Applying the two-resistance theory to contaminant volatilization in showers

Little, John C.

doi:10.1021/es00031a010

Cited by 103 publications

(81 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The descriptions and values of all the parameters are summarized in Table A2. The concentrations of THMs in the air (C air ) were estimated by two-resistance model proposed by Little [25], and the comprehensive formulas were summarized in Table A2. Shower frequency (F) and shower duration (t) were estimated from the shower-habit survey data in one northern city (Shijiazhuang) and two southern cities (Ningbo and Xiamen) to characterize the big difference of shower-habit between northern and southern China.…”

Section: Exposure Assessmentmentioning

confidence: 99%

Cancer risk assessment on trihalomethanes and haloacetic acids in drinking water of China using disability-adjusted life years

Pan

et al. 2014

Journal of Hazardous Materials

View full text Add to dashboard Cite

Section: Exposure Assessmentmentioning

confidence: 99%

Cancer risk assessment on trihalomethanes and haloacetic acids in drinking water of China using disability-adjusted life years

Pan

et al. 2014

Journal of Hazardous Materials

View full text Add to dashboard Cite

“…Step 3: Calculate the rate of mass transfer across the interface using either equation (22) or equation (23) [43,47]:…”

Section: Modificationmentioning

confidence: 99%

A Framework for Modelling Non-Steady-State Concentrations of Semivolatile Organic Compounds Indoors – I: Emissions from Diffusional Sources and Sorption by Interior Surfaces

Guo

2013

Indoor and Built Environment

View full text Add to dashboard Cite

Over the past two decades, more than 20 mass transfer models have been developed for building materials, furnishings, and consumer products as contaminant sources, sinks, and barriers. While these models have greatly improved our understanding of contaminant movements in buildings, their applications in the real world have been somewhat limited because of the incompatibility and computational complexity of the models. In this paper, a framework is proposed for modelling the dynamic concentrations of semivolatile organic compounds in indoor media. Based on a discretization method, which transfers continuous models into discrete counterparts, this framework can perform the functions of the existing mass transfer models and, at the same time, solves the model incompatibility problem and reduces the computational complexity. This framework complements and supplements the existing multimedia semivolatile organic compound models by providing more details of the distribution of semivolatile organic compounds among indoor media, helping check the validity of certain assumptions that have been used in those steady-state models, and providing more flexibility to allow evaluation of risk management options such as source removal, encapsulation, and variable ventilation rate. This framework will be described in two parts. Part I, this paper, discusses the representation of diffusional sources and sorption by interior surfaces. Interactions of semivolatile organic compounds with particulate matter will be discussed in a subsequent publication.

show abstract

“…Although the general practice was to set non-detectable values equal to half the detection limit, the practice was not implemented here because the detection limit was not reported (Kerger et al, 2000). The mass transfer coefficient for chloroform was measured in various shower systems (Little, 1992) (Table 4). The mass transfer coefficients for other THMs were calculated from a two-resistance mass-transfer theory model (Little, 1992) using chloroform as a reference chemical (Table 4).…”

Section: Model Structurementioning

confidence: 99%

“…The mass transfer coefficient for chloroform was measured in various shower systems (Little, 1992) (Table 4). The mass transfer coefficients for other THMs were calculated from a two-resistance mass-transfer theory model (Little, 1992) using chloroform as a reference chemical (Table 4). The PBPK models were coded with the graphical simulation tool SIMULINK s , which is part of the MATLAB s technical computing product family (The MathWorks Inc., Natick, MA, USA).…”

Section: Model Structurementioning

confidence: 99%

“…j Assumed normally distributed: the mean was calculated as the average of eight observed values (Little, 1992), and coefficient of variance (CV) was set to 40%, so that all observed values were contained within the 95% bounds of the distribution. k Assumed normally distributed: the mean was calculated using an estimation method (Little, 1992) with chloroform being a reference chemical, and CV was set to 40% to match the setting for chloroform.…”

Section: Model Evaluation -Individual Models For Chloroform Bdcm Dbmentioning

confidence: 99%

See 1 more Smart Citation

Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling

Tan¹,

Liao²,

Clewell³

2006

J Expo Sci Environ Epidemiol

139

View full text Add to dashboard Cite

Biomonitoring data provide evidence of exposure of environmental chemicals but are not, by themselves, direct measures of exposure. To use biomonitoring data in understanding exposure, physiologically based pharmacokinetic (PBPK) modeling can be used in a reverse dosimetry approach to assess a distribution of exposures possibly associated with specific blood or urine levels of compounds. Reverse dosimetry integrates PBPK modeling with exposure pattern characterization, Monte Carlo analysis, and statistical tools to estimate a distribution of exposures that are consistent with biomonitoring data in a population. The present study used an existing PBPK model for chloroform as a generic framework to develop PBPK models for other trihalomethanes (THMs). Using Monte Carlo sampling techniques, probabilistic information about pharmacokinetics and exposure patterns was included to estimate distributions of THMs concentrations in blood in relation to various exposure patterns in a diverse population. In addition, the possibility of inhibition of hepatic metabolism among THMs was evaluated under the scenarios of household exposure. These studies demonstrated how PBPK modeling can be used as a tool to estimate a population distribution of exposures that could have resulted in particular biomonitoring results. When toxicity level is known, this tool can also be used to estimate proportion of population above levels associated with health risk.

show abstract

Applying the two-resistance theory to contaminant volatilization in showers

Cited by 103 publications

References 17 publications

Cancer risk assessment on trihalomethanes and haloacetic acids in drinking water of China using disability-adjusted life years

Cancer risk assessment on trihalomethanes and haloacetic acids in drinking water of China using disability-adjusted life years

A Framework for Modelling Non-Steady-State Concentrations of Semivolatile Organic Compounds Indoors – I: Emissions from Diffusional Sources and Sorption by Interior Surfaces

Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling

Contact Info

Product

Resources

About