Royce A. Francis

A metric and frameworks for resilience analysis of engineered and infrastructure systems

Abstract In this paper, we have reviewed various approaches to defining resilience and the assessment of resilience. We have seen that while resilience is a useful concept, its diversity in usage complicates its interpretation and measurement. In this paper, we have proposed a resilience analysis framework and a metric for measuring resilience. Our analysis framework consists of system identification, resilience objective setting, vulnerability analysis, and stakeholder engagement. The implementation of this framework is focused on the achievement of three resilience capacities: adaptive capacity, absorptive capacity, and recoverability. These three capacities also form the basis of our proposed resilience factor and uncertainty-weighted resilience metric. We have also identified two important unresolved discussions emerging in the literature: the idea of resilience as an epistemological versus inherent property of the system, and design for ecological versus engineered resilience in socio-technical systems. While we have not resolved this tension, we have shown that our framework and metric promote the development of methodologies for investigating “deep” uncertainties in resilience assessment while retaining the use of probability for expressing uncertainties about highly uncertain, unforeseeable, or unknowable hazards in design and management activities.

Bayesian Belief Networks for predicting drinking water distribution system pipe breaks

Abstract In this paper, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone. To the authors’ knowledge, this is the first attempt to model drinking water distribution system pipe breaks using BBNs. Development of expert systems such as BBNs for analyzing drinking water distribution system data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate data-based distribution system monitoring and asset management. Due to the difficulties in collecting, preparing, and managing drinking water distribution system data, most pipe break models can be classified as “statistical–physical” or “hypothesis-generating.” We develop the BBN with the hope of contributing to the “hypothesis-generating” class of models, while demonstrating the possibility that BBNs might also be used as “statistical–physical” models. Our model is learned from pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. BBN models are learned using a constraint-based method, a score-based method, and a hybrid method. Model evaluation is based on log-likelihood scoring. Sensitivity analysis using mutual information criterion is also reported. While our results indicate general agreement with prior results reported in pipe break modeling studies, they also suggest that it may be difficult to select among model alternatives. This model uncertainty may mean that more research is needed for understanding whether additional pipe break risk factors beyond age, break history, pipe material, and pipe diameter might be important for asset management planning.

Bayesian statistical modeling of disinfection byproduct (DBP) bromine incorporation in the ICR database.

Statistical models are developed for bromine incorporation in the trihalomethane (THM), trihaloacetic acids (THAA), dihaloacetic acid (DHAA), and dihaloacetonitrile (DHAN) subclasses of disinfection byproducts (DBPs) using distribution system samples from plants applying only free chlorine as a primary or residual disinfectant in the Information Collection Rule (ICR) database. The objective of this study is to characterize the effect of water quality conditions before, during, and post-treatment on distribution system bromine incorporation into DBP mixtures. Bayesian Markov Chain Monte Carlo (MCMC) methods are used to model individual DBP concentrations and estimate the coefficients of the linear models used to predict the bromine incorporation fraction for distribution system DBP mixtures in each of the four priority DBP classes. The bromine incorporation models achieve good agreement with the data. The most important predictors of bromine incorporation fraction across DBP classes are alkalinity, specific UV absorption (SUVA), and the bromide to total organic carbon ratio (Br:TOC) at the first point of chlorine addition. Free chlorine residual in the distribution system, distribution system residence time, distribution system pH, turbidity, and temperature only slightly influence bromine incorporation. The bromide to applied chlorine (Br:Cl) ratio is not a significant predictor of the bromine incorporation fraction (BIF) in any of the four classes studied. These results indicate that removal of natural organic matter and the location of chlorine addition are important treatment decisions that have substantial implications for bromine incorporation into disinfection byproduct in drinking waters.

An Approach to Integrating Occupational Safety and Health into Life Cycle Assessment: Development and Application of Work Environment Characterization Factors

Integrating occupational safety and health (OSH) into life cycle assessment (LCA) may provide decision makers with insights and opportunities to prevent burden shifting of human health impacts between the nonwork environment and the work environment. We propose an integration approach that uses industry‐level work environment characterization factors (WE‐CFs) to convert industry activity into damage to human health attributable to the work environment, assessed as disability‐adjusted life years (DALYs). WE‐CFs are ratios of work‐related fatal and nonfatal injuries and illnesses occurring in the U.S. worker population to the amount of physical output from U.S. industries; they represent workplace hazards and exposures and are compatible with the life cycle inventory (LCI) structure common to process‐based LCA. A proof of concept demonstrates application of the WE‐CFs in an LCA of municipal solid waste landfill and incineration systems. Results from the proof of concept indicate that estimates of DALYs attributable to the work environment are comparable in magnitude to DALYs attributable to environmental emissions. Construction and infrastructure‐related work processes contributed the most to the work environment DALYs. A sensitivity analysis revealed that uncertainty in the physical output from industries had the most effect on the WE‐CFs. The results encourage implementation of WE‐CFs in future LCA studies, additional refinement of LCI processes to accurately capture industry outputs, and inclusion of infrastructure‐related processes in LCAs that evaluate OSH impacts.

The work environment disability-adjusted life year for use with life cycle assessment: a methodological approach.

BackgroundLife cycle assessment (LCA) is a systems-based method used to determine potential impacts to the environment associated with a product throughout its life cycle. Conclusions from LCA studies can be applied to support decisions regarding product design or public policy, therefore, all relevant inputs (e.g., raw materials, energy) and outputs (e.g., emissions, waste) to the product system should be evaluated to estimate impacts. Currently, work-related impacts are not routinely considered in LCA. The objectives of this paper are: 1) introduce the work environment disability-adjusted life year (WE-DALY), one portion of a characterization factor used to express the magnitude of impacts to human health attributable to work-related exposures to workplace hazards; 2) outline the methods for calculating the WE-DALY; 3) demonstrate the calculation; and 4) highlight strengths and weaknesses of the methodological approach.MethodsThe concept of the WE-DALY and the methodological approach to its calculation is grounded in the World Health Organization’s disability-adjusted life year (DALY). Like the DALY, the WE-DALY equation considers the years of life lost due to premature mortality and the years of life lived with disability outcomes to estimate the total number of years of healthy life lost in a population. The equation requires input in the form of the number of fatal and nonfatal injuries and illnesses that occur in the industries relevant to the product system evaluated in the LCA study, the age of the worker at the time of the fatal or nonfatal injury or illness, the severity of the injury or illness, and the duration of time lived with the outcomes of the injury or illness.ResultsThe methodological approach for the WE-DALY requires data from various sources, multi-step instructions to determine each variable used in the WE-DALY equation, and assumptions based on professional opinion.ConclusionsResults support the use of the WE-DALY in a characterization factor in LCA. Integrating occupational health into LCA studies will provide opportunities to prevent shifting of impacts between the work environment and the environment external to the workplace and co-optimize human health, to include worker health, and environmental health.

Disinfection byproduct regulatory compliance surrogates and bromide-associated risk

Natural and anthropogenic factors can alter bromide concentrations in drinking water sources. Increasing source water bromide concentrations increases the formation and alters the speciation of disinfection byproducts (DBPs) formed during drinking water treatment. Brominated DBPs are more toxic than their chlorinated analogs, and thus have a greater impact on human health. However, DBPs are regulated based on the mass sum of DBPs within a given class (e.g., trihalomethanes and haloacetic acids), not based on species-specific risk or extent of bromine incorporation. The regulated surrogate measures are intended to protect against not only the species they directly represent, but also against unregulated DBPs that are not routinely measured. Surrogates that do not incorporate effects of increasing bromide may not adequately capture human health risk associated with drinking water when source water bromide is elevated. The present study analyzes trihalomethanes (THMs), measured as TTHM, with varying source water bromide concentrations, and assesses its correlation with brominated THM, TTHM risk and species-specific THM concentrations and associated risk. Alternative potential surrogates are evaluated to assess their ability to capture THM risk under different source water bromide concentration conditions. The results of the present study indicate that TTHM does not adequately capture risk of the regulated species when source water bromide concentrations are elevated, and thus would also likely be an inadequate surrogate for many unregulated brominated species. Alternative surrogate measures, including THM3 and the bromodichloromethane concentration, are more robust surrogates for species-specific THM risk at varying source water bromide concentrations.

Drought risk modelling for thermoelectric power plants siting using an excess over threshold approach

Water availability is among the most important elements of thermoelectric power plant site selection and evaluation criteria. With increased variability and changes in hydrologic statistical stationarity, one concern is the increased occurrence of extreme drought events that may be attributable to climatic changes. As hydrological systems are altered, operators of thermoelectric power plants need to ensure a reliable supply of water for cooling and generation requirements. The effects of climate change are expected to influence hydrological systems at multiple scales, possibly leading to reduced efficiency of thermoelectric power plants. In this paper, we model drought characteristics from a thermoelectric systems operational and regulation perspective. A systematic approach to characterise a stream environment in relation to extreme drought occurrence, duration and deficit-volume is proposed and demonstrated. This approach can potentially enhance early stage decisions in identifying candidate sites for a thermoelectric power plant application and allow investigation and assessment of varying degrees of drought risk during more advanced stages of the siting process.

Statistical tools for populating/predicting input data of risk analysis models

By quantifying economic risk due to damage to building stock, regional loss models for natural hazards are critical in the creation of regional policies, including evacuation strategies and zoning. The increasingly complex interaction between natural hazards and human activities requires more and more accurate data to describe the regional exposure to potential loss from physical damage to buildings and infrastructure. While databases contain information on the distribution and features of the building stock, infrastructure, transportation, etc., it is not unusual that portions of the information are missing from the available databases. Missing or low quality data compromise the validity of regional loss projections. Consequently, this paper uses Bayesian Belief Networks and Classification and Regression Trees to populate the missing information inside a database based on the structure of the available data. A case study is presented to evaluate results.

Part I––Comparing Noncancer Chronic Human Health Reference Values: An Analysis of Science Policy Choices

The goal of this study was to systematically evaluate the choices made in deriving a chronic oral noncancer human health reference value (HHRV) for a given chemical by different organizations, specifically those from the U.S. Environmental Protection Agency, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This analysis presents a methodological approach for comparing both the HHRVs and the specific choices made in the process of deriving an HHRV across these organizations. Overall, across the 96 unique chemicals and 171 two-way organizational comparisons, the HHRV agreed approximately 26% of the time. A qualitative method for identifying the primary factors influencing these HHRV differences was also developed, using arrays of HHRVs across organizations for the same chemical. The primary factors identified were disagreement on the critical or principal study and differential application of the total uncertainty factor across organizations. Of the cases where the total UF was the primary factor influencing HHRV disagreement, the database UF had the greatest influence.