Exposure Database Research Articles

Using remote sensing for exposure and seismic vulnerability evaluation: is it reliable?

ABSTRACT Conducting field surveys for exposure and seismic vulnerability evaluation is the most costly, resource-intensive task when assessing earthquake risk. During the past decade, risk analysts have been trying to alleviate this using remote sensing for building characterization. However, the use of vulnerability databases created with remote sensing had not been sufficiently validated thus far. In this paper, we have created an exposure and seismic vulnerability database in Port Prince (Haiti) using freely accessible aerial ortho-imagery and LiDAR points. We have validated this database against two reference datasets from different, independent studies. Then, we have computed an earthquake damage scenario to test whether remotely sensed data are actually valid for seismic risk evaluation. We have seen how our vulnerability database yields an accurate damage distribution with a low Mean Absolute Percentage Error of 3.78% when compared to the damage obtained with the reference vulnerability dataset. Further, we have conducted a thorough comparison of the cost that entails creating a vulnerability database using remote sensing with a traditional field survey. Twelve international experts have collaborated in the cost estimation of a typical in-field building inspection. As a result, we have found that using remote sensing techniques allows for saving up to 75% of the cost and 85% of the time. These outcomes seem to prove both, the technical and economic feasibility of remote sensing for seismic vulnerability assessment. Thus, we have proposed a 5-step approach for evaluating building vulnerability that combines both, the analysis of remotely sensed data and a reduced, targeted field survey to optimize time and cost. The final goal is to help cities reach the Sustainable Development Goal nr. 11.B to increase their resilience against disasters.

EVMFuzz: Differential fuzz testing of Ethereum virtual machine

AbstractThe vulnerabilities in Ethereum virtual machine (EVM) may lead to serious problems for the Ethereum ecosystem. With lots of techniques being developed for the validation of smart contracts, the testing of EVM has not been well‐studied. In this paper, we propose EVMFuzz, the first that uses the differential fuzzing technique to detect vulnerabilities in EVM. The core idea of EVMFuzz is to continuously generate seed contracts for different EVMs' execution, so as to find as many inconsistencies among execution results as possible, and eventually discover vulnerabilities with output cross‐referencing. First, we present the evaluation metric for the internal inconsistency indicator. Then, we construct seed contracts via predefined mutators and employ a dynamic priority scheduling algorithm to guide seed contract selection and maximize the inconsistency. Finally, we leverage different EVMs as cross‐referencing oracles avoiding manual checking. For evaluation, we selected four widely used EVMs for the test, conducted large‐scale mutation on 36,295 real‐world smart contracts, and generated 253,153 smart contracts as initial seeds. Accompanied by manual root cause analysis, we found five previously unknown security bugs and all had been included in the common vulnerabilities and exposures (CVE) database.

A stochastic exposure model for seismic risk assessment and pricing of catastrophe bonds

Risk-based catastrophe bonds require the estimation of losses from the convolution of hazard, exposure and vulnerability models. These models are affected by different uncertainties that arise from the definition of their input parameters. In this paper, we propose a stochastic approach to treat the uncertainty in the asset location and attributes of the exposure model. The proposed method uses the Monte Carlo sampling approach to generate a stochastic exposure database, where each asset location is generated randomly within the geometric bound of the administration, while the asset attributes (i.e. construction type and material, number of storey, building activity type and number of dwelling) are sampled from distributions built from census data. Finally, a sensitivity analysis is performed to investigate the influence of the spatial resolution of the exposure model on the average annual losses (AAL) and catastrophe bond prices. To this end, we implement four exposure models, with spatial resolution at the asset, municipality and province levels, on a study region comprising ten provinces in southern Italy. Compared to the proposed model, the exposure model where assets are relocated and aggregated at the geometric centroid of the municipality underestimates AAL by 8%, while a higher difference (up to 20%) is observed for the exposure model where assets are relocated and aggregated at the geometric centroid of the province. We also consider an exposure model whose asset locations are extracted from publicly available building footprints. Yet, this latter model was incomplete for some provinces resulting in underestimation of AAL up to 90%. Differences in catastrophe bond prices obtained from the four exposure models are less evident, with the exposure model built based on the building footprints showing a difference up to 9.5%.

Engineering surveys of Sri Lankan schools exposed to tsunami

The 2004 Indian Ocean tsunami affected 5% of Sri Lanka’s schools, severely damaging 108 and destroying 74. The catastrophe highlighted the critical role of schools in providing educational continuity during community recovery. Sri Lanka has since rehabilitated and rebuilt most of the destroyed schools along the coastline. However, there is a limited understanding of current levels of school exposure to tsunami. This hampers preparedness and risk reduction interventions that can improve community and educational tsunami resilience. This paper presents a multi-disciplinary school exposure database relevant to both vulnerability and loss modelling. The repository includes data on 38 schools and 86 classroom buildings, surveyed across the coastal districts of Ampara, Batticaloa and Galle in Sri Lanka, which were heavily affected by the 2004 tsunami. A new engineering rapid visual survey tool is presented that was used to conduct the physical assessment of schools for the exposure repository. School damage mechanisms observed in past tsunami inform the survey forms, which are designed to capture information at both school compound and building levels. The tsunami engineering survey tools are universally applicable for the visual assessment of schools exposed to tsunami. The surveys show that most Sri Lankan school buildings can be classified into three building archetypes. This means that future risk assessments can be conducted considering a small number of index buildings that are based on these archetypes with differing partition arrangements and structural health conditions. The surveys also raise three significant concerns. Firstly, most schools affected by the 2004 tsunami remain in the same exposed locations without any consideration for tsunami design or strengthening provisions. Secondly, Sri Lankan schools are fragile to tsunami loading and many of the schools in the Galle district suffer from severe corrosion, which will further affect their tsunami vulnerability. Thirdly, schools do not appear prepared for tsunami, and do not have adequate tsunami warnings nor evacuation protocols in place. These observations raise the urgent need to mitigate tsunami risk, including a holistic plan for tsunami retrofitting and for interventions to improve the tsunami preparedness of schools in Sri Lanka.

Linking the Urban Environment and Health: An Innovative Methodology for Measuring Individual-Level Environmental Exposures.

Environmental exposures (EE) are increasingly recognised as important determinants of health and well-being. Understanding the influences of EE on health is critical for effective policymaking, but better-quality spatial data is needed. This article outlines the theoretical and technical foundations used for the construction of individual-level environmental exposure measurements for the population of a northern English city, Bradford. The work supports 'Connected Bradford', an entire population database linking health, education, social care, environmental and other local government data over a period of forty years. We argue that our current understanding of environmental effects on health outcomes is limited both by methodological shortcomings in the quantification of the environment and by a lack of consistency in the measurement of built environment features. To address these shortcomings, we measure the environmental exposure for a series of different domains including air quality, greenspace and greenness, public transport, walkability, traffic, buildings and the built form, street centrality, land-use intensity, and food environments as well as indoor dwelling qualities. We utilise general practitioners' historical patient information to identify the precise geolocation and duration of a person's residence. We model a person's local neighbourhood, and the probable routes to key urban functions aggregated across the city. We outline the specific geospatial procedure used to quantify the environmental exposure for each domain and use the example of exposure to fast-food outlets to illustrate the methodological challenges in the creation of city and nationwide environmental exposure databases. The proposed EE measures will enable critical research into the relationship and causal links between the built environment and health, informing planning and policy-making.

Temporal Trends in Variability of Respirable Dust and Respirable Quartz Concentrations in the European Industrial Minerals Sector.

While between- and within-worker variability have been studied quite extensively, hardly any research is available that examines long-term trends in the variability of occupational exposure. In this first study on trends in occupational exposure variability temporal changes in the variability of respirable dust and respirable quartz concentrations within the European industrial minerals sector were demonstrated. Since 2000 the European Industrial Minerals Association's Dust Monitoring Program (IMA-DMP) has systematically collected respirable dust and respirable quartz measurements. The resulting IMA-DMP occupational exposure database contains at present approximately 40 000 personal full-shift measurements, collected at 177 sites owned by 39 companies, located in 23 European countries. Repeated measurements of workers performing their duties within a specific site-job-campaign combination allowed estimation of within- and between-worker variability in exposure concentrations. Overall day-to-day variability predominated the between-worker variability for both respirable dust concentrations and quartz concentrations. The within-worker variability in concentrations by job was two to three times higher for respirable quartz than for respirable dust. The median between-worker variability in respirable dust concentrations was low and further reduced over time. For quartz concentrations the same phenomenon albeit somewhat less strong was observed. In contrast, for the within-worker variability in concentrations downward and upward temporal trends were apparent for both respirable dust and respirable quartz. The study shows that the (relative) size of temporal variability is large and unpredictable and therefore regular measurement campaigns are needed to ascertain compliance to occupational exposure limit values.

Pandemic publishing: A bibliometric review of COVID‐19 research in the crisis and disaster literature

Iconic events have traditionally instigated progression in the fields of crisis and disaster science. In the wake of the COVID-19 pandemic, the pressing question is how this global health emergency impacted the research agendas of our field. We reviewed contributions in ten important crisis and disaster journals in the two and a half years following the COVID-19 outbreak from 1 January 2020 to 30 June 2022. Specifically, we conducted a bibliometric review using thematic mapping analysis to distill the major themes covered by the emerging COVID-19 literature within crisis and disaster science (N = 239 articles). Our results indicate that several well-known topics are applied to the COVID-19 pandemic, such as risk, crisis communication, governance, resilience and vulnerability. The pandemic also gave rise to new topics, such as citizen behavior, state power, and the business and mental health impact of crisis measures. Several studies are already looking ahead by identifying lessons for preparedness and mitigation of future pandemics. By taking stock of the surge of COVID-19 studies while this academic literature is still taking shape, this review sets the stage for future contributions to the crisis and disaster literatures. It provides valuable lessons for what topics are studied and what themes need more attention. The COVID-19 pandemic is destined to become an iconic event for our literature that not only strengthens and deepens existing debates, but also clearly offers the opportunity to draw in new perspectives and broaden the horizon of crisis and disaster science.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

Is residential proximity to polluted sites during pregnancy associated with preterm birth or low birth weight? Results from an integrated exposure database in North Carolina (2003-2015).

Preterm birth (PTB) and term low birth weight (LBW) have been associated with pollution and other environmental exposures, but the relationship between these adverse outcomes and specific characteristics of polluted sites is not well studied. We conducted a retrospective cohort study to examine relationships between residential proximity to polluted sites in North Carolina (NC) and PTB and LBW. We further stratified exposure to polluted sites by route of contaminant emissions and specific contaminants released at each site. We created an integrated exposure geodatabase of polluted sites in NC from 2002 to 2015 including all landfills, Superfund sites, and industrial sites. Using birth certificates, we assembled a cohort of 1,494,651 singleton births in NC from 2003 to 2015. We geocoded the gestational parent residential address on the birth certificate, and defined exposure to polluted sites as residence within one mile of a site. We used log-binomial regression models to estimate adjusted risk ratios (aRR) and 95% confidence intervals (CI). Binomial models were used to estimate adjusted risk differences (aRD) per 10,000 births and 95% CIs for associations between exposure to polluted sites and PTB or LBW. We observed weak associations between residential proximity to polluted sites and PTB [aRR(95% CI): 1.07(1.06,1.09); aRD(95% CI): 61(48,74)] and LBW [aRR(95% CI): 1.09(1.06,1.12); aRD(95% CI): 24(17,31)]. Secondary analyses showed increased risk of both PTB and LBW among births exposed to sites characterized by water emissions, air emissions, and land impoundment. In analyses of specific contaminants, increased risk of PTB was associated with proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons. LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury. This study provides evidence for potential reproductive health effects of polluted sites, and underscores the importance of accounting for heterogeneity between polluted sites when considering these exposures. We documented an overall increased risk of both PTB and LBW in births with gestational exposure to polluted sites using a harmonized geodatabase of three site types, and further examined exposures stratified by site characteristics (route of emission, specific contaminants present). We observed increased risk of both PTB and LBW among births exposed to sites with water emissions or air emissions, across site types. Increased risk of PTB was associated with gestational proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons; increased risk of LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury.

A Pilot Establishment of the Job-Exposure Matrix of Lead Using the Standard Process Code of Nationwide Exposure Databases in Korea

BackgroundThe purpose of this study is to construct a job-exposure matrix for lead that accounts for industry and work processes within industries using a nationwide exposure database. MethodsWe used the work environment measurement data (WEMD) of lead monitored nationwide from 2015 to 2016. Industrial hygienists standardized the work process codes in the database to 37 standard process and extracted key index words for each process. A total of 37 standardized process codes were allocated to each measurement based on an automated key word search based on the degree of agreement between the measurement information and the standard process index. Summary statistics, including the arithmetic mean, geometric mean, and 95th percentile level (X95), was calculated according to industry, process, and industry process. Using statistical parameters of contrast and precision, we compared the similarity of exposure groups by industry, process, and industry process. ResultsThe exposure intensity of lead was estimated for 583 exposure groups combined with 128 industry and 35 process. The X95 value of the “casting” process of the “manufacture of basic precious and non-ferrous metals” industry was 53.29 μg/m3, exceeding the occupational exposure limit of 50 μg/m3. Regardless of the limitation of the minimum number of samples in the exposure group, higher contrast was observed when the exposure groups were by industry process than by industry or process. ConclusionWe evaluated the exposure intensities of lead by combination of industry and process. The results will be helpful in determining more accurate information regarding exposure in lead-related epidemiological studies.

Toxicological and Exposure Database Inventory: A review.

Knowledge of hazards and exposure data about chemical pollutants are essential for epidemiological studies for assessing health risks and preventing the development of diseases. Access to this type of information is clearly needed and although numerous databases (DBs) have been compiled, they are often extremely difficult to find and navigate. This paper presents the creation of an online inventory of toxicological and exposure DBs to support human health risk assessment resulting from the chemical exposome. A free searchable online inventory, called TEDI (Toxicological and Exposure Database Inventory), was set up to collect meta-data on toxicological and exposure DBs resulting from a review of the literature conducted on PubMed. Only chemical agents, including drugs, natural and manufactured/synthetic chemicals and air pollutants, were considered. Regarding exposure DBs, only human exposure to chemicals were included. Because of time and resource constraints, only an inventory was performed, and no effort was made to rate, assess or rank DBs on quality criteria. A total of 715 DBs were identified, catalogued, described and made available on TEDI. The number of chemicals ranged from one single chemical to 90 million for toxicological DBs and from 1 to 103,817 chemicals for exposure DBs. Some DBs were restricted to specific chemicals, with for instance 71 exposure DBs reporting polycyclic aromatic hydrocarbon measurements. Toxicological DBs (n=362) provided a broad range of different information such as toxicity data (57%), physicochemical properties (38%), experimental study results (28%) or prediction data (16%). A total of 382 exposure DBs were found, including exposure measurements DBs (71%), web application/tool (21%), exposure and risk assessment models (19%) and epidemiological DBs (13%). TEDI is of value to the broader community and could support human health risk assessment to chemicals in various contexts. This inventory remains open for further additions, to enlarge its coverage, increase the meta-data collected and include newly developed DBs.

Neonatal abstinence syndrome and mother's own milk at discharge.

To describe factors impacting receipt of mother's own milk (MOM) at discharge among California infants diagnosed with neonatal abstinence syndrome (NAS). Cohort study of the California Perinatal Quality Care Collaborative's Maternal Substance Exposure Database for infants with NAS and gestational age ≥ 34 weeks from 2019 to 2020. 245 infants with NAS were identified. Variables with an increased likelihood of being discharged on MOM included maternal medication assisted treatment (p = 0.001), use of maternal addiction services (p < 0.001), receiving donor human milk (p = 0.001), being treated in the well baby unit (p < 0.001), rooming-in (p < 0.001), and kangaroo care (p < 0.001). Among infants with NAS for whom MOM was recommended (n = 84), rooming-in was the only factor associated with being discharged on MOM (p = 0.002); receiving formula was the only inversely associated factor (p < 0.001). Results suggest supporting the mother-infant dyad and using non-pharmacologic treatment methods, such as rooming-in, increase receipt of MOM at discharge.

Development of Korean CARcinogen EXposure: Assessment of the Exposure Intensity of Carcinogens by Industry

BackgroundOccupational cancer is a global health issue. The Korean CARcinogen EXposure (K-CAREX), a database of CARcinogen EXposure, was developed for the Korean labor force to estimate the number of workers exposed to carcinogens by industry. The present study aimed to estimate the intensity of exposure to carcinogens by industry, in order to supply complementary information about CARcinogen EXposure intensity to the K-CAREX. MethodsWe used nationwide workplace monitoring data from 2014 to 2016 and selected target carcinogens based on the K-CAREX list. We computed the 95th percentile levels of measurements for each industry by carcinogens. Based on the 95th percentile level relative to the occupational exposure limit, we classified the CARcinogen EXposure intensity into five exposure ratings (1–5) for each industry. ResultsThe exposure ratings were estimated for 21 carcinogenic agents in each of the 228 minor industry groups. For example, 3,058 samples were measured for benzene in the manufacturing industry of basic chemicals. This industry was assigned a benzene exposure rating of 3. ConclusionsWe evaluated the CARcinogen EXposure ratings across industries in Korean workers. The results will provide information on the exposure intensity to carcinogens for integration into the K-CAREX. Furthermore, it will aid in prioritizing control efforts and identifying industries of concern.

Estimating cobalt exposure in respirable dust from cobalt in inhalable dust.

Cobalt is a commonly used element in metal industry. Exposure to workers occurs mainly by inhalation of cobalt-containing dust. For the evaluation of cobalt exposure, risk assessment and investigations on occupational diseases, measurements of cobalt in respirable dust are needed. Up to now, often only data for cobalt in inhalable dust are available, which is due to the earlier classification of the limit value in this fraction. Therefore, a possibility to convert cobalt concentrations mathematically from inhalable into respirable concentrations is desirable. In this study, 639 parallel measurements of cobalt concentrations in inhalable (cI(Co)) and respirable dust fractions (cR(Co)) were extracted from the non-public exposure database MEGA (Measurement data relating to workplace exposure to hazardous substances, maintained at the Institute for Occupational Safety and Health of the German Social Accident Insurance) and investigated by regression analysis. For the whole dataset regression shows high quality measures (correlation coefficient R=0.888, adjusted coefficient of determination adj. R2=0.788 - R2 is adjusted to sample size). Further description of the data is achieved by splitting the dataset according to the type of sampling ('stationary' and 'personal') and three working activity groups, 'high temperature processing', 'filling/transport/storage', and 'machining/abrasive techniques' (0.845 ≤ R≤0.876; 0.711 ≤ adj. R2 ≤ 0.762). As subgroups of 'high temperature processing' and 'machining/abrasive techniques' two further groups could be determined. These groups are called heuristic groups, since they have to be formed non-systematically by trial and error. These heuristic groups are 'welding' and 'grinding'. They are more selective on the included working activities with adj. R2 of 0.703 and 0.748 respectively. The resulting conversion functions of all groups are power functions with exponents between 0.704 and 0.794. For the estimation of cobalt in respirable dust in other studies, it is possible to use the conversion functions of the heuristic and working activity groups. Limitations of the possibility to use the conversion functions are discussed.

Detecting and Augmenting Missing Key Aspects in Vulnerability Descriptions

Security vulnerabilities have been continually disclosed and documented. For the effective understanding, management, and mitigation of the fast-growing number of vulnerabilities, an important practice in documenting vulnerabilities is to describe the key vulnerability aspects, such as vulnerability type, root cause, affected product, impact, attacker type, and attack vector. In this article, we first investigate 133,639 vulnerability reports in the Common Vulnerabilities and Exposures (CVE) database over the past 20 years. We find that 56%, 85%, 38%, and 28% of CVEs miss vulnerability type, root cause, attack vector, and attacker type, respectively. By comparing the differences of the latest updated CVE reports across different databases, we observe that 1,476 missing key aspects in 1,320 CVE descriptions were augmented manually in the National Vulnerability Database (NVD) , which indicates that the vulnerability database maintainers try to complete the vulnerability descriptions in practice to mitigate such a problem. To help complete the missing information of key vulnerability aspects and reduce human efforts, we propose a neural-network-based approach called PMA to predict the missing key aspects of a vulnerability based on its known aspects. We systematically explore the design space of the neural network models and empirically identify the most effective model design in the scenario. Our ablation study reveals the prominent correlations among vulnerability aspects when predicting. Trained with historical CVEs, our model achieves 88%, 71%, 61%, and 81% in F1 for predicting the missing vulnerability type, root cause, attacker type, and attack vector of 8,623 “future” CVEs across 3 years, respectively. Furthermore, we validate the predicting performance of key aspect augmentation of CVEs based on the manually augmented CVE data collected from NVD, which confirms the practicality of our approach. We finally highlight that PMA has the ability to reduce human efforts by recommending and augmenting missing key aspects for vulnerability databases, and to facilitate other research works such as severity level prediction of CVEs based on the vulnerability descriptions.

High spatial-resolution loss estimation using dense array strong-motion near-fault records. Case study for Hveragerði and the [formula omitted]6.3 Ölfus earthquake, South Iceland

The most recent and costliest damaging earthquake in Iceland is the Mw6.3 29-May-2008 Ölfus earthquake to date. In particular, Hveragerði town located in the extreme near-fault region, suffered intense horizontal peak ground accelerations (PGA) of ∼ 40–90%g and large amplitude and long-period near-fault pulses, recorded on a dense urban strong-motion array in the town. In this study we collated a high-spatial resolution exposure database (building-by-building) complete with actual reported losses and classified the buildings by building materials and construction year according to the code design requirements in place at the time.We took advantage of the array data and evaluated a set of well-known ground motion intensity measures (IM), including PGA, pseudo-acceleration response spectra at short-to-long periods, Arias Intensity and Cumulative Absolute Velocity. We applied empirical Bayesian kriging geostatistical analyses to generate high-resolution shakemaps and provide IM estimates for each building. The shakemaps showed a significant and systematic variation of the IMs across the small study area, with the lowest ground motions observed centrally and highest values in the outskirts. Furthermore, correlation analysis was carried out for the damage ratio and the exposure data IMs, but only low-to-moderate correlations were observed. A key reason is the incurred losses were primarily due to damage to non-structural components, to which the code design requirements do not apply. We carried out a seismic loss assessment in Hveragerði for the earthquake scenario of the Ölfus earthquake both on building-by-building, and municipality levels of spatial resolution. We applied both local and global fragility models associated with detail building typologies identified based on the SERA taxonomy scheme.The results show that the global fragility functions severely underestimate the seismic performance of the building stock, except for one-story reinforced concrete buildings, while overall the masonry buildings were associated with the most predicted and observed losses. On the other hand, the local models predicted losses that conformed well with the observed damages to timber and concrete buildings. The high-spatial-resolution predictions of losses gave results that better correlated with the observed losses in most typologies.

Characterizing uncertainty of general building stock exposure data

Effective disaster risk management (DRM) and disaster risk reduction (DRR) require modeling potential and post-event impacts using building exposure data. The data used to develop building exposure databases will influence the accuracy of risk assessments and the appropriateness of subsequent decisions. This article proposes a framework for classifying approaches of developing building exposure databases into levels. To examine the uncertainty introduced through using various approaches to exposure development, a probabilistic seismic risk assessment was run with the exposure data corresponding to each proposed level using the County of Los Angeles as the study area. A factor of ∼2.5 was observed in the final loss estimates. The variance was less dependent on the spatial scale of data than on key values, most notably estimates of building size and replacement cost.

A Building Classification System for Multi-hazard Risk Assessment

A uniform and comprehensive classification system, often referred to as taxonomy, is fundamental for the characterization of building portfolios for natural hazard risk assessment. A building taxonomy characterizes assets according to attributes that can influence the likelihood of damage due to the effects of natural hazards. Within the scope of the Global Earthquake Model (GEM) initiative, a building taxonomy (GEM Building Taxonomy V2.0) was developed with the goal of classifying buildings according to their seismic vulnerability. This taxonomy contained 13 building attributes, including the main material of construction, lateral load-resisting system, date of construction and number of stories. Since its release in 2012, the taxonomy has been used by hundreds of experts working on exposure and risk modeling efforts. These applications allowed the identification of several limitations, which led to the improvement and expansion of this taxonomy into a new classification system compatible with multi-hazard risk assessment. This expanded taxonomy (named GED4ALL) includes more attributes and several details relevant for buildings exposed to natural hazards beyond earthquakes. GED4ALL has been applied in several international initiatives, enabling the identification of the most common building classes in the world, and facilitating compatibility between exposure models and databases of vulnerability and damage databases.

Application of Pattern Mining Methods to Assess Exposures to Multiple Airborne Chemical Agents in Two Large Occupational Exposure Databases from France.

Surveys of the French working population estimate that approximately 15% of all workers may be exposed to at least three different chemical agents, but the most prevalent coexposure situations and their associated health risks remain relatively understudied. To characterize occupational coexposure situations in France, we extracted personal measurement data from COLCHIC and SCOLA, two large administrative occupation exposure databases. We selected 118 chemical agents that had ≥100 measurements with detected concentrations over the period 2010–2019, including 31 carcinogens (IARC groups 1, 2A, and 2B). We grouped measurements by work situations (WS, combination of sector, occupation, task, and year). We characterized the mixtures across WS using frequent itemset mining and association rules mining. The 275,213 measurements extracted came from 32,670 WS and encompassing 4692 unique mixtures. Workers in 32% of all WS were exposed to ≥2 agents (median 3 agents/WS) and 13% of all WS contained ≥2 carcinogens (median 2 carcinogens/WS). The most frequent coexposures were ethylbenzene-xylene (1550 WS), quartz-cristobalite (1417 WS), and toluene-xylene (1305 WS). Prevalent combinations of carcinogens also included hexavalent chromium-lead (368 WS) and benzene-ethylbenzene (314 WS). Wood dust (6% of WS exposed to at least one other agent) and asbestos (8%) had the least amount of WS coexposed with other agents. Tasks with the highest proportions of coexposure to carcinogens include electric arc welding (37% of WS with coexposure), polymerization and distillation (34%), and construction drilling and excavating (34%). Overall, the coexposure to multiple chemical agents, including carcinogens, was highly prevalent in the databases, and should be taken into account when assessing exposure risks in the workplace.

HoneyCar: A Framework to Configure Honeypot Vulnerabilities on the Internet of Vehicles

The Internet of Vehicles (IoV), whereby interconnected vehicles that communicate with each other and with road infrastructure on a common network, has promising socio-economic benefits but also poses new cyber-physical threats. To protect these entities and learn about adversaries, data on attackers can be realistically gathered using decoy systems like honeypots. Admittedly, honeypots introduces a trade-off between the level of honeypot-attacker interactions and incurred overheads and costs for implementing and monitoring these systems. Deception through honeypots can be achieved by strategically configuring the honeypots to represent components of the IoV to engage attackers and collect cyber threat intelligence. Here, we present HoneyCar, a novel decision support framework for honeypot deception in IoV. HoneyCar benefits from the repository of known vulnerabilities of the autonomous and connected vehicles found in the Common Vulnerabilities and Exposure (CVE) database to compute optimal honeypot configuration strategies. The adversarial interaction is modelled as a repeated imperfect-information zero-sum game where the IoV network administrator strategically chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability to exploit under uncertainty. Our investigation examines two different versions of the game, with and without the re-configuration cost, to empower the network administrator to determine optimal honeypot investment strategies given a budget. We show the feasibility of this approach in a case study that consists of the vulnerabilities in autonomous and connected vehicles gathered from the CVE database and data extracted from the Common Vulnerability Scoring System (CVSS).