Journal of Exposure Analysis and Environmental Epidemiology (2004) 14, 154–163 r 2004 Nature Publishing GroupAll rights reserved 1053-4245/04/$25.00

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Simulation of working exposures to carbon monoxide using EXPOLIS-Milan microenvironment concentration and time-activity data

YURI BRUINEN DE BRUIN,a,b,c OTTO HA¨ NNINEN,b PAOLO CARRER,a MARCO MARONI,a STYLIANOS KEPHALOPOULOS,c GRETA SCOTTO DI MARCOc AND MATTI JANTUNENb aDepartment of Occupational Health, University of Milan, Via San Barnaba 8, 20122 Milan, Italy bKTL, Department of Environmental Health, P.O. Box 95, FIN-70701, Kuopio, Finland cEC Joint Research Centre, Institute for Health and Protection, Via E. Fermi, 21020 (VA), Ispra, Italy

Current air levels have been shown to affect human health. Probabilistic modeling can be used to assess exposure distributions in selected target . Modeling can and should be used to compare exposures in alternative future scenarios to guide society development. Such models, however, must first be validated using existing data for a past situation. This study applied probabilistic modeling to carbon monoxide (CO) exposures using EXPOLIS-Milan data. In the current work, the model performance was evaluated by comparing modeled exposure distributions to observed ones. Model performance was studied in detail in two dimensions; (i) for different averaging times (1, 8 and 24 h) and (ii) using different detail in defining the microenvironments in the model (two, five and 11 microenvironments). (iii) The number of exposure events leading to 8-h guideline exceedance was estimated. Population time activity was modeled using a fractions-of-time approach assuming that some time is spent in each microenvironment used in the model. This approach is best suited for averaging times from 24 h upwards. In this study, we tested how this approach affects results when used for shorter averaging times, 1 and 8 h. Models for each averaging time were run with two, five and 11 microenvironments. The two-microenvironment models underestimated the means and standard deviations (SDs) slightly for all averaging times. The five- and 11-microenvironment models matched the means quite well but underestimated SDs in several cases. For 1- and 24-h averaging times the simulated SDs are slightly smaller than the corresponding observed values. The 8-h model matched the observed exposure levels best. The results show that for CO (i) the modeling approach can be applied for averaging times from 8 to 24 h and as a screening model even to an averaging time of 1 h; (ii) the number of microenvironments affects only weakly the results and in the studied cases only exposure levels below the 80th percentile; (iii) this kind of model can be used to estimate the number of high-exposure events related to adverse health effects. By extrapolation beyond the observed data, it was shown that Milanese office workers may experience adverse health effects caused by CO. Journal of Exposure Analysis and Environmental Epidemiology (2004) 14, 154–163. doi:10.1038/sj.jea.7500308

Keywords: air pollution, carbon monoxide, EXPOLIS, exposure assessment, exposure modeling, time activity.

Introduction similar findings have been reported also for CO. The Air Pollution and Health: a European Approach (APHEA) Carbon monoxide (CO) is one of the air pollutants of which study results from Athens (Greece) showed a significant acute health effects have been studied and described well association between daily mortality and ambient CO (Maroni et al., 1995). Recent epidemiological studies have concentrations (Touloumi et al., 1996). In a Canadian study, shown a significant association between current urban air CO was found to be the strongest predictor of hospitalization pollution levels and premature deaths and other adverse of the elderly due to congestive heart failure in comparison health outcomes. This association seems to be strongest for with sulfur dioxide, nitrogen dioxide and ozone (Burnett fine particulate matter (PM2.5) (e.g., Pope et al., 2002), but et al., 1997). To be able to control air pollution and to reduce these health effects efficiently, modeling methods must be developed to assess population exposures in current and 1. Address all correspondence to: Dr. Yuri Bruinen de Bruin, c/o Dr. alternative future scenarios (e.g., Jantunen et al., 1998). The Kephalopoulos, Stelios, European Commission - Joint Research Centre, World Health Organization (WHO, 2000a) recognizes Institute for Health & Consumer Protection, Physical & Chemical modeling as one of the major methodologies for assessing Exposure Unit, Sector Indoor Air Quality, Human Exposure Modelling air quality, population exposures and related health effects. and Urban Noise, Via E. Fermi, 21020 Ispra (VA), Italy. Tel.: +39-0332- 789871. Fax: +39-0332-785867. In many circumstances, air quality and exposure measure- E-mail: [email protected], [email protected] ments are insufficient, impractical or impossible to conduct. Received 12 December 2002; accepted 28 July 2003 In particular, probabilistic techniques allow for building Simulation of CO exposures in Milan Bruinen de Bruin et al. relatively simple models for assessing exposure distributions and work district, demographic properties and use of for large target populations (Jantunen et al., 1998). residential gas cooking. The total number of microenviron- At least two research groups in the United States have ments was 13, simulating homes with and without gas stoves developed probabilistic simulation models for carbon mon- separately. During this evaluation, both the 8- and 1-h oxide exposures using the so-called Denver 1982–1983 CO- simulated maximum exposures overestimated levels at the data consisting of 336 paired days of observations. Ott et al. lower percentiles (Z40% at 5th percentile) and under- (1988) developed the SHAPE model using Denver micro- estimated levels at higher percentiles (about 30% at 95th environment concentration data from day 1 together with percentile). At the low end (below 5th percentile) the time-activity data for day 2 to predict day 2 exposures. In overestimation was, however, marginal (o1.6 p.p.m.) in total, 22 microenvironments were identified from the time- both gas-stove categories. The median values were slightly activity data. Day 1 ambient hourly background levels were underestimated (3–4%) for the 1-h exposures and over- first subtracted from the microenvironment concentrations estimated for the 8-h exposures (homes with gas stoves 19% and then day 2 background concentrations were added in the and without 7%). Law et al. suggested several explanations simulation. Ott et al. (1988) found that the mean simulated for the differences: (i) only two of all known indoor sources maximum running 8-h levels compared well to the observed were included in the model; (ii) autocorrelation of population levels (observed vs. simulated, 4.9 vs. 4.8 p.p.m.). The time activity were not modeled; (iii) the time-activity exposure variation, however, was underestimated (observed database included two cities in addition to Denver and (iv) vs. simulated SD, 4.2 vs. 2.4 p.p.m.). The simulated 1-h empirical constants and mass-balance model parameters used maximum exposure distribution was similar to the observed in the model may have led to underestimation of high data (observed vs. simulated mean 10.2 and 10.6 p.p.m., SD exposures. 8.9 and 6.0 p.p.m., respectively). Ott et al. suggested that the Kruize et al. (2003) developed an Excel-based simulation smaller variability of the simulated exposures was caused framework for building probabilistic simulation models as a by (i) the Monte Carlo sampling from observed micro- partoftheEXPOLIS (Air Pollution Exposure Distributions environment concentration distributions, (ii) autocorrelation of Adult Urban Populations in Europe) study. They of microenvironment concentrations not taken into demonstrated the use of the framework by simulating the account in the model and (iii) repetition of personal daily PM2.5 exposure distributions of adults in four EXPOLIS activities. cities (Helsinki, Basle, Prague and Athens) and the PM10 Johnson et al. (1992) tested the probabilistic National exposures of the whole Dutch population. The simulation Ambient Air Quality Standards (NAAQS) Exposure Model framework was based on a microenvironment approach applied to carbon monoxide (pNEM/CO) using the same suggested by Duan (1982), Letz et al. (1984) and Ryan et al. Denver data as Ott et al. (1988). Data covering a 4-month (1986). Models using this approach are, in principle, period were analyzed to determine short-term daily max- applicable for any averaging time, any number of micro- imum CO exposures on the basis of the presence of a gas environments or any air pollutant. As the framework was stove in the subjects’ resident. The model inputs accounted built so that all microenvironments entered in a model are for autocorrelation in the people’s daily activities and for always sampled, the framework is best suited for models ambient temporal and spatial correlations. Specific algo- targeting averaging times from 24 h to lifetime. The mean rithms estimated blood COHb levels according to both PM2.5 exposures in the EXPOLIS cities were predicted with activity and physiological characteristics. For both gas-stove better than 20% accuracy. The variance, however, was categories, distributions of measured and simulated 1-h overestimated in three out of four cities. For each simulation exposures agreed generally well, but simulated exposures run, 2000 iterations with Latin hypercube sampling2 were were overestimated above the 99th percentile. In contrast, 8- selected without truncation of the lognormal concentration h distributions did not match that well; below the 90th distributions. High values sampled from parametric lognor- percentiles simulated exposures were overestimated, while at mal distribution were suggested to be one of the causes for higher percentiles exposures were underestimated. The the overestimation of the variance. authors explained the latter results due to a lack of Ha¨ nninen et al. (2003) used the EXPOLIS simulation autocorrelation over time to adequately represent extended framework to built PM2.5 simulation models for Helsinki. series of short-term exposures in the upper end of the They used EXPOLIS-Helsinki time-activity and microenvi- exposure distribution. Increasing the autocorrelation in the algorithms determining the sequence of outdoor CO concentrations for each microenvironment and actual use 2. In Latin hypercube sampling, the parametric probability distribution of gas stoves was suggested. is divided into slices of equal probability according to the selected number of iterations. Then one sample is drawn randomly from each In a later evaluation of the NAAQS pNEM/CO exposure slice. Compared to the standard Monte Carlo sampling, the generated model using the same Denver data, Law et al. (1997) divided random number sequence better represents the full range of the the target population into 84 cohorts according to the home probability distribution.

Journal of Exposure Analysis and Environmental Epidemiology (2004) 14(2) 155 Bruinen de Bruin et al. Simulation of CO exposures in Milan ronment concentrations measured separately as inputs to simplified approach for the 1- and 8-h averaging times for simulate 48-h PM2.5 exposure distributions. The models were which CO guidelines have been set. built using two or three indoor microenvironments. Two Varying numbers of microenvironments have been used in models targeted the whole adult population including different simulation models (Ott et al., 1988; Law et al., environmental tobacco smoke (ETS) exposures and two 1997; Ha¨ nninen and Jantunen, 2003; Ha¨ nninen et al., 2003; non-ETS exposed adults only. The overall fits between the Kruize et al., 2003). The second goal of this work was to test observed and simulated exposure distributions were reason- how the number of microenvironments in the model affects ably good also for models including ETS exposures, but the the agreement between the modeled and measured data for models excluding ETS exposures matched the corresponding different averaging times. observed exposure distributions more closely. The third goal in this work is to apply the simulation

Ha¨ nninen and Jantunen (2003) modeled PM2.5 concen- technique to assess guideline exceedance. The number of 8-h trations using fixed-station measurements together with exposure events exceeding the guideline level (9 p.p.m.) and infiltration factors and indoor sources analyzed from the potentially causing the COHb to raise to levels with known EXPOLIS microenvironment data as parametric probabil- health consequences (2.5%) is estimated. istic inputs. Four microenvironments were defined and time Ambient fixed site monitoring has been suggested to rather activities modeled for the working and nonworking sub- poorly estimate exposure distributions of urban populations populations separately. Four models with 2000 iterations and (Cortese and Spengler, 1976; Akland et al., 1985; Alm et al., Latin hypercube sampling were run using different ambient 1994; Vellopoulou and Ashmore, 1998; Bruinen de Bruin inputs. Models 1–3, which targeted the non-ETS exposed et al., 2003). In this work, we compare the 8- and 1-h adult subpopulation in Helsinki, reconstructed the popula- ambient concentrations with the exposure distributions. tion exposure distribution very well. Best results were obtained using 1-h ambient concentrations to describe ambient concentrations for all microenvironments. Model 4 Methods added the ETS sources; results of this model underestimated the highest exposure levels. EXPOLIS is a European population based exposure study Health effects of CO exposure have been extensively originally conducted in six European cities in 1996–1998. studied. Binding of CO with hemoglobin forms carboxy- Personal exposures of several major air pollutants (CO, hemoglobin (COHb), reducing the capacity of blood to carry PM2.5, nitrogen dioxide, and 30 volatile organic compounds) oxygen (Maroni et al., 1995). The binding of CO depends and simultaneous microenvironment concentrations were slightly on the subject’s physical activity and consequent measured and 48-h time-activity diaries and general exposure breathing rate. At prolonged exposures to very high questionnaires were collected in Athens (Greece), Basle concentrations, CO causes acute intoxication and death (Switzerland), Grenoble (France), Helsinki (Finland), Milan when blood levels of COHb exceed 50% (Alberts, 1994). In (Italy), and Prague (Czech Republic). Similar sampling order to protect nonsmoking, middle aged and elderly protocols and questionnaires were used in all centers population groups for intoxication, and to protect the fetuses (Jantunen et al., 1998). of nonsmoking pregnant women from problematic hypoxis The EXPOLIS simulation framework used in this study is effects, WHO (2000a, b) stated that a COHb level of 2.5% an Excel-based environment supporting building of prob- should not be exceeded. In order not to exceed this level, for abilistic simulation models to assess population exposure a normal subject engaged in light or moderate exercise, WHO distributions. Framework models generate distributions of set a series of guidelines for different averaging times: personal exposures for a defined averaging time (E)by 90 p.p.m. (15 min), 50 p.p.m. (30 min), 25 p.p.m. (1 h) and summing partial exposures over all microenvironments as 10 p.p.m. (8 h) (WHO, 2000a, b). The maximum average 8-h presented below: guideline level set by United States Environmental Protection Xn  Agency (EPA) is 9 p.p.m., not to be exceeded more than E ¼ fiÂCi ð1Þ once per year (EPA, 1995). i¼1

The time-activity modeling approach used in the EX- in which fi is the fraction of time spent in microenvironment i, POLIS simulation framework assumes that some time is Ci the concentration in microenvironment i (p.p.m.) and n spent in each microenvironment defined (Kruize et al., 2003). the number of microenvironments in the model. For averaging times upfrom 24 h, this assumptionis The fraction of time spent in each microenvironment is reasonable, but becomes questionable for shorter averaging described with a beta distribution with zero as a lowest limit times, like 8 h, especially when using very detailed micro- and 1 as an upper limit. The distributions were fitted using environment separation. When even shorter averaging times, the method of matching moments (using beta-distribution like 1 h, are used, this assumption is clearly wrong. The first with equal values for the two first moments, mean and SD). goal of our study was to quantify model errors caused by this Microenvironment concentrations are assumed to be lognor-

156 Journal of Exposure Analysis and Environmental Epidemiology (2004) 14(2) Simulation of CO exposures in Milan Bruinen de Bruin et al.

mally distributed. Thus, fractions of time (fi)aresampled ‘‘traffic’’ microenvironment and ‘‘home outdoor’’, ‘‘work from beta distributions and concentrations (Ci)from outdoor’’ and ‘‘other outdoor’’ into the ‘‘outdoor’’ micro- lognormal distributions. This calculation is repeated a user- environment; diary microenvironments ‘‘home indoor’’, specified number of times, and the generated group of ‘‘work indoor’’ and ‘‘other indoor’’ were used unchanged. simulated personal exposure levels represents the distribution To create the inputs for the 8-h simulation model, the of exposures in the target population. The simulation mean of the first 8 h of a person’s exposure was calculated. framework is described in more detail by Kruize et al. (2003). Then the next 15-min value was added and the first one was The original data, stored in the EXPOLIS database dropped, creating a new 8-h average. This process was containing data from all the EXPOLIS cities (Ha¨ nninen repeated until the last complete 8-h average was calculated. et al., 2002), consist of 48-h time series of 1-min personal CO These observed exposures were later used in evaluating the exposures obtained from 45 Milanese subjects. The simulation models. The average concentrations, time frac- EXPOLIS-Milan CO data, described in more detail by tions spent in each microenvironment, and data for 24- and Bruinen de Bruin et al. (2003), were used to create the inputs 1-h averaging times were calculated using the same for the simulation models. Data consisted of concentrations procedure. The observed mean and SD values of the input assigned to microenvironments according to the 15-min time- parameters were used to fit the parametric input distributions activity diaries. Multiple entries, less than 0.5% of the total for the models. The EXPOLIS simulation framework uses a diary data, were filtered out using Access count function. two-parameter lognormal distribution for concentrations and Nine simulation models were run using three different two-parameter beta distribution for time fractions. The target-averaging times (24, 8 and 1 h). Three simulation simulation inputs are presented in Table 1a and b. models were run for each averaging time, each with different The simulations were run with Excel version 97 (Micro- number of microenvironments (11, five and two). The 11- soft, Seattle, WA, USA) and @Risk add-on software version microenvironment model used the EXPOLIS time-activity 3.5 (Palisade, Newfield, NY, USA) using Latin hypercube diary microenvironments directly. The two-microenviron- sampling, 500 iterations, and pseudorandom number seed 1. ment model included only ‘home indoor’ and ‘work indoor’ The model outputs are presented as main percentiles and as microenvironments ignoring time activity and concentrations population means and SDs. The model outputs are in other places. The five-microenvironment model combined compared with the corresponding observed exposure dis- some original diary microenvironments into aggregates. The tributions. diary microenvironments ‘‘walk/bike’’, ‘‘motorbike’’, ‘‘car/ The two-microenvironment model targeting 8-h exposures taxi’’, ‘‘bus/tram’’ and ‘‘train/metro’’ were combined into the was repeated with 400,000 iterations to assess the occurrence

Ta bl e 1 . EXPOLIS simulation input parameters (mean7standard deviation).

Indoor Outdoor Traffic Avg. Number time (h) of mE’s Home Work Other Home Work Other Walking Train Bus Motorbike Car

(a) Beta distributed fraction of time spent in each microenvironment (mE) 24 2 0.5570.10 0.3170.07 5 0.5570.10 0.3170.07 0.0570.07 0.0270.03 0.0870.03 11 0.5570.10 0.3170.07 0.0570.07 0.0070.01 0.0170.01 0.0170.02 0.0370.02 0.0170.01 0.0370.02 0.0070.01 0.0270.03 8 2 0.5970.36 0.2770.29 5 0.5970.36 0.2770.29 0.0570.11 0.0270.04 0.0870.07 11 0.5970.36 0.2770.29 0.0570.11 0.0070.02 0.0070.02 0.0170.03 0.0270.04 0.0170.02 0.0270.04 0.0070.01 0.0270.04 1 2 0.5670.48 0.2970.42 5 0.5670.48 0.2970.42 0.0570.18 0.0270.09 0.0870.18 11 0.5670.48 0.2970.42 0.0570.18 0.0070.03 0.0170.05 0.0170.07 0.0270.09 0.0170.05 0.0270.09 0.0070.02 0.0270.10

(b) Lognormal distributed microenvironment (mE) concentration data (p.p.m.) 24 2 1.971.1 1.971.0 5 1.971.1 1.971.0 2.772.0 2.671.7 4.071.9 11 1.971.1 1.971.0 2.772.0 2.671.7 2.271.3 2.772.3 2.871.8 3.171.3 4.071.6 4.171.6 5.372.7 8 2 1.971.2 2.071.5 5 1.971.2 2.071.5 2.672.0 2.471.9 3.972.4 11 1.971.2 2.071.5 2.672.0 2.171.5 2.371.5 2.572.1 2.872.0 3.271.4 3.971.9 4.771.8 5.273.0 1 2 1.971.3 2.071.7 5 1.971.3 2.071.7 2.772.3 2.472.0 3.872.9 11 1.971.3 2.071.7 2.772.3 2.371.4 2.371.8 2.572.27 2.972.4 3.271.5 3.972.0 4.672.2 5.473.5

Journal of Exposure Analysis and Environmental Epidemiology (2004) 14(2) 157 Bruinen de Bruin et al. Simulation of CO exposures in Milan

of the highest 8-h exposure events among the Milanese office Table 2 . Means and Standard Deviations (SDs) (a) and relative deviations workers. The cumulative distribution of 8-h exposure event (b) of the nine models. levels was simulated upto the 99.999th percentile. The simulated distribution of 8-h exposure levels was compared (a) Mean (AM) and SD of different observed (Obs) and simulated (Sim) with EPA (1995) and WHO (2000a, b) ambient air exposure levels (p.p.m.) guidelines. The simulated exposure levels were converted Sim into predicted percentage of COHb in the blood using Number of mEs Running Running Running 1-h transformation factor based on the Coburn–Foster–Kane in the model 24-h average 8-h average average equation (EPA, 1991). The conversion is based on a linear 2 1.970.8 1.971.1 1.971.3 relationshipbetween percentage of COHb in blood and CO 5 2.170.6 2.271.1 2.271.6 concentration in the breathing air (WHO, 2000a). Typically, 11 2.170.7 2.171.0 2.271.5 office work involves light level of exercise, which corresponds to an average breathing rate of 20 l/min. Obs 2.170.9 2.171.1 2.171.7 The theoretical number of target population subjects (b) Relative deviations (%) of simulated mean and SD (in parentheses) affected was estimated assuming 500,000 office workers in values from the observed ones Milan. Using Eq. (2), we calculated that these workers experience 17,504,500,000 8-h exposure events (EE) Sim vs. Obs (%) annually. Number of mEs Running Running Running EE ¼ððdÂhÂqÞÀiÞÂw ð2Þ in the model 24-h average 8-h average 1-h average in which EE is the number of annual 8-h exposure events in 2 À10 (À16) À7(À3) À11 (À24) the target population, d the number of days/year per subject 50(À25) +6 (À3) +7 (À4) 11 À1(À23) +2 (À9) +3 (À9) (365), h the number of hours/day per subject (24), q the number of quarters/hour per subject (4), i the number of mEs=microenvironments. incomplete 8-h exposure events (8  4À1 ¼ 31) and w the estimated number of office workers in Milan (500,000). are slightly smaller than the observed values. The relative The assessment method of highest exposure events is deviations are shown in Table 2b. The simulated variance of outlined in Figure 3. Using the simulated percentiles for the exposures was underestimated in all models (up to 25%). exceedances of the concentration guideline level (9 p.p.m.) or The 8- and 1-h models slightly overestimated the means and COHb level (2.5%), the number of potentially affected produced the most accurate estimates of the variance. In individuals was calculated. The minimum estimate was general, models targeting 8-h exposures matched the calculated assuming that all high-exposure events are observed exposure levels best. accumulated to the same individuals; the maximum estimate Observed and simulated 8-h exposure distributions are was based on the assumption that the high exposure events graphically compared in Figure 1. Overall, the simulations are evenly distributed among the whole target population. match the observed values rather well. Below the 5th The observed personal distributions for 1- and 8-h percentile, all simulated levels overestimate the observed exposures were compared with simulated five-microenviron- ones; the highest overestimation at the 5th percentile is 29% ment model and the distribution of corresponding ambient for both the five- and 11-microenvironment models. On the fixed station data for the 1-year period. The ambient data absolute scale, the differences range, however, only between used were average concentrations of six fixed-site city 0.2 and 0.4 p.p.m. In the higher percentiles, all models monitors (network operated by Provincia di Milano/Settore converge towards the observed values. Ecologia). The US EPA 8-h National Ambient Air Quality Standards (NAAQS) of 9 p.p.m. was exceeded in one out of 1000 cases (at the 99.9 percentile) (Figure 1). When these Results 21,661,819 exceedances are being translated to an estimate of a theoretical minimum number of office workers affected, ThemeansandSDsoftheninemodelsareshowninTable2a using the assumption that high exposure events are together with corresponding observed exposures. The simu- accumulated to the same persons, 619 subjects (out of an lated means of all models are almost identical as could be assumed number of 500,000 office workers in the whole city) expected. Only the three two-microenvironment models would be continuously exposed to concentrations higher than underestimate the means slightly, probably because some 9 p.p.m. (Figure 3). When exceedances are assumed to be high-concentration microenvironments are missing from evenly distributed among the whole population and assuming these simplest models. The variation increases for shorter that these exceedances are occurring in sequences lasting on averaging times as should be expected. The simulated SDs average 12 h, this translates to 2.7 days/year during which

158 Journal of Exposure Analysis and Environmental Epidemiology (2004) 14(2) Simulation of CO exposures in Milan Bruinen de Bruin et al.

30

20 WHO guideline: 2.5% COHb 2.5

10 EPA guideline: 9 ppm 9 8 1.0 7 6 5 4 0.5

[ppm]

lood 3

in b 2

% COHb

Exposure level 1 0.9 0.8 0.1 0.7 0.6 Observed 0.5 Sim. (2 µEs) + extrapolation 0.4 Simulated (5 µEs) 0.05 0.3 Simulated (11 µEs)

0.2 0.5 1122551010 20 30 40 50 60 70 80 90 95 98 99 99.5 99.9 99.99 99.999 Percentage of population 8-hour exposures Figure 1. Comparison of simulation results from models with two, five and 11 microenvironments to the observed 8-h exposures. The two- microenvironment model was repeated with 400,000 iterations to extrapolate the exposure distribution up to the guideline levels set by WHO and EPA to estimate the potential number of exceedances. each one of the 500,000 office workers is exposed to levels observed exposure levels. This is a rather remarkable result exceeding the guideline (Figure 3). since the fraction of time spent in many microenvironments Exceedance of the maximum 8-h concentration (20 p.p.m.) during the 1-h exposure events is frequently zero. The model corresponding to the 2.5% of COHb in blood, occurs at the assumes that in each microenvironment at least some time is 99.999 percentile (1 out of 100,000) (Figure 1). Using the spent, which is incorrect for the short averaging times. This similar approach as described above, the theoretical mini- causes the beta fit, with 0 and 1 as minimum and maximum, mum and maximum estimated numbers of persons exceeding respectively, to move leftwards and causes the mode to be the recommendation not to experience more than 2.5% of misplaced compared to the observed data. To handle this COHb in blood are 8 and 262,568 persons, respectively specifically in the model, the probability peak at zero should (Figure 3). be separately parameterized or actual time-activity diaries The observed and simulated 8- and 1-h exposure should be used. distributions are compared with the corresponding ambient distributions in Figures 2a and b. Respective percentiles are Number of Microenvironments in the Model tabulated in Table 3a and b. In the lower percentiles, the Previous data analyses (Bruinen de Bruin et al., 2003) ambient distribution is between the simulated (higher) and showed that, on an average, 48% of the daily total exposure observed (lower) exposure distributions. In the higher of Milanese office workers was derived from time spent at percentiles, the ambient concentration distributions measured home, 27% from time spent in office and about 6% from in Milan are very close to the observed exposure levels. At ‘other’ indoor microenvironments. Time spent in outdoor the 95th percentile, the relative difference between the microenvironments and traffic contributed about 2 and 16%, ambient and observed exposure distributions are 10 and respectively, to the total daily population’s exposure. 6% for the 8- and 1-h averaging time, respectively. On an In our models, the difference between the two-, five- and absolute scale, these differences are 0.4 and 0.3 p.p.m., 11-microenvironmental models in the higher percentiles of respectively. simulated exposure levels was small. The effect of leaving microenvironments out of a model depends naturally on the concentration levels in the ignored microenvironments in Discussion relation to the microenvironments taken into the model. In our work, exposure distributions of the Milanese working-in- Applicability of the Framework for Averaging Times below office population were satisfactorily simulated using only the 24 h two microenvironments; home and work. Although these Results showed that the simulated exposure levels match the two microenvironments contribute more than 75% to the observed ones reasonably well for all averaging times. Even total daily exposures (Bruinen de Bruin et al., 2003), when the shortest 1-h averaging time resulted in a good match with alternative exposure scenarios are being compared, however,

Journal of Exposure Analysis and Environmental Epidemiology (2004) 14(2) 159 Bruinen de Bruin et al. Simulation of CO exposures in Milan

10 The simulation models calculated the average exposure a 9 8 8-hour 7 levels by random sampling from the microenvironment 6 5 concentrations. This would mean that when a smoker’s 4 exposure was simulated, it is not likely that sampled 3 concentrations of all microenvironments are consistently 2 higher than for a nonsmoker. The fact that in our simulations the autocorrelations between the different microenvironments 1 0.9 were ignored was probably the main reason for the under- 0.8 0.7 0.6 estimation of the exposure variance. 0.5 0.4

Exposure levels [ppm] 0.3 Statistical Tests Observed 0.2 Simulated In the previous work published by Ha¨ nninen et al. (2003), Ambient the agreement of simulated distributions with the observed

0.1 exposure was tested for statistical significance using: the 0.5 1122551010 20 30 40 50 60 70 80 90 95 98 99 99.5 t-test, the Wilcoxon rank-sum test, and the Kolmogorov– Percentage of population Smirnov test. The number of iterations used in the simulation influences the statistical significance; the more iterations are b 10 9 used, the more likely it is to find statistically significant 8 1-hour 7 6 differences between the compared distributions. Thus, we did 5 4 not include such tests in this work. 3

2 Number of Iterations Several models built using the EXPOLIS simulation frame-

1 work (Ha¨ nninen et al., 2003; Kruize et al., 2003) showed 0.9 0.8 0.7 that using 2000 iterations with Latin hypercube sampling 0.6 0.5 occasionally led to unreasonably large values when sampling 0.4 randomly from the highest stratification, where concentra- Exposure levels [ppm] 0.3 Observed tions approach infinity. Although the lognormal distribution 0.2 Simulated Ambient has no upper limit, real-life concentrations do. To prevent unrealistic values, the authors suggested truncation of 0.1 0.5 1122551010 20 30 40 50 60 70 80 90 95 98 99 99.5 parametric lognormal distributions. Percentage of population Ott (1990) studied the nature of lognormally distributed concentrations. Ott stated that towards the high end of a Figure 2. Distributions of observed (a) 8-h exposure levels and (b) 1-h exposure levels in comparison to corresponding simulated (five- logarithmic distribution, the concentration levels fall below microenvironments) exposures and ambient levels at fixed monitoring those predicted by the parametric lognormal distribution. stations. The diluted concentrations in an environment can never exceed the emission concentrations at the source. all microenvionments and activities involved should also be In the current nine model runs, 500 iterations were used to incorporated in the model. If not, the differences between lower the probability of sampling from the extreme high end scenarios, which could be small, do not represent real-life of the concentration tail. All models were also run with 2000 conditions. iterations and the resulting distributions were compared (data not shown). The higher iteration number produced some- Effect of Ignoring Autocorrelations what higher maximum exposure levels, but the other In reality, the microenvironment concentrations are corre- distribution parameters were similar. The unrealistically high lated because of meteorological (e.g., regional atmospheric concentration levels reported by Ha¨ nninen et al. (2003) were stagnation) and behavioral factors (e.g., smoking) (Klepeis, not found in any of the model runs performed in this work, 1999). Therefore, the time series of ambient concentrations neither using 500 nor 2000 iterations. are more or less autocorrelated. During a high pollution day, the ambient components of indoor concentrations will also be Comparison of Exposures and Ambient Concentrations high; this introduces the autocorrelation of the ambient Bruinen de Bruin et al. (2003) showed that ambient CO component into the indoor level as well. On the other hand, levels in Milan were no good predictors of simultaneous behavioral factors have similar effects; for example, a smoker personal exposures for short averaging times. In this work, is probably exposed to higher concentrations in many we wanted to compare ambient concentration distributions microenvironments than a nonsmoker is. with simulated and observed exposure distributions.

160 Journal of Exposure Analysis and Environmental Epidemiology (2004) 14(2) Simulation of CO exposures in Milan Bruinen de Bruin et al.

EXCEEDANCES PER 400 000 EXPOSURE EVENTS

9 ppm (USEPA, 1995): 495 2.5% COHb (WHO, 2000a) 20 ppm: 6

: 400 000 : 400 000

Fraction: 0.0012375 Fraction: 0.000015

* 17 504 500 000 * 17 504 500 000

Accumulated 21 661 818.75 262 567.5 exceedances 8h exceeding exposure events in Milan 8h exceeding exposure events in Milan

Exceedances evenly : 35009 : 35009 distributed 618.75 subjects (=minimum) 7.5 subjects (=minimum) among population

21 661 818.75 subjects (=maximum) 262 567.5 subjects (=maximum)

: 500 000 : 500 000

43.3 exceedances per subject 52.5% of population exceeds once per year 2.5% COHb in blood.

: 16 (assuming that exceedances last maximum 4 hours out of each 8 hour period translates that the maximum duration is 12 hours. With 15-min increment this means 16 running 8-h averages with 12h duration)

43 days per year per subject 2.7 days per year per subject

Figure 3. Method of assessment of highest exposure events in Milan using the two-microenvironment model (repeated with 400,000 iterations) targeting running 8-h exposure.

Ta bl e 3 . Observed (obs) and simulated (sim) (a) 8-h exposures and (b) 1-h Ambient concentration distributions based on 48- and 24- exposures modeled using five microenvironments compared to the h averaging times match corresponding exposure distribu- distribution of their respective ambient levels (p.p.m.). tions rather well (data not shown). At the higher percentiles, the ambient levels are higher than the 48- and 24-h (a) Observed (obs) and simulated (sim) 8-h exposures modeled using five exposures. The concentration differences, however, remain microenvironments compared to the distribution of 8-h ambient levels (p.p.m.) below 1.5 p.p.m. The good match of the 1-h distributions contrasted our Percentile Running 8-h obs Running 8-h sim Average 8-h ambient expectations. Recent personal exposure literature has sug- 5 0.7 0.9 0.8 gested that the use of fixed-site monitoring data does not 10 0.9 1.1 0.9 necessarily result in reliable exposure estimates, especially for 25 1.2 1.4 1.2 short averaging times (Cortese and Spengler, 1976; Akland 50 1.8 2.0 1.7 et al., 1985; Alm et al., 1994; Vellopoulou and Ashmore, 75 2.7 2.7 2.6 90 3.5 3.4 3.8 1998). Although the variation of the 1-h fixed monitoring 95 4.0 4.2 4.4 data is lower than the variation of the population exposures, the overall match between these two variables is good. A (b) Observed (obs) and simulated (sim) 1-h exposures modeled using five similar observation is reported by Johnson et al. (1992). microenvironments compared to the distribution of 1-h ambient levels Fixed-site stations are often situated in traffic-oriented (p.p.m.) locations near high-traffic streets representing high ambient Percentile Running 1-h obs Running 1-h sim Average 1-h ambient CO levels. On the other hand, ambient measurements do not include any indoor sources. How well these opposing factors 5 0.4 0.7 0.5 10 0.7 0.8 0.7 cancel each other depends on how the monitors are situated 25 1.0 1.2 1.0 and the frequency of significant sources in each city. A good 50 1.6 1.8 1.6 matchinonecity,suchasobservedinMilan,impliesno 75 2.7 2.8 2.6 generality and is no indication that the match would be good 90 4.1 4.2 3.9 in another city. How well fixed-site monitoring data can 95 5.3 5.0 5.0 represent indoor concentrations needs to be determined case

Journal of Exposure Analysis and Environmental Epidemiology (2004) 14(2) 161 Bruinen de Bruin et al. Simulation of CO exposures in Milan by case. Ha¨ nninen and Jantunen (2003) calculated micro- experience harmful effects caused by CO exposures every environment concentrations for PM2.5 by multiplication of year. Some of the harmful effects known to be caused by CO the infiltration factor with sampled ambient concentration include headache, impaired vigilance, decreased exercise from a selected macroenvironment. The additional concen- capacity; and angina with coronary artery disease (Alberts, trations caused by the various local (indoor) sources were 1994). These symptoms definitely have an impact on then added. Partial exposures in each microenvironment were at work and thus potentially have also calculated by multiplying the microenvironment concentra- economical impacts. tions by the fractions of time spent in that microenvironment for each simulated imaginary population member. To further Conclusions investigate the potential use of the ambient data, we recommend additional modeling of indoor CO concentra- A probabilistic exposure simulation model was applied to the tions using fixed-station measurements, as demonstrated by Milanese office worker population. Model sensitivity to the Law et al. (1997), and infiltration factor and parameteriza- selected averaging time and number of microenvironments tion of indoor sources (gas cooking and ETS) as demon- defined in the model was tested by calculating nine models for strated for fine particles by Ha¨ nninen and Jantunen (2003). each combination of averaging times (24, 8 and 1 h), and In our case, however, the geographic distribution of the number of microenvironments (two, five and 11). The model limited number of locations of indoor microenvironments results were then used to assess the potential number of visited by our relatively few subjects around the fixed-site exceedances of health related guideline levels (9 p.p.m. of CO monitoring stations, is not expected to result in any useful in air for 8 h and 2.5% COHb in blood). statistical evaluation. Our conclusions are as follows:

Exceedance of the Health-based Guidelines (1) The framework was demonstrated to be applicable for Exposure events exceeding the WHO ambient 8-h guideline exposure averaging times from 8 to 24 h. level (9 p.p.m.) and recommendation that a COHb level of (2) The differences between models with different number 2.5% should not be exceeded, were simulated to demonstrate of microenvironments were relatively small and affected the use of the simulation framework as a tool for health only exposure levels below the 80th percentile. protection purposes. To highlight the potency of the (3) There seems to be a risk of exceedance of health-related modeling framework, the quantified exceedances were exposure levels caused by the current ambient and translated to a theoretical minimum and maximum number microenvironment levels of CO in Milan. This modeling of office workers affected. The range generated by this result is, however, a result of extrapolation beyond the approach is very wide, but it seems reasonable to assume that limited observed data using the probabilistic modeling the true number of affected individuals is captured by these technique based on assumption of lognormality of the limits. In reality, due to residential and workplace locations, concentration distributions in the microenvironments. commuting and other near-field sources, some individuals are likely to experience more exposure exceedances than others, and many none. Acknowledgments Health risks due to exposures to multiple air pollutants are expected to concern more people than our lowest estimate of This work has been supported by EC Environment and number of office workers experiencing exceedance of the CO Climate 1994–1998 Programme Contract No. ENV4-CT96- guideline. The simulation of the exceedance of the guideline 0202 (DG 12-DTEE) and C.E. Ispra No. 18161-2001-07 concerns only our target population being office workers. It F1ED ISP IT. can be assumed that office workers are among the subpopulations experiencing lowest CO exposures. The References remaining 25% of the Milanese adult working population include individuals who do not work in offices like policemen, Akland G.G., Hartwell T.D., Johnson T.R., and Whitmore R.W. Measuring taxi drivers, street workers (exposed to traffic emissions), human exposure to carbon monoxide in Washington, DC, and Denver, Colorado, during the winter of 1982–83. Environ Sci Technol 1985: 19: 911– personnel working in bars and restaurants (with high ETS 918. exposures), etc. These subpopulations probably experience Alberts W.M. Indoor air pollution: NO, NO2,COandCO2. JAllergyClin higher exposures than office workers do. To use the Immunol 1994: 94: 289–295. Alm S., Reponen K., Mukala K., Pasanen P., Tuomisto J., and Jantunen M. framework for exposure modeling of different subpopula- Personal exposures of pre-school children to carbon monoxide: roles of tions, additional exposure data could and should be ambient air quality and gas stoves. Atmos Environ 1994: 28: 3577–3580. generated. Bruinen de Bruin Y., Carrer P., Jantunen M., Ha¨ nninen O., Scotto di Marco G., Kephalopoulos S., Cavallo D., and Maroni M. Personal carbon monoxide According to the model results presented in this work, it exposure levels; contribution of local sources to exposures and microenviron- seems likely that a fraction of the Milanese office workers mental concentrations in Milan. 2003, submitted.

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