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Mark Nicas

Publications and source records attributed to Mark Nicas.

15 recordsLinked to original sources
PubMed2006

Estimating benzene exposure at a solvent parts washer.

A mathematical model is described for estimating benzene exposure at a parts washer using petroleum distillates solvent containing benzene. The basic assumptions are that the benzene mass emission rate exponentially decreases over time, and that the air above the parts washer basin to which a worker is exposed is part of a well-mixed air zone termed the near field (relative to the source location). Two previously conducted simulations of the parts washer process are described. A single 1-hour time-weighted average (TWA) benzene concentration was measured during Simulation #1, and two 4-hour TWA benzene concentrations were measured during Simulation #2. The initial benzene concentrations in the solvents were known, and the exponential loss rate constants were estimated from subsequent determinations of the benzene concentrations. Values for the interzonal airflow rate were estimated based on the conceptual geometry of the near field zone and sparse information on air speed near the parts washers. Minimum values for the room supply/exhaust air rate were estimated based on the room volumes and ventilation conditions. The modeled benzene concentrations were within a multiplicative range of one-half to twofold the measured concentrations. Uncertainty in a model estimate was quantified by Monte Carlo analysis; the distributions of model estimates exhibited coefficients of variation of approximately 40%. Issues related to uncertainty in exposure estimates made by mathematical modeling are discussed.

Air Pollutants, Occupational↗
PubMed2006

An integrated model of infection risk in a health-care environment.

Certain respiratory tract infections can be transmitted by hand-to-mucous-membrane contact, inhalation, and/or direct respiratory droplet spray. In a room occupied by a patient with such a transmissible infection, pathogens present on textile and nontextile surfaces, and pathogens present in the air, provide sources of exposure for an attending health-care worker (HCW); in addition, close contact with the patient when the latter coughs allows for droplet spray exposure. We present an integrated model of pertinent source-environment-receptor pathways, and represent physical elements in these pathways as "states" in a discrete-time Markov chain model. We estimate the rates of transfer at various steps in the pathways, and their relationship to the probability that a pathogen in one state has moved to another state by the end of a specified time interval. Given initial pathogen loads on textile and nontextile surfaces and in room air, we use the model to estimate the expected pathogen dose to a HCW's mucous membranes and respiratory tract. In turn, using a nonthreshold infectious dose model, we relate the expected dose to infection risk. The system is illustrated with a hypothetical but plausible scenario involving a viral pathogen emitted via coughing. We also use the model to show that a biocidal finish on textile surfaces has the potential to substantially reduce infection risk via the hand-to-mucous-membrane exposure pathway.

Cough↗
PubMed2005

Evaluation of COSHH Essentials for vapor degreasing and bag filling operations.

COSHH Essentials is a system of workplace risk management developed by the UK Health and Safety Executive for use by proprietors of small and medium sized enterprises. COSHH Essentials recommends exposure control approaches based on a chemical's potential health hazards, scale of use and ability to become airborne. More specifically, chemicals are grouped into hazard bands based on their potential health hazards, and each hazard band is associated with a 10-fold range of 8 h time weighted average airborne concentrations, termed exposure bands. The recommended control approaches are intended to limit air concentrations to within or below the exposure bands. Using air monitoring data from NIOSH Health Hazard Evaluations and Control Technology Assessments, we evaluated the ability of COSHH Essentials to select adequate control technology for vapor degreasing and bag filling operations, and the ability of the recommended control approaches to successfully limit air concentrations. We identified two types of misclassification errors. 'Under-controlled' errors were instances in which the airborne concentration exceeded the upper limit of the chemical's exposure band in the presence of control technology; such errors were observed in 78% (139/179) and 48% (76/159) of measurements collected at vapor degreasing and bag filling operations, respectively. 'Over-controlled' errors were instances in which the airborne concentration was within or below the chemical's exposure band in the absence of control technology, although conditions of use prompt COSHH Essentials to recommend controls; such errors were observed in 61% (102/167) and 8% (3/26) of measurements collected at vapor degreasing and bag filling operations, respectively. In our use of COSHH Essentials, we found that for many particulate substances toxicological information was difficult to obtain. Given the high prevalence of the control errors, we judge it is important that COSHH Essentials provide the exposure bands and information on the evaluation of control technology performance to users. In addition, we identify a number of questions for further research and outline a prospective study, which will systematically describe how small business owners use COSHH Essentials, and the frequency of under-controlled errors in practice.

Air Pollutants, Occupational↗
PubMed2005

Margins of safety provided by COSHH Essentials and the ILO Chemical Control Toolkit.

COSHH Essentials, developed by the UK Health and Safety Executive, and the Chemical Control Toolkit (Toolkit) proposed by the International Labor Organization, are 'control banding' approaches to workplace risk management intended for use by proprietors of small and medium-sized businesses. Both systems group chemical substances into hazard bands based on toxicological endpoint and potency. COSSH Essentials uses the European Union's Risk-phrases (R-phrases), whereas the Toolkit uses R-phrases and the Globally Harmonized System (GHS) of Classification and Labeling of Chemicals. Each hazard band is associated with a range of airborne concentrations, termed exposure bands, which are to be attained by the implementation of recommended control technologies. Here we analyze the margin of safety afforded by the systems and, for each hazard band, define the minimal margin as the ratio of the minimum airborne concentration that produced the toxicological endpoint of interest in experimental animals to the maximum concentration in workplace air permitted by the exposure band. We found that the minimal margins were always <100, with some ranging to <1, and inversely related to molecular weight. The Toolkit-GHS system generally produced margins equal to or larger than COSHH Essentials, suggesting that the Toolkit-GHS system is more protective of worker health. Although, these systems predict exposures comparable with current occupational exposure limits, we argue that the minimal margins are better indicators of health protection. Further, given the small margins observed, we feel it is important that revisions of these systems provide the exposure bands to users, so as to permit evaluation of control technology capture efficiency.

Air Pollutants, Occupational↗
PubMed2005

Toward understanding the risk of secondary airborne infection: emission of respirable pathogens.

Certain respiratory tract infections are transmitted through air. Coughing and sneezing by an infected person can emit pathogen-containing particles with diameters less than 10 microm that can reach the alveolar region. Based on our analysis of the sparse literature on respiratory aerosols, we estimated that emitted particles quickly decrease in diameter due to water loss to one-half the initial values, and that in one cough the volume in particles with initial diameters less than 20 microm is 60 x 10(-8) mL. The pathogen emission rate from a source case depends on the frequency of expiratory events, the respirable particle volume, and the pathogen concentration in respiratory fluid. Viable airborne pathogens are removed by exhaust ventilation, particle settling, die-off, and air disinfection methods; each removal mechanism can be assigned a first-order rate constant. The pathogen concentration in well-mixed room air depends on the emission rate, the size distribution of respirable particles carrying pathogens, and the removal rate constants. The particle settling rate and the alveolar deposition fraction depend on particle size. Given these inputs plus a susceptible person's breathing rate and exposure duration to room air, an expected alveolar dosemicrois estimated. If the infectious dose is one organism, as appears to be true for tuberculosis, infection risk is estimated by the expression: R = 1-exp(-micro). Using published tuberculosis data concerning cough frequency, bacilli concentration in respiratory fluid, and die-off rate, we illustrate the model via a plausible scenario for a person visiting the room of a pulmonary tuberculosis case. We suggest that patients termed "superspreaders" or "dangerous disseminators" are those infrequently encountered persons with high values of cough and/or sneeze frequency, elevated pathogen concentration in respiratory fluid, and/or increased respirable aerosol volume per expiratory event such that their pathogen emission rate is much higher than average.

Aerosols↗
PubMed2004

Respiratory protection against Mycobacterium tuberculosis: quantitative fit test outcomes for five type N95 filtering-facepiece respirators.

In preparing to fit test a large workforce, a respirator program manager needs to initially choose respirators that will fit the greatest proportion of employees and achieve the best fits. This article discusses our strategy in selecting respirators from an initial array of seven NIOSH-certified Type N95 filtering-facepiece devices for a respiratory protection program against Mycobacterium tuberculosis (M. tb) aerosol. The seven respirators were screened based on manufacturer-provided fit test data, comfort, and cost. From these 7 devices, 5 were chosen for quantitative fit testing on 40 subjects who were a convenience sample from a cohort of approximately 30,000 workers scheduled to undergo fit testing. Across the five brands, medium/regular-size respirators fit from 8% to 95% of the subjects; providing another size of the same brand improved the pass rates slightly. Gender was not found to significantly affect fit test pass rates for any respirator brand. Among test panel members, an Aearo Corporation respirator (TC 84A-2630) and a 3M Company respirator (TC 84A-0006) provided the highest overall pass rates of 98% and 90%, respectively. We selected these two brands for fit testing in the larger worker cohort. To date, these two respirators have provided overall pass rates of 98% (1793/1830) and 88% (50/57), respectively, which are similar to the test panel results. Among 1850 individuals who have been fit tested, 1843 (99.6%) have been successfully fitted with one or the other brand. In a separate analysis, we used the test panel pass rates to estimate the reduction in M. tb infection risk afforded by the medium/regular-size of five filtering-facepiece respirators. We posed a low-exposure versus a high-exposure scenario for health care workers and assumed that respirators could be assigned without conducting fit testing, as proposed by many hospital infection control practitioners. Among those who would pass versus fail the fit test, we assumed an average respirator penetration (primarily due to faceseal leakage) of .04 and 0.3, respectively. The respirator with the highest overall pass rate (95%) reduced M. tb infection risk by 95%, while the respirator with the lowest pass rate (8%) reduced M. tb infection risk by only 70%. To promote the marketing of respirators that will successfully fit the highest proportion of wearers, and to increase protection for workers who might use respirators without the benefit of being fit tested, we recommend that fit testing be part of the NIOSH certification process for negative-pressure air-purifying respirators with tightly fitting facepieces. At a minimum, we recommend that respirator manufacturers generate and provide pass rate data to assist in selecting candidate respirators. In any event, program managers can initially select candidate respirators by comparing quantitative fit tests for a representative sample of their employee population.

Adult↗
PubMed2004

Variability in respiratory protection and the assigned protection factor.

The workplace protection factor (WPF) for a given respirator wearer shows substantial variability from wearing to wearing; this variability is commonly assumed to be lognormal in nature. Further, when multiple WPFs are measured for each of multiple wearers, the aggregated WPFs appear to follow a lognormal distribution. However, the analysis typically applied to WPF data does not apportion variability within versus between wearers. We present an analytical framework based on a normal random effects model of log-transformed penetration P values (P = 1/WPF). Data from seven studies of negative-pressure air-purifying half-mask respirators, and from two studies of hemlet-and-visor type powered air-purifying respirators were analyzed by the method of maximum likelihood in the context of the model. More specifically, analyses were performed for log-transformed P values and for logit-transformed P values. Parameter estimates included within-wearer and between-wearer variance components. In general, the within-wearer component dominated the between-wearer component. We also propose a method for establishing an assigned protection factor, APF, that properly accounts for these variance components. Our method provides an APF satisfying two criteria: (1) for a given wearer, an acceptable WPF distribution has no more than 5% of WPFs below the APF value; and (2) for a wearer population, no more than 5% of wearers have unacceptable WPF distributions. The method incorporates an one-sided confidence limit to account for sampling variability. Alternative confidence limits were computed based on large sample variance estimates of random effects model parameters versus a bootstrap method. In general, there was good agreement between the APF values based on log-transformed versus logit-transformed P data, and between APF values based on the large sample variance estimates versus the bootstrap method. Based on large sample variance estimates for the logit-transformed P data from the seven half-mask studies, estimated APFs ranged from 1.4 to 250, with 5/7 studies yielding an APF </= 5.3. Given these results and related considerations, we recommend that the current half-mask APF be reduced from 10 to 5.

Air Pollution, Indoor↗
PubMed2003

Estimating methyl bromide exposure due to offgassing from fumigated commodities.

Methyl bromide (MB) is used to fumigate diverse commodities. During fumigation, the commodity can sorb a substantial mass of MB, which does not chemically react and is termed a residue. During subsequent commodity handling, the residue offgasses and can lead to MB inhalation exposure among processing workers. Although MB has a low 1 ppm 8-hr TLV-TWA as recommended by the American Conference of Governmental Industrial Hygienists (ACGIH(R)) and is considered a potential occupational carcinogen by the National Institute for Occupational Safety and Health, the recent industrial hygiene literature contains no pertinent exposure data. Limited measurements made in 1992 by the California Department of Pesticide Regulation are summarized here, but associated information on exposure determinants is lacking. In this article, mathematical models are used to integrate data on MB residue offgassing with several processing scenarios to estimate potential exposure levels. The main finding is that if a large volume of commodity rapidly offgasses MB and is handled under conditions of low ventilation, the potential exists for MB exposures above the 1 ppm TLV-TWA value. However, the combination of handling a smaller commodity volume and less rapid offgassing may be the more typical scenario. It is recommended that a pilot study be conducted to measure current MB exposure levels, test the validity of the mathematical models, and collect industry-wide data on exposure determinants. By using the latter data as inputs for validated models, public health scientists could estimate the distribution of MB exposure levels across the commodity processing industry.

Agrochemicals↗
PubMed2002

A risk analysis for airborne pathogens with low infectious doses: application to respirator selection against Coccidioides immitis spores.

Probability models incorporating a deterministic versus stochastic infectious dose are described for estimating infection risk due to airborne pathogens that infect at low doses. Such pathogens can be occupational hazards or candidate agents for bioterrorism. Inputs include parameters for the infectious dose model, distribution parameters for ambient pathogen concentrations, the breathing rate, the duration of an exposure period, the anticipated number of exposure periods, and, if a respirator device is used, distribution parameters for respirator penetration values. Application of the models is illustrated with a hypothetical scenario involving exposure to Coccidioides immitis, a fungus present in soil in areas of the southwestern United States Inhaling C. immitis spores causes a respiratory tract infection and is a recognized occupational hazard in jobs involving soil dust exposure in endemic areas An uncertainty analysis is applied to risk estimation in the context of selecting respiratory protection with a desired degree of efficacy.

Air Microbiology↗
PubMedDate not supplied

Uncertainty in exposure estimates made by modeling versus monitoring.

To conduct an initial exposure assessment for an airborne toxicant, industrial hygienists usually prefer air monitoring to mathematical modeling, even if only one exposure value is to be measured. This article argues that mathematical modeling may provide a more accurate (less uncertain) exposure estimate than monitoring if only a few air samples are to be collected, if anticipated exposure variability is high, and if information on exposure determinants is not too uncertain. To explore this idea, a hypothetical "true" distribution of 8-hour time-weighted average airborne exposure values, C, is posited based on an NF exposure model. The C distribution is approximately lognormal. Estimation of the mean value, microC (the long-term average exposure level), is considered. Based on simple random sampling of workdays and use of the sample mean C to estimate microC, accuracy (uncertainty) in the estimate is measured by the mean square error, MSE(C). In the alternative, a modeling estimate can be made using estimates of the mean chemical emission rate microG, the mean room dilution supply air rate microQ, and the mean dilution ventilation rate in the NF of the source mu beta. By positing uniform distributions for the estimates microG, microQ, and mu beta, an equation for the modeling mean square error MSE(microC) is presented. It is shown that for a sample size of three or fewer workdays, mathematical modeling rather than air monitoring should provide a more accurate estimate of microC if the anticipated geometric standard deviation for the C distribution exceeds 2.3.

Air Pollution, Indoor↗
PubMedDate not supplied

A risk analysis approach to selecting respiratory protection against airborne pathogens used for bioterrorism.

The authors present a quantitative risk analysis approach to estimating infection risk due to airborne pathogens exhibiting relatively large infectious dose values. The method is applied to hypothetical scenarios involving airborne spores of Bacillus anthracis. The method combines the estimated parameters for exposure intensity, the pathogen dose-response relationship, and respirator penetration values (if respiratory protection is used). Because knowledge of the true parameter values will be uncertain, an uncertainty analysis is an essential part of the process. Given a specified value for acceptable infection risk, the method permits choosing a respirator that sufficiently reduces exposure to meet the acceptable risk criterion. A strength of the risk analysis approach is its transparency, in that the model structure and data inputs are explicitly identified. Further, risk analysis informs the expert judgment that must typically be applied in selecting respiratory protection against airborne pathogens.

Anthrax↗
PubMedDate not supplied

Using a spreadsheet to compute contaminant exposure concentrations given a variable emission rate.

Two key elements of mathematical exposure models are the contaminant's emission rate and pattern of dispersion in room air. Assuming that the mass emission rate is constant and room air is perfectly mixed affords relative mathematical simplicity. However, treating a highly variable emission rate as constant underestimates peak exposure intensity, which may be toxicologically important, and assuming a well-mixed condition underestimates exposure intensity near the source. In the past decade multizone models and turbulent diffusion models have been used to account for spatial variability in airborne concentrations, and variable emission rate functions have been described for different processes. Due to the greater complexity of these models, closed-form equations for concentration as a function of time may not be available. This article presents a numerical method that combines a variable contaminant emission rate function with the three dispersion constructs most commonly used by industrial hygienists-the well-mixed room, the near field/far field, and hemispherical turbulent eddy diffusion. The article describes how the numerical method is implemented by a computer spreadsheet program, and illustrates the method using a sinusoidal contaminant emission rate function.

Air Pollution, Indoor↗
PubMedDate not supplied

Predicting room vapor concentrations due to spills of organic solvents.

Relatively small spills of volatile liquids can result in short-term, high-concentration exposures. Because of the transient nature of these exposures, air sampling may be precluded. As an alternative, exposure assessment can be done by mathematical modeling. The vapor emission rate from small spills is highest immediately following the spill and decreases as the surface area available for mass transfer decreases and evaporation cools the liquid. This decreasing emission rate is not described by any of the existing evaporation rate models. The authors present an evaporation rate model that describes the changing emissions as exponentially decreasing. The rate of decrease is governed by an evaporation rate parameter alpha, which has the unit of min(-1) and can be estimated based on experimental measurements. The authors measured alpha for a suite of compounds and different sizes of spill. They found that alpha can be estimated for hydrocarbons containing only C, H, and O with the equation: alpha=0.000524 VP + 0.0108 SA/Vol, where VP is the vapor pressure of the liquid and SA/Vol is the surface area to volume ratio. Next, the authors integrated the exponentially decreasing emission rate into a well-mixed room versus a near field/far field dispersion construct to predict vapor concentrations. A preliminary experiment was conducted in a test room to compare measured concentrations with the concentrations predicted by the models. The well-mixed room model performed well based on ANSI indoor air model evaluation criteria. The predicted near field concentrations showed a poor fit to the measured values based on the ANSI criteria, although overall they did capture the observed time profile.

Air Movements↗