APPLICATION OF THE ROSETTA STONE EQUATIONS

Transcription

1 APPLICATION OF THE ROSETTA STONE EQUATIONS TO CASINO WORKERS SECONDHAND SMOKE EXPOSURE: AN ANALYSIS OF A NIOSH HEALTH HAZARD EVALUATION James L. Repace, Repace Associates, Inc. Bally s Casino, Atlantic City, New Jersey May 1, 2015

2 Abstract In response to a complaint by workers in Bally s Casino in Atlantic City, New Jersey, Trout and Decker (1995) conducted a NIOSH Health Hazard Evaluation, measuring workers plasma and urine cotinine and both area and personal breathing zone nicotine as well as area RSP. Trout et al. (1998) subsequently published a summary of this data in JOEM, omitting the data on RSP. Using the Rosetta Stone model, I estimated mean and median plasma and urine cotinine values for 14 of 29 Bally s Casino workers for which adequate data was reported. The model estimates differ from the NIOSH-measured values by 10 to 15% for the medians, and 7 to 17% for the means. This reasonable agreement between theory and experiment suggests that the Rosetta Stone model is useful for estimating the secondhand smoke doses that nonsmoking workers get from workplace secondhand smoke. In addition, the Habitual Smoker model (Repace, 2007), using NIOSH-measured CO 2 levels to calculate the range in air exchange rates, and using ASHRAE default occupancy plus U.S. smoking prevalence predicts area air nicotine concentrations for this casino ranging from 5 to 20 µg/m 3. By comparison, NIOSH measured area nicotine concentrations ranged from 6 to 16 µg/m 3. Based upon these analyses, I conclude that the Rosetta Stone and Habitual Smoker models are useful for estimating nonsmoking workers exposure and dose from secondhand smoke exposure, and in analyzing measured data with a reasonable degree of accuracy. By contrast, an article by James et al. (2008) reviewing the same data suggested that the Rosetta Stone modeling equations yield unacceptably high error rates when estimating workers exposures to secondhand smoke RSP and nicotine from cotinine dose. However the article by James et al. (2008) is deeply flawed due to improper methods of calculation of nicotine and cotinine in their use of the Rosetta Stone model. In addition, James et al. (20080 failed to recognize that the RSP data reported in the original Health Hazard Evaluation Report by Trout and Decker (1995) but not included in their published peer-reviewed journal article, was clearly erroneous due to monitor malfunction. 2 Introduction Tobacco smoke, which comprises an aerosol (a mixture of solid and liquid particles) and gases, has thousands of chemical components, including many well-characterized toxins and carcinogens (International Agency for Research on Cancer [IARC] 2004). Many of these components are in the gas phase, and others are components of the particles. Repace, Al- Delaimy, and Bernert (2006) derived a pharmacokinetic model, called the Rosetta Stone Equations to relate atmospheric (nicotine and fine particles (PM 2.5 ) and biomarkers (serum, saliva, and urine cotinine) of secondhand smoke (SHS) exposure. These equations have proved useful in evaluating the exposure, dose, and using a dose-response relationship, the risk to workers from secondhand smoke exposure. The risk model has been cited by Trout et al. (1998), Zitting et al. (2002), Kolb et al (2010), and Samet and Wang (1999), while the exposure and dose models were cited by Jaakkola and Samet (1999), Spengler (1999), and Hammond (1999) as well as Ott (1999). The latter models allowed both point-estimates of the measures of central tendency such as the mean and median, as well as probabilistic (Monte Carlo) estimates of the entire distribution of nonsmokers exposures and doses for the population and subgroups such as office workers. The predictions of the models agreed well with both environmental studies of atmospheric nicotine in offices, and with clinical epidemiological studies of saliva cotinine in

3 3 office workers, as well as blood and urine cotinine in various population subgroups (Repace and Lowrey, 1993; Repace et al., 1998). The author has also found them useful in litigation by workers who have been injured by secondhand smoke [e.g., Horseshoe Casino Report, Repace (2006)]. Interestingly, James et al. (2008) cite the Horseshoe Casino Report despite the fact that it was not in the public domain in 2008 but had been submitted in litigation over injury to several dozen casino workers in Hammond Indiana. This suggests that James et al. may have been experts hired by the defense in that case, which was settled out of court by arbitration in favor of the plaintiff casino workers. More recent applications of the model are illustrated in Repace, Zhang et al., 2012) and Hedley et al. (2006). Despite the success of the Rosetta Stone model in estimating worker s exposure dose and risk from secondhand smoke (SHS), It was recently drawn to my attention that James et al. (2008) have questioned its value. James et al. (2008) applied the Rosetta Stone model to evaluate a NIOSH study of casino workers conducted by Trout et al. (1998). James et al. (2008) asserted that: Abstract: SH&E professionals may be asked to determine secondhand smoke (SHS) exposures as a part of worker safety or exposure assessments. This article critically examines a recently proposed set of equations for predicting concentrations of SHS markers in air. It concludes that such models should not be employed until they are fully validated and asserts that use of measured, population specific, SHS concentrations should remain the preferred practice in job safety assessment for this potential workplace hazard. James et al. conclude: The present attempt to validate the model using the NIOSH casino study data revealed a high error rate. Applying the model s equations overstated the NIOSH-measured RSP levels in at least 27 of 28 individuals (only 1 of 28 individuals yielded a urinary cotinine level that resulted in a calculated RSP level that arguably fell within the range measured by NIOSH investigators). Thus, for this study, the proposed calculations yield a 96% (or greater) error rate when estimating the RSP levels from the collected urinary cotinine samples. For workplace safety and risk management decisions, such a high error rate in an exposure assessment tool is unacceptable. This high error rate demonstrates, as much as any line of evidence, that the use of this model would tend to grossly overestimate nicotine/rsp levels when calculated from measured urinary cotinine levels. However, James et al. s claims that the model s equations overstated the NIOSHmeasured RSP levels in at least 27 of 28 individuals is a very peculiar statement, since NIOSH did not measure personal breathing zone (PZB) RSP levels for any of the dealers or supervisors. Rather, they measured area RSP levels using a gravimetric method with a very high limit of detection (20 µg/m 3 ). Moreover, NIOSH measured area RSP in just 12 locations that were distal to the workers. And in three of those locations, while NIOSH measured levels of nicotine comparable to other areas where RSP was detected, NIOSH failed to detect any RSP in these locations, suggesting a lack of reliability for NIOSH s RSP measurements. In fact although Trout and Decker (1995) reported RSP measurements in their Health Hazard evaluation report,

4 4 Trout et al. (1998) did not report those measurements in their JOEM journal publication. Finally, James et al. failed to distinguish the error that results from a comparison of area and personal RSP exposures caused by the proximity effect. McBride et al. (1999) report a pronounced proximity effect for fine particles emitted by a burning incense source: The proximity effect can be seen clearly in the median and mean ratios for the finer particles (0.5 to 2.5 µm). Before the source is on, the mean and median ratios for the monitor location 1.0 m from the source is 1.8 to 5.5 times the mean and 1.3 to 3.7 times the median of the reference (SIM) location 5.4 meters away. Further, Klepeis et al. (2009) predicted that a cigarette smoker would cause average fine particle levels of approximately µg/m 3 at horizontal distances of m. These are distances from a smoker that a table games dealer might experience in a casino. In fact, Repace (2013) in a field survey of a Los Angeles poker casino with a habitual smoker density of 2.1 habitual smokers per 100m 3, measured 55 minute mean area concentrations of PM 2.5 of 153 µg/m 3 and 74 minute average concentrations of PM 2.5 of 196 µg/m 3 at two blackjack tables for a proximity effect ratio of the means of 1.3, while an isolated poker room measured 3.4 µg/m 3 little different from nonsmoking outdoor PM 2.5 measurements that averaged 3.13 µg/m 3. James et al. (2008) derived their conclusions concerning nicotine and cotinine from a flawed application of the Rosetta Stone modeling equations. THE NIOSH STUDY OF BALLY S CASINO In order to understand the application of the Rosetta Stone model to workers SHS exposure, it is first necessary to examine the study of Bally s Casino in New Jersey by Trout et al. (1998). In 1995 the National Institute for Occupational Safety and Health (NIOSH) received complaints about secondhand smoke from workers at Bally s Casino in Atlantic City, New Jersey. Atlantic City is on the seacoast of New Jersey as shown at (A) in the photo below.

5 5 Seaside Location of Bally s Casino, Atlantic City, New Jersey. In response to this request, NIOSH conducted a field study to evaluate the exposure of gaming floor employees to ETS using both environmental and biologic measures of exposure. Trout et al. (1998) reported that: The casino that was evaluated was constructed in 1979 and offers a variety of gaming activities, including slot machines, roulette, blackjack, baccarat, craps, and poker. The gaming floor has an area of 71,380 square feet (ft 2 ); a separate poker area has an area of 8679 ft 2. Gaming activities are in operation 24 hours per day, seven days a week. The maximum occupancy of the casino is 9560 persons. The casino employs approximately 800 persons who work on the casino floor; approximately 330 are full-time dealers and approximately 180 are full-time dealer supervisors. Specific game or area assignments for dealers and supervisors were made at the start of each shift and changed daily (and sometimes within a given work shift). Other casino floor employees include waitresses, cashiers, and security personnel. The heating, ventilating, and air conditioning system was controlled by a Honeywell building management system (Honeywell Inc., Minneapolis, MN). There were 17 air-handling units, each rated to supply 47,000 cubic feet per minute of conditioned air. Assuming a maximum casino capacity (9560 persons) and a reported minimum 30% outdoor air intake, an outside air rate of 25 cubic feet per minute per person (cfm/person) can be calculated. Although the ventilation system was not inspected, carbon dioxide (CO 2 ) measurements (which

6 ranged from 425 to 850 parts per million) were consistent with the calculated outdoor air supply rates. Tobacco smoking by customers is permitted throughout the casino floor; employees do not smoke while on duty. Although some gaming tables are designated as non-smoking, the non-smoking tables are generally located adjacent to tables where smoking is permitted. The employee cafeteria has smoking and non-smoking areas, but these areas are not physically partitioned, and tobacco smoke is evident in the nonsmoking area. Employee lounges are designated non-smoking areas. The field study was performed in March 1996 and consisted of environmental and medical evaluations. Employee representatives and management were notified in advance of the NIOSH site visit. The study population consisted of dealers and supervisors; there were 279 dealers and supervisors, including both smokers and non-smokers, scheduled to work the second shift (generally the busiest shift of the day) during the two days of the evaluation (Thursday and Friday nights). Dealers and supervisors were chosen as the study population because these were the only employees for whom adequate work schedule information was available. 6 Trout et al. (1998) conducted measurements on 29 casino workers, all dealers or supervisors. They divided the workers into two groups: those reporting SHS exposure at work only (17 workers), and those reporting such exposure outside of work as well (12 workers). They measured personal breathing zone (PBZ) nicotine on a subset of 9 workers exposed at work only, and an additional 9 workers who were exposed both at work and away from work, for a total of 18 workers. NIOSH measured both serum (plasma) cotinine and total urine cotinine (cotinine plus cotinine glucuronide) in a total of 28 workers. However, one worker s cotinine level was consistent with active smoking, and he was eliminated. Trout et al. (1998) found that The geometric mean serum cotinine level of the 27 participants who provided serum samples was 1.34 nanograms per milliliter (ng/ml) (pre-shift) and 1.85 ng/ml (post-shift). Both measurements greatly exceeded the geometric mean value of 0.65 ng/ml for participants in the Third National Health and Nutrition Examination Survey (NHANES III) who reported exposure to ETS at work. This evaluation demonstrates that a sample of employees working in a casino gaming area were exposed to ETS at levels greater than those observed in a representative sample of the US population, and that the serum and urine cotinine of these employees increased during the workshift. In fact, as the NHANES III data show, in 1999, the average adult U.S. nonsmoker had a median serum cotinine level of ng/ml, the 90 th percentile was 0.63 ng/ml, and the 95 th percentile was 1.5 ng/ml (95% CI ng/ml) [CDC, 2012]. So the post-shift cotinine values for the Bally s workers, at 1.6 ng/ml, was actually triple that for all workers combined. For our model evaluation purposes, there were only 14 workers, 9 exposed at work only, and 5 exposed at work and away from work who had measurements of both PBZ nicotine and cotinine in both blood and urine. In addition, NIOSH made area measurements of respirable particulate near the center tables of 9 gaming pits using gravimetric methods. It is this subset of 14 nonsmoking workers that is able to provide a complete dataset for Rosetta Stone modeling comparison. It is important to note that the Rosetta Stone equations use free cotinine in urine, not total cotinine. The relationship between the two is given by the equation U Free =

7 (0.508)(U Total ). Table 1 below summarizes the NIOSH data for the 14 nonsmoking workers: 7 TABLE 1. ANALYSIS OF WORKER COTININE AND AIR NICOTINE DATA FOR 14 NONSMOKING CASINO WORKERS STUDIED BY NIOSH; D = DEALER, S = SUPERVISOR. 1. Bally s Worker # from Trout & Decker (1995) 2. Job 3. NIOSH 8-hour shift PBZ Nicotine 4. NIOSH P o = Pre- Shift Plasma 5. NIOSH P F = Post- Shift Plasma 6. NIOSH* * U o = Pre- Shift Free Urine 7. NIOSH* * U F = Post- Shift Free Urine 9. Modeled Postshift Plasma, P o +ΔP 11. Modeled Post-shift Free Urine, U o +ΔU 8. Modeled increase in plasma cotinine ΔP = PBZ Nicotine = 20.8 P o 10. Modeled Postshift Increase In Free Urine ΔU = 6.5 ΔP 1 D D S S D D S D D * S * D * D * D * D Mean Median Geometric mean *(exposed to SHS both at work and away from work). The Rosetta Stone equation posits that the increase in plasma cotinine as a function of inhaled nicotine N in units of micrograms per cubic meter (µg/m 3 ) for 8 hours is given by the Equation ΔP = N 8 /20.8 (Repace, Al-Delaimy & Bernert, 2006). Thus, exposure of a worker whose initial plasma cotinine is P o = 1.14 nanogram per milliliter (ng/ml) to an air nicotine concentration of 10 µg/m 3 for 8 hours would yield an estimated post-shift plasma cotinine P F = ΔP + P o = (10/20.8) = = 1.62 ng/ml, as shown for worker #8 in Table 1, column 9. Similarly the Rosetta Stone Equation relating the free Urine cotinine to plasma cotinine given by Repace et al. (2006) is ΔU F = 6.5 ΔP. Thus for worker #8, ΔU F = 6.5 ΔP = (6.5)(0.48) = 3.12, as shown in Table 1, column 10, and the estimated post-shift free cotinine in column 11 would then be U 0 + ΔU F = = 17.0 as per column 11 in Table 1.

8 8 TABLE 2. COMPARISON OF WORKER COTININE MEASURED AND ESTIMATED FROM AIR NICOTINE DATA USING THE ROSETTA STONE EQUATIONS FOR 14 NONSMOKING CASINO WORKERS STUDIED BY NIOSH. Statistic Measured Post Shift Plasma Estimated Post Shift Plasma Measured Post Shift Free Urine Estimated Post-Shift Free Urine Percent Difference Plasma Percent Difference Urine Minimum Maximum Points Mean Std (0.568) (0.720) (22.5) (18.7) Deviation Median So, for the means of the 14 workers, the Rosetta Stone model yields a 6.5% difference between NIOSH-measured worker mean plasma cotinine and a 16.5% difference with the NIOSHmeasured worker mean urine free cotinine. And for the medians of the 14 workers, the Rosetta Stone model yields a 11.2% difference between NIOSH-measured worker median plasma cotinine and a 15.0% difference with the NIOSH-measured worker median urine free cotinine. The reason for the difference between these results and those calculated by James et al. (2008) is that James et al. use incorrect methods of analysis for this dataset. [Note that worker #9 in Table 1 is an outlier who experienced a ten-fold increase in serum cotinine and a nearly 8-fold increase in urine cotinine. If worker # 9 were omitted, the mean difference between the estimated urine cotinine and measured urine cotinine for the remaining 13 workers would be 15%, and the median difference would be 4%.] The proper method is to start with the available baseline data of pre-shift cotinine, and then input the measured nicotine to the Rosetta Stone model to calculate the increase in cotinine acquired from secondhand smoke exposure during the shift. This is then added to the pre-shift cotinine level to calculate the post-shift cotinine level. Then the urine cotinine can be estimated from the Rosetta Stone Equation, ΔU F = 6.5 ΔP = 3.12 ng/ml If the equation yields results within a reasonable degree of accuracy, as shown in Table 2, then the model is valid. However, if measured nicotine is available, as it is in this case, then simply multiplying it by a factor of ten will yield the estimated RSP level from secondhand smoke. On the other hand, if the nicotine level has not been measured, then it may be interpolated from the measured increase in cotinine level after the 8-hour shift: N 8 = 20.8 ΔP, or similarly from the increase in urine cotinine level, N 8 = (20.8)ΔU/6.5 = (3.20)(ΔU). Sometimes the post-shift cotinine is less than the pre-shift cotinine due to heavy exposure on the previous day and light exposure the next. Or only a post-shift cotinine is available. If only the post-shift plasma cotinine level is available, then the steady-state value is present, which assumes that nicotine exposure occurs over a 24 hour averaging period. That equation, for an H = 24-hour average plasma cotinine is given by P = (0.006)ρHN for a respiration rate ρ = 1m 3 /hour, and solving for N, this yield the equation N = P/(0.006)H = P F /H where P is in units of nanograms per milliliter (Table 3, Repace, Al-Delaimy and Bernert, 2006). From the

9 geometric mean of the measured Post-shift plasma cotinine in Table 2, P F = 1.91 ng/ml, and N 24 = P F /ρh = (166.7)P/(24) = 6.94 P F = (6.94)(1.91) = µg/m 3, which implies an 8 hour nicotine exposure (24/8) = 3 times as great, or nearly 40 µg/m 3. What accounts for this difference? Is this in fact an error in the model, as James et al. (2008) conclude? In Table 1, the ratio of the geometric means for ΔP/P F = 0.447/1.91 = To the contrary, the answer lies in the fact that for workers exposed daily during a 40 or more hour work week, secondhand smoke byproducts build up in extravascular compartments of the human body (Repace and Lowrey, 1993; Repace et al. 1998), so that the cotinine concentration does not decline significantly, as the large pre-shift plasma cotinine value 1.20 ng/ml, indicates. The geometric mean pre-shift plasma cotinine is (1.20/1.91) = 62% of the post-shift value. It would take 5 half-lives or 85 hours for this body burden of cotinine to be purged from the body in the absence of secondhand smoke exposure. Moreover, for the more harmful components of secondhand smoke, e.g., the fine particulate (RSP) concentration, the situation would be much worse as half-lives for removal are far longer than for nicotine and its metabolites. The deposition of particles in the lung is governed by particle characteristics, anatomy of the respiratory tract, tidal volume and breathing pattern. Lung size, airway branching pattern, airway diameters and lengths as well as frequency, depth and flow rate of breathing influence particle deposition. In humans, the effect of differences in activity levels (and hence, breathing patterns) on total lung deposition of particles of various aerodynamic diameters seems to be slight. Small particles, with a mean aerodynamic diameter less than about 2.5 µm, reach the lungs, where they deposit in airways and alveoli by impaction, sedimentation, or diffusion. Total lung deposition of such particles in humans is about 60% for particles with aerodynamic diameters of < 0.1 µm, decreasing to about 20% for particles of µm. Assessment of toxic effects of cigarette smoke in the respiratory tract requires consideration of the complexity of the mixture inhaled and the possibility of synergistic interactions among its many components. Although it is little studied, the possibility of numerous interactions has great plausibility because of the myriad components of cigarette smoke and the interlocking pathways of lung injury (Surgeon General, 2010). 9 DOSIMETRY OF TOBACCO SMOKE IN THE RESPIRATORY SYSTEM To protect the lungs from injury, the respiratory tract has an elegant set of mechanisms for handling the particles and gases in inhaled air (Figure 7.1). These defenses include physical barriers, reflexes and the cough response, the sorptive capacity of the epithelial lining, the mucociliary apparatus, alveolar macrophages, and immune responses of the lung. These defenses are critical because of the substantial volume of air inhaled daily: about 10,000 liters per day are inhaled by an adult. Even harmful substances present at low concentrations may eventually achieve a toxic dose after sustained exposure. In addition, high-level exposures, particularly when sustained, may overwhelm the lung s defenses, and some agents have the potential to reduce the efficacy of these defenses. Cigarette smoke, for example, contains components that impair mucociliary clearance (Surgeon General, 2010). Once deposited in the lung, most particles are removed by the various clearance mechanisms. Insoluble particles deposited on ciliated airways are generally cleared from the respiratory tract by mucociliary activity in hours. Clearance from the pulmonary region may occur through the action of alveolar macrophages or alternative mechanisms. Uptake of

10 10 deposited particles by macrophages is rapid, but removal of these macrophages from the lungs takes several weeks. Overall, clearance of insoluble particles deposited in the pulmonary region of the lung has half-times that are measured in weeks to months or even years. Clearance mechanisms themselves may be adversely affected by inhaled toxicants, so that clearance may take even longer because of the influence of the particles and co-pollutants such as ozone on the clearance mechanisms themselves. The deposition of particles in the lungs of human patients with poor lung function, asymptomatic smokers or mild bronchitics with normal spirometry has been shown to be increased. This may put subjects with pre-existing airway obstruction at increased risk for the adverse effects of inhaled particles (WHO, 2000). < data/assets/pdf_file/0019/123085/aqg2nded_7_3particulate-matter.pdf>. Then the Rosetta Stone equation can be used to estimate the RSP exposure and dose from secondhand smoke from the nicotine and cotinine levels respectively. Table 3, column 4, shows the estimated personal RSP levels based on the measured PBZ nicotine levels using a SHS- RSP/Nicotine ratio of 10:1. The mean is 87.4 and the median is 100 µg/m 3 above background. Table 3 column 6 shows the total RSP measurements made by Trout and Decker (1995) in their report for 2 consecutive days. The smoker density was not reported. The limit of detection for their equipment is very high, at 20 µg/m 3. The measured range is 20 to 90 µg/m 3, setting the minimum at the limit of detection. This raises the question of the reliability of NIOSH s RSP measurements. There is substantial evidence that the RSP levels measured by Trout and Decker (1995) are not reliable. Table 3, columns 6 and 7 show respectively ten Area nicotine and RSP levels measured by Trout and Decker (1995). Four of those nicotine values, ranging from 7 to 10 µg/m 3 registered 20 µg/m 3, while the remaining six nicotine values, ranging from 6 to 16 µg/m 3 registered ranged from 78 to 117 µg/m 3, indicating that the RSP monitors were not registering accurately. James et al. (2008) unaccountably took these RSP values at face value, when this was clearly not warranted. So the inference drawn by James et al. (2008) that our model underestimates RSP is invalid.

11 TABLE 3. PBZ NICOTINE AND AREA RSP LEVELS MEASURED BY TROUT & DECKER (1995). THE WORK AREAS OCCUPIED BY THE WORKERS WERE NOT IDENTIFIED. ALL UNITS µg/m Worker 2. Job 3. 8-hour PBZ Nicotine 4. Rosetta Model estimated PBZ RSP 5. Area Sampled for RSP by Trout & Decker 6. NIOSH Measured Area Nicotine Values 7. NIOSH Measured Area RSP Values 1 D A D B S A S A D Poker D A S A D A D Poker * S * D * D * D * D Mean 96.4 Median 100 *Exposed both at work and away from work. 11 A plot (Figure 1) of Trout and Decker s (1995) RSP and Nicotine values from Table 3, columns 7 and 6 respectively, shows the anomalous value of RSP for similar values of RSP. Nicotine 1 and RSP1 are the paired levels for five areas labeled in column 5 of Table 3, as shown in column 7. The average measured nicotine level for Nicotine1 is 10.8 µg/m 3, and the average measured RSP level for RSP1 is 80 µg/m 3. By contrast, for the remaining four areas, the average measured nicotine level for Nicotine2 is 24% lower, at 8.25 µg/m 3, but the concomitant anomalous RSP2 level averages at or below the limit of detection, 20 µg/m 3, which is at best 75% lower. This is clearly an indication of a malfunction in NIOSH s RSP monitoring in the latter four areas for which RSP2 was measured.

12 12 Figure 1. Plot of NIOSH-measured (Trout and Decker, 1995) Nicotine and RSP from Table 3 showing the anomaly for RSP 2. Nicotine 1 and Nicotine 2 are similar, while RSP 1 and RSP 2 differ by a factor of 3, indicating a problem with the RSP measurements. MODELING RSP LEVELS FROM SECONDHAND SMOKE An alternative method of estimating RSP from secondhand smoke (SHS-RSP) during NIOSH s visit is by modeling. Repace (2007) has summarized the Habitual Smoker Model, which posits that each smoker smokes identical cigarettes each emitting 14 mg of tar to the air at an identical rate of 2 cigarettes per hour at 10 minutes per cigarette. The model estimates SHS- RSP as directly proportional to the smoker density and inversely proportional to the air exchange rate of the building. If the smoker density is unavailable, default values can be estimated from ASHRAE Standard 62, which gives maximum default occupancy-based design ventilation rates for gambling casinos and other commercial venues such as restaurants and bars. Also, ASHRAE Standard 62 gives an equation whereby the expected carbon dioxide concentrations in a space can be calculated, or alternatively, if they have been actually measured, as they were by NIOSH

13 13 for Bally s Casino, the range in occupancy and air exchange rate can also be calculated. If the smoking prevalence is known or can be estimated, then based on default occupancy, the smoker density can then be estimated for input to the model. The following calculation shows the method and results. BALLY S VENTILATION RATES Bally s carbon dioxide levels as measured by NIOSH ranged from 425 to 850 parts per million). ASHRAE standard specified an equation for C s, the equilibrium CO 2 concentration in parts per million (ppm) in a building. If equilibrium is not present (i.e., if the CO 2 concentration has not reached steady state), ventilation rates will be overestimated. The ASHRAE equation is: C s = (G/V o ) + C o, where V o is the outdoor airflow rate per occupant, in units of liters/sec-occupant recommended by ASHRAE for gambling casinos that permit smoking (30 ft 3 /min per occupant, or 15 L/sec per occupant), C o is the measured outdoor air CO 2 concentration (300 ppm) (Trout and Decker, 1995), and G is the conversion factor (5000 ppm/l/sec per occupant). Solving this equation for V o yields: V o = G/(C s - C o ), and substituting the NIOSH-measured CO 2 range of values: V o = 5000/( ), to V o = 5000/( ), yields: 9 L/s-occupant to 40 L/s-occupant (80 cfm/occ). The average CO 2 value reported by Trout and Decker (1995) during the 7:30 pm to 12:30 am busy period ranged from 425 to 650 ppm and averaged 532 ppm, median 525 ppm, with the geometric means on two successive days reading 527 and 597 ppm respectively, for an average geometric mean of 562 ppm for the two days. The 425 ppm value suggests this datum was collected at a time of low occupancy, and the former at a time of high occupancy. In general, for an ASHRAE standard casino, the expected CO 2 concentration for the default occupancy of 120 gamblers per 1000 ft 2, a default ceiling height of 14 and prescribed ventilation rate is C s = (5000/15)+300 = 633 ppm. At V o = 9 L/s-occupant (approximately 18 cfm/occ), this is equivalent to C v = (18 ft 3 /min per occupant)(120 occupants/ ft 3 )(60 min/hour) = 9.3 outdoor air changes per hour, at the highest occupancy. At the lowest level of occupancy, it would be C v = (80 ft 3 /min per occupant)(120 occupants/ ft 3 )(60 min/hour) = 41 outdoor air changes per hour. So this was a well-ventilated casino by ASHRAE standard 62. At the average geometric mean level of C s = 562 ppm, V o = G/(C s - C o ) = 5000/( ) = 19 L/s-occupant (approximately 38 cfm/occ), equivalent to C v = (38 ft 3 /min per occupant)(120 occupants/ ft 3 )(60 min/hour) = 19.5 outdoor air changes per hour, as an overall estimated geometric mean air exchange rate. These values, a range in C v of from 9 to 41 air changes per hour with a measure of central tendency of 19.5 air changes per hour can be used as inputs to the model to estimate SHS-RSP. BALLY S ESTIMATED SECONDHAND SMOKE AREA NICOTINE AND RSP CONCENTRATIONS USING THE HABITUAL SMOKER MODEL (REPACE, 2007). Nicotine: In general, the percentage of gamblers who smoke is less than or equal to the percentage of smokers in the adult population. In 1995, the U.S. average smoking prevalence was about 33%. At that prevalence, the smoker density D HS = (33 habitual smokers /100 occupants)(120 occupants/396 m 3 ) = 10 habitual smokers per 100m 3. What is the expected nicotine concentration at the peak level of occupancy? N peak =21.7 D HS / C v = (21.7)(10/11) = 19.7 µg/m 3. At the minimum level of occupancy, it would be N min =21.7 D HS / C v = (21.7)(10/41) = 5.3 µg/m 3. At the average geometric mean air exchange rate of C v = 19.5 air changes per hour, N ave =21.7 D HS / C v = (21.7)(10/19.5) = 11.1 µg/m 3. By comparison, Trout

14 14 and Decker (1995) measured 8-hour average area nicotine concentrations ranging from 6 to 16 µg/m 3, with an average of 9.8 µg/m 3, a median value of 10 µg/m 3, and a geometric mean of 9.4 µg/m 3. The model best estimate differs from the measured geometric mean by 18%, and from the median value by 11%. Thus the range and best estimate calculated using the model using measured CO 2 rates plus the ASHRAE Standard and average national smoking prevalence yields results that are reasonably close to actual nicotine measurements by NIOSH. Figure 4 shows a plot of the Habitual Smoker model predictions for nicotine for Bally s Casino at ASHRAE standard ventilation and default occupancy. In summary, the Habitual Smoker Model yields predicted nicotine values well into the range of NIOSH s observations in Bally s casino. SHS-RSP: As for Habitual Smoker modeled RSP from secondhand smoke, the predicted range in area concentration is ten times the predicted nicotine concentrations, or 60 to 160 µg/m 3, with a geometric mean of 110 µg/m 3. How do these estimates compare with RSP levels measured in actual U.S. casinos using real-time methods? Repace et al. (2011) summarized the measured SHS-RSP data for 66 U.S. casinos. The figure below is reproduced from that publication:

15 Figure 2 [Repace et al., 2011] shows a range in measured RSP levels in 66 U.S. casinos ranges from about 20 µg/m 3 to 200 µg/m 3, with a median value of 57 µg/m 3, compared to outdoor levels ranging from about 1 µg/m 3 to about 30 µg/m 3, with a median value of 4.5 µg/m 3. The modelestimated value of total RSP for Bally s casino with a 5 µg/m 3 estimated outdoor background RSP level is then 115 µg/m 3, which is approximately at the 85 th percentile for the remaining 66 casinos with smoking, with a range of from 65 to 165 µg/m 3, which is roughly from the 50 th to 15

16 16 the 95 th percentiles measured in real casinos. So the modeled RSP levels are consistent with realistic results. Figure 3 below shows the Rosetta Stone modeled values for plasma and urine cotinine derived from Repace, Al Delaimy, and Bernert (2006) as applied to the values measured by Trout et al for Bally s casino workers whose PBZ nicotine and body fluid cotinine were measured (n=14). Figure 3. Plot of NIOSH-measured plasma and free urine cotinine (Trout and Decker 1995; Trout et al., 1998) versus Rosetta Stone modeled values Repace et al. (2006), showing that the model is in good agreement with the data measured for workers. The Habitual Smoker model (Repace, 2007) can be used to model RSP levels from smoking in Bally s casino. The model assumes a habitual smoker smokes at the rate of 2 cigarettes per hour (Repace and Lowrey, 1980), and spends one-third of the hour smoking. The model posits that the RSP concentration from secondhand smoke is directly proportional to the

17 17 smoker density D HS, and inversely proportional to the air exchange rate C v, and uses a 10:1 RSP/Nicotine ratio for secondhand smoke (Repace, 2007). FIGURE 4. MINIMUM AND MAXIMUM PREDICTIONS OF THE HABITUAL SMOKER MODEL VS. NIOSH AREA MEASUREMENTS ON BALLY S CASINO IN ATLANTIC CITY, NEW JERSEY IN 1995.

18 INHALED LUNG EXPOSURE TO RSP FROM SECONDHAND SMOKE BY BALLY S CASINO WORKERS Now we turn to the issue of secondhand smoke RSP inhalation by the workers. From Table 3, the median PBZ RSP of the Bally s workers is 100 µg/m 3. At a respiration rate of 1 m 3 /hour, a nonsmoking worker would inhale 800 µg of tobacco smoke particles into the lung during the course of an 8-hour work shift. Over the course of a 40-hour work week, 4000 µg of tobacco smoke particles will be inhaled. Figure 5 shows that about 20% of those fine particles, or 800 µg per week will be deposited in the lung, where the solid particulate is subject to removal only by phagocytes and the water and lipid soluble particulate will be absorbed, an thus will remain in the lung for weeks, months or even years. 18 Figure 5. Fractional deposition of inhaled particles in the human respiratory tract (Surgeon General, 2010). This raises the issue of estimation of the inhaled RSP lung exposure from the cotinine dose. The post-shift geometric mean plasma cotinine dose is 1.91 ng/ml. Since SHS-RSP = 10 N, then R 24 = 1667 P F /ρh = (1667)P F /(24) = 69.4 P F = (69.4)(1.91) = 133 µg/m 3, and assuming a 24-hour average respiration rate of 10 m 3 /day, a steady-state particulate load of 1333 µg in the lung for each day of exposure, or 6.7 milligrams per week. This is equivalent to smoking about a half cigarette per week.

19 IN SUMMARY: Properly estimated using the Rosetta Stone model, the mean and median estimated plasma and urine cotinine values for the 14 Bally s workers for which all data was reported differs from the NIOSH-measured values by 10 to 15% for the medians, and 7 to 17% for the means. This good agreement suggests that the Rosetta Stone model is useful for estimating the secondhand smoke doses that nonsmoking workers get from workplace smoking. 2. The Habitual Smoker model predicts area nicotine concentrations ranging from 5 to 20 µg/m 3, mean, 11.1 µg/m 3, compared to NIOSH measured area nicotine concentrations of from 6 to 16 µg/m 3, mean 10 1 µg/m This analysis concludes that based upon NIOSH plasma and urine cotinine data measured in Bally s casino workers, and nicotine in casino air, that the Rosetta Stone and Habitual Smoker models are useful for estimating nonsmoking workers exposure and dose from secondhand smoke exposure, and in analyzing measured data. 4. The paper by James et al. (2008) is flawed due to improper methods of calculation of nicotine and cotinine and failure to recognize that the RSP data reported in the Health Hazard Evaluation Report by Trout and Decker (1995) but not by Trout et al. (1999) in their published paper, was flawed. REFERENCES Centers for Disease Control (2012) Fourth National Report on Human Exposure to Environmental Chemicals, Updated Tables, Tobacco Smoke,. September Hammond SK (1999) Exposure of U.S. Workers to Environmental Tobacco Smoke. Environmental Health Perspectives 107, Supplement 2, May Jaakkola MS, Samet JM. Occupational exposure to environmental tobacco smoke and health risk assessment. Environmental Health Perspectives 107, Suppl. 2: (1999). James HR, Barfield L, Britt JK, and James RC (2008) Occupationl Hazards - Worker Exposure to Secondhand Smoke Evaluating a prediction model. PROFESSIONAL SAFETY SEPTEMBER Kolb S, Bruckner U, Nowak D, Radon K et al (2010) Quantification of ETS exposure in hospitality workers who have never smoked. Environmental Health 2010, 9:49 doi: / x McBride SJ, Ferro AR, Ott WR, Switzer P, Hildemann LM. Investigations of the proximity effect for pollutants in the indoor environment. JESEE 9: (1999). Repace (2013) AIR QUALITY STUDY OF COMMERCE CASINO CARD ROOMS, REPACE ASSOCIATES, INC, FEB. 27, 2013.

20 Repace (2006) EXPERT REPORT ON THE EMPRESS HORSESHOE CASINO. In The Matter of Januszewski et al., v. Horseshoe Hammond, USDC, NDIN, Hammond Div., 2:00CV352JM, March 14, Repace J, Zhang B, Bondy SJ, Benowitz N, Ferrence F. Air Quality, Mortality, and Economic Benefits of a Smoke-Free Workplace Law for Non-Smoking Ontario Bar Workers. Indoor Air 2013; 23: Hedley et al. (2006). Samet JM, Wang SS. Environmental tobacco smoke. Ch. 10, in: Environmental Toxicants, Human Exposures and their Health Effects. M. Lippmann, Ed. John Wiley & Sons, New York, Spengler JD. Buildings operations and ETS exposure. Environmental Health Perspectives 107, Suppl. 2: (1999). Trout D, Decker J, Mueller C, Bernert JT, Pirkle J. (1998) Exposure of Casino Employees to Environmental Tobacco Smoke. JOEM 40, (1998). Trout D. and Decker J. (1995) HETA , Bally s Park Place Casino Hotel Atlantic City, New Jersey. Acknowledgement. I wish to thank Dr. J.T. Bernert for his comments on a draft of this commentary.