Roberta Dyck

Trihalomethane exposures in indoor swimming pools: a level III fugacity model.

The potential for generation of disinfection byproducts (DBPs) in swimming pools is high due to the concentrations of chlorine required to maintain adequate disinfection, and the presence of organics introduced by the swimmers. Health Canada set guidelines for trihalomethanes (THMs) in drinking water; however, no such guideline exists for swimming pool waters. Exposure occurs through ingestion, inhalation and dermal contact in swimming pools. In this research, a multimedia model is developed to evaluate exposure concentrations of THMs in the air and water of an indoor swimming pool. THM water concentration data were obtained from 15 indoor swimming pool facilities in Quebec (Canada). A level III fugacity model is used to estimate inhalation, dermal contact and ingestion exposure doses. The results of the proposed model will be useful to perform a human health risk assessment and develop risk management strategies including developing health-based guidelines for disinfection practices and the design of ventilation system for indoor swimming pools.

Treatment, residual chlorine and season as factors affecting variability of trihalomethanes in small drinking water systems

Seasonal variability in source water can lead to challenges for drinking water providers related to operational optimization and process control in treatment facilities. The objective of this study is to investigate seasonal variability of water quality in municipal small water systems (<3000 residents) supplied by surface waters. Residual chlorine and trihalomethanes (THM) were measured over seven years (2003–2009). Comparisons are made within each system over time, as well as between systems according to the type of their treatment technologies. THM concentrations are generally higher in the summer and autumn. The seasonal variability was generally more pronounced in systems using chlorination plus additional treatment. Chloroform, total THM (TTHM) and residual chlorine concentrations were generally lower in systems using chlorination plus additional treatment. Conversely, brominated THM concentrations were higher in systems using additional treatment. Residual chlorine was highest in the winter and lowest in the spring and summer. Seasonal variations were most pronounced for residual chlorine in systems with additional treatment. There was generally poor correlation between THM concentrations and concentrations of residual chlorine. Further study with these data will be beneficial in finding determinants and indicators for both quantity and variability of disinfection byproducts and other water quality parameters.

Data Fusion Methods for Human Health Risk Assessment: Review and Application

ABSTRACT The improved accessibility to data that can be used in human health risk assessment (HHRA) necessitates advanced methods to optimally incorporate them in HHRA analyses. This article investigates the application of data fusion methods to handling multiple sources of data in HHRA and its components. This application can be performed at two levels, first, as an integrative framework that incorporates various pieces of information with knowledge bases to build an improved knowledge about an entity and its behavior, and second, in a more specific manner, to combine multiple values for a state of a certain feature or variable (e.g., toxicity) into a single estimation. This work first reviews data fusion formalisms in terms of architectures and techniques that correspond to each of the two mentioned levels. Then, by handling several data fusion problems related to HHRA components, it illustrates the benefits and challenges in their application.

Data fusion-based risk assessment framework: an example of benzene

Environmental and health risk assessment projects involve processing of a large volume of uncertain information. To reduce uncertainties in the assessment process, the risk assessor evaluates a large volume of data from toxicological, exposure and health related databases. Being a data intensive assessment, for improved risk assessment, there is a need for these data to be harmonized and integrated by incorporating information from various sources and biological organizational criteria (e.g., gene, cellular, tissue, organ level health effects, if available). The main objective of this study is to have a front end or upstream approach towards an effective dynamic data fusion (DF)-based risk assessments. To achieve this goal, a generalized human health risk assessment and a system biology-based DF framework have been proposed. The proposed approach would be able to detect different trends and patterns and integrates various toxicological datasets from different biological organizational criteria (e.g., gene, cellular, tissue, organ level, if available) and integrates data from disparate sources. To demonstrate the effectiveness of the approach, both the proposed frameworks have been implemented in human health risk assessment for benzene originated from an illustrative example of a contaminated site. The application in this study shows that this approach can increase the efficiency of dynamic data integration and incorporation of toxicity pathway or system biology-based information in the human health risk analysis. Currently, the system biology facilitated DF framework is based on hypothesis driven modeling relationships between different bio-organizational criteria. Further work is needed to prove these relationships. For a data rich chemical example such as benzene, these relationships can be established based on the existing and the emerging knowledge-base on benzene toxicology.

A comparison of membership function shapes in a fuzzy-based fugacity model for disinfection byproducts in indoor swimming pools

Aleatory and epistemic uncertainty in human health risk assessment is virtually unavoidable. While probabilistic methods may adequately address exposure and risk model parameters with variability (body weight, exposure duration, exposure frequency), other methods are more helpful for handling uncertainty that arises from an incomplete understanding of the processes being modeled (mass transport). Due to the potential for cancer and other health risks, it is essential to understand of the concentrations of disinfection byproducts in swimming pool facilities and the related exposures. This study builds on previous probabilistic and fuzzy based fugacity models which estimate exposure to disinfection byproducts in swimming pools (Dyck, Water Res 45:5084–5098, 2011; Annual meeting of the North American Fuzzy Information Processing Society (NAFIPS), 2012). Those models estimated environmental concentrations based on mass transfer processes for which there are many possible formulas. The influence of membership function shape on the final estimated concentration required further investigation. In this study, three different trapezoidal membership functions are derived for the gas-side mass transfer coefficient kG. One triangular and one trapezoidal membership function are derived for the liquid-side mass transfer coefficient, kL. According to graphical output and percent difference between the actual concentration and the defuzzified output of the fuzzy based model, the best combination of shapes to use is the trapezoidal kG membership function which uses seven calculated values (to define the shape of the trapezoid) with either the triangular or trapezoidal kL membership function. Further study is recommended to combine the fuzzy based methods for poorly understood model processes with probabilistic methods for model parameters with aleatory uncertainty.

Fuzzy-based fugacity model for propagating uncertainty in assessing swimmer exposures to disinfection byproducts

Aleatory and epistemic uncertainty are widely accepted as unavoidable components of human health risk assessment. Model uncertainty and variability in exposure factors contribute to the overall uncertainty in the assessed risk. Probabilistic methods are commonly used to characterize these uncertainties. Exposures to disinfection byproducts have been linked to cancer as well as reproductive and respiratory health effects. Swimmers are exposed to disinfection byproducts mainly through inhalation and dermal contact and to a lesser extent through ingestion. A fugacity model has previously been used to estimate inhalation, dermal contact and ingestion exposures for swimmers. There is no consensus in literature regarding the most applicable models to use to estimate these parameters. Uncertainties related to differences between these models have been addressed here using fuzzy set theory. Possibilistic treatment of these uncertainties provides a helpful alternative to arbitrarily chosen models or probability distributions. A case study using disinfection byproduct concentrations from Italian swimming pools is presented.

Investigating the effects of design and management factors on DBPs levels in indoor aquatic centres

Disinfection by-products (DBPs) in indoor swimming pool water and air have long been a critical human health risk concern. This study investigated the effects of several indoor swimming pool design and management factors (e.g. ventilation, water treatment, pool operations, pool type) on the concentrations of DBPs, such as trihalomethanes (THMs) and chloramines, in pool water and air. Two sampling campaigns, A and B, were carried out to measure the concentrations of DBPs under different conditions. In both campaigns, 46 pool water samples, seven tap water samples, and 28 ambient air samples were collected and analyzed. Regression models were also developed and validated for investigating the combined effects of design and management factors on total trihalomethanes (TTHM) and trichloramine. The model results show that pool water characteristics (e.g., total organic content, temperature, conductivity, pH and alkalinity) and management factors (e.g., the number of bathers and sprayers) have direct effects on DBP concentrations. Pool water characteristics such as UV absorbance, hardness, and oxidation-reduction potential and a management factor UV intensity have inverse effects on DBPs levels. Based on the correlation analysis, other factors such as fan speed, fresh air, pool age, and basin area were found to be correlated with the concentrations of individual THMs and trichloramine in both water and air. It was also observed that the concentration of THMs varies with pool type. It is note worthy that the effects of the number of sprayers was quantified for the first time. This study comprehensively assessed pool design and management factors and identified their effects on DBPs, providing indoor swimming pool facilities with useful information to control DBPs in the indoor swimming environment.

Fuzzy Physiologically Based Pharmacokinetic (PBPK) Model of Chloroform in Swimming Pools

Chloroform is one of the most prevalent disinfection byproducts (DBPs) formed in swimming pools through reactions between disinfectants and organic contaminants. Chloroform and related DBPs have been a subject of research in exposure and human health risk assessments over the last several decades. Physiologically based pharmacokinetic (PBPK) models are one tool that is being used increasingly by researchers to evaluate the health impacts of swimming pool exposures. These models simulate the absorption, distribution, metabolism and excretion of chemicals in the human body to assess doses to sensitive organs. As with any model, uncertainties arise from variability and imprecision in inputs. Among the most uncertain model parameters are the partition coefficients which describe uptake and distribution of chemical to different tissues of the body. In this paper, a fuzzy based model is presented for improving the description and incorporation of uncertain parameters into the model. The fuzzy PBPK model compares well with the deterministic model and measured concentrations while providing more information about uncertainty.