined. The results show the presence of moderate spatial inequality in population activity recovery in Hurricane Harvey, based upon which the inequality of recovery is measured. Results of this study can benefit post-disaster recovery resource allocation as well as improve community resilience towards future natural hazards. [ABSTRACT FROM AUTHOR]

• Period of recovery was quantified from observational power outage data. • Findings evidence social and spatial inequalities in power outage events. • Need for future investment in data collection and analysis of power outages. • Importance of integrating equity practices in power restoration management. Due to the effects of climate change and urbanization, the severity and frequency of hazard events is expected to increase. The energy sector in the United States is ever more vulnerable to extreme climatic hazards. Hurricane winds can damage electrical lines, causing hazard-induced power outages. Extreme heat and freezing temperatures can imbalance the supply and demand for energy resulting in managed power outages. Utility companies reportedly prioritize the restoration of power systems based on the number of outages and the size of affected populations. This approach fails to account for unequal impacts of hazard-induced and managed power outages. Research in equitable infrastructure emphasizes that certain populations, such as lower income and racial-ethnic minority households, are disproportionately impacted by disruptions in the power system. Moreover, the connected network qualities of the power system suggests an element of spatial vulnerabilities. However, little empirical evidence exists regarding the presence and extent of energy inequality. A main roadblock is the data collection process, in that outage data is often perishable and not found at granular spatial scales to allow the undertaking of a comprehensive analysis on impacts of power losses. Recognizing this important gap, this study collected and analyzed observational data related to the managed power outages during Winter Storm Uri (2021) and the hazard-induced outages during Hurricane Ida (2021). The research quantified the period of recovery at a granular spatial scale using an equitable-focused analysis to detect social and spatial inequalities through an exploratory lens. In extreme cases of power outage, census tracts of lower income and higher percentage of Hispanic population had longer median durations of recovery during Winter Storm Uri. In the hazard-induced outages of Hurricane Ida, non-coastal zip codes with lower income had a 1.00-day longer median duration of recovery and higher percentage of Black population had a 2.00-day longer median duration of recovery while coastal zip codes with higher percentage of Black population had a 1.00-day longer median of recovery. Non-coastal regions had 63% greater spatial Gini values and 16% greater value in infrastructure inequality when compared to coastal regions. The managed power outages resulted in a 3% to 19% greater value of infrastructure inequality to the hazard-induced power outages. The findings provide evidence of pervasive social and spatial inequality in power outages during climate hazards and highlight the importance of integrating equity into the manner in which utility managers and emergency planners restore power outages. [ABSTRACT FROM AUTHOR]

Vulcan XC-72R carbon supported Pd and Pd–Mo alloys of different Pd:Mo atomic ratios were prepared by hydrothermal synthesis method. The bimetallic nanoalloys were characterized by powder X-ray diffractometry and inductively coupled plasma-atomic emission spectroscopy to determine their crystal structures and elemental compositions. Alloy formation of the nanocatalysts was proven by energy dispersive X-ray spectroscopy line profiles using field emission scanning electron microscopy. The performance of as-prepared nanocatalysts was evaluated for the reactions of methanol, ethanol, ethylene glycol and glycerol electrooxidation in alkaline media by cyclic voltammetry, linear sweep voltammetry and chronoamperometric measurements. It was found that bimetallic Pd–Mo/VC catalysts have higher activity due to different electronic structure as compared to the monometallic palladium. Also, the Pd 3 Mo/VC catalyst showed excellent catalytic activity, high durability and stability which indeed propose it to be as a promising electrocatalyst for future direct alcohol fuel cells. [ABSTRACT FROM AUTHOR]

The objective of this study is to empirically and systematically assess the combination of inherent susceptibility factors, protective actions, and factors of hazard exposure that influence a household's level of hardship experienced due to disruptions in critical infrastructure services during disasters. Classification and regression tree (CART) decision tree models and survey data from three major hurricane events were used to: (1) identify the pathways leading to impact(s) due to service disruptions and explore the differences in pathways across vulnerable population groups; and (2) identify the points of intervention to mitigate well-being impacts in households due to disruptions in water, energy, food, and road transportation services. The results reveal how the associative pathways between these factors change between socioeconomic and demographic groups in the impacted community and for different infrastructure service system types. The findings suggest that not all vulnerable households experienced high hardship outcomes despite prolonged outages. Finally, the hardship pathways suggest recommendations for improving resilience in infrastructure systems in a more equitable manner. The findings can be used by emergency and infrastructure managers and operators to better prioritize resource allocation for hazard mitigation investments and restorations. Accordingly, this study contributes to the theory of human-centric infrastructure resilience. [ABSTRACT FROM AUTHOR]

The objective of this paper is to discover the emergent energy performance and determinants of energy waste in buildings. Electricity consumption in the U.S. attributes to 73% of energy waste in buildings and much of this waste is due to improper design, operation, and use of appliances. In particular, the operation or use phase of buildings and the way occupants behave significantly contribute to energy waste. Understanding the determinants of energy waste during the operation phase of buildings is a challenging task due to the complex interactions between the occupants, building units, and appliances. To decode these complex interactions and facilitate a better understanding of the determinants of energy waste, a simulation approach is used in this study. An agent-based simulation model was developed to capture the diverse attributes and dynamic behaviors of building occupants at the interface of human-building-appliance interactions. The application of the proposed model is demonstrated in a case study. Using simulation experiments, the interactions between occupant, building unit and appliance on energy consumption were investigated. The simulation model also was used for estimating determinants of energy waste. In addition, the simulation model includes a visualization interface that facilitates communication of strategies between the buildings users and facility managers. The results will highlight the significant attributes and effective strategies for energy waste reduction at the interface of human-building-appliance interactions. This information has potentially significant implications for building designers, facility managers, and users through a better understanding of emergent energy performance of buildings. [ABSTRACT FROM AUTHOR]

Sustainability of infrastructure systems involves multiple interrelated objectives which are dependent on the dynamic behaviors of stakeholders, interdependencies between infrastructure assets, and evolving conditions in the social and environmental systems. This research contributes to the body of knowledge by creating a framework that integrates: (1) different dimensions of sustainability in infrastructure networks, and (2) dynamic changes in the behaviors of stakeholders, asset performance conditions, and the social and environmental conditions. The proposed framework includes an agent based simulation model to capture the dynamic behaviors of decision makers and the evolving performance of infrastructure assets under different socio-environmental conditions. The outcomes of the simulation model include the short- and long-term costs and environmental impacts of infrastructure networks under different scenarios related to the changes in the socio-environmental conditions. The application of the framework is shown in a numerical case study of a road network. The results include: (i) identification of infrastructure management strategies (e.g., maintenance planning and funding allocation) that optimize the tradeoff between annual cost and environmental impacts of the network; and (ii) discovering robust management strategies that lead to highly-likely desired sustainability outcomes across different uncertain scenarios related to the future socio-environmental conditions. The results show the capability of the proposed framework for sustainability assessment of infrastructure systems based on integrated analysis of the dynamic behaviors of decision-makers, infrastructure characteristics, and evolving socio-environmental conditions. Hence, it provides a predictive quantitative tool to inform decision-making and policy formulation toward improving the sustainability of infrastructure networks. [ABSTRACT FROM AUTHOR]

This study explores the role of access to essential facilities and emergency services during hazard weather events in shaping individuals' risk perceptions. We develop a framework in which residents' views of required actions facing extreme weather are influenced by their level of access to essential facilities to obtain medical and emergency services. The effect of access is complemented by perceptions about the status of local infrastructure conditions as enabling access. Using a sample of Texas residents collected during April 2021, we demonstrate the role of restricted access and views of local infrastructure conditions as important predictors of increased concerns during natural disasters. The results demonstrate the effects of factors such as access and status of local infrastructure on the risk assessments of individuals in vulnerable communities who face increased risks from extreme weather. Accordingly, the findings advance our understanding of the unexplored relationship between access of essential facilities and risk perceptions, and could inform disaster managers and public officials regarding the importance of evaluating access as an element of public risk perceptions facing extreme weather events. [ABSTRACT FROM AUTHOR]

A novel carbon paste electrode modified with ZnO nanorods and 3-(4′-amino-3′-hydroxy-biphenyl-4-yl)-acrylic acid (3,4′AAZCPE) was fabricated. The electrochemical study of the modified electrode, as well as its efficiency for electrocatalytic oxidation of dopamine, is described. It has been found that the oxidation of dopamine at the surface of modified electrode occurs at a potential of about 180 mV less positive than that of an unmodified carbon paste electrode. The detection limit of 5.6 × 10 −8 M for dopamine was obtained using square wave voltammetry (SWV). In addition, this modified electrode was used for simultaneous determination of dopamine, melatonin, methionine and caffeine. Finally, the modified electrode was used for determination of dopamine, melatonin, methionine and caffeine in human blood serum and urine samples. [ABSTRACT FROM AUTHOR]

While traditional financing approaches such as federal and state grants funded by taxation are insufficient to address the existing need, innovative financing such as novel public private partnership models, credit enhancement tools, and new bonding instruments has emerged to expand the fiscal space of public agencies for infrastructure development. Formulating innovative financing approaches is one of the challenges faced by policymakers to address the ever growing need for restoring the failing civil infrastructure in the U.S. Public support/opposition is one of the major drivers/disruptors of innovative financing. Assessment of public perceptions is a major component towards gaining public support and developing sustainable infrastructure financing policies. The objective of this study is to investigate the determinants of public perceptions of innovative financing. Data obtained from public survey of 616 individuals from 50 states in the U.S. is analyzed to investigate the effects of: (a) economic factors, (b) infrastructure conditions, and (c) personal characteristics of the public on the knowledge, awareness, perceptions, and attitudes of the public towards innovative financing. The findings reveal that the likelihood of public support of innovative financing is insensitive to economic conditions and is affected by the factors related to infrastructure condition such as the level of need for infrastructure renewal, the ability to protect the public against natural disasters, and the history of using innovative financing for infrastructure renewal. The findings also show that 57% of the sample population in the U.S. were not knowledgeable about different methods for financing infrastructure projects and 77% of the sample population in the U.S. were not aware of the activities of public organizations for finding innovative financing solutions for infrastructure projects. Based on the findings, two main strategies have been proposed for infrastructure agencies to enhance their efforts towards gaining public acceptance of innovative financing: (i) enhance strategies to facilitate learning for the public about innovative financing in infrastructure projects and (ii) improve marketing and citizen involvement strategies to get the public to buy-in to innovative financing methods. The findings can enhance the current efforts of public agencies related to educating the public and citizen involvement strategies by incorporating public preferences in policy development to enhance the likelihood of public support of innovative financing methods. Potential improvements can be made in changing the key messages in educating the public, using more effective strategies for communicating complicated financing concepts, and highlighting the success stories and benefits of innovative financing in other states and countries. [ABSTRACT FROM AUTHOR]

The importance of public perceptions and their role in climate change adaptation for infrastructure has been highlighted in previous studies. However, public perception of water infrastructure at risk of flooding has not been explicitly addressed. Therefore, the purpose of this study is to investigate flood risk perception, the factors that influence it, and evaluations of water infrastructure systems. To examine this, data were obtained from a public survey of 755 respondents in Miami-Dade County, Florida, United States. Risk perception is measured as three components: worry, awareness, and preparedness. Structural equation modeling was used to develop and test a model tracing the interrelationships between risk perception, disaster experience, satisfaction with infrastructure services, knowledge of water infrastructure, socioeconomic characteristics, and political views. Results show that flood risk perception elements (awareness, worry, preparedness) significantly influence public evaluation of water infrastructure conditions and mediate the impact of flood experience, service satisfaction, and knowledge. Evaluation of water infrastructure is positively associated with knowledge and service satisfaction but negatively with flood experience. The study also confirms the importance of socio-economic characteristics in shaping public risk perception and evaluation of infrastructure. These findings imply multiple ways that decision-makers may enhance flood risk management plans and adaptation processes of water infrastructure systems in coastal urban areas. [ABSTRACT FROM AUTHOR]

Social media are a rich source of situational information useful conveyed with more immediacy conventional by mass media. The content of social media posts and the embedded geo-coordinates in social media analytics can be harnessed to enhance disaster situational awareness; however, the informal nature of social media post content can compromise the accuracy of an event's location. This study explored the consistency of the locations mentioned in disaster-related tweets with their embedded geo-coordinates. Natural language processing and fine-grained analytics approaches enable us to retrieve important information from them. Relating this retrieved information with embedded geolocations promotes understanding of whether this information is reliable and actionable for disaster management teams, researchers, and disaster victims. The efficiency of rescue operations can be significantly improved if misleading or under-informed posts are separated from the correct information posts. Using a semi-supervised event detection approach and manual annotations, this study explored the consistency between the mentioned locations in social media posts and their geo-coordinates using a case study of 4227 tweets posted during Hurricane Harvey in Houston, Texas. Findings show that 64% of the total tweets have mentioned locations in their contents. Also, most tweets mentioning a specific location during Hurricane Harvey are about the location of the author. Specifically, we found a very high consistency level (94%) between the point-type mentioned locations and the geotags in the tweets. Our findings can support the disaster management teams and policymakers in the design and implementation of effective response strategies. [ABSTRACT FROM AUTHOR]

• An adaptive reinforcement learning model is developed to learn human travel behaviors in regular situations. • The model is capable of simulating the traffic conditions and evaluate the perturbations of disasters on urban mobility. • A case study of the 2017 Hurricane Harvey in Houston is conducted to examine the application of the model. • The model and outcomes of applications can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility. The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and evaluating human mobility patterns, such as destination and trajectory selection, can inform emerging congestion and road closures raised by disruptions in emergencies. Data related to human movement trajectories are scarce, especially in the context of emergencies, which places a limitation on applications of existing urban mobility models learned from empirical data. Models with the capability of learning the mobility patterns from data generated in normal situations and which can adapt to emergency situations are needed to inform emergency response and urban resilience assessments. To address this gap, this study creates and tests an adaptive reinforcement learning model that can predict the destinations of movements, estimate the trajectory for each origin and destination pair, and examine the impact of perturbations on humans' decisions related to destinations and movement trajectories. Employing millions of trajectory data from INRIX, the application of the proposed model is shown in the context of Houston and the flooding scenario caused by Hurricane Harvey in August 2017. The results show that the model can achieve more than 76% precision and recall at the model learning stage. The mean percentage error of the travel distance in predicted trajectories is 4.29%, compared to the travel distances in empirical data. In addition, predicted density of vehicles in grid cells are negatively associated with the traffic speed on road segments and inundation intensity in grid cells during the flooding. The results from the simulation further show that the model could predict traffic patterns and congestion resulting from urban flooding. The outcomes of the analysis demonstrate the capabilities of the model for analyzing urban mobility during crises, which can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility. [ABSTRACT FROM AUTHOR]

Hurricane season brings new and complex challenges as we continue to battle the COVID-19 pandemic. In May 2020, the National Oceanic and Atmospheric Administration has predicted nearly twice the normal number of tropical storms and hurricanes this season, while projections of COVID-19 models continue to rise in the United States as the Atlantic hurricane season progresses. Our research examines the critical intersection of hurricane response and public health in Harris County, Texas. We examine a hypothetical case of the 2017 Hurricane Harvey occurring amid the current pandemic. This research uses point of interest visitations as location intelligence data provided by SafeGraph together with Social Vulnerability Index and historical flood data to examine the critical intersection of natural hazard planning and response and the COVID-19 pandemic to assess the risks of a compound hazard situation. COVID-19 transmission hotspots and businesses in a community due to storm preparation activity were identified. The main drivers of transmission risk arise from overall pandemic exposure and increased interpersonal contact during hurricane preparation. Residents of health-risk areas will need to make logistical arrangements to visit alternative medical facilities for treatments related to either COVID-19 or physical impacts, such as injuries, due to the hurricane risks. Points of interest needed for disaster preparation are more likely to be situated in high-risk areas, therefore making cross-community spread more likely. Moreover, greater susceptibility could arise from social vulnerability (socioeconomic status and demographic factors) and disrupted access to healthcare facilities. Results from this study can be used to identify high-risk areas for COVID-19 transmission for prioritization in planning for temporary healthcare centers and other essential services in low-risk areas. Understanding the interplay between disaster preparation and the restrictive environment laid out by the pandemic is critical for community leaders and public health officials for ensuring the population has sufficient access to essential infrastructure services. The findings from this study can help guide the direction of disaster planning and pandemic response strategies and policies. [ABSTRACT FROM AUTHOR]

The objective of this paper is to develop household susceptibility indices related to disruptions in infrastructure services during disasters. The prolonged disruptions in infrastructure services create great hardship for the households of a community affected by natural hazards, especially shelter-in-place households who are at a high risk of such disruptions. The literature currently lacks tools and methods to assist decision makers in considering the tolerance and susceptibility of the household in communities to the infrastructure service disruptions. While there are some vulnerability indices in the disaster literature, the scores have not developed to specifically consider the risks associated with the infrastructure service disruptions. Hence, in this study, we developed and implemented Disruption Tolerance Index (DTI) to enable understanding of spatial patterns of susceptibility to service disruptions. In particular, we create susceptibility scores and maps for disruptions in power and transportation systems as two essential services on which households rely during their daily life. These service disruptions include power outages and road closures due to the natural hazards which disrupted households' access to these services. The factors for determining DTI scores are selected through an empirical analysis of households' susceptibility to the disruptions, the scores are calculated by implementing Principal Components Analysis, and the scale of the scores is discussed. The study reveals the hot spots and cold spots of compound susceptibility to the disruptions. Unveiling the spatial clusters of susceptibility provides decision makers with the necessary information to consider the households' susceptibility in emergency plans and risk mitigation practices. The approach developed in this study could be adapted for different locations to enable understanding of the unique susceptibility of the households from the infrastructure services during disasters. Such understanding provides the decision-makers with the essential information to plan and mitigate the risk mitigation approaches and preparedness actions related to the risks associated with infrastructure service disruptions. [ABSTRACT FROM AUTHOR]

Emerging influential contributors (EICs) on social media platforms—meteorologists, news reporters, journalists, and employees of public sector agencies—play a pivotal role in disaster public information and warning by consistently disseminating disaster situational information public. However, the fairness in Emerging Influential Contributors' (EICs') attention to different urban areas when posting disaster situational information has not been examined. It is unknown whether vulnerable communities where low-income, minority, and older individuals live are omitted from the content of posts made by EICs. The study used tweets from Houston, Texas, related to Hurricane Harvey to test the proposed approaches and examine the existence of bias in the content of social media posts made by EICs. This study first formulated the problem by identifying its correspondence with the well-explored areas of fairness assessment and then proposed two fairness assessment methods for evaluation of fair information dissemination on social media. This study's findings show that biases exist in the amount of attention given to areas in which vulnerable populations in the content of posts made by EICs on Twitter to inform about disaster situations. Such biases negatively affect the representativeness of social media content disseminated by EICs to inform emergency response and relief efforts. This study's outcomes address important knowledge gaps related to fairness in disaster informatics and information dissemination biases in online social networks during crises. • Emerging influential contributors (EICs) play a pivotal role in disaster information dissemination. • Whether vulnerable communities are omitted from the content of posts made by EICs is assessed. • Two approaches for assessing the fairness of information dissemination are developed. • Biases exist in the amount of attention given by EICs to the vulnerable communities. [ABSTRACT FROM AUTHOR]

• Survival models are developed to determine tolerance level for service disruptions. • Empirical data in the aftermath of 3 major hurricane events are used to develop survival models. • Susceptibility curves enable understanding household-level disturbance level from disruptions. In natural hazard engineering, fragility curves are used to determine the likelihood of damage to an engineered system under different magnitudes of hazard intensity. Analogous to fragility curves for engineered systems, survival models developed in the present study determine the extent of disturbances for shelter-in-place households caused by infrastructure service disruptions during disasters. This study used empirical data from household surveys collected in the aftermath of Hurricane Harvey, Hurricane Florence, and Hurricane Michael to create empirical survival models for determining household-level disturbances related to eight infrastructure services: power, water, communication, sewer systems, transportation, solid waste collection, grocery stores, and healthcare facilities. The survival models considered various influencing factors, such as sociodemographic factors, previous experience, risk perception, and access to resources to determine what percentage of households in a community would experience considerable hardship under varying durations of service disruptions. The developed curves suggested that although the susceptibility patterns are similar for short durations of infrastructure service disruptions, prolonged service disruptions pose varying levels of disturbance in different communities based on the household characteristics and contextual factors. Susceptibility curves could be implemented with current tools for assessing the reliability and resilience of infrastructure systems to promote understanding of the societal impacts that disruptions in these services pose to the affected communities. The resulting empirical survival models provide necessary tools and insights for determining the susceptibility of communities to disruptions of various infrastructure services during disasters. Hence, the outcomes of this study provide new empirical insights and models enabling decision-makers to integrate human-centric dimensions into infrastructure retrofit and restoration processes to more equitably reduce societal impacts of service disruptions. Such human-centric approaches enable designing socially resilient cities and contribute to designing sustainable infrastructure systems. [ABSTRACT FROM AUTHOR]

How populations use social media in disasters is essential for evaluating the representation of subpopulations while analyzing social media data for emergency response and disaster research. Existing machine learning models can extract, characterize and make sense of digital trace data from social media, but are unable to account for diversity in population groups and use of social media. Consequently, the reliability of their decision-making ability remains questionable. This paper presents an exploratory analysis of empirical household survey data on the information seeking, sharing activity, and perceptions of information reliability on social media platforms across different population groups during three major hurricane storm events in the United States between 2017 and 2018. The results of this analysis suggest significant associations between social media use and socioeconomic factors: (1) Socioeconomic factors along with geographic effects play a role in determining not only platform uptake but both motivations for information seeking and the action of information sharing on social media, (2) The type of social media platform influences the type of information people seek, (3) Households from lower socioeconomic and minority backgrounds were more likely to use social media platforms to sought out different information on social media than their peer, (4) perceptions of information reliability are also influenced by social divides, where households in rural areas, lower income groups, and racial minorities were more likely to report greater unreliability in social media information. These findings provide new insights into the roles of social media use in creating or dismantling the digital divide during disasters. [ABSTRACT FROM AUTHOR]

This paper provides empirical information for understanding the susceptibility of households to the infrastructure service disruptions caused by natural hazards. Understanding household-level susceptibility is critical to determine the risks associated with staying shelter-in-place during disasters. This information is essential for various stakeholders such as community leaders, emergency planners, and utility managers to prioritize and restore infrastructure services for the public. However, there is limited empirical information regarding the susceptibility of shelter-in-place households to the disruptions of different services. Hence, this study presents an exploratory analysis of empirical data collected from affected communities to identify the influencing factors of the households' susceptibility. We utilized survey data collected from Hurricane Harvey (850 respondents), Hurricane Florence (362 respondents), and Hurricane Michael (583 respondents) to study the anatomy of susceptibility to eight infrastructure service disruptions. The descriptive analysis compared the similarities, such as rating the sewer and water systems as most important services, and differences, such as the varying levels of expectation for the service disruptions, of the three regions impacted by the disasters. Correlation analysis considered which underlying factors, including sociodemographic characteristics, protective actions and adjustments, previous experience and previous damage, social capital, and need for service of individual households along with the contextual and communal factors of the community, such as urbanization and previous disaster declarations, were associated with the ability of residents to respond to and withstand service disruptions. Although there were consistencies in the relationship of influencing factors to the level of susceptibility, the findings highlight that some variation in the influence of these factors was event-specific or service-specific. Finally, the contextual and communal factors of a community can bring unique insights to the anatomy of susceptibility to the service disruptions, as each location has inherent characteristics that would, directly and indirectly, influence households' susceptibility to service disruptions. These findings provide the necessary empirical information to inform infrastructure prioritization decisions and emergency response actions to reduce the societal impacts of infrastructure service disruptions on vulnerable populations. • Susceptibility to infrastructure service disruptions is examined by implementing empirical household survey data. • Service disruptions discriminately impact low-income, minority, and renter households. • Protective actions, including increased expectations and longer forewarning, decrease the households' susceptibility level. • While influencing factors universally impact the susceptibility level, some variations are event-specific or service-specific. • Contextual and communal factors of the affected regions could explain differences in levels of susceptibility. [ABSTRACT FROM AUTHOR]