Heterogeneity of Coarse Particles in an Urban Area - Environmental

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Heterogeneity of Coarse Particles in an Urban Area Uma Ramesh K. Lagudu,† Suresh Raja,† Philip K. Hopke,†,* David C. Chalupa,‡ Mark J. Utell,‡ Gary Casuccio,§ Traci L. Lersch,§ and Roger R. West§ †

Center for Air Resource Engineering and Science, Clarkson University, Potsdam, New York 13699, United States Departments of Medicine and Environmental Medicine, University of Rochester Medical Center, Rochester, New York 14642, United States § RJ Lee Group, Monroeville, Pennsylvania 15146, United States ‡

bS Supporting Information ABSTRACT: The variation in composition and concentration of coarse particles in Rochester, a medium-sized city in western New York, was studied using UNC passive samplers and computer-controlled scanning electron microscopy (CCSEM). The samplers were deployed in a 5 5 grid (2 km 2 km per grid cell) for 23 week periods in two seasons (September 2008 and May 2009) at 25 different sites across Rochester. CCSEM analysis yielded size and elemental composition for individual particles and analyzed more than 800 coarse particles per sample. Based on the composition as reflected in the fluoresced X-ray spectrum, the particles were grouped into classes with similar chemical compositions using an adaptive resonance theory (ART) network. The mass fractions of particles in the identified classes were then used to assess the homogeneity of composition and concentration across the measurement domain. These results illustrate how particle sampling using the UNC passive sampler coupled with CCSEM/ART can be used to determine the concentration and source of the coarse particulate matter at multiple sites. The particle compositions were dominated by elements suggesting that the major particle sources are road dust and biological particles. Considerable heterogeneity in both composition and concentration were observed between adjacent sites as indicated by cofficient of divergence analyses.

’ INTRODUCTION Particulate matter (PM) exposures have drawn considerable attention because of links to human mortality and morbidity.13 Size is one of the important parameters that determine the environmental and health effects of aerosol particles along with concentration, structure, and chemical composition. Relationships between particles and adverse health effects including mortality have been observed even at low levels of exposure.4,5 The health effects of coarse particles need to be considered differently from those of the fine fraction since they deposit differently in the respiratory system and may produce different health effects compared to fine particles.6 The type and severity of health effects depend on the particle size, concentration, and composition.7 Clinical studies8,9 have shown that acute exposure to coarse particles can produce cardiopulmonary changes. Because of their relatively short atmospheric lifetime, previous studies have shown that coarse particles are spatially heterogeneous.1012 Burton et al.13 collected PM samples from eight sites in a metropolitan area in Philadelphia and showed that the coarse PM concentrations varied spatially, while PM2.5 and PM10 r 2011 American Chemical Society

concentrations were more homogeneous. Therefore, to assess community level exposures, large numbers of samplers at multiple locations are required.10 Hwang et al.14 highlighted significant potential for exposure misclassification in time series epidemiologic studies when regressing health outcomes against source contributions estimated at a single central monitoring site. Conventional active samplers used to assess area level particulate exposures are costly and labor intensive to obtain acceptable spatial resolution. Passive samplers are available that are inexpensive and easier to use than conventional samplers. Therefore, a large number of samplers can be deployed yielding more representative spatial measurements with better measures of precision (duplicate samples). Ott et al.1517 have shown passive sampling to be a feasible method to sample coarse particulate matter in an urban area. Passive particulate samplers are intended to monitor ambient, Received: November 15, 2010 Accepted: March 10, 2011 Revised: March 4, 2011 Published: March 24, 2011 3288

dx.doi.org/10.1021/es103831w | Environ. Sci. Technol. 2011, 45, 3288–3296

Environmental Science & Technology

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Figure 1. Map showing locations of the sampling sites and relationship of sampling domain to New York state.

indoor, or occupational exposure over a period of hours to weeks. They have the potential to be used as an area monitor or as a personal sampler.18,19 Since particles deposit on the sampler by diffusion and gravity, longer sampling times are required but provide assessments of long-term average exposures. To date, very little information has been available to characterize the spatial variation of PM size and composition on a local scale. Previous studies have explored intracity variations of PM concentration, size, and composition of airborne particles.20,21 However, these studies do not give relevant information on local variability, for example in an urban area, in order to be able to assess epidemiological exposure of PM. In this work, UNC passive samplers2224 were deployed at 25 locations in Rochester, NY to collect and analyze integrated particle samples (particle size between 0.5 and 15 μm). Computer-controlled scanning electron microscopy (CCSEM) was used to measure individual particle size, shape, and elemental composition of the coarse PM collected on the samplers. Adaptive Resonance Theory (ART) neural networks were then used to classify the measured particle composition into homogeneous groups. Methods to determine the spatial variation in PM concentration, size, and composition are presented and discussed.

’ EXPERIMENTAL METHODS Sample Collection. The UNC Passive Sampler was developed by Wagner and Leith.2224 The passive sampler shelter design described by Ott et al.16 was used during this campaign to avoid any effects of precipitation getting into the samplers. The samplers were deployed at 25 locations in the Rochester, NY metropolitan area. They were placed on telephone poles and

other similar structures at heights of approxmately 3 m above the ground in a 5 5 grid (2 km 2 km per grid cell) for 23 week periods in two seasons (September 2008 and May 2009). Locations were selected to minimize the impact of local vegetation (trees) so that the surroundings were as open as possible. During the first campaign in September, samplers were placed on September 17, 2008 and removed on October 1, 2008. During the spring, the samplers were placed on April 30, 2009 and removed on May 21, 2009. Figure 1 shows the locations of the samplers installed in Rochester. The UNC passive sampler consists of a standard scanning electron microscope (SEM) stub, a collection substrate, and a protective mesh cap. During sampling, particles are transported by gravity and diffusion through the 150-μm-diameter holes in the mesh cap and deposit on a substrate mounted on the stub. The stub is oriented such that the substrate is horizontal. After sampling, the mesh cap is removed, the stub is placed in a scanning electron microscope (Aspex Personal SEM), and the particles are counted, sized, and composition assayed using CCSEM analytical procedures. CCSEM. PM samples collected on the sampler substrate were analyzed using CCSEM.25,26 Briefly, CCSEM scans the collection substrate of the SEM stub for individual particles and provides a fluoresced X-ray spectrum and an image of each particle. The analysis is performed by rastering the electron beam over the sample while monitoring the resultant backscattered signal. At each point, the image intensity is compared to a preset threshold level. Once a coordinate is reached where the signal is above the threshold level, the electron beam is driven across the particle in a preset pattern to determine the size of the particle. Upon measurement of the particle size, the elemental composition of the particle is determined through collection of characteristic 3289

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Figure 2. Spatial variation of coarse PM mass concentrations (ng/m3) in Rochester, NY. Solid dots represent the sampling locations.

Figure 3. Wind roses for the fall 2008 (left) and spring 2009 (right) sampling periods.

X-rays using energy dispersive spectroscopy (EDS) techniques. Individual particles characterized during the CCSEM analysis are grouped into particle type classes based on their elemental composition. The mass of an individual particle was calculated by multiplying the assigned density of the particle by its volume. Each particle was assigned a density based on a common oxide in proportion to the elements present as determined by the EDS

analysis.25,26 Because of the small mass being analyzed and the relatively short accumulation time for each spectrum, only a limited suite of elements are observed after application of the noise reduction procedure described in the next section. The observed elements include carbon (C), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), 3290



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Table 1. Particle Classes Obtained from Clustering of Particle Data Obtained from Two Seasons Together cluster number

particle type

source type

mean fall

mean spring

concentration (ng/m3)

concentration (ng/m3)

1

Ca

road dust

115.7

30.1

2

CP (negligible P)

biological

55.4

27.3

3

CCaZn (High C)

C road dust

4

NaMgCa (High CaMg)

salt

6.8

7.4

13.6

89.3

5

CMgAlSiKMnFe (CSiAlFeMg)

C road dust

7.6

7.0

6

PCa (Ca)

biological

3.1

105.6

7 8

STiMnFeCuBaPb (Fe) MgAlSiKMnFe (SiAl)

road dust road dust

4.5 62.0

0.5 16.1

9

AlSiK (SiAl)

road dust

19.2

16.8

10

AlSiK (SiAl) More Si, Al

road dust

98.0

66.7

0.4

12.5

11

CPKNi (C)

C road dust

12

Si

road dust

77.6

1.7

13

AlSiKMn (SiAl)

road dust

11.5

154.9

14

CSiCr (CSi)

C road dust

2.5

25.9

15 16

NaMgCa (CaMgNa) CrMnFeNiCuZnBaPb (Fe)

salt Cr-rich

163.2 43.8

13.6 2.8

17

CAlSiTiMnFeNiCuZnBaPb (CSiAl)

C road dust

0.4

27.2

18

CAlSiK-Mn (SiCAl)

C road dust

24.4

24.9

19

MgAlSiSKMnPb (SiAl)

road dust

20

CAlZn (CAl)

C road dust

208.5

61.2

12.2

25.3

9.5

24.8

21

CAlSiK (CSiAl)

C road dust

22

Si

road dust

157.3

68.4

23 24

CP CPCrZn (C)

biological biological

4.7 62.8

59.1 0.3

25

CTiCrFeNiCuZnBaPb (FeC)

C road dust

2.4

7.7

26

CrFeBa (Fe)

Cr-rich

3.5

16.2

chromium (Cr), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), barium (Ba), and lead (Pb). Sampling and Analysis Precision. Replicate samplers were deployed at 5 locations during each sampling campaign to assess the reproducibility of the mass measurements. The resulting data have been separated by size into μm intervals and the results are presented in Figure S1 in the Supporting Information. There is a moderate correlation between the replicate samples, but the limited number of particles in any given size interval reduced the correlation among the results. Data Analysis. CCSEM measurements provide a representation of the particle composition, but can rarely be used to provide accurate elemental concentrations on a particle-by-particle basis. Therefore, in order to use the semiquantitative data to obtain new quantitative variables, particles need to be classified into homogeneous groups. Particle classes or groups represent the types of particles present in the air and the mass of particles in a given class is a quantitative measure of particle composition.27 Because there are peaks in the spectrum that arise from statistical fluctuations in the X-ray spectrum arising from the short acquistion time (5 s/particle) and influence of the background substrate, the data were subjected to noise reduction and composition data were normalized to unit length. Detailed steps for pretreatment of single particle data obtained from CCSEM measurements have been given by Kim et al.28 Any X-ray count for a given element less than twice the square root of total X-ray count for an individual particle was set to zero. After noise reduction, the X-ray spectrum for each particle data was normalized to unit length. The volume was estimated

from the maximum diameter (dmax) and perpendicular diameter (dperp).29 The mass of each particle was then estimated from the volume of a spheroid of revolution and an estimated density based on the elemental composition. The concentration of each particle has been estimated with the deposition velocity model discussed by Ott et al.16 using a surface roughness length of 0.530 to determine the friction velocity. The diffusivity of the particle was estimated using the StokesEinstein equation. The total concentration of all coarse particles at a single site was estimated by summing the concentration contribution of all the particles at that site. These data were used finding the spatial variation of the coarse particles over the sampling locations. The quality of these results has been assessed using the replicate samples. The analysis of the replicate sample data is presented in the Supporting Information. For those sites with replicate samples, the average value of the two samples was used. There was generally good agreement among the replicate samples. Application of ART-2a Model. The chemical information derived from CCSEM serves as a basis for classification of particles into classes with similar composition. The normalized unit length data were analyzed with an adaptive resonance theory (ART-2A) algorithm.31 This dynamic classification system has been used previously for classification of particles collected on filter samples.32 In the present work, particle classification using ART-2a was performed for different vigilance factors, and a vigilance factor of 0.5 was found to yield a good classification with homogeneous classes in terms of the chemical elements contained in each group. The ART2a algorithm was run with a vigilance factor of 0.5 and a learning rate of 0.5 for all of the samples from both seasons (fall and spring). 3291



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Figure 4. Spatial variation of mass concentration (ng/m3) of combined soil-related clusters during fall (left) and spring (right). Solid dots represent sampling locations.

The numbers of measured particles for the fall and spring season were 21 596 and 29 000, respectively. Particles with diameters less than 2.5 μm and above 10 μm were discarded, and the remaining “coarse” particles are the subject of this paper. The fall and spring data sets were combined with a total coarse particle number of 11 550. The ART-2a algorithm was run using a standard UNIX network cluster. Only resulting particle groups/clusters representing more than 1% of the total number of particles were considered for further analysis. Each cluster obtained from the ART-2a analysis had elemental information, aerodynamic diameter, and mass of each particle. The masses were summed to yield the total mass in each cluster. The elemental average of each of the elements in each cluster was compared to the elemental average of all the coarse particles for that season. If a particular element was found to have a higher average value than the overall elemental average, it was considered to be a defining element in the class. In this way all the classes of the clusters have been identified. The spatial variations have been plotted using a spline interpolation scheme (ArcGIS V9.2). Although various interpolation schemes such as kriging and inverse distance weighting, etc. were tried, spline interpolation provided smoother and more realistic plots.

’ RESULTS AND DISCUSSION Spatial Variation of Coarse Particle Mass. Figure 2 shows the spatial variation in the mass concentrations (ng/m3) of coarse PM across the 25 sites in Rochester during the fall-08 and the spring-09 sampling campaigns. The wind roses for these two sampling periods are shown in Figure 3. These plots present

the spatial variation across the sampling domain. The spatial distributions of coarse PM in Rochester were highly variable with concentrations ranging from 100 to 3230 ng/m3 in the fall and 410 to 2050 ng/m3 in the spring (Supporting Information Table S1). During the fall 2008 period, the wind direction was predominately from the southwest. It can be seen from the fall plot (left panel Figure 2) that there is a high concentration in the southeastern corner of the sampling domain. This site is adjacent to an agricultural field so that harvesting likely contributed to the higher observed concentrations. For the spring samples (right panel), high mass concentrations are observed in the northwestern corner of the sampling domain. This area is close to a major industrial zone in Rochester. Wind directions during this late April to May period were predominately westerly. Particle Class Membership. Clustering of the coarse particles yielded 26 different particle types with mass values greater than 1% of the total estimated mass. Table 1 presents the defined particle classes and the mass concentrations for each season. The particle types are dominated by the presence of silicon, calcium, magnesium. and carbon. Some particle types appear to be very similar in their elemental content (e.g., clusters 21 and 22 in Table 1). However, these clusters show differences in the ratios of the constituent elements. These clusters are likely to arise from the same source type. From Table 1, high concentrations of various species, e.g., Ca, C, CaMg, and SiAl, can also be seen in a given cluster. SiAl generally indicates crustal material. In general, there is quite limited direct suspension of soil particles because of the limited amount of open space in urban areas that is not covered with vegetation. 3292



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Figure 5. Spatial variation of mass concentration (ng/m3) of combined carbon/soil-related clusters during fall (left) and spring (right). Solid dots represent sampling locations.

However, soil and other materials deposited on road surfaces are resuspended by traffic. Thus, particle types such as in Clusters 1, 710, 12, 13, 19, and 22 are likely to be road dust/soil. The presence of carbon along with these elements can still indicate that such particle types are road dust but with the addition of carbonaceous material. Road abrasion of asphalt roads or tire wear deposited on road surfaces can be sources of carbon in addition to tailpipe emissions or deposition of primary biological materials. Particle types where carbon was found to be associated with elements like Zn suggests that such coarse particle types are derived from combusted lubricating oil and/or tire wear.3337 Ba and Cu are observed in several clusters and suggest brake wear particles.38 Thus, there is a second group of road dust clusters including cluster numbers 3, 5, 14, 17, 18, 20, 21, and 25 that include crustal elements plus high concentrations of carbon. There are several clusters with carbon and/or phosphorus that may represent biologically derived particles.39 These clusters are 2, 6, 11, 23, and 24. Because of the constraints on this project, images are not available for the individual particles so we cannot confirm these assignments based on morphology. Future work is planned that will also acquire images so confirmatory studies can be performed using individual particle morphology. There are two classes that are high in sodium suggesting a residue of salt that is used on Rochester roads during the winter months. This group includes cluster numbers 5 and 15. Finally, two clusters (Nos. 10 and 26) had measurable chromium that could derive from yellow road paint that is commonly used on roads, but also could arise from particles being worn from the

main line railroad40 that runs through the sampling domain and from an industrial area that is in the upper left corner of the sampled domain. Spatial Variation of Particle Types. To examine the spatial distributions of these groups of clusters that likely derive from the same underlying sources, the mass contributions for each cluster have been summed and the resulting mass concentrations of the road dust, high carbon road dust, and biological particles are plotted in Figures 4 to 6. The spatial variation of the Cr-rich and salt groups are shown in Supporting Information Figures S2 and S3, respectively. The spatial variations of each of the identified particle types are provided in the Supporting Information for this paper as Figures S4 to S30. It can be seen from Figure 4 that the spatial patterns are very similar to those seen for the total particle mass shown in Figure 2. This crustal element dominated group of clusters represents 64% of the fall particle mass and 47% of the spring mass. As mentioned above, there is an agricultural area in the southeastern corner of the domain that was actively harvesting vegetables during the fall sampling period. Winds during the fall were predominately from the southwest (Figure 3) bringing particles into the sampling domain from the south. In the spring samples, Figure 4 (right), there appear to be high concentrations in the vicinity of I-590 in the southwest corner of the domain. Winds in the spring were stronger and more westerly in direction bringing particles from the industrial area in the northwestern corner. For the highcarbon road dust particle types, the areas of high values are generally clustered around the major roads shown in Figure 5. 3293



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Figure 6. Spatial variation of mass concentration (ng/m3) of combined biological clusters during fall (left) and spring (right). Solid dots represent sampling locations.

These particle types contributed similar fractions of the coarse aerosol mass (11%) in both seasons. Because the carbon is being attributed to tire wear and other motor vehicle related material, the spatial patterns are logical. The carbonaceous soil also appeared to be emitted by the agricultural area. The biological particles (Figure 6) showed a very different spatial pattern with higher values in the residential areas away from the highways. This group contributed about 3 times more mass in the spring than in the fall suggesting pollen as a major portion of the biological material. Biological material represented 6% of the fall coarse particle mass and 17% of the average spring mass concentration. The sodium-dominated particle types (Figure S2) show higher contributions in the fall than the spring. The other elements in these clusters are Ca and Mg. For the spring samples, the pattern of high concentrations is strongly related to the I-590/I-490 area. Thus, there appears to be some residual from the winter salt application to the roads. However, the fall pattern is less clear with the high values observed in three corners of the domain and the highest values again in the southeastern agricultural area. Examining the meteorological data for the sampling period, there were no periods of snow or freezing precipitation. There could be application of soil amendments during the fall (liming the soil to control pH for example), but the exact nature of the fall Na particles is unknown. The spatial patterns of the Cr-rich particles are shown in Figure S3. For the fall samples, there is a high area at the southern edge of the domain and in the northeastern corner. In the spring samples, the high areas appear to be downwind of the railroad.

Examination of one individual cluster plot (Figures S19) shows a pattern that suggests a road source, while the other plot (Figure S29) appears to be related to the railroad. Coefficient of Divergence (COD). There is a visual degree of heterogeneity observable in the plots described in the prior section. However, to provide a quantitative measure of spatial heterogeneity, the coefficient of divergence (COD)41 was calculated using the particle type concentrations at the sampling sites. The COD is defined as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !ffi u u1 n xij xik 2 CODjk ¼ t n i ¼ 1 xij þ xik

∑

where xij is the source contribution per sampling interval, i, estimated at site j, j and k represent two sites, and n is the number of sampling intervals. The COD provides a measure of the degree of uniformity among sampling sites. As the estimated particle concentrations at two sites become more similar, the COD approaches zero. Alternatively, as the estimated concentrations diverge, the COD approaches one. The maximum, minimum, and average values of all the coefficients of divergence (COD) are given in Table 2. All of the COD values are presented in SI Tables S2 and S3. The average values indicate that the sites are rather dissimilar during both seasons. The sites are relatively better correlated in the spring compared to the fall. During the spring, the COD values among the sites were higher and did not exhibit any specific trends showing that the sites are more independent of each other. The value of the 3294



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Table 2. Maximum and Minimum Coefficient of Divergence, Fall and Spring, Rochester site #

max

min

avg

site #

max

min

avg

S#2243

0.870

0.420

0.597

S#1580

0.936

0.555

0.629

S#2244 S#2246

0.806 0.829

0.365 0.474

0.549 0.601

S#1582 S#1585

0.910 0.913

0.547 0.481

0.648 0.571

S#2247

0.853

0.406

0.605

S#1586

0.891

0.482

0.566

S#2248

0.831

0.424

0.561

S#1587

0.897

0.439

0.539

S#2250

0.816

0.412

0.547

S#1588

0.867

0.513

0.607

S#2251

0.847

0.441

0.569

S#1592

0.870

0.557

0.659

S#2252

0.809

0.365

0.540

S#1598

0.868

0.506

0.615

S#2253

0.821

0.402

0.570

S#1604

0.877

0.481

0.606

S#2254 S#2255

0.922 0.812

0.640 0.395

0.775 0.577

S#1609 S#1618

0.893 0.870

0.519 0.494

0.624 0.591

S#2256

0.834

0.480

0.588

S#1620

0.908

0.563

0.634

S#2258

0.835

0.479

0.602

S#1648

0.879

0.477

0.588

S#2259

0.878

0.505

0.646

S#1666

0.871

0.481

0.554

S#2260

0.855

0.461

0.570

S#1671

0.917

0.516

0.625

S#2261

0.857

0.536

0.619

S#1688

0.890

0.534

0.605

S#2262

0.840

0.462

0.571

S#1707

0.903

0.541

0.649

S#2263 S#2265

0.808 0.868

0.395 0.406

0.585 0.594

S#1712 S#1718

0.942 0.867

0.509 0.482

0.607 0.593

S#2266

0.843

0.513

0.611

S#1721

0.927

0.439

0.582

S#2267

0.839

0.476

0.567

S#1722

0.870

0.511

0.595

S#2269

0.864

0.424

0.586

S#1737

0.846

0.480

0.586

S#2270

0.942

0.640

0.758

S#1745

0.890

0.514

0.613

S#2271

0.851

0.475

0.633

S#1747

0.852

0.480

0.578

maximum COD is always above 0.7, greater than the criterion suggested by the USEPA to indicate heterogeneity (COD > 0.2).42 High values of COD are found in the present study with a minimum value of 0.365. Lower values might be expected when sampling sites are only 2 km apart and the averaging times are 21 days to 30 days. Thus, there is very high spatial variability for coarse particles across this urban area. Further analyses of these data could be pursued using the spatial variation in a factor analysis approach such as RMAPS43 or that described by Paatero et al.44 These approaches will be explored on a larger data set for which such analyses may be more applicable, but they should permit quantitative identification and apportionment of the major coarse particle sources. This work demonstrates the utility of passive sampling coupled with CCSEM/ART to determine the concentration and composition of the coarse particulate matter at multiple sites and hence, to assess the spatial variability. The spatial variation plots showed the temporal (spring vs fall) as well as the spatial variability to be significant. These results illustrate that there could be significant exposure misclassification in epidemiological studies that depend on data from one or a few central monitoring stations. However, the passive monitor method could be used to assess exposure to coarse PM at a local scale. The resulting concentration estimates could then be incorporated into epidemiological studies to better assess the association between health effects and exposure to coarse particles.

’ ASSOCIATED CONTENT

bS

Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Fax: (315) 268 4410; e-mail: [email protected].

’ ACKNOWLEDGMENT This work was supported by U.S. Environmental Protection Agency’s Science to Achieve Results (STAR) Program through a subcontract from the University of Rochester PM and Health Center Grant RD832415. Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency, it has not been subjected to the Agency’s required peer and policy review and therefore, does not necessarily reflect the views of the Agency and no official endorsement should be inferred. ’ REFERENCES (1) Brunekreef, B.; Holgate, S. T. Air pollution and health. Lancet 2002, 360, 1233–1242. (2) Stenfors, N.; Nordenhall, C.; Salvi, S. S.; Mudway, I.; Soderberg, M.; Blomberg, A.; Helleday, R.; Levinz, J.-O.; Holgate, S. T.; Kelly, F. J.; Frew, A. J.; Sandstrom, T. Different airway inflammatory responses in asthmatic and healthy humans exposed to diesel. Eur. Respir. J. 2004, 23, 82–86. (3) Dockery, D.; Pope, C. A., III; Xu, X.; Spengler, J. D.; Ware, J. H.; Fay, M. E.; Ferris, B. J.; Speizer, F. E. An association between air pollution and mortality in 6 US cities. N. Engl. J. Med. 1993, 329, 1753–1759. (4) Vedal, S.; Brauer, M.; White, R.; Petkau, J. Air pollution and daily mortality in a city with low levels of pollution. Environ. Health Perspect. 2003, 111, 45–51. (5) Bell, M. L.; Samet, J. M.; Dominici, F. Time-series studies of particulate matter. Ann. Rev. Public Health 2004, 25, 247–280. (6) Sandstrom, T.; Nowak, D.; Van Bree, L. Health effects of coarse particles in ambient air: messages for research and decision-making. Eur. Respir. J. 2005, 26, 187–188. (7) Hinds, W. C. Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles; J. Wiley & Sons, Inc.: New York, 1999. (8) Graff, D. W.; Cascio, W. E.; Rappold, A.; Zhou, H.; Huang, Y. C. T.; Devlin, R. B. Exposure to concentrated coarse air pollution particles causes mild cardiopulmonary effects in healthy young adults. Environ. Health Perspect. 2009, 117, 1089–1094. (9) Huang, Y. C.; Ghio, A. J. Vascular effects of ambient pollutant particles and metals. Curr. Vasc. Pharmacol. 2006, 4, 199–203. (10) Jerrett, M.; Burnett, R. T.; Ma, R. J.; Pope, C. A.; Krewski, D.; Newbold, K. B.; Thurston, G.; Shi, Y,L; Finkelstein, N.; Calle, E. E.; Thun, M. J. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 2005, 16, 727–736. (11) Monn, C. Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone. Atmos. Environ. 2001, 35, 1–32. (12) Wilson, W. E.; Suh, H. H. Fine particles and coarse particles: concentration realtionships relevant to epidemiological studies. J. Air Waste Manage. Assoc. 1997, 47, 1238–1249. (13) Burton, R. M.; Suh, H. H.; Koutrakis, P. Spatial variation in particulate concentrations within metropolitan Philadelphia. Environ. Sci. Technol. 1996, 30, 400–407. (14) Hwang, I.; Hopke, P. K.; Pinto, J. P. Source apportionment and spatial distributions of coarse particles during the Regional Air Pollution Study. Environ. Sci. Technol. 2008, 42, 3524–3530. (15) Ott, D. K.; Cyrs, W.; Peters, T. M. Passive measurement of coarse particulate matter, PM102.5. J. Aerosol Sci. 2008, 39, 156–167. 3295


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Heterogeneity of Coarse Particles in an Urban Area - Environmental

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