Although ambient particulate matter has been associated with a range of health outcomes, the health risks for individuals depend in part on their daily activities. Information about particle mass concentrations and size distributions in indoor and outdoor microenvironments can help identify high-risk individuals and the significant contributors to personal exposure. To address these issues in an urban setting, we measured particle count concentrations in four size ranges and particulate matter [is less than or equal to] 10 [micro]m ([PM.sub.10]) concentrations outdoors and in seven indoor microenvironments in Boston, Massachusetts. Particle counts and [PM.sub.10] concentrations were continuously)measured with two light-scattering devices. Because of the autocorrelation between sequential measurements, we used linear mixed effects models with an AR-1 autoregressive correlation structure to evaluate whether differences between microenvironments were statistically significant. In general, larger particles were elevated in the vicinity of significant human activity, and smaller particles were elevated in the vicinity of combustion sources, with indoor [PM.sub.10] concentrations significantly higher than the outdoors on buses and trolleys. Statistical models demonstrated significant variability among some indoor microenvironments, with greater variability for smaller particles. These findings imply that personal exposures can depend on activity patterns and that microenvironmental concentration information can improve the accuracy of personal exposure estimation. Key words. air pollution, exposure assessment, indoor air, microenvironments, particulate matter. Environ Health Perspect 108:1051-1057 (2000). [Online 16 October 2000]
http://ehpnet1.niehs.nih.gov/docs/2000/108p1051-1057levy/abstract.html
Recent epidemiologic studies have established relationships between particulate matter and both morbidity and premature mortality and presented evidence that fine particulate matter (the fraction of particulate matter [is less than] 2.5 [micro]m in diameter) may be responsible for these adverse outcomes (1-6). Although some physiologic and toxicologic evidence exists (7-9), most of the evidence for particulate matter health effects is taken from epidemiologic studies that use fixed-site ambient measurements as estimates of exposure.
Critics of the positive epidemiologic findings consider the disconnect between ambient monitors and actual exposure to be a potential source of error (10,11). However, although some studies have found poor correlations between personal exposure and ambient concentrations (12-14), the correlations have been stronger when evaluated within individuals across time (15,16). Furthermore, any errors induced by using fixed-site monitors to represent personal exposure would likely be "Berkson errors," which would not induce bias if the dose-response relationship were linear (17).
Despite these facts, knowledge about personal exposure to particulate matter is crucial in a risk assessment and public policy context. Estimates of the distribution of exposures can help identify high-risk individuals and risks to susceptible subpopulations, and understanding the primary contributors to personal exposure can lead to well-designed control policies. Because individuals spend a significant fraction of the day indoors, with variable ventilation rates and differing indoor sources, the differences in personal exposures between individuals represented by the same fixed monitor could be substantial.
Because it would be implausible to measure the personal exposures of a significant number of people, a theoretically sound alternative is to measure concentrations in a number of microenvironments and determine the time spent by individuals in these microenvironments. A microenvironment can be defined as a physical compartment or defined space with relatively homogeneous air pollution concentrations (18). Simple microenvironmental models could involve estimates of indoor and outdoor concentrations and the amount of time spent in each of these two settings. For more complex models, such as the Probabilistic National Ambient Air Quality Standards Exposure Model (pNEM) (19) or the Simulation of Human Air Pollutant Exposure (SHAPE) for carbon monoxide (20), there is a need to understand concentration patterns across a number of different microenvironments that have not been well characterized to date.
Along with particulate matter mass concentrations, there are compelling reasons to estimate the particle counts and the size distributions of those particles. From a health effects standpoint, it has been argued that particle surface area or number could be more important than particle mass, due to the potential impairment of macrophage functions associated with clearance (21). In addition, because different source types provide different particle sizes and chemical compositions, understanding the sizes of particles present in different microenvironments can establish a framework for source attribution. Recent evidence that particulate matter from combustion sources may have greater effects than crustal particulate matter (22) gives source attribution added significance.
Continuous real-time monitoring can provide the information necessary to detect the influence of a local source or changes in local circumstances on particulate matter counts or mass concentrations. In addition, continuous monitoring allows us to evaluate short-term particle exposures, a topic for which very little exposure or health information has been collected (2.3).
To address these issues, we continuously monitored particulate matter mass concentrations and particle counts in a number of indoor microenvironments in an urban area over a period of 4 days, with outdoor measurements taken outside each microenvironment. We considered building or source factors as well as temporal trends in pollution concentrations to determine the significance of differences among indoor microenvironments. By determining microenvironments that might contribute significantly to personal exposure and by capturing the degree of microenvironmental variability in an urban area, we provide a template to better estimate personal exposures to particulate matter.
Methods
We measured particle counts and mass concentrations in seven indoor microenvironments as well as outdoors over a 4-day period in June 1998. To measure particle counts, we used an APC-1000 Airborne Particle Counter (Biotest Diagnostics, Denville, NJ). The APC-1000 is a light-scattering device that simultaneously measures particle counts above four device-specified size thresholds: 0.3 [micro]m, 0.5 [micro]m, 1.0 [micro]m, and 5.0 [micro]m in diameter. According to the manufacturer, the theoretical upper bound for the largest particle size category is on the order of 1,000 [micro]m, although particle counts are generally minimal above 5 [micro]m because particles much larger would not be suspended in ambient air. The APC-1000 also provided measurements of temperature and relative humidity and was factory-calibrated using isotropic polystyrene spheres within the year before use. Past exposure assessment studies have found the APC-1,000 to be useful for evaluating short-term concentration peaks and making preliminary source attributions (23).
We measured particulate matter mass concentrations using a DustTrak 8520 (TSI, Minneapolis, MN), a laser photometer designed to measure particles between 0.1 and 10 [micro]m. The DustTrak was factory-calibrated using A1 test dust (Arizona Test Dust, ISO 12103-1) within the year before use and was calibrated to a zero filter during the sampling period.
Nine students from the Harvard School of Public Health Summer Program in Biostatistics were trained in the operation of all equipment and divided into three sampling groups. The Summer Program in Biostatistics is a short-term program funded by the National Institute of Environmental Health Sciences, which is intended to introduce undergraduate mathematics majors from underrepresented minority groups to biostatistics, environmental health, and public health research.
On each of the 4 sampling days, each group sampled for up to three sessions in designated microenvironments in the Boston, Massachusetts, area. To normalize for temporal trends, measurements were taken between 1100 and 1700 hr on all 4 days. The students followed a detailed script that directed them to spend 20-40 min in each of several sampling locations. Along with the monitoring data, the groups recorded other information about site characteristics that might affect particulate matter concentrations, including the presence of smokers or air conditioning, whether windows were kept open or closed, and the distance from the street.
The seven indoor microenvironments selected were categorized as bus, gymnasium, hospital, museum, restaurant, store, and subway (Table 1). In Boston, the buses are largely diesel fueled, and the subway line is electric and consists of both a street-level and an underground section. These microenvironments were chosen strategically to represent typical activities of urban residents that had not been previously incorporated into many microenvironmental models. The students mimicked the typical behaviors within microenvironments (i.e., walking in stores, sitting in restaurants), so that their personal influence on particle concentrations would not alter true exposure patterns. In addition, outdoor measurements were taken in the vicinity of all microenvironments, with additional measurements taken in parks and on sidewalks.
Table 1. Descriptions of indoor and outdoor microenvironments.
Microenvironment Description
Bus Diesel-fueled city buses and medical area shuttle
(all urban travel)
Subway Electric-powered subway traveling both above
ground (on-street trolley) and underground
Gymnasium Athletic facility located near medical area
Hospital Two hospitals located across the street from
one another, along bus route
Museum Art museum in urban area
Restaurant Small pizza place, fast-food restaurant, coffee
shop
Store Three large shopping malls downtown, shops
near medical area and other urban areas
Sidewalk High-traffic roads near medical area, other
urban areas
Park Park areas downtown, close to traffic
No. of sessions
Microenvironment (indoor, outdoor)
Bus 15
(7, 8)
Subway 21
(12, 9)
Gymnasium 2
(1, 1)
Hospital 6
(3, 3)
Museum 2
(1, 1)
Restaurant 10
(5, 5)
Store 15
(8, 7)
Sidewalk 8
(0, 8)
Park 6
(0, 6)
Because the APC-1000 requires a 15-sec standby period between measurements, we used a 2-min averaging time for the APC-1000 and a 135-sec averaging time for the DustTrak. This ensured that the two instruments were synchronized throughout the measurement period. The sampling interval was selected to provide a reasonable sample size within each microenvironment while dampening the effects of short-term spikes in concentrations.
Statistics
Aside from estimating particle mass and count concentrations and size profiles within different microenvironments, the primary goal of this study was to understand the degree of variability between microenvironments, controlling for common factors. However, standard statistical comparisons between microenvironments are impeded by two aspects of our study design. First, environmental measurements were taken with short averaging times, making it unlikely that the measurements are independent of one another within microenvironments (a required assumption for many standard statistical methods). Second, when comparing a number of microenvironments, random variability could be the cause of significant deviations, and adjustments for multiple comparisons are needed.
The anticipated autocorrelation between sequential measurements was confirmed by exploratory analysis using the variogram technique (24). Given this, we sought a statistical model that could account for the autocorrelation. We considered using generalized estimating equations (GEEs) within our regression analysis, but preliminary simulations suggested that a linear mixed effects (LME) model (25) had better properties, particularly in terms of Type I error. Consequently, we applied LMEs within our regression analysis, assuming an AR-1 autoregressive correlation structure within each session. Thus, for each session i and replicate j, we statistically modeled each measurement [y.sub.ij] as
[y.sub.ij] = [x.sub.i][Beta] + [b.sub.i] + [u.sub.ij],
where [x.sub.i] is a row-vector consisting of the covariates of interest, [Beta] is a corresponding set of regression coefficients to be estimated, [b.sub.i] is a normally distributed random effect intercept, and [u.sub.ij] is a normally distributed error with AR-1 structure
[u.sub.ij] = [Rho][u.sub.ij-1]+ [e.sub.ij],
(each [e.sub.ij] being independent and normally distributed). We constructed separate models for indoors and outdoors, given differences in relevant factors as well as a desire to determine the degree of heterogeneity among both types of microenvironments. All particle count and mass concentration parameters were log-transformed to more closely approximate normal distributions.
For indoor microenvironments, the predictors considered for the model include indicator variables for microenvironment, open windows, and presence of central air conditioning, number of people nearby, and temperature and relative humidity. For outdoor microenvironments, we considered indicator variables for microenvironment, presence of smokers nearby, as well as number of people nearby and temperature and relative humidity. It should be noted that no smokers were present in any indoor microenvironments, explaining its exclusion from the indoor model. In addition, we incorporated a term for date of measurement into both models, to account for meteorological or other differences that could influence ambient concentrations.
Once the LME models have established whether there are significant differences among microenvironments in indoor or outdoor settings, we need to determine which microenvironments differ significantly from one another. To make this comparison, we used Wald tests on the estimated group means for different microenvironments. However, this technique can result in specious statistical findings if the effect of multiple comparisons is not properly accounted for. In ordinary least-squares regression, multiple comparison methods such as the Tukey and Scheffe tests have been developed to control the Type I error probability without significantly increasing the likelihood of a Type II error (26). However, these techniques are not applicable to LME.
To address this issue, we conducted multiple comparisons using two procedures at opposite extremes. First, we effectively ignored the multiple comparisons issue, rejecting the null hypotheses if p [is less than] 0.05. This adequately controls for Type II errors but could yield extremely high Type I errors. On the other extreme, we used the Bonferroni probability of 0.05/n (where n is the number of comparisons) to yield an overall Type I error rate of 0.05 while increasing Type II errors. These two extreme methods should bracket the correct statistical inferences.
For all statistical assessments, we evaluated the particle counts within specified size ranges rather than the measured threshold values. In other words, the APC-1000 measures the number of particles per unit volume that have diameters of at least 0.3 [micro]m, 0.5 [micro]m, 1.0 [micro]m, and 5.0 [micro]m. We analyzed the differences between these categories, reflecting the particle size ranges of 0.3-0.5 [micro]m, 0.5-1.0 [micro]m, 1.0-5.0 [micro]m, and [is greater than] 5.0 [micro]m. Past studies have found that these ranges correspond to aerodynamic diameters of 0.9-1.2 [micro]m, 1.2-1.7 [micro]m, 1.7-3.7 [micro]m, and [is greater than] 3.7 [micro]m (23).
Results
In total, the microenvironmental sampling yielded 578 measurements taken within 85 measurement sessions. Due to equipment issues, there were only 381 measurements for which all four particle size counts from the APC-1000 and the [PM.sub.10] concentration using the DustTrak were valid (66%). Because of our interest in evaluating variability in and predictors of particle counts within specified size ranges, we focused our analysis on these 381 measurements, even though this reduced the statistical power of our analysis. Descriptive statistics as well as statistical models did not differ significantly when applied to the full set of data when appropriate.
For all descriptive statistics, we estimated the geometric mean and geometric standard deviation to account for the logarithmic distribution of pollution concentrations and to increase comparability with the study by Brauer and colleagues (23). Taken across all microenvironments, outdoor [PM.sub.10] concentrations ranged between 10 and 90 [micro]g/[m.sup.3], with a geometric mean of 19 [micro]g/[m.sup.3] and a geometric standard deviation (GSD) of 1.9 (Table 2). Indoor [PM.sub.10] concentrations were generally higher and more variable, with a geometric mean concentration of 35 [micro]g/[m.sup.3] (range 0-380 [micro]g/[m.sup.3]) and a GSD of 3.0. The patterns are similar for the four particle size ranges, with higher geometric mean count concentrations as well as greater variability indoors.
Table 2. Particle count and mass concentrations aggregated
across urban microenvironments.
[PM.sub.0.3-0.5]
(particles/
Sample [cm.sup.3])
size GM GSD
Outdoors
All 147 1.1 2.0
RH [is less than or equal to] 30% 66 0.9 1.9
RH > 30% 81 1.4 1.9
Temperature [is less than or 79 1.0 1.9
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 68 1.3 2.1
Nonsmoking 47 1.1 1.8
Smoking 100 1.2 2.1
Indoors
All 234 1.5 2.4
RH [is less than or equal to] 30% 62 1.5 2.2
RH > 30% 172 1.5 2.5
Temperature [is less than or 143 1.3 2.6
equal to] 24 [degrees] C
Temperature > 24 degrees] C 91 1.9 2.1
No AC 10 1.2 1.5
AC 224 1.5 2.5
[PM.sub.0.5-1.0]
(particles/[cm.sub.3])
GM GSD
Outdoors
All 0.4 2.4
RH [is less than or equal to] 30% 0.4 2.3
RH > 30% 0.3 2.5
Temperature [is less than or 0.3 2.4
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 0.5 2.3
Nonsmoking 0.3 2.4
Smoking 0.4 2.4
Indoors
All 0.8 3.8
RH [is less than or equal to] 30% 1.2 3.3
RH > 30% 0.7 3.8
Temperature [is less than or 0.6 3.6
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 1.4 3.3
No AC 0.4 1.5
AC 0.8 3.8
[PM.sub.1.0-5.0]
(particles/[cm.sub.3])
GM GM
Outdoors
All 0.08 1.8
RH [is less than or equal to] 30% 0.07 1.9
RH > 30% 0.09 1.8
Temperature [is less than or 0.08 1.9
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 0.08 1.8
Nonsmoking 0.07 1.8
Smoking 0.08 1.9
Indoors
All 0.15 2.5
RH [is less than or equal to] 30% 0.17 2.6
RH > 30% 0.15 2.5
Temperature [is less than or 0.12 2.4
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 0.23 2.4
No AC 0.10 1.5
AC 0.16 2.6
[PM.sub.5.0+]
(particles/[cm.sup.3]
GSD GM
Outdoors
All 0.004 2.0
RH [is less than or equal to] 30% 0.004 1.8
RH > 30% 0.003 2.2
Temperature [is less than or 0.004 2.0
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 0.004 2.1
Nonsmoking 0.004 1.7
Smoking 0.004 2.2
Indoors
All 0.007 2.9
RH [is less than or equal to] 30% 0.008 2.5
RH > 30% 0.007 3.0
Temperature [is less than or 0.005 2.5
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 0.012 2.8
No AC 0.003 1.4
AC 0.007 2.9
[PM.sub.10]
[micro]g/[m.sup.3]
GM GSD
Outdoors
All 19 1.9
RH [is less than or equal to] 30% 15 1.8
RH > 30% 24 1.9
Temperature [is less than or 18 1.7
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 21 2.1
Nonsmoking 17 1.7
Smoking 20 2.0
Indoors
All 35 3.0
RH [is less than or equal to] 30% 33 2.8
RH > 30% 36 3.0
Temperature [is less than or 30 3.2
equal to] 24 [degrees] C
Temperature > 24 [degrees] C 46 2.4
No AC 21 1.4
AC 36 3.0
Abbreviations: AC, air conditioning; GM, geometric mean;
RH, relative humidity.
To compare the two instruments, we followed the methodology of Brauer and colleagues (23) and used an assumed particle density of 2.8 g/[cm.sup.3] (7) to convert particle counts into particle mass. Assuming spherical shape and using the midpoint of the particle diameter ranges (7.5 [micro]m assumed for the largest size range), the calculated particle mass from the APC-1000 is well correlated with the [PM.sub.10] mass concentration measures (r = 0.87). In addition, we can compare our measurements to concentrations from fixed monitors. Although [PM.sub.10] data are only available every 6 days from the nearest U.S. Environmental Protection Agency (U.S. EPA) monitoring station (Kenmore Square, Boston), the ambient concentration was approximately 20 [micro]g/[m.sup.3], similar to our geometric mean outdoor value.
When we consider some simple comparisons by site characteristic, we see some systematic differences (Table 2). Particle counts and mass concentrations tend to be greater with higher temperatures and higher relative humidity, both in indoor and outdoor microenvironments. Particle counts and mass concentrations are slightly higher in outdoor microenvironments with smokers and in indoor microenvironments with central air conditioning, although few measurements were taken indoors without air conditioning (n = 10).
Stratifying by microenvironment, we can see some systematic differences between indoor and outdoor particle mass concentrations (Figure 1). Indoor [PM.sub.10] concentrations appear to be elevated over outdoor concentrations in the subway, bus, and museum microenvironments, with occasional peaks within restaurants. Indoor [PM.sub.10] concentrations also appear to vary more substantially across microenvironments than outdoor concentrations, an expected finding given the similarity among the urban outdoor settings. To determine whether the outdoor mass concentrations were homogeneous and could therefore be considered a single microenvironment, we applied a Wald test to the LME model for outdoor [PM.sub.10] measurements, controlling for temperature, relative humidity, and date of measurement. With this model, there was no evidence of outdoor microenvironment heterogeneity (p = 0.75), a finding that was not altered by the inclusion of additional covariates.
[GRAPH OMITTED]
Similarly, outdoor particle count concentrations and size distributions appear relatively similar across microenvironments (Figure 2). Wald tests on the outdoor particle count data (controlling for temperature, relative humidity, and date of measurement) found relatively little evidence of significant heterogeneity (p = 0.03 for 0.3-0.5 [micro]m; p = 0.005 for 0.5-1.0 [micro]m; p = 0.11 for 1.0-5.0 [micro] m; p = 0.97 for 5.0 [micro] m). Although we cannot reject heterogeneity for the smaller particles, multiple comparisons reveal that no differences are significant at the Bonferroni 5%. For pairwise 5% comparisons, only the park microenvironment differs significantly from other microenvironments for 0.3-0.5 [micro]m, with a small number of significant comparisons for 0.5-1.0 [micro]m. Thus, the overall evidence suggests that our outdoor count measurements are relatively homogeneous, and we therefore consider the outdoors as a single microenvironment in the multiple comparisons below.
[GRAPH OMITTED]
Within indoor microenvironments (Figure 3), both the total particle count concentrations and the size distributions appear to differ significantly. For example, the subway microenvironment has its highest median particle count concentration within the 0.5-1.0 [micro]m range, whereas the store microenvironment has relatively more coarse particles. In general, particle counts for larger particle sizes are greater in microenvironments with significant pedestrian traffic (i.e., museum and store), whereas particle counts for smaller particle sizes are highest near combustion sources (i.e., subway, bus, and restaurant).
[GRAPH OMITTED]
Considering indoor/outdoor ratios stratified by microenvironment (defined as the ratio between the geometric mean concentrations indoors and outdoors, measured sequentially) helps to emphasize the differences among microenvironments (Table 3). For the subway, bus, and restaurant microenvironments, indoor count concentrations were most significantly elevated over outdoor concentrations for [PM.sub.0.5-1.0], a particle-size range associated with fuel combustion (both from proximity to traffic and indoor combustion sources). In the remaining four microenvironments, the greatest elevation occurred for [PM.sub.5.0+], indicative of dust or other coarse particles generated by human activities. The information from particle size counts provides more extensive evidence of source contributions than [PM.sub.10] mass measures. For example, the [PM.sub.10] indoor/outdoor ratios are almost identical in the museum and subway microenvironments, despite the differences in the types of particles penetrating from the outdoors and generated indoors. In general, the fact that the indoor/outdoor ratio is [is less than] 1 only for the store microenvironment indicates that significant particle exposures occur in many indoor environments.
Table 3. Ratios between geometric mean indoor and
outdoor particle counts (particles/[cm.sup.3]) and [PM.sub.10]
concentrations ([micro]g/[m.sup.3]), stratified across
urban microenvironments.
[PM.sub.0.3-0.5] [PM.sub.0.5-1.0] [PM.sub.1.0-5.0]
Subway 2.0 4.5(a) 3.0
Bus 1.8 3.3(a) 2.2
Restaurant 1.0 1.4(a) 1.2
Hospital 0.8 1.4 1.7
Gymnasium 0.8 0.7 0.8
Museum 0.6 1.9 4.0
Store 0.5 0.6 1.1
[PM.sub.5.0+] [PM.sub.10]
Subway 2.2 2.3
Bus 1.6 2.1
Restaurant 0.9 1.2
Hospital 1.8(a) 1.0
Gymnasium 0.9(a) 1.1
Museum 4.1(a) 2.2
Store 2.5(a) 0.8
(a) The largest particle count concentration ratio within
each microenvironment.
To move from these qualitative descriptions to quantitative comparisons between microenvironments, we constructed LME models for the eight microenvironments (seven indoor microenvironments and the pooled outdoor microenvironment). For our primary model, we controlled for date of measurement, temperature, and relative humidity. The indoor microenvironments with hypothesized proximity to combustion sources (subway, bus, and restaurant) tended to have significantly greater particle counts and mass concentrations than other microenvironments, particularly for smaller particle sizes (Table 4). For particles [is greater than] 5.0 [micro]m, store and museum had the highest count concentrations, although none of the differences between indoor microenvironments were statistically significant. In general, greater microenvironmental variability was seen for [PM.sub.0.3-0.5] and [PM.sub.0.5-1.0] than for larger particles.
Table 4. Multiple comparisons among microenvironments
using LME model.(a)
Subway Bus
[PM.sub.0.3-0.5] (no./[cm.sup.3])
Subway = =
Bus = =
Restaurant < =
Outdoor << <
Gymnasium << <
Store << <<
Hospital << <<
Museum << <<
[PM.sub.0.5-1.0](no./[cm.sup.3]) Subway Bus
Subway = >
Bus < =
Restaurant << =
Outdoor << <
Store << <
Museum << =
Gymnasium << <
Hospital << <
[PM.sub.1.0-5.0](no./[cm.sup.3]) Subway Bus
Subway = =
Bus = =
Restaurant < =
Museum = =
Store << =
Outdoor << <
Gymnasium << <
Hospital << <
[PM.sub.5.0+](no./[cm.sup.3]) Store Museum
Store = =
Museum = =
Subway = =
Restaurant = =
Bus = =
Hospital = =
Outdoor < =
Gymnasium
[PM.sub.10]([micro]g/[m.sup.3]) Subway Bus
Subway = >
Bus < =
Museum = =
Restaurant << =
Outdoor << <
Store << <
Gymnasium << =
Hospital << <<
Restaurant Outdoor
[PM.sub.0.3-0.5] (no./[cm.sup.3])
Subway > >>
Bus = >
Restaurant = =
Outdoor = =
Gymnasium = =
Store < <<
Hospital << <<
Museum < <
[PM.sub.0.5-1.0](no./[cm.sup.3]) Restaurant Outdoor
Subway >> >>
Bus = >
Restaurant = =
Outdoor = =
Store = =
Museum = =
Gymnasium = =
Hospital < <
[PM.sub.1.0-5.0](no./[cm.sup.3]) Restaurant Museum
Subway > =
Bus = =
Restaurant = =
Museum = =
Store = =
Outdoor = =
Gymnasium = =
Hospital < =
[PM.sub.5.0+](no./[cm.sup.3]) Subway Restaurant
Store
Museum = =
Subway = =
Restaurant = =
Bus = =
Hospital = =
Outdoor < =
Gymnasium = =
[PM.sub.10]([micro]g/[m.sup.3]) Museum Restaurant
Subway = >>
Bus = =
Museum = =
Restaurant = =
Outdoor = =
Store = =
Gymnasium = =
Hospital = <
Gymnasium Store
[PM.sub.0.3-0.5] (no./[cm.sup.3])
Subway >> >>
Bus > >>
Restaurant = >
Outdoor = >>
Gymnasium = =
Store = =
Hospital = =
Museum = =
[PM.sub.0.5-1.0](no./[cm.sup.3]) Store Museum
Subway >> >>
Bus > =
Restaurant = =
Outdoor = =
Store = =
Museum = =
Gymnasium = =
Hospital = =
[PM.sub.1.0-5.0](no./[cm.sup.3]) Store Outdoor
Subway >> >>
Bus = >
Restaurant = =
Museum = =
Store = =
Outdoor = =
Gymnasium = =
Hospital = =
[PM.sub.5.0+](no./[cm.sup.3]) Bus Hospital
Store = =
Museum = =
Subway = =
Restaurant = =
Bus = =
Hospital = =
Outdoor = =
Gymnasium = =
[PM.sub.10]([micro]g/[m.sup.3]) Outdoor Store
Subway >> >>
Bus > >
Museum = =
Restaurant = =
Outdoor = =
Store = =
Gymnasium = =
Hospital < <