Who contributes to disaster preparedness? Predicting decision making in social dilemmas pertaining to community resilience.

Planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments. Using statistical learning techniques trained on the results of a web-based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. Given participants' dispositions and in-game circumstances, Bayesian additive regression tree (BART) models are able to accurately predict deviations from players' decisions that would reasonably lead to Pareto-efficient outcomes for their communities. Participants tend to overcontribute relative to these Pareto-efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. However, higher trait Openness scores reflect an individual's tendency to follow a risk-neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. In addition, several input variables have nonlinear effects on decisions, suggesting that it may be warranted to use more sophisticated statistical learning methods to reexamine results from previous studies that assume linear relationships between individuals' dispositions and responses in applications of game theory or decision theory.

Factors impacting bike crash severity in urban areas.

INTRODUCTION: Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks. METHOD: The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee's two largest urban areas, Nashville and Memphis. RESULTS: The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified. CONCLUSION: Based on these results, a series of specific, suggested policy changes are presented for implementation consideration. PRACTICAL APPLICATIONS: There is existing guidance in FHWA Lighting Handbook and TDOT's Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested.

A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards.

The concepts of vulnerability and resilience help explain why natural hazards of similar type and magnitude can have disparate impacts on varying communities. Numerous frameworks have been developed to measure these concepts, but a clear and consistent method of comparing them is lacking. Here, we develop a data-driven approach for reconciling a popular class of frameworks known as vulnerability and resilience indices. In particular, we conduct an exploratory factor analysis on a comprehensive set of variables from established indices measuring community vulnerability and resilience at the U.S. county level. The resulting factor model suggests that 50 of the 130 analyzed variables effectively load onto five dimensions: wealth, poverty, agencies per capita, elderly populations, and non-English-speaking populations. Additionally, the factor structure establishes an objective and intuitive schema for relating the constituent elements of vulnerability and resilience indices, in turn affording researchers a flexible yet robust baseline for validating and expanding upon current approaches.

Selecting Indicators for Assessing Community Sustainable Resilience.

Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is rapidly gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system-wide capability to maintain desired performance levels while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time. To enable assessment of community sustainable resilience, we review current literature, consolidate available indicators and metrics, and develop a classification scheme and organizational structure to aid in identification, selection, and application of indicators within a dynamic assessment framework. A nonduplicative set of community sustainable resilience indicators and metrics is provided that can be tailored to a community's needs, thereby enhancing the ability to operationalize the assessment process.

Cost-benefit analysis of different air change rates in an operating room environment.

BACKGROUND: Hospitals face growing pressure to meet the dual but often competing goals of providing a safe environment while controlling operating costs. Evidence-based data are needed to provide insight for facility management practices to support these goals. METHODS: The quality of the air in 3 operating rooms was measured at different ventilation rates. The energy cost to provide the heating, ventilation, and air conditioning to the rooms was estimated to provide a cost-benefit comparison of the effectiveness of different ventilation rates currently used in the health care industry. RESULTS: Simply increasing air change rates in the operating rooms tested did not necessarily provide an overall cleaner environment, but did substantially increase energy consumption and costs. Additionally, and unexpectedly, significant differences in microbial load and air velocity were detected between the sterile fields and back instrument tables. CONCLUSIONS: Increasing the ventilation rates in operating rooms in an effort to improve clinical outcomes and potentially reduce surgical site infections does not necessarily provide cleaner air, but does typically increase operating costs. Efficient distribution or management of the air can improve quality indicators and potentially reduce the number of air changes required. Measurable environmental quality indicators could be used in lieu of or in addition to air change rate requirements to optimize cost and quality for an operating room and other critical environments.

Methodology for analyzing environmental quality indicators in a dynamic operating room environment.

BACKGROUND: Sufficient quantities of quality air and controlled, unidirectional flow are important elements in providing a safe building environment for operating rooms. METHODS: To make dynamic assessments of an operating room environment, a validated method of testing the multiple factors influencing the air quality in health care settings needed to be constructed. These include the following: temperature, humidity, particle load, number of microbial contaminants, pressurization, air velocity, and air distribution. The team developed the name environmental quality indicators (EQIs) to describe the overall air quality based on the actual measurements of these properties taken during the mock surgical procedures. These indicators were measured at 3 different hospitals during mock surgical procedures to simulate actual operating room conditions. EQIs included microbial assessments at the operating table and the back instrument table and real-time analysis of particle counts at 9 different defined locations in the operating suites. Air velocities were measured at the face of the supply diffusers, at the sterile field, at the back table, and at a return grille. RESULTS: The testing protocol provided consistent and comparable measurements of air quality indicators between institutions. At 20 air changes per hour (ACH), and an average temperature of 66.3°F, the median of the microbial contaminants for the 3 operating room sites ranged from 3-22 colony forming units (CFU)/m3 at the sterile field and 5-27 CFU/m3 at the back table. At 20 ACH, the median levels of the 0.5-µm particles at the 3 sites were 85,079, 85,325, and 912,232 in particles per cubic meter, with a predictable increase in particle load in the non-high-efficiency particulate air-filtered operating room site. Using a comparison with cleanroom standards, the microbial and particle counts in all 3 operating rooms were equivalent to International Organization for Standardization classifications 7 and 8 during the mock surgical procedures. CONCLUSIONS: The EQI protocol was measurable and repeatable and therefore can be safely used to evaluate air quality within the health care environment to provide guidance for operational practices and regulatory requirements.

A methodology for modeling regional terrorism risk.

Over the past decade, terrorism risk has become a prominent consideration in protecting the well-being of individuals and organizations. More recently, there has been interest in not only quantifying terrorism risk, but also placing it in the context of an all-hazards environment in which consideration is given to accidents and natural hazards, as well as intentional acts. This article discusses the development of a regional terrorism risk assessment model designed for this purpose. The approach taken is to model terrorism risk as a dependent variable, expressed in expected annual monetary terms, as a function of attributes of population concentration and critical infrastructure. This allows for an assessment of regional terrorism risk in and of itself, as well as in relation to man-made accident and natural hazard risks, so that mitigation resources can be allocated in an effective manner. The adopted methodology incorporates elements of two terrorism risk modeling approaches (event-based models and risk indicators), producing results that can be utilized at various jurisdictional levels. The validity, strengths, and limitations of the model are discussed in the context of a case study application within the United States.

Application of an enhanced spill management information system to inland waterways.

Spill response managers on inland waterways have indicated the need for an improved decision-support system, one that provides advanced modeling technology within a visual framework. Efforts to address these considerations led the authors to develop an enhanced version of the Spill Management Information System (SMIS 2.0). SMIS 2.0 represents a state-of-the-art 3D hydrodynamic and chemical spill modeling system tool that provides for improved predictive spill fate and transport capability, combined with a geographic information systems (GIS) spatial environment in which to communicate propagation risks and locate response resources. This paper focuses on the application of SMIS 2.0 in a case study of several spill scenarios involving the release of diesel fuel and trichloroethylene (TCE) that were simulated on the Kentucky Lake portion of the Tennessee River, each analyzed at low, average, and high flow conditions. A discussion of the decision-support implications of the model results is also included, as are suggestions for future enhancements to this evolving platform.

Development of a terrestrial chemical spill management system.

Adequately preparing for and responding to terrestrial (land-based) chemical spills are critical to the protection of human health and the environment. To facilitate analysis and support decision-making for such events, the authors have developed an environmental risk management system that characterizes the ability of a spilled chemical to immediately impact human health, groundwater, surface water, and soil resources, and incorporates these four risk areas into an overall measure of terrestrial chemical risk. This system incorporates a risk index model, leverages geographic information systems (GIS) technology, and contains a comprehensive chemical and environmental database to assess and delineate the immediate threat posed by a terrestrial chemical spill. It is designed to serve a variety of stakeholders, including managers and policy-makers, who would benefit from generating screening-level environmental risk assessments without requiring a technical background or collection of detailed environmental and chemical data. Areas of potential application include transportation routing, industrial zoning, environmental regulatory compliance and enforcement, spill response, and security planning.

Estimation of terrestrial chemical spill risk factors using a modified Delphi approach.

The large number of chemical spills each year in the United States presents a potentially significant risk to human health and the environment. In an effort to manage this risk, the authors are developing a screening tool to assess the immediate threat to human and environmental receptors from land-based chemical spills. As part of this development effort, a modified Delphi survey was employed to determine the most important factors governing this risk and the relative importance of these factors. Results of the survey indicate that accounting for the attributes of the spilled chemical as well as the characteristics of the surrounding environment is imperative in making informed decisions regarding spill planning and mitigation. Survey results further indicate the greatest concern during spill events to be the risk to human health, which must be considered directly as well as factored into decisions concerning the protection of environmental receptors.

Development of a GIS-based spill management information system.

Spill Management Information System (SMIS) is a geographic information system (GIS)-based decision support system designed to effectively manage the risks associated with accidental or intentional releases of a hazardous material into an inland waterway. SMIS provides critical planning and impact information to emergency responders in anticipation of, or following such an incident. SMIS couples GIS and database management systems (DBMS) with the 2-D surface water model CE-QUAL-W2 Version 3.1 and the air contaminant model Computer-Aided Management of Emergency Operations (CAMEO) while retaining full GIS risk analysis and interpretive capabilities. Live 'real-time' data links are established within the spill management software to utilize current meteorological information and flowrates within the waterway. Capabilities include rapid modification of modeling conditions to allow for immediate scenario analysis and evaluation of 'what-if' scenarios. The functionality of the model is illustrated through a case study of the Cheatham Reach of the Cumberland River near Nashville, TN.

Development of a centralized inland marine hazardous materials response database.

The state of the practice for obtaining chemical reference at a typical hazardous materials transportation accident scene is to consult multiple printed references. This often leads to confusion. This paper describes the design and development of a centralized response database. This is accomplished by identifying the most commonly used emergency response databases for all modes of transportation, developing relationships between the data, and building intuitive interfaces that allow for rapid information retrieval. The tool is subsequently applied to a previous accident to demonstrate the value-added from its availability in a response scenario. By combining all datasets in one application, data redundancy, errors and lags between updates of the data sets can be reduced. The linkages between the database and supporting files enables the data to be easily updated. While the database is designed to aid response to marine transportation accidents, the tool could also be applied to other modes of transportation. Moreover, facility and vessel operators could benefit from having a comprehensive chemical source accessible in case of release or human contact with the material. Finally, the inclusion of commodity flow information enables decision makers to prepare for high risk commodities.