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.

Elusive Critical Elements of Transformative Risk Assessment Practice and Interpretation: Is Alternatives Analysis the Next Step?

This article argues that "game-changing" approaches to risk analysis must focus on "democratizing" risk analysis in the same way that information technologies have democratized access to, and production of, knowledge. This argument is motivated by the author's reading of Goble and Bier's analysis, "Risk Assessment Can Be a Game-Changing Information Technology-But Too Often It Isn't" (Risk Analysis, 2013; 33: 1942-1951), in which living risk assessments are shown to be "game changing" in probabilistic risk analysis. In this author's opinion, Goble and Bier's article focuses on living risk assessment's potential for transforming risk analysis from the perspective of risk professionals-yet, the game-changing nature of information technologies has typically achieved a much broader reach. Specifically, information technologies change who has access to, and who can produce, information. From this perspective, the author argues that risk assessment is not a game-changing technology in the same way as the printing press or the Internet because transformative information technologies reduce the cost of production of, and access to, privileged knowledge bases. The author argues that risk analysis does not reduce these costs. The author applies Goble and Bier's metaphor to the chemical risk analysis context, and in doing so proposes key features that transformative risk analysis technology should possess. The author also discusses the challenges and opportunities facing risk analysis in this context. These key features include: clarity in information structure and problem representation, economical information dissemination, increased transparency to nonspecialists, democratized manufacture and transmission of knowledge, and democratic ownership, control, and interpretation of knowledge. The chemical safety decision-making context illustrates the impact of changing the way information is produced and accessed in the risk context. Ultimately, the author concludes that although new chemical safety regulations do transform access to risk information, they do not transform the costs of producing this information-rather, they change the bearer of these costs. The need for further risk assessment transformation continues to motivate new practical and theoretical developments in risk analysis and management.

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

BACKGROUND: Life 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: Results 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.

Characterizing the performance of the Conway-Maxwell Poisson generalized linear model.

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression.

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.

Multivariate distributions of disinfection by-products in chlorinated drinking water.

Drinking water disinfection by-product (DBP) occurrence research is important in supporting risk assessment and regulatory performance assessment. Recent DBP occurrence surveys have expanded their scope to include non-regulated priority DBPs as well as regulated DBPs. This study applies a Box-Cox transformed multivariate normal model and data augmentation methods for left-censored and missing observations to US EPA Information Collection Rule (ICR) drinking water data to describe the variability in the trihalomethane (THM4), trihaloacetic acid (THAA), dihaloacetic acid (DHAA), and dihaloacetonitrile (DHAN) DBP classes, the relationship between class-sum and the occurrence of individual DBPs within these classes. Inferences about bromine incorporation in these classes are then compared to those made by Obolensky and Singer (2005). Results reported herein show that class-based and individual DBP concentrations are strongly related to bromine substitution, and that speciation and bromine substitution patterns are consistent across DBP classes. In addition, the multiple imputation approach employed reveals that uncertainties related to missing and left-censored DBPs have important implications for understanding bromine substitution in the THAA class. These concerns should be considered through alternative approaches to DBP regulation in subsequent Stage II D/DBP assessment and revisions, where appropriate.