# Chapter 3. Source to Hu man Exposure

3.1. Introduction

This chapter describes basic concepts and new and established findings in atmospheric sciences and human exposure assessment relevant to PM to establish a foundation for the health and ecological effects discussed in subsequent chapters. Information in this chapter builds on previous AQCDs for PM using new data and re-interpretations of extant studies as well. This includes new knowledge of PM chemistry, the latest developm ents in monitoring methodologies, recent national and local measurements and trends in PM co ncentrations as a function of size range and composition, advances in receptor and chemistr y-transport modeling, revised estimates of policyrelevant background PM, and recent work on exposure assessment. The chapter and its associated annex materi al are organized as follows: Section 3.2 presents an overview of basic information related to the size di stribution and composition of airborne particles. Section 3.3 provides a brief description of the sources , emissions, and deposition of PM, including discussions of possible mechanisms of secondary PM formation from gaseous precursors and of the atmospheric processes that deposit PM to the earth’s surface. Issues related to the measurement of PM and its chemical components and to monito rs and networks in the U.S. are covered in Section 3.4 ; supplementary material on these topics is c ontained in Annex A, Section A.1. Analyses of data for ambient concentrations of PM and its components are characterized in Section 3.5 , and supplementary information can be found in Annex A, Section A.2. Section 3.6 describes methods for determining source contributions to ambient samples by receptor models and presents results from recent receptor modeling studies. In addition, the construction of chemistry-transport models (CTMs) to determine pollutant concentrations is described in Section 3.6 . Supplementary information about receptor model methods and result s is given in Annex A, Section A.3. Policy relevant background concentrations of PM, i.e., th ose concentrations defined to result from natural sources everywhere in the world together with an thropogenic sources outside of Canada, the United States, and Mexico, are presented in Section 3.7 . Issues related to human exposure assessment including sources of exposure and implications for epidemiologic studies are discussed in Section 3.8 . Supplementary information on exposure studies is included in Annex A, Section A.4. Finally, the summary and conclusions from Chapter 3 are presented in Section 3.9

.



# 3.2. Overview of Basic Aerosol Properties

Unlike gas-phase pollutants such as SO 2 

### , CO, H 2 

### CO and O 3 

### , which are well-defined chemical entities, atmospheric PM varies in size, shape, an d chemical composition. Atmospheric chemical and microphysical processing of direct emissions of PM and its precursors together with mechanical generation of particles tend to produce distinct lognormal modes (Whitby, 1978, 071181 ) as shown in Figure 3-1 . To the extent that information is available, discussions in this and subsequent chapters will focus on particles in specific size ranges (i.e., PM 2.5 

### , PM 10-2.5 

### and PM 10 

### ). The subscripts after PM

Note: Hyperlinks to the reference citations throughout this doc ument will take you to the NCEA HERO database (Health and Environmental Research Online) at http://epa.gov/hero . HERO is a database of scientific literature used by U.S. EPA in the process of developing science assessments such as the Integrated Science Assessments (ISA) and the Integrated Risk Information System (IRIS)

.

December 2009 3-1

### refer to the aerodynamic diameter 1 

### (d

ae 

### ) in micrometers (μm) of 50% cut points of sampling devices. For example, EPA defines PM 10 

### as particles collected by a sampler with an upper 50% cut point of 10 μm d

ae 

### and a specific, fairly sharp, penetration cu rve, as defined in the Code of Federal Regulations (40 CFR Part 58). PM 2.5 

### is defined in an analogous wa y. Ultrafine particles (UFPs), defined here as particles with a diameter ≤ 0.1 μm (typically based on physical size, thermal diffusivity, or electrical mobili ty), will also be discussed. The terms “fine particles” and “coarse particles” have lost the precise meaning as defined in Whitby (1978, 071181 ), where “fine particles” refers to all particles in the nucleation, Aitken, and accumulation modes; and “coarse particles” characterizes all particles larger than these. UFPs correspond loosely to the nucleation plus Aitken m odes (in earlier literature, these modes were not separated and the combination, unresolved by older instruments, was called the Aitken mode). Now, the term “fine particles” is m ost often associated with the PM 2.5 

### fraction, which includes the nucleation, Aitken and accumulation modes and some particles from the lower-size tail of the coarse particle mode between about 1 and 2.5 μm aerodynamic diameter. “Thoracic coarse” is frequently used in reference to PM 10-2.5 

### , which does not include the low-end tail of the coarse particle mode. With high relative humidity, larger particles in th e accumulation mode could also extend into the 1 to 3 μm size range. These relationships can be seen in Figure 3-1 , which shows the number distribution for UPFs and the volume distribution (or mass distribution if particle density is constant across the size range) for fine and (thoracic) coarse particles. The figure is arranged th is way because particle number is most highly concentrated in the ultraf ine (UF) size range but volume (or mass) is most concentrated in the larger size ranges.

Source:Reprinted with Permission of Cambridge University Press from Pandis (2004, 156838 ).



## Figure 3-1. Particle size distributions by number and volume. Dashed lines refer to values in individual modes and solid lines to their sum. Note that ultrafine particles are a subset of fine particles.

1 Aerodynamic diameter is the diameter of a unit density (1 g/cm 3 ) sphere that has the same gravitational settling velocity as the particle of

interest and is a useful metric for characterizing particles >~ 1 μm. For sub-micron particles, forces other than gravity incre ase in importance in determining a particle’s motion and air can no l onger be considered a continuum. Aerodynamic diameter is frequent ly reported down to ~ 0.1 μm where the assumptions used in its derivation no longer hold. A useful metric for characterizing parti cles <~ 0.5 μm is the mobility diameter defined as the diameter of a particle having the same diffusiv ity or electrical mobility in air as the particle of interest. In the region between ~ 0.5–1.0 μm, aerodyna mic and mobility diameters are not necessarily the same. The question of how best to merge these diameters is unresolved and depends on the particle properties of interest.

December 2009 3-2

### Characterizing particle size is important because different size particles penetrate to different regions of the human respiratory tract. Thoracic partic les refer to particles that travel past the larynx to reach the lung airways and the gas-exchange region of the lung, and respirable particles are those that reach the gas-exchange region. The selection of PM 10 

### as an indicator of thoracic particles was based in large part on dosimetry (U.S. EPA, 1996, 079380 ). However, the selection of PM 2.5 

### to characterize respirable particles was driven ma inly by considerations related to measurement techniques available at the time rather than dosime try. Currently, cut points other than 2.5 μm are attainable and frequently put into use. For exampl e, the American Conference of Governmental Industrial Hygienists (ACGIH, 2005, 156188 ), the International Standards Organization, and the European Standardization Committee have adopted a 50% cut point of 4 μm as an indicator of respirable particles. Most commonly, however, PM 2.5 

### is used as an indicator of respirable particles,
PM 10-2.5 

### is used as an indicator of the thoracic component of coarse particles that is sometimes referred to as thoracic coarse (noting that it excl udes some coarse particles below 2.5 μm and above 10 μm), and PM 10 

### is used as an indicator of thoracic particles. As can be seen from Table 3-1 , particles in individual size modes are characterized by rather distinct sources, composition, chemical pr operties, lifetimes in the atmosphere ( τ ) and distances over which they can travel. Whereas particles in the sm aller size modes are formed mainly by combustion processes and by nucleation and condensation of gase s, coarse particles are generated mainly by mechanical activity, such as by the action of wi nd on either the ground or the sea surface or by construction or by resuspension by traffic. Particles in the UF size range are either emitted directly to the atmosphere or are formed by nucleation of gaseous constituents in the atmosphere. The properties of fibers and engineered nano-objects and nanometer scale products (e.g., dots, hollow spheres, rods, fibers, and tubes) are not reviewed in this chapter because these classes of objects are found mainly in certain occupational settings rather than in ambient air.

December 2009 3-3

## Table 3-1. Characteristics of ambi ent fine (ultrafine plus accumulation-mode) and coarse particles.

Fine

Ultrafine Accumulation Coarse

Formation Processes Combustion, high-temperature processes, and atmospheric reactions Break-up of large solids/droplets

Formed by Nucleation of atmospheric gases including H 2 SO 4 , NH 3 and some organic compounds

Condensation of gases Condensation of gases

Coagulation of smaller particles

Reactions of gases in or on particles

Evaporation of fog and cloud droplets in which gases have dissolved and reacted Mechanical disruption (crushing, grinding, abrasion of surfaces)

Evaporation of sprays

Suspension of dusts

Reactions of gases in or on particles

Composed of Sulfate

EC

Metal compounds

Organic compounds with very low saturation vapor pressure at ambient temperature Sulfate, nitrate, ammonium, and hydrogen ions

EC

Large variety of organic compounds

Metals: compounds of Pb, Cd, V, Ni, Cu, Zn, Mn, Fe, etc.

Particle-bound water

Bacteria, viruses Nitrates/chlorides/sulfates from HNO 3 /HCl/SO 2 reactions with coarse particles

Oxides of crustal elements (Si, Al, Ti, Fe)

CaCO 3 , CaSO 4 , NaCl, sea salt

Bacteria, pollen, mold, fungal spores, plant and animal debris

Solubility Not well characteri zed Largely soluble, hygroscopic, and deli quescent Largely insoluble and nonhygroscopic

Sources High temperature combustion

Atmospheric reactions of primary, gaseous compounds. Combustion of fossil and biomass fuels, and high temperature industrial processes, smelters, refineries, steel mills etc.

Atmospheric oxidation of NO 2 , SO 2 , and organic compounds, including biogenic organic species (e.g., terpenes) Resuspension of particles deposited onto roads

Tire, brake pad, and road wear debris

Suspension from disturbed soil (e.g., farming, mining, unpaved roads)

Construction and demolition

Fly ash from uncontrolled combustion of coal, oil, and wood

Ocean spray

Atmospheric half-life Minutes to hours Days to weeks Minutes to hours

Removal Processes Grows into accumulation mode

Diffuses to raindrops and other surfaces Forms cloud droplets and rains out

Dry deposition Dry deposition by fallout

Scavenging by falling rain drops

Travel distance <1 to 10s of km 100s to 1000s of km <1 to 10s of km (100s to 1,000s of km in dust storms for the small size tail)

Source: Adapted with Permission of the Air & Waste Management Association from Wilson and Suh (1997, 077408 )

December 2009 3-4Source: National Exposure Research Laboratory.



## Figure 3-2. X-ray spectra and scanning electron microscopy images of individual particles. These include: (1) an aluminum-silicate fly ash sphere emitted from a coal-fired power plant; (2) an iron oxide sphere emitted from a steel manufacturing facility; (3) an aluminum-silicate particle, probably of crustal origin; (4) a carbon soot aggregate from a diesel engine consisting of many sub-micron size carbon particles; (5) a sodium chloride crystal, potentially of marine origin; and (6) a partially collapsed pollen particle. The polycarbonate filter substrate used to collect the particles is visible in the ba ckground of each image and contributes to the carbon peak in each spectrum.

Particles appear in a wide variety of shapes su ch as spheres, ellipsoids, cubes, and irregular or fractal geometries. This is one reason why a standa rd metric such as aerodynamic diameter is useful for describing the mechanical properties of the part icles. The shape of particles is important for determining the optical properties of the particles. The directionality of sunl ight scattered by certain shapes of particles, such as plates, also depe nds strongly on their physical orientation while suspended. The shape of particles also affects the surf ace area of the particles in contact with the surface it is deposited on, including cell membranes. Images of six types of individual partic les obtained using scanning electron microscope (SEM) and their corresponding x-ray spectra showi ng their major elemental composition are shown

December 2009 3-5

### in Figure 3-2 . The images show particles sitting on a thin polycarbonate film with pores and a 1 μm scale bar for size reference. Air is pulled through the filter pores with a vacuum pump and the particles are left behind on the surface. The meta l dominated spherical particles (image 2) were formed at high temperatures and were quickly cooled. Particles which are liquid such as sulfate are also spherical. Sodium chloride (NaCl) crystals are c ubic (image 5); this particular particle could be marine sea salt due to the proximity to the ocea n where the sample was collected. Other particles, such as the carbon chain agglomerates from diesel engines have much more irregular and complex shapes (image 4). Note that these particles were placed under vacuum resulting in volatilization of water and other volatile components and partial co llapse of the pollen grain (image 6). Changes in composition and possibly morphology could occur duri ng the sampling, collection and analysis of aerosol samples. For example, particles may be coat ed with semi-volatile material that evolves off the particles under vacuum and under the electron beam. Similarly, particles in ambient air are generally mixtures or agglomerates of particles coming from multiple sources and have a diverse chemical make-up.



# 3.3. Sources, Emissions and De position of Primary and Secondary PM

PM is composed of both primary (derived direct ly from emissions) or secondary (derived from atmospheric reactions involving gaseous precursors) components. Table 3-4 summarizes anthropogenic and natural sources for the major prim ary and secondary aerosol constituents of fine and coarse particles. Anthropogenic sources can be further divided into stationary and mobile sources. Stationary sources include fuel combustion for electrical utilities, residential space heating and cooking; industrial processes; construction and demolition; metal, mineral, and petrochemical processing; wood products processing; mills and elevators used in agriculture; erosion from tilled lands; waste disposal and recycling; and biomass combustion. Biomass combustion encompasses many emission activities including burning of wood for fuel, burning of vegetation to clear land for agriculture and construction, to dispose of agricultural and domestic waste, to control the growth of animal or plant pests, and to manage forest resources (prescribed burning). Wildlands also burn due to lightning strikes and arson. Mobile or transporta tion-related sources include direct emissions of primary PM and secondary PM precursors from highw ay vehicles and non-road sources as well as fugitive dust from paved and unpaved roads. Also shown in Table 3-2 are sources for several precursor gases, the oxidation of which can form secondary PM. An overview of estimates of emissions of primary PM and precursors to secondary PM from major sources is given in Section 3.3.1 . The transformations from gaseous precursors shown in Table 3-2 to secondary PM are described in Section 3.3.2

. In general, the sources of fine PM are very different from those of coarse PM. Some of the mass in the fine size fraction forms during com bustion from material that has volatilized in combustion chambers and then recondensed before emission to the atmosphere. Some ambient PM 2.5 

### forms in the atmosphere from photochemical reac tions involving precursor gases. Included in this category is the formation of new UFPs by (1) hom ogeneous nucleation of precursor gases and (2) the condensation of gases on pre-existing particles. Biol ogical material also exists in the fine fraction including many types of microorganisms, especially viruses and bacteria and fragments of pollens and fungal spores. PM 10-2.5 

### is mainly primary in origin, as it is produced by surface abrasion or by suspension of biological material and fragments of living things (e.g., plant and insect debris). In addition, atmospheric reaction products condense on coarse particles. Because precursor gases undergo mixing during transport from their sources a nd chemical reactions, and the oxidation of different gases can produce the same reaction products , it is difficult to identify individual sources of secondary PM. Transport and transformation of pr ecursors can occur over distances of hundreds of kilometers. PM 10-2.5 

### has a shorter lifetime in the atmosphere, so its effects tend to be more localized. However, intercontinental transport of dust from Afri can and Asian deserts occurs and some of this material is in the PM 10-2.5 

### size range. Major intercontinental dust events are highly episodic but small contributions can be present at other times (see Section 3.7 ).

December 2009 3-6

## Table 3-2. Constituents of atmospheri c particles and their major sources.

Primary (PM <2.5 μm) Primary (PM >2.5 μm) Secondary PM Precursors (PM <2.5 μm)

Aerosol species Natural Anthropogenic Natu ral Anthropogenic Natural Anthropogenic

Sulfate (SO 4 2– ) Sea spray Fossil fuel combustion Sea spray — Oxidation of reduced sulfur gases emitted by the oceans and wetlands and SO 2 and H 2 S emitted by volcanism and forest fires Oxidation of SO 2 emitted from fossil fuel combustion

Nitrate (NO 3 − ) — Mobile source exhaust — — Oxidation of NO X produced by soils, forest fires, and lightning Oxidation of NO X emitted from fossil fuel combustion and in motor vehicle exhaust

Minerals Erosion and reentrainment Fugitive dust from paved and unpaved roads, agriculture, forestry, construction, and

demolition Erosion and reentrainment Fugitive dust, paved and unpaved road dust, agriculture, forestry, construction, and

demolition — —

Ammonium (NH 4 + ) — Mobile source exhaust — — Emissions of NH 3 from wild animals, and undisturbed soil Emissions of NH 3 from motor vehicles, animal husbandry, sewage, and fertilized land

Organic carbon (OC) Wildfires Prescribed burning, wood burning, mobile source exhaust, cooking, tire wear and industrial
processes Soil humic matter Tire and asphalt wear, paved and unpaved road dust Oxidation of hydrocarbons emitted by vegetation (terpenes, waxes) and wild fires Oxidation of hydrocarbons emitted by motor vehicles, prescribed burning, wood burning, solvent use and industrial
processes

EC Wildfires Mobile source exhaust (mainly diesel), wood biomass burning,

and cooking — Tire and asphalt wear, paved and unpaved road dust — —

Metals Volcanic activity Fossil fuel combustion, smelting and other metallurgical processes, and

brake wear Erosion, reentrainment, and organic debris —

. — —

Bioaerosols Viruses and bacteria — Plant and insect fragments, pollen, fungal spores, and bacterial agglomerates — — —

Dash (—) indicates either very minor source or no known source of component

. Source: U.S. EPA (2004, 056905 ).



### Only major sources for each constituent within each broad category shown at the top of Table 3-2 are listed. Not all sources are equal in magn itude. Chemical characterizations of primary particulate emissions for a wide variety of natural and anthropogenic sources (as shown in Table 3-2 ) were given in Chapter 5 of the 1996 PM AQCD (U.S. EPA, 1996, 079380 ). Summary tables of the composition of source emissions presented in the 1996 PM AQCD (U.S. EPA, 1996, 079380 ) and updates to that information are provided in Appendix 3D to the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). Source composition profiles are archived by the EPA at http://www.epa.gov/ttn/chief/software/speciate/ . The profiles of source composition were based in large measure on the results of studies that collected source signatures for use in source apportionment studies.

December 2009 3-7

### Natural sources of primary PM include windblown dust from undisturbed land, sea spray, and biological material. The oxidation of a fraction of terpenes emitted by vegetation and reduced sulfur species from anaerobic environments leads to secondary PM formation. Ammonium (NH 4 + 

### ) ions, which play a major role in regulating the pH of particles, are derived from emissions of NH 3 

### gas. Source categories for NH 3 

### have been divided into emissions from undisturbed soils (natural) and emissions that are related to human activities (e.g., fe rtilized lands, domestic and farm animal waste). There is ongoing debate about characterizing em issions from wildfires as either natural or anthropogenic. Wildfires have been listed in Table 3-2 as natural in origin, but land management practices and other human actions affect the occurr ence and scope of wildfires. For example, fire suppression practices allow the buildup of combustible fuels and increase the susceptibility of forests to more severe and infrequent fires from whatever cause, including lightning strikes. Similarly, prescribed burning is listed as anthropogenic, but can be viewed as a substitute for wildfires that would otherwise occur eventually on the same land.



# 3.3.1. Emissions of Primary PM and Precursors to Secondary PM

U.S. na tional average emissions of primary PM 2.5 

### , PM 10 

### and gaseous precursor species (SO 2 

### , NO X 

### , NH 3 

### and VOCs) from different source categories are shown in Figure 3-3 . Note that the entries refer mainly to anthropogenic sources, with lit tle information about natural sources. However, for categories such as VOCs, the contribution from bi ogenic emissions of isoprene and terpenes can be quite large. The entries are continually undergoing revision and are subject to varying degrees of uncertainty. For example, almost all of the sulfur in fuel is released as volatile components (SO 2 

### or SO 3 

### ) during combustion. Hence, sulfur emissions can be calculated on the basis of sulfur content in fuels to a greater accuracy than can be done for other pollutants like nitrogen oxides or primary PM. There have been notable downward revisions to the inventories since 2002 in the emissions of dust from roads. These have resulted in large measure from incorporation of em issions test data with updated methods for measuring dust emissions. Also, the spatial and temporal characterization of wildfire emissions has improved since 2002 by integra ting satellite-derived fire detection and stateof-the-art fuels characterization and consumption models (Pouliot et al., 2008, 156883 ). Emission measurements from high-temperatur e combustion sources are sensitive to the dilution, temperature, and pre-treatment of dilution air (England et al., 2007, 156420 ; England et al., 2007, 156421 ; Sheya et al., 2008, 156977 ). To a large extent, especially with regard to th e contribution of road dust to PM, refinements in emission estimates have been guided by the use of receptor modeling. See Section 3.6.1 for a description of receptor modeling techni ques and the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) for the role of receptor models in refining emissions estimates. Note that since the estimates given in Figure 3-3 are U.S. national averages, they may not accu rately reflect the contribution of specific local sources determining a person’s exposures to PM at any given time and location. As can be seen from a comparison of the total U. S. emissions (in million metric tons) shown in Figure 3-3 , estimates of total emissions of potential pr ecursors to secondary PM formation including SO 2 

### , NO X 

### , NH 3 

### and VOCs are considerably larger than those for primary PM sources. However, translating the emissions of precursors into pr oduction rates of secondary PM or using these emissions as a guide to estimate PM composition is highly problematic. A significant fraction of gaseous precursors are lost before they could be converted to PM. Dry deposition and precipitation scavenging of some of these gaseous precursors and their intermediate oxidation products occur before they are transformed in the atmosphere, and most VOCs emitted are oxidized to carbon dioxide (CO 2 

### ) rather than to PM. Some of these precursors are also transported outside the United States. Even if gaseous precursors are converted to PM components, the effects of atmospheric transformations must also be considered (discussed in Section 3.3.2 below). As a result of these transformations, ratios of masses of particle phase products to each other will not be the same as those in the emissions inventories for their precursors. For example, SO 2 

### , NO X 

### and NH 3 

### are converted to secondary PM as SO 4 2- 

### , NO 3 - 

### and NH 4 + 

### . The ratios of molecular weights of PM products (SO 4 2- 

### , NO 3 - 

### and NH 4 + 

### ) to gaseous precursors (SO 2 

### , NO X 

### and NH 3 

### ) are 1.5, 1.35, and 1.07, respectively. Estimating a conversion factor fo r carbon in VOCs is less straightforward. The oxidation of VOCs leads to the formation of sec ondary OC in PM. As the result of atmospheric transformations, nitrogen and oxygen are added to th e carbon originally present in VOCs. Turpin and Lim (2001, 017093 ) recommend adjustment factors ranging from 1.4 to 2.0 to account for the presence of oxygen and nitrogen in organic compounds in OC in the aerosol phase. Because of all

December 2009 3-8

### the above issues, the resultant mass and composition of ambient PM is quite different from what might be inferred from examining the emissions inventories alone.

Source: U.S. EPA (2006, 157070 )



## Figure 3-3. Detailed source categorization of anthropogenic emissions of primary PM 2.5 

## , PM 10 

## and gaseous precursor species SO 2 

## , NO X 

## , NH 3 

## and VOCs for 2002 in units of million metric tons (MMT). EGUs = Electricity Generating Units.

December 2009 3-9

# 3.3.2.

3.3.2.1. 

# Formation of Secondary PM

Precur sors to secondary PM have natural and anthropogenic sources, just as primary PM has natural and anthropogenic sources. A substantial fraction of the fine particle mass, especially during the warmer months of the year, is secondary in na ture, formed as the result of atmospheric reactions involving both inorganic and organic gaseous pr ecursors. The major atmospheric chemical transformations leading to the formation of particulate nitrate (pNO 3 

### ) and particulate sulfate (pSO 4 

### ) are relatively well understood; whereas those i nvolving the formation of secondary organic aerosol (SOA) are less well understood and are subject to much current investigation. A large number of organic precursors are involved and many of the kine tic details still need to be determined. Also, many of the products of the oxidation of hydrocarbons have yet to be identified. However, there has been substantial progress made in understanding the chemistry of SOA formation in the past few

years.



# Formation of Nitrate and Sulfate

The basi c mechanism of the gas and aqueous phase oxidation of NO 2 

### and SO 2 

### has long been studied and can be found in numerous texts on at mospheric chemistry, e.g., Seinfeld and Pandis (1998, 018352 ), Finlayson-Pitts and Pitts (2000, 055565 ), Jacob (1999, 091122 ), and Jacobson (2002, 090667 ). The reader is referred to the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) as well as the 2008 NO X 

### and SO X 

### ISAs (U.S. EPA, 2008, 157073 ; U.S. EPA, 2008, 157074 ; U.S. EPA, 2008, 157075 ) where these processes are described in great detail.



# 3.3.2.2. Formation of Secondary Organic Aerosol

Some key new findings have altered percepti ons of SOA formation since the 2004 PM AQCD (see especially the reviews by Kroll and Seinfeld (2008, 155910 ) and Rudich et al. (2007, 156059 )). New measurement techniques for estimating the sp eciation and water solubility of organic aerosols have noted the dominant contribution of oxygenat ed species in atmospheric particles. Recent measurements show that the abundance of oxidized SOA exceeds that of more reduced hydrocarbon like organic aerosol in Pittsburgh (Zhang et al., 2005, 157185 ) and in about 30 other cities across the Northern Hemisphere (Zhang et al., 2007, 101119 ). Based on aircraft and ship-based sampling of organic aerosols in coastal waters downwind of no rtheastern U.S. cities, de Gouw et al. (2008, 191757 ) reported that 40-70% of measured orga nic mass was water soluble and estimated that approximately 37% of SOA is attributable to aromatic precursors, based on PM yields estimated for NO X 

### -limited conditions. However, the remaining ma ss of estimated SOA (63%) was unexplained, possibly due to oxidation of semivolatile precursors not measured by standard gas chromatography. Aerosol yields from the oxidation of aromatic compounds have been reported to be higher when reactions with NO X 

### are not dominant, suggesting that transport of less reactive compounds (e.g., benzene) out of source regions with high NO X 

### levels could result in greater overall SOA yields than previously estimated (Ng et al., 2007, 199528 ). Furthermore, Zhang et al. (2007, 189998 ) noted that the most common mass spectrum of oxygenated OA m easured in ambient air resembles mass spectra measured in irradiated diesel exha ust reported by Robinson et al. (2007, 156053 ) and Sage et al. (2008, 191758 ). Typical dilution sampling of combustion sources employ dilution rate, temperature, pressure, and background aerosol concentrations that can differ substantially from ambient conditions. Lipsky and Robinson (2006, 189891 ) and Robinson et al. (2007, 156053 ) showed that under higher dilution conditions, the fraction of diesel engine organic emissions that volatilizes is higher than that measured using common test methods. Murphy and Pandis (2009, 190095 ) pointed out the importance of characterizing the volatility distribution of emissions of organic species from combustion sources for more accurately predicting the abundance and oxidation state of SOA in both urban and surrounding regional background environments. They note that in urban areas, volat ile emissions can be photochemically oxidized to more non-volatile compounds which then conde nse, forming oxidized SOA. Braun (2009, 189997 ) suggested that the weathering of di esel exhaust particles involves the desorption of semi-volatile organic compounds followed by the decomposition and reaction of the amorphous non-volatile carbon. These reactions would result in the formation of a number of functional groups on the

December 2009 3-10

### surface of the carbon core including quinones, carbohydroxide and carboxyl groups as well as sulfate. In general, all the above studies undersco re the importance of accurately describing the phase distribution of semivolatile organic compounds emitted by co mbustion sources under atmospheric conditions, and of atmospheric photoc hemical reactions in modifying the composition of emissions. Until a few years ago, the oxidation of terpenes and aromatic compou nds were considered as sources of SOA, and the oxidation of isoprene wa s not considered a source of SOA. However, observations of 2-methyl tetrols in ambient samples from a number of different environments suggest that small but not insignificant quantit ies of SOA are formed from isoprene oxidation (Claeys et al., 2004, 058608 ). Laboratory studies also indicate the formation of 2-methyl tetrols from isoprene oxidation (Edney et al., 2005, 155760 ; Kleindienst et al., 2006, 156650 ). Xia and Hopke (2006, 179947 ) observed the seasonal variations for the two major diastereoisomers produced during the oxidation of isoprene with highest concentrations occurring during summer and lowest concentrations occurring during winter. During su mmer, the maximum contribution of these two diastereoisomers to OC was 2.8%, however it is not clear if more SOA could have been produced from isoprene oxidation. Kroll and Seinfeld (2008, 155910 ) and Rudich et al. (2007, 156059 ) noted that the composition of SOA evolves from repeated cycles of volatilization and condensation of chemical reaction products in both the particle and gas phases. Rudich et al. (2007, 156059 ) focused on the oxidation of particle phase species by reaction with gas phase oxidants. Kroll and Seinfeld (2008, 155910 ) identified three factors that determine the SOA forming potential of organic compounds in the atmosphere:

1. Oxidation reactions of gas-phase organic species . These species include alkanes, alkenes, aromatics, cyclic olefins, isoprene and terp enes. Note that oxidation reactions can either lower volatility by addition of functional groups or increase volatility by cleavage of carbon-carbon bonds; 2. Reactions in the particle, or condensed, phase that can change volatility either by oxidation or formation of high-molecular-weight species. These reactions can lead to the formation of oligomers, thereby decreasing volatility or to the formation of more volatile products; and 3. Ongoing reactions that result from the varied volatility of oxidation products.

Ot her detailed work has focused on the formati on of higher molecular weight particle-phase oligomers (Gao et al., 2004, 156460 ; Kalberer et al., 2004, 156619 ; Tolocka et al., 2004, 087578 ), the importance of cloud processing in the evolution of SOA (Blando and Turpin, 2000, 155692 ; Gelencser and Varga, 2005, 156463 ), and the role of acid seeds in oligomer formation (Tolocka et al., 2004, 087578 ). These results imply that ambient samp les could contain mixtures of SOA from different sources at different stages of pro cessing, some with common reaction products making source identification of SOA problematic. Figure 3-4 shows a schematic of processes involved in the formation of SOA.

December 2009 3-11

# Aqueous phase

reactions Organic Compounds Gas phase

reactions of dicarbonyls, organic acids and ·OH, ·NO 2 

### , O 3 

### of alkanes, aromatics, alkenes, olefins, etc

and ·OH, O 3 

### , NO 2



### Gas/particle partitioning Nucleation, sorption, conde nsation/evaporation 

# Aerosol phase

reactions

products Droplet evaporation

products products PRIMARY SECONDARY



## Figure 3-4. Primary emissions and formation of SOA through gas, cloud and condensed phase reactions.

It should be noted that many of the products of terpene oxidation are oxidative in nature, and are not merely nonreactive oxidation products. Organi c peroxides represent an important class of reactive oxygen species (ROS) that have high oxidizing potential and could cause oxidative stress in cells on which these species deposit. For example, Docherty (2005, 087613 ) found evidence for the substantial production of organic hydroperoxides in secondary organic aerosol (SOA) resulting from the reaction of monoterpenes with O 3 

### . Analysis of the SOA formed in their environmental chamber indicated that the SOA was mainly organic hydroperoxides. In particular, they obtained yields of 47% and 85% of organic peroxides from the oxidation of α - and β -pinene. The hydroperoxides then react with aldehydes in particles to form peroxyhemiacetals, which can either rearrange to form other compounds such as alcohols and acids or revert back to the hydroperoxides. The aldehydes are also produced in large measure during the ozonolysis of the monoterpenes. Monoterpenes also react with OH radicals resulting, however, in the production of more lower molecular weight products than in their reaction with O 3 

### . Various terpenoid compounds are used in a number of household products and can be oxidized by ozone that has infiltrated fr om outdoors. The oxidation of terpenoid compounds indoors produces UFPs as described in the 2006 O 3 

### AQCD (U.S. EPA, 2006, 088089 ).



# 3.3.2.3. Formation of New Particles

In additi on to being emitted by high temperatur e combustion sources, ne w particles can form by nucleation of atmospheric gases. New particle formation has been observed in environments ranging from relatively unpolluted marine and con tinental environments to polluted urban areas (Kulmala et al., 2004, 089159 ). These new, nucleation mode particles are formed from molecular clusters. Competition between condensation of gases and clusters onto existing particles and coagulation of clusters determines which process will dominate (McMurry et al., 2005, 191759 ). Because of this competition, it is expected that particle number concentrations are dominated by primary anthropogenic emissions in highly polluted settings and by nucleation in remote continental sites. However, nucleation still occurs in urban environments and can still be the major source under certain conditions. The composition of UFPs will differ depending on the nature of their sources.

December 2009 3-12

### Nucleation is observed in the morning and extending into the afternoon, and occurs at higher rates during summer than during winter, consistent with a photochemical process. New particle formation events have been observed to occur over distances of several hundred kilometers in what have been called regional nucleation events (Shi et al., 2007, 191760 ). The major gas phase nucleating species involved are sulfuric acid vapor and water vapor. Kuang et al. (2008, 191196 ) suggested that the rate of nucleation is second order with respect to
H 2 

### SO 4 

### vapor depending on mechanism. However, other studies (e.g., Kulmala et al., 2007, 097838 ) have suggested that the nucleation rate is first order with respect to H 2 

### SO 4 

### vapor. These differences imply that a number of mechanistic details still remain to be determined, including the interactions with other species. However, this disparity is sm all compared to classical thermodynamic binary nucleation theory involving H 2 

### SO 4 

### and water vapor, in which the nucleation rate is given by H 2 

### SO 4 

### vapor to at least the 10th power (Kulmala et al., 1998, 129411 ). H 2 

### SO 4 

### vapor is produced by the gas phase oxidation of SO 2 

### by OH radicals (U.S. EPA, 2008, 157075 ). Ammonia (Gaydos et al., 2005, 191762 ) and organic acids and bases (amines) are also involved to some extent (Smith et al., 2008, 199529 ). The formation of UFPs indoors from the oxidation of terpenoids by O 3 

### as mentioned above also indicates that nucleation occurs indoors as well (U.S. EPA, 2006, 088089 ).



# 3.3.3.

3.3. 

# Mobile Source Emissions

3.1. Em issions from Gasoline Fueled Engines

PM emitt ed from gasoline fueled engines is a mix of OC, EC and small quantities of trace metals and sulfates, with OC constituting anyw here from 26-88% of PM (Cadle et al., 1999, 007636 ; Geller et al., 2006, 139644 ; Schauer et al., 2002, 035332 ). Most of the compounds in OC have yet to be characterized. High molecular weight and large PAHs have been identified in gasoline fueled vehicle emissions (Phuleria et al., 2006, 156867 ; Riddle et al., 2007, 115272 ). EPA exhaust emission standards do not control PM from gasoline vehicles as stringently as diesel vehicles. PM emissions from gasoline fueled vehicles decreased greatly as other exhaust emissions (primarily hydrocarbons and carbon monoxide) were controlled by improvements in the catalytic converter and better control of air-to-fuel mixture ratios for the engine intake. When leaded gasoline was used in pre-1975 model year vehicles, gasoline engine PM emissions were relatively large (about 300 mg/mile) and consisted largely of lead salts from combustion of the lead a dditive. A current gasoline fueled vehicle emits far lower PM, about 1-10 mg/mile. Emissions of gasoline PM increase at colder ambient temperatures and recent rulemaking for air toxics will result in significant reduction of gasoline PM at colder temperatures. Further details a bout the composition of motor vehi cle emissions in the context of source apportionment modeling can be found in Section 3.6.1

.



# 3.3.3.2. Emissions from Diesel Fueled Engines

Matti Ma ricq (2007, 155973 ) presents a conceptual model of diesel PM as a mix of nucleation-mode SO 4 2– 

### and hydrocarbons from unspent fuel and soot embedded with trace metals on which SO 4 2– 

### and hydrocarbons condense. PM emissions from pre-2007 diesels consist largely of EC (about 70% by mass) and OC (high molecular weight compounds derived from both diesel fuel and lubricating oil) which is responsible for about 25% of the PM (Maricq, 2007, 155973 ). The EC is non-volatile, while the organic material present exhibits temperature-dependent evaporation in similar fashion to a mixture of C24-C32 alkanes (Sakurai et al., 2003, 113924 ). Sulfates constitute about 5% of the PM. A small fraction of the diesel fuel sulfur (typically about 1-2%) is oxidized to sulfate. Trace elements (such as Zn and halogens, mainly from lubricating oil; and others) are also present. Mass spectra of organic diesel particles from pre-2007 engines appear to be largely similar to engine lube oil with minor contributions from unburned diesel fuel. Effective with the 2007 model year for on-road diesel heavy-duty highway truck engines, the new EPA PM standard (0.01 g per brake horsepower-hour [g/bhp-h]) reduced PM emission limits by 90% from the prior standard (0.10 g/bhp-h). By co mparison, uncontrolled heavy-duty diesels (pre1988 model years) emitted about 1-2 g/bhp-h of PM. The 2007 standard resulted in the introduction of new emission control technology, mainly the diesel particulate filter (DPF). Other elements of the

December 2009 3-13

### new control technology also include water-cooled e xhaust gas recirculation (mostly for control of NO X 

### ), a diesel oxidation catalyst (DOC) used in some vehicles and improved fuel injection systems. Besides the large reduction in diesel PM on a mass basis, the composition of diesel PM changed greatly. EPA regulations required that diesel fuel for on-road vehicles contain no more than 15 ppm sulfur as of January 2007 (Lim et al., 2007, 155931 ). Prior to that, the limit on diesel fuel sulfur established in 1995 for on-road vehicles was 500 ppm. The HEI-ACES study characterizes emissions from four engines and shows that PM emissions are about 0.001 g/bhp-h or 90% below the level of the current (2007) emission standard (Shimpi et al., 2009, 189888 ). This study also characterizes the composi tion of the much lower mass of PM emitted with this new technology. PM samples collected over a composite type test c onsisted of 53% sulfate, 30% OC, 13% EC and 4% other components, including metals. A substantial frac tion of sulfur is converted to sulfate over the diesel particulate filter resulting in the higher fr actional content of sulfate emissions. However, due to the much lower mass of PM being emitted (over a 90% reduction compared to earlier diesels) as well as the low sulfur content of the fuel, the tota l mass of sulfate emitted is somewhat less than that from earlier diesels. This work also shows that UFP number emissions are lower (about 90% lower) and that a number of other emissions are also controlled, including PAHs, nitro-PAHs, carbonyls (such as aldehdyes), and metals. Pre-2007 engines can be retrofitted with e xhaust aftertreatment devices, including DPFs, DOCs, and selective catalytic reduction (SCR ) systems to reduce emi ssions (U.S. EPA, 2009, 189885 ). Hu et al. (2009, 189886 ) examined emissions of various metals (V, Pt) from various diesel retrofit systems including those using V-SCR and a zeolite-based SCR with a DPF. Pakbin et al. (2009, 189893 ) shows significant reductions in emissions of various PAHs for diesels with SCR retrofit systems for NO X 

### control. Biswas et al. (2008, 189969 ) examined PM size distribution and composition (including semi-volatile s and non-volatiles) from several advanced technology diesels including those with SCR. They showed major reductions in PM number in most driving conditions but did not show a reduction in PM number und er cruise conditions. Biswas et al. (2009, 189880 ) examined four heavy-duty diesel vehicles with va rious retrofits showing, in general, large reductions in PM but, in some cases, somewhat higher emi ssions (or smaller decreases than expected) for EC and OC. In general, under light load conditions such as id le, diesel PM has a higher percentage of OC emissions than at high load conditions. Under lighter loads, organic compounds are not oxidized as effectively as under high loads. Under higher loads, PM contains more EC than under light loads. Also, newer model year diesels through the 2006 m odel year tend to have a higher fraction of PM that is EC than older models. Emissions have been measured with the new technology engines under a number of driving cycles besides the Federal Test Procedure. The HEI ACES study examined emissions during a range of test procedures. In general, the low-load test cycle resulted in lower exhaust temperatures and higher emission than did the high-load cycles. Regeneration events also produced short-term increases in particle emissions. However, particle emission measurements on the ACES engines were consistently lower than those on a typical 2004 engine (Shimpi et al., 2009, 189888 ). EPA standards will result in non-road diesels also having technology like catalyzed diesel particulate filters starting in 2012. Similar standards have also been promulgated for locomotives powered by diesel engines. Some work has been done with prototype SCR systems for diesel NO X 

### control such as would be used for the 2010 diesel NO X 

### standard. This standard will also result in reductions for NO X 

### similar to those seen for PM in the 2007 standard. There is no information on emissions from diesel engines with this new technology at temperatures of ≤ 10° C. In general, the ratio of emissions under cold start conditions at low temperatures to emissions at 24° C is significan tly higher for diesel engines with new technology compared to emissions from non-catalyst systems. Note that the engines with the newer technology require time to allow the catalyst to reach norm al operating temperatures for full emission reductions. During the period of catalyst heating, pa rticles will be trapped in the DPF, but volatile components can pass through. As a result of this pa rticle-trapping, post-2007 model year engines still emit less than the older engines, even under cold start conditions.



# 3.3.4. Deposition of PM

Wet an d dry deposition are important processes for removing PM and other pollutants from the atmosphere on urban, regional, and global scales. Th e conceptual model for dry deposition is to view

December 2009 3-14

### the flux deposited on a surface as the product of a concentration (mass or moles of a pollutant/m 3 

### ) times a deposition velocity (V d 

### ) (m/s). Therefore, deposition has the units of mass per unit time per unit area, or flux. The general approach used to estimate V d 

### for gases or very small particles is the resistance-in-series method represented by Equation 3-1 :


## )RRR/(1V cba d 

## + +

=
Equation 3-1



### where R

a 

### , R b 

### , and R

c 

### represent the resistance due to atmospheric turbulence, transport in the fluid sublayer very near the elements of the surface, such as leaves or soil, and the resistance to uptake of the surface itself, respectively. Typically, these resi stances are empirically derived and can vary as a function of wind speed, solar radiation, plant ch aracteristics, precipitation/moisture, and soil/air temperature. These processes are shown schematically in Figure 3-5 . This approach works for a range of substances, although it is inappropriate for species with substantial re-emissions from the surface or for species where deposition to the surfa ce depends on concentrations at the surface itself. The approach is also modified somewhat for aerosols where R b 

### and R
c 

### are replaced with a surface V d 

### to account for gravitational settling.

Source: Courtesy of T. Pierce, USEPA / OR D / NERL / Atmospheric Modeling Division.



## Figure 3-5. Schematic of the resistance-in-series analogy for atmospheric deposition.

Wesley and Hicks (2000, 025018 ) listed several shortcomings of the then-current knowledge of dry deposition. Among those shortcomings were diff iculties in representing dry deposition over

December 2009 3-15

### varying terrain where horizontal advection plays a significant role in determining the magnitude of
R

a 

### and difficulties in adequately determining V d 

### for extremely stable conditions such as those occurring at night; see the discussion by Mahrt (1998, 048210 ). Under optimal conditions, when a model is exercised over a relatively small area wh ere dry deposition measurements have been made, models still generally showed uncertainties on the order of ± 30% (e.g., Brook et al., 1996, 024023 ; Massman et al., 1994, 043681 ; Padro, 1996, 052446 ; Wesely and Hicks, 2000, 025018 ). Wesley and Hicks (2000, 025018 ) concluded that an important result of those comparisons was that the level of sophistication of most dry deposition models was relatively low, and that deposition estimates, therefore, must rely heavily on empirical data. Still larger uncertainties exist when the surface features in the built environment are not well known or when the surface comprises a patchwork of different surface types, as is common in the eastern U.S.



# 3.3.4.1. Deposition Forms

Wet Depos ition

Wet deposition results from the incorporation of atmospheric particles and gases into cloud droplets and their subsequent precipitation as rain or snow, or from the scavenging of particles and gases by raindrops or snowflakes as they fall (Lovett, 1994, 024049 ). Wet deposition depends on precipitation amount and ambient pollutant concentr ations. Vegetation surface properties have little effect on wet deposition, although leaves can retain liquid and solubilized PM. Landscape characteristics can affect wet deposition via orographic effects and by the closer aerodynamic coupling to the atmosphere of tall fore st canopies as compared to the shorter shrub and herbaceous canopies. Following wet deposition, humid ity and temperature conditions further affect the extent of drying versus concentrating of solutions on foliar surfaces, which influence the rate of metabolic uptake of surface solutes (Swietlik and Faust, 1984, 046678 ). The net consequence of these factors on direct physical effects of wet deposited PM on leaves is not known (U.S. EPA, 2004, 056905 ). Rainfall introduces new wet deposition and also redistributes throughout the canopy previously dry-deposited particles (Peters and Eiden, 1992, 045277 ). The concentrations of suspended and dissolved materials are typically highest at the onset of precipitation and decline with duration of individual precipitation events (Hansen et al., 1994, 046634 ). Sustained rainfall removes much of the accumulation of dry-deposited partic les from foliar surfaces, reducing direct foliar effects and combining the associated chemical bur den with the wet-deposited material (Lovett, 1994, 024049 ) for transfer to the soil. Intense rainfall may contribute substantial total particulate inputs to the soil, but it also removes bioavailable or injurious pollutants from foliar surfaces. This washing effect, combined with differential foliar uptake and foliar leaching of different chemical constituents from particles, alters the compos ition of the rainwater that reaches the soil and the pollutant burden that is taken-up by plants. Once in the soil, thes e particle constituents may affect biogeochemical cycles of major, minor, and trace elements. Low intensity precipitation events, in contrast, may deposit significantly more particulate pollutants to foliar-surfaces than high intensity precipitation events. Additionally, low-intensity events may en hance foliar uptake through the hydrating of some previously dry-deposited par ticles (U.S. EPA, 2004, 056905 )



## Dry Deposition

Dry particulate deposition, especially of heavy me tals, base cations, and organic contaminants, is a complex and poorly characterized process. It appears to be controlled primarily by such variables as atmospheric stability, macro- a nd micro-surface roughness, particle diameter, and surface characteristics (Hosker and Lindberg, 1982, 019118 ). The range of particle sizes, the diversity of canopy surfaces, and the variety of chemi cal constituents in airborne particles have made it difficult to predict and to estimate dry particulate deposition (U.S. EPA, 2004, 056905 ). Dry deposition of atmospheric particles to pl ant and soil surfaces affects all exposed surfaces. Larger particles >5 μ m diameter are dry-deposited mainly by gravitational sedimentation and inertial impaction. Smaller particles, especially those with diameters between 0.2 and 2.0 μ m, are not readily

December 2009 3-16

### dry-deposited and may travel long distances in th e atmosphere until their eventual deposition, most often via precipitation. Plant parts of all types, along with exposed soil and water surfaces, receive steady deposits of dry dusts, EC, and heterogeneous secondary particles formed from gaseous precursors (U.S. EPA, 1982, 017610 ). Estimates of regional particulate dry deposition infer fluxes from the product of variable and uncertain measured or modeled particulate concentr ations in the atmosphere and even more variable and uncertain estimates of V d 

### parameterized for a variety of specific surfaces (e.g., Brook et al., 1996, 024023 ). Even for specific sites and well-defined particles, uncertainties are large. Modeling the dry deposition of particles to vegetation is at a re latively early stage of development, and it is not currently possible to identify a best or most generally applicable modeling approach (U.S. EPA, 2004, 056905 ).



## Deposition from Clouds and Fog

The occurrence of cloud and fog deposition tends to be geographically restricted to coastal and high mountain areas. Several factors make it particularly effective for the delivery of dissolved and suspended particles to vegetation. Concentrations of particulate-derived materials are often manyfold higher in cloud or fog water than in precipita tion or ambient air due to orographic effects and gas-liquid partitioning. In addition, fog and cloud water deliver particulate chemical species in a bioavailable-hydrated form to foliar surfaces. This enhances deposition by sedimentation and impaction of submicron aerosol particles that exhibit low V d 

### before fog droplet formation (Fowler et al., 1989, 002515 ). Deposition to vegetation in fog droplets is proportional to wind speed, droplet size, concentration, and fog density. In some ar eas, typically along foggy coastlines or at high elevations, this deposition represents a substantial fraction of total deposition to foliar surfaces (Fowler et al., 1991, 046630 ).



# 3.3.4.2. Methods for Estimating Dry Deposition

Methods f or estimating dry deposition of particles ar e more restricted than for gaseous species and fall into two major categories: surface analysis methods, which include all types of estimates of contaminant accumulation on surfaces of interest, a nd atmospheric deposition rate methods, which use measurements of contaminant concentrations in the atmosphere and descriptions of surrounding surface elements to estimate deposition rates (Davidson and Wu, 1990, 036799 ). Surface extraction or washing methods characterize the accumulation of pa rticles on natural surfaces of interest or on experimental surrogate surfaces. These techniques rely on methods designed specifically to remove only surface-deposited material. Total surface rinsate may be equated to accumulated deposition or to the difference in concentrations in rinsate between exposed and control (sheltered) surfaces and may be used to refine estimates of deposition. Foliar extraction techniques may underestimate deposition to leaves because of uptake and transl ocation processes that remove pollutants from the leaf surface (Garten and Hanson, 1990, 036803 ; Taylor et al., 1988, 019289 ). Foliar extraction methods also cannot distinguish gas from particle-phase sources (Bytnerowicz et al., 1987, 036493 ; Dasch, 1987, 036496 ; Kelly, 1988, 037379 ; Lindberg and Lovett, 1985, 036530 ; Van Aalst, 1982, 036481 ). The National Dry Deposition Network was esta blished in 1986 to document the magnitude, spatial variability, and trends in dry deposition across the United States. Currently, the network operates as a component of the CASTNet (Clarke et al., 1997, 025022 ). A significant limitation on current capacity to estimate regional effects of NO X 

### and SO X 

### deposition is inadequate knowledge of the mechanisms and factors governing particle dr y deposition to diverse surfaces (U.S. EPA, 2004, 056905 ). Collection and analysis of stem flow and th roughfall can also provide useful estimates of particulate deposition when compared to directly sa mpled precipitation. The method is most precise for particle deposition when gaseous deposition is a small component of the total dry deposition and when leaching or uptake of compounds of interest out of or into the foliage is not a significant fraction of the deposition because these lead to positive and negative artifacts in the calculated totals. Foliar washing, whether using precipitation or experimental lavage, is one of the best available methods to determine dry deposition to vegetated ecosystems. Major limitations include the site specificity of the measurements and the restriction to elements that are largely conserved within the

December 2009 3-17

### vegetative system. Surrogate surfaces have not been found that can adequately replicate essential features of natural surfaces, and therefore do not produce reliable estimates of particle deposition to the landscape. Micrometeorological methods employ eddy covari ance, eddy accumulation, or flux gradient protocols for quantifying dry deposition. These t echniques require measurements of particulate concentrations and of atmospheric transport processes. They are currently well developed for ideal conditions of flat, homogeneous, and extensive la ndscapes and for chemical species for which accurate and rapid sensors are available. Additional studies are needed to extend these techniques to more complex terrain and more chemical species. The eddy covariance technique measures ver tical fluxes of gases and fine particles from calculations of the mean covariance between the vertical component of wind velocity and pollutant concentration (Wesely et al., 1982, 036564 ) using sensors acquiring concentration data at 5-20 Hz. For the flux gradient or profile techniques, ver tical fluxes are calculated from a concentration difference and an eddy exchange coefficient determined at discrete heights (Erisman et al., 1988, 036510 ; Huebert et al., 1988, 036569 ). Most measurements of eddy transport of PM have used chemical sensors (rather than mass or particle c ounting) to focus on specific PM components. These techniques have not been well developed for generalized particles and may be less suitable for coarse particles that are transported efficiently in high frequency eddies (Gallagher et al., 1988, 046631 ).



# 3.3.4.3. Factors Affecting Dry Deposition Rates and Totals

In the siz e range of ~0.1-1.0 μ m where V d 

### is relatively independent of particle diameter as shown in Figure 3-6 , particulate deposition is controlled by roughness of the surface and by the stability and turbulence of the atmospheric surface layer. Impaction and interception dominate over diffusion as dry deposition processes, and the V d 

### is considerably lower than for particles that are either smaller than ~0.1 μm or larger than ~1.0 μm. Deposition of particles between 1 and 10 μ m diameter is strongly dependent on particle size as shown in Figure 3-6 . Larger particles within this size range are collected more efficiently at typical wind speeds than are smaller particles (Clough, 1975, 070850 ), suggesting the importance of impaction. Impaction is related to wind speed, the square of the particle diameter, and the inverse of the deposition surface cross-section. As a deposi ting particle's trajectory deviates from the streamlines of the air in which it is suspended, increasing either wind speed or the ratio of particle size to the deposition surface cross-section in creases the probability of collision. Empirical estimates of V d 

### for fine particles under wind tunnel and field conditions are often several-fold greater than predicted by theory (Unsworth and Wilshaw, 1989, 046682 ). A large number of transport phenomena, including streamlin ing of foliar obstacles, turbulence structure near surfaces, and various phoretic transport mechanis ms are not well characterized (U.S. EPA, 2004, 056905 ). The discrepancy between estimated and predicted values of V d 

### may reflect model limitations or experimental limitations in the specification of the effective size and number of deposition obstacles. Previous reviews (e.g., U.S. EPA, 1996, 079380 ; U.S. EPA, 2004, 056905 ) suggest the following generalizations: (1) particles >10 μ m exhibit variable V d 

### between 0.5 and 1.1 cm/s depending on friction veloci ties, whereas a minimum particle V d 

### of 0.03 cm/s exists for particles in the size range 0.1 to 1.0 μ m; (2) the V d 

### of particles is approximately a linear function of friction velocity; and (3) deposition of particles from the atmosphere to a forest canopy is from 2 to 16 times greater than deposition in adjacent open terrain like grasslands or other low vegetation.

December 2009 3-18Source: U.S. EPA (2004, 056905 ).



## Figure 3-6. The relationship between particle diameter and V d 

## for particles. Values measured in wind tunnels by Little and Wiffen (1977, 070869 ) over short grass with wind speed of 2.5 mi/s closely approximate th e theoretical distribut ion determined by Peters and Eiden (1992, 045277 ) for a tall spruce forest. These distributions reflect the interaction of Brownian diffusivity (descending dashed line), which decreases with particle size and sedimentation velocity (ascending dotted line derived from Stokes Law), which increases with particle size. Intermediate-sized particles (0.1-1.0 μ m) are influenced strongly by both particle size and sedimentation velocity, and deposition is relatively independent of size.



## Leaf Surface Effects on V d



### The chemical composition of a particle is not usually considered to be a primary determinant of its V d 

### . Rather, the plant leaf surface ha s an important influence on the V d 

### of particles, and therefore on the flux of dry deposition to the terrestri al environment. Relevant leaf surface properties include stickiness, microscale roughness, and cr oss-sectional area. These properties affect the probability of impaction and particle bounce. The efficiency of deposition to vegetation also varies with leaf shape. Particles impact more frequently on the adaxial (upper) surface than on the abaxial (lower) surface. Most particles accumulate in th e midvein, central portion of leaves. The greatest particle loading on dicotyledonous leaves is frequently on the adaxial surface at the base of the blade, just above the petiole junction. Precipitation washing probably plays an important role in this distribution pattern (U.S. EPA, 2004, 056905 ). Lead particles have been shown to accumulate to a greater extent on older as compared with younger needles and twigs of white pine, suggesting that wind and rain may be insufficient to fully wash the foliage. Fungal mycelia (derived from windborne spores) were frequently observed in intimate contact with other partic les on leaves, which may reflect minimal re-entrainment of the spore due to shelter by the particles, mycelia development near sources of soluble nutrients provided by the particles, or simply co-de position (Smith and Staskawicz, 1977, 046675 ). Leaves with complex shapes tend to collect more particles than do those with shapes that are more regular. For example, conifer needles are more efficient than broad leaves in collecting particles by impaction as a result of the small cross-s ection of the needles relative to the larger leaf laminae of broadleaves allowing for greater penetrat ion of wind into conifer canopies than broadleaf ones (U.S. EPA, 2004, 056905 ).

December 2009 3-19

## Canopy Surface Effects on V d



### Surface roughness increases particulate deposition, and V d 

### is usually greater for a forest than for a nonforested area and greater for a field than for a water surface. Different size particles have different transport properties and V d 

### . The upwind leading edges of forests, hedgerows, and individual plants are primary sites of coarse par ticle deposition. Impaction at high wind speed and the sedimentation that follows the reduction in wi nd speed and carrying capacity of the air in these areas lead to preferential deposition of larger particles (U.S. EPA, 2004, 056905 ). Air movement is slowed in proximity to vegetate d surfaces. Canopies of uneven age or with a diversity of species are typically aerodynamically rougher and receive larger inputs of dry-deposited pollutants than do smooth, low, or monoculture vegetation (Garner et al., 1989, 042085 ; U.S. EPA, 2004, 056905 ). Canopies on slopes facing the prevailing winds receive larger inputs of pollutants than more sheltered, interior canopy regions. All foliar surfaces within a forest canopy are not equally exposed to particle deposition. Upper canopy foliage tends to receive maximum exposure to coarse and fine particles, but foliage within the canopy tends to receive primarily fine aeros ol exposures. The dry deposition of fine-mode particles and unreactive gases tends to be more evenly distributed throughout the canopy. Both uptake and release of PM constituents can occur within the canopy. The leaf surface is a region of leaching and uptake. Exchange also occurs with epiphytic organisms and bark and through solubilization of previously dry-deposited PM. Vegetation emits a variety of particles and particulate precursor materials.



# 3.4.

3.4.1.

3.4. 

# Monitoring of PM

Ambi ent Measurement Techniques

1.1. PM Mass

Federal r eference methods (FRMs) and federa l equivelance methods (FEMs) for PM were discussed in detail in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). Issues discussed there include the definition or description of FRMs and FEMs for PM 2.5 

### and PM 10 

### , and measurement methods for
PM 10-2.5 

### . Also included are detailed descriptions of the WINS impactor, virtual and cascade multistage impactors for PM 10-2.5 

### measurement, high-volume and low-volume PM 10 

### samplers, and real-time or continuous methods for PM 2.5 

### and PM 10 

### including:

 Tapered Element Oscillating Microbalance (TEOM) operated at various temperatures;

 Sample Equilibration System (SES)-TEOM;

 Differential TEOM;

 β -Gauge Techniques (BGT);

 Piezoelectric Microbalance;

 Real-Time Total Ambient Mass Sampler (RAMS);

 Continuous Ambient Mass Monitor (CAMM);

 Continuous Coarse Particle Monitor (CCPM);

 Micro-orifice Uniform Deposit Impactor (MOUDI);

December 2009 3-20

###  Multichannel diffusion denuder sampling system (BOSS); and

 Light scattering photometric instruments.

I n this section, FRMs and FEMs for PM 2.5 

### , PM 10-2.5 

### , and PM 10 

### will be revisited and evaluated based on the cumulative understanding of these methods with a focus on evaluations performed following the 2004 PM AQCD (U.S. EPA, 2004, 056905 ), followed by the discussion of new techniques under development or evaluation.



## Federal Reference Method and Federal Equivalent Method

FRM and FEM PM samplers are designed to measure the mass concentrations of ambient particulate matter. The FRMs for measuring PM 2.5 

### , PM 10-2.5 

### , and PM 10 

### are specified in CFR 40 Part 50, Appendices L, O and J, respectively. The FRM for PM 2.5 

### is a hybrid-based method that specifies certain aspects of the method (e.g., component dimensions and tolerances, sample handling and analysis) by design specifications and other aspe cts (e.g., flow control) by performance specifications (U.S. EPA, 2004, 056905 ). The PM 10 

### FRM is performance-based; particles are inertially separated with a penetration efficiency of 50% at 10 ± 0.5 μ m aerodynamic diameter. The required collection efficiency as a function of particle size larger and smaller than 10 μm aerodynamic diameter is explicitly specified by a penetration curve in the CFR. Particles larger than 10 μm in aerodynamic diameter are co llected on the filter with diminishing collection efficiency as particle size increases. Likewise, particles sm aller than 10 μm in aerodynamic diameter are collected on the filter with increasing collection ef ficiency as particle size decreases. The FRM for
PM 10-2.5 

### concentration is computed as the numeric difference between concurrent and co-located
PM 10 

### and PM 2.5 

### concentrations obtained from low-volume FRM samplers of the same make and model. It should be noted that while the FRM for PM 2.5 

### and PM 10-2.5 

### reports data under local conditions, the FRM for PM 10 

### reports data corrected to standard temperature (298 K) and pressure (101.3 kPa) (STP). A very sharp cut cyclone (VSCC) wa s approved in 2004 as a Class II PM 2.5 

### FEM method (Kenny et al., 2004, 155895 ). The VSCC provides superior perf ormance over long sampling periods under heavy loading and was also incorporated as an optional second-stage separator for the PM 2.5 

### FRM (71 FR 61214, October 17, 2006). In 2006, EP A finalized new performance criteria (40 CFR Part 53) for the approval of FEMs as Class II equivalent methods when based on integrated filter sampling and as Class III equivalent methods when based on continuous technologies that can provide at least hourly data reporting. The perform ance criteria include evaluating additive bias (intercept) and multiplicative bias (slope) as well as correlation with co-located candidate and FRM methods at field studies covering multiple seasons a nd sampling locations. As a result of these new performance criteria, EPA has recently approved two filter-based PM 2.5 

### and two filter-based PM 10-2.5 

### Class II FEMs based on the virtual impactor techniques (dichotomous sampler), and five PM 2.5 

### and
one PM 10-2.5 

### Class III continuous FEMs based on BGT or TEOM techniques. The most recent list of FRMs and FEMs can be found in Annex A, Table A-2 and on the following EPA web site: http://www.epa.gov/ttn/amtic/f iles/ambient/criteria/refere nce-equivalent-methods-list.pdf

. Evaluations of FRMs and FEMs were conducted both in supersite studies and in other research studies (Ayers, 2004, 097440 ; Brown et al., 2006, 097665 ; Butler et al., 2003, 156313 ; Cabada et al., 2004, 148859 ; Chang and Tsai, 2003, 155718 ; Charron et al., 2004, 053849 ; Grover et al., 2005, 090044 ; Hains et al., 2007, 091039 ; Hering et al., 2004, 155837 ; Jaques et al., 2004, 155878 ; Krieger et al., 2007, 129657 ; Lee et al., 2005, 155925 ; Lee et al., 2005, 128139 ; Price et al., 2003, 098082 ; Rees et al., 2004, 097164 ; Russell et al., 2004, 082453 ; Salminen and Karlsson, 2003, 156070 ; Schwab et al., 2004, 098450 ; Schwab et al., 2006, 098449 ; Solomon et al., 2003, 156994 ; Tsai et al., 2006, 098312 ; Vega et al., 2003, 105974 ; Wilson et al., 2006, 091142 ; Yi et al., 2004, 156169 ; Zhu et al., 2007, 098367 ) (see Annex A, Tables A-3, A-5 and A-11 ). In general, the co-located FRMs showed very good precision with coefficient of variat ion (CV) <5%. For different co-located FRMs, the regression slope of one sampler on another is commonly close to unity with R 2 

### >0.95. The PM 2.5 

### and PM 10 

### concentrations measured by dichotomous samplers were within 10% of the FRM methods, and the differences can be attributed to the sa mpling artifacts of semi-volatile components; see Section 3.4.1.2 for details. The precision of various TEOMs ranges from 10-30%. The concentration measured by the TEOM operated at 50°C was consistently lower than those measured by the TEOM operated at 30°C. The differences between these moni tors were also found to be a function of season

December 2009 3-21

### and location. BGTs were highly correlated with FRMs but BGT mass could be higher than the FRM mass (30% higher at the Fresno supersite) (Chow et al., 2008, 156355 ). Additionally, a number of techniques have been developed to reduce positive and negative sampling artifacts. These are described in the ISA for NO X 

### and SO X 

### – Ecological Criteria (U.S. EPA, 2008, 157074 ). Several papers (Buser et al., 2007, 156310 ; Buser et al., 2007, 156311 ; Buser et al., 2007, 156312 ) published since the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) claim that the EPA FRM samplers for PM 10 

### “oversample certain agricultural and ot her source emissions.” These claims are based on the erroneous assumption that the "true" PM 10 

### concentration is what would be given by a
PM 10 

### sampler that excluded all particles greater than 10 μm aerodynamic diameter and included all particles less than 10 μm aerodynamic diameter. The legal definitions for PM 2.5 

### and PM 10 

### , as defined in the CFR (40 CFR Part 58), include both a 50% cut-point and a penetration curve. For PM 10 

### , the 50% cut-point of 10 ± 0.5 μm aerodynamic diameter means that 50% of particles with aerodynamic diameter of 10 ± 0.5 μm are removed by the inle t and 50% pass through the inlet where they are collected on the filter. The penetration curve specifies, as a function of particle size, the fraction of particles larger than 10 μm that pass through the inlet and the fraction of particles less than 10 μm that are intercepted by the inlet. No effort was made in the development of the FRM to have the
PM 10 

### sampler collect all particles less than 10 μm and no particles greater than 10 μm since the sampler was designed to collect a fraction of atmospheric particles similar to the “inhalable” or thoracic fraction, i.e., those particles that would pass through the nose and throat and reach the lungs (Miller et al., 1979, 070577 ; U.S. EPA, 2004, 056905 ). Thus, the FRM PM 10 

### sampler correctly and intentionally collects particles greater than 10 μm.
For PM 2.5 

### and PM 10 

### , it has long been known that FRMs are subject to sampling artifacts including particle bounce on heavily-loaded impa ction substrates and the loss of semi-volatile components of PM (e.g., NH 4 

### NO 3 

### , and some organics). Although there are no standard reference materials that provide a test of accuracy of the sampling method for airborne PM mass, in comparison with other sampling techniques that ca n measure both semi-volatile and nonvolatile PM, FRMs reported PM 2.5 

### or PM 10 

### mass concentrations biased low by as much as 10-30% (Chow et al., 2008, 156355 ). The bias of the FRMs depends on the composition of ambient PM and the sampling conditions (e.g., ambient temperature and relative humidity), which vary from day to day and from season to season. Another limitation of the current FRM sampling protocol is that filter samples are typically collected every 3 or 6 days (only

∼ 150 of the 900+ PM 2.5 

### FRMs operating in 2007 were scheduled to sample every day). Under this operating condition, the concentration-response relationship in air pollution health studies (especi ally in time-series studies) cannot be fully evaluated in terms of lag structures and distributed lags between ambient concentration and health outcome (Lippmann, 2009, 190083 ; Solomon and Hopke, 2008, 156997 ).



## Development and Evaluation of New Techniques

Several new innovations have recently emerged to measure both fine and coarse PM fractions in the ambient air. These techniques include the Filter Dynamics Measurement System-TEOM (FDMS-TEOM) (Grover et al., 2006, 138080 ) for real-time measurement of PM 2.5 

### or PM 10 

### and several new methods for measurement of PM 10-2.5 

### . In addition, several new techniques exist for measuring UFPs (discussed later in Section 3.4.1.4 ) and for estimating PM mass concentration indirectly using particle size (discussed later in Section 3.4.1.5 ).

Real-time Measurement of PM 2.5



### or PM 10 

### using the FDMS-TEOM

The FDMS-TEOM incorporates self-referencing capability to the traditional TEOM by alternating measurement of ambient air and chilled clean air (particles and semivolatile gases are removed by filtration at 4°C after which the clean ai r is reheated to 30°C) in 6-min intervals. As clean air flows over the sample filter, the semivola tile PM on the sample filt er is evaporated. Thus, the instrument provides direct measurements of th e nonvolatile particle mass and incorporates an adjustment for the semivolatile NH 4 

### NO 3 

### and organic material. In a comparison between the TEOM, FDMS-TEOM, and FRM mass, the PM 2.5 

### concentration measured by the TEOM operated at 50°C was consistently lower than those measured by the TEOM operated at 30°C. The TEOM operated at 30°C provided concentrations 50% lower than the FDMS-TEOM, and the FDMS-TEOM provided concentrations 10-30% higher than the FRM mass (Chow et al., 2008, 156355 ; Schwab et al., 2006, 098449 ).

December 2009 3-22

### Techniques for Measurement of PM 10-2.5 

### Methods developed to measure PM 10-2.5 

### are based on three measurement techniques: (1) virtual impactors using low-volume, high-volume, and real -time techniques; (2) cascade impactors; and (3) passive samplers. A low-volume dichotomous sampler (operated at 16 .7 L/min), based on virtual impaction, was described in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). Since then, a high-volume dichotomous sampler was developed to operate at 1,000 L/min (Sardar et al., 2006, 156071 ). The sampler was evaluated in the field by comparison with a MOUDI sampler, and the measured PM 10-2.5 

### mass concentrations were within 10%. The high-volum e dichotomous sampler provides sufficient mass collection for comprehensive standard chemical analyses over short sampling intervals. Using the virtual impactor technique in conjunction with a TEOM or BGT as the detector, continuous PM 10-2.5 

### measurement techniques were developed (Misra et al., 2001, 018998 ; Misra et al., 2003, 195001 ; Solomon and Sioutas, 2008, 190139 ). The TEOM method was highly correlated with the PM 10-2.5 

### FRM, but mass concentrations measured by the TEOM were 20-30% lower (Solomon and Sioutas, 2008, 190139 ). The BGT method also agreed well with the PM 10-2.5 

### FRM (slopes = 0.88-1.17, and
R 2 

### >0.95) (Solomon and Sioutas, 2008, 190139 ). Case et al. (2008, 155149 ) evaluated a cascade PM sampler designed to collect PM 10-2.5 

### on a foam impactor. Particle bouncing on impactors has long been a concern for PM collection. Porous foam was used to serve as the impactor substrate to reduce particle bounce and to collect relatively large amounts of particles (Demokritou et al., 2004, 186901 ; Demokritou et al., 2004, 190115 ; Huang et al., 2005, 186991 ; Kuo et al., 2005, 186997 ). The sampler was operated at 5 L/min, and it agreed with a low-volume dichotomous sampler within ±20%. The precision of the sampler was 20% as determined by the CV. An inexpensive passive sampler for PM 10-2.5 

### was also developed (Leith et al., 2007, 098241 ; Ott et al., 2008, 195004 ; Wagner and Leith, 2001, 190153 ; Wagner and Leith, 2001, 190154 ). The passive sampler collects particles by gravity, diffusion, and convective diffusion onto a glass coverslip, and then an image analysis is conducted on the collected particles to estimate mass flux as a function of aerodynamic diam eter. Leith et al. (2007, 098241 ) conducted a field evaluation of the passive sampler, and the difference between a FRM and the co-located passive sampler was within
1 σ of concentrations measured with PM 10-2.5 

### FRM samplers. Ott et al. (2008, 119394 ) reported the precision of the sampler was 11.6% (CV), and the detection limit was 2.3 μ g/m 3 

### for a 5-day sample.



# 3.4.1.2. PM Speciation

The follo wing sections describe recent developments regarding measurement techniques to ascertain quantities of particle-bound water, ca tions and anions, elemental composition, carbon, and organic species.



## Particle-Bound Water

Particle-bound water is an important co mponent of ambient PM (U.S. EPA, 2004, 056905 ). Recently, a differential method was developed to measure particle-bound water (Santarpia et al., 2004, 156944 ; Stanier et al., 2004, 095955 ). The dry ambient aerosol size spectrometer (DAASS) can measure particle-bound water in the particle size range from 3 nm-10 μm (Stanier et al., 2004, 095955 ), by alternatively measuring ambient PM size di stribution at low relative humidity (RH) and ambient RH. A comparison of the two size distributions provides information on the water absorption and change in particle size due to RH. Khlystov et al. (2005, 156635 ) reported that the particle-bound water, measur ed by DAASS, was underestimated for particles <200 nm and overestimated for particles >200 nm compared with thermodynamic models. The loss of semi-volatile components during measurement ma y bias the particle-bound water measurement results. Methods and analytical specifications for particle-bound water are listed in Annex A, Table A-12.

December 2009 3-23

## Cations and Anions

The measurement of cations and anions including SO 4 2– 

### , NO 3 – 

### , NH 4 + 

### , Cl
- 

### , Na + 

### , and K + 

### still relies primarily on filter-based collection, water based extraction and ion chromatography (IC) based chemical speciation and quantification. In add ition, denuders are frequently used in the sampling system to adjust for sampling artifacts. These methods have been reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). Filter-denuder based integr ated sampling methods for SO 4 2– 

### , NO 3 – 

### , and
NH 4 + 

### have been detailed in the 2008 SO X 

### - Health ISA (U.S. EPA, 2008, 157075 ) and the 2008 NO X 

### and SO X 

### - Ecological ISA (U.S. EPA, 2008, 157074 ). Recent developments in multiple ion measurem ents have focused on the coupling of IC and a sample dissolution system, represented by the Partic le into Liquid Sampler-Ion Chromatography (PILS-IC) and the Ambient Ion Monitor (AIM) (Orsini et al., 2003, 156008 ; Weber et al., 2001, 024640 ). When ambient PM passes through the PILS -IC system, water droplets are generated by mixing ambient PM with saturated water vapor and collected by impaction. The resulting liquid stream is then introduced into the IC system for ion speciation and quantification. Hourly concentrations of multiple ions can be obtained w ith the system, with a CV of 10%. For the AIM system, a parallel plate denuder is used to remove the interfering gases, and then particles enter a super-saturation chamber to form droplets. The collect ed droplets are then introduced into the IC for analysis. The AIM system can provide hourly concen trations for multiple ions. The particle mass spectrometer is another advance in multiple PM component measurements, but most of these types of measurements are semi-quantitative a nd will be detailed later in Section 3.4.1.3 . Note that measurement and analytical specifications for ions other than SO 4 2– 

### and NO 3 – 

### are listed in Annex A, Table A-9.

Sulfate

Methods used for continuous (sampling interval of minutes) measurements of SO 4 2– 

### include Aerosol Mass Spectrometry (A MS) (Drewnick et al., 2003, 099160 ; Hogrefe et al., 2004, 099003 ), PILS-IC (Weber et al., 2001, 024640 ), flash volatilization techniques (Bae, 2007, 155669 ; Stolzenburg and Hering, 2000, 013289 ) and the Harvard School of Public Health (HSPH) tube furnace to convert SO 4 2– 

### to SO 2 

### for detection by a SO 2 

### analyzer (Allen et al., 2001, 156205 ). These methods are described in detail by Drewnick et al. (2003, 099160 ), along with an inter-sampler comparison that found overall agreement within 2.9% for all continuous instruments with R 2 

### of 0.87 or better. When compared with filter samples, Drewnick et al. (2003, 099160 ) showed differences were less than 25% for the AMS, PILS, flash vaporization, and HSPH continuous SO 4 2– 

### monitors. The Thermo 5020 particulate sulfate analyzer (bas ed on the HSPH technique) compared within 80% of 24-h filter-based measurement at a rural site in New York (Schwab et al., 2006, 098449 ). Annex A, Tables A-8 and A-14, list detailed met hods and analytical specifications for sampling SO 4 2–



### .

Nitrate

In addition to the nylon filter-based method and the new developments mentioned for SO 4 2–



### , methods based on flash volatilization-chem iluminescence analysis and catalytic conversion-chemiluminescence analysis have also been developed for continuous NO 3 – 

### measurement (averaging time 30 s-10 min). For the flash volatilization system (Fine et al., 2003, 155775 ; Stolzenburg and Hering, 2000, 013289 ; Stolzenburg et al., 2003, 156102 ), particles are collected by a humidified impaction process and analyzed in place by flash vaporization and chemiluminescent detection of the evolved NO X 

### . For the catalytic conversion-chemiluminescence analysis system (Weber et al., 2003, 157129 ), NO 3 – 

### was measured by conversion of particle NO 3 – 

### into NO, and then detected with the chemilumine scence method. Field and lab comparisons were conducted to compare the different instruments mentioned above. Although the R&P 8400N ambient particulate NO 3 – 

### monitor, which is based on the Stolzenburg flash vaporization technique, could provide 10-min resolution data and showed exce llent precision (with a CV <10%) (Harrison et al., 2004, 136787 ; Hogrefe et al., 2004, 099003 ; Long and McClenny, 2006, 098214 ; Rattigan et al., 2006, 115897 ), it consistently reported NO 3 – 

### concentrations ~30% lower than the denuder-filter systems in both the Baltimore supersite and the mu ltiyear field study in New York (Harrison et al., 2004, 136787 ; Hogrefe et al., 2004, 099003 ; Rattigan et al., 2006, 115897 ). In the New York measurement campaign, an AMS was also co-located with other instruments to obtain the real-time NO 3 – 

### information. AMS did not always agree well with the denuder-filte r system for reasons not

December 2009 3-24

### entirely apparent. However, Bae et al. (2007, 156244 ) reported that some organic compounds can also produce signals at mass-to-charge ratio m/z = 30, which is one of the characteristic m/z for NO 3 – 

### . Therefore, the disagreement between the AMS a nd the filter-based method could be a result of the interference of organic compounds using the AMS. Annex A, Tables A-7 and A-13, list methods and analytical specifications for sampling NO 3 –



### .

Ammonium

Several continuous and semi-continuous instru ments can be used to monitor ambient ammonium concentrations (Al-Horr et al., 2003, 153951 ; Bae, 2007, 155669 ) including many listed above for SO 4 2– 

### and NO 3 – 

### . Bae et al. (2007, 155669 ) conducted an inter-comparison of three semi-continuous instruments during the New York multiyear air sampling campaign: a PILS-IC, an AMS, and a wet scrubbing-long path absor ption photometer. Bae et al. (2007, 155669 ) reported the inter-sampler coefficients of determination (R 2 

### ) between these instruments were above 0.75, and the slopes (with zero intercept) were between 0.71 and 1.04. Annex A, Table A-9 describes measurement of ions other than NO 3 – 

### and SO 4 2– 

### , including NH 4 +



### .



## Elemental Composition

Techniques for measuring the elemental composition of PM samples were reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). These methods include:

 Energy dispersive X-ray fluorescence (ED-XRF);

 Synchrotron X-ray fluorescence (SXRF);

 Particle-induced X-ray emission (PIXE);

 Particle elastic scattering analysis (PESA);

 Total reflection X-ray fluorescence (TR-XRF);

 Instrumental neutron activation analysis (INAA);

 Atomic absorption spectrophotometry (AAS);

 Inductively-coupled plasma-atomic emission spectroscopy (ICP-AES);

 Inductively-coupled plasma-mass spectrometry (ICP-MS); and

 Scanning electron microscopy (SEM).

R ecent development in this area focused on the semi-continuous measurement methods, in which elements were analyzed in the lab using the methods mentioned above on time-resolved and/or size resolved samples (Kidwell and Ondov, 2004, 155898 ). The concentrated slurry/graphite furnace atomic absorption spectrometry (GFAAS) method collects ambient PM as a slurry using impactors, and then the collected PM is analyzed by AAS in the lab. Laser induced breakdown spectroscopy (LIBS) was used to measure seve n metals at the Pittsburgh supersite. LIBS concentrates ambient PM using a virtual impactor into a sample cell, and then a Nd:YAG laser-spectrometer is used to identify and quantify different elements. A full listing of measurement techniques and analytical specifications for trace elements is provided in Annex A, Table A-6.



## Elemental and Organic Carbon

The large variety of aspects of carbon an alyses were reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). Measurement and analytical specifications for carbon measurements are listed in Annex A, Tables A-10 and A-15. Asp ects of the measurements include sampling artifacts

December 2009 3-25

### associated with the integrated filter-based OC and EC sampling methods, the IMPROVE vs. CSN thermal optical protocols (i.e., different thermal optical methods) and optical techniques to measure light-absorption or BC. One significant change taking pl ace in the CSN is that the method for carbon measurements is being changed from the CSN method to a method designed to be consistent with the IMPROVE carbon analysis protocol. This is a phased process that began in May of 2007 with the conversion of 56 stations. Phase 2 of the carbon sampler conversion occurred in April of 2009 with another 62 stations. The balance of the CSN is scheduled to be converted to IMPROVE-like sampling and IMPROVE analysis in late 2009 (Henderson, 2005, 156537 ). The CSN network was implemented to support the PM 2.5 

### NAAQS and provides data for PM 2.5 

### mass, SO 4 2– 

### , NO 3 – 

### , NH 4 + 

### , Na, K, EC, OC, and select trace elements (Al through Pb) at many sites across the U.S. This conversion will increase consistency between these two networ ks. Also, since the release of the 2004 PM AQCD (U.S. EPA, 2004, 056905 ), more studies have been conducted to extend the understanding of sampling artifact issues (Chow et al., 2008, 156355 ; Watson et al., 2005, 157125 ), evaluate different thermal and optical proce dures (Chen et al., 2004, 199501 ; Chow et al., 2004, 156347 ; Chow et al., 2005, 155728 ; Chow et al., 2007, 156354 ; Conny et al., 2003, 145948 ; Han et al., 2007, 155823 ; Subramanian et al., 2004, 081203 ; Watson et al., 2005, 157125 ), develop reference materials (Klouda et al., 2005, 130382 ; Lee, 2007, 155926 ), create water soluble organic carbon (WSOC) measurement techniques (Andracchio et al., 2002, 155657 ; Yang et al., 2003, 156167 ), develop semi-continuous/continuous/real-time carbon m easurement techniques (Chow et al., 2008, 156355 ; Watson et al., 2005, 157125 ), and introduce isotope identification into the OC/EC measurement (Huang et al., 2006, 097654 ). OC sampling artifact issues were further addr essed in various studies (Arhami et al., 2006, 156224 ; Bae et al., 2004, 156243 ; Chow et al., 2005, 155728 ; Fan et al., 2003, 058628 ; Fan et al., 2004, 155770 ; Grover et al., 2008, 156502 ; Lim et al., 2003, 037037 ; Mader et al., 2003, 155955 ; Matsumoto et al., 2003, 124293 ; Muller et al., 2004, 097109 ; Offenberg et al., 2007, 098101 ; Olson and Norris, 2005, 156005 ; Park et al., 2006, 098104 ; Rice, 2004, 156049 ; Subramanian et al., 2004, 081203 ; ten Brink et al., 2004, 097110 ; ten Brink et al., 2005, 156115 ; Viana et al., 2006, 179987 ), and were well summarized by Watson et al. (2005, 157125 ) and Chow et al. (2008, 156355 ). There are two commonly used methods to correct OC sampling artifacts: the filter with backup filter system (TBQ: placing a backup quartz-fiber filter behind the front Teflon-membrane filter; QBQ: placing a backup quartz-fiber filter behind the front quartz-fiber filter); and the denuder-filter-adsorbent system. Subramanian et al. (2004, 081203 ) and Chow et al. (2006, 099031 ) reported that during the Pittsburgh and Fresno supersite studies the positive artifact (organic gases condensed on filters) from TBQ (24-34%, up to 4 μg/m 3 

### OC) was nearly twice that from QBQ (13-17%). With the denuder-filter-adsorbent system, the negative artifact (OC evaporating from the filter) was 5-10%. Watson and Chow (2002, 037873 ) reported that the XAD-coated denuder could function as efficiently as a parallel plate denuder using carbon-impregnated charcoal filters (CIF) with frequent denuder changes. Huebert and Charlson (2000, 156577 ) reported that using tandem filter packs may hinder a quantitative analysis of the artifacts. Different temperature protocols and optical co rrection methods in thermal-optical analyses were further evaluated by Watson et al. (2005, 157125 ), Chow et al. (2004, 156347 ; 2005, 155728 ; 2007, 156354 ), Subramanian et al. (2006, 156107 ), Conny et al. (2003, 145948 ), Han et al. (2007, 155823 ), Chen et al. (2004, 199501 ) and (Conny et al., 2009, 191999 ). Solomon et al. (2003, 156994 ) reported a 20-50% difference for OC and a 20-200% difference for EC using 11 filter samples and 4 different analytical protocols. In an assessment of the different thermal-optical analysis protocols used around the world, Watson et al. (2005, 157125 ) reported that differences of a factor of 2 to 7 in EC between different methods could be observed, and a factor of 2 was common, while the relative differences in OC between diff erent methods were small. As Watson et al. (2005, 157125 ) stated, there are 12 major differences among the thermal methods: (1) analysis atmosphere; (2) temperature ramping rates; (3) temperature pl ateaus; (4) residence time at each plateau; (5) optical pyrolysis monitoring configuration and wave length; (6) standardization; (7) oxidation and reduction catalysts; (8) sample aliquot and size; (9) evolved carbon detection method; (10) carrier gas flow through or across the sample; (11) locati on of the temperature monitor relative to the sample; and (12) oven flushing conditions. Chow et al. (2004, 156347 ) and Chen et al. (2004, 199501 ) addressed the difference between optical tr ansmission and optical reflectance methods for charring correction, and they reported that the charring OC on the surface of or inside a filter dominated the differences between these two correction methods. The differences between different sampling and measurement methods are also applie d to the in-situ/semi-continuous methods, since

December 2009 3-26

### most of these methods are also based on thermal-opti cal analysis of collected filters. Most of these methods agree with integrated filter methods within 30%. The differences observed between methods for OC and EC come largely from how OC and EC are defined. They are defined on an operati onal basis, as there are no standard reference materials. Initial efforts have been made to produce OC/EC reference ma terials at the National Institute of Science and Technology (NIST) (Klouda et al., 2005, 130382 ; Lee, 2007, 155926 ). Klouda et al. (2005, 130382 ) described the development of Reference Material 8785: Air Particulate Matter on Filter Media. Each reference filter is uniquely identified by its air PM number and its gravimetrically determined mass of fine Standard Reference Material (SRM) 1649a, and each filter has values assigned for total carbon, EC, and organic carbon mass fractions measured according to both IMPROVE and NIOSH protocols. Lee et al. (2007, 155926 ) reported a method to create a reference filter with a known amount of OC (as potassium hydrogen phthalate), and EC (as carbon black hydrosol). Measurement methods for WSOC have been de veloped recently (Miyazaki et al., 2006, 156767 ; Sullivan and Weber, 2006, 157031 ; Sullivan et al., 2004, 157029 ; Sullivan et al., 2006, 157030 ; Sullivan et al., 2007, 100083 ; Yu et al., 2004, 156172 ). WSOC can be measured on integrated filter samples, or in-situ measuremen t can be conducted by coupling with the PILS-IC (Sullivan et al., 2004, 157029 ). For integrated filter samples, filters are extracted with deionized water and followed by oxidation of total WSOC to CO 2 

### . CO 2 

### can then be detected by either infrared spectroscopy (IR) (Decesari et al., 2000, 155748 ; Kiss et al., 2002, 156646 ; Yang et al., 2003, 156167 ), FID (Yang et al., 2003, 156167 ), or pyrolysis gas chromatography/mass spectrometry (GC/MS) (Gelencsér et al., 2000, 155785 ). A correlation coefficient of 0.84 was reported by Sullivan et al. (Sullivan et al., 2004, 157029 ) between in-situ and filter based measurement of WSOC. Further development and evaluation has been conducted on the measurement of BC with light absorption instruments (Andreae and Gelencsér, 2006, 156215 ; Arnott et al., 2003, 037711 ; Bae et al., 2004, 156243 ; Borak et al., 2003, 156284 ; Cyrys et al., 2003, 049634 ; Kurniawan and SchmidtOtt, 2006, 098823 ; Park et al., 2006, 098104 ; Saathoff et al., 2003, 156066 ; Sadezky et al., 2005, 097499 ; Slowik et al., 2007, 096177 ; Taha et al., 2007, 096277 ; Virkkula et al., 2007, 157098 ; Wallace, 2000, 000803 ; Weingartner et al., 2003, 156149 ; Williams et al., 2006, 157148 ; Wu et al., 2005, 157155 ). These instruments include the aethalometer, particle absorption photometer, and photoacoustic analyzer. However, these instrume nts are subject to interferences by particle scattering, interactions with the filter substrate, pa rticle loading on filters, and other pollutants (e.g., NO 2 

### ). Uncertainties of up to 50% were observed in the studies mentioned above by comparing these methods with integrated filter methods and thermal analysis methods. Huang et al. (2006, 097654 ) reported the measurement of a stable isotope, 13 

### C, in OC and EC with a thermal optical transmission analyzer coupled with gas chromatography-isotope ratio mass spectrometer (TOT-GC-IRMS). The ratio of 13 

### C/ 12 

### C in OC and EC can provide useful information on OC/EC source categories and origin. The method was applied to Pacific2001 aerosol samples from the greater Vancouver area in Canada and produced a precision of ~0.03%. Gustafsson et al. (2009, 192000 ) applied the radiocarbon measurement technique and quantified the source contributions of carbonaceous aerosols to the Indian Ocean “brown cloud,” with particular relevance for understanding and mitigating the climate effects of EC/BC.



## Organic Speciation

Organic matter makes up a substantial fraction of PM in all regions of the U.S. (U.S. EPA, 2004, 056905 ), and 10-40% of the total organic matter is currently quantifiable at the individual compound level (Pöschl, 2005, 156882 ). Recent advancements in traditional solvent extraction GC/MS and high pressure liquid chromatography (H PLC) as well as application of thermal desorption (TD) techniques are helping to expa nd the understanding of the composition of organic matter as well as improving detection limits for quantification of organic molecular marker (OMM) compounds (Robinson et al., 2006, 156918 ; Schnelle-Kreis et al., 2005, 112944 ; Sheesley et al., 2007, 112017 ; Shrivastava et al., 2007, 111594 ). In addition, information about organic functional groups can be obtained with Fourier transform infr ared spectrometry (FTIR) (Tsai and Kuo, 2006, 156127 ). Recent advancements in GC/MS technology including inert electron ionization sources and improved instrument sensitivity and scan rates fo r better OMM quantification, have increased its

December 2009 3-27

### application in organic aerosol char acterization studies (Cass, 1998, 155716 ; Dutton et al., 2009, 194887 ; Fraser et al., 2003, 042231 ; Graham et al., 2003, 156489 ; Hays et al., 2002, 026104 ; Robinson et al., 2006, 156918 ; Schauer et al., 1996, 051162 ; Sheesley et al., 2007, 112017 ; Subramanian et al., 2006, 156107 ; Watson et al., 1998, 012257 ; Zheng et al., 2002, 026100 ; Zheng et al., 2006, 157189 ). Incorporation of high volume injection using programmable temperature vaporization (PTV) (Engewald et al., 1999, 155765 ) has further lowered detection limits for trace level OMM compounds. High volume injection has the added benefit of preventing the loss of semivolatile compounds (S wartz et al., 2003, 157035 ), and has been applied for analysis of PAHs using low volume samplers (down to 5 L/min), allowing for smaller required mass loadings (Bruno et al., 2007, 155706 ; Crimmins and Baker, 2006, 097008 ). Since last review, HPLC analysis with fluorescence detection has also been used frequently for quantification of semivolatile organic compounds in both the particle and gas phase (Albinet et al., 2007, 154426 ; Barreto et al., 2007, 155676 ; Chow, 2007, 157209 ; Eiguren-Fernandez et al., 2003, 142609 ; Goriaux et al., 2006, 156484 ; Murahashi, 2003, 096539 ; Rynö et al., 2006, 156065 ; Stracquadanio et al., 2005, 156104 ; TemimeRoussel et al., 2004, 098530 ; Temime-Roussel et al., 2004, 098521 ). Lengthy extraction and analysis times remain a limiting factor for these methods. TD techniques bypass one of the time consuming steps in traditional solvent extraction analysis for nonpolar organic compounds (n-alkanes, branched alkanes, cyclohexanes, hopanes, steranes, alkenes, phthalates and PAHs). This is achieved by vaporizing and analyzing organic constituents directly from the collection substrate, thereby bypassing the extraction step (Chow, 2007, 157209 ). Methods exist for both off-line TD analysis of previously collected filter samples and semi-continuous TD analysis. Annex A, Table A-17 is adapted from Chow et al. (2007, 157209 ) and summarizes recent TD-GC/MS studies. The most common off-line method is TD-GC/MS (Hays and Lavrich, 2007, 155831 ). Continuous or semi-continuous methods have been developed for direct analysis of individual organic constituents by coupling TD with various forms of mass spectrometry (Smith et al., 2004, 156090 ; Tobias and Ziemann, 1999, 157053 ; Tobias et al., 2000, 156121 ; Voisin et al., 2003, 156141 ; Williams et al., 2006, 156157 ). A comparison of measurement and analytical specifications for filter analysis using solvent extr action and TD methods for organic speciation are summarized in Annex A, Table A-17.



# 3.4.1.3. Multiple-Component Measurements on Individual Particles

The 2004 PM AQCD (U.S. EPA, 2004, 056905 ) discussed the aerosol time-of-flight mass spectrometry (ATOFMS). Recently, the ATOFMS and several other aerosol mass spectrometry methods have been further developed. Both lab and field comparisons have been conducted to evaluate the reliability of these types of instruments. There are four types of commonly used aerosol mass spectrometry: (1) particle analysis by laser MS (PALMS; National Oceanic and Atmospheric Administration [NOAA]); (2) rapid single particle mass spectrometer (RSMS; University of Delaware); (3) aerosol time-of-flight MS (ATOFMS; TSI, Inc.); and (4) AMS (Aerodyne) (Chow et al., 2008, 156355 ; Nash et al., 2006, 199502 ). The differences between these instruments primarily come from the particle sizing methods of mass spectrometers, as shown in Annex A, Table A-16. Although the technique varies, the underlying principle is to fragment each particle into ions, using either a high-power laser or a heated surface, and then a mass spectrometer to measure the mass to charge ratio of each ion fragment in a

vacuum. These instruments were evaluated at the Atlant a, Houston, Fresno, Pittsburgh, New York, and Baltimore supersites (Bein et al., 2005, 156265 ; Drewnick et al., 2004, 155755 ; Drewnick et al., 2004, 155754 ; Hogrefe et al., 2004, 099003 ; Jimenez et al., 2003, 156611 ; Lake et al., 2003, 156669 ; Lake et al., 2004, 088411 ; Middlebrook et al., 2003, 042932 ; Phares et al., 2003, 156866 ; Qin and Prather, 2006, 156895 ; Wenzel et al., 2003, 157139 ). Measurements of the gross composition and abundance of particles by these instruments were generally semi-quantitative, with the exception of AMS. Particles of similar composition (e.g., OC/SO 4 2– 

### , Na/K/SO 4 2– 

### , soot/hydrocarbon, and mineral particle types) were characterized by these i nstruments during the studies mentioned above. NO 3 – 

### and SO 4 2– 

### concentrations measured with AMS were comparable with other continuous and filter-based methods, as mentioned in Section 3.4.1.2 . In addition, concentrations of different particle types can be obtained by the co-location of thes e aerosol mass spectrometers and other particle sizing instruments, such as particle counters or the Micro-Orifice Uniform Deposit Impactor (MOUDI).

December 2009 3-28

# 3.4.1.4. UFPs: Mass, Surface Area, and Number

Instrume nts for measuring UPFs developed duri ng the past decade permit measurement of size distributions of particles down to 3 nm in diamet er with mobility partic le sizers. Concentrations down to this size range can be obtained by a MOUDI. The recently developed low pressure-drop UFP impactor coupled with a β Attenuation Monitor (nano-BAM) can also provide UFP (<150 nm) mass concentrations (Chakrabarti et al., 2004, 157426 ). A high correlation coefficient was observed between MOUDIs and nano-BAMs, with a correlation of 0.96. A 50% cut point (d 50 

### ) of 13-200 nm can be achieved by a high-volume slot-type UFP virtual impactor (Middha and Wexler, 2006, 155982 ). Methods are also being developed to measure the su rface area of UFPs. Particle surface area is usually measured by attaching labeled (radioactive or electrical labeling) molecules to particles and detecting the radioactive or electrical properties of the attached molecules. Wilson et al. (2007, 098398 ) suggested that the electrical aerosol detector (EAD, based on diffusion charging) measurement might be a useful indicator of the pa rticle surface area deposited in the lung. This method can be potentially useful for examining the association between health effects and particle surface areas. Developments involving the condensation particle counter include use of de-ionized water as a condensation medium in lieu of butanol or n-propanol in condensation particle counters (Hering et al., 2005, 155838 ; Hermann et al., 2007, 155840 ; Petäjä et al., 2006, 156021 ). This development makes the condensation particle counter (CPC) easier to use in field studies because water does not have some of the same chemical properties (w ith respect to hazard and odor) as butanol or n-propanol. The performance of this CPC was report ed to be similar to the conventional butanol based CPC (Hering et al., 2005, 155838 ). Use of a battery of water and butanol-based CPCs was demonstrated to detect a range of solubilities in nucleation-mode particles (Kulmala et al., 2007, 155911 ). Additionally, CPCs have been used to measure particles in the smaller end of the UF scale through adjustment of CPC cut-off diameters through tuning the temperature difference between the CPC saturator and condense r (Kulmala et al., 2007, 155911 ) and improved charge reduction techniques (Winkler et al., 2008, 156160 ). The latter method was effective in reducing the size of particles detected by a CPC to <2 nm. These studies include assessment of errors related to these developments with the CPC and generally show that counting efficiencies with these devices is upwards of 95% (Hermann et al., 2007, 155840 ). Additionally, recent advancements have been made in development of fast scanning methods for UFP size distributions, including diffusion screens (DS) (Feldpausch et al., 2006, 155773 ) and fast integrated mobility sc anners (FIMS) (Olfert et al., 2008, 156004 ).



# 3.4.1.5. PM Size Distribution

Along wi th particle density and shape (U.S. EPA, 2004, 056905 ), the particle size distribution can be used to estimate PM mass concentrations. For particles >0.1 μm, several instruments, including DRUM, MOUDIs, and aerodynamic particle sizer (APS), are available to measure mass-based or count-based particle size distributi on. An APS incorporating very sharp cut points between 0.1 and 10 μ m is now available (Peters, 2006, 156860 ; Zeng, 2006, 098375 ). For particles in this range, inertial forces are used to separate particles based on impaction. For particles <0.1 μm, particles can be separated by their electrical mobility , and as a result, electrical mobility diameter is often used to describe UFP size distribution in lieu of aerodynamic diameter. It has been necessary to develop techniques to convert mobility diameters, measured by the scanning mobility particle sizer (SMPS) or the Engine Exhaust Particle Sizer ( EEPS), to aerodynamic diameters, measured by the APS, or vice versa, in order to merge the di stributions spanning the UF, accumulation, and coarse modes. A variety of techniques for combining SMPS and APS diameters have been reported in the literature (Hand and Kreidenweis, 2002, 155824 ; Khlystov et al., 2004, 155897 ; Morawska et al., 1999, 007609 ; Morawska et al., 2007, 155990 ; Shen et al., 2002, 156086 ; TSI, 2005, 157196 ). However, each of these techniques incurs some uncertainty of which the user must be aware.



# 3.4.1.6. Satellite Measurement

Instrume nts sensing back scattered solar radiati on on satellites have made it possible to derive information about tropospheric aerosol properties on th e global scale. The satellite borne instruments

December 2009 3-29

### vary in their complexity and in the aerosol properties they can measure. Satellite instruments measure radiance (or brightness temperature) that can then be used to provide information on the aerosol column amount, or the aerosol optical de pth (AOD). Depending on the wavelengths sampled and the spectral resolution of the instruments, in formation about the composition of particles of diameter <2 μm and particles of diameter >2μm can be obtained. Data fro m two main instruments, the moderate resolution imaging spectroradi ometer (MODIS) and the multiangle imaging spectroradiometer (MISR) have been used to estimate surface PM in the U.S. MODIS measures the intensity of back scattered sunlight at seven wave lengths through the visible to the near infrared at one viewing direction; and MISR measures the inte nsity at four wavelengths (from the visible to the near IR) and the same ground pixel at nine viewi ng angles. The spatial resolution of reported AOD is 17.6×17.6 km for MISR and either 10×10 or 1×1 km for MODIS, depending on retrieval algorithm. Since both instruments are located on the same satell ite, their times of overpass are the same, about 1330 local time. Due to precession of the satellite’s orbit, the satellite does not pass over the same path every day, and instruments cannot sense aerosol properties beneath cloud tops. The problem of using satellite data to retrieve pr operties of the atmospheric aerosol is complex because the surface contribution to satellite measured reflectance must be separated from the aerosol signal. Difficulties can arise when attempting to derive aerosol information over land surfaces because of uncertainties in surface reflectivity, similarities between aerosol and surface composition, and high signal-to-noise ratio when viewing AOD over reflective surfaces such as desert and snow. To overcome this difficulty, data from MODIS have been applied over dark land surfaces and ongoing improvements in retrieval algorithms are being developed. Instruments such as the MISR that sense at multiple viewing angles can better cope with the problems over land surfaces because they can use the information on the angular dependence of reflection from the surface and the atmosphere to distinguish between their signals. Not only can total AODs be derived, but fractional AODs that reflect external mixtures characterized by particle shape, effective radius, and single scattering albedo can also be derived. These properties can then be used to infer particle composition. Retrievals over the oceans have had less difficulty because the optical properties of sunlight reflected from the sea surface are much be tter known, and reflectivities are low over most zenith angles at less than gazing incidence. Kokhanovsky et al. (2007, 190009 ) examined the errors associated with MODIS, MISR, and a number of other satellite instruments with respect to associated retrieval algorithms for retrievals over Central Europe. They found a correlation coefficient between MODIS and MISR AOD of 0.62. Both MODIS and MISR AOD tended to underestimate ground-based AOD measurements from AERONET (NASA’s AErosol RObotic NETwork) sli ghtly with MODIS generally retrieving higher AODs than MISR. Chu et al. (2003, 190049 ) found correlations between MODIS and AERONET AODs ranging from 0.82-0.91; and Kahn et al. (2005, 189961 ) found correlations of 0.7-0.9 between MISR and AERONET AODs. Further complexity is added when attempting to relate surface PM 2.5 

### to aerosol optical depths. The detailed comparisons of surface measurement and sa tellite measurements are given in Chapter 9.



# 3.4.2.

3.4. 

# Ambient Network Design

2.1. Mo nitor Siting Requirements

The EPA A ir Quality System database (AQS ) contains measurements of air pollutant concentrations in the 50 states, plus the District of Columbia, Puerto Rico, and the Virgin Islands, for the 6 criteria air pollutants as well as a more lim ited dataset of hazardous air pollutants. In 2007, there were 4,693 PM 10 

### monitors and 2,194 PM 2.5 

### monitors reporting values to the AQS. Where SLAMS PM 10 

### and PM 2.5 

### monitoring is required, at least one of the sites must be a maximum concentration site for that specific area. The appropriate spatial scales for PM 2.5 

### , PM 10-2.5 

### , and PM 10 

### monitoring differ given the contrasting spatial gradie nts of coarse PM relative to fine. The relevant scales for each size classification ar e provided in Annex A, Table A-18. Criteria for siting ambient monitors for PM at national monitoring networks are summarized below by PM size, and details are given in the CFR 40 Part 58 Appendix D, and SLAMS/NAMS/PAMS Network Review Guidance (U.S. EPA, 1998, 093211 ). Table A-19 in Annex A provides a summary of the number of sites and operating specifications of these networks. Probing

December 2009 3-30

### and monitoring path siting criteria for any specific monitoring site are given in CFR 40 Part 58 Appendix E, including horizontal and vertical placement, spacing from minor source, spacing from obstructions, spacing from trees, and spacing from roadways.

PM 2.5


### The minimum number of PM 2.5 

### monitors required in a metropolitan statistical area is determined by the population and the air quality in the area, as specified in Appendix D of 40 CFR Part 58. The required minimum number of PM 2.5 

### monitors ranges from 0 to 3 in any given metropolitan statistical area. Continuous PM 2.5 

### monitors must be operated in no fewer than one-half of the minimum required sites in each area. Most PM 2.5 

### monitoring in urban areas should be representative of a neighborhood scale (for trends a nd compliance with standards). Urban or regional scale sites are located to characterize regional transport of PM 2.5 

### . In certain instances where population-oriented micro- or middle-scale PM 2.5 

### monitoring are determined by the Regional Administrator to represent many such locations thr oughout a metropolitan area, these smaller scales can be considered to represent community-wide air quality. PM 2.5 

### measurements are obtained at local temperature and pressure across the NAM S/SLAMS networks (40 CFR Part 58).
PM 2.5 

### chemical speciation monitoring is currently conducted at 197 CSN sites ( http://www.epa.gov/ttn/amtic/specgen.html ). Within the CSN network, 53 locations are recognized as the Speciation Trends Network (STN) operating on a sample schedule of one in every three days, while the rest of the CSN typically operates every sixth day.

PM 10-2.5



### PM 10-2.5 

### monitoring has not been required at SLAMS sites, but will be required at NCore 1 

### Stations (which is a sub-set of the SLAMS) by January 1, 2011. Middle and neighborhood scale measurements are the most important station classifications for PM 10-2.5 

### to assess the variation in coarse particle concentrations that would be expect ed across populated areas that are in proximity to large emissions sources. PM 10-2.5 

### chemical speciation monitoring and analyses will also be required at NCore sites by January 1, 2011. EPA has already approved FRMs and FEMs for PM 10-2.5 

### mass; however, methods for PM 10-2.5 

### speciation are still being developed (Henderson, 2009, 192001 ). PM 10-

2.5 

### measurements are obtained at local temperat ure and pressure by recalculating the co-located PM 10 

### for local conditions.

PM 10


### As for PM 2.5 

### , the minimum number of PM 10 

### monitors required in a metropolitan statistical area is determined by the population and the air quality in the area, as specified in Appendix D of 40 CFR Part 58. The required minimum number of PM 10 

### monitors ranges from 0 to 8 in any given metropolitan statistical area. Except for some circumstances where microscale (<100 m, for maximum PM 10 

### exposure) monitoring may be appropriate, the most important scales to characterize the emissions of PM 10 

### effectively from both mobile and stationary sources are the middle scale (for short-term public exposure) and neighborhood scal e (for trends and compliance with standards).
PM 10 

### measurements are obtained at standard te mperature and pressure across the NAMS/SLAMS networks (40 CFR Part 58).



# 3.4.2.2. Spatial and Temporal Coverage

Location s of PM 2.5 

## and PM 10 

## Monitors in Selected Metropolitan Areas in the U.S.

Fifteen metropolitan regions were chosen for closer investigation of monitor siting based on their distribution across the nation and relevance to h ealth studies analyzed in subsequent chapters of this ISA. These regions were: Atlanta, Birmingham , Boston, Chicago, Denver, Detroit, Houston, Los Angeles, New York City, Philadelphia, Phoenix, P ittsburgh, Riverside, Seattle, and St. Louis. Core1 For more information on NCore, see the NCore web site at: http://www.epa.gov/ttn/amtic/ncore/index.html

.

December 2009 3-31

### Based Statistical Areas (CBSAs) and Combined Statistical Areas (CSAs), as defined by the U.S. Census Bureau ( http://www.census.gov/ ), were used to determine wh ich counties, and hence which monitors, to include for each metropolitan region. 1 

### Figure 3-7 and Figure 3-8 display PM 2.5 

### and
PM 10 

### monitor density, respectively, with respect to population density in Boston. Annex A includes similar information for all fifteen metropolit an regions (Figure A-1 through Figure A-3 0).

1 A CBSA represents a county-based region surrounding an urban center of at least 10,000 people determined using 2000 census dat a and

replaces the older Metropolitan Statistical Area (MSA) definition from 1990. The CSA represents an aggregate of adjacent CBSAs tied by specific commuting behaviors. The broader CSA definition was used when selecting monitors for the cities listed above with t he exception of Los Angeles, Riverside and Phoenix. Los Angeles and Riverside are contained within th e same CSA, so the smaller CB SA
definition was used to delineate these two c ities. Phoenix is not contained within a CS A, so the smaller CBSA definition was us ed for this city as well.

December 2009 3-32

## Figure 3-7. PM 2.5 

## monitor distribution in comparison with population density, Boston CSA.

December 2009 3-33

## Figure 3-8. PM 10 

## monitor distribution in comparison with population density, Boston CSA.

December 2009 3-34

## Table 3-3. Proximity to PM 2.5 

## and PM 10 

## monitors for total population a 

## by city.

Proximity to PM Monitors b

Region Total CSA/CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km

N N % N % N % N %

PROXIMITY TO PM 2.5

MONITORS

Atlanta 5,316,742 23,461 0.44 581,461 10.94 1,990,477 37.44 3,179,844 59.81

Birmingham 1,166,100 12,925 1.11 240,383 20.61 666,926 57.19 848,447 72.76

Boston 7,502,707 185,457 2.47 1,877,180 25.02 3,356,019 44.73 4,641,175 61.86

Chicago 9,754,262 177,076 1.82 3,091,573 31.69 6,473,463 66.37 8,185,010 83.91

Denver 2,952,039 40,601 1.38 649,953 22.02 1,548,976 52.47 2,252,657 76.31

5,553,465 54,997 0.99 1,174,733 21.15 2,791,555 50.27 3 

## , 845 

## , 190 69.24 Detroit

Houston 5,503,320 11,586 0.21 213,708 3.88 905,007 16.44 1,599,079 29.06

Los Angeles 13,061,361 115,477 0.88 2,579,809 19.75 7,544,466 57.76 10,792,727 82.63

New York 22,050,940 717,094 3.25 8,107,764 36.77 13,493,867 61.19 16,571,764 75.15

Philadelphia 6,388,913 117,389 1.84 1,878,373 29.40 3,517,321 55.05 4,393,136 68.76

Phoenix 3,818,147 37,133 0.97 490,072 12.84 1,099,069 28.79 1,739,542 45.56

Pittsburgh 2,515,383 40,574 1.61 587,148 23.34 1,331,230 52.92 1,883,301 74.87

Riverside 3,781,063 43,739 1.16 723,829 19.14 1,855,296 49.07 2,344,394 62.00

Seattle 3,962,434 13,723 0.35 287,373 7.25 931,630 23.51 1,561,792 39.41

St. Louis 2,869,955 37,329 1.30 563,176 19.62 1,338,349 46.63 1,760,985 61.36

PROXIMITY TO PM 10 MONITORS

Atlanta 5,316,742 30,973 0.58 416,440 7.83 1,090,497 20.51 1,837,983 34.57

Birmingham 1,166,100 23,943 2.05 251,310 21.55 473,054 40.57 638,472 54.75

Boston 7,502,707 63,614 0.85 1,090,172 14.53 2,087,770 27.83 2,939,870 39.18

Chicago 9,754,262 55,642 0.57 844,714 8.66 2,374,972 24.35 3,844,297 39.41

Denver 2,952,039 38,449 1.30 521,201 17.66 1,146,286 38.83 1,799,187 60.95

Detroit 5,553,465 14,050 0.25 309,623 5.58 748,971 13.49 1,300,995 23.43

Houston 5,503,320 36,795 0.67 832,767 15.13 2,227,314 40.47 3,141,150 57.08

Los Angeles 13,061,361 52,052 0.40 1,404,389 10.75 4,899,254 37.51 9,075,863 69.49

New York 22,050,940 19,842 0.09 292,105 1.32 592,631 2.69 773,962 3.51

Philadelphia 6,388,913 23,988 0.38 376,966 5.90 1,091,532 17.08 2,238,309 35.03

Phoenix 3,818,147 99,520 2.61 1,255,430 32.88 2,615,738 68.51 3,416,682 89.49

Pittsburgh 2,515,383 65,906 2.62 706,413 28.08 1,291,700 51.35 1,705,451 67.80

Riverside 3,781,063 61,356 1.62 895,615 23.69 2,360,272 62.42 2,922,799 77.30

Seattle 3,962,434 4,851 0.12 220,539 5.57 709,887 17.92 1,211,430 30.57

St. Louis 2,869,955 27,872 0.97 380,411 13.25 891,695 31.07 1,212,543 42.25

a Based on 2005 population totals.

b Percentages are given with respect to the total population per city provided.

December 2009 3-35

### Table 3-3 shows the population density around PM 2.5 

### and PM 10 

### monitors for the total population for each CSA/CBSA individually. Popula tion totals within various distances of PM monitors were calculated assuming equal intern al population distribution for individual census blocks. Between-city disparities in population density were large and were dependent primarily on the location and number of PM monitoring sites per CSA/CBSA. For PM 2.5 

### , Los Angeles (83%) and Denver (76%) had the largest proportion of the total population within 15 km of a monitor. Houston (29%) had the least population coverage with their PM 2.5 

### monitors. For PM 10 

### , Phoenix (89%) had the largest proportion of the total population within 15 km of a monitor. Detroit (23%), Boston (39%), Seattle (31%), and Philadelphia (35%) had the smallest proportions of the population within 15 km of a PM 10 

### monitor. Proximity to monitoring stations is considered further in Section 3.5 and Section 3.8 regarding spatial variability within cities. Figure 3-7 shows that the PM 2.5 

### network more closely samples near population centers in the Boston CSA compared with the PM 10 

### network shown in Figure 3-8 , although both PM 2.5 

### and PM 10 

### networks place at least one monitor in the city center.



# 3.4.2.3. Network Application for Exposure Assessment with Respect to Susceptible Populations

Subject A ge

Table 3-4 breaks down the population density around PM 2.5 

### and PM 10 

### monitors for sub-populations of children age 0-4 yr, children age 5-17 yr, and elderly adults age 65 yr and over cumulatively for the 15 CSAs/CBSAs examined. Table 3-5 shows the distribution for adults age 65 years and over for each CSA/CBSA individually. This de tail of information is not provided for the 0- to 4-yr and 5- to 17-yr age groups because variation in percentage within a certain radius of the monitor was generally fairly low for each city ac ross the child age groups when compared to total population. In the cases of Denver, Detroit, Phoenix, Riverside, and St. Louis for PM 2.5 

### and Birmingham, Denver, Riverside, and St. Louis for PM 10 

### , the elderly population’s distribution around the samplers varied more from the total population compared to other age groups. When all CSAs/CBSAs were considered cumulatively, the perc entage of the population within 15 km of a monitor was similar for all age groups for both PM 2.5 

### and PM 10 

### . Between-city disparities in elderly population density within a sampler radius were larger. For PM 2.5 

### , Chicago (87%) and Denver (84%) had the largest proportion of the elderly population within 15 km of a monitor. Houston (31%) had the least population coverage with their PM 2.5 

### monitors. For PM 10 

### , Phoenix (90%) had the largest proportion of the total population within 15 km of a monitor. New York (4%), Detroit (27%), Seattle (32%), and Philadelphia (39%) had the smallest proportions of the population within 15 km of a
PM 10 

### monitor. These differences may reflect overall density of the samplers within a given city, with
PM 2.5 

### monitors more numerous than PM 10 

### monitors in most of the CSAs/CBSAs, and retirement and settlement trends among elderly adults.

December 2009 3-36

## Table 3-4. Proximity to PM 2.5 

## and PM 10 

## monitors for children age 0- 4 yr, children age 5-17 yr, and adults age 65 yr and older. a 

## The figures presented here are cumulative for the 15 CSAs/CBSAs examined in Chapter 3.

Proximity to PM Monitors b

Age Grouping Total CSA/CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km

N N % N % N % N %

PROXIMITY TO PM 2.5

MONITORS

0-4 6,400,785 109,466 1.71 1,603,000 25.04 3,361,922 52.52 4,462,403 69.72

5-17 17,212,825 275,427 1.60 4,164,132 24.19 8,814,179 51.21 11,813,997 68.63

≥ 65 10,391,023 175,113 1.69 2,570,909 24.74 5,483,776 52.77 7,288,049 70.14

PROXIMITY TO PM 10 MONITORS

0-4 6,400,785 44,384 0.69 695,120 10.86 1,725,419 26.96 2,636,782 41.19

5-17 17,212,825 110,882 0.64 1,756,246 10.20 4,441,239 25.80 6,942,001 40.33

≥ 65 10,391,023 68,367 0.66 1,056,375 10.17 2,631,243 25.32 4,041,802 38.90

a Based on 2000 population totals.
b Percentages are given with respect to the total population per city provided.



## Race and Hispanic Origin

Table 3-6 shows the percent of the population self-i dentified as white or black and having a Hispanic or non-Hispanic origin within 1, 5, 10, or 15 km distances from PM 2.5 

### and PM 10 

### monitors cumulatively across the fifteen CSAs/CBSAs. For PM 2.5 

### , blacks and Hispanics had similar percentages of the population within 15 km of a monitor (86% and 82%, respectively), while a smaller proportion of whites and non-Hispanics we re within that same distance (63% and 67%, respectively), across the fift een CSAs/CBSAs studied. For PM 10, 

### Hispanics (54%) represented the subpopulation with the largest percentage of total population within 15 km of a monitor across the fifteen CSAs/CBSAs studied. The percentage of bl acks within that same distance was marginally lower (48%), whereas the percentage of whites and non-Hispanics within 15 km of a monitor was approximately two-thirds that of Hispanics (35% and 37%, respectively). Higher percentages of individual ethnic subpopulations within 15 km of a PM 2.5 

### monitor most likely represents the fact that
more PM 2.5 

### monitors are currently deployed compared with PM 10 

### monitors. While no ethnic subpopulation appears to be well represented at the neighborhood scale, greater percentages of the black and Hispanic populations (1% each) are within 1 km of a PM 10 

### monitor than the corresponding white and non-Hispanic populations (0.5% each). Li kewise, 2.5% of the black population and 2.8% of the Hispanic population reside within 1 km of a PM 2.5 

### monitor compared to 1.4% of the white population and 1.5% of the non-Hispanic population. Furthermore, it is notable that at any scale shown in Table 3-6 for both PM 2.5 

### and PM 10 

### monitors, those self-identified as black or Hispanic actually have greater representation by the monitors than those identified as white or non-Hispanic.

December 2009 3-37

## Table 3-5. Proximity to PM 2.5 

## and PM 10 

## monitors for adults age 65 yr and older a 

## by city.

Proximity to PM Monitors b

Age Grouping Total CSA/CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km

N N % N % N % N %

PROXIMITY TO PM 2.5

MONITORS

Atlanta 362,201 1,757 0.49 36,772 10.15 136,179 37.60 207,122 57.18

Birmingham 145,905 1,619 1.11 29,952 20.53 84,223 57.72 106,488 72.98

945,790 18,821 1.99 224,628 23.75 438,920 46.41 606 

## , 231 64.10 Boston

Chicago 1,018,983 18,539 1.82 348,656 34.22 713,194 69.99 883,112 86.67

Denver 232,974 3,891 1.67 59,625 25.59 140,523 60.32 196,361 84.28

Detroit 626,216 5,765 0.92 138,672 22.14 345,808 55.22 469,462 74.97

Houston 377,586 1,010 0.27 14,911 3.95 66,741 17.68 117,661 31.16

Los Angeles 1,207,436 9,653 0.80 229,893 19.04 688,844 57.05 984,889 81.57

New York 2,710,675 78,918 2.91 921,599 34.00 1,619,177 59.73 2,048,842 75.58

Philadelphia 834,110 13,323 1.60 251,459 30.15 487,003 58.39 605,663 72.61

Phoenix 388,150 2,738 0.71 39,833 10.26 90,304 23.27 142,084 36.61

Pittsburgh 449,544 8,933 1.99 111,050 24.70 249,269 55.45 347,711 77.35

Riverside 342,334 3,024 0.88 50,901 14.87 129,836 37.93 170,933 49.93

Seattle 390,372 1,721 0.44 29,429 7.54 101,223 25.93 156,562 40.11

St. Louis 358,747 5,401 1.51 83,528 23.28 192,532 53.67 244,929 68.27

PROXIMITY TO PM 10 MONITORS

362,201 2 

## , 115 0.58 35,448 9.79 93,903 25.93 139,240 38.44 Atlanta

145,905 3 

## , 663 2.51 35,628 24.42 66,839 45.81 86,299 59.15 Birmingham

945,790 6 

## , 852 0.72 124,911 13.21 262,854 27.79 385,046 40.71 Boston

1,018,983 7 

## , 619 0.75 107,540 10.55 291,705 28.63 441,771 43.35 Chicago

232,974 3 

## , 675 1.58 43,658 18.74 107,548 46.16 168,447 72.30 Denver

626,216 1 

## , 555 0.25 41,833 6.68 99,680 15.92 167,760 26.79 Detroit

377,586 2 

## , 085 0.55 57,413 15.21 166,715 44.15 219,615 58.16 Houston

1,207,436 4 

## , 693 0.39 126,696 10.49 422,725 35.01 810,078 67.09 Los Angeles

2,710,675 2 

## , 463 0.09 37,580 1.39 80,222 2.96 104,951 3.87 New York

834,110 2 

## , 740 0.33 49,413 5.92 154,535 18.53 322,700 38.69 Philadelphia

388,150 8 

## , 605 2.22 119,306 30.74 267,456 68.91 348,464 89.78 Phoenix

449,544 13 

## , 302 2.96 133,285 29.65 243,723 54.22 314,941 70.06 Pittsburgh

342,334 4 

## , 181 1.22 65,499 19.13 182,615 53.34 236,900 69.20 Riverside

Seattle 390,372 503 0.13 22,333 5.72 72,979 18.69 123,054 31.52

358,747 4 

## , 316 1.20 55,833 15.56 117,743 32.82 172,535 48.09 St. Louis

+ Based on 2000 population totals.

b Percentages are given with respect to the total population per city provided.

December 2009 3-38

## Table 3-6. Proximity to PM 2.5 

## and PM 10 

## monitors based on the populati on identified as white, black, Hispanic, or non-Hispanic a



## . The figures presented here are cumulative for the 15 CSAs/CBSAs examined in Chapter 3.

Proximity to PM Monitors b

Race or Hispanic

Origin Total CSA/CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km

N N % N % N % N %

PROXIMITY TO PM 2.5

MONITORS

White 61,936,855 863,823 1.39 12,257,978 19.79 27,553,900 44.49 39,030,037 63.02

Black 12,668,004 320,447 2.53 4,780,620 37.74 9,241,172 72.95 10,906,346 86.09

Hispanic 15,916,208 445,126 2.80 5,782,482 36.33 10,661,947 66.99 13,094,618 82.27

Non-Hispanic 74,611,962 1,135,999 1.52 16,553,574 22.19 36,318,474 48.68 49,629,054 66.52

PROXIMITY TO PM 10 MONITORS

White 61,936,855 325,771 0.53 5,554,906 8.97 14,041,215 22.67 21,913,907 35.38

Black 12,668,004 134,174 1.06 1,611,263 12.72 3,867,436 30.53 6,020,348 47.52

Hispanic 15,916,208 169,305 1.06 2,496,959 15.69 5,905,322 37.10 8,589,819 53.97

Non-Hispanic 74,611,962 421,917 0.57 6,767,187 9.07 17,261,734 23.14 27,254,421 36.53

a Based on 2000 population totals

b Percentages are given with respect to the total population per city provided.



## Socioeconomic Status

Table 3-7 shows the percent of the population below and above the poverty level and the percent of the population over age 25 years stratified by education level that reside within 1 km, 5 km, 10 km, and 15 km of a PM 2.5 

### and PM 10 

### monitor cumulatively across the 15 CSAs/CBSAs. For
PM 2.5 

### , 80% of the population below poverty level a nd 77% of the population with less than high school education are within 15 km of a monito r for the 15 CSAs/CBSAs studied. Populations of those above the poverty line, those with a high school or more education, and those with a college education within 15 km of a monitor were less than the low SES groups (67% for each). For PM 10 

### , 47% of the population below the poverty level and 45% of the population with less than a high school education are within 15 km of a monitor, wh ereas the percentage of those above the poverty line (39%), those with a high school or more edu cation (38%), and those with a college education (35%) were slightly less. Hi gher percentages of individual SES subpopulations within a given distance of PM 2.5 

### monitors relative to PM 10 

### monitors likely reflect the fact that more PM 2.5 

### monitors are currently deployed within the 15 CSAs/CBSAs studied compared with PM 10 

### monitors. Lower SES groups are not shown to be well-represented at the neighborhood scale, with 1.2% of the population below the poverty level and 1.0% of the population with less than a high school education residing within 1 km of a PM 10 

### monitor. Likewise, 3.1% of the population below the poverty level and 2.4% of the population with less than high school education reside within 1 km of a PM 2.5 

### monitor. However, the populations of low SES groups are more represented at the neighborhood scale than those for higher SES groups. For exam ple, only about 1.5-1.7% of those above the poverty line, those with a high school or more educati on, or those with a college education are within 1 km of a PM 2.5 

### monitor. Moreover, it is notable that at any scale shown in Table 3-7 and for both
PM 2.5 

### and PM 10 

### , those living under the poverty line and those age 25 years and older with less than high school education have greater representation by the monitors than those above the poverty line or those age 25 and older with high school or college education.

December 2009 3-39

## Table 3-7. Proximity to PM 2.5 

## and PM 10 

## monitors based on the popu lation below or above the poverty line, population over age 25 with less than high school education, population over 25 with high school education, and population over 25 with college education or

more a



## . The figures presented here are cumulative for the 15 CSAs/CBSAs examined in Chapter 3.

Proximity to PM Monitors b

Total CSA/CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km SES

N % N % N % N % N

PROXIMITY TO PM 2.5

MONITORS

Below poverty line 10,645,411 330,970 3.11 3,951,549 37.12 7,107,192 66.76 8,528,731 80.12

Above poverty line 85,551,420 1,297,591 1.52 19,094,985 22.32 41,736,460 48.79 57,070,312 66.71

Less than HS education 11,606,042 276,942 2.39 3,806,208 32.80 7,225,291 62.25 8,930,174 76.94

HS education 30,583,598 444,262 1.45 6,940,261 22.69 15,152,047 49.54 20,489,904 67.00

College education 16,433,811 280,810 1.71 3,451,717 21.00 7,776,218 47. 32 11,000,917 66.94

PROXIMITY TO PM 10 MONITORS

Below poverty line 10,645,411 132,979 1.25 1,626,694 15.28 3,504,957 32.92 5,024,714 47.20

Above poverty line 85,551,420 485,874 0.57 8,171,398 9.55 21,096,615 24.66 33,034,279 38.61

Less than HS education 11,606,042 112,901 0.97 1,544,594 13.31 3,537,414 30.48 5,186,441 44.69

HS education 30,583,598 185,439 0.61 2,975,200 9.73 7,580,000 24.78 11,747,653 38.41

College education 16,433,811 59,892 0.36 1,229,885 7.48 3,447,148 20.98 5,722,347 34.82

a Based on 2000 population totals

b Percentages are given with respect to the total population per city provided.



# 3.5. Ambient PM Concentrations

This section describes measurements of ambient PM mass and composition made since the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) including analyses using AQS data as well as published findings. Emphasis is placed on the period fro m 2005-2007 which incorporates the most recent validated AQS data available at the time this document was prepared. When the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) was written, the full nationwide PM 2.5 

### compliance monitoring network had only recently been deployed, providing three years (1999-2001) of completed measurements. Based on observations from these first three years, the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) found that PM 2.5 

### in eastern cities was generally more highly correlated across monitoring sites than in western cities. Th e higher spatial correlations in the eastern cities resulted from the more regionally dispersed sources of PM 2.5 

### in the East. Although PM 2.5 

### concentrations at sites within an urban area ca n be highly correlated, significant differences in concentrations can occur on any given day. The ratio of PM 2.5 

### to PM 10 

### was found to be higher in the East than in the West in general, and values for this ratio are consistent with those found in numerous earlier studies presented in the 1996 PM AQCD (U.S. EPA, 1996, 079380 ). Differences in the composition of PM 2.5 

### between eastern and western cities we re also found to be consistent with differences found in the 1996 PM AQCD (U.S. EPA, 1996, 079380 ). Much more limited data were

December 2009 3-40

### available for describing the spatial variability of coarse particulate mass measured as PM 10-2.5 

### , UFPs, and PM composition. The 2004 PM AQCD (U.S. EPA, 2004, 056905 ) noted that components produced by area (e.g., traffic) and point sources are more spatially variable than regionally dispersed components (e.g., secondary SO 4 2– 

### ). Spatial variability will affect estimates of communityscale human exposure and caution should be exercise d in extrapolating conclusions from one area to another, particularly on a regional scale. For this PM ISA, the PM 2.5 

### monitoring network has been active for 8 or 9 years depending on location. Observations and analyses based on PM 2.5 

### measurements reported to AQS are included in this chapter. Furthermore, by selecting locations where PM 10 

### and PM 2.5 

### measurements are co-located, information about the spatiotemporal distribution of the PM 10-2.5 

### size fraction is investigated. Given the form of the curren t standard and the relative abundance of PM 10 

### monitors in the AQS network, PM 10 

### mass concentrations are also included in this section with the understanding that PM 10 

### includes mass contributions from the smaller size fractions and therefore overlaps with
PM 2.5 

### , PM 10-2.5 

### and UFP mass concentrations. Although co mpliance monitoring does not apply for UFPs because there is no ambient standard for them, new observational information is available from detailed studies in several cities. Similarly, advancements have been made in understanding PM composition from the CSN and IMPROVE networks. Descriptions of UFPs and speciated PM are covered throughout this section where information is available. Unless otherwise specified, the PM 2.5 

### , PM 10-2.5 

### and PM 10 

### data utilized in this section comes from the AQS. Based on the population and exposure re quirements for monitor siting in 40 CFR Part 58 described in Section 3.4.2 , monitors reporting to the AQS are not uniformly distributed across the U.S. Monitors are far more abundant in urban areas than rural ones, so actual rural spatiotemporal distributions may differ considerably from those re ported here. Furthermore, biases exist for some PM constituents (and hence, total mass) owing to volatilization losses of NO 3 – 

### and other semi-volatile compounds and, conversely, rete ntion of particle-bound water with hygroscopic species. The magnitude of these effect s is likely to be region-specific. Spatial distributions of PM across a range of geographic scales are covered in Section 3.5.1

. Temporal behavior including trends, seasonality and hourly variability are covered in Section 3.5.2

. Finally, statistical associations between different size fractions of PM and copollutants including CO, NO 2 

### , O 3 

### and SO 2 

### are covered in Section 3.5.3

.



# 3.5.1. Spatial Distribution

Spatia l scales of interest for PM range from global and continental scales (>1000 km) down to micro scale (~5-100 m). Variation in PM concentr ation depends on the spatial scale and magnitude of PM sources, formation and removal mechanis ms, and transport and dispersion of PM. These different sources and processes can cause substantia l variation in particle size distribution and chemistry. This section addresses the spatial va riability of PM by focusing primarily on AQS data across three different scales: variability across the U.S., urban-scale variab ility and neighborhoodscale variability. These sections are further subdivided to the extent possible into PM size fractions and composition.

December 2009 3-41

# 3.5.1.1. Variability across the U.S.

PM 2.5 

### Figure 3-9 shows the 3-yr mean of the 24-h PM 2.5 

### concentrations by county across the U.S. for 2005-2007. The data used in generating this map are from FRM or FRM-like 1 

### data obtained fromthe AQS database after applying a completeness criterion of 75% per quarter (i.e., 11 out of 15 quarterly measurements for a 1-in-6 day sampling schedule). Counties shown in white did not contain sufficient PM 2.5 

### data between 2005-2007 to meet the completeness criterion as a result of either a lack of monitoring sites or a lack of adequate or complete data from existing monitoring sites within the county. Of the 3,225 U.S. counties, 540 (17%) had PM 2.5 

### data meeting the completeness criterion in all three years (2005-2007). These 540 counties represent roughly 63% of the U.S. population. The fraction of the population residing within each county-average concentration range is shown on the left-hand margin of Figure 3-9 . Given the number of counties with no data, the varying size of counties, and the non-uniform spacing of the monitors and population within each reporting county, this should only be taken as a rough estimate of the relationship between population and average ambient concentrations. As seen in Figure 3-9 ,Kern County, CA reported the highest 3-yr avg 24-h
PM 2.5 

### concentration in excess of 20 μ g/m 3 

### . Average concentrations between 18 and 20 μg/m 3 

### were reported for several counties in the San Joaquin Valley and inland southern California as well as Jefferson County, AL containing Birmingham and Allegheny County, PA containing Pittsburgh.

1 FRM-like refers to PM 2.5 concehntration data associated with the parameter code “88502 - Acceptable PM 2.5 AQI and Speciation Mass”

in the AQS. These data were collected by continuous instruments wh ich are not approved as FRM or FEM, and consequently EPA does not use these data for regulatory purposes. These data are denoted as “FRM-like” because state and local monitoring agencies ha ve individually decided that the continuous inst ruments reporting these data have a degree of agreement with FRM/FEM methods that is sufficient in their opinion for the data to be used in public advisories regarding current air quality. In some cases, these da ta include statistical adjustments by the state/local monitoring agency base d on one-time or ongoing correlation analysis with co-located FRM/FEM monitors, intended to improve the “FRM-likeness” of the continuous concentration data (e.g., Bortnick et al., 2002, 156285 ). State/local monitoring agency decisions about whether to adjust continuous PM 2.5 data and whether their raw or adjusted continuous PM 2.5 data should be associated with parameter code 88502 were informed by non-binding EPA guidance issued in 2006 (Technical Note on Reporting PM 2.5 Continuous Monitoring and Speciation Data to AQS http://www.epa.gov/ttn/amtic/files/ambient/pm25/datamang/contrept.pdf ).

December 2009 3-42

## Figure 3-9. Three-yr avg 24-h PM 2.5 

## concentration by county derived from FRM or FRM-like data, 2005-2007. The population bar show s the number of people residing within counties that reported county-wide averag e concentrations within the specified
ranges.

Table 3-8 contains summary statistics for PM 2.5 

### reported to AQS for the period 2005-2007. All 24-h FRM and 1-h FRM-like data reported to AQS and meeting the completeness criterion outlined above are included in the table. The table provide s a distributional comparison between annual, 24-h and 1-h averaging times, calendar years (2005, 2006 and 2007) and seasons: winter (DecemberFebruary), spring (March-May), summer (June -August), and fall (September-November). In addition, 15 CSAs/CBSAs were chosen for their importance in recent PM health studies, as described in Section 3.4 , and have been included individually in the table. The distribution of PM 2.5 

### annual averages (calculated without seasonal weighting and presented in Table 3-8 ) was generated from 2,382 individual annual means reported by 794 24-h FRM monitors reporting to AQS between 2005 and 2007. The mean of the annual averages was 12 μg/m 3 

### , equivalent to the mean of the individual 24-h avg. The maximum annual average PM 2.5 

### concentration calculated from 24-h FRM data over these 3 yr was 23 μg/m 3 

### in Bakersfield, CA (AQS monitor ID: 060290010) during 2007. This site is located in the heavily populated portion of the San Joaquin Valley where air pollution frequently be comes trapped at ground level due to local topography. The distribution of the 24-h and 1-h avg, both generated from the same 1-h FRM-like data, are comparable up to the 90th percentile. The 1-h avg is 3 μg/m 3 

### higher than the 24-h avg at the 95 th 

### percentile and 7 μg/m 3 

### higher at the 99th percentile. Th is deviation between 1-h and 24-h averaging times is a result of short duration spikes in PM 2.5 

### mass lasting long enough to influence the upper percentiles of the 1-h distribution but not n ecessarily the 24-h avg distribution. Exceptional events were not removed from this data set and are responsible for at least some of the higher

December 2009 3-43

### concentrations observed. For example, the maximum 1-h reading of 828 μg/m 3 

### was reported by a monitor in Boise, ID (AQS monitor ID: 160010011) on July 4, 2007. Nine of the top 12 1-h PM 2.5 

### concentrations reported across the country also occu rred on July 4th, implicating fireworks as the common source for these high values.



## Table 3-8. PM 2.5 

## distributions derived from AQS data (concentration in μg/m 3 

## ).

Percentiles

n Mean 1 5 10 25 50 75 90 95 99 Max

2005-2007 PM 2.5

FOR DIFFERENT AVERAGING PERIODS

Annual avg a (24-h FRM) 2,382 12 5 7 8 10 12 14 16 17 19 23

24-h avg (24-h FRM) 349,028 12 2 4 4 7 10 16 23 28 39 193

24-h avg (1-h FRM-like) 183,057 10 1 2 3 5 8 13 19 24 35 126

1-h avg (1-h FRM-like) 4,403,817 10 0 1 2 4 8 13 21 27 42 828

PM 2.5

ANNUAL AND SEASONAL STRATIFICATION USING 24-H AVG FRM DATA

2005 114,346 13 2 4 5 7 11 17 24 30 42 133

2006 113,197 12 2 4 4 7 10 15 21 26 36 193

2007 121,485 12 2 4 4 7 10 16 22 27 40 145

Winter (December-February) 86,286 12 2 4 5 7 10 15 22 27 44 193

Spring (March-May) 88,489 11 2 3 4 6 9 14 20 24 33 145

Summer (June-August) 86,830 14 2 4 5 8 12 19 26 31 40 133

Fall (September-November) 87,423 12 2 3 4 6 10 15 22 26 39 126

2005-2007 PM 2.5

IN INDIVIDUAL CSAS/CBSAS USING 24-H AVG FRM DATA

Atlanta 4,939 15 4 6 7 10 14 19 25 29 37 145

Birmingham 4,869 16 4 6 7 10 15 21 29 34 47 64

Boston 8,464 10 2 3 4 5 9 13 20 24 32 50

Chicago 10,308 14 3 4 6 8 13 18 25 31 42 65

Denver 4,192 9 2 3 4 6 8 10 14 18 31 61

Detroit 5,223 14 2 3 5 7 12 19 26 31 45 82

Houston 1,342 15 4 6 8 10 14 18 23 26 34 44

Los Angeles 6,600 15 3 5 6 9 13 18 25 32 50 133

New York 15,826 13 2 4 4 6 10 17 24 29 39 58

Philadelphia 7,541 14 3 4 5 8 12 18 25 30 38 63

Phoenix 1,634 10 2 3 4 6 9 12 17 21 32 77

Pittsburgh 5,783 16 3 5 6 9 13 20 29 36 52 101

Riverside 2,751 17 3 5 6 10 14 21 31 40 58 106

Seattle 1,297 9 2 3 3 4 7 10 20 29 43 68

St. Louis 6,887 14 3 5 6 9 13 18 24 29 40 50

All 15 CSAs/CBSAs 87,656 14 2 4 5 7 12 17 25 30 42 145

Not in the 15 CSAs/CBSAs 261,372 12 2 3 4 6 10 15 22 27 38 193

a Straight annual average without quarterly weighting.

December 2009 3-44

### The distribution of the 24-h FRM PM 2.5 

### data was similar across the 3 years (2005-2007) investigated. Summer (June-August) had the highest mean and median relative to other seasons, but only by a small margin. For the 99th percentile, wi nter (December-February) was slightly higher than the other seasons. This is consistent with wi ntertime stagnation events resulting in short-term elevated PM 2.5 

### concentrations. Of the 15 CSAs/CBSAs inve stigated, the highest mean of 24-h PM 2.5 

### concentrations was reported for Riverside (17 μg/m 3 

### ), Birmingham (16 μg/m 3 

### ) and Pittsburgh (16 μg/m 3 

### ); the lowest was reported for Denver (9 μg/m 3 

### ) and Seattle (9 μg/m 3 

### ).

PM 10-2.5


### Since PM 10-2.5 

### is not routinely measured and reported to AQS, co-located PM 10 

### and PM 2.5 

### measurements from the AQS network were used to investigate the spatial distribution in PM 10-2.5



### . Only low-volume FRM or FRM-like samplers were considered in calculating PM 10-2.5 

### to avoid complications with vastly different sampling prot ocols (e.g., flow rates) between the independent
PM 10 

### and PM 2.5 

### measurements. The same 11+ days per quarter completeness criterion discussed above was applied to the PM 10 

### and PM 2.5 

### measurements. The PM 2.5 

### concentrations are reported to AQS at local conditions whereas the PM 10 

### concentrations are reported at standard conditions. Therefore, prior to calculating PM 10-2.5 

### by subtraction, the PM 10 

### AQS data were adjusted to local conditions on a daily basis using temperature and pressure measurements from the nearest National Weather Service station. Figure 3-10 shows the 3-yr mean of the 24-h PM 10-2.5 

### concentration by county across the U.S. for 2005-2007. There is considerably less coverage for PM 10-2.5 

### than for PM 2.5 

### or PM 10 

### alone since only a small subset of PM monitors are co-located and low-volume. The 40 counties included in Figure 3-10 incorporate less than 5% of the U.S. population. Of the 3,225 U.S. counties, only 40 (1%) met the completeness and co-l ocation criteria in all 3 yr (2005-2007), and therefore the available measurements do not pr ovide sufficient information to adequately characterize regional-scale coarse PM spatial concentration distributions. Table 3-9 contains summary statistics for PM 10-2.5 

### for the period 2005-2007 similar to those reported in Table 3-8 for PM 2.5 

### . Only six of the 15 CSAs/CBSAs had sufficient data for inclusion in Table 3-9 . Although fewer monitoring sites within these CSAs/CBSAs were used for PM 10-2.5 

### than for PM 2.5 

### , Table 3-8 and Table 3-9 provide a rough comparison of the PM present in the fine and thoracic coarse modes for these six cities. The easte rn cities including Atlanta, Boston, Chicago and New York all had a higher fraction in the fine mode w ith the greatest ratio of fine to thoracic coarse in Chicago (14 μg/m 3 

### PM 2.5, 

### 5 μg/m 3 

### PM 10-2.5, 

### ratio = 2.8). In contrast, Denver (9 μg/m 3 

### PM 2.5 

### , 20 μg/m 3 

### PM 10-2.5 

### , ratio = 0.45) and Phoenix (10 μg/m 3 

### PM 2.5 

### , 22 μg/m 3 

### PM 10-2.5 

### , ratio = 0.45) had a higher fraction in the thoracic coarse mode. Give n the limited information available from AQS for
PM 10-2.5 

### and the current NAAQS for PM 10 

### , the next section characterizes the more prevalent PM 10 

### data, acknowledging that PM 10 

### incorporates both thoracic coarse and fine particles.

December 2009 3-45

## Figure 3-10. Three-yr avg 24-h PM 10-2.5 

## concentration by county derived from co-located low volume FRM PM 10 

## and PM 2.5 

## monitors, 2005-2007. The population bar shows the number of people residing within counties that reported county-wide average concentrations within the specified ranges.

December 2009 3-46

## Table 3-9. PM 10-2.5 

## distributions derived from AQS data (concentration in μg/m 3 

## ).

Percentiles

n Mean 5 10 25 50 75 90 95 99 Max 1

2005-2007 PM 10-2.5 FOR DIFFERENT AVERAGING PERIODS

Annual avg a (low volume FRM) 130 12 3 5 6 9 11 14 19 23 39 43

24-h avg (low volume FRM) 12,027 13 -3 1 2 6 10 17 26 33 54 246

PM 10-2.5 ANNUAL AND SEASONAL STRATIFICATION USING 24-H AVG LOW VOLUME FRM DATA

2005 3,990 12 -5 0 2 5 10 16 26 33 52 246

2006 4,037 13 -2 1 2 6 10 17 27 34 56 182

2007 4,000 13 -2 1 3 6 11 18 26 33 56 148

Winter (December-February) 2,942 11 -5 -1 1 4 8 15 27 34 56 246

Spring (March-May) 3,088 13 -2 1 2 5 10 17 26 33 62 151

Summer (June-August) 2,968 14 -2 3 5 8 12 18 25 31 44 93

Fall (September-November) 3,029 14 -2 1 3 6 11 18 28 34 60 148

2005-2007 PM 10-2.5 IN INDIVIDUAL CSAS/CBSAS USING 24-H AVG LOW VOLUME FRM DATA b

Atlanta 167 10 -4 1 2 5 9 13 18 21 30 46

Boston 340 7 -2 1 2 4 6 9 12 16 25 27

Chicago 161 5 -8 -4 -3 1 4 8 14 19 37 37

Denver 353 20 0 4 6 11 19 28 36 42 59 78

New York 338 9 -16 -2 1 5 8 12 17 23 34 56

Phoenix 163 22 -3 8 11 16 20 29 35 46 67 70

All 6 CSAs/CBSAs 1,522 12 -6 0 2 5 10 17 27 34 51 78

Not in the 6 CSAs/CBSAs 10,505 13 -2 1 2 6 10 17 26 33 56 246

a Straight annual average without quarterly weighting.

b No co-located low-volume FRM PM 10 and FRM-like PM 2.5 monitors available for Birmingham, Detroit, Houston, Los Angeles, Philadelphia, Pittsburgh, Riverside, Seattle or St. Louis.



### PM 10 

### Figure 3-11 shows the 3-yr mean of the 24-h PM 10 

### concentrations by county across the U.S. for 2005-2007. Both FRM and FEM PM 10 

### data reported to AQS were included and the same 11+ days per quarter completeness criterion described above for PM 2.5 

### was applied. The highest 3-yr avg for PM 10 

### (>50 μg/m 3 

### ) occurred in inland southern California and the populous counties of southern Arizona and central New Mexico. Of the 3,225 U.S. counties, 676 (12%) contained PM 10 

### data meeting the completeness criterion in all three years; these 676 counties incorporate approximately 43% of the U.S. population.

December 2009 3-47

## Figure 3-11. Three-yr avg 24-h PM 10 

## concentration by county derived from FRM or FEM monitors, 2005-2007. The population bar shows the number of people residing within counties that reported county-wide average concentrations within the specified ranges.

Table 3-10 contains summary statistics for PM 10 

### reported to AQS for the period 2005-2007. Both 24-h FRM and 1-h FEM data are included in the table. To facilitate a distributional comparison between averaging times, annual, 24-h and 1-h averaging times using the FRM and FEM data have been included separately in Table 3-10 . As in the earlier tables, the data is also stratified by year and season and includes the 15 CSAs/CBSAs individually.

December 2009 3-48

## Table 3-10. PM 10 

## distributions derived from AQS data (concentration in μg/m 3 

## ).

Percentiles

n Mean 1 5 10 25 50 75 90 95 99 Max

2005-2007 PM 10 FOR DIFFERENT AVERAGING PERIODS

Annual avg a (24-h FRM and 1-h FEM) 2022 25 10 14 16 19 23 28 35 44 60 85

24-h avg (24-h FRM and 1-h FEM) 326,675 26 3 6 9 14 21 32 46 59 97 8299

24-h avg (24-h FRM) 167,310 25 2 6 9 14 21 31 45 57 91 8299

24-h avg (1-h FEM) 156,931 26 4 7 9 14 21 32 48 62 105 979

1-h avg (1-h FEM) 3,767,533 27 1 4 6 11 19 32 51 69 145 8540

PM 10 ANNUAL AND SEASONAL STRATIFICATION USING 24-H AVG FRM AND FEM DATA

2005 107,524 25 2 6 9 13 21 31 46 58 93 1441

2006 109,505 26 3 6 9 13 21 32 46 59 101 8299

2007 109,646 26 4 7 9 14 21 32 47 60 99 2253

Winter (December-February) 80,959 23 2 5 7 11 17 27 42 57 99 8299

Spring (March-May) 82,772 25 2 6 8 13 20 31 45 58 96 2253

Summer (June-August) 81,351 29 6 10 12 18 25 35 49 60 92 1839

Fall (September-November) 81,593 26 3 7 9 14 21 32 48 62 102 1212

2005-2007 PM 10 IN INDIVIDUAL CSAS/CBSAS USING 24-H AVG FRM AND FEM DATA

Atlanta 1,868 24 6 9 11 16 23 31 39 44 57 108

Birmingham 5,478 34 6 9 12 19 28 43 64 82 120 241

Boston 1,412 17 2 5 7 10 15 22 30 36 50 58

Chicago 6,165 26 6 9 11 16 23 32 45 55 78 214

Denver 4,706 28 5 10 12 18 25 35 47 54 75 118

Detroit 1,407 30 7 10 12 18 26 38 53 64 81 182

Houston 1,397 31 7 10 12 17 23 34 56 80 137 248

Los Angeles 2,020 27 4 8 11 18 25 33 42 51 74 489

New York 514 19 2 6 7 11 17 25 35 40 51 83

Philadelphia 4,207 19 4 7 9 12 17 24 34 40 52 84

Phoenix 12,005 52 7 14 19 29 44 65 91 112 166 2253

Pittsburgh 12,677 24 4 7 9 13 19 31 45 57 83 157

Riverside 4,327 35 4 8 11 19 30 45 64 75 111 1212

Seattle 2,136 19 5 7 9 12 17 23 31 37 52 79

St. Louis 2,464 33 6 10 12 18 28 42 59 74 114 315

All 15 CSAs/CBSAs 62,783 32 5 8 10 16 25 39 60 77 120 2253

Not in the 15 CSAs/CBSAs 263,892 24 2 6 8 13 20 30 43 54 88 8299

a Straight annual average without quarterly weighting

December 2009 3-49

### The maximum annual average PM 10 

### concentration calculated from 24-h FRM data over these three years was 85 μg/m 3 

### in Stanfield, AZ (AQS monitor ID: 040213008) during 2007. Stanfield is a small agricultural town (2007 population = 1074) approximately 64 km south of Phoenix and is in a region heavily influenced by windblown dust. Many of the maximum 24-h and 1-h avg PM 10 

### concentrations in Table 3-10 exceed 1,000 μg/m 3 

### , but these represent rare events given the much lower 99th percentiles. Exceptional events were not removed from this data set and are responsible for at least some of the higher concentrations observed. The distribution of the 24-h FRM and FEM PM 10 

### data was similar across the three years (2005-2007) investigated. Summer (June-August) had the highest mean and median relative to other seasons, consistent with PM 2.5 

### observations. Of the 15 CSAs/CBSAs investigated, the highest mean of 24-h PM 10 

### concentrations was reported for Phoenix (52 μg/m 3 

### ), considerably higher than the means for the other CSAs/CBSAs investigated. The lowest was reported for Boston (17 μg/m 3 

### ) with New York, Philadelphia and Seattle only slightly higher (19 μg/m 3 

### ). On average using the 2005-2007 data for PM 2.5 

### in Table 3-8 and PM 10 

### in Table 3-10 , the distribution between fine and coarse PM varies substantially by location. A larger fraction of PM mass is present in the thoracic coarse mode in Phoenix and Denver (3-yr mean PM 2.5 

### /PM 10 

### ratios of 0.19 and 0.32, respectively). In cont rast, a larger fraction is present in the fine mode in Philadelphia (0.74), New York (0.68) and Pi ttsburgh (0.67). Comparisons of PM 2.5 

### to PM 10 

### as reported to AQS should be used with caution, however, since PM 2.5 

### concentrations are reported for local conditions while PM 10 

### concentrations are converted to STP before reporting. Nevertheless, these findings are consistent with those in Table 3-9 for PM 10-2.5 

### in the subset of 6 cities with available co-located lowvolume PM data that have been properly adjusted for temperature and pressure. These findings are also consistent with those reported in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) where ratios of
PM 2.5 

### to PM 10 

### were observed to be highest in the northeast (0.70), southeast (0.70), and industrial Midwest (0.70) and lower in the upper Midwest (0.53) , northwest (0.50), southern California (0.47) and southwest (0.38).

UFPs

Little is known about the spatiotemporal distribution or composition of UFPs on a regional scale. New particle formation has been observed in environments ranging from relatively unpolluted marine and continental environments to pollute d urban areas as an ongoing background process and during nucleation events (Kulmala et al., 2004, 089159 ). During nucleation events, which may last for several hours, UFP number concentrations can exceed 10 4 

### per cm 3 

### over distances of several hundred kilometers (Kulmala et al., 2004, 089159 ; Qian et al., 2007, 116435 ). These events occur throughout the year on 5-40% of days, depending on location (Qian et al., 2007, 116435 ). Cloud condensation nuclei, with diameters between ~10 a nd ~100 nm have been monitored for several years at a number of nonurban sites in the U.S. ( http://cmdl.noaa.gov/aero/data/ ). Average particle number counts at these sites in the U.S. range from several hundred to several thousand per cm 3 

### . The particles are formed by nucleation of atmospheric gases with additional contribution from primary emissions in these environments (Pierce and Adams, 2009, 191189 ). In an urban setting, a large percentage of UFPs come from combustion-related emissions from mobile sources (Sioutas et al., 2005, 088428 ). UFP number concentrations drop off quickly with distance from the roadway (Levy et al., 2003, 052661 ; Reponen et al., 2003, 088425 ; Zhu et al., 2005, 157191 ), and therefore concentrations can be highly heterogeneous in the near-road environment depending on traffic, meteorological and topographic conditions (Baldauf et al., 2008, 190239 ). Studies characterizing spatial variability in UF Ps are currently limited to a handful of close proximity locations and therefore are discussed in Sections 3.5.1.2 and 3.5.1.3 in the context of urban- and neighborhood-scale variability. Further elaboration on the composition of UFPs is included below.

PM Constituents

Only PM 2.5 

### is collected routinely at CSN network sites so the majority of this section on PM constituents is devoted to PM 2.5 

### composition. PM 10-2.5 

### and UFP composition is discussed to the extent possible below. Figure 3-12 through Figure 3-16 contain U.S. concentration maps for OC, EC, SO 4 2– 

### , NO 3 - 

### , and NH 4 + 

### mass from PM 2.5 

### measurements taken as part of the CSN network for the period 2005-2007. Data used in these figures are as reported to AQS: no correction was applied to OC for non-carbon mass and NO 3 - 

### represents total particulate nitrate. Figure 3-12 shows regions of

December 2009 3-50

### high PM 2.5 

### OC mass concentration with annual average concentrations greater than 5 μg/m 3 

### in the western and the southeastern U.S. Concentrations at the western monitors peak in the fall and winter while those in the Southeast peak anywhere from spring through fall. The central and northeastern portions of the U.S. generally contain lower measured OC. Bell et al. (2007, 155683 ) present a similar map for estimated organic carbon mass (OCM) from 2000-2005 calculated by multiplying the blank corrected OC measurement by 1.4 to account for non-carbon mass. There are a range of estimates in the literature for suggested scaling factors (Turpin and Lim, 2001, 017093 ), depending predominantly on how highly oxygenated the aerosol is (Pang et al., 2006, 156012 ). Fresh PM, more common in urban regions, has undergone limited ch emical transformation. As the aerosol is transported to rural regions, it becomes more oxygenated. Turpin and Lim (2001, 017093 ) recommended ratios of 1.6 ± 0.2 for urban and 2. 1 ± 0.2 for non-urban aerosols. Estimates range from 1.6 to 2.6 for rural IMPROVE monitors (El-Zanan et al., 2005, 155764 ). Therefore, applying one correction factor of 1.4 across the entire U.S. w ill lead to an underestimate of the OCM in rural regions. Therefore, the OC data in Figure 3-12 is presented as measured with a national blank correction, but no adjustment to OCM. Figure 3-13 contains a similar map for PM 2.5 

### EC mass concentration that exhibits smaller seasonal variability than OC, particularly in the eastern half of the U.S. There are isolated monitors spread throughout the country that measure high annual average EC concentrations. These EC ‘hot spots’ are primarily associated with larger metropolit an areas such as Los Angeles, Pittsburgh, and New York, but El Paso, TX, also reported high annual average EC concentrations (driven by a wintertime average concentration greater than 2 μg/m 3 

### ). In a similar analysis for EC by Bell et al. (2007, 155683 ) for 2000-2005 data, there were also high wintertime EC concentrations in eastern Kentucky and western Montana. These particular locations do not stand out in the 2005-2007 data in Figure 3-13

. Figure 3-14 contains a map for PM 2.5 

### SO 4 2– 

### mass concentration which shows that SO 4 2– 

### is more prevalent in the eastern U.S. owing to the strong west-to-east gradient in SO 2 

### emissions. This gradient is magnified in the summer months when more sunlight is available for photochemical formation of SO 4 2– 

### . In contrast, PM 2.5 

### NO 3 – 

### mass concentration in Figure 3-15 is highest in the west, particularly in California. There are also elevated concentrations of NO 3 – 

### in the upper Midwest. The seasonal plots show generally higher NO 3 – 

### in the wintertime as a result of temperature driven partitioning. Exceptions exist in Los Angeles and Riverside where high NO 3 –

### readings appear yearround. The PM 2.5 

### NH 4 + 

### mass concentration maps in Figure 3-16 shows spatial patterns related to both SO 4 2– 

### and NO 3 – 

### resulting from its presence in both (NH 4 

### ) 2 

### SO 4 

### and NH 4 

### NO 3 

### . Figure A-31 through Figure A-36 in Annex A show similar U.S. concentration maps for PM 2.5 

### Cu, Fe, Ni, Pb, Se and V mass concentrations as measured by XRF. There is considerably less seasonal variation in the concentration profile for these metals than OC or the ions. For the 15 metropolitan areas identified earlier, the contribution of the major component classes to total PM 2.5 

### mass was derived using the measured sulfate, adjusted NO 3 – 

### , derived water, inferred carbonaceous mass appro ach (SANDWICH) (Frank, 2006, 098909 ). This approach uses the measured FRM PM 2.5 

### mass and co-located CSN chemical constituents to perform a mass balancebased estimation of the PM 2.5 

### mass fraction attributed to SO 4 2– 

### , NO 3 – 

### , EC, OCM, and crustal material. SO 4 2– 

### and NO 3 – 

### include associated NH 4 + 

### mass and estimated particle-bound water. Furthermore, NO 3 – 

### is assumed to be fully neutralized as NH 4 

### NO 3 

### and has been adjusted to represent the amount retained by the FRM monitor. EC is taken as measured, and the crustal component is derived from common oxides contained in the Earth’s crust (Pettijohn, 1957, 156862 ), but can also include significant anthropogenic contributions, such as coal fly ash that are unrelated to soil resuspension. Finally, OCM is estimated using mass balance by subtracting the sum of all other constituents from the FRM PM 2.5 

### mass. The SANDWICH method takes into account passive collection of semi-volatile or handling-relate d mass on the FRM filters in the mass balance calculation. The magnitude of this artifact is assigned a nominal value of 0.5 μg/m 3 

### , which is derived from limited analysis of FRM field blanks. Other c onstituents such as salt and other metallic oxides, however, are not included in these calculations a nd therefore the OCM fraction estimated by mass balance represents an upper bound on the FRM retained OCM. The calculations and assumptions that go into the SANDWICH method are di scussed in detail in Frank (2006, 098909 )with further information available on EPA’s AirExplorer web site http://www.epa.gov/cgi-bin/htmSQL/mxplorer/query_spe.hsql

December 2009 3-51

## Figure 3-12. Three-yr avg 24-h PM 2.5 

## OC concentrations measured at CSN sites across the U.S., 2005-2007.

December 2009 3-52

## Figure 3-13. Three-yr avg 24-h PM 2.5 

## EC concentrations measured at CSN sites across the U.S., 2005-2007.

December 2009 3-53

## Figure 3-14. Three-yr avg 24-h PM 2.5 

## SO 4 2– 

## concentrations measured at CSN sites across the U.S., 2005-2007.

December 2009 3-54

## Figure 3-15. Three-yr avg 24-h PM 2.5 

## NO 3 – 

## concentrations measured at CSN sites across the U.S., 2005-2007.

December 2009 3-55

## Figure 3-16. Three-yr avg 24-h PM 2.5 

## NH 4 + 

## concentrations measured at CSN sites across the U.S., 2005-2007.

December 2009 3-56

### Figure 3-17 shows the PM 2.5 

### compositional breakdown for the 15 CSAs/CBSAs. All available monitoring sites with co-located FRM PM 2.5 

### and CSN speciation data reporting in all four seasons for at least one calendar year from 2005-2007 were included. Furthermore, each season was required to contain five reported values for mass and the major PM 2.5 

### constituents. This resulted in a varying number of sites (ranging from one to seve n, as indicated in the caption to Figure 3-17 ) used to create the averages shown in the figure. Variability in PM 2.5 

### composition within each CSA/CBSA where multiple monitors were available and trends in composition over time are discussed in subsequent sections.



## Figure 3-17. Three-yr avg PM 2.5 

## speciation estimates for 2005-2007 derived using the SANDWICH method. For the following 15 CS As/CBSAs (with the number of sites per CSA/CBSA listed in parenthesis): Atlanta, GA (1); Birmingham, AL (3); Boston, MA (4); Chicago, IL (7); Denver, CO (2); Detroit, MI (4); Houston, TX (1); Los Angeles, CA (1); New York City, NY (7); Philadelphia, PA (6); Phoenix, AZ (2); Pittsburgh, PA (4); Riverside, CA (1); Seattle, WA (4); and St. Louis, MO (3). SO 4 2–


## and NO 3 – 

## estimates include NH 4 + 

## and particle bound water and the circle area is scaled in proportion to FRM PM 2.5 

## mass as indicated in the legend.

On an annual average basis, SO 4 2– 

### is a dominant PM component in the easte rn U.S. cities. For the presented cities, this includes everything east of Houston where the SO 4 2– 

### fraction of PM 2.5 

### ranges from 42% in Chicago to 56% in Pittsburgh on an annual average basis. OCM is the next largest component in the east ranging from 27% in Pittsburgh to 42% in Birmingham. In the west, OCM is the largest constituent on an annual basis, ranging from 34% in Los Angeles to 58% in Seattle. SO 4 2– 

### , NO 3 – 

### and crustal material are also important in many of the included western cities. In the west, fractional SO 4 2– 

### ranges from 18% in Denver to 32% in Los Angeles while fractional NO 3 – 

### is relatively large in Riverside (22%), Los An geles (19%) and Denver (15%) and less important on an annual basis in Phoenix (1%) and Seattle (2%). Crustal material is particularly prevalent in Phoenix (28%). EC makes up a smaller fraction of the PM 2.5 

### (4-11%), but it is consistently present in all included cities regardless of region.

December 2009 3-57

## Figure 3-18. Seasonally-stratified 3-yr avg PM 2.5 

## speciation estimates for 2005-2007 derived using the SANDWICH method. For the foll owing 15 CSAs/CBSAs: Atlanta, GA; Birmingham, AL; Boston, MA; Chicago, IL; Denver, CO; Detroit, MI; Houston, TX; Los Angeles, CA; New York City, NY; Phila delphia, PA; Phoenix, AZ; Pittsburgh, PA; Riverside, CA; Seattle, WA; and St. Louis, MO. SO 4 2– 

## and NO 3 – 

## estimates include NH 4 + 

## and particle bound water and the circle area is scaled in proportion to FRM PM 2.5 

## mass as indicated in the legend.

The seasonal variation in PM 2.5 

### composition across the 15 CSAs/CBSAs is shown in Figure 3-18 where the seasons are defined as before. SO 4 2– 

### dominates in most metropolitan areas in the summertime, while NO 3 – 

### becomes important in the colder wintertime months. Notable exceptions include Denver, Phoenix, Riverside, and Seattle where summertime SO 4 2– 

### makes up a smaller fraction of the PM 2.5 

### mass compared with other regions. Likewise, NO 3 – 

### is less pronounced in the wintertime in Atlanta, Birmingham, Houston, Phoeni x, and Seattle compared with other regions. Los Angeles and Riverside exhibit elevated NO 3 – 

### from fall through spring. Crustal material is a substantial summertime component in Houston (26%), an d is generally low elsewhere in the East in all seasons. In the West, crustal material repr esents a substantial component year-round in Phoenix and Denver. The only PM size fraction routinely collected at CSN network sites is PM 2.5 

### , resulting in less available information on speciated PM 10-2.5 

### . Edgerton et al. (2005, 088686 ; 2009, 180385 ) published speciated measurements for PM 2.5 

### and PM 10-2.5 

### obtained using dichotomous samplers from four locations included in the Southeastern Aerosol Research and Characterization (SEARCH) study: Yorkville, GA, Centreville, AL, Birmingham, AL a nd Atlanta, GA. Samples were collected between 1999 and 2003 on a 1-in-3 day or 1-in-6 day schedule, depending on site. Speciated measurements for both PM 2.5 

### and PM 10-2.5 

### included SO 4 2– 

### , NO 3 - 

### , NH 4 + 

### , and major metal oxides (MMO). In addition, OC and either black carbon (BC) or EC were reported for PM 2.5 

### over the entire study period and for
PM 10-2.5 

### for a subset of samples extending from April 2003 to April 2004. For the Atlanta and Birmingham SEARCH sites, the annual average NO 3 - 

### mass fraction was approximately equal for PM 2.5 

### (5.6% and 5.0%, respectively, for Atlanta and Birmingham) and PM 10-

2.5 

### (4.9% and 3.3%). Likewise, the OC mass fraction was approximately equal for PM 2.5 

### (26% and 26%) and PM 10-2.5 

### (24% and 27%). MMO contributed an order of magnitude smaller mass fraction to
PM 2.5 

### (2.6% and 4.7%) than PM 10-2.5 

### (38% and 35%). In contrast, SO 4 2– 

### contributed an order of

December 2009 3-58

### magnitude greater mass fraction to PM 2.5 

### (25.1% and 24.1%) than PM 10-2.5 

### (2.8 and 2.1%). BC also contributed a larger mass fraction of PM 2.5 

### (8.6% and 10.5%) than EC did for PM 10-2.5 

### (2.9% and 2.4%). Based on these findings, MMO are present primarily in the thoracic coarse mode, while SO 4 2– 

### and EC/BC are present primarily in the fine mode. NO 3 – 

### and OC are present in both modes in approximately equal mass fractions. These results are specific to Atlanta and Birmingham and may not represent other geographic regions. Information about the composition of ambient UF Ps directly emitted by sources is still sparse compared to that for the larger size modes. However, their composition is expected to reflect that of their sources. As noted in Section 3.3 (and references therein), particle number emissions from motor vehicles are dominantly in the UF size range. The composition of gasoline vehicle emissions consists mainly of a mix of OC, EC and small quantities of trace metals and sulfates, with OC constituting anywhere from 26-88% of PM. Diesel PM is generally comprised of an EC and trace metal ash core onto which organic material and nucleation-mode SO 4 2– 

### condense. With the introduction of new diesel emissions standards in 2007, total emissions have decreased dramatically, particularly for carbon. In areas where atmospheric nucleation is the dominant source of UFPs, sulfate along with ammonium, and secondary organic compounds ar e the likely major components of UFPs. In a study conducted at several urban sites in Southern California, Cass et al. (2000, 020680 ) found that the composition of UFPs ranged from 32-67% OC, 3.5-17.5% EC, 1-18% SO 4 2- 

### , 0-19% NO 3 – 

### , 0-9% NH 4 + 

### , 1-26% metal oxides, 0-2% Na, and 0-2% Cl. Thus carbon, in various forms, was found to be the major contributor to the mass of UFPs. However, ammonium was found to contribute 33% of the mass of UFPs at one site in Riversid e. Fe was the most abundant metal found in the UFPs. Chung et al. (2001, 017105 ) found that carbon was the major component of the mass of UFPs in a study conducted during January of 1999 in Bakersfield, CA. However, in the study of Chung et al. (2001, 017105 ), the contribution of carbonaceous species (OC and EC; typically 20-30%) was much lower than that found in the cities in Southern California. They found that Ca was the dominant cation, accounting for about 20% of th e mass of UFPs in their samples. Sizablecontributions from Si (0-4%) and Al (6-14%) were also found. MOUDIs are used to collect sizesegregated filter samples in the UFP compositiona l analyses described above. Coarse particle bounce is a concern when using MOUDIs and further st udies, including scanning electron microscopy, may be needed to quantify the effect of this sampling artifact on UFP compositional analyses. Herner et al. (2005, 135983 ) reported a gradual increase in OC mass fraction as particle size decreases from 1 μm (20% OC) to 100 nm (80% OC ) in the San Joaquin Valley of California. Sardar et al. (2005, 180086 ) found OC to be the major component of UFPs at four locations in California, with higher OC mass fraction in the wintertime relative to summertime. EC and SO 4 2– 

### were also present in the UFP samples, but at much smaller mass fractions. EC was present year-round, whereas SO 4 2– 

### had a summertime increase. More detailed chemical characterization of the OC fraction of ambient UFPs is extremely limited, but recent studies have identified specific organic molecular markers affiliated with motor vehicle emissions including hopanes and PAHs (Fine et al., 2004, 141283 ; Ning et al., 2007, 156809 ; Phuleria et al., 2007, 117816 ). As noted in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ), primary biological aerosol particles (PBAP), which include microorganisms, fragments of living things, and organic compounds of miscellaneous origin in surface deposits on filters, are not distinguishable in analyses of total OC. A clear distinction should be made between PBAP and primary OC that is produced by organisms (e.g., waxes coating the surfaces of organisms) an d precursors to secondary OC such as isoprene and terpenes. Indeed, the fields of view of many photomicrographs of PM samples obtained by scanning electron microscopy are often dominated by large numbers of pollen spores, plant and insect fragments, and microorganisms. Bioaerosols such as pollen, fungal spores, and most bacteria are expected to be found mainly in the coarse size fraction (see Figure 3-2 for an illustrative example of a pollen particle). However, allergens from po llens can also be found in respirable particles (Edgerton et al., 2009, 180385 ; Taylor, 2002, 025693 ). Matthias-Maser et al. (2000, 155972 ) summarized information about the size distribution of PBAP in and around Mainz, Germany in what is perhaps the most complete study of this sort. Matthias-Maser found that PBAP constituted up to 30% of total particle number and volume in the approximate size range from 0.35-50 μm on an annual basis. Additionally, whereas the contributio n of PBAP to the total aerosol volume did not change appreciably with season, the contribution of PBAP to total particle number ranged from about 10% in December and March to about 25% in June and October. Bauer et al. (2008, 189986 ) measured contributions of fungal spores to OC at an urban and a suburban site in Vienna, Austria in spring and summer. Fungal spores at the suburban site contributed on average 10% to OC in PM 10

December 2009 3-59

### and 5% at the urban site. At the suburban site, in summer, fungal spores accounted on average for 60% of the OC (0.56 μg/m 3 

### ) in PM 10-2.1 

### (2.6 μg/m 3 

### ). The contribution to PM 2.1 

### was estimated to be about 10% that in PM 10-2.1 

### . Womiloju et al. (2003, 179954 ) estimated that fungal spores contribute 14-22% of OC in PM 2.5 

### in and around Toronto. Edgerton et al. (2009, 180385 ) found that PBAP contributed 60-70% of OC (average ~1.7 μg/m 3 

### ) in PM 10-2.5 

### at an urban and a rural site in Alabama in fall of 2000 and spring of 2001. The percentage contributions were similar at bot h sites and higher concentrations were found in spring than in fall. Although results for the U.S. are more limited, they are broadly consistent with the results of the other studies in illustrating the importance of PBAP, at least for fungal spores in OC.



# 3.5.1.2. Urban-Scale Variability

PM 2.5


### Data from the 15 CSAs/CBSAs were used to i nvestigate urban-scale variability in PM reported to AQS. PM 2.5 

### has a longer residence time in the atmosphere compared to PM 10-2.5 

### resulting from a slower V d 

### . As a result, PM 2.5 

### exhibits increased spatial homo geneity with relatively less localized influence from point sources. Maps of PM 2.5 

### monitor locations and box plots of seasonal
PM 2.5 

### mass concentration data are provided for Boston ( Figure 3-19 and Figure 3-20 ), Pittsburgh ( Figure 3-21 and Figure 3-22 ), and Los Angeles ( Figure 3-23 and Figure 3-24 ). Figures A-37 through A-80 in Annex A contain similar information for all 15 CSAs/CBSAs under investigation. With very few exceptions, the PM 2.5 

### concentration is quite uniformly distributed across the monitors. Los Angeles has one monitor (Site I) that reported noticeably less PM 2.5 

### in all four seasons than the rest of the monitors in the region. This monitor is locat ed at Lancaster CA, separated from the rest of the Los Angeles region by the San Gabriel Mountains. In general, however, PM 2.5 

### varies approximately the same magnitude between monitors as it does between seasons for th e 15 selected cities. Table 3-11 through Table 3-13 contain pair-wise monitor si te comparison statistics for PM 2.5 

### in Boston, Pittsburgh, and Los Angeles, respectively. Tables A-20 through A-34 in Annex A contain the same statistics for PM 2.5 

### measured within all 15 of the CSAs/CBSAs investigated. Comparison statistics shown include the Pearson correlation coef ficient (R), the 90th percentile of the absolute difference in concentrations (P90), the coefficient of divergence (COD) and the number of paired observations (n). The COD provides an indication of the variability across the monitoring sites in each CSA/CBSA and is defined as follows:

2

1 

## 1 

# ∑

= 

## ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ −

= p

i ikij ikij jk 

## XX XX

p COD

Equation 3-2



### where X ij 

### and X ik 

### represent observed concentrations averaged over some measurement averaging period i (hourly, daily, etc.) at sites j and k , and p is the number of paired observations. A COD of 0 indicates there are no differences between concentrations at paired sites (spatial homogeneity), while a COD approaching 1 indicates extreme spatial heterogeneity. Temporal correlations between 24-h PM 2.5 

### concentrations in Boston range from 0.61 to 0.97 in Table 3-11 . The lowest correlation in this CSA was betw een Site A located in Fall River, MA, 1 km from the Narragansett Bay and Site L located in Na shua, NH, on the bank of the Merrimack River, 120 km north. The highest correlation was between S ites P and R, located less than a kilometer apart in Providence, RI. In Pittsburgh, 24-h PM 2.5 

### correlations range from 0.65 to 0.97. The lowest correlation in this CSA was between Sites B and D, located diamet rically opposite downtown Pittsburgh and 33 km apart. The highest correlation was for Sites K and D, located 21 km apart and both west of downtown. The prevailing wind in Pittsburgh is from the west, which explains the higher correlation between the two upwind sites. In Los Angeles, 24-h PM 2.5 

### correlations range from 0.21 to 0.96. The lowest correlation was between Sites I and J located 123 km apart and separated by the San Gabriel Mountains as discussed

December 2009 3-60

### earlier. The highest correlation was for sites G and H, located 3.7 km apart and both in Long Beach, CA. Therefore, while distance between monitors plays an important role in how well any two monitors correlate, other factors such as mete orology and topography can be important as well.



## Figure 3-19. Locations of PM 2.5 

## monitors and major highways, Boston, MA.

December 2009 3-61concentration (μg/m3)

0 10 20 30 40

1234 1234 1234 1234 1234 1234 1234 1234 1234 1=winter 2=spring
3=summer
4=fall teK

teL 33-001-2004

teM 33-011-1015

teN 33-013-1006

teO 33-015-0014

teP 44-007-0022

teQ 44-007-0026

teR 44-007-0028

teS 44-007-1010 AQSSiteID SD 7.4 5.3 6.7 6.0 5.8 6.6 6.9 6.5 6.5

Si

Si

Si

Si

Si

Si

Si

Si

Si K L MN O P QRS

Mean 11.4 7.2 10.0 9.7 8.9 10.1 11.9 10.5 9.7

Obs 346 183 361 362 362 1027 321 313 998

25-027-0023



## Figure 3-20. Seasonal distribution of 24-h avg PM 2.5 

## concentrations by site for Boston, MA, 2005-2007. Box plots show the median and interquartile range with whiskers extending to the 5 th 

## and 95 th 

## percentiles at each site during (1) winter (December-February), (2) spring (March-May), (3) summer (June-August) and (4) fall (September-November).

December 2009 3-62

## Table 3-11. Inter-sampler comparison statistics for each pair of 24-h PM 2.5 

## monitors reporting to AQS for Boston, MA.

Site A B C D E F G H I J

A 1.00 0.80 0.77 0.71 0.84 0.79 0.78 0.79 0.79 0.77 (0.0, 0.00) (6.6, 0.21) (6.2, 0.22) (6.9 , 0.23) (4.8, 0.19) (8.1, 0.23) (7.7, 0. 24) (6.8, 0.22) (7.9, 0.25) (7.5, 0.24)

341 326 318 323 329 318 319 325 338 310
B 1.00 0.92 0.87 0.87 0.90 0.90 0.90 0.90 0.85 (0.0, 0.00) (4.1, 0.17) (4.1, 0.18) (4.7, 0.19) (6.3 , 0.21) (6.2, 0.23) (4.9, 0. 19) (7.1, 0.26) (5.5, 0.21)

350 328 331 339 326 323 333 343 317
C 1.00 0.90 0.85 0.90 0.89 0.90 0.88 0.86 (0.0, 0.00) (3.5, 0.17) (5.3, 0.21) (6.3, 0.23) (6.3, 0.24) (5.0, 0.20) (6.8, 0.26) (6.2, 0.21)

342 321 331 316 318 326 336 311
D 1.00 0.80 0.88 0.88 0.86 0.86 0.87 (0.0, 0.00) (5.6, 0.20) (5.8, 0.21) (5.8 , 0.22) (4.6, 0.19) (7.0, 0.26) (5.8, 0.19)

355 336 324 329 332 345 313
E 1.00 0.90 0.90 0.89 0.87 0.87 LEGEND
Pearson R (P90, COD)

n (0.0, 0.00) (5.9, 0.19) (5.8, 0.21) (5.0, 0.19) (6.9, 0.24) (5.4, 0.20)
357 330 333 340 350 322
F 1.00 0.94 0.94 0.92 0.92 (0.0, 0.00) (3.8, 0.14) (3.5, 0.15) (4.5, 0.17) (5.4, 0.18)

349 324 324 339 310
G 1.00 0.94 0.94 0.89 (0.0, 0.00) (4.0, 0.16) (4.3, 0.15) (5.7, 0.20)

398 325 338 308
H 1.00 0.93 0.89 (0.0, 0.00) (4.7, 0.19) (5.0, 0.17)

349 342 318
I 1.00 0.86 (0.0, 0.00) (6.9, 0.23)

1015 330
J 1.00 (0.0, 0.00)

335

December 2009 3-63Site K L M N O P Q R S

A 0.77 0.61 0.71 0.68 0.73 0.87 0.81 0.85 0.86 (8.1, 0.23) (8.3, 0.29) (8.0, 0.23) (7.9, 0.23) (7.0, 0.22) (5.3, 0.18) (7.2, 0.23) (5.6, 0.20) (5.2, 0.18)

320 173 324 334 331 326 292 285 306
B 0.86 0.80 0.87 0.83 0.88 0.86 0.80 0.85 0.85 (6.6, 0.21) (6.2, 0.23) (5.3, 0.19) (6.0, 0.21) (4.7, 0.18) (5.6, 0.19) (7.9, 0.26) (5.7, 0.21) (6.0, 0.19)

329 175 331 341 336 335 300 288 314
C 0.86 0.89 0.93 0.90 0.93 0.83 0.79 0.81 0.82 (6.9, 0.21) (4.8, 0.23) (4.4, 0.17) (4.6, 0.19) (3.8, 0.18) (5.9, 0.21) (7.8, 0.26) (6.2, 0.23) (6.0, 0.21)

321 173 323 335 328 329 290 281 309
D 0.88 0.79 0.91 0.85 0.86 0.80 0.75 0.79 0.80 (6.4, 0.19) (5.7, 0.25) (3.5, 0.16) (4.7, 0.19) (4.2, 0.18) (6.2, 0.20) (7.8, 0.25) (6.2, 0.21) (5.8, 0.20)

325 174 329 339 334 342 300 287 321
E 0.87 0.72 0.83 0.79 0.84 0.91 0.86 0.88 0.91 (6.3, 0.20) (8.3, 0.27) (5.8, 0.17) (6.3, 0.20) (4.8, 0.18) (4.5, 0.17) (6.3, 0.22) (4.9, 0.18) (3.9, 0.17)

333 179 338 347 343 343 306 295 324
F 0.91 0.78 0.90 0.85 0.85 0.89 0.86 0.88 0.89 (4.7, 0.17) (9.6, 0.33) (5.3, 0.18) (6.4, 0.20) (7.5, 0.22) (5.2, 0.16) (6.0, 0.16) (4.9, 0.16) (5.5, 0.17)

323 168 323 334 330 336 295 281 316
G 0.90 0.77 0.90 0.85 0.87 0.88 0.86 0.87 0.88 (5.0, 0.19) (9.0, 0.33) (5.3, 0.19) (6.3, 0.20) (7.0, 0.22) (5.5, 0.17) (5.3, 0.17) (5.2, 0.17) (5.7, 0.19)

320 172 326 335 329 383 296 282 356
H 0.90 0.75 0.88 0.83 0.84 0.89 0.86 0.87 0.88 (4.4, 0.17) (9.4, 0.30) (4.9, 0.18) (5.6, 0.21) (6.8, 0.21) (4.5, 0.16) (6.0, 0.19) (4.5, 0.16) (5.1, 0.17)

327 175 332 341 336 335 299 289 314
I 0.87 0.75 0.86 0.82 0.83 0.88 0.84 0.85 0.87 (6.1, 0.20) (10.0, 0.36) (6.7, 0.22) (7.2, 0.23) (8.2, 0.25) (6.1, 0.20) (6.0, 0.16) (6.0, 0.18) (6.3, 0.21)

341 181 352 356 357 957 314 306 936
J 0.95 0.73 0.87 0.84 0.80 0.90 0.86 0.87 0.88 (3.0, 0.14) (9.2, 0.28) (5.2, 0.18) (5.9, 0.20) (7.5, 0.22) (5.0, 0.17) (5.9, 0.20) (5.3, 0.17) (5.2, 0.18)

316 167 314 326 323 321 283 272 302
K 1.00 0.71 0.88 0.85 0.81 0.89 0.86 0.87 0.88 (0.0, 0.00) (10.3, 0.31) (6.0, 0.16) (6.5, 0.19) (8.2, 0.22) (5.2, 0.16) (5.8, 0.18) (5.5, 0.16) (5.5, 0.18)

346 170 326 337 332 331 296 286 313
L 1.00 0.89 0.91 0.90 0.68 0.63 0.72 0.69 (0.0, 0.00) (6.7, 0.24) (5.9, 0.23) (4.8, 0.21) (10.0, 0.29) (12.1, 0.35) (9.1, 0.30) (9.8, 0.29)

183 176 181 177 181 153 149 164
M 1.00 0.94 0.90 0.83 0.81 0.82 0.84 (0.0, 0.00) (3.8, 0.13) (4.6, 0.16) (5.5 , 0.16) (7.4, 0.20) (5.8, 0.17) (5.1, 0.16)

361 341 336 345 300 288 326
N 1.00 0.90 0.77 0.75 0.78 0.78 (0.0, 0.00) (4.4, 0.17) (6.7, 0. 19) (8.1, 0.22) (6.4, 0.20) (6.2, 0.19)

362 346 347 309 297 327
O 1.00 0.80 0.75 0.79 0.80 (0.0, 0.00) (5.8, 0.19) (8 .8, 0.25) (6.8, 0.21) (6.0, 0.19)

362 348 304 292 330
P 1.00 0.95 0.97 0.97 (0.0, 0.00) (3.6, 0.14) (2.0, 0.09) (2.1, 0.08) LEGEND
Pearson R (P90, COD)

n

1027 307 299 943
Q 1.00 0.92 0.94 (0.0, 0.00) (3.1, 0.13) (4.0, 0.16)

321 268 290
R 1.00 0.94 (0.0, 0.00) (2.7, 0.12)

313 280
S 1.00 (0.0, 0.00)

998

December 2009 3-64

## Figure 3-21. Locations of PM 2.5 

## monitors and major highways, Pittsburgh, PA.

December 2009 3-65

## Figure 3-22. Seasonal distribution of 24-h avg PM 2.5 

## concentrations by site for Pittsburgh, PA, 2005-2007. Box plots show the median and interquartile range with whiskers extending to the 5th and 95th percentiles at each site during (1) winter (December-February), (2) spring (March-May), (3) summer (June-August) and (4) fall (September-November).

December 2009 3-66

## Table 3-12. Inter-sampler comparison statistics for each pair of 24-h PM 2.5 

## monitors reporting to AQS for Pittsburgh, PA.

A B C D E F G H I J K L

A 1.00 0.79 0.95 0.92 0.93 0.95 0.95 0.85 0.90 0.93 0.91 0.88 (0.0, 0.00) (15.9, 0.19) (5.6, 0.13) (4.7, 0.11) (4.7, 0.11) (4.9, 0.10) (3.8, 0.10) (6.4, 0. 13) (6.4, 0.13) (5.0, 0.12) (6.0 , 0.13) (5.6, 0.12)

1063 1035 298 164 323 329 170 319 344 337 934 340
B 1.00 0.71 0.65 0.80 0.85 0.76 0.69 0.71 0.68 0.68 0.67 (0.0, 0.00) (16.9, 0.24) (17.4, 0.25) ( 14.4, 0.19) (12.5, 0.14) (15.7, 0.20) (17.0, 0.19) (15.7, 0.21) (17.8, 0.23) (19.3, 0. 25) (15.9, 0.21)

1066 303 165 329 335 171 324 350 341 938 346
C 1.00 0.93 0.90 0.91 0.94 0.80 0.93 0.96 0.95 0.91 (0.0, 0.00) (2.8, 0.09) (6.6, 0.16) (8.7, 0.17) (6.0, 0.14) (9.4, 0.19) (6.7, 0.15) (4.6 , 0.12) (4.5, 0.10) (6.5, 0.15)

306 144 282 282 148 268 290 286 270 286
D 1.00 0.84 0.87 0.91 0.79 0.89 0.91 0.97 0.85 (0.0, 0.00) (6.4, 0.15) (8.5, 0.16) (5.8, 0.13) (9.2, 0.17) (5.9, 0.13) (4.6, 0.11) (3.1, 0.08) (6.5, 0.15)

165 153 161 158 156 158 155 146 157
E 1.00 0.90 0.90 0.84 0.85 0.86 0.88 0.83 (0.0, 0.00) (6.4, 0.13) (6.5, 0.13) (6.8, 0.14) (8.3, 0.16) (7.7, 0.16) (7.6, 0.15) (7.3, 0.15)

332 313 157 295 320 315 290 318
F 1.00 0.91 0.82 0.88 0.88 0.89 0.86 (0.0, 0.00) (6.7, 0.13) (7.4, 0.14) (7 .1, 0.15) (7.9, 0.15) (8.8, 0.17) (7.0, 0.14)

337 167 302 327 319 296 322
G LEGEND

Pearson R (P90, COD)

n 1.00 0.78 0.94 0.93 0.90 0.91 (0.0, 0.00) (7.3, 0.16) (4 .0, 0.10) (5.0, 0.11) (6.6, 0.15) (5.0, 0.13)

171 159 163 159 149 161
H 1.00 0.80 0.78 0.82 0.70 (0.0, 0.00) (8.4, 0.15) (8.2, 0.17) (9.0, 0.18) (9.2, 0.18)

328 317 309 288 314
I 1.00 0.93 0.89 0.88 (0.0, 0.00) (5.0, 0.11) (7.2, 0.16) (6.0, 0.13)

354 334 310 339
J 1.00 0.93 0.88 (0.0, 0.00) (5.5, 0.12) (5.9, 0.13)

345 302 331
K 1.00 0.86 (0.0, 0.00) (6.9, 0.15)

966 306
L 1.00 (0.0, 0.00)

350

December 2009 3-67

## Figure 3-23. Locations of PM 2.5 

## monitors and major highways, Los Angeles, CA.

December 2009 3-68

## Figure 3-24. Seasonal distribution of 24-h avg PM 2.5 

## concentrations by site for Los Angeles, CA, 2005-2007. Box plots show the median and interquartile range with whiskers extending to the 5th and 95th percentiles at each site during (1) winter (December-February), (2) spring (March-May), (3) summer (June-August) and (4) fall (September-November).

Table 3-13. Inter-sampler comparison statistics for each pair of 24-h PM 2.5 

## monitors reporting to AQS for Los Angeles, CA.

A B C D E F G H I J K

A 1.00 0.86 0.87 0.81 0.80 0.88 0.68 0.64 0.30 0.70 0.82 (0.0, 0.00) (9.0, 0.18) (7.7, 0.16) (9.0, 0.19) (9.7, 0.21) (5.8, 0.14) (11.5, 0.22) (12.4, 0.23) (18.0, 0.36) (10.5, 0.21) ( 11.4, 0.23)

862 252 803 238 262 269 761 793 179 804 259
B 1.00 0.92 0.87 0.83 0.88 0.77 0.73 0.31 0.74 0.71 (0.0, 0.00) (5.5, 0.11) (9.1, 0.19) (9.0, 0.15) (7.6, 0.15) (9.8, 0.17) (11.6, 0.18) (24.1, 0. 38) (11.9, 0.19) (15.0, 0.27)

308 293 250 278 279 268 282 177 292 277
C 1.00 0.80 0.89 0.92 0.84 0.79 0.29 0.82 0.78 (0.0, 0.00) (9.6, 0.20) (5.8, 0.11) (6.4, 0.13) (9.0 , 0.15) (10.0, 0.17) (18.6, 0. 38) (9.4, 0.16) (13.2, 0.25)

1004 274 315 319 880 913 213 920 305
D 1.00 0.69 0.77 0.63 0.60 0.41 0.64 0.60 (0.0, 0.00) (10.9, 0.23) (7.4, 0.18) (11.3, 0.22) (11.1, 0.22) (14.8, 0.31) (9.6, 0.21) (11.6, 0.23)

291 263 263 256 268 164 274 261
E 1.00 0.79 0.95 0.92 0.34 0.88 0.76 (0.0, 0.00) (9.1, 0.19) (5.9, 0.11) (7.6, 0.13) (19.7, 0.39) (8 .2, 0.15) (13.7, 0.27)

327 301 289 301 192 307 291
F 1.00 0.70 0.70 0.33 0.69 0.72 (0.0, 0.00) (10.5, 0.18) (9.2, 0. 19) (14.8, 0.34) (9.8, 0.19) (9.9, 0.21)

334 290 302 184 311 293
G 1.00 0.96 0.23 0.92 0.78 LEGEND Pearson R (P90, COD)

n (0.0, 0.00) (4.0, 0.09) ( 17.0, 0.35) (5.4, 0.12) (11.0, 0.21)
946 859 194 882 277
H 1.00 0.26 0.91 0.78 (0.0, 0.00) (15.3, 0.34) (5.9, 0.12) (9.5, 0.21)

990 208 914 294
I 1.00 0.21 0.31 (0.0, 0.00) (18.3, 0.35) (9.7, 0.28)

221 205 180
J 1.00 0.84 (0.0, 0.00) (9.8, 0.19)

999 298
K 1.00 (0.0, 0.00)

318 

### To further investigate the relationship between correlation and distance, Figure 3-25 through Figure 3-27 plot inter-sampler correlation as a function of distance between monitors for PM 2.5 

### in

December 2009 3-69

### Boston, Pittsburgh, and Los Angeles. These three cities were selected to illustrate how this relationship varies across urban areas with differ ent topography and climatology as well as different
PM 2.5 

### sources, compositions and monitor densities. Plots are provided in Annex A for all 15 CSAs/CBSAs under investigation beginning with Figure A-39. The Boston data exhibit the strongest relationship between inter-sampler correlation and distance, with average inter-sampler correlation remaining higher than 80% when samplers are 95 km apart (R 2 

### = 0.55). This small amount of variability is expected given the consistency between distributions shown in the corresponding box plots ( Figure 3-20 ). The Pittsburgh data show some reductions in inter-sampler correlations at short distances, with the samplers at Sites B and G having only 76% correlation with a distance of less than 4 km. Site B is located in Liberty, PA, a mountainous suburb of Pittsburgh where emissions from steel manufacturing and frequent stable conditions in the planetary boundary layer cause localized events of elevated concentration. In contra st, Site G is in the neighboring town of Clairton, PA, located at a lower elevation on the bank of the opposite side of the Monongahela River from Liberty. On average, inter-sampler correlation remained higher than 80% when samplers were separated by 61 km, but in this case with much greater scatter (R 2 

### = 0.22) than observed in the Boston data. This scatter is driven by the measurements at Site B; Figure 3-22 shows an elevated mean and variability for this site compared with other monitors situated around the Pittsburgh CSA. When data from Site B are removed, the inter-sa mpler correlation vs. distance plot for Pittsburgh
PM 2.5 

### resembles the one from Boston (with R 2 

### increasing to 0.68). The Los Angeles data exhibit a much steeper slope, with average inter-sampler correlation remaining higher than 80% when samplers are 29 km apart (R 2 

### = 0.74). This suggests that other factors, such as mountainous topography separating monitors, the distribution of traffic, re suspension of crustal components, and occurrence of stable boundary layers, may cause more spatial variation in the PM 2.5 

### concentration profile within the Los Angeles region when compar ed with other parts of the country. The Site I monitor, separated from the rest of the Los Angeles region by the San Gabriel Mountains as mentioned above, provides the low correlations grouped in the lower right portion of Figure 3-27 . It should also be noted in examining Figure 3-19

, Figure 3-21 , and Figure 3-23 that some monitors are often located close to major interstate highways whil e others in the same urban area are not. These differences in proximity of monitors to nearby major roads may also result in lower inter-monitor correlations.

December 2009 3-700 0.2 0.4 0.6 0.8

1

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Distance Between Samplers (km)Correlation



## Figure 3-25. Inter-sampler correlations for 24-h PM 2.5 

## as a function of distance between monitors in Boston, MA.

0 0.2 0.4 0.6 0.8

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0

Distance Between Samplers (km)Correlation 1

0



## Figure 3-26. Inter-sampler correlations for 24-h PM 2.5 

## as a function of distance between monitors in Pittsburgh, PA.

December 2009 3-710 0.2 0.4 0.6 0.8

1

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Distance Between Samplers (km)Correlation



## Figure 3-27. Inter-sampler correlations for 24-h PM 2.5 

## as a function of distance between monitors in Los Angeles, CA.

PM 10-2.5


### Given the limited number of co-located low-volume FRM PM 10 

### and FRM PM 2.5 

### monitors, only a very limited investigation into the intra-urban spatial variability of PM 10-2.5 

### was possible using AQS data. Of the 15 cities under investigation, only six (Atlanta, Boston, Chicago, Denver, New York and Phoenix) contained data sufficient for calculating PM 10-2.5 

### according to the data completeness and monitor specification requirements discussed earlier. Figure 3-28 contains box plots of PM 10-2.5 

### for one or two available sites per CSA/CBSA providing adequate PM 10-2.5 

### concentration data. For Boston, the correlation between the two sites for PM 10-2.5 

### was 0.45 compared with 0.73 for PM 2.5 

### alone and 0.84 for PM 10 

### alone (using the same two monitoring sites). For New York, the correlation was slightly higher for the two sites: 0.74 for PM 10-2.5 

### compared with 0.93 for
PM 2.5 

### alone and 0.82 for PM 10 

### alone. The COD for PM 10-2.5 

### also increases in both cities compared with PM 2.5 

### and PM 10 

### alone, suggesting less spatial homogeneity for thoracic coarse particles compared with fine particles. Wilson and Suh (1997, 077408 ) reported PM 10-2.5 

### correlations between eight sites in Philadelphia ranging from 0.14 to 0.63 with an average of 0.38. This was considerably less than the corresponding average correlation for PM 2.5 

### (r = 0.90) and PM 10 

### (r = 0.87) from the same study. Thornburg et al. (2009, 190999 ) also reported a high degree of spatial variability in
PM 10-2.5 

### in Detroit with between-monitor correlations ranging from 0.03 to 0.76. These results suggest that local sources can have a substantial impact on PM 10-2.5 

### concentrations, resulting in a higher degree of spatial variability in PM 10-2.5 

### relative to PM 2.5 

### or PM 10



### .

December 2009 3-72

## Figure 3-28. Seasonal distribution of 24-h avg PM 10-2.5 

## concentrations by site for Atlanta, GA; Boston, MA; Chicago, IL; Denver, CO; New York City, NY; and Phoenix, AZ; 2005-2007. Box plots show the median and interquartile range with whiskers extending to the 5th and 95th percentiles at each site during (1) winter (December-February), (2) spring (March-May), (3) summer (June-August) and (4) fall (September-November). Note the different concentration scales on the y-axes.

PM 10



### PM 10 

### mass concentration has been shown to vary as much as a factor of five over urban-scale distances of 100 km or less, and by a factor of 2 or more on scales as small as 30 km in an analysis of California air quality (Alexis et al., 2001, 079886 ). This can be attributed to the rapid V d 

### and resulting short atmospheric lifetime of the coarse-mode particles making up much of PM 10 

### mass. As a result, local emission sources often dominate PM 10 

### annual average mass at certain monitors. Data from the 15 CSAs/CBSAs were used to i nvestigate urban variability in PM 10 

### reported to the AQS database. Maps of PM 10 

### monitor locations and box plots of seasonal PM 10 

### mass concentration data are provided for Boston ( Figure 3-29 and Figure 3-30 ), Pittsburgh ( Figure 3-31 and Figure 3-32 ), and Los Angeles ( Figure 3-33 and Figure 3-34 ) similar to the PM 2.5 

### maps and box plots shown earlier in Figure 3-19 through Figure 3-24 . Annex A, Figures A-82 through A-125 incorporate similar information for all 15 CSAs/CBSAs. Table 3-14 through Table 3-16 contain pair wise, within-city comparison statistics (R, P90, COD and n, as defined above) for PM 10 

### measured at the available monitors in Boston ( Table 3-14 ), Pittsburgh ( Table 3-15 ) and Los Angeles ( Table 3-16 ); all 15 CSAs/CBSAs are included in Annex A, Tables A-35 through A-49. Boston is an example of a city with a wide rang e in concentrations measured at different sites. Inter-monitor variation in PM 10 

### is frequently larger than the seasonal variation measured at any given site. Pairwise correlations between monitors in Boston range from 0.45 to 0.95 in Table 3-14

. Pittsburgh is an example of a city with a large number of PM 10 

### monitors providing consistent values with a select few reporting higher concentrations (sites D, H, I and K in Figure 3-32 ). This illustrates the potential influence of localized point or ar ea sources or topography. Correlations between monitors in Pittsburgh range from 0.47 to 0.97 in Table 3-15 . Los Angeles shows a high degree of between-season and within-season variability, wh ich is on the order of the between-monitor variation. Correlations between monitors in Los Angeles range from 0.29 to 0.93 in Table 3-16 . Once again, the lowest correlations are with the moni tor separated from the other monitors by the San Gabriel Mountains (Site E in Figure 3-33 ).

December 2009 3-73

## Figure 3-29. Locations of PM 10 

## monitors and major highways, Boston, MA.

December 2009 3-74

## Figure 3-30. Seasonal distribution of 24-h avg PM 10 

## concentrations by site for Boston, MA, 2005-2007. Box plots show the median and interquartile range with whiskers extending to the 5th and 95th percentiles at each site during (1) winter (December-February), (2) spring (March-May), (3) summer (June-August) and (4) fall (September-November).

Table 3-14. Inter-sampler comparison statistics for each pair of 24-h PM 10 

## monitors reporting to AQS for Boston, MA.

Site A H B C D E F G

A 1.00 0.69 0.69 0.73 0.71 0.84 0.70 0.79 (0.0, 0.00) (15.0, 0.22) (12.0, 0.20) (10.0, 0.22) (13.0, 0.30) (8.0 , 0.14) (15.0, 0.20) (10.0, 0.17)

191 169 179 173 171 182 169 167
B 1.00 0.66 0.56 0.45 0.69 0.77 0.65 (0.0, 0.00) (17.0, 0.24) (19. 0, 0.28) (24.0, 0.39) (15.0, 0. 21) (12.0, 0.17) (16.0, 0.20)

174 167 161 158 169 156 154
C 1.00 0.72 0.47 0.62 0.64 0.59 (0.0, 0.00) (10.0, 0.22) (17.0, 0. 33) (12.0, 0.21) (16.0, 0.26) (16.0, 0.24)

182 170 168 179 166 164
D 1.00 0.63 0.68 0.59 0.69 (0.0, 0.00) (11.0, 0.29) (10. 0, 0.23) (19.0, 0.30) (13.0, 0.26)

175 163 173 161 158
E 1.00 0.84 0.58 0.80 (0.0, 0.00) (13.0, 0.29) (22.0, 0.38) (15.0, 0.33)

171 171 161 157
F LEGEND Pearson R (P90, COD)

n 1.00 0.81 0.95 (0.0, 0.00) (11.0, 0.16) (5.0, 0.11)

182 169 167
G 1.00 0.79 (0.0, 0.00) (10.0, 0.13)

169 154
H 1.00 (0.0, 0.00)

168

December 2009 3-75

## Figure 3-31. Locations of PM 10 

## monitors and major highways, Pittsburgh, PA.

December 2009 3-76

## Figure 3-32. Seasonal distribution of 24-h avg PM 10 

## concentrations by site for Pittsburgh, PA, 2005-2007. Box plots show the median and interquartile range with whiskers extending to the 5th and 95th percentiles at each site during (1) winter (December-February), (2) spring (March-May), (3) summer (June-August) and (4) fall (September-November).

December 2009 3-77

## Table 3-15. Inter-sampler comparison statistics for each pair of 24-h PM 10 

## monitors reporting to AQS for Pittsburgh, PA.

Site A B C D E F G H I

A 1.00 0.93 0.93 0.80 0.92 0.89 0.93 0.79 0.86 (0.0, 0.00) (9.0, 0.15) (8.0, 0.14) ( 23.0, 0.21) (8.0, 0.12) (14.0, 0.18) (8 .0, 0.14) (16.0, 0.17) (18.0, 0.18)

1077 1002 1065 1070 175 178 960 181 1005
B 1.00 0.96 0.80 0.91 0.92 0.97 0.81 0.89 (0.0, 0.00) (8.0, 0.15) (29.0, 0.24) (11.0, 0.20) (6.0, 0.16) (5.0, 0.10) (25.0, 0.29) (22.0, 0.20)

1019 1007 1012 163 166 911 169 954
C 1.00 0.81 0.94 0.93 0.94 0.77 0.87 (0.0, 0.00) (23.0, 0.20) (6.0, 0.11) (7.0 , 0.12) (8.0, 0.13) (21. 0, 0.22) (19.0, 0.17)

1083 1075 173 176 966 179 1010
D 1.00 0.72 0.66 0.76 0.83 0.88 (0.0, 0.00) (21.0, 0.20) (26.0, 0.24) (27.0, 0.24) (14.0, 0.18) (16.0, 0.14)

1087 176 179 970 182 1014
E 1.00 0.90 0.90 0.78 0.77 (0.0, 0.00) (10.0, 0.14) (10. 0, 0.17) (20.0, 0.20) (20.0, 0.19)

176 173 154 175 166
F 1.00 0.94 0.70 0.74 (0.0, 0.00) (7.0, 0.12) (25.0, 0.27) (25.0, 0.22) LEGEND
Pearson R (P90, COD)

n 179 157 178 168
G 1.00 0.70 0.87 (0.0, 0.00) (22.0, 0.28) (20.0, 0.19)

978 160 910
H 1.00 0.76 (0.0, 0.00) (17.0, 0.20)

182 171
I 1.00 (0.0, 0.00)

1022

December 2009 3-78J K L M N O P Q

A 0.84 0.76 0.88 0.85 0.86 0.77 0.78 0.86 (14.0, 0.20) (40.0, 0.30) (15.0, 0.18) (16.0, 0.19) (11.0, 0.16) (16.0, 0.22) (15.0, 0.19) (11.0, 0.15)

176 1044 1033 1052 1074 166 177 1061
B 0.93 0.76 0.88 0.81 0.91 0.76 0.83 0.88 (7.0, 0.16) (43.0, 0.36) (19.0, 0. 23) (20.0, 0.26) (10.0, 0.16) (12.0, 0.19) (18. 0, 0.28) (10.0, 0.18)

164 986 982 994 1016 157 165 1003
C 0.90 0.75 0.88 0.83 0.89 0.78 0.88 0.90 (8.0, 0.13) (39.0, 0.30) (14.0, 0.17) (15.0, 0.19) (9.0, 0.12) (12. 0, 0.18) (13.0, 0.19) (9.0, 0.12)

174 1049 1039 1057 1080 164 175 1067
D 0.73 0.84 0.80 0.78 0.76 0.57 0.64 0.74 (24.0, 0.22) (24.0, 0.22) (20.0, 0.18) (20.0, 0.20) (25.0, 0.20) (28.0, 0.26) (20.0, 0.25) (26.0, 0.21)

177 1055 1043 1061 1084 167 178 1071
E 0.86 0.65 0.83 0.80 0.84 0.77 0.84 0.85 (10.0, 0.16) (36.0, 0.29) (16.0, 0.16) (14.0, 0.17) (12.0, 0.14) (14.0, 0.19) (13.0, 0.16) (11.0, 0.15)

171 169 169 172 176 161 172 174
F 0.90 0.57 0.82 0.75 0.86 0.83 0.84 0.86 (7.0, 0.12) (41.0, 0.34) (20.0, 0.20) (19.0, 0.22) (11.0, 0.14) (9 .0, 0.15) (16.0, 0.22) (9.0, 0.14)

174 172 172 175 179 164 175 177
G 0.92 0.73 0.87 0.78 0.89 0.81 0.84 0.86 (7.0, 0.13) (45.0, 0.35) (18.0, 0. 21) (19.0, 0.24) (9.0, 0.15) (11.0, 0.17) (17.0, 0.26) (10.0, 0.16)

156 955 938 952 975 146 157 967
H 0.74 0.68 0.77 0.78 0.74 0.60 0.65 0.76 (23.0, 0.26) (26.0, 0.22) (15.0, 0.18) (17.0, 0.18) (21.0, 0.22) (27.0, 0.29) (19.0, 0.22) (21.5, 0.24)

176 175 175 178 182 167 177 180
I 0.79 0.83 0.82 0.78 0.81 0.66 0.69 0.78 (22.0, 0.20) (30.0, 0.25) (16.0, 0.17) (18.0, 0.20) (20.0, 0.17) (26.0, 0.24) (21.0, 0.25) (22.0, 0.19)

166 992 978 998 1019 158 167 1009
J 1.00 0.66 0.79 0.72 0.88 0.78 0.86 0.86 (0.0, 0.00) (44.5, 0.33) (18.0, 0.20) (18.0, 0.22) (8.0, 0.13) (11. 0, 0.17) (16.0, 0.21) (8.0, 0.15)

177 170 170 173 177 163 173 175
K 1.00 0.74 0.75 0.70 0.47 0.58 0.68 (0.0, 0.00) (31.0, 0.26) (33. 0, 0.24) (40.0, 0.30) (44.0, 0. 36) (34.0, 0.30) (43.0, 0.30)

1061 1017 1035 1058 160 171 1048
L 1.00 0.87 0.85 0.70 0.74 0.80 (0.0, 0.00) (13.0, 0.16) (16.0, 0.17) (22.0, 0.24) (17.0, 0.21) (18.0, 0.19)

1051 1025 1048 160 171 1035
M 1.00 0.74 0.64 0.67 0.77 (0.0, 0.00) (18.0, 0.21) (19.0, 0.26) (17.0, 0.22) (18.0, 0.19)

1069 1067 163 174 1053
N 1.00 0.72 0.86 0.86 (0.0, 0.00) (13.0, 0. 18) (14.0, 0.20) (10.0, 0.14)

1092 167 178 1076
O 1.00 0.75 0.69 (0.0, 0.00) (18.0, 0.25) (14.0, 0.19)

167 163 165
P 1.00 0.84 (0.0, 0.00) (15.0, 0.21)

178 176
Q 1.00 (0.0, 0.00)

1079

December 2009 3-79

## Figure 3-33. Locations of PM 10 

## monitors and major highways, Los Angeles, CA.

December 2009 3-80

## Figure 3-34. Seasonal distribution of 24-h avg PM 10 

## concentrations by site for Los Angeles, CA, 2005-2007. Box plots show the median and interquartile range with whiskers extending to the 5th and 95th percentiles at each site during (1) winter (December-February), (2) spring (March-May), (3) summer (June-August) and (4) fall (September-November).

Table 3-16. Inter-sampler comparison statistics for each pair of 24-h PM 10 

## monitors reporting to AQS for Los Angeles, CA.

Site A B C D E F G

A 1.00 0.73 0.44 0.73 0.47 0.41 0.65 (0.0, 0.00) (17.0, 0.17) (27. 0, 0.24) (24.0, 0.22) (28.0, 0. 26) (29.0, 0.24) (30.0, 0.28)

169 153 154 157 169 155 143
B 1.00 0.61 0.57 0.52 0.42 0.73 (0.0, 0.00) (14.0, 0.14) (21.0, 0.24) (23.0, 0.23) (15.0, 0.16) (20.0, 0.23)

175 159 159 173 162 149
C 1.00 0.65 0.43 0.93 0.73 (0.0, 0.00) (27.0, 0.28) (22. 0, 0.24) (11.0, 0.11) (21.0, 0.22)

178 158 176 159 148
D 1.00 0.70 0.65 0.57 LEGEND Pearson R (P90, COD)

n (0.0, 0.00) (16.0, 0.20) (26.0, 0.28) (19.5, 0.24)
176 175 161 150
E 1.00 0.29 0.38 (0.0, 0.00) (26.0, 0.25) (20.0, 0.24)

985 173 159
F 1.00 0.65 (0.0, 0.00) (21.5, 0.22)

175 150
G 1.00 (0.0, 0.00)

162

December 2009 3-81

### Figure 3-35 through Figure 3-37 illustrate the relationship between inter-sampler correlation and distance between sites for PM 10 

### measurements obtained in Boston, Pittsburgh and Los Angeles. Annex A contains similar plots for all 15 CSAs/CBSA s under investigation beginning with Figure A84. In each plot, substantially more scatter is observed when compared to those for PM 2.5 

### ( Figure 3-25 through Figure 3-27 ). This is consistent with the variability observed in the seasonal box plots of concentration shown in Figure 3-30

, Figure 3-32 , and Figure 3-34 . The Boston data exhibit the strongest relationship between inter-sampler correl ation and distance, with average inter-sampler correlation remaining higher than 80% when samplers are 44 km apart (R 2 

### = 0.61). The lowest correlations on this plot originate from comparisons between Site B (rural Worcester, MA) and samplers located at Sites E (West Greenwich, RI) and G (Providence, RI). Boston is subject to long range transport of SO 4 2– 

### , which is a regional pollutant and is a major component of PM 2.5 

### and PM 10 

### in the eastern U.S. The Pittsburgh data shows some lower inter-sampler correlations, with one sampler pair having only 66% correlation within a distance of 2 km. On average, inter-sampler correlation remained higher than 80% when samplers were also separated by 44 km, but in this case with much greater scatter (R 2 

### = 0.28) than observed in the Boston data. As seen for the Pittsburgh
PM 10 

### box plots in Figure 3-32 , sites D, H, I, and K have elevated means and high variability that is driving the observed scatter. These four site s are all located in mountainous suburbs of Pittsburgh (North Braddock, PA, Liberty, PA, Lincoln Boro , PA, and Beaver Falls, PA, respectively), where emissions from steel manufacturing and frequent st able conditions in the planetary boundary layer cause localized events of elevated concentrati on. When those four sites are removed, scatter decreases greatly (R 2 

### = 0.56). The Los Angeles data exhibi t a much steeper slope, with average inter-sampler correlation remaining higher than 80% when samplers are only 30 km apart (R 2 

### = 0.56). The lower inter-sampler correlations in part reflect the fact that some of these monitoring sites are separated from each other by hills or, in the case of one sited at Lancaster, CA (Site I), by the San Gabriel Mountains. The Los Angeles data exhibit greater scatter than the Pittsburgh data. However, the smallest inter-sampler separation distance is 23 km, and there are relatively fewer PM 10 

### samplers. Given the present data, it is not possible to judge how data would correlate on smaller spatial scales.

0 0.2 0.4 0.6 0.8

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0

Distance Between Samplers (km)Correlation 1

0



## Figure 3-35. Inter-sampler correlations for 24-h PM 10 

## as a function of distance between monitors in Boston, MA.

December 2009 3-821

0 0.2 0.4 0.6 0.8

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Distance Between Samplers (km)Correlation



## Figure 3-36. Inter-sampler correlations for 24-h PM 10 

## as a function of distance between monitors in Pittsburgh, PA.

0 0.2 0.4 0.6 0.8

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0

Distance Between Samplers (km)Correlation 1

0



## Figure 3-37. Inter-sampler correlations for 24-h PM 10 

## as a function of distance between monitors in Los Angeles, CA.

December 2009 3-83

### UFPs

Relatively few studies compare UFP measuremen ts at multiple locations within an urban center. An early study by Buzorius et al. (1999, 081205 ) suggested spatial homogeneity in total particle number concentrations between multiple locations in Helsinki, Finland. They found correlations in 10-min average at three sites within the city as high as 0.84. The sites, however, were relatively close together (2 km) and all near the same roadway. There was a high degree of correlation between traffic intensity and total aero sol number concentrations, suggesting that traffic was the primary source of the measured particles and the driving force behind the high correlations. Weekend correlations (0.28-0.47) and correlations wi th a fourth monitor located 22 km outside the city (0.05-0.64) were much lower. Tuch et al. (2006, 157060 ) found more spatial heterogeneity in UFP concentrations measured for an entire year at two locations 1.5 km apart in Leipzig, Germany. Figure 3-38 shows the correlation as a function of particle size (mobili ty diameter) dropping off as the particle size decreases from 0.5 at 100 nm down to 0.2 at 3 nm. Table A-50 in Annex A contains correlation coefficients of hourly and daily average particle number, surface area and volume concentrations as a function of particle diameter adapted from the Tuch et al. (2006, 157060 ) study. For all days (N = 5481 hourly observations), the correlation between UFPs (10-100 nm) measured at the two sites was 0.31.

Source: Reprinted with Permission of Nature Publishing Group from Tuch et al. (2006, 157060 )



## Figure 3-38. Bin-wise Spearman correlation coefficients in aerosol particle number concentrations between the Ift (urban background) and the Eisenbahn-strasse (city/urban center) sites in Leipzig, Germany.

The two sites represented in Figure 3-38 and Table A-44 were relatively close to each other, but one was located in a mixed semi-industrial region while the other was in a street canyon in a residential neighborhood near busy roadways. This sugge sts a high degree of spatial heterogeneity in UFPs driven primarily by differences in near by source characteristics. Sioutas et al. (2005, 088428 ) reviewed studies of the distribution of UFPs and came to the similar conclusion that mobile sources make a large contribution to UFPs and theref ore UFP concentrations can exhibit substantial variability in space and time. This is to be expe cted since UFP concentrations drop off much quicker with distance from roadways than larger particle sizes (Levy et al., 2003, 052661 ; Reponen et al., 2003, 088425 ; Zhu et al., 2005, 157191 ). Hagler et al. (2009, 191185 ) showed similar exponential decreases in UFP number concentrations with dist ance from the road for multiple locations in the U.S. Neighborhood-scale variability and near-roadway concentration gradients for UFPs are discussed further in Section 3.5.1.3

.

December 2009 3-84

### PM Constituents

The pie charts showing PM 2.5 

### composition that were generated using the SANDWICH method for the 15 CSAs/CBSAs presented earlier in Figure 3-17 and Figure 3-18 represent the average of all available monitors within each region. Individual pi e charts for each monitor are included in Figures A-127 through A-141 in Annex A and provide an i ndication of the urban-scale spatial variability in
PM 2.5 

### composition. In the instances where multiple m onitors were available, there was a fair degree of spatial homogeneity in PM 2.5 

### bulk chemistry within each metropolitan area. Some notable exceptions exist, however. Birmingham and Detroit show variation in the amount of crustal material, both spatially and seasonally. Denver e xhibits some spatial variation in NO 3 – 

### during the winter, the season with the highest measured PM 2.5 

### mass. Several sites in New York and one in Pittsburgh have elevated fractions of EC relative to the other sites within the respective cities, and several sites in New York have been shown to have elevated Ni concentrations in PM 2.5 

### samples when compared with surrounding areas (Peltier and Lippmann, 2009, 197455 ; Peltier et al., 2008, 197452 ). In Phoenix, high winter PM 2.5 

### mass is site specific and appears to be associated with high OC; the crustal component also varies and is invers ely proportional to total measured mass.



# 3.5.1.3. Neighborhood-Scale Variability

Neighbor hood scale spatial variability in the par ticle concentration profile is affected by land and building topography, meteorology, particle size di stribution, particle co mposition, and particle volatility. Population density at the neighborhood scale is also an important determinant of the spatial distribution of PM concentration beca use population density impacts source prevalence, source magnitude, topographical-drive n ventilation, and heat island effects (Crist et al., 2008, 156372 ; Makar et al., 2006, 155959 ; Mfula et al., 2005, 123359 ; Rigby and Toumi, 2008, 156050 ). A number of computational and wind tunnel modeling street canyon studies have demonstrated the potential variability in pollutant concentrations within a street canyon (Borrego et al., 2006, 155697 ; Chang and Meroney, 2003, 090298 ; Kastner-Klein and Plate, 1999, 001961 ; So et al., 2005, 110746 ; Xiaomin et al., 2006, 156165 ). Influential parameters include street canyon height to width ratio (H/W), source positioning, wind spe ed and direction, building shape and upstream configuration of buildings. Figure 3-39 shows pollutant concentrations obtained from wind tunnel and computational fluid dynamics simulations of tra nsport and dispersion in an infinitely long street canyon with a line source centered at the bottom of the canyon (Xiaomin et al., 2006, 156165 ). When the canyon height was equal to the street width (typical of moderate density suburban or urban fringe residential neighborhoods) and lower bac kground wind speed existed, concentrations on the leeward canyon wall were four times those of th e windward wall near ground level. When the canyon height was twice the street width (typical of higher-density urban planning) and background winds were somewhat higher, near ground level concentrations on the windward canyon wall were roughly three times higher than those measured at the leeward wall. Baldauf et al. (2008, 191017 ; 2009, 191766 ) noted that the presence of noise barrie rs, vegetation, or changes in topography adjacent to the road can also alter particle dispersion characteristics. Specifically, depressed road segments, where the road bed is below the surrounding terrain, leads to increased air turbulence and pollutant dispersion. These results suggest that mi cro- and neighborhood-scale variation related to urban topography may have a significant impact on pollutant concentrations at this scale.

December 2009 3-85Source : Reprinted with Permission of El sevier Ltd. From Xiaomin et al. (2006, 156165 ).



## Figure 3-39. Dimensionless concentration as a function of height at windward and leeward locations and street canyon aspect ratios (H/W). (a) Dimensionless concentration on the windward and leeward sides of the canyon when H/W = 1 and wind speed = 3 m/s. (b) Dimensionless concentration on the windward and leeward sides of the canyon when H/W = 2 and wind speed = 5 m/s. Computational fluid dynamics modeling was performed, and measurements were obtained in wind tunnel simulations.

PM 2.5



### and PM 10 

### Knowledge of neighborhood-scale variability is important for interpreting data from PM 2.5 

### and
PM 10 

### community monitors. Figure 3-40 shows data derived from the 15 CSAs/CBSAs for PM 2.5 

### and
PM 10 

### discussed in Section 3.5.1.2 . This figure is limited to the inter-sampler correlations obtained for sampler pairs located within a distance of 4 km (i.e., neighborhood scale). PM 2.5 

### data exhibit a flatter slope, with average correlation maintained at 93% within 4 km (R 2 

### = 0.22). There is more scatter and variability among the PM 10 

### data, with an average correlation of 70% within 4 km (R 2 

### = 0.03). The degree of variability in PM 10 

### compared with PM 2.5 

### relates to transport and dispersion of the PM 10-2.5 

### component of PM 10 

### compared with PM 2.5 

### . However, differences in composition, source location, topography, and monitor height—all of which could affect concentrations—could drive the relatively high degree of scatter for both size cla sses, considering the low computed R 2 

### values for each of these

curves.

December 2009 3-86R PM2.5 = -0.0163D + 1

R 2 = 0.2178

R PM10 = -0.0748D + 1

R 2 = 0.0346

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

0 0

. 511

. 522

. 533

. 5 4

Distance Between Samplers (km)Correlation

PM2.5

PM10
Linear (PM2.5)
Linear (PM10)



## Figure 3-40. Inter-sampler correlations for 24-h PM 2.5 

## and PM 10 

## as a function of distance between monitors for samplers located within 4 km (neighborhood scale).

Isakov et al. (2007, 156588 ) compared PM 2.5 

### concentrations from a central monitoring site in Wilmington, DE with PM 0.3 

### measurements taken on a mobile platform driven through mostly quite residential streets within a 4 km×4 km grid containing the central monitor. Correlations were generally high (average r = 0.87) over all time periods and locations monitored, consistent with the range of correlations for PM 2.5 

### shown in Figure 3-40

.

PM 10-2.5 

### Neighborhood-scale variability in PM 10-2.5 

### was investigated by Chen et al. (2007, 147318 ) in the Raleigh/Durham area of NC. The average corr elation between 26 residential monitors located throughout the region and a centrally located mon itor representing a maximum inter-sampler range of 60 km was found to be 0.75 for PM 10-2.5 

### compared with 0.92 and 0.94 for PM 2.5 

### and PM 10 

### , respectively. Based on this study, neighborhood-scale variability is greater for PM 10-2.5 

### than for PM 2.5 

### or PM 10 

### , matching the conclusion drawn above on the broader urban-scale.

UFPs

Moore et al. (2009, 191004 ) monitored UFP concentrations throughout the Ports of Los Angeles and Long Beach, through which an interstate highway runs and found that concentrations varied by a factor of 5-7 across sites with substan tial differences in the daily concentration time series at each site. Such variability reflects dive rsity of the sources (some near the Interstate, some near the Port), and the influence of changing meteorology over an urban area. In a mobile platform sampling study, Westerdahl et al. (2005, 086502 ) and Fruin et al. (2008, 097183 ) also reported substantial peaks in UFP concentration when sampling at highways in comparison with a background site (the University of Southern California) using the same data set. Near roadway environments can exhibit high conc entration gradients, particularly for UFPs. Ntziachristos et al. (2007, 089164 ) observed that the near-road particle size distribution was

December 2009 3-87

### substantially higher in the UF mobility diameter rang e and that these results were very sensitive to meteorology (rain) and time of day. Baldauf et al. (2008, 190239 ) reported elevated UFP number concentrations downwind of a highway in Rale igh, NC, when compared to measurements approximately 100 m upwind of the road. Hagler et al. (2009, 191185 ) noted a 5-12% decrease in number concentrations per 10 m distance from the road for a number of studies in the U.S. with unobstructed air flow. After initial emission from a motor vehicle, the evolution of the PM distribution within the plume is a function of (1) the turbulence that di lutes the plume and (2) evaporation or condensation of the volatile portion of the aerosol that results from rapid cooling of the exhaust. Figure 3-41 shows the size distribution measured by Zhu et al. (2002, 041553 ) at distances of 17-300 m away from the roadway (in this case, Highway 710 in Los Angeles) and at an upwind site. It can be seen that a mode originally measured around 9 nm increases in diameter and decreases in number concentration as distance from the highway incr eases. Smaller secondary modes appear around 30 m from the roadway with multiple modes at some particle sizes. By 150 m away from the highway, the size distribution flattens with a small mode around 50 nm. It is clear from th e bottom figure that the number concentration of larger particles (i.e., 100-220 nm) does not vary as much as UFPs (<100 nm) with increasing distance downwind from the roadway.

Source: Reprinted with Permission of El sevier Ltd. From Zhu et al. (2002, 041553 ).



## Figure 3-41. Particle size distributions measured at various distances from the 710 freeway in Los Angeles, CA (top), an d particle number concentr ation as a function of distance from the 710 freeway (bottom).

December 2009 3-88

### Zhou and Levy (2007, 098633 ) performed a meta-analysis of traffic-related air pollution literature and found that background pollution and meteorology can have important impacts on the size of the elevated concentration region around the highway. Zhu et al. (2002, 041553 ) and Zhang et al. (2005, 157185 ) noted in field measurements of UFPs that small particles can be lost due to evaporation or to coagulation during Brownian diffusion to form bigger particles, resulting in an upward shift in mode diameter with distance from the roadway. Studies of particle sizes on roads (Kittelson et al., 2006, 156649 ; Kittelson et al., 2006, 156648 ), in tunnels (Venkataraman et al., 1994, 002475 ), and upwind and downwind of roads (Zhu et al., 2002, 041553 ) suggest that for wellmaintained spark-ignition vehicles, a large fracti on of the mass of particles emitted from the vehicles are in the nuclei mode (i.e., smaller than accumu lation mode). High-speed highway driving may be associated with a larger fraction of particle mass being emitted in the UF size range, while lower speed operation results in a higher mass fraction in the accumulation mode (Cadle et al., 2001, 017192 ). In situations in which the dilution rates are lower than in a short tunnel or downwind of a road way, condensation of vapors can give rise to particles in the accumulation mode (Kittelson, 1998, 051098 ). Diesel engines, in particular, emit black carbon in the lower end of the accumulation mode, with number emissions domi nated by semi-volatile material in the nuclei mode (Kittelson, 1998, 051098 ; Kittelson et al., 2006, 156649 ; Kittelson et al., 2006, 156648 ). Sharp gradients in black carbon mass have been observed along roadways with high diesel traffic (Zhu et al., 2002, 041553 ). As the traffic pollution moves downwind, th e UFPs may grow into the accumulation mode by coagulation or condensation. In addition to Ga ussian dispersion and wind eddies caused by the presence of natural and anthropogenic barriers, Sahlodin et al. (2007, 114058 ) demonstrated that turbulence produced by vehicles can result in modification of the plume emanating from the highway. Hence, on-road turbulence could potentially alter the aerosol size distribution. This added turbulence could cause some evaporation of tiny nu cleation particles that have not absorbed or adsorbed onto soot nuclei, which may affect the rate of coagulation (Jacobson et al., 2005, 191187 ). The roadway configuration may also affect par ticle transport and dispersion. Depressed road sections, where the road bed is below the surroundi ng terrain, leads to increased air turbulence and mixing as air flows up and out of the road depression. This configuration can result in lower particulate concentrations and flatten concentration decay curves away from the road. On the other hand, configurations with the road bed at-grade with surrounding terrain, or elevated above the surrounding terrain with solid fill material resulte d in the highest pollutant concentrations and sharpest concentration gradients downwind from the road.

PM Constituents

The composition of PM will also vary on the neighborhood-scale in response to local sources and differential dispersion, resulting in variable spatial distribution of individual components. Krudysz et al. (2008, 190064 ) investigated spatial variation in size-fractionated (<0.25 μm, 0.25-2.5 μm, >2.5 μm) PM composition data at four si tes located within 3-6 km of each other in the Long Beach, CA area. Inter-site R 2 

### values in the 0.25-2.5 μm size range were higher for mass (ranging from 0.56-0.91) than for EC (0.02-0.71) for pair wise site comparisons. Spatial heterogeneity in all size ranges investigated wa s also found for several elements associated with motor vehicle emissions and resuspended road dust including Cu, Mg, Ba, Ca and Al. Viana et al. (2008, 156135 ) observed higher concentrations of crustal elements in PM 2.5 

### and PM 10 

### samples in rural neighborhoods and higher concen trations of combustion-derived PM 2.5 

### and PM 10 

### , such as EC and NO 3 – 

### , in higher density urban areas. Gutiérrez-Dabán et al. (2005, 155818 ) examined the mass distribution of various PAHs under different traffic and urban density conditions. Figure 3-42 displays the distributions for benz[a]pyrene (BaP) at high and low traffic sites at the urban center, periphery, and industrial areas in Sev ille, Spain (Gutiérrez- Dabán et al., 2005, 155818 ). Concentrations were nearly an order of magnitude lower for the low traffic urban periphery location when compared with the high traffic or industrial locations. Particles smaller than ~ 600 nm had roughly an order of magnitude higher concentration th an those at larger sizes and tended to have a larger spread in concentrations among sampling sites. Figure 3-43 shows the distributions for sixteen PAHs at a high traffic location at the city cente r in Seville, Spain (Gu tiérrez-Dabán et al., 2005, 155818 ). PAH species varied in concentration by up to two orders of magnitude for each particle size bin, and the highest concentrations of individual PAHs were generally found for particles smaller than approximately 600 nm. Olson and McDow (2009, 191188 ) reported decreases by a factor of 1.04-2.37 in select PAH and organic source mark er concentrations when comparing measurements 10 m and 275 m from a highway in Raleigh, North Carolina. Phuleria et al. (2006, 156867 ) sampled

December 2009 3-89

### UFPs and PM 2.5 

### concentrations and PAH species at the mouth of the Caldecott Tunnel in Orinda, CA and found that the two size classes were highly correlated (R 2 

### = 0.97). Given the size differentials of each size bin presented in the Gutiérrez-Dabán et al. (2005, 155818 ) study, it is possible that the
PM 2.5 

### sampled at the tunnel mouth in the latter study represented secondary PM 2.5 

### that grew from UFP emissions trapped within the tunnel.

1 10 100 1000 10000

>10 10–4.9 4.9–2.7 2.7–1.3 1.3–0.6 <0.6

Size bin ( μ m)Concentration (pg m-3)

HTC

HTP

LTC

LTP

LTIP

Source: Adapted with Permission of Springer-V erlag from Gutiérrez-Dabán et al. (2005, 155818 ).



## Figure 3-42. Mass distributions for BaP at a high traffic urban center (HTC), high traffic urban periphery (HTP), low traffic urban center (LTC), low traffic urban periphery (LTP), and low traffic industrial urban periphery (LTIP) in Seville, Spain.

December 2009 3-901 10 100 1000 10000

>10 10–4.9 4.9–2.7 2.7–1.3 1.3–0.6 <0.6

Size bin ( μ m)Concentration (pg m-3) Naph

Ace

Acey

Flu

Phen

Ant

Flua

Pyr

BaA

Chry

BbF

BkF

BaP

InP

DbA

Bper

Source: Adapted with Permission of Springer-V erlag from Gutiérrez-Dabán et al. (2005, 155818 ).



## Figure 3-43. Mass distributions for 16 PAHs at a high traffic city center in Seville, Spain.



# 3.5.2.

3.5. 

# Temporal Variability

Tempor al variability is another important factor in characterizing PM. This section addresses trends as well as seasonal and hourly variability. Trends in PM 2.5 

### and PM 10 

### are addressed in Section 3.5.2.1 based on AQS data. Seasonality is coupled with spatial variability and has been discussed in the regional context above. Section 3.5.2.2 below briefly investigates the seasonality on a finer time scale, thereby addressing issues relating to the seasonal definitions used earlier. Section 0 addresses hourly patterns, an issue particul arly important to understanding the behavior of PM concentrations in reference to sources, human activity patterns a nd exposure. Hourly patterns are investigated using AQS data on a national basis for PM 2.5 

### and PM 10 

### . Data for UFPs and PM constituents are presented where available.



# 2.1. Regional Trends

This sect ion summarizes available informati on on trends in PM mass and composition. Mass concentration trends are based on AQS data and incorporate 9 years (1999-2007) of PM 2.5 

### data and 20 years (1988-2007) of PM 10 

### data. Composition trends are based on six years of available CSN data (2002-2007). Several monitoring site s were excluded from the following trend analyses to provide a consistent basis for comparison over the desired year s of monitoring. This included exclusion of sites when there was no corresponding site in later or earl ier years. Region-average trends were calculated to facilitate presentation and extrapolation of the results. These region-averages, however, may not necessarily represent the trends that are being o bserved at any individual monitor or geographical location within the specified region.

December 2009 3-91

### PM 2.5 

### Figure 3-44 shows trends in U.S. ambient 24-h PM 2.5 

### concentrations from 1999-2007. In the period 2005-2007, the 3-yr avg of the 98th percentile of 24-h PM 2.5 

### concentrations fell 10% relative to the 1999-2001 period (see Figure 3-44 A). The number of sites reporting values greater than the 24-h NAAQS was shown to decline 40% in Figure 3-44 B. Figure 3-44 C illustrates the downward trend in the 98th percentile of 24-h PM 2.5 

### concentrations for three consecutive calendar years in all U.S. EPA regions. This trend is most pronounced in Region 9 incorporating Arizona, California and Nevada where this value dropped 25% from the 1999-2001 period to the 2005-2007 period.

Source: U.S. EPA (2008, 157076 )



## Figure 3-44. Ambient 24-h PM 2.5 

## concentrations in the U.S., 1999-2007, showing A) ambient concentrations, B) number of trends sites above the 24-h NAAQS and C) trends by U.S. EPA Region.

Figure 3-45 contains similar trend information for the annual PM 2.5 

### NAAQS. The seasonally weighted 3-yr avg PM 2.5 

### concentrations for the years 2005-2007 were at the lowest since national monitoring began in 1999 ( Figure 3-45 A). The seasonally weighted 3-yr avg fell 10% between the 1999-2001 averaging period and the 2005-2007 averaging period. The number of sites reporting concentrations above the annual average PM 2.5 

### NAAQS fell 56% over these same periods in Figure 3-45 B. Figure 3-45 C illustrates the annual trends in PM 2.5 

### by U.S. EPA region. Declines were the greatest in Region 9 again where annual PM 2.5 

### concentrations fell 20% from the 1999-2001 averaging period to the 2005-2007 averaging period.

December 2009 3-92Source: U.S. EPA (2008, 157076 ).



## Figure 3-45. Ambient annual PM 2.5 

## concentrations in the U.S., 1999-2007, showing A) ambient concentrations, B) number of trends sites above the annual NAAQS and C) trends by U.S. EPA Region.

PM 10 

### Figure 3-46 shows trends in U.S. ambient 24-h PM 10 

### concentrations from 1988-2007. In 2007, the U.S. national average second highest PM 10 

### concentration was 37% lower than in 1988 ( Figure 3-46 A). Of 281 sites used in this trend analysis, th e number reporting concentrations above the 24-h
PM 10 

### NAAQS (150 μg/m 3 

### ) fell from 23 in 1988 to 5 in 2007 with a max of 29 in 1989 ( Figure 3-46 B). Figure 3-46 C shows trends in the second highest 24-h PM 10 

### concentrations broken down by U.S. EPA region. All regions exhibit an overall decrease from 1988-2007. Largest decreases occurred in EPA Region 10, which incorporates Washington, Oregon, Idaho and Alaska. Most of the decrease occurred between 1988-1995.

December 2009 3-93Source: U.S. EPA (2008, 157076 ).



## Figure 3-46. Ambient 24-h PM 10 

## concentrations in the U.S., 1988-2007, showing A) ambient concentrations, B) number of trends sites above the 24-h NAAQS and C) trends by U.S. EPA Region.

PM Constituents

The SANDWICH method discussed in Section 3.5.1.1 for estimating PM 2.5 

### composition from FRM mass measurements and CSN bulk composition measurements was used to evaluate trends in
PM 2.5 

### constituents. Figure 3-47 includes stacked bar charts of PM 2.5 

### composition from 2002 to 2007 stratified by region and season. The regions used in Figure 3-47 were selected based on common aerosol characteristics including trends, seasonal ity, size distributions and/or composition as described in chapter 6 of the 1996 PM AQCD (U.S. EPA, 1996, 079380 ) and differ from the EPA regions used in the preceding figures. Figure 3-47 is based on 42 monitoring locations reporting complete CSN data with 2002 being the first year with sufficient speciation data. The Southwest region incorporating Arizona, New Mexico and pa rts of Texas and Oklahoma did not contain any complete data and therefore is not represented in this analysis. Two seasons representing different temperature ranges–cool (October-April) and wa rm (May-September)–were considered in the figure since many PM 2.5 

### components exhibit strong seasonal dependence.

December 2009 3-94Source: U.S. EPA (2008, 191190 ).



## Figure 3-47. Regional and seasonal trends in annual PM 2.5 

## compostion from 2002 to 2007 derived using the SANDWICH method. Data from the 42 monitoring locations shown on the map were stratified by re gion and season including cool months (October-April) and warm months (May-September). SO 4 2– 

## and NO 3 – 

## estimates include NH 4 + 

## and particle bound water.

Most of the components showed little discernable trend over the 6-yr period. SO 4 2– 

### showed a peak during the warm months of 2005 in the Southeast, Northeast and Midwest, partly due to atypical weather conditions (U.S. EPA, 2008, 191190 ). However, no trend over the 6-yr time period is present for SO 4 2– 

### in any of the regions or seasons. The same is true for EC and crustal material. A slight decline in OC was observed for the Northeast during warm months and in Southern California year-round. The largest decline was for NO 3 - 

### in Southern California during both cool and warm

December 2009 3-95

### months. A smaller decline in NO 3 - 

### is also observed in the other regions with the exception of the Northwest where no discernible trend is present. This analysis is limited in time and space by the availability of CSN data so a high de gree of uncertainty remains regarding PM 2.5 

### compositional trends. However, with the exception of NO 3 - 

### concentrations in Southern California, no major changes in PM 2.5 

### composition are evident based on available CSN data from 2002-2007. This is consistent with Figure 3-44 and Figure 3-45 where the downward trend in PM 2.5 

### mass begins to level off after 2002.



# 3.5.2.2. Seasonal Variations

Many of th e figures and tables presented in th e preceding sections have included a seasonal break-down based on the following climatological seasons: winter (December-February), spring (March-May), summer (June-August) and fall (S eptember-November). Figures A-142 through A-156 in Annex A show bar charts of PM 2.5 

### composition by individual month, illustrating intra-annual variability on a finer time scale. The same 15 CS As/CBSAs are investigated and included in these plots; they are generated from the same data used in the seasonal and annual pie charts based on the SANDWICH method discussed in Section 3.5.1.1 and illustrated in Figure 3-17 and Figure 3-18

. Monthly plots for most of the areas reveal heterogeneity in PM composition within the 3-month long seasonal bins defined earlier. This is especially true in the spring and fall when daily average weather conditions (e.g., te mperature) are changing most rapidly, driving fluctuation in
PM 2.5 

### composition on relatively short timescales in many cities. For example, the NO 3 – 

### mass in Los Angeles (Figure A-129) and Riverside (Figure A-1 34) can vary from a small fraction to the most prevalent fraction of PM 2.5 

### mass in a month’s time based on the 3-yr aggregate data. Therefore, selecting a different delineation point for the se asons can have an influence on the seasonal composition analysis, specifically for constit uents that fluctuate rapidly (e.g., NO 3 – 

### ). Relatively little is known about the sea sonal variability in UFPs. Kuhn et al. (2005, 129448 ) and Zhu et al. (2004, 156184 ) found that the concentrations in the UF mode in Los Angeles, CA can be much higher during winter, particularly during evenings, because atmospheric dilution is reduced in response to lower mixing heights ( Figure 3-48 ). Jeong et al. (2004, 180350 ) made similar observations in Rochester, NY, suggesting an i nverse relationship between temperature and UFP formation in the 11-470 nm size range. Singh et al. (2006, 190136 ) reported higher particle number concentrations during winter months, relative to summer and spring, at urban sites in Southern California, and that afternoon particle number concentrations in warm months either occurred during a peak in ozone concentrations or followed shortly th ereafter, suggesting a role for photochemistry in addition to meteorological changes in the formati on of aerosols. The study also reported increased concentrations of 60-200 nm particles during a labor strike at the Port of Long Beach, suggesting contributions from idling ships. Moore et al. (2009, 191004 ) also report higher particle number concentrations during cooler months at 14 sites in Long Beach, San Pedro, and Wilmington, CA, a location with diverse industrial and transportation sources. However, they noted substantial heterogeneity in seasonal trends between sites with seasonal numerical size distributions not generalizable across the study area with a maximum monitor separation of under 10 km.

December 2009 3-96Source:Reprinted with Permission of Elsevier Ltd. from Kuhn et al. (2005, 129448 ).



## Figure 3-48. UFP size distribution at highway (site A) and background (site B) sites in Los Angeles, CA, during summer and winter seasons, with winter broken into day and evening distributions.

Studies reporting higher cold-season particle number concentrations are consistent with vehicle emission studies that found particle emission rates elevated during lower ambient temperatures (Baldauf et al., 2005, 191184 ; Mathis et al., 2005, 155970 ; U.S. EPA, 2008, 191767 ). Mathis et al. (2005, 155970 ) found that cold-start conditions pr oduce roughly an order of magnitude greater PM number emissions in gasoline engines and more than two orders of magnitude higher PM number emissions in diesel engines when compared with warm start conditions.



# 3.5.2.3. Hourly Variability

Hourly PM 2.5 

### and PM 10 

### measurements are conducted at many sites using beta gauge or TEOM monitors. Many of the hourly measurements for PM 10 

### have FRM or FEM status. All available hourly data from FRM, FEM and FRM-like monitors in th e 15 CSAs/CBSAs discussed ea rlier were used to investigate diel variation in PM. Of the 15 CSAs/ CBSAs, Atlanta, Chicago, Pittsburgh, Seattle and St. Louis had qualifying hourly PM 2.5 

### and PM 10 

### data available. Houston and New York had only qualifying PM 2.5 

### data. Denver, Detroit, Los Angeles, Philadelphia, Phoenix, and Riverside had only qualifying hourly PM 10 

### data. Birmingham and Boston had no qualifying hourly PM 2.5 

### or PM 10 

### data. Diel plots for PM 2.5 

### stratified by weekdays and weekends for seven of the 15 CSAs/CBSAs with available data between 2005 and 2007 are included in Annex A, Figures A-157 through A-163. In most cities investigated, a morning PM 2.5 

### peak is present starting at approximately 6:00 a.m., corresponding with the start of the morning rush hour just before the break-up of overnight stagnation. In Pittsburgh, dispersion behavior during the night results in elevated PM 2.5

December 2009 3-97

### concentrations throughout the night that blend in with any morning peak. With the exception of Pittsburgh, all seven metropolitan areas show two distinct daily peaks on both the weekdays and weekends. The evening PM 2.5 

### concentration peak is broader than the morning peak and extends to overnight hours, reflecting the concentration increa se caused by a drop in boundary layer height at night. Figure 3-49 compares the two-peak diel distribution in PM 2.5 

### for Seattle with the one-peak distribution in PM 2.5 

### for Pittsburgh Since these figures re present the distribution of hourly observations over a 3-yr period, any fluctuations or changes in the timing of the daily peaks would result in a broadening of the curves shown in the diel plot.



## Figure 3-49. Diel plot generated from hourly FRM-like PM 2.5 

## data ( μ g/m 3 

## ) stratified by weekday (left) and weekend (right) for Pittsburgh, PA, and Seattle, WA, 2005-2007. Included are the number of monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for each hour of the day shown on the horizontal axis.

December 2009 3-98

### Annex A, Figures A-164 through A-174 show diel patterns for PM 10 

### stratified by weekdays and weekends for eleven of the 15 CSAs/CBSAs with available data between 2005 and 2007. All cities show a gradual morning increase in mean PM 10 

### starting at approximately 6:00 a.m. on weekdays, corresponding with the start of the mo rning rush hour before the break-up of overnight stagnation. The magnitude and duration of this peak , however, varies considerably by area. Phoenix shows the most pronounced morning PM 10 

### peak concentration, which drops off during the day and reappears in the evening. In contrast, Chicago shows a less pronounced peak with the PM 10 

### concentration remaining elevated throughout the day. Figure 3-50 shows the diel plots of PM 10 

### for Chicago and Phoenix. In both instances, the weekend diel pattern is similar in shape to the weekday pattern with less pronounced peaks. Once again, any fluctuations in the timing of the daily peaks could result in a broadening of the peaks in the 3-yr composite diel figures.



## Figure 3-50. Diel plots generated from hourly FEM PM 10 

## data ( μ g/m 3 

## ) stratified by weekday (left) and weekend (right) for Chicago, IL, and Phoenix, AZ, 2005-2007. Included are the number of monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for each hour of the day shown on the horizontal axis.

UFPs in urban environments have been shown to exhibit a similar two-peaked diel pattern in Los Angeles (Moore et al., 2007, 122445 ; Sardar et al., 2005, 180086 ) and the San Joaquin Valley (Herner et al., 2005, 135983 ) in CA, Rochester, NY (Jeong et al., 2004, 180350 ), Raleigh, NC (Baldauf et al., 2008, 190239 ) as well as in Kawasaki City , Japan (Hasegawa et al., 2005, 157355 ) and Copenhagen, Denmark (Ketzel et al., 2003, 131251 ). Figure 3-51 from the Denmark study

December 2009 3-99

### shows a large peak in total particle number (dominated by UFPs) corresponding with the morning rush hour. The morning peak is absent on Sundays, however. Many studies also show a broad afternoon UFP concentration peak, which likely orig inates from a combination of evening rush-hour traffic, decreased atmospheric dilution and form ation of UFPs through nucleation involving products of active photochemistry. Nucleation likely plays an important role since the afternoon peak is present on weekends whereas the morning traffic related peak is absent. This is consistent with observations of particle counts in Atlanta peaki ng during the mid-afternoon for particles <10 nm (Woo et al., 2001, 011702 ) resulting from nucleation.

Source: Adapted with Permission of Elsevier Science Ltd. From Ketzel et al. (2003, 131251 )



## Figure 3-51. Average diel variation in total particle number (ToN) and total particle volume (ToV) on weekdays (left column) and Sundays (right column) from two sites in Denmark: one in a busy street canyon (Jagtv) and one measuring urban background (HCØ).

Hourly variability in particle-phase OC an d EC were investigated by Bae et al. (2004, 156243 ) in the urban St. Louis atmosphere. OC diel pa tterns were similar during weekdays and weekends with a broad morning and evening concentration peak most likely reflecting daily fluctuations in atmospheric mixing height. Weekend EC diel patterns were similar to those for OC, but the weekday patterns showed more abrupt EC concentration pea ks in the morning and afternoon, coinciding with rush-hour traffic. The divergent weekday patterns between OC and EC suggests motor vehicles or other EC sources with temporal profiles tracking traffic patterns are primarily responsible for the daily fluctuations in EC concentrations in St. Louis.



# 3.5.3. Statistical Associations with Copollutants

Associ ations between different PM size fract ions and between PM and other copollutants including SO 2 

### , NO 2 

### , CO and O 3 

### are investigated in this section. AQS data were obtained from all available co-located monitors across the U.S. after application of a completeness criterion of 11 or

December 2009 3-100

### more observations per quarter. P earson correlation coefficients (r) were calculated using 2005-2007 data. The results are displayed graphically in Figure 3-52 for correlations with PM 2.5 

### mass concentration and Figure 3-53 for correlations with PM 10 

### mass concentration. The different PM size fractions are compared and contrasted in this section using temporal correlations which should not be confused with average PM mass fraction (e.g., PM 2.5 

### /PM 10 

### ) comparisons discussed in Section 3.5.1.1

.

Spring

PM1 Winter

0 (d

NO2 (

CO aily avg)

PM10-2.5 (daily avg)

SO2 (daily avg)

daily avg)

(daily avg)

O3 (daily max 8-h)

correlation (r) with PM2.5 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Fall

PM10 ily avg)

10-2.5 (daily avg)

SO2 (daily avg)

NO2 (daily avg)

CO (daily avg)

O3 aily max 8-h) (da

PM

(d

correlation (r) with PM 2.5 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 PM 10 (daily avg)

PM 10-2.5 (daily avg)

SO 2 (daily avg)

NO 2 (daily avg)

CO (daily avg)

O 3 (daily max 8-h)

correlation (r) with PM2.5 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Summer

PM 10 (daily avg)

PM 10-2.5 (daily avg)

SO 2 (daily avg)

NO 2 (daily avg)

CO (daily avg)

O 3 (daily max 8-h)

correlation (r) with PM 2.5 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz



## Figure 3-52. Distribution of correlations between 24-h avg PM 2.5 

## and co-located 24-h avg PM 10 

## , PM 10-2.5 

## , SO 2 

## , NO 2 

## and CO and daily max 8-h avg O 3 

## for the U.S. stratified by season (2005-2007). Statistics shown include the mean (green star), median (red line), inner quartile range (box), 5th/95th percentiles (whiskers) and outliers (black circles).

December 2009 3-101Spring

PM2. Winter

5 (d

(dai

O2

C aily avg)

PM10-2.5 ly avg)

SO2 (daily avg)

N (daily avg)

O (daily avg)

O3 (daily max 8-h)

correlation (r) with PM10 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Fall

PM2.5 ly avg)

PM10-2.5 ily avg)

SO2 (daily avg)

NO2 (daily avg)

CO (daily avg)

3 (daily max 8-h) (dai

(da

O

correlation (r) with PM 10 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 PM 2.5 (daily avg)

PM 10-2.5 (daily avg)

SO 2 (daily avg)

NO 2 (daily avg)

CO (daily avg)

O 3 (daily max 8-h)

correlation (r) with PM10 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Summer

PM 2.5 (daily avg)

PM 10-2.5 (daily avg)

SO 2 (daily avg)

NO 2 (daily avg)

CO (daily avg)

O 3 (daily max 8-h)

correlation (r) with PM 10 (daily avg) -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz



## Figure 3-53. Distribution of correlations between 24-h avg PM 10 

## and co-located 24-h avg PM 2.5 

## , PM 10-2.5 

## , SO 2 

## , NO 2 

## and CO and daily max 8-h avg O 3 

## for the U.S. stratified by season (2005-2007). Statistics shown incl ude the mean (green star), median (red line), inner quartile range (box), 5th/95th percentiles (whiskers) and outliers (black circles).

For both PM 2.5 

### and PM 10 

### national composite copollutant correlations, there is considerable spread in the observed correlations in all four seasons. On average, PM 2.5 

### and PM 10 

### correlate with each other better than with the gaseous copollutants. The correlations between PM 2.5 

### and PM 10 

### are all positive but span the range from just above zero to near one. This illustrates the wide variability in correlation between these two PM metrics. Fewer points are available for correlation with PM 10-2.5 

### because only data from low-vo lume FRM/FRM-like samplers were used to calculate PM 10-2.5 

### . The available data suggest a stronger correlation between PM 10 

### and PM 10-2.5 

### than between PM 2.5 

### and
PM 10-2.5 

### on a national basis. Correlations among copollutants for indivi dual CSAs/CBSAs are included in Annex A, Figure A-175 through Figure A-188 for PM 2.5 

### and Figure A-189 through Figure A-202 for PM 10



### . Each data point in these figures represents a co-loc ated monitor pair. Seattle did not have sufficient co-located data to be included with the other CSAs/CBSAs. As can be seen from the individual CSAs/CBSAs, there can be considerable variation in the correlations even within an individual urban area. For example, correl ations between 24-h average PM 2.5 

### and PM 10 

### concentrations measured at the five co-located monitor pairs in Boston be tween 2005 and 2007 ranged from 0.42 to 0.88 during winter and reach as high as 0.98 during the summer (Figure A-177). Few locations within the 15 CSAs/CBSAs contained adequate data for calculating correlations with PM 10-2.5 

### using low-volume PM data: Boston and New York had two locations each, Atlanta, Chicago, Denver and Phoenix had only one location and the remaining CSAs/CBSAs had no locations. Correlations between PM 2.5 

### and PM 10-2.5 

### varied substantially by CSA/CBSA and season and no general patterns were observed in the limite d data set analyzed here. In contrast, correlations between PM 10 

### and PM 10-2.5 

### did show some trends by location and season and were greater in all locations than correlations between PM 2.5 

### and PM 10-2.5 

### . The highest correlations between PM 10 

### and

December 2009 3-102

### PM 10-2.5 

### were observed in Denver and Phoenix with correlations above 0.88 during all seasons. Atlanta, Boston, Chicago and New York all had lower correlations between PM 10 

### and PM 10-2.5 

### (0.30 ≤ r ≤ 0.88), particularly during the fall (0.30 ≤ r ≤ 0.56). The lowest correlations between PM 10 

### and PM 10-2.5 

### were observed in New York in the fall and Boston in the summer where they dropped to 0.30 and 0.38, respectively, at one of the two mon itor locations in each city. In the four eastern CSAs/CBSAs investigated, correlations between PM 10 

### and PM 10-2.5 

### were highest in the spring, in agreement with the national averages shown in Figure 3-53 . In Denver and Phoenix, there was less seasonal dependence in the correlation. A similar analysis of correlations between PM size fractions by region was reported in Table 3-1 of the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) and Figure 2-20 of the 2005 OAQPS Staff Paper (U.S. EPA, 2005, 090209 ). In all regions, correlations between PM 10 

### and PM 10-2.5 

### were greater than those for PM 2.5 

### and PM 10-2.5 

### . Correlations between PM 10 

### and PM 10-2.5 

### were found to be largest in the southwest and the upper Midwest and smallest in the southeast and the northeast. While these regional analyses used different data inclusion criteria for estimating PM 10-2.5 

### than the criteria used in this analysis for the individual CSAs/CBSAs, the results are generally consistent: higher correlations between PM 10 

### and PM 10-2.5 

### mass concentrations compared with PM 2.5 

### and PM 10-2.5 

### mass concentrations in all regions and higher correlations between PM 10 

### and PM 10-2.5 

### mass concentrations in the west compared to the east. The correlation between PM and the gaseous pollutants included in Figure 3-52 and Figure 3-53 also showed a large range in values based on the national composite data. There was little seasonal variability in the mean correlation between PM and SO 2 

### . NO 2 

### and CO, however, showed higher correlations with PM on average in winter than in the other seasons. This is possibly driven by meteorology with increased frequency of stagnation events in colder months as well as potential concurrent increases in emissions of these compounds from motor vehicles with colder temperatures. The correlation between daily max 8-h avg O 3 

### and 24-h avg PM showed the highest degree of seasonal variability with positive correlations on average in summer (0.56 for PM 2.5 

### and 0.39 for
PM 10 

### ) and negative correlations on average in winter (-0.30 for PM 2.5 

### and -0.18 for PM 10 

### ). During the transition seasons, spring and fall, correlations were mixed but on average were still positive.
PM 2.5 

### is both primary and secondary in origin, whereas O 3 

### is only secondary. Photochemical production of O 3 

### and secondary PM in the planetary boundary layer (PBL) is much slower during the winter than during other seasons. Primary pollutant concentrations (e.g., primary PM 2.5 

### components, NO and NO 2 

### ) in many urban areas are elevated in winter as the result of heating emissions, cold starts and low mixing heights. O 3 

### in the PBL during winter is mainly associated with air subsiding from above the boundary layer following the passage of cold fronts, and this subsiding air has much lower PM concentrations than are present in the PBL. Therefore, a negative association between O 3 

### and PM is frequently observed in the winter. During summer, both O 3 

### and secondary
PM 2.5 

### are produced in the PBL and in the lower free troposphere at faster rates compared to winter, and so they tend to be positively correlated. Bell et al. (2007, 093256 ) also observed wintertime minima in same-day correlati ons between 24-h avg PM (both PM 2.5 

### and PM 10 

### ) and 24-h avg O 3 

### using data from 98 U.S. urban communities. The average correlations were positive in winter, unlike those shown in Figure 3-53 . Furthermore, the highest national average correlations were in spring and fall in the Bell et al. (2007, 093256 ) analysis rather than summer as observed in Figure 3-52 and Figure 3-53 . This discrepancy could be a result of the different averaging times used for O 3 

### or the selection of different monitoring networks and/or time periods. For the PM 2.5 

### city-specific correlations shown in Annex A , Figure A-175 through Figure A188, all selected cities with sufficient data showed negative correlations in the wintertime with daily max 8-h avg O 3 

### (including Birmingham, Boston, Chi cago, Denver, Houston, Los Angeles, Philadelphia, Phoenix, Pittsburgh, Riverside an d St. Louis). The remaining four CSAs/CBSAs had insufficient data. In Baltimore, Sarnat et al. (2001, 019401 ) found a significant (at the p <0.05 level) positive (0.67) and negative (-0.72) correlation between daily PM 2.5 

### and O 3 

### in the summer (June 19-August 23, 1998) and winter (February 2-March 13, 1999), respectively. For PM 10 

### , the cityspecific correlations with max 8-h avg O 3 

### shown in Annex A, Figure A-189 through Figure A-202 were more variable. Birmingham, Boston, and St . Louis all showed positive wintertime correlations between PM 10 

### and daily maximum 8-h avg O 3 

### while Denver, Detroit, Houston, Los Angeles and Phoenix showed negative wintertime correla tions. The remaining seven CSAs/CBSAs had insufficient data. These copollutant correlations il lustrate the importance of considering seasonality when assessing temporal relationships between air pollutants, particularly PM and O 3



### .

December 2009 3-103

# 3.6.

3.6.1.

3.6.1.1. 

# Mathematical Modeling of PM

Ther e are two main classes of models used to study atmospheric PM, receptor models and CTMs. Receptor models are statistical models wh ereas CTMs are numerical models, i.e., they approximate derivatives by finite difference approximations. Finite element models are also numerical models but have not been used as extensi vely for applications described here, and so are not discussed further. Receptor models are diagnostic in their approach, in that they try to derive source contributions at monitoring locations using either ambient data alone or in combination with data for the chemical composition of sources or in combination with meteorological data. Threedimensional CTMs are formulated in a prognostic, or predictive manner, that is, they attempt to predict species concentrations by solving a set of coupled, non-linear partial differential equations (continuity equations) for chemical species that include terms based on emissions inventories, atmospheric transport, chemical tr ansformations, and deposition. Mon itoring data is used to evaluate the performance of CTMs. Each of these approaches has its own advantages and disadvantages.



# Estimating Source Contributions to PM Using Receptor Models

Method s for analyzing the composition of ambien t PM samples in terms of contributions from different sources are reviewed in this section. Associations between exposures to ambient PM, as represented by ambient monitors, and health outcome s have been extensively studied. Some health studies, described in Section 6.6, have used sou rce apportionment modeling to evaluate relationships between health outcomes and PM (mainly PM 2.5 

### ) from different sources. This section is intended to provide background concerning the uses of source apportionment techniques in such studies. Understanding the contribution of different emissions sources to ambient PM has also been used extensively in evaluating air quality data for use in developing control strategies.



# Receptor Models

Receptor models have been used mainly as part of the development of air quality management plans. However, there have been several publi cations relating apportioned source types based on receptor models to human health effects. Discussions in this section will focus mainly on those methods that have been used to relate health outcomes to sources. More complete descriptions of a large number of types of receptor models currently in use are given in Watson et al. (2008, 157128 ), who summarize the properties of these methods, including the strengths and weaknesses. This compilation of receptor models, broken down into different approaches (i.e., chemical mass balance, factor analysis, tracer-based, meteorology based) is included in Tables A-51 through A-54 in Annex A. Receptor models such as the chemical mass balance (CMB) model (Watson et al., 1990, 004848 ) relate source category contributions to ambient PM concentrations based on analyses of the compositional profiles of ambient and source emissi ons samples. It uses as its basis a mass balance equation that represents all chemical species in an aerosol sample as linear combinations of contributions from a fixed number of independent sources plus an error term representing the portion of the measurement that cannot be fit by the model. The compositional profiles used in receptor models can be extensive (see for example the SPECIATE data base, http://www.epa.gov/ttnchie1/ software/speciate/index.html ) for a comprehensive collection of results from a large number of studies. As an example, several studies have identified EC and over 100 organic carbon compounds in gasoline PM emissions, including alkanes, PAHs, oxy-PAHs, steranes, hopa nes, and organic acids (Maricq, 2007, 155973 ; Schauer et al., 1999, 010582 ; Schauer et al., 2002, 035332 ). This breakdown in identifiable groups of organic compounds is illustrated in Figure 3-54 and Table 3-17 shows emissions factors for trace elements. Data for the compositional profiles for several othe r important sources of PM that could be used for CMB modeling are shown in Table A-55 in Annex A. Source categories are amenable to refinement and to analysis as information on tracers becomes available. For example, PBAP have long b een known to be significant constituents of the atmospheric aerosol, but not many studies have eval uated their contributions, largely because of the lack of suitable tracers and the additional equipmen t needs for sampling and analysis of bioaerosols.

December 2009 3-104

### Bauer et al. (2008, 189986 ) reviewed studies estimating the contribution of fungal spores to PM 2.5 

### and PM 10-2.5 

### as fungal spores were expected to be ma jor contributors to PBAP. They proposed the use of arabitol and mannitol as unique tracers with an estimated accuracy of ± 50% to apportion the contribution of fungal spores to OC in both PM 2.5 

### and PM 10-2.5 

### . They estimated 24-h avg contributions of ~ 40% to OC in PM 10-2.5 

### during spring and summer in Vienna, with a smaller contribution to PM 2.5



### .

Source: Reprinted with Permission of Elsevier Science Ltd. From Fraser et al. (1999, 010819 )



## Figure 3-54. Schematic of organic composition of particulate emissions from gasoline-fueled vehicles.

One recently-identified concern in the appli cation of CMB-based receptor models with detailed organic marker compounds is the photoche mical stability of those species. Robinson et al. (2006, 156918 ) reported evidence of significant summertime photooxidation of hopanes and longchain alkenoic acids, low-volatility compounds of ten used as mobile source and cooking emissions, respectively. Seasonal differences in hopanes/EC ratios differed in a manner consistent with oxidation. Photochemical loss of particle-phase ma rker species mass complicates the interpretation of model results, as long-range transport and phot ochemistry may result in the loss of markers for distant sources. Furthermore, photochemical break down of organic marker species may cause losses in CMB model performance criteria and possible bias in source contribution estimates. The photooxidation of condensed-phase organic compounds al so may affect the polarity and volatility of these compounds. In other methods, various forms of factor anal ysis are used that rely on the varying mix of species present in ambient observations of compos itional data to derive the source contributions. Standard factor analytic approaches such as Pr incipal Component Analysis (PCA) have been used, but PCA alone can apportion only the variance, not the mass, in an aerosol composition data set. Additional steps in particular the identification of source tracers is required in Absolute Principal Components Scores (APCS) to apporti on mass from PCA (Miller et al., 2002, 030661 ; Thurston and Spengler, 1985, 056074 ). However, it can be difficult to find suitable tracers for some sources because many elements are emitted by more than one source. In Positive Matrix Factorization (PMF) (Paatero and Tapper, 1994, 086998 ), the ambient compositional data matrix is decomposed into the product of a matrix representing the source contri butions and one representing the source profiles. Solutions are obtained by minimizing an object function with respect to these two matrices, and solutions are subject to non-negativity constraints. PMF also allows for the treatment of missing data and data near or below detection limits by weighting elements inversely according to their uncertainties. The PMF approach requires a large number of samples (n typically >50) and are most often applied to time series data, whereas CMB can be applied to a single sample. Both the CMB and the PMF approaches find solutions based on least squares fitting and minimization of an object function. Both methods provide error estimates for the solutions based on estimates of the errors in the input parameters. It should be noted, though, that the error estimates for both methods often contain subjective judgments about the magnitude of the analytical and monitoring errors.

December 2009 3-105

## Table 3-17. Example of emissions factors (ng/km) for trace elements under variable speed and steady speed driving conditions for PM emitted by diesel and gasoline engines. Note that emissions are highly variable.

Diesel Gasoline Element Transient Steady State Transient Steady State

Al 9108 (5224) 2706 2273 (545) 252

Ca 69,443 (23,640) 16,128 18,247 (3044) 2324

Fe 22,910 (21,448) 2036 10,266 (9928) 138

K 4672 (752) 1191 1935 (558) 117

Mg 3087 (461 997 5183 (1706) 183

Na 7736 (1751) 1945 2237 (1125) 321

Ba 583 (349) 73 331 (55) 4.8

Be 26 (12) 23 6.7 (1.1) 1.5

Cr 634 (354) 93 138 (6.7) 8.6

Cu 1944 (679) 627 1745 (1803) 16

Li 13 (0.2) 7.9 3.0 (1.4) 0.9

Mn 368 (183) 76 152 (85) 3.4

Ni 2310 (656) 644 107 (0.7) 21

Pb 793 (593) 79 237 (2.3) 11

S 23,750 (5295) 6713 8705 (3375) 349

Ti 2036 (320) 345 118 (9.3) 24

V 28 (9.4) 11 15 (11) 1.8

Zn 21,118 (4422) 5620 4650 (1225) 198

Standard deviations are presented in parenthesis when multiple tests have been averaged.

Source: Adapted with Permission of Elsevier Ltd. from Geller et al. (2006, 139644 )

. 

### The nature of the solutions in terms of source categories is different in the CMB and PMF approaches. In the CMB approach, the composition of the source emissions is assumed to be known based on measurements. These assumptions may or may not reflect the composition of emissions affecting a particular site at any given time or place. However, there may be variations in the composition of individual source categories (e.g., soils, motor vehicle emissions) across a given airshed and even in the composition of the same sour ce with time. Source profiles can also be altered between emission and receptor locations resulti ng from atmospheric reactions, depending on the source type and species under analysis. The CMB technique was developed for apportioning source categories of primary PM and was not formulated to include sources of secondary PM. CMB might not explain all the mass or produce a valid result un less there is information for the composition of all major sources affecting a given site, and there is confidence that the existing source profiles are specific to those sources. For example, Volckens et al. (2008, 105465 ) describe PAH emission profiles from hand-held gasoline lawn and garden equipment as found in some CMB source profiles for motor vehicles. In PMF, the source solutions are more general in that they contain information about the entrainment of emissions from additional sources during transport, the ti me dependence of the composition of emissions from particular sources, th e formation of secondary species and local differences in source compositions. PMF differs from CMB because it derives the mix of factors from measured data. However, the procedure used to find a solution results in some rotational ambiguity (Paatero and Tapper, 1994, 086998 ). The assignment of sources to PMF factors depends largely on past experience and judgments. Judgments are based to large extent on comparison with data for source profiles and also on the factors that could modify the assignments. These issues are

December 2009 3-106

### alleviated to some degree by incorporating inform ation about local wind fields and other physical parameters. The UNMIX model takes a geometric approach that exploits the covariance of the ambient data to determine the number of independent sources, the composition and contributions of the sources, and corresponding uncertainties (Henry, 1997, 020941 ). UNMIX uses PCA to find edges in m-dimensional space, where m is the number of ambient species. Success of the UNMIX model hinges on the ability to find these “edges” in the ambient data from which the number of source types and the source compositions are extracted. In simp lest terms, the approach can be seen to be similar to that for deriving ternary mixing diagrams, except there is extension to higher dimensionality. Measurement errors in the ambient data “fuzz” the edges, making them difficult to find. UNMIX employs an “edge-finding” algorithm to find the best edges in the presence of error. UNMIX does not make explicit use of errors or un certainties in the ambient concentrations, unlike the methods outlined above. Rather they are implicitly incorporated into the analyses. PMF and UNMIX have also used data for particle size distributions to obtain further information about

sources. Partial least squares (PLS) is another mathematic al model related to PCA which has been used in a limited number of PM toxicology studies to establish a relationship between PM constituents and health outcomes (McDonald et al., 2004, 087458 ; Seagrave et al., 2006, 091291 ; Veranth et al., 2006, 087479 ). Although not really a receptor model and not designed as such, PLS shares some similarities with certain receptor models; and more importantly attempts to link PM components with health outcomes. Unlike PCA and other receptor models discussed in this section, PLS incorporates both predictor variables (e.g., PM component concentrations) and outcome variables (e.g., toxicological responses) into one coupled regression model. Like PCA, PLS groups the observable variables into a reduced number of latent variables, thereby reducing the dimensionality of the model. Typically, PM toxicology studies ha ve been limited to two- component models (two latent variables on the predictor side compared with two on the outcome side), thereby producing a 2×2 loading plot revealing relationships between predictors and outcomes. PLS is particularly useful when there are more predictor variables than observations, which is a situation that other multivariate factor analysis approaches do not hand le well. However, since PLS is a variance based approach, it shares the same shortcomings discusse d earlier for PCA. PLS has also traditionally been limited to two-component applications even though this is not a strict mathematical limitation.



## Results from Receptor Models

Results from receptor modeling calculations indicate that PM 2.5 

### is most often produced mainly by fossil fuel combustion. Fugitive dust, found mainly in the PM 10-2.5 

### size range, represents the largest source of measured ambient PM 10 

### in many locations in the western U.S. Quoted uncertainties in the source apportionment of constituents in ambient aerosol samples typically range from 10 to 50%. It is apparent that a relatively small number of broadly defined source categories, compared to the total number of chemical species that typi cally are measured in ambient monitoring-source receptor model studies, are needed to account for the majority of the observed mass of PM in these studies. Trying to be more specific about contributions from source categories could result in ambiguity. For example, some stationary sources (e .g., agriculture use engines) and quite different mobile sources (e.g., trucks and locomotives) rely on diesel power and ancillary data is required to resolve contributions from these sources. Compilations of source attribution studies using CMB for
PM 10 

### have appeared in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) and using PMF for PM 2.5 

### in Engel-Cox and Weber (2007, 156419 ). Results of the compilation by Engel-Cox and Weber (2007, 156419 ) for the eastern U.S. are shown in Figure 3-53 . There are only three main source categories in the figure constituting most of the PM 2.5 

### mass (i.e., sulfate, nitrate, mobile). Two of these are predominantly secondary and not identified by sources of precursors. Tables A-56 and A-57 in Annex A list results of other receptor modeling studies for PM 2.5 

### and PM 10 

### , many of which are in the western U.S.

December 2009 3-107Source: Reprinted with Permission of Air & Waste Management Association from Engel-Cox and Weber (2007, 156419 ).



## Figure 3-55. Source category contributions to PM 2.5 

## at a number of sites in the East derived using PMF.



## Spatial Variability in Source Contribut ions to PM Based on Receptor Models

Spatial variability in source contributions across urban areas is an important consideration in assessing the likelihood of exposure measurement e rror in epidemiologic studies relating health endpoints to sources. Arguments similar to those fo r using ambient concentrations as surrogates for personal exposures apply here. Studies for PM 2.5 

### (Kim et al., 2005, 083181 ; Wongphatarakul et al., 1998, 049281 ) indicate that intra-urban variability increas es in the following order: regional sources (e.g., secondary SO 4 2– 

### originating from EGUs) < area sources (e.g., on-road mobile sources) < point sources (e.g., stacks). This point is illustrated in Figure 3-56 . The only study available for PM 10-2.5 

### (Hwang et al., 2008, 134420 ) indicates a similar ordering, but without a regional component (resulting from the short lifetime of coarse PM compared to transport times on the regional scale) as shown in Figure 3-57

.

December 2009 3-108Source: Reprinted with Permission of ACS from Kim et al. (2005, 083181 )



## Figure 3-56. Pearson correlation coefficients for source category contributions to PM 2.5 

## between the 10 Regional Air Pollution Study/Regional Air Monitoring System (RAPS/RAMS) monitoring sites in St. Louis.

Source: Reprinted with Permission of ACS from Hwang et al. (2008, 194533 )



## Figure 3-57. Pearson correlation coefficients for source contributions to PM 10-2.5 

## between the 10 Regional Air Pollution Study/Regional Air Monitoring System (RAPS/RAMS) monitoring sites in St. Louis.



# 3.6.2. Chemistry Transport Models

CTMs a re the prime tools used to compute the interactions among atmospheric pollutants and their transformation products, the production of se condary aerosols, the evolution of particle size distribution, and transport and deposition of poll utants. CTMs are driven by emissions inventories for primary species such as NO X 

### , SO X 

### , NH 3 

### , VOCs, and primary PM, and by meteorological fields produced by other numerical prediction models. Va lues for meteorological state variables such as winds and temperatures are taken from operational analyses, reanalyses, or weather circulation models. In most cases, these are off-line meteorologi cal analyses, meaning that they are not modified

December 2009 3-109

### by radiatively active species generated by the ai r quality model (AQM). Work to integrate meteorology and chemistry was done in the mid-1990s by Lu et al. (1997, 048202 and references therein; 1997, 191768 ) although limits to computing power prev ented their wide-spread application. More recently, new, integrated models of meteor ology and chemistry are now available as well; see, for example, Binkowski et al. (2007, 090563 ) and the Weather Research and Forecast model with chemistry (WRF-Chem) ( http://ruc.fsl.noaa.gov/wrf/WG11 ). CTMs have been developed for application over a wide range of spatial scales ranging up from neighborhood to global. CTMs are used to: (1) obtain better understanding of the processes controlling the formation, transport, and destruction of gas- and particle-phase criteria and hazardous air pollutants; (2) understand the relations between concentrations of secondary pollutant products and concentrations of their precursors; (3) underst and relations among the concentration patterns of various pollutants that may exert adverse effects; and (4) evaluate how changes in emissions propagate through the atmospheric system to secondary products and deposition. Emissions of precursor compounds can be divided into anthropogenic and natural source categories. Natural sources can be further divide d into biogenic from vegetation, microbes, and animals, and abiotic from biomass burning, li ghtning, and geogenic sources. However, the distinction between natural sources and anthropogenic sources is often difficult to make in practice, as human activities affect directly or indirectly emissions from what would have been considered natural sources during the preindustrial era. T hus, emissions from plants and animals used in agriculture have been referred to as anthropogenic or biogenic in different applications. Wildfire emissions may be considered natural, except that forest management practices can lead to buildup of fuels on the forest floor, thereby altering the frequency and severity of forest fires. The initial conditions, or starting concentration fields of all species computed by a model, and the boundary conditions, or concentrations of speci es along the horizontal and upper boundaries of the model domain throughout the simulation, must be specified at the beginning of the simulation. Both initial and boundary conditions can be estimated from models or data or, more generally, model + data hybrids. Because data for vertical profiles of most species of interest are very sparse, results of model simulations over larger, usually global, domains are often used. As might be expected, the influence of boundary conditions depends on the lifet ime of the species under consideration and the time scales for transport from the boundaries to the interior of the model. Each of the model components described above has associated uncertainties and the relative importance of these uncertainties varies with the modeling application. The largest errors in photochemical modeling are still thought to arise from the meteorological and emissions inputs to the model (Russell and Dennis, 2000, 035563 ). While the effects of poorly specified boundary conditions propagate through the model’s domain, the effects of these errors remain undetermined. Because many meteorological processes occur on spat ial scales smaller than the model’s vertical or horizontal grid spacing and thus are not calculated explicitly, parameterizations of these processes must be used. These parameterizations introduc e additional uncertainty. Because the chemical production and loss terms in the continuity equati ons for individual species are numerically coupled, the chemical calculations must be performed iteratively until calculated concentrations converge to within some preset criterion. The number of iterations and the convergence criteria chosen also can introduce error. CTMs in current use mostly have one of two forms. The first, grid-based or Eulerian air quality models subdivide the region to be modele d, the modeling domain, into a three-dimensional array of grid cells. Spatial derivatives in the species continuity equations are cast in finite-difference form over this grid and a system of equations for th e concentrations of all the chemical species in the model are solved numerically at each grid point . Time-dependent continuity or mass conservation equations are solved for each species including te rms for transport, chemical production and destruction, and emissions and deposition (if relevant ), in each grid cell. Chemical processes are simulated with ordinary differential equations, a nd transport processes are simulated with partial differential equations. Because of a number of factors such as the different time scales inherent in different processes, the coupled, nonlinear nature of the chemical process terms, and computer storage limitations, not all of the terms in the equations are solved simultaneously in three dimensions. Instead, operator splitting, in whic h terms in the continuity equation involving individual processes are solved sequentially, is used. In the second common CTM formulation, trajectory or Lagrangian models, a number of hypothetical air parcels are specified as though fo llowing wind trajectories. In these models, the

December 2009 3-110

### original system of partial differential equations is transformed into a system of ordinary differential equations. A less common approach is to use a hybrid Lagrangian-Eulerian model, in which certain aspects of atmospheric chemistry and transport are treated with a Lagrangian approach and others are treaded in an Eulerian manner (e.g., Stein et al., 2000, 048341 ). Each approach has advantages and disadvantages. The Eulerian approach is more general in that it includes processes that mix air parcels and allows integrations to be carried out for long periods during which individual air parcels lose thei r identity. There are, however, techniques for including the effects of mixing in Lagrangian models such as FLEXPART (Zanis et al., 2003, 053423 ), ATTILA (Reithmeier and Sausen, 2002, 053447 ), and CLaMS (McKenna et al., 2002, 053445 ). Because both the accuracy and the computat ional intensity of Eulerian models depend strongly on the size of the horizontal and verti cal grid spacing, speed and fidelity to actual atmospheric conditions must sometimes be traded-off; that is to say, while finer grid spacing will often capture effects missed at larger grid intervals, models set up in this way require longer to solve. In a similar manner, the accuracy of Lagrangian models depends on the number of air parcels deployed; thus they, too, become computationally intensive when higher-order accuracy is desired. More detailed discussion of CTM applications appears in the 2008 ISA for NO X 

### and SO X 

### – Ecological Criteria (U.S. EPA, 2008, 157074 ).



# 3.6.2.1. Global Scale

Global-s cale CTMs are used to address issues associated with climate change and stratospheric O 3 

### depletion to characterize long-range air pollution transport, and to provide boundary conditions for the regional-scale models. The CT Ms include parameterizations of atmospheric transport; the transfer of solar radiation through the atmosphere; chemical reactions; and removal to the surface by turbulent motions and precipitation for emitted pollutants. Th e upper boundaries of the CTMs extend anywhere from the tropopause (~8 km at the poles to ~16 km in the tropics) to the mesopause at ~80 km in order to obtain more realistic boundary conditions for problems involving stratospheric dynamics and chemistry. Global simulations are typically conducted with a horizontal grid spacing of 200 km or more, although some models such as GEOS-Chem have been run at grid spacings of about 100 km (e.g., Wu et al., 2008, 190039 ) and efforts are being made to achieve even higher spatial resolution. Simulations of the effects of long-range transport at particular locations link multiple horizontal resolutions from the global to the local scale. Finer resolution can only improve scientific understanding to the extent that the governing proce sses are more accurately described at that scale. Consequently, there is a crucial need for observations at the appropriate scales to evaluate the scientific understanding represented by the models.



# 3.6.2.2. Regional Scale

Most majo r regional-scale air-related modeling efforts at EPA use the Community Multi-scale Air Quality modeling system (CM AQ) (Byun and Ching, 1999, 156314 ; Byun and Schere, 2006, 090560 ). A number of other modeling platforms using Lagrangian and Eulerian frameworks were reviewed in the 2006 O 3 

### AQCD (U.S. EPA, 2006, 088089 ) and in Russell and Dennis (2000, 035563 ). The capabilities of a number of CTMs designed to study local- and regional-scale air pollution problems were summarized by Russell and Dennis (2000, 035563 ). Evaluations of the performance of CMAQ are given in Arnold et al. (2003, 087579 ), Eder and Yu (2005, 089229 ), Appel et al. (2005, 089227 ), and Fuentes and Raftery (2005, 087580 ). CMAQ’s horizontal domain can extend from a few hundred kilometers on a side to the entire hemisphere. CMAQ is most often driven by the MM5 mesoscale meteorological model (Seaman, 2000, 035562 ), though it may be driven by other meteorological models including WRF and the Regional Atmospheric Modeling System (RAMS); see http://atmet.com . Simulations of pollution episodes over regional domains have been performed with a horizontal resolution as low as 1 km; see the application and general survey results reported in Ching et al. (2006, 090300 ). However, simulations at such high resolutions require better parameterizations of meteorological processes such as boundary layer fluxes, deep convection and clouds (Seaman, 2000, 035562 ). Finer spatial resolution is necessary to resolve features such as urban heat island circulation; sea, bay, and land breezes; mountain and valley breezes; and the nocturnal low-level jet, all of which can affect pollutant concentrations.

December 2009 3-111

### The most common approach to setting up the horizontal domain is to nest a finer grid within a larger domain of coarser resolution. However, ther e are other strategies such as the stretched grid and the adaptive grid. In a stretched grid, the grid’s resolution continuously varies throughout the domain, thereby eliminating any potential problems with the sudden change from one resolution to another at the boundary. Caution should be exercised in using such a formulation because certain parameterizations like those for convection might be valid on a relatively coarse grid scale but may not be valid on finer scales. Adaptive grids are not fixed at the start of the simulation, but instead adapt to the needs of the simulation as it evolves. They have the advantage that they can resolve processes at relevant spatial scales. However, they can be very slow if the situation to be modeled is complex. Additionally, if adaptive grids are us ed for separate meteorological, emissions, and photochemical models, there is no reason a priori w hy the resolution of each grid should match, and the gains realized from increased re solution in one model will be wasted in the transition to another model. The use of finer horizontal resolution in CT Ms will necessitate finer-scale inventories of land use and better knowledge of the exact paths of roads, locations of factories, and, in general, better methods for locating sources and estimating their emissions. The vertical resolution of these CTMs is variab le and usually configured to have more layers in the PBL and fewer higher up. Because the height of the boundary layer is of critical importance in simulations of air quality, improved resolution of the boundary layer height would likely improve air quality simulations. Additionally, cu rrent CTMs do not adeq uately resolve fine-scale features such as the nocturnal low-level jet in part because lit tle is known about the nighttime boundary layer. CTMs require time-dependent, three-dimensional wind fields for the period of simulation. The winds may be generated either by a model using ini tial fields alone or with four-dimensional data assimilation to improve the model’s performance; i.e., model equations can be updated periodically to bring results into agreement with observations. Modeling series durations can range from simulations of several days duration, the typical time scale for individual O 3 

### episodes, to several months or multiple seasons of the year. The current trend in modeling applications is towards annual simulations. This trend is driven in part by the need to improve understanding of observations of periods of high wintertime PM (Blanchard et al., 2002, 047598 ) and the need to simulate O 3 

### episodes occurring in spring, fall, and winter. Chemical kinetics mechanisms representing the important reactions occurring in the atmosphere are used in CTMs to estimate the rates of chemical formation and destruction of each pollutant simulated as a function of time. Mechanisms that treat the reactions of all individual reactive species explicitly are computationally too demanding to be incorporated into CTMs for regulatory use. Similarly, very extensive “master mechanisms” (Derwent et al., 2001, 047912 ) that include approximately 10,500 reactions involving 3,603 chemical species (Derwent et al., 2001, 047912 ) can be combined into mechanisms that group together compounds with similar chemistry. Because of different approaches to the lumping of organic compounds into surrogate groups for computational efficiency, chemical mechanisms can produce different results under similar conditions. The Carbon Bond chemical mechanisms starting with CB-IV (Gery et al., 1989, 043039 ), the RADM II mechanism (S tockwell et al., 1990, 043095 ), the SAPRC (e.g., Carter, 1990, 042893 ; Wang et al., 2000, 048357 ; Wang et al., 2000, 048365 ), and the RACM mechanisms can be used in CMAQ. Jimenez et al. (2003, 156611 ) provided brief descriptions of the features of the main mechanisms in use and compared concentrations of several key species predicted by seven chemical mechanisms in a box-model simulation over 24 h. CMAQ and other state-of-the-science CTMs inco rporate processes and interactions of aerosolphase chemistry (Binkowski and Roselle, 2003, 191769 ; Gaydos et al., 2007, 139738 ; Zhang and Wexler, 2008, 191770 ). There have also been several attempts to study the feedbacks of chemistry on atmospheric dynamics using meteorological mode ls like MM5 and WRF (Grell et al., 2000, 048047 ; Liu et al., 2001, 048201 ; Lu et al., 1997, 048202 ; Park et al., 2001, 044169 ). This coupling is necessary to accurately simulate feedbacks which may be caused by the heavy aerosol loading found in forest fire plumes (Lu et al., 1997, 048202 ; Park et al., 2001, 044169 ) or in heavily polluted areas. Photolysis rates in CMAQ can now be calculated interactively with model produced O 3 

### , NO 2 

### , and aerosol fields (Binkowski et al., 2007, 090563 ). Spatial and temporal characterizations of anth ropogenic and biogenic precursor emissions must be specified as inputs to a CTM. Emissions inventories have been compiled on grids of varying resolution for many hydrocarbons, aldehydes, ketones, CO, NH 3 

### , and NO X 

### . Emissions inventories for many species require the application of algorithms for calculating the dependence of emissions on physical variables, such as temperature, and to convert the inventories into formatted emission

December 2009 3-112

### files which can be used by a CTM. For example, preprocessing of emissions data for CMAQ often is done by the Spare-Matrix Operator Kernel Emissions (SMOKE) system ( http://smoke-model.org ). For many species, information concerning the tempor al variability of emissions is lacking, so longterm annual averages are used in short-term, epi sodic simulations. Annual emissions estimates are often modified by the emissions model to produce emi ssions more characteristic of the time of day and season. Significant errors in emissions can oc cur if inappropriate time dependence is used. Additional complexity arises in model calculations because different chemical mechanisms can include different species, and inventories constructed for use with one mechanism must be adjusted to reflect these differences in another.



# 3.6.2.3. Local or Neighborhood Scale

The grid spacing in regional CTMs, usually between 1 and 12 km 2 

### , is usually too coarse to resolve spatial variations on the neighborhood scal e. The interface between regional scale models and models of smaller exposure scales is provided by smaller scale dispersion models. Several models could be used to simulate concentration fi elds near roads, each with its own set of strengths and weaknesses. The California Department of Transportation’s most recent line dispersion model is CALINE4; see http://www.dot.ca.gov/hq/e nv/air/pages/calinesw.htm . The CALINE family of models is not supported by the California Departme nt of Transportation for modeling of highwaysource PM, however, but only for roadway CO, although PM work with CALINE has been performed for more than ten years; see Wu et al. (2009, 191773 ) and references therein. In addition, AERMOD ( http://www.epa.gov/scram 001/dispersion_prefrec.htm ) is a steadystate plume model formulated as a replacement to the ISC3 dispersion model. In the stable boundary layer (SBL), it assumes the concentration distribution to be Gaussian in both the vertical and horizontal dimensions. In the convective boundary layer, the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is described with a bi-Gaussian probability density function (pdf). AERMOD has provisions that can be applied to flat and complex terrain and multiple source types (including, point, area and volume sources) in both urban and rural areas. It incorporates air dispersion based on the structure of turbulence in the PBL and scaling concepts and is meant to treat surface and elevated sources, in both simple and complex terrain in rural and urban areas. The dispersion of emissions from line sources like highways in AEROMOD is handled as a source with dimensions set using an area or volume source algorithm in the model; however, actual emissions are usually not in steady state and ther e are different functional relationships between buoyant plume rise in point and line sources. Moreover, most simple dispersion models including AERMOD are designed without chemical mechanisms and so cannot produce secondary pollutants from their primary emissions . There are also non-steady state models that in corporate plume rise explicitly from different types of sources. For example, CALPUFF ( http://www.src.com/calpuff/calpuff1.htm ), which is EPA’s recommended dispersion model for transport in ranges >50 km, is a non-steady-state puff dispersion model that simulates the effects of time- and space-varying meteorological conditions on pollution transport, transformation, and removal and has provisions for calculating dispersion from surface sources. However, CALPUFF was not desi gned to treat the dispersion of emissions from roads, and like AERMOD does not include product ion of secondary pollutants. The distinction between a steady-state and time varying model coul d be unimportant for long time scales; however, at short time scales, the temporal variability in traffic emissions could result in underestimation of peak concentration and exposures.



# 3.6.3. Air Quality Model Evaluation for Air Concentrations

Urban and regional air quality is determined by a complex system of coupled chemical and physical processes including emissions of pollutants and pollutant precursors, complex chemical reactions, physical transport and diffus ion, and wet and dry deposition. NO X 

### in these systems has long been known to (1) act nonlinearly in the production of O 3 

### and other secondary pollutants (Dodge, 1977, 038646 ); and (2) involve complicated cross-media environmental issues, such as acidic or nutrient deposition to sensitive biota and degradation of visibility. NO Y 

### species emitted and transfor med from emissions control the production and fate of both O 3 

### and aerosols by sustaining or suppressing OH cycling. Correctly characterizing the interrelated

December 2009 3-113

### NO Y 

### and OH dynamics for O 3 

### formation and fate in the polluted troposphere depends on new techniques using combinations of several NO Y 

### species for diagnostically probing the complex atmospheric dynamics in typical urban and regional airsheds. Evaluation results from a recent EPA exercise of CMAQ in the Tampa Bay, FL, airshed are presented here as an example of the present level of skill of state-of-the-science AQMs for predicting atmospheric concentrations of some of the releva nt species for this PM NAAQS assessment. This modeling series exercised CMAQ version 4.4 and with the University of California at Davis (UCD) sectional aerosol module in place of the standard CMAQ modal aerosol module and was driven by meteorology from MM5 v3.6 and with NEI emissions as augmented by continuous emissions monitoring data where available. The UCD size-segregated module was preferred for this application because of the importance of sea salt particles in the bay airshed. Testing of this new engineering extension to CMAQ (termed CMAQ-UCD below) reveal ed that its performance was very similar to that of CMAQ’s standard modal module; hence, mo del behavior and performance reported here can stand as a general indication of CMAQ’s skill. The CTM was run with 21 vertical layers for the month of May 2002. For this evaluation, CMAQ-UCD was run in a one-way nested series of three domains with 32 km, 8 km, and 2 km horizontal grid spacings from the CONUS (32 km) to central Florida and the eastern Gulf of Mexico (2 km). Depictions of the 8 km and 2 km domains used here zoomed over the central Tampa area are shown in Figure 3-58 and Figure 3-59

.

December 2009 3-114

## Figure 3-58. Eight km southeast U.S. CMAQ -UCD domain zoomed over Tampa Bay, FL.

Figure 3-59. Two km southeast U.S. CMAQ-U CD domain zoomed over Tampa Bay, FL.

December 2009 3-1150 1 2 3 4 5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hours (EST)NO (ppb)

0 2 4 6 8 10 12 14 16 18 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hours (EST)NO2(ppb)

0 2 4 6 8 10 12 14 16 18 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hours (EST)NOX(ppb)



## Figure 3-60. Hourly average CMAQ-UCD predictions and measured observations of NO (top), NO 2 

## (middle), and total NO X 

## (bottom) concentrations for May 1-31, 2002. Green squares = 8 km solution, red diamonds = 2 km solution, blue circles = observations.

December 2009 3-1160 1 2 3 4

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10- May Hours (date mark = 0000 EST)Ethene (ppb)

0 1 2 3 4

11- May 12- May 13- May 14- May 15- May 16- May 17- May 18- May 19- May 20- May Hours (date mark = 0000 EST)Ethene (ppb)

0 1 2 3 4

21- May 22- May 23- May 24- May 25- May 26- May 27- May 28- May 29- May 30- May 31- May Hours (date mark = 0000 EST)Ethene (ppb)



## Figure 3-61. CMAQ-UCD predictions and measured observations of ethene concentrations at Sydney, FL for May 1-31, 2002. Green squares = 8 km solution, blue circles = observations.

December 2009 3-1170 2 4 6 8

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10- May Hours (date mark = 0000 EST)Isoprene (ppb)

0 2 4 6 8

11-May12-May13-May14-May15-May16-May17-May18-May19-May20-May

Hours (date mark = 0000 EST)Isoprene (ppb)

0 1 2 3 4

21- May 22- May 23- May 24- May 25- May 26- May 27- May 28- May 29- May 30- May 31- May

Hours (date mark = 0000 EST)Isoprene (ppb)(note change of scale)



## Figure 3-62. CMAQ-UCD predictions and measur ed observations of isoprene concentrations at Sydney, FL for May 1-31, 2002. Green squares = 8 km solution, blue circles = observations.

December 2009 3-1180 10 20 30 40 50 60

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10- May Hours (date mark = 0000 EST)PM2.5(ug/m3)

0 10 20 30 40 50 60

11-May12-May13-May14-May15-May16-May17-May18-May19-May20-May

Hours (date mark = 0000 EST)PM2.5(ug/m3)

0 10 20 30 40 50 60

21-

May 22- May 23- May 24- May 25- May 26- May 27- May 28- May 29- May 30- May 31- May

Hours (date mark = 0000 EST)PM2.5(ug/m3)



## Figure 3-63. CMAQ-UCD predictions and measured observations of PM 2.5 

## concentrations at Sidney, FL for May 1-31, 2002. Green squares = 8 km solution, blue circles = observations.

December 2009 3-119

# 3.6.3.1.

3.6.3.2. Ground-based Comparisons of Photochemical Dynamics

Errors in the NO X 

### concentrations in the model, most likely from on-road emissions, affected NO X 

### predictions shown in Figure 3-60 , but CMAQ-UCD’s general responses were reasonable. The model also replicated anthropogenic and biogenic VOC emissions well; see Figure 3-61 and Figure 3-62 , respectively. After initial errors leading to underprediction in the first 21 days, CMAQ-UCD’s predictions of hourly PM 2.5 

### concentrations and trends over the whole month also replicated the observed concentrations well ( Figure 3-63 ).



# Predicted Chemistry for Nitrates and Related Compounds

Particul ate NO 3 – 

### (pNO 3 – 

### ) plays a crucial and complex role in the health of aquatic and estuarine ecosystems and human drinking water systems. Gas-phase NO 3 – 

### replacement of Cl – 

### on sea salt particles is often favored thermodynamically and the V d 

### of the coarse pNO 3 – 

### formed through this replacement is more than an order of magnitude greater than for fine pNO 3 – 

### . Over open bodies of salt water such as the Gulf of Mexico and Tampa Bay, FL, pNO 3 – 

### from this reaction dominates dry deposition and is estimated to be of the same order as pNO 3 – 

### wet deposition. However, total NO 3 – 

### concentrations are driven, buffered, and altered by a wide range of photochemical gas-phase reactions, heterogeneous reactions, and aerosol dynamics, making them especially difficult to model. Because pNO 3 – 

### is derived mostly from gas-phase HNO 3 

### and will interact with Na + 

### , NH 4 + 

### , Cl

– 

### , and SO 4 2– 

### , all these species and the physical parameters governing their creation, transport, transformation, and fate m ust be accurately replicated to predict pNO 3 – 

### with high fidelity. This has historically been a difficult pr oblem for numerical process models, owing in large part to the pervasive dearth of reliable ambient measurements of NO 3 – 

### in its various forms. Normalized mean error (NME) for the large-scale Eulerian CTM-predicted pNO 3 – 

### has typically been on the order of a factor of 3 greater than the NME for particulate SO 4 2– 

### (pSO 4 2– 

### ) (Odman et al., 2002, 092474 ; Pun et al., 2003, 047775 ). SO 4 2

− 

### , NH 4 + 

### , Na + 

### , and Cl – 

### were all predicted to within a factor of 2 and with no significant bias during the photochemical day in the 8 km CMAQ-UCD solution, although a significant bias in Na +


### and Cl – 

### was evident in the 2 km solution for two near-wat er sites. This grid-size dependent bias is still being explored. Size segregation maxima were correct to within two size bins every day for which there were observations for both SO 4 2– 

### and NH 4 + 

### (0.2 to 1.0 μ m), and Na + 

### and Cl – 

### (2.0-10.0 μ m). Cl – 

### concentrations were greatly overpredicted during dark hours, but were nearer to observed values during the photochemical day. CMAQ-UCD performance for HNO 3 

### and NH 3 

### are shown in Figure 3-64 and Figure 3-65 , respectively. Figure 3-66 shows that CMAQ-UCD systematically underp redicted the hourly time series of measured pNO 3 – 

### concentrations at the Sydney supersite, the only location with discrete pNO 3 – 

### data. These time series data establish that CMAQ-UCD’s largest errors were on 4 days in the first 2 wk of the month, but that the total peak pNO 3 – 

### concentrations were nearly all underpredicted. Since pNO 3 – 

### is derived in large part from gas-phase HNO 3 

### , its underprediction may be due to an underprediction of HNO 3 

### concentrations or an underrepresentation of the gas- to aerosol-phase change. At Sydney, FL, in fact, both these conditions held. Figure 3-64 depicts the model’s bias for HNO 3 

### underprediction in both the 8 km and 2 km solutions, except for four days of very large peak overpredictions. This pattern of underpredictions wa s especially evident overnight. On 8 other days the model overpredicted the one hour peak concentration as well, though not so substantially, but the chief effect was still one of an artificial a nd inappropriate N limitation in the model. A time series molar equivalent ratio of HNO 3 

### to total NO 3 

### depicts which phase stores the NO 3 – 

### and how that storage ratio changes over time. Figure 3-67 shows that at Sydney, FL, CMAQ-UCD stored too much NO 3 – 

### in the gas phase as HNO 3 

### (and recall that the daytime HNO 3 

### concentrations were sometimes overpredicted by the model) and too little in the gas phase overnight, when the model was regularly low against the measurements; compare Figure 3-64 and Figure 3-65 . Note again here the similarity of the 8 km and 2 km solutions in this comparison. Interestingly, the 23-h integrated data did not reveal this important difference in NO 3 – 

### form between the model and measurements as Figure 3-68 shows in the stacked bar percentage plots of fine and coarse pNO 3 – 

### together with gas-phase HNO 3 

### . Both the 8 km ( Figure 3-68 , middle panel) and the 2 km ( Figure 3-68 , bottom panel) solutions predicted distributions between the two general ranges of aerosol size, and be tween gas and aerosol phases, with good fidelity to the daily observations ( Figure 3-68 , top panel) at Sydney, FL. This result illustrates that while discrete time

December 2009 3-120

### series data are crucial for diagnosing model behavior, on the integrated total daily and longer basis used for computing total annual N loads, CMAQ -UCD predicted approximately the correct distributions for pNO 3 – 

### , even though the total NO 3 – 

### concentration prediction was biased low.

0
1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10- May Hours (Date Mark = 0000 EST) 1 2 3 4 5 6HNO3(ppb)

0
11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May Hours (Date Mark = 0000 EST) 1 2 3 4 5 6HNO3(ppb)

0 1 2 3 4 5 6

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May Hours (Date Mark = 0000 EST)HNO3(ppb)



## Figure 3-64. CMAQ-UCD predictions of HNO 3 – 

## concentrations and corresponding measured observations at Sydney, FL, for May 1-31, 2002. Green x = 8 km solution, red diamonds = 2 km solution, blue circles = observations.

December 2009 3-1210 2 4 6 8 10 12 14 16 18 20 22

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10- May Hours (date mark = 0000 EST)NH3(ug/m3)

0 2 4 6 8 10 12 14 16 18 20 22

11-

May 12- May 13- May 14- May 15- May 16- May 17- May 18- May 19- May 20- May Hours (date mark = 0000 EST)NH3(ug/m3)

0 2 4 6 8 10 12 14 16 18 20 22

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May Hours (date mark = 0000 EST)NH3(ug/m3)



## Figure 3-65. CMAQ-UCD predictions of NH 3 

## concentrations and corresponding measured observations at Sydney, FL, for May 1-31, 2002. Green x = 8 km solution, red diamonds = 2 km solution, blue circles = observations.

December 2009 3-1220 1 2 3 4 5 6

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10- May Hours (date mark = 0000 EST)Total Particulate NO3(ug/m3)

0 1 2 3 4 5 6

11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May Hours (date mark = 0000 EST)Total Particulate NO3(ug/m3)

0 1 2 3 4 5 6

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May Hours (date mark = 0000 EST)Total Particulate NO3(ug/m3)



## Figure 3-66. CMAQ-UCD predictions of pNO 3 – 

## concentrations and corresponding measured observations at Sydney, FL, for 1-31 May, 2002. Green x = 8 km solution, red diamonds = 2 km solution, blue circles = observations.

December 2009 3-1230.0 0.2 0.4 0.6 0.8 1.0

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-May Hours (date mark = 0000 EST)HNO3/ Total NO3

0.0 0.2 0.4 0.6 0.8 1.0

11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May

Hours (date mark = 0000 EST)HNO3/ Total NO3

0.0 0.2 0.4 0.6 0.8 1.0

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May

Hours (date mark = 0000 EST)HNO3/ Total NO3



## Figure 3-67. CMAQ-UCD predictions of the ratio of HNO 3 

## to total NO 3 

## and corresponding measured observations at Sydney, FL, for May 1-31, 2002. Green x = 8 km solution, red diamonds = 2 km solu tion, blue circles = observations.

December 2009 3-124MOUDI aerosol NO3 and ARA HNO3 (23 h daily mean)

0% 20% 40% 60% 80% 100%

5/1 5/3 5/5 5/7 5/9 5/11 5/13 5/15 5/17 5/19 5/21 5/23 5/25 5/27 5/29 5/31Contribution to Total NO3

Date, 2002
< 2.5 um 2.5 - 10 um HNO3

CMAQ-UCD 8 km solution (23 h daily mean)

0% 20% 40% 60% 80% 100%

5/1 5/3 5/5 5/7 5/9 5/11 5/13 5/15 5/17 5/19 5/21 5/23 5/25 5/27 5/29 5/31

Date, 2002Contribution to Total NO3

< 2.5 um 2.5 - 10 um HNO3

CMAQ-UCD 2 km solution (23 Hour Means)

0% 20% 40% 60% 80% 100%

5/1 5/3 5/5 5/7 5/9 5/11 5/13 5/15 5/17 5/19 5/21 5/23 5/25 5/27 5/29 5/31

Date, 2002Contribution to Total NO3

< 2.5 um 2.5 - 10 um HNO3



## Figure 3-68. CMAQ-UCD predicted size and chemical-form fractions of total NO 3 – 

## for days in May 2002 with measured observations. Measured concentrations (top panel); 8 km solution (middle panel); 2 km solution (bottom panel). Red bars = pNO 3 – 

## <2.5
μ m; blue bars = pNO 3 – 

## 2.5-10 μ m; green bars = HNO 3


## .

December 2009 3-125

### While inorganic aerosol anion totals were dominated by NO 3 – 

### in the coarse fraction and by SO 4 2– 

### in the fine fraction, there was sufficient NH X 

### (NH X 

### = NH 3 

### + NH 4 + 

### ) at Sydney, FL, to form fine aerosol NH 4 

### NO 3 

### in some circumstances. Figure 3-65 depicts the hourly mass concentration of NH 3 

### at Sydney, FL, showing again the strong similarity of the 8 km and 2 km solutions. Each solution, however, underpredicted the measured NH 3 

### concentrations consistently, and especially for the nine very large excursions of 10-20 μ g/m 3 

### during the month. Overall, CMAQ-UCD was found to be operationally sound in this evaluation of its 8 km and 2 km solutions for the Tampa Bay airshed using the ground-based and aloft data (not shown here) from the May 2002 field intensive. Moreover, results from diagnostic tests of the model’s photochemical dynamics were generally in excellent agreement with results from the ambient atmosphere. However, CMAQ-UCD was biased low in this application for total NO 3 

### and for NO 3 

### present as gas-phase HNO 3 

### . In addition, the model was biased low for the HO X 

### radical reservoir species CH 2 

### O and H 2 

### O 2 

### (not shown here), though this bias appeared to have been limited to these species. Performance of the new UCD aerosol module was judged to be entirely adequate, allocating aerosols by chemical makeup to the appropriate si ze fractions. Model performance for fine-mode aerosols was also judged to be fully adequate.



# 3.6.4.

3.6. 

# Evaluating Concentrations and Deposition of PM Components with CTMs

4.1. Gl obal CTM Performance

The wet a nd dry deposition processes described in Section 3.3 are of necessity highly parameterized in all CTMs. While all current models implement resistance schemes for dry deposition, the V d 

### generated from different models can vary highly across terrain types (Stevenson et al., 2006, 089222 ).The accuracy of wet deposition in global CTMs is tied to spatial and temporal distribution of model precipitation and the treatment of chemical scavenging. Dentener et al. (2006, 088434 ) compared wet deposition across 23 models with available measurements around the globe. Figure 3-69 and Figure 3-70 extract results of a comparison of the 23-model mean versus observations over the eastern U.S. for pNO 3 – 

### and pSO 4 2– 

### deposition, respectively. The mean model results were strongly correlated with the observations (r >0.8), and usually captured the magnitude of wet deposition to within a factor of two over the eastern U.S. Dentener et al. (2006, 088434 ) concluded that 60-70% of the participating models captured the measurements to within 50% in regions with quality controlled observations.

December 2009 3-126Source: Adapted with Permission of American Geophysical Union from Dentener et al. (2006, 088434 ).



## Figure 3-69. Scatter plot of total nitrate (HNO 3 

## plus pNO 3 – 

## ) wet deposition (mg N/m 2 

## /yr) of the model mean versus measurements for the North American Deposition Program (NADP) network. Dashed lines indicate a factor of two. The gray line is a linear regression through zero.

Source: Adapted with Permission of American Geophysical Union from Dentener et al. (2006, 088434 ).



## Figure 3-70. Scatter plot of total SO 4 2– 

## wet deposition (mg S/m 2 

## /yr) of the model mean versus measurements for the National Atmos pheric Deposition Program (NADP) network. Dashed lines indicate a factor of two. The gray line is a linear regression through zero.



# 3.6.4.2. Regional CTM Performance

Regional CTM performance for concentration and de position of some of the most relevant PM species is illustrated here with examples from CMAQ version 4.6.1 as configured and run for

December 2009 3-127

### exposure and risk assessments reported in the Risk and Exposure Assessment for the Review of the Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur (U.S. EPA, 2009, 191774 ); additional details on the model co nfiguration and application are found there. A map of the 36 km parent domain and two 12 km (east and west) progeny domains appears in Figure 3-71

.



## Figure 3-71. CMAQ modeling domains for the OAQPS risk and exposure assessments: 36 km outer parent domain in black; 12 km we stern U.S. (WUS) domain in red; 12 km eastern U.S. (EUS) domain in blue.

Comparisons from the 2002 annual run of CMAQ for the exposure assessment are shown here against measured concentrations and deposition to tals from nodes in three networks: IMPROVE, CSN (labeled STN in the plots) and CASTNet. Comparisons were made as model-observation pairs at all sites having sufficient data for the seasonal or the 2002 annual time period in the two 12 km east and west domains and were evaluated with the fo llowing descriptive statistics: correlation, root mean square error, normalized mean bias, and normalized mean error. Summertime pSO 4 2– 

### concentrations are well predicted by CMAQ, to within a factor of 2 at nearly every point, and with R 2 

### >0.8 across all three networks ( Figure 3-72 ). This result tracks the generally well-predicted SO 4 2– 

### concentrations found in earlier CMAQ evaluations: see Eder and Yu (2005, 089229 ), Mebust et al. (2003, 156749 ) and Tesche et al. (2006, 157050 ). Since pSO 4 2– 

### concentrations are strongly a function of precipit ation, care must be taken to ensure that the meteorological solution driving individual CMAQ chemical applications produces precipitation fields with low bias as discussed by Appel et al. (2008, 155660 ).

December 2009 3-128

## Figure 3-72. 12-km EUS Summer sulfate PM. Each data point represents a paired monthly averaged (June/July/August) observation and CMAQ prediction at a particular IMPROVE, STN, and CASTNet site. Solid lines indicate a factor of two around the 1:1 line shown between them.

Wintertime pNO 3 – 

### ( Figure 3-73 ) and total NO 3 

### (HNO 3 

### +pNO 3 – 

### ) ( Figure 3-74 ) concentrations are predicted less well by CMAQ, but NO 3 

### is a pervasively difficult species to measure and model. Still, at the CASTNet nodes where the total NO 3 

### concentrations are higher than they are at all but a few of the remote IMPROVE sites, CMAQ predicts concentrations for nearly every node to within a factor of 2 and with an R 2 

### >0.8. These CMAQ-predicted concentrations, coupled with modeled cloud and precipitation fields produce wet deposition fields for SO 4 2– 

### and NO 3 − 

### in the east domain as shown in Figure 3-75 and Figure 3-76 , respectively.

December 2009 3-129

## Figure 3-73. 12-km EUS Winter nitrate PM. Each data point represents a paired monthly averaged (December/January/February) observation and CMAQ prediction at a particular IMPROVE and STN site. Solid lines indicate a factor of two around the 1:1 line shown between them.

December 2009 3-130

## Figure 3-74. 12-km EUS Winter total nitrate (HNO 3 

## + total pNO 3 – 

## ). Each data point represents a paired monthly averaged (December/January/February) observation and CMAQ prediction at a particular CASTNet site. Solid lines indicate a factor of two around the 1:1 line shown between them.

December 2009 3-131

## Figure 3-75. 12-km EUS annual sulfate wet deposit ion. Each data point represents an annual average paired observation and CMAQ prediction at a particular NADP site. Solid lines indicate the factor of 2 aroun d the 1:1 line shown between them.

December 2009 3-132

## Figure 3-76. 12-km EUS annual nitrate wet deposi tion. Each data point represents an annual average paired observation and CMAQ prediction at a particular NADP site. Solid lines indicate a factor of two around the 1:1 line shown between them.

Importantly, CMAQ captured the chief spatial pa tterns and magnitudes of air concentrations and wet deposition relevant to computing concentration and deposition budgets, as shown in Figure 3-77 for concentrations and Figure 3-78 for deposition. More specifically, CMAQ’s predictions of
NH 3 

### and SO 4 2– 

### for both high and low concentration sites are well within the range of observed measurements ( Figure 3-79 ).

December 2009 3-133

## Figure 3-77. CMAQ vs. measured air concentrations from east-coast sites in the IMPROVE, CSN (labeled STN), and CASTNet sites in the summer of 2002 for sulfate (left) and ammonium (right). Solid lines indicate a factor of 2 around the 1:1 line shown between them.

2003 200 200 

### NADP Ammonium Ion Wet Deposition CMAQ Ammonium Ion Wet Deposition



## Figure 3-78. Comparison of CMAQ-predicted and NADP-measured NH 4 + 

## wet deposition : (top left) CMAQ prediction; (bottom left) NADP-measurements; (right) regression and smoothed median line through CMAQ pred ictions and NADP measurements with sites in the Chesapeake Bay watershed highlighted.

December 2009 3-134Kenansville Ammonia July 2004

12-hour Averages: 6am-6pm

0 5 10 15 20 25 30 35 40 45

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)NH3 (ug/m3)

Kenansville NH3 CMAQ-J4c NH3 Kenansville Sulfate July 2004

12-hour Averages: 6am-6pm

0 2 4 6 8 10 12 14 16 18 20

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)SO4 (ug/m3)

Kenansville SO4 CMAQ-J4c ASO4

Millbrook (Raleigh) Ammonia July 2004

12-hour Averages: 6am-6pm

0 1 2 3 4 5 6

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)NH3 (ug/m3)

Millbrook NH3 CMAQ-J4c NH3 Millbrook (Raleigh) Sulfate July 2004

12-hour Averages: 6am-6pm

0 2 4 6 8 10 12 14 16 18 20

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)SO4 (ug/m3)

Millbrook SO4 CMAQ-J4c ASO4



## Figure 3-79. CMAQ-predicted (red symbols an d lines) and 12-h measured (blue symbols and lines) NH 3 

## and SO 4 2– 

## surface concentrations at high and low concentration grid cells in North Carolina in July 2004. (top left) High concentration NH 3 

## in Kenansville; (top right) high concentration SO 4 2– 

## in Kenansville; (bottom left) low concentration NH 3 

## in Raleigh; (bottom right) low concentration SO 4 2– 

## in Raleigh.

Deposition velocities are difficult to esti mate for reasons described in Section 3.3.3 . Recent work in EPA’s Atmospheric Modeling and Analysis Division with CMAQ showed that the original
V d 

### for NH 3 

### was very likely too high and should be nearer to the values for SO 2 

### deposition, or even lower over some land use surface types. A sensitivity study with the model was performed to test the effects of changing V d 

### for NH 3 

### on the fraction of NH 3 

### available for transport away from grid cells with high emissions concentrations. Comparisons were made for the surface grid cells and total column NH 3 

### concentrations. In the highest emissions grid cells during June 2002, the surface NH X 

### budget was dominated by turbulent transport or vertical mixing moving a majority of the surface NH 3 

### emissions up and away from the surface into the mixed layer. Figure 3-80 depicts the NH X 

### budget under the base case (Base V d 

### ) and the sensitivity case (SO 2 

### V d 

### ) for which the NH 3 

### V d 

### was set equal to the SO 2 

### V d 

### . Lower NH 3 

### V d 

### decreased NH X 

### deposition to the surface from 15 to 8%, leaving more NH X 

### for transport horizontally, 22% up from 20% in the base case, and vertically, 69% up from 64% in the base case. Typically, ~67% of surface emissions were moved aloft where most was advected away from the high emissions grid cell, with a small fraction converted to pNH 4 + 

### and an even smaller fraction wetdeposited to the surface. The total column analyses for NH 3 

### and NH X 

### are shown in Figure 3-81

.

December 2009 3-135

## Free Troposphere

Ga s to
P article 

## Mixed Layer

Surface 38m (

− 2 km )



### NH 3 

### Emissions Dry Deposition Vertical
Diffusion

Horizontal
Advection



### NH 3 

### Layer 1 Analysis Top of Model – 16 km



### 8-15% 22-20% 69-64%

0.04%

Right Number: BaseVd
Left Number: SO 2 Vd



## Figure 3-80. Surface grid cell (layer 1) analysis of the sensitivity of NH X 

## deposition and transport to the change in NH 3 

## V d 

## in CMAQ.

Free Troposphere

Mixed Layer (

− 2 km ) Top of Model – 16 km

NH 3 Emissions Dr y Deposition Horizontal
Advection

NH X Column Analysis Wet Deposition

8-15% 0.58-0.57% 91-84%

Right Number: BaseVd

Left Number: SO 2 Vd Gas to Particle
Conversion (unusually low)

NH 3 Emissions D ry Deposition Horizontal
Advection

NH 3 Column Analysis Wet Deposition

8-15% 2.1-2.2%

0.55-0.54% 89-82%



## Figure 3-81. Total column analysis for NH 3 

## (left) and NH X 

## (right) showing modeled NH 3 

## emissions, transformation, and transport throughout the mixed layer and up to the free troposphere.

Local total deposition (wet + dry) is a significant but not dominant loss pathway for surface
NH 3 

### emissions. In these simulations, CMAQ deposited ~25% of the NH 3 

### emissions from the single high concentration grid cell in Sampson County, NC, back into that grid cell. By far, the largest contribution to the local deposition total was dry deposition. Dry-to-wet deposition ratios for the Sampson County high emissions grid cell and surrounding surface grid cells ranged from 2 to 10. Deposition to grid cells farther away from th e high concentration, immediately surrounding grid cells, was significantly affected by the change in NH 3 

### V d 

### tested in this case. Figure 3-82 depicts

December 2009 3-136

### the range of influence of the high concentration grid cell, where that range is defined to be the distance by which 50% of the emissions attributable to that grid cell have deposited. The range of influence of the high concentration Sampson County grid cell was extended in the V d 

### sensitivity tested here from ~180 km in the base case to ~ 400 km in the case using the lower, more realistic V d 

### for NH 3 

### . The areal extent of this difference in range of influence is mapped in Figure 3-83

.

June 2002 NHx Ran g e of Influence: BaseVd vs. SO2Vd Sampson County (single cell)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.06305478102126150174198222246270294318342366390414438462486



### Distance from Center (km)Fraction of Emissions

Wet+Dry Dep BaseVd Advection BaseVd
Wet+Dry Dep SO2Vd Advection SO2Vd 

## BaseVd

− 180km SO 2 

## V d

− 400km

Figure 3-82. Range of influe nce (where 50% of emitted NH 3 

## deposits) from the high concentration Sampson County grid cell in the June 2002 CMAQ simulation of V d 

## sensitivities. Base case and sensitivity case total deposition (blue symbols and lines); base case and sensitivity case advection totals (red symbols and lines).

December 2009 3-137

# BaseVd SO 2 

# Vd

Figure 3-83. Areal extent of the change in NH X 

## range of influence as predicted by CMAQ for the Sampson County high concentration grid cell (center of range circles) in June 2002 using the base case and sensitivity case V d


## .

December 2009 3-138

# 3.7.

3.7.1. 

# Background PM

The background concentrations of PM that are useful for risk and policy assessments informing decisions about the NAAQS are referr ed to as policy-relevant background (PRB) concentrations. PRB concentrations have historical ly been defined by EPA as those concentrations that would occur in the U.S. in the absence of anthropogenic emissions in continental North America defined here as the U.S., Canada, and Mexico . For this document, PRB concentrations include contributions from natural sources everywhere in th e world and from anthropogenic sources outside continental North America. Background concentrat ions so defined facilitated separation of pollution that can be controlled by U.S. regulations or through international agreements with neighboring countries from those that were judged to be generally uncontrollable by the U.S. Over time, consideration of potential broader ranging intern ational agreements may lead to alternative determinations of which PM source contributions should be considered by EPA as part of PRB.



# Contributors to PRB Concentrations of PM

Contri butions to PRB concentrations of PM in clude both primary and secondary natural and anthropogenic components. Natural sources includ e wind erosion of natural surfaces (Gillette and Hanson, 1989, 030212 ); volcanic production of SO 4 2– 

### ; PBAP; wildfires producing EC, OC, and inorganic and organic PM precursors; and SOA produced by oxidation of biogenic hydrocarbons such as isoprene and terpenes. However, human intervention can be involved in the formation of SOA, as production of natural SOA depends to a la rge extent on the presence of anthropogenic NO X



### . As described earlier in Section 3.3 , prescribed fires are considered part of PRB. In addition to emissions from forest fires in the U.S., emissions from forest fires in other countries can be transported to the U.S. For example, Boreal forest fires in Canada (Mathur, 2008, 156742 ) and Siberia (Generoso et al., 2007, 155786 ) and tropical forest fires in the Yucatan Peninsula and Central America (Wang et al., 2006, 157109 ) have affected PM concentrations in the U.S. PRB PM varies across the contiguous Unites States (CONUS) by region and season as a function of the complex mechanisms of transport, dispersion, deposition, and reentrainment. Dust from the Sahara desert and the Sahel in North Africa (Chiapello et al., 2005, 156339 ) affects mainly the eastern U.S.; dust from the Gobi and Taklimikan deserts in Asia (VanCuren and Cahill, 2002, 157087 ; Yu et al., 2008, 157168 ) have the largest effects in the western U.S. but also affect air quality in the eastern U.S. Husar et al. (2001, 024947 ) report that the average PM 10 

### concentration at 25 reporting stations thr oughout the northwestern U.S. reached 65 μ g/m 3 

### during an episode in the last week in April 1998, compared to an average of 10-25 μ g/m 3 

### during the rest of April and May. This was accompanied by visual reports of milky-white discoloration of the normally blue sky in non-urban areas along the west coast. PRB contributions to PM 2.5 

### , PM 10-2.5 

### , and PM 10 

### can also be viewed as coming from two conceptually separate components: a reasonably consistent “baseline” component and an episodic component. The baseline component consists of cont ributions that are generally well characterized by a reasonably consistent distribution of daily values each year, although there is variability by region and season. The episodic component consi sts of infrequent, sporadic contributions from natural high-concentration events occurring over shorte r periods of time (e.g., hours to several days) both within North America (e.g., volcanic eruptions, large forest fires, dust storms) and outside North America (e.g., transport related to dust storms from deserts in North Africa and China and storms at sea). These episodic natural events, as we ll as events like the uncontrolled biomass burning in Central America, are essentially uncontrollable and do not necessarily occur in all years. In-situ measurements provide evidence for th e transport of anthropogenic PM from Asia on Mt. Batchelor, OR (Jaffe et al., 2003, 052229 ). These data show sporadic but well correlated increases in CO, O 3 

### , total Hg, and aerosol backscatter associated with air coming from Asia. The ITCT-2K2 campaign also found evidence for the oxidation of SO 2 

### to H 2 

### SO 4 

### during trans-Pacific transport of Asian emissions. If particulate SO 4 2– 

### were to be formed in the polluted boundary layer where it originated, it would likely be deposited prior to transport across the Pacific Ocean (Brock et al., 2004, 156295 ). Thus, primary species emitted direct ly and secondary species formed during transport contribute to PRB concentrations. Satellite data have provided images to track clouds of dust and pollution across the oceans and have been used for some quantitative estimation of the flux of material leaving continents. Yu et al. (2008, 157168 ) used optical thickness data to estimate

December 2009 3-139

### column loadings from the MODIS along with sate llite assimilated wind fields to estimate the transport of PM from Asia. Three-dimensional, globa l-scale CTMs have also been used to estimate intercontinental transport of PM pollution (UNCEC, 2007, 157078 ) and trans-Pacific transport of mineral dust from Asian deserts (Fairlie et al., 2007, 141923 ) and the Sahara Desert (McKendry et al., 2007, 156748 ). Estimates for the contribution of PBAP are hi ghly problematic. Heald and Spracklen (2009, 190014 ) estimated the contribution of fungal spores to PM 2.5 

### based on GEOS-Chem simulations of mannitol, considered to be a unique tracer for fungal spores (Bauer et al., 2008, 189986 ). They estimated an annual mean contribution of fungal spores to OC ranging from <0.1 μg/m 3 

### in the desert Southwest to ~ 0.5 μg/m 3 

### in the more humid Southeast. It should be noted that these are model derived estimates that still require evaluation agai nst measurements in the U.S. They do, however, provide the only quantitative estimates of PBAP concentrations across the continental U.S.



# 3.7.1.1. Estimates of PRB Concentrations in Previous Assessments

Estimate s of PRB concentrations reported in the 1996 PM AQCD (U.S. EPA, 1996, 079380 ) and earlier PM AQCDs were based in large measure on estimates by Trijonis et al. (1990, 157058 ) for the National Acid Precipitation Asse ssment Program (NAPAP) as shown in Table 3-18 . The importance of different sources is likely to be quite different for natural background compared to current conditions in the US, resulting in large ch anges in relations among different size fractions. For example, PM 10-2.5 

### might be expected to dominate under certain conditions in the absence of primary and secondary PM 2.5 

### from anthropogenic sources. Differen t approaches for estimating PRB concentrations in the western and eastern U.S. were taken in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). Data obtained at IMPROVE monitoring sites in the western U.S. shown in Figure 3-84 were chosen as estimates of the distribution of daily average PRB concentrations in the West because they were thought to be among the least likely in fluenced by regional pollution sources especially at the upper end of the concentration distribution. This conclusion was drawn from back trajectory analyses and examination of the trace elemental composition at IMPROVE sites. Because of likely unresolved contamination from pollution sources at other IMPROVE sites, it was recommended to use averaged data from these sites throughout the West. Concentrations distributions from 1988 through 2001 can be found in Appendix 3E of the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). Median concentrations were ~3 μg/m 3 

### . Little interannual variability was observed below the 90th percentile values. However, at the upper end of the concentration distribution substantial interannual variability was observed due mainly to forest fires and dust transport from Asian deserts. It was also recognized that this method would likely overestimate PRB concentrations.



## Table 3-18. Estimates of annual average natural background c oncentrations of PM 2.5 

## and PM 10 

## (μg/m 3 

## ) from Trijonis et al. (1990, 157058 ). Estimates of PM 10-2.5 

## were obtained by subtraction.

PM 2.5 PM 10 PM 10-2.5

East 2-5 5-11 ≤ 1-9

West 1-4 4-8 ≤ 1-7

December 2009 3-140

## Figure 3-84. IMPROVE monitoring site locations.

Table 3-19 shows annual and quarterly average PM 2.5 

### concentrations measured at the IMPROVE sites shown in Figure 3-84 for 2004. Annual average concentrations tend to be slightly higher in the East, particularly in Brigantine and Dolly Sods. When the data are broken down by season, a more complex picture emerges. The highest concentrations in the East and Midwest are found during the 3rd calendar quarter, whereas in the West highest quarterly averages can occur during other quarters. As can also be seen from a comparison with values shown in Table 3-18 , PM 2.5 

### values measured in the East are much higher than the PRB concentration estimates by Trijonis et al. (1990, 157058 ) for the NAPAP.

December 2009 3-141

## Table 3-19. Annual and quarterly mean PM 2.5 

## concentrations ( μ g/m 3 

## ) measured at IMPROVE sites in 2004.

Mean January-March April-June July-September October-December

EAST

Acadia 4.5 3.9 4.6 6.0 3.5

Brigantine 9.5 8.1 11.3 11.6 7.3

Dolly Sods 9.5 6.7 9.8 15.5 5.7

MIDWEST

Voyageurs 3.8 4.1 3.1 4.2 3.6

WEST

Bridger 2.1 1.2 3.1 2.8 1.3

Canyonlands 2.6 2.2 3.2 2.9 2.1

Gila 2.9 2.0 4.0 3.8 1.8

Glacier 4.8 4.6 4.2 5.3 5.0

Redwood 3.5 2.7 3.6 3.7 3.9



### Thus, estimating daily average PRB concentrations in the eastern U.S. using observations is highly problematic because of the widespread mixi ng of precursors and anthropogenic PM generated in the East. Therefore, results from receptor modeling studies using PMF by Song et al. (2001, 036064 ) were used in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) for the East to separate contributions from likely regional pollution sources from natural and imported pollution. The “background” sources contribute about 7% to annual average PM 2.5 

### concentrations at Underhill, VT and about 12% at Brigantine, NJ, i.e., values between 1 and 2 μg/m 3 

### . These sites were chosen because they were outside of urban areas making it easier to separate pollution from background components. However, some contribution of region al anthropogenic pollution was still present. The PM Staff Paper (U.S. EPA, 2006, 157071 ) adopted a different approach for estimating PRB concentrations. This approach separated ou t components mainly thought to be emitted by regional pollution sources such as SO 4 2– 

### , which are obtained directly from observations at many more IMPROVE sites than are shown in Figure 3-84 , and to use the remaining PM components in both the East and the West to estimate PRB. Removing regional pollution from data obtained at IMPROVE sites is problematic as it involves assumptions about the relative contributions of regional pollution and background sources. Although sulfate in the East is mainly produced by regional pollution sources, it is not the only component with a regional pollution source. By comparison, sulfate is a very minor component of PM 2.5 

### in the West, leading to substantial overestimates in populated states like California. Annual mean estimates in the continental U.S. ranged from 2.5 μg/m 3 

### in the Central West (ID, MT, WY, ND, SD, CO, UT, NZ, AZ) to 5.2 μg/m 3 

### for the Southwest Coast (most of California), the latter value likely reflecting contributions from local, nonsulfate pollution. In general, the methods outlined in both these documents are of limited utility for two reasons: (1) they lack detailed spatial coverage across the whole U.S., since both methods rely on monitoring data that are limited both spatially and temporally ; and (2) PM measurements from even the limited, remote sites used in the previous estimates of PRB can not be completely devoid of contributions from anthropogenic PM. Because of these limitations, numerical modeling can provide superior PM background estimates, as described just below.



# 3.7.1.2. Chemistry Transport Models for Predicting PRB Concentrations

CTMs can be used to estimate the PRB concentrations of atmospheric components including PM using a “zero-out” approach in which anthropo genic emissions inside continental North America are set to zero while global biogenic emissions and anthropogenic emissions outside continental

December 2009 3-142

### North America remain. Numerical modeling can provi de more precision in the estimate of PRB PM than measurements since even the most remote measurement sites like some of those in the IMPROVE network (see the discussion in Section 3.7.1.1 ) will necessarily be affected by non-local non-biogenic pollution, thereby confusing the cont ributions from these sources. Numerical models are also capable of supplying estimates of PRB con centrations at much higher spatial and temporal resolution than can be obtained by relying on meas urements obtained at even the most remote monitoring sites. In this approach, the monitoring da ta are used to evaluate the CTM’s performance. For this assessment, the global-scale circul ation model GEOS-Chem was coupled with the regional scale air quality model CMAQ (Section 3.6.2.2 ) to simulate one year of air quality data over the CONUS in two series of runs, the first annu al series with all anthropogenic and biogenic emissions included and the second annual series with the zero-out approach employed. The global-scale scale circulation model was se t up as follows. GEOS-Chem, version 7, was used, with modifications to include aromatic and biogenic SOA formation; emissions were computed from a variety of sources including the Global Emissions Inventory Activity (GEIA) (Benkovitz et al., 1996, 156267 ), and Emissions Database for Global At mospheric Research, version 2 (EDGAR) (Olivier et al., 1996, 156828 ; Olivier et al., 1999, 156829 ). Particularized emissions in specific areas used the European Monitoring and Evaluation Program (EMEP) for Europe (Auvray and Bey, 2005, 156237 ), BRAVO (Kuhns and Knipping, 2005, 156663 ) for Mexico, and Streets et al. (2006, 157019 ) for Asia. Emissions from these studies were supplemented with data from Martin et al. (2002, 089380 ) for additional NO X 

### emissions from biofuels, lightning, and ship traffic, Bond et al. (2004, 056389 ) for global primary organic aerosols, and Cooke et al. (1999, 156365 ) and Park et al. (2003, 156842 ) for U.S. primary organic aerosols. Biomass burning emissions are not climatological, but were computed with GFEDv2 (Giglio et al., 2006, 156469 ; van der Werf et al., 2006, 157084 ); monthly values computed using active fire observations from MODIS; global dust fields computed off-line using GOCART (see emissions from DEAD: http://dust.ess.uci.edu/dead/ ) to make annual adjustments to photolysis rates and heterogeneous-phase chemistry. The regional CTM was set up as follows. CMAQ, version 4.7, (excluding the dynamic coarse mode updates) was used with the SAPRC_99 chemical mechanism and AERO5 aerosol module; emissions were processed through SMOKE ( http://smoke-model.org ), version 2.4, based on the 2004 projections from the NEI with specific CEM, biogeni cs, and fire updates; MM5, version 3.7.4, was used with the Asymmetric Convective Mixing, vers ion 2.2, PBL scheme; and data nudging was used to analyze fields for winds and temperature.



## Model Evaluation

Details from evaluations of the performance of a number of CMAQ applications are given in Arnold et al. (2003, 087579 ), Eder and Yu (2006, 142721 ), Appel et al. (2005, 089227 ), and Fuentes and Raftery (2005, 087580 ). In an annual simulation series for 2002 using CMAQ, version 4.6.1, in two 12-km domains for the CONUS ( Figure 3-85 ), predicted concentrations of summertime particulate SO 4 

### , often a major determinant of surface-layer PM concentrations, were well-predic ted by CMAQ at a 12-km grid spacing, to within a factor of 2 at nearly every point of comparison and with R 2 

### >0.8 across all three national networks (CASTNet, IMPROVE and CSN); a more detailed description is included in the 2008 NO X 

### SO X 

### ISA (U.S. EPA, 2008, 157074 ). This result for CMAQ, version 4.6.1, for 2002 tracks the generally well-predicted SO 4 2– 

### concentrations found in most earlier CMAQ evaluations: see Mebust et al. (2003, 156749 ), Eder and Yu (2006, 142721 ), and Tesche et al. (2006, 157050 ). Since particulate SO 4 2– 

### concentrations are strongly a function of precipitation, care must be taken to ensure that the meteorological solution driving individual CMAQ chemical applications produces precipitation fields with low bias as discussed by Appel et al. (2008, 155660 ).

December 2009 3-143

## Figure 3-85. 12-km EUS Summer SO 4 2– 

## PM. Each data point represents a paired monthly averaged (June/July/August) observation and CMAQ prediction at a particular IMPROVE, STN, and CASTNet site. Solid lines indicate the factor of 2 around the 1:1 line shown between them.

Wintertime particulate NO 3 – 

### ( Figure 3-86 ) and total NO 3 

### (HNO 3 

### + particulate NO 3 – 

### ) ( Figure 3-87 ) concentrations are predicted as well by CMAQ; however, NO 3 – 

### is a pervasively difficult species to measure and model. Still, at the CASTNet nodes where the total NO 3 – 

### concentrations are higher than they are at all but a few of the re mote IMPROVE sites, CMAQ predicts concentrations for nearly every node to within a factor of 2 and with an R 2 

### >0.8. A “base case” in which conditions for 2004 including all the anthropogenic and natural sources both within and outside of continental North America was run for comparison with measurements. A PRB simulation was also run by shutting off the anthropogenic sources of primary PM and precursors to secondary PM i nside continental North America.

December 2009 3-144

## Figure 3-86 12-km EUS Winter NO 3 - 

## PM. Each data point represents a paired monthly averaged (December/January/February) observation and CMAQ prediction at a particular IMPROVE and STN site. Solid lines indicate the factor of 2 around the 1:1 line shown between them.

Figure 3-87. 12-km EUS Winter total nitrate (HNO 3 

## + total particulate NO 3 – 

## ). Each data point represents a paired monthly averaged (December/January/February) observation and CMAQ prediction at a particular CASTNet site. Solid lines indicate the factor of 2 around the 1:1 line shown between them.

December 2009 3-145

### Figure 3-88 and Figure 3-89 show monthly average concentrations, and Figure 3-90 and Figure 3-91 show 24-h avg concentration distributions for 2004 predicted by CMAQ for the base case and for PRB. Measurements are also included for the five western and four eastern/midwestern IMPROVE sites shown in Figure 3-84 . Wildfires could have affected the grid cell containing the midwestern Voyageurs site resulting in the high PRB values found for the July average compared to the PRB values for the rest of the year. The “base case” simulations tend to underestimate concentrations throughout most western sites as shown in Figure 3-89 and Figure 3-91 . These underestimates are still within the range of a few μg/m 3 

### . However, the base case simulation also greatly over-predicts PM 2.5 

### concentrations at the upper end of the distribution at the Redwoods site ( Figure 3-91 ). This over-prediction results from emissions from wildfires in northern California that are included in the grid cell containing the Redw oods site, but may not have affected the site. However, wildfires indicated by MODIS would have af fected other areas either close to these sites or could have affected other locations in be tween the IMPROVE sites. The simulated monthly average PRB concentrations in the east/mi dwest range from a minimum of 0.6 μg/m 3 

### at Acadia National Park (NP) in July to 3.7 μg/m 3 

### at Voyageurs NP in July. However, most values are <1 μg/m 3 

### . The monthly average PRB concentrations calculate d for the West tend to be lower than for the East and range from 0.2 μg/m 3 

### at Bridger and Glacier NPs in January and February, respectively, to 8.7 μg/m 3 

### at Redwoods NP in November. Excluding values at Redwoods NP which greatly exceed measurements, the highest monthly average concentration was 3.7 μg/m 3 

### at Voyageurs NP in the East/Midwest and 2.4 μg/m 3 

### at Gila NP in the West.



## Figure 3-88. Monthly average of PM 2.5 

## concentrations measured at IMPROVE sites in the East and Midwest for 2004. Also shown are distributions of PM 2.5 

## concentrations calculated by CMAQ for the base case and for PRB.

December 2009 3-146

## Figure 3-89. Monthly average of PM 2.5 

## concentrations measured at IMPROVE sites in the West for 2004. Also shown are distributions of PM 2.5 

## concentrations calculated by CMAQ for the base case and for PRB.

December 2009 3-147

## Figure 3-90. Distribution of PM 2.5 

## concentrations measured at IMPROVE sites in the East and Midwest for 2004. Also shown are distributions of PM 2.5 

## concentrations calculated by CMAQ for the base case and for PRB.

December 2009 3-148

## Figure 3-91. Distribution of PM 2.5 

## concentrations measured at IMPROVE sites in the West for 2004. Also shown are distributions of PM 2.5 

## concentrations calculated by CMAQ for the base case and for PRB. Note the scale change on the y-axis for Redwoods NP.

Table 3-20 gives the annual and quarterly average CMAQ predictions at IMPROVE sites for the “base case” and the ratio of CMAQ predictions to the measured concentrations at those sites in 2004. CMAQ performance for the annual average concentrations and most of the seasonal averages is very good in the East and Midwest, generally fa lling within 35%. In the West, CMAQ’s prediction of PM 2.5 

### mass averages at these remote sites is generally too low in all seasons, often by 50%. Air quality model predictions in the mountainous West are often not as good as those over the flatter

December 2009 3-149

### terrain in the East and Midwest because the model’ s grid spacing (36 km in this case) smoothes over significant variation at the surface that results in differences at such remote sites. However, the model’s trend relative to the geospatial difference is correct: the predicted PM 2.5 

### concentrations are lower at the western sites than they are at the eastern sites, just as the measurements are. Table 3-21 shows corresponding annual and quarterly mean PRB PM 2.5 

### concentrations at IMPROVE sites.



## Table 3-20. Annual and quarterly mean PM 2.5 

## concentrations ( μ g/m 3 

## ) for the CMAQ “base case” at IMPROVE sites in 2004.

Annual; mod/obs Jan-March; mod/obs Apr-Jun; mod/obs Jul-Sep; mod/ obs Oct-Dec; mod/obs

EAST

Acadia 4.7; 1.04 5.6; 1.44 4.0; 0.87 4.6; 0.77 4.6; 1.31

Brigantine 10.2; 1.07 10.9; 1.35 10.3; 0.91 10.2; 0.88 9.4; 1.29

Dolly Sods 9.8; 1.03 8.3; 1.24 8.6; 0.88 14.0; 0.90 8.0; 1.40

MIDWEST

Voyageurs 4.0; 1.05 4.9; 1.19 2.2; 0.71 3.9; 0.93 4.9; 1.36

WEST

Bridger 1.6; 0.76 1.3; 1.08 1.6; 0.52 1.8; 0.64 1.7; 1.30

Canyonlands 1.6; 0.62 1.9; 0.86 1.4; 0.44 1.5; 0.52 1.6; .76

Gila 1.6; 0.55 1.4; 0.70 2.2; 0.55 1.7; 0.45 1.1; 0.61

Glacier 2.2; 0.45 1.8; 0.39 2.1; 0.50 2.1; 0.40 2.8; 0.56

Redwood 4.6; 1.31 4.0; 1.48 3.0; 0.83 2.9; 0.78 8.4; 2.15



### Table 3-22 illustrates CMAQ predictions of sea sonal variation in the base case PM 2.5 

### concentrations across regions of the CONUS, while Table 3-23 shows CMAQ predictions of the seasonal variation in regional PRB PM 2.5 

### concentrations. Highest base case PM 2.5 

### concentrations were observed for the Northeast, Southeast, and industrial Midwest, with highest concentrations during the fall and winter (and comparably high concentrations in the summer for the Industrial Midwest). PRB PM 2.5 

### concentrations were highest on an annual basis in the Southeast, and peaking during the winter. In the summer, PRB PM 2.5 

### is roughly comparable for the Northwest and Southern California and elevated, but slightly lower for the Southwest. These results also likely indicate the influence of sources that are more strongly relate d to hot and dry conditions such as wildfires and dust suspension.

December 2009 3-150

## Table 3-21. Annual and quarterly mean PM 2.5 

## concentrations ( μ g/m 3 

## ) for the CMAQ PRB simulations at IMPROVE sites in 2004.

Annual January-March April-June July-September October-December

EAST

Acadia 0.70 0.76 0.76 0.65 0.65

Brigantine 0.77 0.86 0.91 0.70 0.63

Dolly Sods 0.79 0.88 0.83 0.75 0.66

MIDWEST

Voyageurs 1.2 0.83 0.91 2.0 0.93

WEST

Bridger 0.57 0.33 0.57 0.76 0.61

Canyonlands 0.49 0.38 0.54 0.68 0.35

Gila 0.74 0.42 1.4 0.80 0.32

Glacier 0.91 0.36 0.87 1.1 1.3

Redwood 2.8 2.4 1.5 1.1 6.1



## Table 3-22. Annual and quarterly mean of the CMAQ-predicted base case PM 2.5 

## concentrations ( μ g/m 3 

## ) in the U.S. EPA CONUS regions in 2004.

Annual January-March April-June July-September October-December

Northeast 9.76 10. 74 8.38 9.55 10.38

Southeast 10.05 12. 28 7.72 9.78 10.42

Industrial Midwest 11. 38 12.22 9.37 11.89 12.00

Upper Midwest 6.70 8.83 4.95 5.34 7.67

Southwest 3.30 4. 08 2.77 3.31 3.03

Northwest 2.72 2. 49 2.21 2.71 3.44

Southern California 4. 43 4.64 3.93 4.34 4.82

December 2009 3-151

## Table 3-23. Annual and quarterly mean of the CMAQ-predicted PRB PM 2.5 

## concentrations ( μ g/m 3 

## ) in the U.S. EPA CONUS regions in 2004.

Annual January-March April-June July-September October-December

Northeast 0.74 0. 85 0.78 0.67 0.68

Southeast 1.72 2.43 1.41 1.41 1.64

Industrial Midwest 0. 86 0.89 0.89 0.94 0.73

Upper Midwest 0.84 0.79 0.93 0.99 0.66

Southwest 0.62 0. 61 0.76 0.70 0.40

Northwest 1.01 0. 48 0.81 1.42 1.32

Southern California 0. 84 0.54 0.92 1.21 0.67



# 3.8. Issues in Exposure Assessment for PM and its Components

The purpose of this section is to present the latest exposure assessment studies to characterize the exposure of individuals and populations to PM of ambient origin. Such information will aid the interpretation of epidemiologic studies described in subsequent chapters of this ISA. This section includes descriptions of modeling and monitoring techniques used to capture personal PM exposure, observations reported in the literature at various relevant spatial scales, observations related to PM composition and PM in a mix of copol lutants, and the effect of exposure estimates on epidemiologic results. Attention is given to use of community-based monitors at urban spatial scales and use of personal and microenvironmental exposure data to present how each metric can be used in exposure assessment and what errors and uncert ainties exist for each approach. Understanding of exposure errors is important because exposure error can pot entially bias an estimate of a health effect endpoint, or increase the size of confidence intervals around a health effect estimate. Typically, exposure error biases analyses of health effect s towards the null (i.e., no relationship between exposure and health effect). The information presented in this section builds upon the key findings of the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). One key finding was that separation of total PM exposures into ambient and nonambient components reduces potential uncertainties in the analysis and interpretation of PM health effects data. At the time of the 2004 PM AQCD (U.S. EPA, 2004, 056905 ), one study reported that individual daily values of both the total and nonambient personal PM exposure were poorly correlated with the daily ambient PM concentrations, while individual daily values of ambient PM exposure and daily ambient PM concentrations were highly correlated. In poo led studies (different subjects measured on different days), individual, daily values of the total PM exposure were generally shown to be poorly correlated with the daily ambient PM concentrations. In longitudinal studies (each subject measured for multiple days), i ndividual, daily values of the total PM personal exposure and the daily ambient PM concentrations were found to be highly correlated for some, but not all, subjects. Using the PTEAM study data, the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) also analyzed exposure measurement errors in the cont ext of time-series epidemiology to show that the error introduced by using ambient PM concentrations as a surrogate for ambient PM exposures negatively biases the estimation of health risk coefficients by the ratio of ambient PM exposure to ambient PM concentration. However, it was concluded that the health risk coefficient determined using ambient PM concentrations provides the correct information on the change in health risks that would be produced by a change in ambient concentrations. Personal exposure to PM can vary considerably depending on PM size and composition, source strength and proximity, season, time of day, region of the country, population density of the environment, personal activity pa tterns, and ventilation of indoor environments. Table A-58 of Annex A, which summarizes findings from U.S. panel studies of personal exposure to PM with no

December 2009 3-152

### indoor sources published between 2002 and 2008 broke n down by region of the country, illustrates this variability. For example, Table A-58 presents 24-h personal PM 2.5 

### exposures that range from roughly 1-55 μ g/m 3 

### with highest exposures in the southwest and northeastern regions of the country and during the summer season. Section 3.8 is designed to present current theory and field results regarding exposure to PM. To illustrate th e concept of personal exposure within various microenvironments, a general exposure model is presented in Section 3.8.1 . New developments in techniques for measuring personal and indoor PM are presented in Section 3.8.2 , followed by exposure modeling techniques in Section 3.8.3 . In Section 3.8.4 , exposure assessment field studies in the literature are presented. Attention is given to ambient exposure over multiple spatial scales including near-road, in-vehicle, and indoor environments. Section 3.8.5 presents issues related to PM composition and PM in multipollutant mixtures. Fi nally, implications of exposure assessment issues for epidemiologic studies are presented in Section 3.8.6

.



# 3.8.1. General Exposure Concepts

A theo retical model of personal exposure is pres ented to highlight what is measurable and what uncertainties exist in this framework. An individual’s time-integrated total exposure to airborne PM can be described based on a compartmentaliza tion of the person’s activities during a given time period: 

# ∫

= dtCE j T

naaT 

## EEE Equation 3-3



### where E T 

### = total exposure over a time period of interest, C j 

### = airborne PM concentration at microenvironment j , and dt = portion of the time-period spent in microenvironment j. Equation 3-3 can be decomposed into a microenvironmental model that accounts for exposure to PM of ambient ( E

a 

### ) and nonambient ( E

na 

### ) origin of the form:



## = +

Equation 3-4



### Figure 3-92 illustrates Equation 3-4 . Examples of ambient PM sources include industrial and mobile source emissions, resuspended dust, biom ass combustion, and secondary formation. Examples of nonambient sources include smoking, c ooking, home heating, cleaning, and indoor air chemistry. PM concentrations generated by ambien t and nonambient sources are subject to spatial and temporal variability that can affect estimate s of exposure and resulting health effects. Exposure factors affecting interpretation of epidem iology are discussed in detail in Section 3.8.6

.

December 2009 3-153Source: Adapted with permission of Nature Publishing Group from Wilson and Brauer (2006, 088933 ).



## Figure 3-92. Model of total personal exposure to PM as a function of ambient and nonambient

sources.

This assessment focuses on the ambient component of exposure because this is more relevant to the NAAQS review. E

a 

### can be decomposed into the fraction of time spent in various outdoor and indoor microenvironments (Wallace et al., 2006, 089190 ; Wilson et al., 2000, 010288 ): 

# ∑ ∑ += ioii oo a 

## CFfCfE ,inf, Equation 3-5



### where f = fraction of the relevant time period (equivalent to dt in Equation 3-2 ), subscript o = index of outdoor microenvironments, subscript i = index of indoor microenvironments, subscript o,i = index of outdoor microenvironments adj acent to a given indoor microenvironment i , and F inf, i 

### = infiltration factor for indoor microenvironment i

. Equation 3-5 is subject to the constraint Σ f

o 

### + Σ

f

i 

### = 1, and each term on the right hand side of the equation has a summation because it reflects various microenvironmental exposures. Here, “indoors” refers to being inside any aspect of the built environment, e.g., home, office buildings, encl osed vehicles (automobiles, trains, buses), and recreational facilities (movies, restaurants, bars ). “Outdoor” exposure can occur in parks or yards, on sidewalks, and on bicycles or motorcycles.
F

inf



### represents the equilibrium fraction of the PM concentration outside the microenvironment that penetrates inside the microenvironment a nd remains suspended. It is a function of the microenvironmental air exchange characteristics and the particle properties. Assuming steady state conditions, the infiltration factor is a function of the penetration, P , of PM (a fractional quantity representing the portion of outdoor PM that passes through the building envelope), the air exchange rate, a , of the indoor microenvironment, and the rate of PM loss, k , within the indoor microenvironment: F inf



### = Pa /( a + k ). Determination of E

a 

### can be complicated by PM loss through chemical and physical processes in microenvironmen ts, and the composition of PM can be modified during infiltration of outdoor air into microenvironments (Meng et al., 2007, 194618 ; Sarnat et al., 2006, 089166 ). In the context of interpreting epidemiologic st udies of the effects of ambient pollutants on human health, the association between E

a 

### and concentrations from a central site monitor, C

a 

### , is more relevant than the association between E T 

### and C

a 

### because nonambient PM is uncorrelated with C

a 

### , as discussed in Section 3.8.4 . In ecologic studies of large panels or cohorts, C

a 

### is often used in lieu of outdoor microenvironmental data to represent these e xposures based on the availability of data. Thus it is often assumed that C

o 

### = C

a 

### and that the fraction of time spent outdoors can be expressed cumulatively as f

o 

### ; the indoor terms still retain a summation because infiltration differs among

December 2009 3-154

### different microenvironments. Under these assumptions, an individual’s exposure to ambient PM, first given in Equation 3-5 , can be re-expressed as a function of C

a 

### . The following approximation has been employed in the literature to describe ambient exposure based on these assumptions (Wallace et al., 2006, 089190 ; Wilson and Brauer, 2006, 088933 ; Wilson et al., 2000, 010288 ): 

# ( )

aii oa 

## CFffE 

# ∑ += inf, Equation 3-6



### Particle size, particle composition, meteorology , urban and natural topography, and other factors determine whether or not Equation 3-6 is a reasonable approximation for Equation 3-5 . Errors and uncertainties inherent in the use of Equation 3-6 in lieu of Equation 3-5 are described in Section 3.8.6 with respect to implications for epidemiology. If concentration measured at a central site monitor is used to represent ambient concentration, then α , the ratio between personal exposure to ambient PM and the ambient concentration of PM, can be defined as:

a a 

## C E

= α

Equation 3-7



### If the assumptions forming the basis for Equation 3-6 are valid, then α is the proportionality factor in Equation 3-6 : 

# ∑ += ii o 

## Fff inf, 

## α

Equation 3-8



### α varies between 0 and 1. If a person’s exposure occurs in a single microenvironment, the ambient component of a microenvironmental PM concentrat ion can be represented as the product of the ambient concentration and F inf 

### . Wallace et al. (2006, 089190 ) note that time-activity data and corresponding estimates of F inf



### for each microenvironmental exposure are needed to compute an individual’s α with accuracy. If significant local sources and sinks are not captured by central site monitors, then the ambient component of outdoor air must be estimated using dispersion models, land use regression (LUR) models, receptor models, fine scale CTMs or some combination of these techniques. Modeling methods are described in Section 3.8.3

.



# 3.8.2. Personal and Microenvironmental Exposure Monitoring

The pu rpose of this section is to present new discoveries related to measuring microenvironmental PM concentrations or personal exposure to PM. A review of over 100 personal and microenvironmental PM exposure studies published since 2002 (see Table A-58 of Annex A) reveals that the majority of the monitoring techni ques in use were previously reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) for PM. Detailed descriptions of these methodologies are provided in that document and therefore will not be repeated in this document. The following sections will include only findings from 2002 or later regarding monitoring and modeling methodologies in common use and significant adva ncements in understanding the capabilities and limitations of these methods for assessment of PM exposure.



# 3.8.2.1. New Developments in Personal Exposure Monitoring Instrumentation

Current p ersonal exposure sampling methodology c onsists largely of integrated filter sampling using a cyclone or personal exposure monitor (PEM) to achieve a cut point at a desired particle size. This method of sampling facilitates speciation work because the filters can be archived for chemical and gravimetric analysis. Additionally, light sca ttering aerosol detection instruments, such as the personal DataRam (pDR) and SidePak personal aer osol monitor have seen some use in personal
PM 2.5 

### , PM 10 

### , and PM 1 

### monitoring (e.g., Lewne et al., 2006, 090556 ; Wallace et al., 2006, 088211 ). Several researchers have noted that relative humidity causes overestimation of the particle mass in

December 2009 3-155

### light-scattering personal exposure monitors (e.g., Lowenthal et al., 1995, 045134 ; Ramachandran et al., 2003, 195017 ); a correction factor has been applied to concentrations to address this issue. In the DEARS, Williams et al. (2008, 191201 ) attempted to reduce humidity of the sample stream by placing a drying column upstream of the pDR’s detector. Although this addressed the humidity issue, the drying column occasionally released particles a nd therefore caused artificial concentration peaks. For this reason, Williams et al. (2008, 191201 ) determined that the humid ity correction approach is preferable. One area of further development is in personal sampling of the thoracic and respirable particle size distribution. Variations of the cascade impactor have been developed for personal sampling and tested for use in field studies (Case et al., 2008, 155149 ; Lee et al., 2006, 098249 ; Singh et al., 2003, 156088 ). The model developed and tested by Lee et al. (2006, 098249 ) operates with a 1- μ m cut point and therefore can characterize respirab le particles well. The Case et al. (2008, 155149 ) twostage cascade impactor separated PM 10-2.5 

### from PM 2.5 

### for personal monitoring and sampled within ± 20% of a reference method. Hsiao et al. (2009, 191001 ) developed a mini-cyclone with a 1 μ m or 0.3 μ m cut point for sampling accumulation mode and UFPs. The Personal Cascade Impactor Sampler (PCIS) has the capability to sample do wn to a cut point of 250 nm (Singh et al., 2003, 156088 ). For PM 2.5 

### , the difference between the PCIS and the MOUDI cascade impactor was 11%, while the difference between the PCIS and the SMPS-APS was only 2%. Differences between the PCIS and the MOUDI for PM 2.5 

### species compared with the MOUDI was generally higher: 11% for SO 4 2– 

### , 22% for NO 3 – 

### , 19% for EC, and 94% for OC. Mass was overestimated by 3%, 16%, and 31% for PM 1-0.5 

### , PM 0.5-0.25 

### , and PM 0.25 

### , respectively, when compared with the SMPS-APS. Similarly, Case et al. (2008, 155149 ) found a mass difference ranging from -11 to +10% for PM 10-2.5 

### with the Personal Respirable Particulate Samp ler (PRPS), and Lee et al. (2006, 098249 ) found a mass difference of -6 to 0% for PM 2.5 

### and -6 to -1% for PM 10 

### when comparing results from this device with those from the PEM. Leith et al. (2007, 098241 ) redesigned the Wagner-Leith passive sampler for measuring PM 10-2.5 

### . In this work, the difference between a PM 10-2.5 

### FRM and the co-located passive sampler was within 1 standard deviation of concentrations measured by the FRM samplers. A number of personal PM monitors are under develo pment as part of the National Institutes of Health Genes, Environment, and Health Initiative ( http://www.gei.nih.gov/exposurebiology/ program/sensor.asp ). Funded projects for miniature personal monitors include a platform that records real-time BC and PM concentrations and archives PM for further analysis, a badge containing a sensor array that detects several compounds found in diesel PM, a micro-ne phelometer recording PM and endotoxin exposure, a complementary me tal-oxide-semiconductor (CMOS) fitting in the nose to measure allergen PM, and a micro-thermofl uidic nanoparticle sensor. The mini-cyclone cited above was designed to operate upwind of the micro-thermofluidic sensor (Hsiao et al., 2009, 191001 ). LeVine et al. (2009, 192091 ) and Schwartz et al. (2008, 192094 ) described use of the CMOS technology for real-time DNA detection. Mulchandani et al. (2001, 191003 ) reviewed amperometric biosensors used for orga nophosphate pesticide detection that are the basis of the diesel detection badge; several additional articles have been published by this group that describe applications of amperometric sensors.



# 3.8.2.2. New Developments in Microenv ironmental Exposure Monitoring Instrumentation

The majo rity of developments since the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) regarding microenvironmental PM characterization have involv ed real-time instrumentation in the UFP size range. Because these methods are also used for ambi ent sampling, they are described in Section 3.4 and in Annex Table A-59. New developments in microenvironmental sampling for exposure assessment have also included construction, testing, and implementation of mobile environmental sampling laboratories for PM mass, particle count, and composition, as well as other criteria pollutants (CO, SO 2 

### , NO 2 

### , O 3 

### ). These mobile laboratories typically contain a suite of real-time equipment with short sampling intervals (e.g., 1-10 min), such as an SMPS with CPC, APS, laser photometers, and aethalometers for aerosols; monitors for the gaseous criteria pollutants; a weather station for meteorological variables; and a Global Positioning System (GPS) for position. Videotape or journal observations are sometimes logged simultaneously to track local on-road sources of pollution. One key application of mobile laboratory technology is assessment of the outdoor microscale environments and in-vehicle microenvironments on roadways for determining e xposure during on-road transportation (Pirjola et

December 2009 3-156

### al., 2004, 117564 ; Sabin et al., 2005, 087728 ; Weijers et al., 2004, 104186 ; Westerdahl et al., 2005, 086502 ). For instance, Sabin et al. (2005, 087728 ) used videotape records to determine whether BC detected on a school bus was the result of local outdoor sources from other vehicles or “self-pollution” from the school bus’s own engine exhaust. Westerdahl et al. (2005, 086502 ) used the GPS time series to determine that minima in the UFP time series corresponded to passage through residential areas of Long Beach and Pasadena, in contrast to the pollution spikes observed along highways. Studies have also shown that detecti on of PM from vehicle exhaust could be improved through use of combined measurement results to improve statistical analysis (Ntziachristos and Samaras, 2006, 116722 ).



# 3.8.3.

3.8.3.1. 

# Exposure Modeling

This se ction describes a variety of techniques used to model PM exposure. Many of these methods are used in combination to link ambien t PM levels in the atmosphere or source characteristics to human exposure among individuals or sample populations. Recent developments in exposure modeling are described in this subsection, a nd errors and uncertainties of these approaches are described in Section 3.8.6.2

.



# Time-Weighted Microenvironmental Models

An indivi dual’s exposure is dictated by his or her activity patterns, as modeled by f

o 

### and f i 

### in Equation 3-5 . A number of panel studies have tracked subject exposures using questionnaires, timeactivity diaries, or global positioning systems(e.g., Cohen et al., 2009, 190639 ; Elgethun et al., 2003, 190640 ; Johnson et al., 2000, 001660 ; Olson and Burke, 2006, 189951 ; Wallace et al., 2006, 089190 ). In many cases, the time-activity tracking is performed in conjunction with personal exposure and/or indoor and outdoor PM concentration monitoring to estimate overall PM exposure. Wu (2005, 086397 ) described a microenvironmental model of total personal exposure:

iiaoaoohT 

## CfCfCfE + + =
Equation 3-9



### where f oh 

### = fraction of time spent outdoors at home, f

oa 

### = fraction of time spent outdoors away from home, C

o 

### = PM concentration outside the home, and C i 

### = indoor PM concentration. In Equation 3-9

, E T 

### can be calculated based on time-activity di ary data and time-resolved PM concentration

measurements. E T 

### can be expressed as a time-resolved value or cumulatively over a time period of interest using this formulation. In this model, ambient and nonambient exposure cannot be separated because it incorporates indoor c oncentrations that are a function of both ambient and nonambient sources. Additionally, Equation 3-9 distinguishes ambient concentration measured at a monitor from that measured immediately outside the home. Liu et al. (2003, 073841 ) found that this model predicted elderly exposures adequately but was a poor predictor of PM 2.5 

### exposure for asthmatic children. Wu et al. (2005, 086397 ) point out that this may be due to lack of availability of the children’s time-activity data in school where children spend a substantial portion of their day. In a study of school children’s exposure patterns, DeCastro et al. (2007, 190996 ) computed odds ratios of a panel subject’s location within a given microenviro nment using multivariate logistic models of the indoor school, indoor home, and outdoor microenvir onments. They found that (1) the city of residence was a significant predictor of being indoo rs at school; (2) having an afterschool job was a significant predictor of being indoors at home; and (3) age and having an afterschool job were significant predictors of being outdoors. The results of the DeCastro et al. (2007, 190996 ) study were designed to predict f i 

### and f

o 

### in exposure modeling. A second approach proposed by Wu et al. (2005, 086397 ) is similar in formulation to Equation 3-5 because it computes ambient PM exposure by c onsidering the amount of outdoor PM infiltrated indoors. This version also incorporates C

o 

### and C

a 

### :

December 2009 3-157oiaoaooha 

## CFfCfCfE inf 

## = + +

Equation 3-10



### Equation 3-10 differs from Equation 3-5 because it accounts for concentrations immediately outside the building rather than considering all outdoor ex posures to be a function of that measured at a community monitor. Factors influencing the contribution of E

a 

### to E T 

### may include sample population characteristics, location of a site for microenvi ronmental monitoring, seasonal trends in PM concentration, and regional differences aff ecting ambient concentration and infiltration. Regression based approaches can also be incorporated into time-weighted microenvironmental modeling. Chang et al. (2003, 053789 ) used data from the Scripted Activity Study and the Older Adults Study, both conducted in Baltimore in 199 8 and 1999, to compute total personal exposure based on time-weighted microenvironmental exposures for each panel subject: 

# ∑ ∑ + =

k k kkoo kkii T 

## CfMECfMEE β β

Equation 3-11



### where ME = microenvironment (indoor or outdoor), β = regression coefficient reflecting the accuracy of the exposure estimate for a given microenvironment, f k 

### = fraction of time performing an activity k , and C k 

### = personal exposure while performing activity k . In this work, Chang et al. (2003, 053789 ) tested the models with hourly personal exposure data, hourly ambient concentration data, and daily ambient concentration data. The study found that time-activity data improved estimates of 24-h PM 2.5 

### exposure in comparison with using 24-h ambient PM 2.5 

### data, but the use of hourly ambient data was comparable to personal microe nvironmental data in estimating exposure. When using ambient concentration data, β reflected infiltration for the indoor microenvironmental estimates for a sample population. Using a simila r activity-based exposure modeling approach for a panel study in Seattle from 1999-2002, Allen et al. (2004, 190089 ) found that subjects’ PM exposures were best represented by modeling ambien t and nonambient exposures separately because ambient PM personal exposure was well correlated w ith PM concentration at central site monitors, while nonambient PM exposure was not.



# 3.8.3.2. Stochastic Population Exposure Models

Populati on-based methods, such as the Air Po llution Exposure (APEX), Stochastic Human Exposure and Dose Simulation (SHEDS), and EXPO LIS (exposure in polis, or cities) models, involve stochastic treatment of the model input factors ( http://www.epa.gov/ttn/fera/human_apex.html ; (Burke et al., 2001, 014050 ; Kruize et al., 2003, 156661 ). These are described in detail in Annex 3.7 of the 2008 NO X 

### ISA (U.S. EPA, 2008, 157073 ). Stochastic models utilize distributions of pollutant -related and individual-level variables, such as ambient and local PM concentration source c ontributions and breathing rate, respectively, to compute the distribution of individual exposures across the modeled population. Using distributions of input parameters in the model framework rath er than point estimates allows the models to explicitly incorporate uncertainty and variability into exposure estimates (Zidek et al., 2007, 190076 ). These models estimate time-weighted exposure for modeled individuals by summing exposure in each microenvironment visited during the exposure period. The models also have the capability to estimate received dose through a dosime try model. The initial set of input data for population exposure models is ambient air quality data, which may come from a monitoring network or model estimates. Estimates of concentrations in a set of microenvironments are generated either by mass balance methods or microenvironmental factors. Microenvironments modeled include residential indoor microenvironments; other indoor microenvironments, such as schools, offices, and public buildings; vehicles; and outdoor microenvironments. The sequence of microenvironments and exertion levels during the exposure period is dete rmined from characteristics of each modeled individual. The APEX model does this by generating a profile for each simulated individual by sampling from distributions of demographic vari ables such as age, gender, and employment; physiological variables, such as height and weight; and situational variables, such as living in a house with a gas stove or air conditioning. Activity patterns from a database such as Consolidated Human Activity Database (CHAD) are assigned to the simulated individual using age, gender, and

December 2009 3-158

### biometric characteristics. Breathing rates are calculated for each activity based on exertion level, and the corresponding received dose is then computed. For APEX, the PM dosimetry algorithm is based on the International Commission on Radiological Protection’s Human Respiratory Tract Model for Radiological Protection (ICRP, 1995, 006988 ), and calculates the rate of mass deposition of PM in the respiratory system (U.S. EPA, 2008, 191775 ). Summaries of individual- and population-level metrics are produced, such as maximum exposure or dose, number of individuals exceeding a specified exposure/dose threshold, and number of person-days at or above certain exposure levels. The models also consider the non-ambient contribution to total exposure. Nonambient source terms are added to the infiltration of ambient pollutant s to calculate the total concentration in the microenvironment. Output from model runs with and without nonambient sources can be compared to estimate the ambient contribution to total exposure and dose. Recent larger-scale human activity databases, such as those developed for CHAD or the National Human Activity Pattern Survey (NHAPS) , have been designed to characterize exposure patterns among much larger population subsets than can be examined during individual panel studies (Klepeis et al., 2001, 002437 ; McCurdy et al., 2000, 000782 ). CHAD consists of a consolidation of human activity data obtained during several panel studies in which diary or retrospective activity data were obtained, while NHAPS acquired sa mple population time-activity data through surveys about human activity (Klepeis et al., 2001, 002437 ). The complex human activity patterns across the population (all ages) for NHAPS are illustrated in Figure 3-93 (Klepeis et al., 2001, 002437 ). This figure is presented to illustrate the diversity of daily activities among the entire population as well as the proportion of time spent in each microenvironment. Different patterns would be anticipated when breaking down activity patterns for subgroups, such as children or the elderly. With data for average PM concentrations in each microenvironment, population exposures can be estimated from this break-down of time-activity data.

Source: Reprinted with Permission of Nature Publishing Group from Klepeis et al. (2001, 002437 ).



## Figure 3-93. Distribution of time sample popula tion spends in various environments, from the National Human Activity Pattern Survey.

December 2009 3-159

### Stochastic and deterministic methods are ofte n combined, as described below. Recently, SHEDS has been linked with the Modeling Environment for Total Risk Studies (MENTOR) model to expand population exposure assessment to i ndividual risk assessment (Georgopoulos et al., 2005, 080269 ). In this formulation, CMAQ was used to predic t initial concentrations at a coarse scale, and then a spatiotemporal random field method (Vyas and Christakos, 1997, 156142 ) was applied to interpolate the concentration to census tract scale in which exposure estimates are made. CHAD can also be incorporated into MENTOR so that estima tes of exposure are related to dose and metabolic distributions to estimate risk of specific health impacts.



# 3.8.3.3. Dispersion Models

Dispersi on models have been used both for di rect estimation of exposure and as inputs for stochastic modeling systems, as described above. Location-based exposures have been predicted using models such as CALINE, AERMOD, CALPUFF, (all described in Section 3.6.2.3 ) or the Operational Street Pollution Model (OSPM) for estimation of street-level PM pollution coupled with infiltration models to represent indoor exposur e to ambient levels (Gilliam et al., 2005, 056749 ; Mensink et al., 2008, 155980 ; Wilson and Zawar-Reza, 2006, 088292 ). For instance, CALPUFF was used to model transport and dispersion in lower Manhattan following the September 11, 2001 World Trade Center collapse to determine average location-based exposures (Gilliam et al., 2005, 056750 ). Wilson and Zawar-Reza (2006, 088292 ) used The Air Pollution Model (TAPM), which integrated an emissions model with a mesoscale meteorological driver, to assess PM 10 

### dispersion and potential for exposure in Christchurch, New Zeal and. Gulliver and Briggs (2005, 191079 ) used the Atmospheric Dispersion Modeling System (ADMS) to model dispe rsion of “line-source” traffic emissions in an urban environment. In a method similar to that employed by Georgopoulos et al. (Georgopoulos et al., 2005, 080269 ) with SHEDS, Wu et al. (2005, 058570 ) used CALINE to predict street-level concentrations of pollutants and input the results of that dispersion model into an individual exposure model that accounts for infiltration of spec ific building characteristics. Wu et al. (2005, 058570 ) employed CHAD to estimate the time-basis of exposures from the CALINE predictions. With an individualized exposure approach, the model is deterministic. However, population exposures can be estimated by performing repeated simulations using various housing characteristics and then computing a posterior probability dist ribution function for exposure. Isakov et al. (2009, 191192 ) developed a methodology to link a CTM (used to compute regional scale spatiotemporallyvarying concentration in an urban area) with stochastic population exposure models to predict annual and seasonal variation in urban population exposure within urban microenvironments.



# 3.8.3.4. Land Use Regression and GIS-Based Models

LUR mode ls have also been developed to desc ribe pollution levels as a function of source characteristics (Briggs et al., 1997, 025950 ; Gilliland et al., 2005, 098820 ; Ryan and LeMasters, 2007, 156063 ). LUR is a regression derived from monitored concentration values as a function of data from a combination of factors (e.g., land use designation, traffic counts, home heating usage, point source strength, and population density). Th e regression is then computed for multiple locations based on the independent variables at loca tions without monitors. At the census tract level, a LUR is a multivariate description of pollution as a function of traffic, land use, and topographic variables (Briggs et al., 1997, 025950 ). Originally, LUR was used for NO 2 

### dispersion, but it was adapted for PM 2.5 

### exposure estimation by Brauer et al. (2003, 155702 ) for Stockholm, Sweden, Munich, Germany, and throughout The Netherlands. This study found a measure of traffic density to be the most significant variable predicting PM 2.5 

### exposure. Ryan et al. (2008, 156064 ) reported on a LUR model for childhood exposure to traffic-derived EC for the Cincinnati Allergy and Air Pollution Study and also found traffic to be the most important determinant of diesel exhaust particle exposure. In this case, wind direction was also factored into the model as a determinant of EC mixing. Like deterministic dispersion models, LUR can be perf ormed over wide areas to develop a posterior probability distribution function of exposure at th e urban scale. However, Hoek et al. (2008, 195851 ) warn of several limitations of LUR, including dis tinguishing real associations between pollutants and covariates from those of correlated copollutants, limitations in spatial resolution from monitor data, applicability of the LUR model under changi ng temporal conditions, and introduction of confounding factors when LUR is used in epidemiologic studies.

December 2009 3-160

### A GIS platform is typically used to organize the independent variable data and map the results. The GIS software creates numerous lattice points for the regression of concentration as a function of the covariates. For instance, Krewski et al. (2009, 191193 ) computed PM 2.5 

### concentrations for the New York City and Los Angeles metropolitan area s. For the Los Angeles analysis, the LUR was applied at 18,000 points in the simulation domain, and an inverse distance weighting kriging method was applied to interpolate the predicted concentrati on. In New York City, the LUR was applied at 49 monitors for a 3-yr model and 36 monitors for a model of winter 2000; kriging was employed only for the purpose of visualizing the concentration between monitors. The models explained 69% and 66% of the variation in PM 2.5 

### in Los Angeles and New York City, respectively. GIS-based spatial smoothing models can be used to estimate PM concentration levels where monitors are not located. Yanosky et al. (2008, 099467 ) described an approach to estimate concentrations using a combination of reported AQS data and GIS-based and meteorological covariates. Temporally stationary covariates include d distance to nearest road for different PM size fractions, urban land use, population density, point sour ce emissions within 1 and 10 km buffers, and elevation above sea level. Time-varying covariates included area source emissions, precipitation, and wind speed. In this analysis, the GIS-based covariates were temporally stationary, while the meteorological and PM monitored concentration inputs were time-varying. This approach was applied to estimate PM 2.5 

### , PM 10-2.5 

### , and PM 10 

### exposures for the Nurse’s Health Study and provided estimates of concentration at approximately 70,000 nodes with PM 2.5 

### and/or PM 10 

### data input from more than 900 AQS sites with good validation of the PM 2.5 

### and PM 10 

### models (Paciorek et al., 2009, 190090 ; Yanosky et al., 2008, 099467 ; Yanosky et al., 2009, 190114 ). GIS-based methods can also be applied to integrate exposures over different microenvironments. For example, Gulliver and Briggs (2005, 191079 ) described development of the Space-Time Exposure Modeling System (STEMS) to model PM 10 

### concentration. STEMS is a multipronged model that links traffic emissions es timates, dispersion, background PM estimates, and time-activity data within a GIS framework to create exposure estimates. Traffic emissions estimates and meteorological parameters are input into the ADMS dispersion model, which along with background PM measurements, are used to create hour ly point estimates of PM concentration. Based on the time-activity data, the concentration estimates were then used to calculate exposures to trafficrelated PM 10 

### along a commuting path while an individual is in transit. PM 10 

### was used by Gulliver and Briggs (2005, 191079 ) because ADMS had not yet been validated for smaller PM size fractions. In an analysis of the sensitivity of incl uded variables, Gulliver and Briggs (2005, 191079 ) showed the model to be most sensitive to fluctuations in local meteorology followed by sudden vehicle speed reductions of 10 km/h. The STEMS model was primarily designed to model exposures during transit, but the authors state that this technique can be applied to modeling other microenvironmental

exposures. Source proximity is sometimes used as a co variate in GIS-based regression models. For instance, Baxter et al. (2007, 092725 ) predicted indoor exposure to PM 2.5 

### , EC, and NO

x 

### based on distance to roadways, indoor source characteristics, window opening, and ambient concentrations in the Boston metropolitan area. In this effort, Baxter et al. examined a variety of factors estimated using GIS including roadway density, roadway length, average daily traffic, and population density to determine which variables were significant predictors. They found that point estimates of PM 2.5 

### were largely influenced by regional ambient PM 2.5 

### while EC estimates were more influenced by local mobile sources. However, Baxter et al. (2008, 191194 ) found no association between distance to a bridge toll booth station and indoor EC concentration in Detroit homes when studying the impact of diesel emissions from traffic on the Ambassador Bridge as part of the Detroit Exposure and Aerosol Research Study (DEARS). Being loca ted downwind of the booth, however, was a significant predictor of indoor EC concentration. Corburn (2007, 155738 ) tested two distinct modeling approaches, the cumulative air toxics surface (CATS) and the U.S. EPA’s National Air Toxics Assessment (NATA) to determine how these approaches can yield estimates of human exposure to diesel exhaust and 33 air toxics for environmental impact assessment. The CATS approach included an exposure term incorporating source density and distance to source, and the sources include traffic as well as bus depots and transfer stations, airports, and industrial point sources. Corburn’s results demonstrated that robust land use data can provide an approximation for urban exposures, although he cautioned that such estimates should not supersede environmental monitoring. In using these approaches, Huang and Batterman (2000, 156572 ) warn that geographic divisions must be sufficiently small to avoid inter-zone variability in source and exposure characteristics. Moreover, the HEI Report on Traffic Related Health Effects (2009, 191009 )

December 2009 3-161

### discourages use of source proximity as a surrogate for traffic exposure in epidemiologic studies because it is not specific to particular pollutants and can be subject to confounding factors such as SES.



# 3.8.4.

3.8.4.1. 

# Exposure Assessment Studies

Table A-61 in Annex A lists exposure assessment studies performed in the U.S. by region of the country with personal, microenvironmental, and ambient mass concentrations presented (note that chemical speciation data, where available, are discussed below). The majority of urban-scale studies focus on PM 2.5 

### because PM 2.5 

### concentrations are more homogeneously distributed. Studies of microscale to neighborhood scale dispersion more co mmonly include data on UF and thoracic coarse PM in addition to PM 2.5 

### because they travel over shorter distances from the site of generation, as described in Section 3.5 . Some of these studies present the outdoor concentration measured outside the test building, while others use ambient concentration obtained from a community site monitor. As would be expected, there is considerable variab ility within and across regions of the country with respect to indoor exposures and ambient concentra tions. Furthermore, some regions are represented by only one or two studies, while other regions have many studies. Most studies have been conducted in only one or two metropolitan areas. Thus, the results presented may not be broadly representative. Results of these studies highlight the uncertainties surrounding various estimates of the ambient contribution to personal exposure. This varia tion can be attributed to a number of factors, including PM size distribution, scope and magn itude of microenvironmental sources, proximity to microenvironmental sources, ambient concentrations of PM, percentages of time spent in various microenvironments, the age and condition of indoor microenvironments, natural and urban topography, and outdoor meteorology. Errors in exposur e estimation are linked to the spatial scale of concentration measurements because pollutant tr ansport and dispersion varies over different spatial scales as a function of the many factors menti oned in the previous sentence. Findings related to identifying the ambient components of personal exposure and modes of PM infiltration indoors are discussed in the subsequent subsections with respect to multiple spatial scales.



# Micro-to-Neighborhood Scale Ambient PM Exposure

Near-Roa d Exposures

Sections 3.3 and 3.5 describe the physical and chemical composition of traffic emissions as well as characterization of the plume away from roads. Table 3-24 contains data from recent studies comparing outdoor personal exposure to fixed si te monitors. Only studies where samples were obtained outdoors and compared with a community-based ambient monitoring site were included because indoor microenvironments have penetrati on losses that affect the comparability of the results. Note that some of these studies include d enclosed transportation microenvironments (e.g., cars, buses, subways), but all studies examin ed personal exposure in the outdoor microscale environment. Also note that studies must be reviewed cautiously because most used different instrumentation for personal, microenvironment al, and ambient measurements, and measurement artifacts related to each instrument may differ. The Violante et al. (2006, 156140 ) study showed that outdoor personal exposure to PM 10 

### was significantly higher than fixed community-based ambient
PM 10 

### measurements in downtown Bologna, It aly. Likewise, the Kaur et al. (2005, 088175 ), Kaur et al. (2005, 086504 ), and Adams et al. (2001, 019350 ) studies showed PM 2.5 

### measurements to be significantly higher than fixed community-based ambient PM 2.5 

### monitoring site measurements in central London, U.K. Kinney et al. (2000, 001774 ) performed personal exposure monitoring on study volunteers on streets in Manhattan and showed that PM 2.5 

### concentrations were not significantly different from ambient PM 2.5 

### measurements; this is more consistent with the urban-scale homogeneity in concentration of PM 2.5



### . Morwaska et al. (2008, 191006 ) stated that UFP number concentrations in the near-road environment were roughly 18 times higher than in a non-urban background environment, while measured concentrations in street canyons and tunnels were 27 and 64 times higher, respectively

December 2009 3-162

### than background. This suggests that trapping of sou rces in a semi-enclosed environment can lead to higher UFP exposures. Additionally, fresh emission of short-lived UFPs would explain substantially higher concentrations near the site of emission. By sampling UFP number concentrations at multiple sites in Los Angeles, Moore et al. (2009, 191004 ) demonstrated five- to seven-fold differences between concentrations measured directly next to a freeway and an oceanside site during morning rush hour with substantial variability among sites throughout the day. When comparing sampling campaign data for clear weather and rainy days next to the I-710 freeway in Los Angeles, Ntziachristos et al. (2007, 089164 ) found that particle number concentration obtained with a CPC was 2.4 times higher in clear weather than when rain ing; particle surface area was 3.7 times larger in clear weather; and, black carbon concentration wa s 1.7 times higher in clear weather. However, SMPS data reported for rainy day particle number c oncentrations were almost 29 times higher in this study. Likewise, Zhou and Levy (2007, 098633 ) noted in a meta-analysis of near-road studies that the concentrations are generally el evated within 300-400 m of a roadway for EC and UFPs. Kinney et al. (2000, 001774 ) showed EC to increase linearly with increasing traffic counts and large spatial variations in two sites that had concentrations significantly higher than ambient measurements. These observations suggest caution should be taken regarding the representativeness of community averaged monitoring data for assessing exposures. Particle chemistry is also an important consi deration, because exposure may differ among PM components. Farmer et al. (2003, 089017 ) found that exposure to particle-bound PAHs, including benzo[a]pyrene, can be 2-3 times higher among th ose routinely exposed to outdoor traffic emissions (e.g., police, bus drivers) compared with cont rol subjects. Particle-bound PAH exposure can also vary with vehicle operation. Fo r example, Kinsey et al. (2007, 190073 ) estimated from continuous idling and restart school bus operating conditions (wit hout retrofitting) that over a 10-min period of waiting at a bus stop, continuous idling resulted in exposure to 33% more particle-bound PAH than in the case where the bus was restarted 2 min into the simulation and idled for 8 min. Continuous idling produced approximately 34 times more partic le-bound PAH than in another scenario where the bus was off for 10 min then restarted.

December 2009 3-163

## Table 3-24. Examples of studies comparing near-r oad personal exposures with fixed site ambient concentrations.

Reference and Site Ambient monitors Personal monitors Microenvironment, other variables Ambient v. Personal Asso ciation Primary Findings

Violante et al. (2006, 156140 )

Bologna, Italy Fixed PM 10 and benzene monitoring station (method not specified). Active pump with PM 10 PEM, passive sample for benzene desorbed and analyzed by GC-MS. Localized traffic density (vehicles/h);

Meteorology (wind speed, wind direction, visibility, relative humidity). Personal: 185.10 ± 38.52 μg/m 3

Fixed: 43.56 ± 24.10 μg/m 3 (p<0.0001) Fixed PM 10 correlated with multivariate model of traffic and meteorology, but not personal PM 10 ; relationship between benzene and PM 10 not explored.

Kaur et al. (2005, 086504 )

London, U.K. Fixed TEOM for PM 2.5 and fixed CO monitor at ambient and curbside sites. High flow personal samplers for PM 2.5 , P-Trak monitors for UFP, Langan T15 and T15v for CO. Exposures stratified by mode of transport (walk, cycle, bus, car, taxi). Average PM 2.5 sampled by TEOM was 3 times lower than average personal PM 2.5 sample, and 8 times lower than maximum personal PM 2.5 sample. PM 2.5 exposures during walking significantly lower than during car and taxi rides, UFP exposures during walking significantly lower than bus and car rides, cycling exposures to PM 2.5 and UFP not significantly different from those on bus, car, or taxi.

Kaur et al. (2005, 088175 )

London, U.K. Fixed TEOM for PM 2.5 and fixed CO monitor at ambient and curbside sites. High flow personal samplers for PM 2.5 analyzed post-sample for reflectance for EC, P-Trak monitors for UFP, Langan T15 and T15v for CO. Volunteers walking at set times and directions along Marylebone Rd in London. Fixed vs. personal PM 2.5 : slope = 0.29, R = 0.6; personal PM 2.5 measurements were >2 times background levels and more than 15 μg/m 3 greater than curbside measurements. Pedestrian exposures were significantly higher than fixed site curbside or ambient measurements. Results indicate that exposure declined up to 10% from curbside to building edge within a street
canyon.

Adams et al. (2001, 019350 )

London, U.K. Fixed TEOM for PM 2.5 and fixed CO monitor at ambient and curbside sites. High flow personal samplers for PM 2.5

. Exposures stratified by mode of transport (cycle, bus, car, subway). Median values: (μg/m 3 )

Summer Winter Cycle 34.5 23.5 Bus 39.0 38.9 Car 37.7 33.7 Subway 247.2 157.3 Fixed 15 13 Curb 24 37 Exposures were 2.3-16.5 times higher than ambient and 1.4-10.3 times higher than curbside during summer. During winter, only subway exposures were appreciably higher (4.3 times) than curbside.

Kinney et al. (2000, 001774 )

New York City, NY (Harlem) Ambient site filter in greased impactor with pump for PM 2.5 ; absorbance testing on filter for EC. Three high traffic sites filter in greased impactor with pump; absorbance testing on filter for EC. Localized traffic density (vehicles/h). Mean values: (μg/m 3 )

PM 2.5 45.7 (10.1) EC Site 1 6.2 (1.9) Site 2 47.1 (16.4) 3.7 (0.6) Site 3 36.6 (10.8) 2.3 (0.9) Ambient 38.7 (10.9) 1.5 (0.5) PM 2.5 at high traffic sites was not significantly higher than ambient; EC was significantly higher than ambient at 2 sites. EC increased linearly with traffic counts.



## In-Vehicle and In-Transit Exposures

In-vehicle pollution has been identified in various studies as a source of exposure to PM 2.5 

### , PM 10, 

### and UFPs (Briggs et al., 2008, 156294 ; Diapouli et al., 2007, 156397 ; Fruin et al., 2008, 097183 ; Gómez-Perales et al., 2004, 054418 ; Gómez-Perales et al., 2007, 138816 ; Gulliver and Briggs, 2004, 053238 ; Gulliver and Briggs, 2007, 155814 ; Rossner et al., 2008, 156927 ; Sabin et al., 2005, 087728 ). Results from recent studies are provided in Table A-60 of Annex A. In many of these studies, in-vehicle exposures are shown to be compar able to or less than that of walkers on the same route. Typically, in-vehicle exposures were also higher than community-based ambient monitor concentrations for TSP and PM 10 

### (Diapouli et al., 2008, 190119 ). Curbside measurements of UFPs and PM 2.5 

### obtained at a fixed site in the Kaur et al. (2005, 088175 ; 2005, 086504 ) studies were generally lower than exposures during transit, incl uding during walking and cycling. In contrast, the Adams et al. (2001, 019350 ) study demonstrated that fixed site PM 2.5 

### concentrations were higher than curbside during the summer and lower than curbside during the winter. As particle size decreased to the fine and UF range, less difference between in-vehicle and ambient concentrations was observed for PM mass or count, with the exception of the Diapouli et al. (2008, 190119 ) study where in-bus UFP concentrations were several times higher than indoor or outdoor residential and school concentrations.

December 2009 3-164

### Fruin et al. (2008, 097183 ) and Westerdahl et al. (2005, 086502 ) observed that in-vehicle UFP concentrations increased for freeways in comparison with arterial roads. They estimated that 36% of exposure to UFPs occurred during a total daily comm uting time of 1.5 h (6% of the day) in Los Angeles; 22% of total exposure occurred during 0.5 h spent on freeways. Gong et al. (2009, 190124 ) demonstrated that UFP deposition rate increased with decreasing particle size (down to ~30 nm) and increasing surface area inside the vehicle, wh ere deposited PM on the seats and dashboard can be resuspended and then inhaled or ingested. UFP dep osition rate also rose slightly with increased number of passengers. Zhu et al. (2007, 179919 ) found that in-vehicle UFP counts were 85% lower than outdoors when the fan was operating in recirc ulation mode. They estimat ed that a 1-h commute (4% of the day) accounts for 10-50% of daily exposure to UFPs generated by traffic. Based on the American Time Use Survey estimation of an average of 70.2 min spent in vehicles per person each day (U.S. Bureau of Labor Statistics http://www.bls.gov/tus/ ), cumulative in-vehicle exposure can become important. In a study of PM 2.5 

### exposure on school buses, Adar et al. (2008, 191200 ) found that PM 2.5 

### on school buses was 2 times higher than on-road levels and 4 times higher than central site measurements. Sabin et al. (2005, 087728 ) demonstrated for school buses that emission control technologies had a significant impact on in-bus concentrations of black carbon mass, and Hammond et al. (2007, 190135 ) demonstrated significant reductions of pa rticle number concentration measured for 0.02-1 μ m particles when comparing buses using clean diesel or retrofits compared with nonretrofitted buses. Although not tested here for ot her vehicle types with respect to PM, these findings suggest that a portion of in-vehicle concentrations are due to self-pollution, defined by Behrentz et al. (2004, 155682 ) as the fraction of a vehicle’s own exha ust entering the vehicle microenvironment. Behrentz et al. (2004, 155682 ) tested self-pollution with school buses using SF 6 

### tracer gas and demonstrated that 0.3% of in-vehicle air comes from self-pollution, and that this number was roughly 10 times greater than in-vehicle concentrations related to self-pollution on newer buses. The Behrentz et al. (2004, 155682 ) study also measured EC and particle-bound PAH and found that 25% of the variability in EC concentration was re lated to self-pollution. Adar et al. (2008, 191200 ) estimated that 35.5% of PM 2.5 

### mass on school buses was from self-pollution. These findings are important for exposure estimation when partitioni ng local and ambient sources of pollution during transport in vehicles.



# 3.8.4.2. Ambient PM Exposure Estimates from Central Site Monitoring Data

The follo wing paragraphs describe studies that estimate personal exposure to ambient PM from central site monitoring data. As shown in Figure 3-93 , the large majority of an individual’s time is spent indoors. Although calculation of infiltrat ion and indoor personal exposure is an important part of this assessment, such exposures are described with respect to central site monitors. Assessing population-level exposure at the urban scale is particularly relevant for epidemiologic studies, which typically provide information on the relationship be tween health effects and community-averaged, rather than individual, exposure. Indoor or other nonambient sources could significant ly affect assessment of a person’s total exposure to many pollutants. For this reason, many studies use PM components to estimate infiltration of ambient PM to indoor environments. Wilson et al. (2000, 010288 ) first proposed that SO 4 2– 

### could be used as a tracer of the ambient PM 2.5 

### infiltration rate. Sarnat et al. (2002, 037056 ) also noted that it is reasonable to assume that the size distribution of ambient SO 4 2– 

### particles is sufficiently similar to the size distribution of ambient PM 2.5 

### , and therefore that the ambient SO 4 2– 

### to personal SO 4 2– 

### ratio is an acceptable surrogate for the ratio of the ambient PM 2.5 

### exposure to the ambient PM 2.5 

### concentration. Sulfate has been used this way in several studies, including Ebelt et al. (2005, 056907 ), Wallace and Williams (2005, 057485 ; 2006, 089190 ) and Wilson and Brauer (2006, 088933 ). For this method to be successful, indoor or other nonambient sources of the tracer must be small compared to ambient sources over the period of sampling. Wilson and Brauer (2006, 088933 ) noted that environmental tobacco smoke and tap water used in showers or humidifiers are indoor sources of SO 4 2– 

### . Other concerns in using SO 4 2– 

### as a tracer for PM 2.5 

### arise because SO 4 2– 

### tends to be concentrated in the accumulation mode and thus it might not capture any coarse PM found in the upper end of the PM 2.5 

### distribution, which can include larger pa rticles in the tail end of the coarse mode (Wallace and Williams, 2005, 057485 ). Strand et al. (2007, 157018 ) suggested that Fe be used as an additional tracer to correct for the infiltration of larger PM 2.5 

### particles. Their study took place in Denver, where indoor sources of Fe were sma ll. However, there could be more substantial

December 2009 3-165

### contributions from tracking iron in soil indoors in other locations. The spatial variability of Fe is also larger than that of PM 2.5 

### across urban areas. Volatilization of nitrate or organic compounds after infiltration of PM 2.5 

### indoors could lead to bias in e xposure estimates (Sarnat et al., 2006, 089166 ). This could be a large problem in areas in whic h PM contains a large semi-volatile component. Figure 3-94 shows total exposure to SO 4 2– 

### as a function of measured ambient SO 4 2–



### concentration. Figure 3-95 shows estimated ambient exposure to PM 2.5 

### as a function of measured ambient PM 2.5 

### concentration, where ambient personal exposure is calculated from the ambient exposure factor for SO 4 2– 

### . Close agreement between these figures can be observed. Figure 3-96 shows total exposure to PM 2.5 

### as a function of measured ambient PM 2.5 

### concentration. However, the total exposure to PM 2.5 

### shows virtually no association with ambient PM 2.5 

### because it contains nonambient contributions to PM 2.5



### .

Source: Reprinted with Permission of Nature Publishing Group from Wilson and Brauer (2006, 088933 )



## Figure 3-94. Total exposure to SO 4 2– 

## as a function of measured ambient SO 4 2– 

## concentration, from the Vancouver study. Vancouver British Columbia, April-September 1998, with 16 non-smoking subjects aged 54-86 yr.

Source: Reprinted with Permission of Nature Publishing Group from Wilson and Brauer (2006, 088933 )



## Figure 3-95. Estimated ambient exposure to PM 2.5 

## as a function of measured ambient PM 2.5

## concentration, from the Vancouver study (Vancouver, British Columbia, AprilSeptember 1998, with 16 non-smoking subjects aged 54-86 yr).

December 2009 3-166Source: Reprinted with Permission of Nature Publishing Group from Wilson and Brauer (2006, 088933 )



## Figure 3-96. Total exposure to PM 2.5 

## as a function of measured ambient PM 2.5 

## concentration, from the Vancouver study. Vancouver, British Columbia, April-September 1998, with 16 non-smoking subjects aged 54-86 yr.

The estimated ambient exposure to PM 2.5 

### is well correlated with measured ambient PM 2.5 

### concentration with zero intercept, implying that nonambient sources were minor. This technique works well in areas where SO 4 2- 

### is a regional pollutant, because its spatial variability is small(Kim et al., 2005, 083181 ; U.S. EPA, 2004, 056905 ). Wilson and Brauer (2006, 088933 ) reported that the pooled Pearson correlation coefficient was 0.79 for personal ambient exposures (estimated by the tracer element method) vs. ambient concentrations of PM 2.5 

### , and it was 0.001 for personal nonambient PM 2.5 

### exposure vs. ambient concentrations. Strand et al. (2006, 089203 ) conducted an exposure study in Denver (2002-2004) for 6-12 year-old school children. Up to 10 personal exposure samples were collected on each day, and ambient concentrations were measured simultaneously at a fixed site located at the school. The daily average personal SO 4 2- 

### exposure was strongly associated with ambient SO 4 2- 

### concentration (r = 0.96, 120 > N > 100). Koutrakis et al. (2005, 095800 ) reported the median Spearman correlation coefficients between personal SO 4 2- 

### exposure and ambient SO 4 2- 

### concentration were above 0.60 during both winterand summer in Boston and Baltimore (15 subjects with 12 consecutive measurements during each season in both Boston and Baltimore). For another Baltimore cohort (15 senior subjects with up to 23 consecutive measurements for each person), Hopke et al. (2003, 095544 ) reported that the median Pearson correlation coefficient between personal exposure to the SO 4 2- 

### factor and the ambient SO 4 2- 

### factor was 0.93 (ranging from 0.56 to 0.98 for different subjects), while the median Pear son correlation coefficients were 0.25 for the crustal factor (ranging from -0.46 to 0.66) and 0.22 for a factor whose origin was not identified (ranging from -0.19 to 0.88). The inferences drawn from using the SO 4 2– 

### component of PM 2.5 

### as an indicator for personal exposure to ambient PM 2.5 

### may apply in areas where SO 4 2– 

### is a minor component of PM 2.5 

### or in the absence of significant nonambient sources of SO 4 2– 

### (Sarnat et al., 2001, 019401 ). Source apportionment techniques could also be used, in principle, to derive ambient personal
PM 2.5 

### exposures. They would be especially useful in areas where the application of a tracer method might be problematic. Hopke et al. (2003, 095544 ) noted that four outdoor factors (NH 4 

### NO 3 – 

### and (NH 4 

### ) 2 

### SO 4 

### , secondary SO 4 2- 

### , OC, motor vehicle exhaust) would constitute an estimate of the personal ambient PM 2.5 

### concentration. However, the data used in this portion of the analysis were obtained only with fixed monitors and did not include measurements made by PEMs. They also used the Multilinear Engine to derive factors that were required to contribute jointly to central indoor and outdoor, individual apartment, and PEM samples of a panel of residents. Hopke et al. (2003, 095544 ) used PMF to derive source contributions to community, outdoor, and indoor PM exposures at a retirement facility in Tow son, MD. Hopke et al. (2003, 095544 ) found three sources: SO 4 2–



### , unknown (perhaps combustion related, according to the authors), and soil, jointly contributing 46%, 13%, and 4% of PM 2.5 

### to the PEM samples, respectively. Further source resolution was not possible because there was a lack of data for a number of components in the PEM samples. The largest and most clearly identified contribution to personal exposure was from the SO 4 2- 

### factor. This study also

December 2009 3-167

### determined that a few minor indoor and personal activity sources contributed <10% of the ambient SO 4 2- 

### source to personal exposures. Wilson and Brauer (2006, 088933 ) presented an adaptation of the SO 4 2– 

### method for estimating exposure to PM 10-2.5



### . α is computed based on the SO 4 2– 

### method as the ratio of exposure to ambient SO 4 2– 

### (as measured by a personal monitor) to ambient SO 4 2– 

### concentration. Then, knowing an individual subjects’ time diary and the penetration and loss properties of SO 4 2– 

### , the air exchange rate for an individual location can be calculated from Equation 3-5 and Equation 3-7 . Finally, the penetration and loss rates of PM 10-2.5 

### from the PTEAM database (Ozkaynak et al., 1996, 073986 ) can be input into the individual exposure model along with the individual activity pattern and residential air exchange rate to compute the ambient PM 10-2.5 

### exposure factor and the ambient PM 10-2.5 

### exposure if PM 10-2.5 

### concentration is measured; in the Wilson and Brauer (2006, 088933 ) paper, PM 10-2.5 

### was estimated from ambient PM 10 

### and PM 2.5 

### concentrations. Given that PM 10-2.5 

### deposits more readily and therefore disperses over a shorter distance than PM 2.5 

### , it is possible that use of ambient PM 10-2.5 

### concentration may incur more error than in using this method for PM 2.5 

### . Ebelt et al. (2005, 056907 ) observed in a Vancouver, Canada panel study that the correlation between ambient PM 10-2.5 

### exposure and ambient PM 10 

### exposure (r = 0.72) was lower than the correlation between ambient PM 2.5 

### exposure and ambient PM 10 

### exposure (r = 0.92). This is attributed to both a smaller F inf



### for PM 10-2.5 

### and PM 2.5 

### comprising a greater fraction of the PM 10 

### for the Vancouver study. In this study, PM 10-2.5 

### mass concentration was calculated from the difference between ambient PM 10 

### and PM 2.5 

### mass concentration. Wilson and Brauer (2006, 088933 ) state that their methodology for computing the ambient exposure factor based on the PM 2.5 

### SO 4 2– 

### method can be applied to PM in the 0.1-0.5 μm size range. Little SO 4 2– 

### mass is found below 0.1 μm, so the SO 4 2– 

### tracer method would not be applicable for UFPs. Given the short atmospheric lifetime of UFPs resulting from particle growth and evaporation processes, primary UFPs are most prevalent at mi croscale rather than at an urban spatial scale (Sioutas et al., 2005, 088428 ). Moore et al. (2009, 191004 ) found substantial spatial, hourly, and daily variability in UFP concentration in a satu ration study of Los Angeles. Moore et al. (2009, 191004 ) and Harrison and Jones (2005, 191005 ) also found that UFPs and PM 2.5 

### measurements were poorly correlated at the monitoring sites.



# 3.8.4.3. Infiltration

F

inf



### var ies substantially given a vast array of conditions, and it can best be modeled dynamically based on a distribution of air exchange and deposition or other UF, accumulation mode, fine, and coarse PM loss rates rather than a single value (Bennett and Koutrakis, 2006, 089184 ; Wallace et al., 2006, 089190 ). Given that air exchange rates within a building vary as a function of ambient temperature and pressure, F inf



### is subject to seasonal and regional changes (Meng et al., 2005, 058595 ; Sarnat et al., 2006, 089166 ; Wallace and Williams, 2005, 057485 ). These factors make F inf



### a more accurate descriptor of infiltration th an a simple I/O ratio because the I/O ratio also includes contributions from indoor sources in addition to PM that infiltrates from outdoors. Wallace et al. (2006, 089190 ) identified several significant factors affecting F inf



### , including window opening, age of an indoor microenvironment, number of occ upants, location on a dirt road, dryer usage, and air conditioning usage. This term becomes even more complex when one considers transformation of the size distribution and chemical composition of th e PM through chemical reactions on the particle surface, agglomeration, growth, and evaporation given that F inf



### depends on particle size (Keller and Siegmann, 2001, 025881 ). F inf



### for PM is influenced by physical mechanisms, such as Brownian diffusion, thermophoresis, and impaction, all of which are functions of particle size (Bennett and Koutrakis, 2006, 089184 ; Tung et al., 1999, 049003 ). These differential effects are summarized below. Recent studies on infiltration are summarized in Table A-64 of Annex A. F inf



### and I/O are listed where available, although it is recognized th at I/O is not as meaningful a descriptor but provides an approximation of F inf



### . A number of studies have examined the impa ct of season on PM infiltration. Season is important because it affects the ventilation prac tices used (e.g., open windows, air conditioning or heating use) and ambient temperature and humidity conditions influence the transport, dispersion, and size distribution of the PM. Pandian et al. (1998, 090552 ) found that nationwide residential air exchange rates vary by season as: summer > spring > winter > fall with summer air exchange roughly 1.5-2 times greater than average air exchange rate for the entire year because the rates are driven by home air conditioning and heating usage. Allen (2003, 053578 ) provided information on

December 2009 3-168

### the range and distribution of F inf



### for PM 2.5 

### at 44 residences in Seattle. The mean F inf



### was calculated using light scattering measurements in a recursiv e mass balance method with no species data. For all sampling days, F inf



### (± SD) was 0.65 ± 0.21. Differences in infiltration were observed for the heating season (0.53 ± 0.16), when windows would be expected to be closed, and for the non-heating season (0.79 ± 0.18). Residences with open windows had a mean F inf



### of 0.69 vs. 0.58 for residences with closed windows. The authors combined the light scattering results with indoor and outdoor sulfur measurements to estimate that 79 ± 17% of indoor PM 2.5 

### was generated outdoors. This study provides important data on the distribution of residential F inf



### values and illustrates the magnitude of the effect of season and window position on infiltration rates. Barn et al. (2008, 156252 ) and Baxter et al. (2007, 092725 ) also noted that window opening was an important variable. Barn et al. (2008, 156252 ) found F inf



### of 0.61 ± 0.27 for 13 homes during summer and 0.27 ± 0.18 for 19 homes during winter in Canada for PM 2.5 

### from forest fires and wood smoke. Likewise, location could impact residential ve ntilation practices and infiltration. Using the SO 4 2- 

### method for estimating PM 2.5 

### infiltration, Cohen et al. (2009, 190639 ) noted differences in median infiltration among eight areas (including three comprising the Los Angeles region and two comprising the New York City region). Indoor-outdoor SO 4 2– 

### ratio was noted to be highest in New York City (median: 0.85) and Los Angeles (median: 0.84) and lowest in St. Paul (median: 0.54). Pandian et al. (1998, 090552 ) observed that residential air exchange rates vary by region as: southwest > southeast > northeast > northwest, which re flects regional use of air conditioning. Sarnat et al. (2006, 089166 ) noted differences in PM 2.5 

### infiltration between coastal and inland residences, although these differences were not statistically significant.



## Differential Infiltration Related to PM Size

Differential infiltration as a function of particle size has been observed to occur. Infiltration factors for particle diameters ranging from 20 nm to 10 μ m were measured using continuous SMPSAPS monitoring in Boston by Long et al. (2001, 011526 ) during summer and fall for nighttime periods, when personal activity patterns would be less likely to generate indoor PM. The maximum infiltration factor was reported for particles be tween 80 and 500 nm to range from 0.8 to 1.0. Summer values were uniformly higher than fall values, consistent with higher observed air exchange rates. The infiltration factor decreased with size above 500 nm, reaching 0.1-0.2 for 6-10 μ m particles. Particles sma ller than 80 nm also were reported to have lower infiltration factors. This demonstrates the size dependence of PM infiltrati on, which has been further studied by recent investigators. Sarnat et al. (2006, 089166 ) examined infiltration as a function of particle size and found that I/O varies by particle diameter, as measured by a SMPS-APS system. Figure 3-97 presents I/O values for size fractions ranging from 0.02 to 10 μ m. The maximum infiltration was observed around the accumulation mode (0.1-0.5 μ m), with I/O = 0.7-0.8. Reduced infiltration was observed for coarse-mode PM (0.1-0.2 for D p 

### = 5-10 μ m) and, to a lesser extent, UFPs (0.5-0.7 for
D

p 

### = 0.02-0.1 μ m). This is consistent with increased re moval mechanisms for those size fractions. Deposition is caused by settling for coarse-mode pa rticles. Deposition of UFP can occur by diffusion leading to agglomeration into larger particles and subsequent settling, as well as losses to walls.

December 2009 3-169Source: Reprinted with Permission of Air & Waste Management Association from Sarnat et al. (2006, 089166 ).



## Figure 3-97. F inf



## as a function of particle size.



# 3.8.5. Multicomponent and Multipollutant PM Exposures

3.8. 5.1. Ex posure Issues Related to PM Composition

Annex A presents exposure studies that include chemical speciation data in Table A-62. Some of these studies focused on SO 4 2– 

### , NO 3 – 

### , or carbonaceous aerosols (EC, OC, particle-bound PAHs), while others measured concentrations of trace elemen ts from crustal (Ca, Fe, Mn, K, Al, S, Cl in salt), mobile (Al, Ca, Fe, K, Mg, Na, Ba, Cr, Cu , Mn, Ni, Pb, S, Ti, V, and Zn), or industrial (particle-bound Hg, Cl, V, Zn, Ti, Cu, Pb) sources. A number of source apportionment studies have been performed over the last five years to dete rmine the contribution of outdoor sources to indoor and personal PM constituents. Source apportionment studies by Kim et al. (2005, 083181 ), Hopke et al. (2003, 095544 ) and Zhao et al. (2006, 156181 ) have shown that secondary SO 4 2– 

### provides the largest ambient contribution to personal and indoor exposures. Thes e studies took place in Baltimore, MD and Raleigh/Chapel Hill, NC. In a source apportionm ent study in Seattle, vegetative burning was the most significant source of outdoor origin (Larson et al., 2004, 098145 ). Zhao et al. (2007, 156182 ) performed a source apportionment study of personal exposure to PM 2.5 

### among residents in Denver and also observed lower contributions from secondary SO 4 2– 

### in comparison with motor vehicle emissions and secondary NO 3 – 

### . This suggests that personal exposure to SO 4 2– 

### in parts of the West is lower than in the Mid-Atlantic. These observati ons are consistent with the composition distribution shown in Figure 3-17 and Figure 3-18 . Viana et al. (2008, 156135 ) selected 4 sites of varying population density to represent exposures of preg nant subjects in an early childhood epidemiologic study. Viana et al. (2008, 156135 ) analyzed PM 2.5 

### and PM 10 

### samples for several species along urban-to-rural gradients centered in Valencia, Sp ain and found gradients for both size fractions in anthropogenically-generated SO 4 2– 

### , OC, EC, NO 3 - 

### , Fe, and NH 4 + 

### , but not in mineral species.

December 2009 3-170

### Combined, these findings suggest urban- and regi onal-scale variation in species composition can influence exposure estimates. Personal PM exposure studies including source apportionment analysis along with chemical speciation are presented in Annex A, Table A-63. Source apportionment for carbonaceous aerosols is complicated by the fact that they can be derived from indoor and outdoor combustion sources. Carbonaceous aerosols are difficult to trace to specific indoor and outdoor sources because combustion is widespread. Sørensen et al. (2005, 089428 ), Ho et al. (2004, 056804 ), Larson et al. (2004, 098145 ), and Jansen et al. (2005, 082236 ) all found that personal and microenvironmental exposure to total carbon or BC was lower than that measured outdoors, while Sarnat et al. (2006, 089166 ) showed significant associations between personal and ambient measurements of EC for measurements taken during the fall for low and high ventilation conditions (slope = 0.66-0.73) and during the summer for high ventilation conditions (slope = 0.41). Wu et al. (2006, 179950 ), Delfino et al. (2006, 090745 ), Olson and Norris (2005, 156005 ) and Turpin et al. (2007, 157062 ) all demonstrated much higher levels of OC compared with EC in personal samples, possibly due to indoor sou rces of OC from cooking and home heating. Reff et al. (2007, 156045 ) and Meng et al. (2007, 194618 ) both reported findings from the Relationships between Indoor, Outdoor, and Personal Air (RIOPA ) study in Los Angeles, Houston, and Elizabeth, NJ. Results from Reff et al. (2007, 156045 ) reveal significantly higher detection of aliphatic C-H functional groups indoors and in personal samples compared with outdoors ( Figure 3-98 ). This information may help to distinguish carbonaceous compounds of indoor and outdoor origin in future source apportionment studies of PM exposure. Little regional variation in the aliphatic, carbonyl, or SO 4 2– 

### groups tested were reported in this study. In Meng et al. (2007, 194618 ), indoor exposures were shown to decrease for secondary formation aerosols including SO 4 2– 

### but not NO 3 – 

### (not tested) when compared with outdoor concentrations. In this study, indoor exposures to mechanically generated aerosols decreased in comparison with outdoors ( Figure 3-99 ). Trace metal studies have shown variable resu lts regarding personal exposure to ambient constituents. For instance, Molnár et al. (2006, 156773 ) found that personal exposure was higher than outdoor and ambient concentrations for mostly crustal Cl, K, Ca, Ti, Fe, and Cu. However, Adgate et al. (2007, 156196 ) found that personal exposures were higher than ambient for Fe, Mg, K, Zn, Cu, Pb, and Mn but lower than ambien t for Al, Na, and Ti. Larson et al. (2004, 098145 ) found that personal exposure to Ca and Cl were higher than concentrations measured at ambient (central site) and residential outdoor monitors, lower for Fe, K, Mn, and As and the same for Al, Br, Cr, and Cu. Source apportionment for trace metals can vary significantly among cities and over seasons. For instance, in a Baltimore source apportionment study, exposure to Mn could be attributed nearly equally to the Quebec wildfires, roadway wear, and soil, while Pb exposure was largely found to be due to a local incinerator (Ogulei et al., 2006, 119973 ). In this case, the Quebec wildfires were a transient episodic source, while roadway wear and incineration were continuous. However, in Larson et al. (2004, 098145 ), Mn and Pb exposures in Seattle were largely attributable to mobile source and stationary source emissions. For this reason, sour ce composition behavior cannot be generalized for characterizing exposures and resulting health effects across multiple locations or times.

December 2009 3-171Source: Reprinted with Permission of El sevier Ltd. from Reff et al. (2007, 156045 )



## Figure 3-98. Apportionment of aliphatic carbon, carbonyl, and SO 4 2– 

## components of outdoor, indoor, and personal PM 2.5 

## samples, for Los Angeles (top), Elizabeth (center), and Houston (bottom).

December 2009 3-172Source: Reprinted with Permission of ACS from Meng et al. (2007, 194618 ).



## Figure 3-99. Apportionment of infiltrated PM from mechanical generation (top), primary combustion (center), and seco ndary combustion (bottom).

December 2009 3-173

## Differential Infiltration Related to PM Composition

A number of chemical factors influence the te ndency for differential infiltration in PM. Lunden et al. (2003, 156718 ) studied infiltration of BC and OC aerosols and found that F inf



### can vary substantially as a function of gas transport propertie s with differing air exchange rates. This study and Sarnat et al. (2006, 089166 ) also showed that BC aerosol infiltration is considerably higher than infiltration of OC, and that carbonaceous aeros ol infiltration differed substantially from NO 3 – 

### and SO 4 2– 

### aerosols under the same building air exchange conditions. These disparities are likely related to differences in the particle size distribution of PM components, as described in Section 3.8.4.3 . As shown in Figure 3-100 , the composition of indoor PM that has infiltrated from outdoors is different from that of outdoor PM (Meng et al., 2007, 194618 ). In this case, the particles containing photochemical products (primarily accumulation mode) have a higher infiltration rate than the larger (primarily coarse mode) mechanically generate d particles or the smaller primary combustion particles (likely to consist mostly of UFPs in the nucleation or Aitken nuclei mode).

Source: Reprinted with Permission of ACS from Meng et al. (2007, 194618 ).



## Figure 3-100. Results of the positive matrix factorization model showing differences in the mass of outdoor PM and PM that has infiltrated indoors based on source category.

PM species enriched in the accumulation mode, such as SO 4 2– 

### , will infiltrate more efficiently than components with larger size distributions, such as iron (Strand et al., 2007, 157018 ). Lunden et al. (2008, 155949 ) also compared I/O ratios for PM 2.5 

### , total carbon, OC, and BC in an unoccupied house and found the lowest ratio for PM 2.5 

### (0.41 ± 0.2), the highest for BC (0.61 ± 0.2), and intermediate values for total carbon (0.50 ± 0.2) and OC (0.47 ± 0.2). The authors attributed the

December 2009 3-174

### lower PM 2.5 

### I/O ratio to indoor loss of NH 4 

### NO 3 

### aerosol. The authors note that their BC I/O of 0.6 is somewhat lower than BC ratios measured in occupied spaces (Polidori et al., 2007, 156877 ). Conversely, indoor sources in occ upied residences contribute to observed OC I/O ratios greater than 1 in other studies (Polidori et al., 2006, 156876 ; Sawant et al., 2004, 056798 ). Analytical results for
PM 2.5 

### components from the Baxter et al. (2007, 092726 ) study found F inf



### of 0.95 ± 0.07 for S and 0.60 ± 0.04 for V, the two components identifi ed as having no indoor sources and which had I/O ratios significantly less than 1 (Baxter et al., 2007, 092726 ). It is possible that association of V with larger particles of lower penetration efficiency co uld contribute to a lower infiltration rate. Meng et al. (2005, 058595 ) also noted that the lack of indoor sources of S and V result in much lower variability in penetration and loss rates. Volatilization of PM during infiltration can cause differences between the composition of indoor and outdoor PM. NO 3 – 

### , a prevalent PM component year-round in the western U.S. and during winter throughout the mid-western and northeastern states, has a decreased F inf



### due to volatilization of NO 3 – 

### indoors. Sarnat et al. (2006, 089166 ) calculated F inf



### values for NO 3 – 

### , PM 2.5 

### , and BC, and found the values to increase in that order. NO 3 – 

### was low (median = 0.18, IQR = 0.12-0.33), while BC was high (median = 0.84, IQR = 0.70-0. 96); the intermediate value of PM 2.5 

### (median = 0.48, IQR = 0.39-0.57) reflected its composition as a mixture of those two components (among others). Indoor volatilization of NO 3 – 

### enriches indoor ambient PM in other components, creating differences in toxicity between indoor and outdoor ambient PM . The high infiltration of non-volatile BC creates additional sorption sites for organics, including indoor-generated compounds. Meng et al. (2007, 194618 ) found that secondary formation accounts for 55% of indoor PM of outdoor origin, while primary combustion accounts for 43%, and mechan ical generation accounts for 2%. Meng et al. (2007, 194618 ) noted that secondary formation processe s often result in more accumulation mode particles, so that diffusion losses are not as great as for primary combustion particles that are composed primarily of nucleation and condensation size modes ( Figure 3-100 ). Likewise, Polidori et al. (2007, 156877 ) suggest that similarities in the EC an d OC size distributions and infiltration factors reflect low vapor pressure secondary orga nic aerosols in the OC component. Sioutas et al. (2005, 088428 ) suggest that volatilization of UFPs while crossing the building envelope may impede infiltration in this size range. Variations in the presence of outdoor PM indoors, and resulting changes in removal behavior once indoors, relate to the species composition of PM.



# 3.8.5.2. Exposure to PM and Copollutants

Analysis of personal exposure to multipollutant mixtures is an area of growing research. Several multipollutant studies involving UF, fine, a nd coarse PM are presented in Table A-65. Sarnat et al. (2001, 019401 ) found significant associations between personal exposure to PM 2.5 

### and ambient concentrations of O 3 

### , NO 2 

### , CO (significant only for winter), and SO 2 

### in a panel study conducted in Baltimore. Personal exposures to PM 2.5 

### and personal exposures to the gases were not correlated in this study. This result may have arisen in part because personal exposures to the gases were often beneath detection limits of the personal monitoring devices. Schwartz et al. (2007, 090220 ) also used data from the Baltimore panel study to simulate distributions of personal exposures and ambient concentrations of PM 2.5 

### , PM 10 

### , SO 4 2– 

### , NO 2 

### , and O 3 

### . They found that personal exposure to ambient
PM 2.5 

### was significantly associated with ambient concentrations of PM 2.5 

### , NO 2, 

### and O 3 

### (O 3 

### in an inverse relationship). They also reported that personal exposure to SO 4 2– 

### was significantly positively associated with ambient PM 2.5 

### and O 3 

### concentrations. There is evidence that associations between ambient gases and personal exposure to PM 2.5 

### of ambient origin exist but are complex and vary by season and region. Seasonality of the associations could be a result of seasonal variability in photochemistry, source generation, and building ventilation. Sarnat et al. (2005, 087531 ) observed associations between personal exposure to total
PM 2.5 

### and ambient concentrations of O 3 

### , NO 2 

### , and SO 2 

### measured at community-based monitors for groups of healthy senior citizens and school childre n in Boston during the summer. In this study, significant associations between personal exposure to ambient PM 2.5 

### and personal O 3 

### exposures were observed in summer and between personal PM 2.5 

### and personal NO 2 

### in winter and summer, unlike the Baltimore study in which only summertime personal PM 2.5 

### and personal NO 2 

### were associated (Sarnat et al., 2001, 019401 ). In their study of personal exposure to ambient air pollutants in Steubenville, OH, Sarnat et al. (2006, 090489 ) found low but significant associations for ambient O 3 

### with personal PM 2.5 

### , SO 4 2– 

### , and EC in the summer. Low but significant associations between ambient SO 2 

### and personal PM 2.5 

### , and between ambient NO 2 

### and personal EC, were also observed. In the fall,

December 2009 3-175

### ambient O 3 

### had a weak but significant association with personal EC, and SO 2 

### had a weak but significant association with personal SO 4 2– 

### . Ambient NO 2 

### was also significantly associated with personal PM 2.5 

### , SO 4 2– 

### , and EC with somewhat higher coefficient of determination ( R 2 

### = 0.25-0.49) in the fall.



# 3.8.6. Implications of Exposure Assessment Issues for Interpretation of Epidemiologic Studies

Enviro nmental epidemiologic study designs vary by many factors, including study sample size, measurement time interval, study duration, m onitor type, and spatial distribution of the study sample. A panel epidemiology study consists of a rela tively small sample (typically tens) of study participants followed over a period of days to m onths. Time-activity diary studies are examples of panel studies (e.g., Cohen et al., 2009, 190639 ; Elgethun et al., 2003, 190640 ; Johnson et al., 2000, 001660 ; Olson and Burke, 2006, 189951 ), and a microenvironmental model might be applied to represent exposure in this case. Community time -series studies may involve millions of people whose exposure and health status is estimated over the course of a few years using a short monitoring interval (hours to days). Because so many people are involved, community-averaged concentration is typically used as a surrogate for exposure in community time-series studies. Exposures and health effects are spatially aggregated over the time intervals of interest because they are designed to examine health effects and their potential causes at the community level (e.g., Dominici et al., 2000, 005828 ; Peng et al., 2005, 087463 ). A longitudinal cohort epidemiology study typically involves hundreds or thousands of subjects followed over several years or decades. Concentrations are generally aggregated over ti me and by community to estimate exposures (e.g., Dockery et al., 1993, 044457 ; Krewski et al., 2000, 012281 ). The importance of exposure misclassification varies with study design based on the spatial and temporal aspects of the design. Other factors that could influence exposure estima tes in PM epidemiologic studies include source characteristics, particle size distribution, and pa rticle composition. Potential issues that could influence estimates of PM exposure include measur ement, modeling, spatial variability, temporal variability, use of surrogates for PM exposure, and compositional diff erences. These are described in detail in the following sections.



# 3.8.6.1. Measurement Error

Measurem ent Error at Community-Based Ambient Monitors and Exposure Assessment

Community-based ambient monitors are employed for time-series and longitudinal studies, although they can be used for panel studies as well. Section 3.4 discusses potential errors in measuring ambient PM in detail. Because there will likely be some random component to instrumental measurement error, the correlation of the measured PM mass with the true PM mass is expected to be <1. Sheppard et al. (2005, 079176 ) indicate that instrument error in the hourly or daily average concentrations has “the effect of attenuating the estimate of α .” Zeger et al. (2000, 001949 ) suggest that in order for this error to cause substantial bias in later estimation of a health outcome, the measurement error must be strongly correlated with the measured concentrations. Positive and negative artifacts resulting from sampli ng volatile PM may therefore lead to lack of association with health endpoints in time-series an d longitudinal studies. In multicity longitudinal studies, where PM composition and associated ar tifacts may vary across ci ties, the cumulative influence of such artifacts on exposure estimates is more difficult to predict.



## Measurement Error for Pers onal Exposure Monitors

PEMs are primarily used in panel exposure studies to measure total exposure to PM (e.g., Cohen et al., 2009, 190639 ; Elgethun et al., 2003, 190640 ; Johnson et al., 2000, 001660 ; Olson and Burke, 2006, 189951 ). PEMs are specialized monitors that, because people must carry them, have to

December 2009 3-176

### be small, light, quiet, and battery operated or passive. As a result, they may have lower face velocities across the filter and lower pressure drops than ambient-based filter measurements of PM, which typically sample at much higher flow rates, and consequently at much higher face velocities. Light scattering measurements are biased when relative humidity is high and are sensitive to size distribution (Lowenthal et al., 1995, 045134 ; Sioutas et al., 2000, 025223 ). Positive artifacts resulting from adsorption of vapor-phase organic compounds and negative artifacts from evaporation of semi-volatile PM also create challenges for inte rpreting personal exposure monitoring data (e.g., Pang et al., 2002, 030353 ). Olson and Norris (2005, 156005 ) attributed more OC particle mass collection to face velocity differences when usi ng PEMS compared with FRMs. Data quality of PEMs is described in much greater detail in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ). The artifacts listed here could result in either negative or positive sampling bias.



# 3.8.6.2. Model-Related Errors

When mod els are used in lieu of or to suppl ement measurements of ambient PM exposure or community-based ambient PM concentration, it is impor tant to identify errors and uncertainties that could affect estimates of PM-related health effects. Model-related errors are determined by four factors: representativeness of the mathematical model, accuracy of model inputs, scale of model resolution, and model sensitivity. If verification errors related to these four factors are minimized, then the model can be evaluated against physical data to determine how well the model truly captures a real situation (Roache, 1998, 156915 ). Detail of the model design and inputs can have significant impact on validation, as observed in Meng et al.(2005, 058595 ) and Hering (2007, 155839 ). Meng et al. (2005, 058595 ) demonstrated how use of an increasingly more detailed mathematical model decreases the variability of the results with respect to modeled indoor PM 2.5 

### concentration of outdoor origin and to modeled infiltration factor. Hering (2007, 155839 ) compared infiltration model results for PM-based EC, NO 3 – 

### , and SO 4 2– 

### . Model inputs were from a central site monitor only, central site monitor with air exchange data, and detailed inputs related to initial outdoor (outside test building) and indoor concentrations. Use of more detailed inputs resulted in significant reductions in error for indoor EC concentration, smaller improvements for indoor SO 4 2–



### , and negligible improvement in model results for indoor NO 3 – 

### . This illustrates the impact of differential infiltrati on discussed in Sections 3.8.4 and 3.8.5

.

December 2009 3-177Source: Reprinted with Permission of ACS from Isakov et al.(2007, 195880 ).



## Figure 3-101. Grid resolution of the CMAQ model in Philadelphia compared with distribution of census tracts in which exposur e assessment is performed.

For any spatial interpolation models, grid resoluti on is another source of error. Isakov et al. (2007, 195880 ) linked CMAQ with the Hazardous Air Pollutant Exposure Model for exposure assessment in Philadelphia. Their simulation was implemented on a 4 km nested grid within 12 km and 36 km grids to bring the scale of their model from national to urban. However, the census tracts in which Isakov et al. (2007, 195880 ) sought to describe exposure were distributed on a much finer scale ( Figure 3-101 ). They supplemented the CMAQ model with an Industrial Source Complex Short Term (ISCST) dispersion model to resolve th e subgrid scale behavior. If concentrations were averaged across the cell in lieu of a more detailed subgrid representation, Isakov et al. (2007, 195880 ) found that exposures were overestimated by a factor of 2. Appel et al. (2008, 155660 ) noted that their 36 km simulations provided a closer estimate of SO 4 2– 

### aerosol concentration than did their 12 km nested simulation, which overestimated concentrations. Hogrefe et al. (2007, 156561 ) also noted overestimation of the CMAQ model at the 12 km scale, where multiple point interpolation was used to obtain subgrid estimates. Model convergence theory would suggest that the 36 km simulation is not actually more accurate but coincidentally closer to the observed concentrations (Roache, 1998, 156915 ). It is possible that if secondary pollutant s are more regionally dispersed, lower spatial resolution would be required to attain a conver ged solution of the spatial concentration field. However, higher spatial resolution in the simula tion should produce very similar results if the solution is convergent. Use of geospatial statistical methods for grid interpolation, as performed in the SHEDS/MENTOR simulation by Georgopoulos et al. (2005, 080269 ), provides another methodology for grid interpolation. Similar to Isakov et al.(2007, 195880 ), Georgopoulos et al. (2005, 080269 ) linked CMAQ with an exposure model for estimation of neighborhood-scale exposures using a 4 km resolution grid nested within 12 km and 36 km grids. The authors found that CMAQ underestimated PM 2.5 

### concentration at many times during the simulation. Kim et al. (2009, 188446 ) compared results from an exposure model using six different levels of spatial resolution. The model predicted PM 2.5 

### at monitor locations as a function of the mean concentration and spatial and random errors. The 6 levels of spatial resol ution were simulated through assignment of the spatial and random error terms and by defining the distance over which spatial errors are correlated.

December 2009 3-178

### Between monitors, Kim et al. (2009, 188446 ) compared results from assigning nearest monitor values and from kriging. They found that model pr ediction error and bias in health effects estimates decreased with increased spatial resolution of the error terms, as well as with kriging over a nearest monitor scheme. For GIS-based models designed to improve spa tial resolution of exposure estimates, Yanosky et al. (2008, 099467 ) described three sources from which they derived an estimate of total model uncertainty: transient model components, stationary model components, and residual spatial and temporal components of variance. When analyzing relative contributions to uncertainty, they found that unexplained local spatial variability was the largest contributor. With model inputs from PM 10 

### monitors for this study, poor model performance and high uncertainty were observed where monitors were sparsely located. High uncertainties were also calculated in a few select urban areas (New York City, Detroit, Cleveland, and Pittsburgh) where con centrations tend to be higher, although the latter may have been related to the Taylor series approximation used for the residual uncertainty term. Spatial and temporal uncertainties were also reduced when temporal resolution was increased in the model implementation. In his review of various exposure assessment modeling techniques, Jerrett et al. (2005, 092864 ) reviewed source proximity and LUR for appl ication to exposure assessment. The literature contains mixed evidence of the association between health effects and source proximity (e.g., Langholz et al., 2002, 191771 ; Maheswaran and Elliott, 2003, 125271 ; Venn et al., 2000, 007895 ; Venn et al., 2001, 023644 ). Jerrett et al. (2005, 092864 ) contend that source proximity modeling is limited because other confounding covariates, such as SES, may be related to source proximity. Additionally, subjects’ time-activity patterns may vary from locations modeled through source proximity. Wind direction and topography may bring PM plumes away from a site located even in very close proximity to the source, so that high concentrations would be found at distances far downwind of a source. Jerrett et al. (2005, 092864 ) state that LUR is an adaptable framework allowing adaptation to localized conditions, but they caution that LUR is limited to fairly homogeneous spatial regions. They point to Briggs’ (2000, 191772 ) simulation of Amsterdam as an example of LUR surfaces produced with little spatial variability. LUR and kriging were both used in the ACS data reanalysis by Krewski et al. (2009, 191193 ) to study mortality as a function of spatial variability in PM 2.5 

### in New York and Los Angeles, as described in Section 3.8.3.4 . The LUR solution produced some observed overpredictions near freeways. Kriged results were compared with LUR for both cities. For New York City, kriging produced slightly attenuated mortality risk estimates, while for Los Angeles, kriging did not exhibit as much spatial variability as LUR. The latter may be due to the fact that the monitoring network in Los Angeles was not situated to capture spatial variability in PM 2.5 

### concentration occurring near areas of high traffic. Despite similarities in the LUR performance, health effects predictions were quite different for New York City and Los Angele s, with increased hazard ratios of 1.56 and 1.39, respectively using the same LUR covariates. Krewski et al. (2009, 191193 ) noted that in New York City, the healthiest (and wealthiest) segment of th e population lived in the most polluted areas, while in Los Angeles, there was a strong association between pollution and mortality. This finding implies that, because geographic regions may differ by multiple factors, such as building and power plant fuel use, roadway design, traffic patterns, and building design, significant variables in an LUR analysis may also differ by region.



# 3.8.6.3. Spatial Variability

For PM, s patial and temporal distribution as a function of particle size and composition also plays a large role in the selection of an exposure model. For instance, use of Equation 3-6 might be employed for a study of ambient PM 2.5 

### exposure because spatial variability in PM 2.5 

### concentration can be low over urban to regional scales in comparison with more spatially variable PM 10-2.5 

### and UFPs, as described in Section 3.8.4 . Spatial issues leading to exposure misclassification are discussed below. In panel studies, exposure error will be introduced if the ambient PM concentration measured at the central site monitor is used as an exposur e surrogate and differs from the actual ambient PM concentration outside a subject’s residence and/or worksite. Filleul et al. (2006, 089862 ) computed exposure based on varying contributions of co mmunity-based ambient monitors (deemed background) and proximal monitors (to represent a receptor) in Le Havre, France for black smoke measurements. They found that using a weighted mean with increasing weight for proximal monitors

December 2009 3-179

### resulted in non-significant but increased mean exposure estimates. Moore et al.’s (2009, 191004 ) finding of high variability in UFPs across Los An geles also suggests that exposure error would occur from using one or a few UFP monitors. In an example using AQS data, PM 10 

### monitors in the Chicago CSA are south of the most populated areas within Chicago (Figure A-8 in Annex A), where intersampler correlations for urban scale PM 10 

### data for several monitor pairs are below 0.4 (Table A23 in Annex A). In another example from AQS data, PM 2.5 

### and PM 10 

### monitor locations in the Riverside CBSA are shown to corre spond more closely with higher p opulation density areas (Figures A-25 and A-26 in Annex A), where urban scale intersampler correlations for both PM 2.5 

### and PM 10 

### are below 0.4 for several monitor pair s. For most cities, intersampler correlation is much higher for
PM 2.5 

### than for PM 10 

### . This is in accord with the findings of Sarnat et al. (2009, 180084 ) where, in an Atlanta time-series study of the effect of spatial variation in concentration on epidemiologic associations, spatially homogeneous PM 2.5 

### and O 3 

### were found to be consistently associated with emergency room visits using any monitor within the study area, while associations were less consistent across monitors for spatially heterogeneous CO and NO 2 

### among the entire population studied. Considering results reported in the litera ture along with inter-sampler correlations (reported in Annex A, Section A.2 for PM 2.5 

### and PM 10 

### , and their corresponding monitor locations shown in Annex A, Section A.1), the magnitude of spatial exposure error likely depends on particle size as well as monitor location, source location, and char acteristics such as urban and natural topography and meteorological trends. Spatial variability among various studies further suggests that use of a single or small number of ambient monitors introduces uncertainty in exp osure assessment panel studies. Violante et al. (2006, 156140 ) studied personal exposures to traffic of parking police in Bologna, Italy to determine how personal exposure to outdoor PM 10 

### and benzene compares with that measured at a community-based monitor. This study found that personal exposures to PM 10 

### were significantly higher than at the community-based monitor, a lthough the authors were not able to demonstrate significant effects of meteorology or traffi c on those exposures. Nerriere et al. (2007, 156801 ) observed spatial heterogeneity of personal exposures to metals in PM 2.5 

### and PM 10 

### , with higher levels found near high-traffic and industrial areas. In a Ba yesian hierarchical model analysis of personal exposure and ambient PM 2.5 

### data from the pilot Baltimore Ep idemiology-Exposure Panel Study of 16 subjects, McBride et al. (2007, 124058 ) showed that community monitors overestimated personal exposures for the panel subjects, and that these results were not sensitive to model selection. For community time-series epid emiology, the community-average concentration, not the concentration at each fixed monitoring site, is the concentration variable of concern (Zeger et al., 2000, 001949 ). Because variation in trends is of interest, bias in the central site monitor data will not affect health effects estimates unless the central site monitor is not correlated with the communityaverage concentration. The latter condition will cause the health effect estimate to be biased towards the null (Sheppard et al., 2005, 079176 ). The correlation between the concentration at a central community ambient monitor and the community-ave rage concentration depends on homogeneity of the spatial distribution and representativeness of the central-site monitor location. Kim et al. (2005, 083181 ) noted that spatial variability among PM species can add uncertainty to exposure estimates in community time-series epidemiology studies exploring source contributions to health effects. The monitoring site is selected to re present the community average of the PM characteristic (mass and/or species) of interest. If the selected site is far from PM sources, then the average measurement may be lower than actual ambient PM concentrations. Likewi se, if the site is selected to measure a “hot spot” or pollution from a nearby source, exposure estimates across the community could be skewed upwards. Intra-urban spatial heterogeneity could affect health effects estimates derived from community time-series studies if a community is divided by urban or natural topographic features or by source locations into several sub-communities that differ in the temporal pattern of pollution. Intra-urban spatial heterogeneity is discussed in detail in Section 3.5 . Community exposure may not be wellrepresented when monitors cover large areas w ith several sub-communitie s having different sources and topographies. This point is illustrated for Los Angeles in Figure 3-27 and Figure 3-37 where intersampler correlation decreases with respect to distance moreso than for other cities shown. Using zip code classified mortality data in a study of SE S and acute cardiovascular mortality in Phoenix, high risk ratios were computed when a small area near the monitoring site was studied (Mar et al., 2003, 042841 ; Wilson et al., 2007, 157149 ), while use of larger-area county-wide data produced non-significant associations (Moolgavkar, 2000, 010305 ; Smith et al., 2000, 010335 ). At least part of the heterogeneity found between cities in multicity studies may be due to the use of a large

December 2009 3-180

### geographic area that is composed of several sub- communities that differ in the spatiotemporal distribution of air pollutants. Note that when zi p codes cover large areas (e.g., in western mountain states) or when counties cover small areas (e.g., in the northeast), then assumptions change regarding use of zip code- or county-level data for ep idemiologic studies. For all metropolitan areas investigated in this assessment, the PM 10 

### data have significantly more scatter than PM 2.5 

### . This suggests that the uncertainty of the community average concentration would increase in the coarse PM range. Metrics have been developed and used to compare the spatial variability of air pollutants (Wongphatarakul et al., 1998, 049281 ). These metrics are useful in assessing the potential for exposure error in the epidemiologic studies, especial ly when different monitors are used on different days to construct city-wide averages. Epidemiologic studies of long-term exposure re ly on differences among communities in longterm average ambient concentrations. If exposure er rors are different in the different communities, the differences in long-term ambient concentrations among communities may not represent the differences in long-term average exposures (Dockery et al., 1993, 044457 ). Thus, in a regression of health effects against average concentration as an indicator for average exposure, there could be a different magnitude and direction of error in th e exposure indicator for each spatial area. This could bias the slope up or down. The following epidemiologi c studies, described in detail in Section 7.6, are cited here to illustrate the effect of spatial exposure error on health effects estimates. The Harvard Six-City Study dealt with this issue by design, where the members of the cohort in each city were located in a relatively small area near the monitor (Dockery et al., 1993, 044457 ). In the ACS study, the spatial area was the Metropolitan Statistical Area (MSA) (Krewski et al., 2000, 012281 ); other studies have used counties as the spatial area (Enstrom, 2005, 087356 ; Lipfert et al., 2000, 004087 ). In a comparison of several of the larger long-te rm cohort studies, those using county level spatial areas (Enstrom, 2005, 087356 ; Lipfert et al., 2000, 004087 ) sometimes did not find significant associations, whereas those using MSAs (Pope et al., 1995, 045159 ; Pope et al., 2002, 024689 ) or cities (Dockery et al., 1993, 044457 ) did find significant associations. Jerrett et al. (2005, 087600 ) used smaller zip code areas within Los Angeles Coun ty and found effects that were both significant and largest in magnitude compared to those reporte d for other long-term cohort studies. Krewski et al. (2009, 191193 ) suggested that significant associations between cardiovascular health effects estimates and PM 2.5 

### observed in Los Angeles but not in New York City were related to spatial homogeneity of PM 2.5 

### concentration in New York City. The Nurses’ Health Study examined associations of mortality with PM 10 

### and found higher and more significant associations when using estimated concentrations at subjects’ indi vidual residences (Puett et al., 2008, 156891 ) in lieu of county-level concentrations (Fuentes et al., 2006, 097647 ). These considerations suggest that studies that include large U.S. counties as spatial areas and find no significant associations of health effects with pollution cannot be considered definitive, beca use the likelihood of exposure error increases in this situation. Reducing the exposure error by usi ng concentrations based on residence address or small zip code areas is associated with larger relative risk than those obtained with county-wide averages of concentrations.



# 3.8.6.4. Temporal Variability

Temporal Correlation

Concentration time series analyzed for commu nity time-series epidemiologic studies can include those averaged over several monitors, a si ngle monitor used as an estimate of the true community average exposure, or a monitor used to represent nearby exposures. Within a city, lack of correlation of relevant time series at various sites results in smoothing the exposure surrogate concentration function over time and resulting loss of peak structure from the data series. Burnett and Goldberg (2003, 042798 ) found that community time-series epidemiology results reflect actual population dynamics only when five conditions are met: environmental covariates are fixed spatially but vary temporally; the probability of the health effect estimate is small at any given time; each member of the population has the same probability of the health effect estimate at any given time after adjusting for risk factors; each member of the population is equally affected by environmental covariates; and, if risk factors are averaged acr oss members of the population, they will exhibit smooth temporal variation. For this study, mortal ity was examined, but the temporal considerations

December 2009 3-181

### are generalizable to other health outcomes. Dominici et al. (2000, 005828 ) note that ensuring correlation between ambient and community-average exposure time series is made difficult by limitations in availability and duration of detaile d ambient concentration and exposure time series data and, as a result, is often a source of uncertainty. Sheppard et al. (2005, 079176 ) also add that the health effect estimate can be biased by time-dependent error in α in a time-series study if the spatial variation in PM concentration is not significant. The direction of bias is related to seasonal correlation between α and C

a 

### .



## Seasonality

Community time-series studies can be designed to investigate seasonal effects by incorporating seasonal interaction terms for the exposure surrogate and/or meteorology (e.g., Dominici et al., 2000, 005828 ). Studies from Section 6.5 are briefl y mentioned here to illustrate how seasonal exposures can influence health effect estimates. Bell et al. (2008, 156266 ) and Peng et al. (2005, 087463 ) observed higher health effect estimates and stronger seasonal dependence in the northeast than in the rest of the country for PM 2.5 

### and PM 10 

### , respectively. Peng et al. (2005, 087463 ) stated that these results generated three hypotheses. First, the PM composition and resulting toxicity might vary with season. Bell et al. (2008, 156266 ) showed seasonal differences between respiratory and cardiovascular effect estimates that the aut hors hypothesized related to seasonal differences in dominance of a given PM species. Second, Peng et al. (2005, 087463 ) suggested that less seasonality in regions other than the northeast may reflect re gional tendencies for spending more or less time outdoors. Exposure estimates for time spent outdoors may be less subject to exposure error because uncertainties related to infiltration are not a factor during that time. At the same time, air conditioning usage, which is more common in the summer and in warm climates (Pandian et al., 1998, 090552 ), has been associated with decreased association between PM 2.5 

### and cardiovascular morbidity (Bell et al., 2009, 191007 ). Third, infectious diseases are more prevalent during winter and so may influence health outcomes. However, it wo uld be expected that regions other than the northeast would be affected by influenza in winter. Uncertainty in sources of seasonal bias may also indicate other unknown factors.



## Data Frequency

Most panel and many time-series studies examin e the associations of health outcomes only with exposure (or exposure surrogates) on the day of exposure (lag 0). Zanobetti et al. (2000, 004133 ) and Lokken et al. (2009, 186774 ) suggest that health effects may not occur until subsequent days or be distributed over several days. When PM measurements are obtained every three or six days, it is difficult to refine the study lag structure do wn to the day-level. In studies of the effects of short-term PM 2.5 

### exposure on cardiovascular and respiratory hospitalization in >200 urban U.S. counties, Bell et al. (2008, 156266 ) and Dominici et al. (2006, 088398 ) worked with a combination of air quality measurements obtained daily, those obtained every 3 days, and daily hospitalization data. Time lags of 0, 1, and 2 days were applied, such that PM 2.5 

### data obtained only on day 0 would be applied as lag 0 for the corresponding day’s hospitalization record, as lag 1 for the next day’s hospitalization, and as lag 2 for the following day. No lag 0 data would be available for day 1, and no lag 0 or 1 data would be available for day 2 in this example. This analysis could be performed with sufficient statistical power despite the reduction in days of PM data because a large number of counties were analyzed. Single city studies using data obtained every three or six days could employ the same approach but would lose statistical power compared with daily data because fewer data points would exist for each lag. Likewise, Dominici et al. (2006, 088398 ) only applied distributed lag analysis to the daily hospitalization data where daily PM 2.5 

### concentration data were also available.

December 2009 3-182

# 3.8.6.5. Use of Surrogates for PM Exposure

Surrogat es for Inf iltration Tracers

In panel studies, a tracer can be used for PM th at infiltrates indoors, such as sulfate as a surrogate for PM 2.5 

### , as described in Section 3.8.4 . For this method to be successful, indoor or other nonambient sources of the tracer must be small compared to ambient sources over the period of sampling. Wallace and Williams (2005, 057485 ) observed that, because SO 4 2– 

### particles are typically smaller than other PM contributing to measurable PM 2.5 

### mass, F inf



### may be biased by using this term to describe PM 2.5 

### infiltration. Other concerns in using SO 4 2– 

### as a tracer for PM 2.5 

### arise because SO 4 2– 

### tends to be concentrated in smaller particles and thus it might be a better tracer for fine mode particles than for the coarse fraction at the upper tail of the PM 2.5 

### particle size distribution. Volatilization of ammonium nitrate or organic compounds after infiltration of PM 2.5 

### indoors results in these components being poor surrogates for ambient PM in exposure estimates (Lunden et al., 2003, 081201 ).



## Use of Ambient PM Concentration in Lieu of Ambient PM Exposure

Ambient PM concentration is often used as a surrogate for exposure to ambient PM in epidemiologic studies. The ambient concentration ma y be based on measurements made just outside the primary microenvironment, at the nearest community monitor, at a single community monitor, or as the average of several community monitors. Based on the information presented in Section 3.8.4 related to urban-scale PM distribution, there is less exposure error for accumulation mode PM because it has a more homogeneous spatial distribut ion and higher infiltration indoors, compared with coarse or UFPs. If appropriate measurements are made, it is also possible to estimate the ambient and nonambient components of total person al exposure and use four exposure surrogates in panel epidemiologic studies: C

a 

### , E T 

### , E

a 

### , and E

na 

### (Ebelt et al., 2005, 056907 ; Koenig et al., 2005, 087384 ; Strand et al., 2006, 089203 ; Wilson and Brauer, 2006, 088933 ). Results from Wilson and Brauer (2006, 088933 ) showed that exposure error is introduced by 1) using C

a 

### instead of E

a 

### and 2) assuming E

a 

### and E

na 

### have the same effects on health outco mes. There was essentially no association of the effect with E T 

### or E

na 

### . Wilson and Brauer (2006, 088933 ) noted that exposure to nonambient PM will not affect the relationship between C

a 

### and E

a 

### , but “the difference between ambient concentration and ambient exposure will bias the re lative risk derived from epidemiologic studies.” Strand et al. (2006, 089203 ) also noted that inclusion of nonambient PM 2.5 

### would not be expected to change health effect estimates because ambient and nonambient PM 2.5 

### calculations were not correlated. Zeger et al. (2000, 001949 ) pointed out that for community time-series epidemiology, it is the correlation of the daily community-average personal exposure to the ambient concentration with daily community-average concentration that is impo rtant, not the correlation of each individual’s daily exposure with the daily community-averag e concentration. Thus, the low correlation of individual daily exposure with the daily community-average concentration, as frequently found in pooled panel exposure studies, is not relevant to error in community time-series epidemiologic analysis. Sheppard et al. (2005, 079176 ) also notes that an insufficient number of total personal exposure samples used in a time-series design woul d introduce large classical measurement errors related to high variability in E

na 

### . Sheppard et al. (2005, 079176 ) further maintain that these errors can be minimized by using the average concentration measured at community-based ambient monitors. However, overestimation of the community-average exposure by substituting C

a 

### for E

a 

### leads to underestimation of the effect estimate per unit mass of ambient PM. City-to-city variations in the indoor air exchange rate, related to differen ces in climate or housing stock, will cause city-tocity differences in the health effect endpoint estimate obtained from the study using C

a 

### even if the endpoint remained the same using the community-average exposure.

December 2009 3-183

## Relationship between PM and Copollutants

Uncertainties in the composition of multipolluta nt mixtures of gases and PM to which the population is exposed can introduce uncertainties in health effects estimates. When copollutant associations exist, as described in Section 3.8.5 , the potential for one pollutant to act as a surrogate for another pollutant or mix of pollutants introduces uncertainty into epidemiologic models (Sarnat et al., 2001, 019401 ). For example, O 3 

### may be an indicator of photochemical oxidation products including organic PM. SO 2 

### may be an indicator of Ni emissions from smelters, V from oil fired power plants, or As, Se or Hg from coal-fired po wer plants. In another example, the HEI Report on traffic-related health effects (2009, 191009 ) lists CO, NO 2 

### , PM 2.5 

### and PM 10 

### mass, UFP count, EC, benzene, and traffic metrics (e.g., count, fuel consu mption) all as potential surrogates for traffic or for the mix of all PM and gaseous pollutants in tr affic because all of these pollutants are found in mobile source emissions. Furthermore, in a multipol lutant model, transfer of association can occur by an increase in the slope of a confounding copollutant and a concurrent decrease in the slope of the truly causal covariate. This can occur when copollu tants are highly correlated with larger error for the true copollutant, smaller error for the confounder, and correlation between the copollutant measurement errors (U.S. EPA, 2004, 056905 ; Zeger et al., 2000, 001949 ; Zidek et al., 1996, 051879 ). For these reasons, this is an important area of uncertainty for interpretation of the multipollutant models discussed in Chapter 6.



# 3.8.6.6. Compositional Differences

Differen ces between the composition of ambient PM and the ambient PM that has infiltrated indoors may affect exposure estimates. Numerous di fferential infiltration studies related to indooroutdoor changes in size distri bution and chemical composition are cited in Sections 3.8.4 and 3.8.5

, respectively. If differential infiltration results in differences in PM size distribution and chemical composition between indoor-ambient PM and outdoor-ambient PM, then use of outdoor-ambient PM could bias health effects estimates related to particular species. Baxter et al. (2007, 092726 ) showed that V tends to have lower F inf



### , perhaps because metals exist more in the coarse range, while S has
F

inf



### close to unity. Epidemiologic studies cited in Section 6.6 indicate that significant associations between health effects estimates and PM trace me tal exposures exist and may be modified by season. Trace metal penetration efficiency estimates are thus relevant to those findings. Section 6.6 also discusses significant associations between health effects endpoints and exposure to EC and OC in PM. After initial emission, traffic-related PM is generally in the accumulation mode with volatile components; accumulation mode PM tends to have the highest infiltration factors, but volatile components may be lost during infiltration (Sarnat et al., 2006, 089166 ). If outdoor residential or central-site measurements are used for an exposure surrogate, differences in indoor and outdoor PM composition related to infiltration could introduce uncertainty into effects estimates. Ebelt et al. (2005, 056907 ) illustrated that exposure error occurs when the PM on one or more days is not representative of the normal community PM. Section 6.3.2.1 discusses this COPD panel study of the association between respiratory and cardiovascular measures (i.e., lung function, blood pressure, heart rate, HRV, and ectopic beats) and PM 2.5 

### , PM 10-2.5 

### , and PM 10 

### . In their analysis, one day of dust from the Gobi Desert caused an increase in the concentration of fine and coarse PM. When this day was deleted from the analysis, the associations of health effects estimates with PM 10 

### and especially with PM 10-2.5 

### became larger and more significant. Similar peaks in PM 10 

### concentration have been observed on the Iberian Peninsula as a result of high transport events carrying dust from the Sahara Desert that could affect epidemiologic associations on given days (Artinano et al., 2001, 190099 ).



# 3.8.6.7. Conclusions

This sect ion presents considerations for exposure assessment and the exposure misclassification issues that can potentially affect health effects estimates. These issues can be categorized into six areas: measurement, modeling, spatial variability, temporal variability, use of surrogates for PM exposure, and compositional differ ences. Potential influences of each of these sources on health effects estimates derived from pane l, time-series, and longitudinal epidemiologic studies are described above. Additionally, error sources often interact with each other and are driven

December 2009 3-184

### by particle size distribution. For example, fresh dies el-generated PM is characterized by UFPs that dynamically grow and change in chemical composition over short time and spatial scales, and lack of spatial and temporal resolution in measurements or models can result in misclassifying this exposure (Moore et al., 2009, 191004 ). For this reason, conclusions regarding UFP exposure cannot be drawn from PM 2.5 

### concentration data, in part because PM 2.5 

### concentration is more spatially homogeneous across a city. In most circumstances, exposure error tends to bias a health effect estimate downward (Sheppard et al., 2005, 079176 ; Zeger et al., 2000, 001949 ). Insufficient spatial or temporal resolution to capture true variabil ity and correlation of PM with copollutants are examples of sources of uncertainty that could widen confidence intervals and so potentially reduce the significance of health effects estimates.



# 3.9.

3.9.1.

3.9.1.1.

3.9.1.2. 

# Summary and Conclusions

Conc entrations and Sour ces of Atmospheric PM

This se ction summarizes sources and concentrations of atmospheric PM. The following summaries cover source characteristics from Section 3.3 , measurement techniques from Section 3.4

, spacial and temporal variability an d copollutant correlations from Section 3.5 , source contributions from Section 3.6 and policy relevant background concentrations from Section 3.7

.



# PM Source Characteristics

PM in the a tmosphere contains both primary (i.e ., emitted directly by sources) and secondary components, which can be anthropogenic or natural in origin. Secondary components are produced by the oxidation of precursor gases such as SO 2 

### and NO X 

### and reactions of acidic products with NH 3 

### and organic compounds. Developments in the chemis try of formation of SOA indicate that oligomers are likely a major component of OC in aerosol sa mples. Recent observations suggest that small, but still significant quantities of SOA are formed from isoprene oxidation. Gasoline engines have been found to emit a mix of OC, EC, and nucleati on-mode heavy and large polycyclic aromatic hydrocarbons on which unspent fuel and trace metals condense, while diesel particles are composed of a soot nucleus on which SO 4 2– 

### and hydrocarbons condense. Data from standard emissions tests in which there is insufficient dilution of fresh exhaust from combustion sources tend to overestimate the primary component of organic aerosol at the expense of the semi-volatile components. These semi-volatile components are precursors to sec ondary organic aerosol formation and their oxidation results in more oxidized forms of SOA than pr eviously considered, both in near source urban environments and further downwind.



# Measurement Techniques

The feder al reference methods for PM 2.5 

### and PM 10 

### are based on criteria outlined in the CFR. They are, however, subject to several limitations that should be kept in mind when using compliance monitoring data for health outcome studies. FRM methods are subject to the loss of semi-volatile species such as organic compounds and ammonium nitrate (especially in the West). Since FRM gravimetric methods involve 24-h integrated filter samples, no information is available for variations over shorter averaging times. However, methods ha ve been developed to measure real-time PM 2.5 

### or PM 10 

### mass concentrations (e.g., FDMS-TEOM). New FRMs and FEMs are available for PM 10-2.5 

### and various methods (dichotomous samplers, cascade impa ctors, and passive sampling techniques) are under evaluation to improve PM 10-2.5 

### measurements. Techniques are available to characterize UFP mass, surface area, and number concentrati ons. Continuous and semi-continuous measurement techniques are also available for PM species, such as PILS for multiple ion analysis and AMS formultiple component analysis. Advances have also been achieved in PM organic speciation (e.g., TDGC/MS).

December 2009 3-185

# 3.9.1.3. Ambient PM Variability and Correlations

Advances in understanding the spatiotemporal distribution of PM mass and constituents have recently been made, particularly with regard to PM 2.5 

### mass and chemical composition and UFP concentrations. Emphasis in this ISA was on the period 2005-2007 so that the most recent validated EPA Air Quality System (AQS) data were used. Note, however, that a majority of U.S. counties were not represented by AQS data since their population fell below the regulatory monitoring threshold for PM. Moreover, monitors reporting to AQS were not uniformly distributed across the U.S. or within counties, and conclusions drawn from AQS data may not apply equally to all parts of a geographic region. Furthermore, biases can exist for some PM constituents (and hence total mass) owing to volatilization losses of nitrates and ot her semi-volatile compounds, and, conversely, to retention of particle-bound water by hygroscopic species . The degree of spatial variability in PM is likely to be region-specific and strongly influe nced by region-specific sources and meteorological and topographic conditions.



## Spatial Variability across the U.S.

County-scale, 24-h avg concentration data for PM 2.5 

### between 2005-2007 showed considerable variability across the U.S.. The highest reported 3-yr avg concentrations were for six counties within the San Joaquin Valley and inland southern California, as well as Jefferson County, AL (containing Birmingham) and Allegheny County, PA (containing Pittsburgh). The lowest reported annual average
PM 2.5 

### concentrations were contained within 237 counties distributed throughout many western and northern states as well as Florida and the Carolinas. Of the 15 individual CSAs/CBSAs selected for detailed investigation based on their geographic di stribution and importance in recent health effect studies, the highest mean 24-h PM 2.5 

### concentrations were reported for Riverside (17 μg/m 3 

### ), Birmingham (16 μg/m 3 

### ) and Pittsburgh (16 μg/m 3 

### ); the lowest were reported for Denver (9 μg/m 3 

### ) and Seattle (9 μg/m 3 

### ). Since PM 10-2.5 

### is not routinely measured and reported to AQS, co-located low-volume PM 10 

### and PM 2.5 

### measurements from the AQS network were used to investigate the spatial distribution in
PM 10-2.5 

### . Current data coverage (see Figure 3-10 ) and measurement errors limit the ability to draw any meaningful conclusions regarding the large-scale spatial distribution of PM 10-2.5 

### in urban areas. Only 6 of the 15 CSAs/CBSAs chosen for closer inve stigation had sufficient data for calculating
PM 10-2.5 

### . In general, in the eastern metropolitan area s including Atlanta, Boston, Chicago and New York, most of the mass of PM 10 

### was in the PM 2.5 

### size fraction, with the highest ratio of PM 2.5 

### to PM 10-2.5 

### in Chicago (14 μg/m 3 

### PM 2.5, 

### 5 μg/m 3 

### PM 10-2.5, 

### ratio = 2.8). In contrast, Denver (9 μg/m 3



### PM 2.5 

### , 20 μg/m 3 

### PM 10-2.5 

### , ratio = 0.45) and Phoenix (10 μg/m 3 

### PM 2.5 

### , 22 μg/m 3 

### PM 10-2.5 

### , ratio = 0.45) contained most of PM 10 

### in the thoracic coarse mode. Given the limited informati on available from AQS for PM 10-2.5 

### and the current National Ambient Air Quality Standard for PM 10 

### , analyses were performed on the more prevalent PM 10 

### data acknowledging that PM 10 

### incorporates both thoracic coarse and fine particles. The highest reported 3-yr avg PM 10 

### concentrations (>51 μg/m 3 

### ) occurred in two counties in southern California and five counties in southern Arizona and central New Mexico. The lowest reported annual average PM 10 

### concentrations ( ≤ 20 μg/m 3 

### ) were within 114 counties distributed fairly uniformly across the U.S. Of the 15 CSAs/CBSAs investigated, the highest mean 24-h PM 10 

### concentrations was reported for Phoenix (52 μg/m 3 

### ), considerably higher th an the means for the othe r CSAs/CBSAs investigated. The lowest was reported for Boston (17 μg/m 3 

### ) with New York, Philadelphia and Seattle only slightly higher (19 μg/m 3 

### ). Spatial variability in PM 2.5 

### components obtained from the CSN varied considerably by species. The highest annual average OC concentrations (>5 μg/m 3 

### ) were observed in the western and southeastern U.S. Concentrations in the West peaked in the fall and winter, while concentrations in the Southeast peaked anytime be tween spring and fall. Of the 15 CSAs/CBSAs investigated, OC was the dominant PM 2.5 

### component on an annual basis in the western cities, ranging from 34% of PM 2.5 

### mass in Los Angeles to 58% in Seattle. EC exhibi ted less seasonal variability than OC and was particularly stable in the eastern half of the U.S. Annual average EC concentrations greater than 1.5 μg/m 3 

### were present in Los Angeles, Pittsburgh, New York and El Paso. Concentrations of SO 4 2– 

### were higher in the eastern U.S. resulting from higher SO 2 

### emissions in the East compared with the West. There is also considerable se asonal variability with higher SO 4 2– 

### concentrations in the summer months when the oxidation of SO 2 

### proceeds at a faster rate th an during the winter. Of the 15

December 2009 3-186

### CSAs/CBSAs selected, sulfate was the dominant PM 2.5 

### component on an annual basis in the eastern cities, ranging from 42% of PM 2.5 

### mass in Chicago to 56% in Pittsburgh. NO 3 – 

### concentrations were highest in California, with annual averages >4 μg/m 3 

### at many monitoring locations. There were also elevated concentrations of NO 3 – 

### in the Midwest (>2 μg/m 3 

### ), with wintertime concentrations exceeding 4 μg/m 3 

### . In general, NO 3 – 

### was higher in the winter across the country, resulting from a number of factors including: (1) lower temperatures which favor partitioning into particles; (2) higher relative humidity, mainly in dry areas; (3) lower sulfate, allowing higher uptake of NO 3 – 

### ; and (4) residential wood burning in specific areas of the U.S., especially in the Northwest. Exceptions existed in Los Angeles and Riverside, where high NO 3 – 

### readings appeared year-round. Crustal material constituted a substantial fraction of PM 2.5 

### year-round in Phoenix (28%) and Denver (16%), and during the summer in Houston (26%). Clearly there are variations in both PM 2.5 

### mass and composition by city resulting from numerous controlling variables (e.g., meteorology , the nature of sources, proximity to sources, topography). These variables are frequently poor ly characterized on a broad scale, making it difficult to draw general conclusions regarding PM 2.5 

### mass and composition across all cities within a given geographic region.



## Spatial Variability on the Urban and Neighborhood Scales

In general, PM 2.5 

### has a longer atmospheric lifetime than PM 10-2.5 

### because larger particles have a higher gravitational settling velocity. For PM 2.5 

### , most metropolitan areas exhibited high correlations (generally >0.75) between monitoring sites out to a distance of 100 km. Notable exceptions were Denver, Los Angeles and Rive rside where correlations dropped below 0.75 somewhere between 20 and 50 km. Insufficient data were available in the 15 metropolitan areas to perform similar analyses for PM 10-2.5 

### using co-located, low volume FRM monitors. More abundant
PM 10 

### data, however, showed larger declines in in ter-monitor correlations as a function of distance relative to PM 2.5 

### . Atlanta, Boston, Denver, Los Angeles, New York City, Philadelphia, Phoenix, Pittsburgh and Riverside all showed an average corr elation of 0.75 at 40 km or greater monitor separation while Birmingham, Chicago, Detroit, Houston and St. Louis had correlations that dropped off much more quickly with distance (average correl ation of 0.75 at 6 km or less monitor separation). Furthermore, correlations between PM 10 

### concentrations exhibited substan tially more scatter relative
to PM 2.5 

### . Shorter atmospheric lifetimes for PM 10 

### can result in local emission sources dominating
PM 10 

### annual average mass concentrations at particular monitors. Although the general understanding of PM differential settling leads to an expectation of greater spatial heterogeneity in the PM 10-2.5 

### fraction relative to the PM 2.5 

### fraction in urban areas, deposition of particles as a function of size depends strongly on local meteorological conditions, in particular on the degree of turbulence in the mixing layer. Therefore, the findings from these 15 CSAs/CBSAs may not apply to all locations or at all times. Population density and associated building dens ity are also important determinants of the spatial distribution of PM concentrations. Inte r-sampler correlations as a function of distance between monitors obtained for sampler pairs located less than 4 km apart (i.e., on a neighborhood scale) showed a shallower slope for PM 2.5 

### than for PM 10 

### . The average correlation was 0.93 for PM 2.5 

### , but it dropped to 0.70 for PM 10



### . Few studies have performed direct comparisons of UFP measurements at multiple locations within an urban area. A decrease in the number of UFPs was demonstrated with shifts from a dominant mode at around 10 nm within 20 m of a freeway to a flattened dominant mode at around 50 nm at a distance of roughly 100-150 m. At th e same time, accumulation mode particle number concentration remained relatively constant to within ~300 m from the freeway. These findings suggest a high degree of spatial heterogeneity in UFPs compared with accumulation mode particles on the urban scale.



# 3.9.1.4. Temporal Variability

A steady decrease in PM 2.5 

### concentrations from 1999 (the beginning of nationwide monitoring for PM 2.5 

### ) to 2007 was observed in all 10 EPA Regions, with the 3-yr avg of the 98th percentile of 24-h PM 2.5 

### concentrations dropping 10% over this time period. Similar trends in PM 10 

### concentrations show a steady decline from 1988 to 2007 in all 10 EPA Regions.

December 2009 3-187

### Using hourly PM observations in the 15 metropol itan areas, diel varia tion showed peaks that differ by PM size fraction and region. For PM 2.5 

### , a morning peak was observed starting at approximately 6:00 a.m., corresponding with the start of morning rush hour. There was also an evening PM 2.5 

### concentration peak that was broader than the morning peak and extended into the overnight period, likely reflecting a combination of evening rush hour and the concentration increase caused by the usual collapse of the mixed layer after sundown. PM 2.5 

### concentrations in Pittsburgh remained elevated throughout the night, obscuring the morning peak. For PM 10 

### , all areas showed a morning and afternoon peak in mean concentrations . The magnitude and duration of this peak varied considerably by metropolitan area. Studies indicate that UFPs in urban environments exhibit similar two-peaked diel patterns in Los Angeles and the San Joaquin Valley as well as in Kawasaki City, Japan and Copenhagen, Denmark. The afternoon peak in UFPs likely represents the combination of primary source emissions such as evening rush-hour traffic and photochemical formation of secondary organic aerosol and sulfate. Comparison between weekdays and Sundays as well as an urban street canyon site and an urban background site in this figure suggest traffic is a major source of UFPs within a street canyon during the morning rush hour. Any fluctuations or changes in the timing of the individual daily peaks during the 3-yr period would result in a broadening of the distribution shown in the diel plots.



# 3.9.1.5.

3.9.1.6. Correlations between Copollutants

Correlat ions between PM size fractions and be tween PM and gaseous copollutants including SO 2 

### , NO 2 

### , CO and O 3 

### varied both seasonally and spatially between and within metropolitan areas. On average, PM 10 

### and PM 2.5 

### were correlated with each other better than with the gaseous copollutants. Correlations between PM 10 

### and PM 10-2.5 

### were greater in all locations than correlations between PM 2.5 

### and PM 10-2.5 

### . Correlations between PM 10 

### and PM 10-2.5 

### were particularly high in Denver and Phoenix (r > 0.88 in a ll seasons). There was relatively little seasonal variability in the mean correlation between PM in both size fractions and SO 2 

### and NO 2 

### . CO, however, showed higher correlations with PM 10 

### and PM 2.5 

### on average in the winter compared with the other seasons. This seasonality results in part because a larger fraction of PM is primary in origin during the winter. To the extent that this primary component of PM is associated with common sources of NO 2 

### and CO, then higher correlations with these gaseous copolluta nts are to be expected. Increased atmospheric stability in colder months would also reinforce these associations. The correlation between daily maximum 8-h avg O 3 

### and PM showed the highest degree of seasonal variability with positive correlations on average in the spring, summer and fall, and negative correlations on average in the winter. This situation arises as the result of seasonal differences in PM primary emissions and photochemical production of secondary PM 2.5 

### and O 3



### .



# Source Contributions to PM

Results o f receptor modeling calculations indicate that PM 2.5 

### is produced mainly by combustion of fossil fuel, either by stationary sour ces or by transportation. It is apparent that a relatively small number of source categories, compar ed to the total number of chemical species that typically are measured in ambient monitoring source receptor model studies, are needed to account for the majority of the observed mass of PM in these studies. Trying to be more specific about contributions from source categories could result in ambiguity. For example, quite different mobile sources (e.g., trucks and locomotives) rely on diesel power and ancillary data is required to resolve contributions from these sources. A compilation of study results shows that secondary sulfate (mainly from EGUs), nitrate (from the oxidation of NO X 

### emitted mainly from transportation and EGUs), and primary mobile source categories constitute most of PM 2.5 

### (and PM 10 

### ) in the East. Fugitive dust, found mainly in the PM 10-2.5 

### size range, represents the largest source of ambient PM 10 

### in many locations in the western U.S. Quoted uncertainties in the source apportionment of constituents in ambient aerosol samples typica lly range from 10 to 50%. A comparison of source apportionment techniques indicated that the same major source categories of PM 2.5 

### were consistently identified by several independent groups working w ith the same data sets. Soil-, sulfate-, residual oil-, and salt-associated mass were most clearly id entified by the groups. Other sources with more ambiguous signatures, such as vegetative burning and traffic-related emissions were less consistently identified.

December 2009 3-188

### Spatial variability in source contributions across urban areas is an important consideration in assessing the likelihood of exposure error in epidemiologic studies relating health endpoints to sources. Concepts similar to those for using ambient concentrations as surrogates for personal exposures apply here. Studies for PM 2.5 

### indicate that intra-urban variability increases in the following order: regional (e.g., secondary SO 4 2– 

### from EGUs) < area (e.g., on-road mobile sources) < point (e.g., stacks) sources. Only one study was available for PM 10-2.5 

### , indicating a similar ordering, but without a regional component (resulting from the short lifetime of PM 10-2.5 

### compared to transport times on the regional scale).



# 3.9.1.7.



# 3.9.2.

3.9.2.1. Policy-Relevant Background

The back ground concentrations of PM that are useful for risk and policy assessments informing decisions about the NAAQS are referr ed to as policy-relevant background (PRB) concentrations. PRB concentrations have historical ly been defined by EPA as those concentrations that would occur in the U.S. in the absence of anthropogenic emissions in continental North America defined here as the U.S., Canada, and Mexico . For this document, PRB concentrations include contributions from natural sources everywhere in th e world and from anthropogenic sources outside continental North America. Background concentrat ions so defined facilitated separation of pollution that can be controlled by U.S. regulations or through international agreements with neighboring countries from those that were judged to be generally uncontrollable by the U.S. Over time consideration of potential broader ranging intern ational agreements may lead to alternative determinations of which PM source contributi ons should be considered by EPA as part of PRB. Contributions to PRB concentrations of PM in clude both primary and secondary natural and anthropogenic components. For this document, PRB concentrations for the continental U.S. were estimated using EPA’s CMAQ modeling system, a deterministic CTM and with GEOS-Chem, a global-scale model for CMAQ boundary conditions. PRB concentrations of PM 2.5 

### were estimated to be less than 1 μg/m 3 

### on an annual basis, with maximum daily average values in a range from 3.1 to 20 μg/m 3 

### and having a peak of 63 μg/m 3 

### at the nine national park sites across the U.S. used to evaluate model performance for this analysis. For further information on methods used in modeling of PRB concentrations see Section 3.6 , and for further information on the results of calculation of PRB concentrations see Section 3.7

.


# Human Exposure

This se ction summarizes the findings from the recent exposure assessment literature, which include the assessment of exposure to ambien t PM, infiltration of ambient PM to indoor environments, and source apportionment of PM exp osure. This summary is intended to support the interpretation of the findings from epidemiologic studies. For more detailed explication see Section 3.8

.



# Characterizing Human Exposure

A number of techniques have been applied in the literature to model human exposure to PM. Several studies have used time-weighted microenviro nmental models to define total or ambient PM exposure. Time-activity diaries or global positioni ng systems have been employed to capture the time-basis for those models. Stochastic populati on exposure models, such as APEX and SHEDS, are applied for PM exposure risk assessment among the population. Concentrations from chemistry transport models have also been used to provide input to the stochastic exposure models at particular locations. LUR models have been applied for i ndividual exposures at the intra-urban scale to examine exposure to pollution surrogates, such as tr affic counts, land use, or topographic variables. Source proximity and kriging have also been applie d. GIS-based models have been used to model exposure over large regions (e.g., for the Nurse’s Health Study) using spatial smoothing models of AQS data and incorporating GIS-based and meteor ological covariates. GIS approaches have also been used for intra-urban scale exposure studies. These methods all have their own uses and caveats. LUR is an adaptable framework allowing adaptati on to localized conditions but might best be applied in relatively spatially homogeneous areas. In a comparison of LUR with kriging, kriging produced slightly attenuated mortality risk estimates for New York City, while for Los Angeles,

December 2009 3-189

### kriging did not exhibit as much spatial variability as LUR. Source proximity modeling is relatively simple to apply but is limited because other c onfounding covariates, such as socioeconomic status, may be related to source proximity . Additionally, source proximity models do not incorporate timeactivity data. New advancements in personal and microenvironmental monitoring techniques have been reported. Personal monitoring developments includ e new models of cascade impactors and cyclones to sample in the UF size range and miniature mon itors for species detection. Additionally, new work on microenvironmental modeling using mobile plat forms and GPS technology has been reported. The reader is referred to the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) for descriptions of most real-time and filter-based personal and microenvi ronmental PM monitors currently available.



# 3.9.2.2. Spatial Scales of PM Exposure Assessment

Assessin g population-level exposure at the urban scal e is particularly relevant for time-series epidemiologic studies, which provide information on the relationship between health effects and community-average exposure, rather than variations in individual exposure. The correlation between the PM concentration measured at a central-site community ambient monitor and the true community average concentration depends on the spatial distribution of the PM, location of the monitoring site chosen to represent the community average, and di vision of the community by terrain features or source locations into several sub-communities that di ffer in the temporal pattern of pollution. Concentrations of SO 4 2– 

### and some components of SOA measured at central-site monitors are expected to be uniform in urban areas given the regi onal nature of their sources. However, this is not true for primary components like EC whose sources ar e strongly spatially variable in urban areas. Given that roughly 90% of an individual’s day is spent indoors, assessment of exposure to infiltrated ambient SO 4 2– 

### , whose formation and dispersion also occurs over urban-to-regional scales and whose size distribution is in the accumulation mode, is commonly used to assess ambient PM 2.5 

### exposure. This technique has also been applied to assess PM 10-2.5 

### exposure but likely with more error than for
PM 2.5 

### because PM 10-2.5 

### is more highly spatially variable than PM 2.5 

### . Source apportionment techniques have also been applied to assess urban-scale PM 2.5 

### exposures using community-based ambient monitoring, outdoor, and indoor samples. At micro-to-neighborhood scales, heterogeneity of sources and topography may cause more variability in exposure. This is particularly true for PM 10-2.5 

### and for UFPs, both of which are more highly spatially variable than PM 2.5. 

### Particle chemistry and source behavior also contribute to spatial heterogeneity of PM concentration. Some studies, conducted mainly in Europe, have found personal
PM 2.5 

### and PM 10 

### exposures for pedestrians in street canyons to be higher than ambient concentrations measured by urban background ambient mon itors. Likewise, microenvironmental UFP concentrations were observed to be substantially higher in near-road environments, street canyons, and tunnels when compared with other environments in urban areas. In-vehicle UFP exposures can also be important. As a result, ambient monitors lo cated at background, central urban, road side, or near-residential sites might not reflect peak exposures to individuals who commute. PM infiltration factors, F inf



### , depend on particle size, chemical composition, season, and region of the country. Infiltration can best be modeled dy namically based on a distribution of air exchange and deposition PM loss rates rather than being repr esented by a single value. There is significant variability within and across regions of the country with respect to indoor exposures to ambient PM. Infiltrated ambient PM concentrations depend in pa rt on the ventilation properties of the building or vehicle in which the person is exposed. Season is important to PM infiltration because it affects the ventilation practices used, and ambient temperat ure and humidity conditions affect the transport, dispersion, and size distribution of PM. Residentia l air exchange rates have been observed to be higher in summer for regions with low air conditioning usage, and regional differences in air exchange rates (Southwest < Southeast < Northeast < Northwest) also reflect ventilation practices. Differential infiltration occurs as a function of PM size and composition. PM infiltration is largest for accumulation mode particles, and decreases for UFPs lost to diffusion and for coarse PM lost through inertial impaction mechanisms. Differential infiltration by size fraction can affect exposure estimates if not properly characterized.

December 2009 3-190

# 3.9.2.3.

3.9.2.4. Multicomponent and Multipollutant PM Exposures

Emission inventories and source apportionment studies suggest that sources of PM exposure vary by region. Comparison of studies performed in the eastern U.S. with studies performed in the western U.S. suggest that the contribution of SO 4 2– 

### to personal exposure is higher for the East (16-46%) compared with the West (~4%) and that motor vehicle emissions and secondary NO 3 - 

### are larger sources of personal exposure for the West (~9%) as compared with the East (~4%). Results of source apportionment studies of personal exposure to SO 4 2– 

### indicate that personal SO 4 2– 

### exposures are mainly attributable to ambient sources. Source apportionment for OC and EC is difficult because they originate from both indoor and outdoor sources. Exposure to OC of indoor and outdoor origin can be distinguished by the presence of alipha tic C-H groups generated indoors, since outdoor concentrations of aliphatic C-H are low. Trace metal studies have shown variable results regarding personal exposure to ambient constituents with significant variation among cities and over seasons that can be related to incinerator operation, f ossil fuel combustion, biomass combustion (wildfires), and presence of crustal materials in the built environment, among other sources. Differential infiltration is also affected by variations in particle composition and volatility. For example EC infiltrates more readily than OC. This can lead to outdoor-indoor differentials in PM toxicity. A number of studies have examined whether gaseous copollutants could act as surrogates for exposure to ambient PM. Several studies have concluded that ambient concentrations of O 3 

### , NO 2 

### , and SO 2 

### are associated with the ambient component of personal exposure to total PM 2.5 

### as opposed to the ambient component of personal exposures to the gases. However, in some studies this result may have arisen in part because personal exposure to the gases was often beneath the detection limits of the personal monitoring devices. Thus, the evidence that ambient gases can be considered surrogates of PM 2.5 

### exposure is mixed. It is likely that associations between ambient gases and personal exposure to PM 2.5 

### of ambient origin exist, but they are complex and vary by season and location.



# Implications for Epidemiologic Studies

The impo rtance of exposure error varies with study design based on the spatial and temporal aspects of the design. For PM epidemiology studies, source characteristics, particle size distribution, and particle composition are also important factors in interpreting exposure error for an epidemiology study. Potential sources of error that could influence estimates of PM exposure include measurements, use of surrogates for PM exposure, mode ling, spatial variability, temporal variability, and compositional differences. PM exposure estimates are subject to monitoring and modeling errors. Ambient and personal exposure monitoring errors can bias health effects es timates if the error is strongly correlated with the measurements of concentration. This can be an issue for sampling semi-volatile organic compounds in PM, especially where PM exposures in cities with different PM composition are compared. Ambient monitor height also affects es timates of exposure beca use PM concentration varies as a function of height. Within a street canyon, changes in wind direction and speed cause significant variability over a small distance. Wind tunnel studies have shown street canyon effects exist for suburban settings as well as for heavily urbanized settings. Ad ditionally, model-based exposure estimates are subject to errors related to the spatial resolution of the modeling technique and the measurement-based inputs used. Variations in PM and its components could lead to errors in using ambient PM measures as surrogates for PM exposure. PM 2.5 

### concentrations are relatively well-correlated across monitors in the urban areas examined. Correlation coefficients tend to be lower, and concentration differences tend to be higher between PM 10 

### monitoring sites than between PM 2.5 

### monitoring sites. Likewise, studies have shown UFPs to be more spatially variable across urban areas. Even if PM 2.5 

### , PM 10-2.5 

### , and UFP concentrations measured at sites within an urban area are highly correlated, significant differences in their concentrations can occur on any given day. The degree of urban-scale spatial variability in PM concentrations varies across the c ountry and with size fraction. Current information suggests that UFPs, PM 10-2.5, 

### and many PM components are more spatially variable than PM 2.5 

### . These factors should be considered in using data obt ained from monitoring networks to estimate community-scale human exposure to ambient PM, and caution should be exercised in extrapolating conclusions obtained from one urban area to another.

December 2009 3-191

### Community time-series epidemiologic studies use the average community PM concentration as a surrogate for the average personal exposure to ambient PM. The resulting health effect risk estimate, based on the average community ambient c oncentration, differs from the risk that would be estimated if the average community ambient exposure were used in the epidemiologic study. However, the risk estimate based on the ambient con centration gives the change in health effects resulting from a change in ambient PM concentrati on and is, therefore, an appropriate measure for risk assessment and risk management. Variations in ambient concentrations across a community, variations in individual ambient exposures around the community average, and seasonal or daily variation in the ambient exposure estimate may incr ease standard errors of PM health effects estimates, making it more difficult to detect a true underlying association between the correct exposure metric and the health outcome studied. Likewise, sampling time in terval and lag time selection both determine whether an epidemiologic model captures the phenomena of interest with sufficient resolution. The use of the community average ambient PM 2.5 

### concentration as a surrogate for the community average personal exposure to ambient PM 2.5 

### is not expected to change the principal conclusions from PM 2.5 

### epidemiologic studies that use community average health and pollution data. Several recent studies support this by showing how the ambient component of personal exposure to PM 2.5 

### could be estimated using vari ous tracer and source apportionment techniques and that it is highly correlated with ambient concentrations of PM 2.5 

### . These studies also show that the non-ambient compone nt of personal exposure to PM 2.5 

### is basically uncorrelated with ambient PM 2.5 

### concentrations. For long-term studies that use differences in long-term community average ambient PM concentrations as an exposure metric, the effect of possible community-tocommunity differences in the average ambient exposure factor or in the average non-ambient exposure are less understood. For panel epidemiologic studies, the most appropriate exposure metric may depend on the health outcome measured. However, sufficient information should be obtained to enable determining the association of the health outcome with ambient concentration, ambient exposure, non-ambient exposure, and total personal exposure. Exposure error may occur if a measured PM component acts as a surrogate for another PM constituent. Differences between composition of outdoor and indoor ambient PM may also cause error in exposure assessment related either to diff erential losses of UF or coarse PM from diffusion, evaporation of semi-volatile PM, or impaction. Th e resulting differences in PM size distribution and chemical composition between indoor-ambient PM and outdoor-ambient PM are expected to cause differences in toxicity that could affect health outcomes. Lack of information regarding these relationships adds uncertainty to the health effects estimate.

December 2009 3-192

# Chapter 3 References

ACGIH (2005). TLVs and BEIs: Based on the documentation of th e threshold limit values for chemical substances and physical agents and biological exposur e indices. American Conference of Governmental Industrial Hygienists. Cincinnati, Ohio. 156188

Adams HS; Nieuwenhuijsen MJ; Colvile RN; McMullen MAS; Khandelwal P (2001). Fine particle (PM25) personal exposure levels in transport microenvironments, London, UK. Sci Total Environ, 279: 29-44. 019350

Adar SD; Davey M; Sullivan JR; Compher M; Szpiro A; Li u L-J (2008). Predicting airborne particle levels aboard Washington State school buses . Atmos Environ, 42: 7590-7599. 191200

Adgate JL; Mongin SJ; Pratt GC; Zhang J; Field MP; Ramach andran G; Sexton K (2007). Relationships between personal, indoor, and outdoor exposures to trace elements in PM(2.5). Sci Total Environ, 386: 21-32. 156196

Al-Horr R; Samanta G; Dasgupta PK (2003). A Continuous Anal yzer for Soluble Anionic Constituents and Ammonium in Atmospheric Particulate Matter. Environ Sci Technol, 37: 5711-5720. 153951

Albinet A; Leoz-Garziandia E; Budzinski H; ViIlenave E (2007). Sampling precautions for th e measurement of nitrated polycyclic aromatic hydrocarbons in ambi ent air. Atmos Environ, 41: 4988-4994. 154426

Alexis A; Garcia A; Nystrom M; Rosenkranz K (2001). The 2001 California almanac of emissions and air quality. Retrieved 10-AUG-05, from http://www.arb.ca. gov/aqd/almanac/almanac01/almanac01.htm. 079886

Allen GA; Harrison D; Koutrakis P (2001). A New Method fo r Continuous Measurement of Sulfate in the Ambient Atmosphere. Presented at American Associati on for Aerosol Research, 20th Annual Conference. 156205

Allen R; Larson T; Sheppard L; Wallace L; Liu L-JS (2003) . Use of real-time light scattering data to estimate the contribution of infiltrated and indoor-generated part icles to indoor air. Environ Sci Technol, 37: 3484-3492. 053578

Allen R; Wallace L; Larson T; Sheppard L; Liu LJ (2004). Es timated hourly personal exposures to ambient and nonambient particulate matter among sensitive populations in Seattle, Washington. J Air Waste Manag Assoc, 54: 1197-1211.

190089

Andracchio A; Cavicchi C; Tonelli D; Zappoli S (2002). A new approach for the fractionation of water-soluble organic carbon in atmospheric aerosols and cloud drops. Atmos Environ, 36: 5097-5107. 155657

Andreae MO; Gelencsér A (2006). Black car bon or brown carbon? The nature of light-absorbing carbonaceous aerosols. Atmos Chem Phys, 6: 3131-3148. 156215

Appel K; Bhave PV; Gilliland AB; Sarwar G; Roselle SJ (2008). Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting m odel performance; Part IIâ€”particulate matter. Atmos Environ, 42: 6057-6066. 155660

Appel KW; Gilliland A; Eder B (2005). An operational evalua tion of the 2005 release of models-3 CMAQ version 45. National Oceanic and Atmospheric Administration–Air Resources. Washington DC. 089227

Arhami M; Kuhn T; Fine PM; Delfino RJ; Sioutas C (2006). Effects of Sampling Artifacts and Operating Parameters on the

Performance of a Semicontinuous Particulate Elemental Ca rbon/Organic Carbon Monitor. Environ Sci Technol, 40:

945-954. 156224

Arnold JR; Dennis RL; Tonnesen GS (2003). Diagnostic evalua tion of numerical air quality models with specialized ambient observations: testing the Community Multiscale Ai r Quality modeling system (CMAQ) at selected SOS 95 ground sites. Atmos Environ, 37: 1185-1198. 087579

Arnott WP; Moosmüller H; Sheridan PJ; Ogren JA; Raspet R; Slaton WV; Hand JL; Kreidenweis SM; Collett JL Jr (2003).

Photoacoustic and filter-based ambient aerosol light absorption measurements: Instrument comparisons and the role of relative humidity. J Geophys Res, 108: AAC 15-1. 037711

Note: Hyperlinks to the reference citations throughout this doc ument will take you to the NCEA HERO database (Health and Environmental Research Online) at http://epa.gov/hero . HERO is a database of scientific literature used by U.S. EPA in the process of developing science assessments such as the Integrated Science Assessments (ISA) and the Integrated Risk Information System (IRIS)

.

December 2009 3-193Artinano B; Querol X; Salvador P; Rodriguez S; Alonso DG; Al astuey A (2001). Assessment of airborne particulate levels in Spain in relation to the new EU-dir ective. Atmos Environ, 35: S43-S53. 190099

Auvray M; Bey I (2005). Long-range trans port to Europe: Seasonal variations and implications for the European ozone budget. J Geophys Res, 1 10: D11303.1-D11303.22. 156237

Ayers GP (2004). Potential for simultaneous measurement of PM10, PM25 and PM1 for air quality monitoring purposes using a single TEOM. Atmos Environ, 38: 3453-3458. 097440

Bae MS (2007). Intercomparison of Real Time Ammonium Me asurements at Urban and Rural Locations in New York. Aerosol Sci Technol, 41: 329-341. 155669

Bae MS; Schauer JJ; Deminter JT; Turner JR (2004). Hourly a nd daily patterns of particle-phase organic and elemental carbon concentrations in the urban atmosphe re. J Air Waste Manag Assoc, 54: 823-833. 156243

Bae MS; Schwab JJ; Zhang Q; Hogrefe O; Demerjian KL; Weimer S; Rhoads K; Orsini D; Venkatachari P; Hopke PK (2007). Interference of organic signals in highly time resolved nitrate measurements by low mass resolution aerosol mass spectrometry. J Geophys Res, 112: D22305-D22305 (1-16). 156244

Baldauf R; Thoma E; Hays M; Shores R; Kinsey J; Gull ett B; Kimbrough S; Isakov V; Long T; Snow R; Khlystov A; Weinstein J; Chen FL; Seila R; Olson D; Gilmour I; Cho SH; Watkins N; Rowley P; Bang J (2008). Traffic and meteorological impacts on near-road air quality: Summary of methods and trends from the Raleigh near-road study. J Air Waste Manag Assoc, 58: 865-878. 190239

Baldauf R; Thoma E; Khlystov A; Isakov V; Bowker G; Long T; Snow R (2008). Impacts of noise barriers on near-road air quality. Atmos Environ, 42: 7502-7507. 191017

Baldauf R; Watkins N; Heist D; Bailey C; Rowley P; Shores R (2009). Near-road air quality monitoring: Factors affecting network design and interpretation of data. Air Qual. Atmos. Health , 2: 1-9. 191766

Baldauf RW; Gabele P; Crews W; Snow R; Cook JR (2005). Criteria and air-toxic emissions from in-use automobiles in the national low-emission vehicle program. J Air Waste Manag Assoc, 55: 1263-1268. 191184

Barn P; Larson T; Noullett M; Kennedy S; Copes R; Brauer M (2008). Infiltration of forest fire and residential wood

smoke: an evaluation of air cleaner effectivene ss. J Expo Sci Environ Epidemiol, 18: 503-511. 156252

Barreto RP; Albuquerque FC; Netto ADP (2007). Optimization of an improved analytical method for the determination of 1-nitropyrene in milligram diesel soot samples by high- performance liquid chromatogr aphy–mass spectrometry. J Chromatogr A, 1163: 219-227. 155676

Bauer H; Schueller E; Weinke G; Berger A; Hitzenberger R; Marr I; Puxbaum H (2008). Significant contributions of fungal spores to the organic carbon and to the aerosol mass balan ce of the urban atmospheric aerosol. Atmos Environ, 42:

5542-5549. 189986

Baxter LK; Barzyk TM; Vette VF; Croghan C; Williams RW ( 2008). Contributions of diesel truck emissions to indoor elemental carbon concentrations in homes in proximity to Ambassador Bridge . Atmos Environ, 42: 9080-9086 .

191194

Baxter LK; Clougherty JE; Laden F; Levy JI (2007). Predictors of concentrations of nitrogen dioxide, fine particulate matter, and particle constituents inside of lower socio economic status urban homes. J Expo Sci Environ Epidemiol,

17: 433-444. 092726

Baxter LK; Clougherty JE; Paciorek CJ; Wright RJ; Levy JI (2007). Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data. Atmos Environ, 41: 6561-6571. 092725

Behrentz E; Fitz DR; Pankratz DV; Sabin LD; Colome SD ; Fruin SA; Winer AM (2004). Measuring self-pollution in school buses using a tracer gas tec hnique. Atmos Environ, 38: 3735-3746. 155682

Bein KJ; Zhao Y; Wexler AS; Johnston MV (2005). Speciation of size-resolved individual ultrafine particles in Pittsburgh, Pennsylvania. J Geophys Res, 110: D07S05. 156265

Bell ML; Dominici F; Ebisu K; Zeger SL; Samet JM (2007) . Spatial and Temporal Variation in PM2. 5 Chemical Composition in the United States for Health Effect s Studies. Environ Health Perspect, 115: 989-995. 155683

Bell ML; Ebisu K; Peng RD; Dominici F (2009). Adverse health effects of particulate air pollution: modification by air conditioning. Epidemiology, 20: 682-686. 191007

December 2009 3-194Bell ML; Ebisu K; Peng RD; Walker J; Samet JM; Zeger SL; Dominic F (2008). Seasonal and regional short-term effects of fine particles on hospital admissions in 202 U.S. counties, 1999-2005. Am J Ep idemiol, 168: 1301-1310. 156266

Bell ML; Kim JY; Dominici F (2007). Potential confounding of particulate matter on the short-term association between ozone and mortality in multisite time-series studies. Environ Health Perspect, 115: 1591-1595. 093256

Benkovitz CM; Scholtz MT; Pacyna J; Tarrason L; Dignon J; Voldner EC; Spiro PA; Logan JA; Graedel TE (1996). Global gridded inventories of anthropogenic emissions of sulfur and nitrogen: North Atlantic Regional Experiment(NARE). J Geophys Res, 101: 29239-29253. 156267

Bennett DH; Koutrakis P (2006). Determining the infiltration of outdoor particles in the indoor environment using a dynamic model. J Aerosol Sci, 37: 766-785. 089184

Binkowski F; Roselle S (2003). Models-3 Community Multiscale Air Quality(CMAQ) model aerosol component 1. Model description. J Geophys Res, 108: 4183. 191769

Binkowski FS; Arunachalam S; Adelman Z; Pinto JP (2007). Exam ining photolysis rates with a prototype online photolysis module in CMAQ. J Appl Meteor Climatol, 46: 1252-1256. 090563

Biswas S; Hu S; Verma V; Herner J; Robertson W; Ayala A; Sioutas C (2008). Physical properties of particulate matter (PM) from late model heavy-duty diesel vehicles operating with advanced PM and NOx emission control technologies. Atmos Environ, 42: 5622-5634. 189969

Biswas S; Verma V; Schauer JJ; Sioutas C (2009). Chemical speciation of PM emissions from heavy-duty diesel vehicles equipped with diesel particulate filter (DPF) and. Atmos Environ, 43: 1917-1925. 189880

Blanchard P; Brook JR; Brazil P (2002). Chemical characterization of the organic fraction of atmospheric aerosol at two sites in Ontario, Canada. J Geophys Res, 107: ICC10.1-ICC10.8 . 047598

Blando JD; Turpin BJ (2000). Secondary organic aerosol forma tion in cloud and fog droplets: a literature evaluation of plausibility. Atmos Environ, 34: 1623-1632. 155692

Bond TC; Streets DG; Yarber KF; Nelson SM; Woo J-H; Klim ont Z (2004). A technology-based global inventory of black and organic carbon emissions from co mbustion. J Geophys Res, 109: D14203. 056389

Borak J; Sirianni G; Cohen HJ; Chemerynski S; Wheel er R (2003). Comparison of NIOSH 5040 Method versus

AethalometerTM to Monitor Diesel Particulate in Sc hool Buses and at Work Sites. AIHA J, 64: 260-268. 156284

Borrego C; Tchepel O; Costa AM; Martins H; Ferreira J; Miranda AI (2006). Traffic-related particulate air pollution exposure in urban areas. At mos Environ, 40: 7205-7214. 155697

Bortnick SM; Coutant BW; Eberly SI (2002). Using continuous PM2.5 monitoring data to report an air quality index. J Air Waste Manag Assoc, 52: 104-112. 156285

Brauer M; Hoek G; van Vliet P; Meliefste K; Fischer P; Gehring U; Heinrich J; Cyrys J; Bellander T; Lewne M; Brunekreef B (2003). Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems. Epidemiology, 14: 228-239. 155702

Braun A (2009). Two-process model for the atmospheric weathe ring, oxidation and ageing of diesel soot. Geophys Res Lett, 36: L07810. 189997

Briggs D (2000). Exposure assessm ent. In Spatial Epidemiology: Methods and Applications (pp. 335-359). Oxford: Oxford University Press. 191772

Briggs DJ; Collins S; Elliott P; Fischer P; Kingham S; Lebret E; Pryl K; Van Reeuwijk H; Smallbone K; Van Der Veen A (1997). Mapping urban air pollution using GIS: a regressi on-based approach. Int J Geogr Inform Sci, 11: 699-718.

025950

Briggs DJ; de Hoogh K; Morris C; Gulliver J (2008). Effects of travel mode on exposures to particulate air pollution. Environ Int, 34: 12-22. 156294

Brock CA; Hudson PK; Lovejoy ER; Sullivan A; Nowak JB; Huey LG; Cooper OR; Cziczo DJ; de Gouw J; Fehsenfeld FC (2004). Particle characteristics followi ng cloud-modified transport from Asia to North America. J Geophys Res,

109: D23S26. 156295

Brook JR; Sirois A; Clarke JF (1996). Comparison of dry deposition velocities for SO2, HNO3, and SO4(2-) estimated with two inferential models. Water Air Soil Pollut, 87: 205-218. 024023

December 2009 3-195Brown AS; Yardley RE; Quincey PG; Butterfield DM (2006). Studi es of the effect of humidity and other factors on some different filter materials used for gr avimetric measurements of ambient pa rticulate matter. Atmos Environ, 40:

4670-4678. 097665

Bruno P; Caselli M; de Gennaro G; Tutino M (2007). Determin ation of polycyclic aromatic hydrocarbons (PAHs) in particulate matter collected with low volume samplers. Talanta, 72: 1357-1361. 155706

Burke JM; Zufall MJ; Ozkaynak H (2001). A population exposure model for particulate matter: case study results for PM2.5 in Philadelphia, PA. J Expo Sci Environ Epidemiol, 11: 470-489. 014050

Burnett RT; Goldberg MS (2003). Size-fractionated particulate mass and daily mortality in eight Canadian cities. Health Effects Institute. Boston, MA. 042798

Buser MD; Parnell Jr CB; Shaw BW; Lacey RE (2007). Particulat e matter sampler errors due to the interaction of particle size and sampler performance characteristics: Am bient PM 10 samplers. Trans ASAE, 50: 229-240. 156311

Buser MD; Parnell Jr CB; Shaw BW; Lacey RE (2007). Particulate matter sampler errors due to the interaction of particle size and sampler performance characteristics: Ambient PM2. 5 samplers. Trans ASAE, 50: 241-254. 156310

Buser MD; Parnell Jr CB; Shaw BW; Lacey RE (2007). Particulate matter sampler errors due to the interaction of particle size and sampler performance characteristics: Background and Theory. Trans ASAE, 50: 221-228. 156312

Butler AJ; Andrew MS; Russell AG (2003). Daily sampling Of PM 2. 5 in Atlanta: Results of the first year of the Assessment of Spatial Aerosol Composition. J Geophys Res, 108: 8415. 156313

Buzorius G; Hameri K; Pekkanen J; Kulmala M (1999). Spatial variation of aerosol number concentration in Helsinki city. Atmos Environ, 33: 553-565. 081205

Bytnerowicz A; Miller PR; Olszyk DM (1987). Dry depositi on of nitrate, ammonium and sulfate to a Ceanothus crassifolius canopy and surrogate surf aces. Atmos Environ, 21: 1749-1757. 036493

Byun D; Schere KL (2006). Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system. Appl. Mech. Rev., 59: 51-77. 090560

Byun DW; Ching JKS (eds) (1999). Science algorithms of th e EPA models-3 community multiscale air quality (CMAQ) modeling system. U.S. Environmental Protection Agency, Office of Research and Development. Washington, DC. EPA/600-R-99-030. http://www.epa.gov/as mdnerl/CMAQ/CMAQscienceDoc.html. 156314

Cabada JC; Rees S; Takahama S; Khlystov A; Pandis SN; Davidson CI; Robinson AL (2004). Mass size distributions and size resolved chemical composition of fine particulat e matter at the Pittsburgh supersite. Atmos Environ, 38: 3127-

3141. 148859

Cadle SH; Mulawa P; Groblicki P; Laroo C; Ragazzi RA; Ne lson K; Gallagher G; Zielinska B (2001). In-use light-duty gasoline vehicle particulate matter emissions on the FTP, REP05, and UC cycles [final report]. 017192

Cadle SH; Mulawa PA; Hunsanger EC; Nelson K; Ragazzi RA; Barrett R; Gallagher GL; Lawson DR; Knapp KT; Snow R (1999). Composition of light-duty motor vehicle exhaust part iculate matter in the Denver, Colorado area. Environ Sci Technol, 33: 2328-2339. 007636

Carter WPL (1990). A detailed mechanism for the gas-phase atmospheric reactions of orga nic compounds. Atmos Environ,

24: 481-518. 042893

Case MW; Williams R; Yeatts K; Chen F-L; Scott J; Svendsen E; Devlin RB (2008). Evaluation of a direct personal coarse particulate matter monitor. At mos Environ, 42(19): 4446-4452. 155149

Cass GR (1998). Organic molecular tracers for particulate air pollution sources. Trends Analyt Chem, 17: 356-366. 155716

Cass GR; Hughes LA; Bhave P; Kleeman MJ; Allen JO; Salm on LG (2000). The chemical composition of atmospheric ultrafine particles. Proc Biol Sci, 358: 2581-2592. 020680

Chakrabarti B; Singh M; Sioutas C (2004). Development of a Near-Continuous Monitor for Measurment of the Sub-150 nm PM Mass Concentration. Aerosol Sci Technol, 38: 239-252. 157426

Chang C-H; Meroney RN (2003). Concentration and flow distributions in urban street canyons: wind tunnel and computational data. J Wind Eng Ind Aerod, 91: 1141-1154. 090298

Chang CT; Tsai CJ (2003). A model for the relative humidity ef fect on the readings of the PM10 beta-gauge monitor. J Aerosol Sci, 34: 1685-1697. 155718

December 2009 3-196Chang L-T; Koutrakis P; Catalano PJ; Suh HH (2003). Assessi ng the importance of differen t exposure metrics and timeactivity data to predict 24-h personal PM25 exposures. J Toxicol Environ Health A Curr Iss, 66: 1825-1846. 053789

Charron A; Harrison RM; Moorcroft S; Booker J (2004). Quantitative interpretation of divergence between PM10 and PM25 mass measurement by TEOM and gravimetric (P artisol) instruments. Atmos Environ, 38: 415-423. 053849

Chen FL; Williams R; Svendsen E; Yeatts K; Creason J; Sc ott J; Terrell D; Case M (2007). Coarse particulate matter

concentrations from residential outdoor sites associat ed with the North Carolina Asthma and Children's Environment Studies (NC-ACES). Atmos Environ, 41: 1200-1208. 147318

Chen LWA; Chow JC; Watson JG; Moosm5uller H; Arnott WP (2004). Modeling reflectance and transmittance of quartzfiber filter samples containing elemental carbon particles: Im plications for thermal/optical analysis. J Aerosol Sci,

35: 765-780. 199501

Chiapello I; Moulin C; Prospero JM (2005). Understanding th e long-term variability of A frican dust transport across the

Atlantic as recorded in both Barbados surface concentr ations and large-scale Total Ozone Mapping Spectrometer (TOMS) optical thickness. J Geophys Res, 110: D18S10. 156339

Ching J; Herwehe J; Swall J (2006). On joint deterministic grid modeling and sub-grid variability conceptual framework for model evaluation. Atmos Environ, 40: 4935-4945. 090300

Chow JC (2007). The application of thermal methods for de termining chemical compositi on of carbonaceous aerosols: A review. J Environ Sci Health A Tox Hazard Subst Environ Eng, 42: 1521-1541. 157209

Chow JC; Doraiswamy P; Watson JG; Chen LW; Ho SS; Sodeman DA (2008). Advances in integrated and continuous measurements for particle mass and chemical composition. J Air Waste Manag Assoc, 58: 141-163. 156355

Chow JC; Watson JG; Chen LW; Chang MC; Robinson NF; Trimble D; Kohl S (2007). The IMPROVE_A temperature protocol for thermal/optical carbon analysis: maintaining consistency with a long-term database. J Air Waste Manag Assoc, 57: 1014-1023. 156354

Chow JC; Watson JG; Chen LWA; Arnott WP; Moosmuller H; Fung K (2004). Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ Sci Technol, 38: 4414-

4422. 156347

Chow JC; Watson JG; Louie PKK; Chen LWA; Sin D (2005) . Comparison of PM2.5 carbon m easurement methods in Hong Kong, China. Environ Pollut, 137: 334-344. 155728

Chow JC; Watson JG; Park K; Lowenthal DH; Robinson NF; Magliano KA (2006). Comparison of particle light scattering and fine particulate matter mass in central Ca lifornia. J Air Waste Manag Assoc, 56: 398-410. 099031

Chu D; Kaufman Y; Zibordi G; Chern J; Mao J; Li C; Ho lben B (2003). Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS). J Geophys Res, 108:

4661. 190049

Chung A; Herner JD; Kleeman MJ (2001). Detection of alkaline ultrafine atmospheric particles at Bakersfield, California. Environ Sci Technol, 35: 2184-2190. 017105

Claeys M; Graham B; Vas G; Wang W; Vermeylen R; Pashynska V; Cafmeyer J; Guyon P; Andreae MO; Artaxo P; Maenhaut W (2004). Formation of secondary organic aer osols through photooxidation of isoprene. Sci New York,

303: 1173-1176. 058608

Clarke JF; Edgerton ES; Martin BE (1997). Dry deposition calcula tions for the clean air status and trends network. Atmos Environ, 31: 3667-3678. 025022

Clough WS (1975). The deposition of particles on moss and grass surfaces. Atmos Environ, 9: 1113-1119. 070850

Cohen M; Adar S; Allen R; Avol E; Curl C; Gould T; Hardie D; Ho A; Kinney P; Larson T (2009). Approach to estimating participant pollutant exposures in the multi-ethnic study of atherosclerosis and air pollution (MESA Air). Environ Sci Technol, 43: 4687-4693. 190639

Conny JM; Klinedinst DB; Wight SA; Paulsen JL (2003). Opti mizing thermal-optical methods for measuring atmospheric elemental (black) carbon: A response surface study. Aerosol Sci Technol, 37: 703-723. 145948

Conny JM; Norris GA; Gould TR (2009). Factorial-based response- surface modeling with confidence intervals for optimizing thermal-optical transmission analysis of atmospheric black carbon. Anal Chim Acta, 635: 144-156.

191999

December 2009 3-197Cooke WF; Liousse C; Cachier H; Feichter J (1999). Constr uction of a 1 ° x 1 ° fossil fuel emission data set for carbonaceous aerosol and implementation and radiative im pact in the ECHAM 4 model. J Geophys Res, 104:

22137-22162. 156365

Corburn J (2007). Urban land use, air t oxics and public health: Assessing hazardous exposures at the neighborhood scale. Environ Impact Assess Rev, 27: 145-160. 155738

Crimmins BS; Baker JE (2006). Improved GC/MS methods for measuring hourly PAH and nitro-PAH concentrations in urban particulate matter. Atmos Environ, 40: 6764-6779. 097008

Crist KC; Liu B; Kim M; Deshpande SR; John K (2008). Char acterization of fine particulate matter in Ohio: Indoor, outdoor, and personal exposures . Environ Res, 106: 62-71. 156372

Cyrys J; Heinrich J; Hoek G; Meliefste K; Lewne M; Gehring U; Bellander T; Fischer P; Van Vliet P; Brauer M; Wichmann HE; Brunekreef B (2003). Comparison between differ ent traffic-related particle indicators: elemental carbon (EC), PM25 mass, and absorbance. J Expo Sci Environ Epidemiol, 13: 134-143. 049634

Dasch JM (1987). Measurement of dry depos ition to surfaces in deciduous and pine canopies. Environ Pollut, 44: 261-277.

036496

Davidson CI; Wu Y-L (1990). Dry deposition of particles and vapors. In Lindberg, S. E.; Page, A. L.; Norton, S. A. (Ed.),Acidic precipitation: v. 3, sources, deposition, and canopy interactionsNew York, NY: Springer-Verlag.

036799

deCastro BR; Sax AN; Chillrud SN; Kinney PL; Spengler JD (2007). Modeling time-location pa tterns of inner-city high school students in New York and Los Angeles using a longitudinal approach with generalized estimating equations. J Expo Sci Environ Epidemiol, 17: 233-247. 190996

Decesari S; Facchini MC; Fuzzi S; Tagliavini E (2000). Characterization of water-soluble organic compounds in atmospheric aerosol: A new appr oach. J Geophys Res, 105: 1481-1489. 155748

Delfino RJ; Staimer N; Gillen D; Tjoa T; Sioutas C; Fung K; George SC; Kleinman MT (2006). Personal and ambient air pollution is associated with increased exhaled nitric oxide in children with asthma. Environ Health Perspect, 114:

1736-1743. 090745

Demokritou P; Lee S; Koutrakis P (2004). Development and evaluation of a high loading PM2.5 speciation sampler. Aerosol Sci Technol, 38: 111-119. 186901

Demokritou P; Lee SJ; Ferguson ST; Koutrakis P (2004). A compact multistage (cascade) impact or for the characterization of atmospheric' aerosols. J Aerosol Sci, 35: 281-299 . 190115

Dentener F; Stevenson D; Ellingsen K; Van Noije T; Schultz M; Amann M; Atherton C; Bell N; Bergmann D; Bey I; Bouwman L; Butler T; Cofala J; Collins B; Drevet J; Doherty R; Eickhout B; Eskes H; Fiore A; Gauss M; Hauglustaine D; Horowitz L; Isaksen ISA; Josse B; Lawr ence M; Krol M; Lamarque JF; Montanaro V; Muller JF; Peuch VH; Pitari G; Pyle J; Rast S; Rodriguez J; Sanderson M; Savage NH; Shindell D; Strahan S; Szopa S; Sudo K; Van Dingenen R; Wild O; Zeng G (2006). The global atmo spheric environment for the next generation. Environ Sci Technol, 40: 3586-3594. 088434

Derwent RG; Collins WJ; Johnson CE; Stev enson DS (2001). Transient behaviour of tropospheric ozone precursors in a global 3-D CTM and their indirect gree nhouse effects. Clim Change, 49: 463-487. 047912

Diapouli E; Chaloulakou A; Spyrellis N (2007). Levels of ultr afine particles in different microenvironments--implications to children exposure. Sci Total Environ, 388: 128-136. 156397

Diapouli E; Grivas G; Chaloulakou A; Spyrellis N (2008). PM10 and ultrafine particles counts in-vehicle and on-road in the Athens area . Water Air Soil Pollut, 8: 89-97. 190119

Docherty KS; Wu W; Lim YB; Ziemann PJ (2005). Contributions of organic peroxides to secondary aerosol formed from reactions of monoterpenes with O3 . Environ Sci Technol, 39: 4049-4059. 087613

Dockery DW; Pope CA III; Xu X; Spengler JD; Ware JH; Fay ME; Ferris BG Jr; Speizer FE (1993). An association between air pollution and mortality in six US cities. N Engl J Med, 329: 1753-1759. 044457

Dodge MC (1977). Combined use of modeling techniques and smog chamber data to derive ozone-precursor relationships.

Presented at International Conference on Photochemical Oxidant Pollution and Its Control, 09/12/1976-09/17/1976, Research Triangle Park, NC. 038646

December 2009 3-198Dominici F; Peng RD; Bell ML; Pham L; McDermott A; Zeger SL; Samet JL (2006). Fine particulate air pollution and hospital admission for cardiovascular and re spiratory diseases. JAMA, 295: 1127-1134. 088398

Dominici F; Zeger SL; Samet JM (2000). A measurement error model for time-series studies of air pollution and mortality. Biostatistics, 1: 157-175. 005828

Drewnick F; Jayne JT; Canagaratna M; Worsnop DR; Demerjian KL (2004). Measur ement of Ambient Aerosol Composition During the PMTACS-NY 2001 Using an Aerosol Mass Spectrometer. Part II: Chemically Speciated Mass Distributions. Aerosol Sci Technol, 38: 104-117. 155755

Drewnick F; Schwab J; Jayne J; Canagaratna M; Worsnop D; Demerjian K (2004). Measurement of Ambient Aerosol Composition During the PMTACS-NY 2001 Using an Aerosol Mass Spectrometer. Part I: Mass Concentrations. Aerosol Sci Technol, 38: 92-103. 155754

Drewnick F; Schwab JJ; Hogrefe O; Peters S; Husain L; Diamond D; Weber R; Demerjian KL (2003). Intercomparison and

evaluation of four semi-continuous PM2.5 sulf ate instruments. Atmos Environ, 37: 3335-3350. 099160

Dutton SJ; Williams DE; Garcia JK; Vedal S; Hannigan MP (20 09). PM2.5 characterization for time series studies: organic molecular marker speciation methods and observations fro m daily measurements in Denver. Atmos Environ, 43:

2018-2030. 194887

Ebelt ST; Wilson WE; Brauer M (2005) . Exposure to ambient and nonambient components of particulate matter: a comparison of health effect s. Epidemiology, 16: 396-405. 056907

Eder B; Kang D; Mathur R; Yu S; Schere K (2006). An ope rational evaluation of the Eta-CM AQ air quality forecast model. Atmos Environ, 40: 4894-4905. 142721

Eder B; Yu S (2005). A performance evaluation of the 2004 release of Models-3 CMAQ. Atmos Environ, 40: 4811-4824.

089229

Edgerton ES; Casucclo GS; Saylor RD; Lersch TL; Hartsell BE; Jansen JJ; Hansen DA (2009). Measurements of OC and EC in coarse particulate matter in the southeastern United States. J Air Waste Manag Assoc, 59: 78-90. 180385

Edgerton ES; Hartsell BE; Saylor RD; Jansen JJ; Hansen DA ; Hidy GM (2005). The Southeastern Aerosol Research and Characterization Study: Part II Filter-based measuremen ts of fine and coarse particulate matter mass and composition. J Air Waste Manag Assoc, 55: 1527-1542. 088686

Edney EO; Lewandowski M; Offenberg JH; Wang W; Claeys M; Kleindienst TE; Jaoui M (2005). Formation of 2-methyl tetrols and 2-methylglyceric acid in secondary organic aerosol from laboratory irradi ated isoprene/NOX/SO 2/air mixtures and their detection in ambient PM2.5 samples co llected in the eastern United States. Atmos Environ, 39:

5281-5289. 155760

Eiguren-Fernandez A; Miguel AH; Jaques PA; Sioutas C (2003) . Evaluation of a denuder-MOUDI-PUF sampling system to measure the size distribution of semi-volatile polycyclic aromatic hydrocarbons in the atmosphere. Aerosol Sci Technol, 37: 201-209. 142609

El-Zanan HS; Lowenthal DH; Zielinska B; Chow JC; Kuma r N (2005). Determination of the organic aerosol mass to organic carbon ratio in IMPROVE samples. Chemosphere, 60: 485-496. 155764

Elgethun K; Fenske R; Yost M; Palcisko G (2003). Time-location analysis for exposure assessment studies of children using a novel global positioning system instrume nt. Environ Health Perspect, 111: 115-122. 190640

Engel-Cox JA; Weber SA (2007). Compilation and assessment of recent positive matrix fact orization and UNMIX receptor model studies on fine particulate matter source apportionm ent for the eastern United States. J Air Waste Manag Assoc, 57: 1307-1316. 156419

Engewald W; Teske J; Efer J (1999). Programmed temperatur e vaporisers-based large volume injection in capillary gas chromatography. J Chromatogr A, 842: 143-161. 155765

England GC; Watson JG; Chow JC; Zielinska B; Chang M (2007). Dilution-Based Emissions Sampling from Stationary Sources: Part 2- Gas-Fired Combustors Compared with Ot her Fuel-Fired Systems. J Air Waste Manag Assoc, 57:

79-93. 156421

England GC; Watson JG; Chow JC; Zielinska B; Oliver Ch ang MC; Loos KR; Hidv GM (2007). Dilution-based emissions sampling from stationary sources: Pa rt 1-Compact sampler methodology and performance. J Air Waste Manag Assoc, 57: 65-78. 156420

December 2009 3-199Enstrom JE (2005). Fine particulate air pollution and total mortality among elderly Californians, 1973-2002. Inhal Toxicol,

17: 803-816. 087356

Erisman J-W; Vermetten AWM; Asman WAH; Waijers-IJpelaan A; Slanina J (1988). Vertical distribution of gases and aerosols: the behaviour of ammonia and related component s in the lower atmosphere. Atmos Environ, 22: 1153-

1160. 036510

Fairlie TD; Jacob DJ; Park RJ (2007). The impact of transp acific transport of mineral dust in the United States. Atmos Environ, 41: 1251-1266. 141923

Fan X; Brook JR; Mabury SA (2003). Sampling atmospheric car bonaceous aerosols using an integrated organic gas and particle sampler. Environ Sci Technol, 37: 3145-3151. 058628

Fan X; Lee PKH; Brook JR; Mabury SA (2004). Improved Measuremen t of Seasonal and Diurnal Differences in the Carbonaceous Components of Urban Particulate Matter Us ing a Denuder-Based Air Sampler. Aerosol Sci Technol,

38: 63-69. 155770

Farmer PB; Singh R; Kaur B; Sram RJ; Binkova B; Kalina I; Popov TA; Garte S; Taioli E; Gabelova A; CebulskaWasilewska A (2003). Molecular epidemiology studies of carcinogenic environmental pollutants: effects of polycyclic aromatic hydrocarbons (PAHs) in environm ental pollution on exogenous and oxidative DNA damage. Mutat Res, 544: 397-402. 089017

Feldpausch P; Fiebig M; Fritzsche L; Petzold A (2006). Measurement of ultrafine aerosol size distributions by a combination of diffusion screen separators and conde nsation particle counters. J Aerosol Sci, 37: 577-597. 155773

Filleul L; Zeghnoun A; Cassadou S; Declercq C; Eilstein D; Le Tertre A; Medina S; Pascal L; Prouvost H; Saviuc P; Quenel P (2006). Influence of set-up conditions of exposur e indicators on the estimate of short-term associations between urban pollution and mortality. Sci Total Environ, 355: 90-97. 089862

Fine PM; Chakrabarti B; Krudysz M; Schauer JJ; Sioutas C (2004). Diurnal Variations of Individual Organic Compound

Constituents of Ultrafine and Accumulation Mode Partic ulate Matter in the Los Angeles Basin. Environ Sci Technol, 38: 1296-1304. 141283

Fine PM; Jaques PA; Hering SV; Sioutas C (2003). Performance Evaluation and Use of a Continuous Monitor for Measuring Size-Fractionated PM 2.5 Nitrate. Aerosol Sci Technol, 37: 342 - 354. 155775

Finlayson-Pitts BJ; Pitts JN Jr (2000). Chemistry of the upper and lower atmos phere: theory, experiments and applications. San Diego, CA: Academic Press. 055565

Fowler D; Cape JN; Unsworth MH (1989). Deposition of atmo spheric pollutants on forests. Proc Biol Sci, 324: 247-265.

002515

Fowler D; Duyzer JH; Baldocchi DD (1991). Inputs of trace gase s, particles and cloud droplets to terrestrial surfaces. Proc Roy Soc Edinb B Biol, 97: 35-59. 046630

Frank NH (2006). Retained nitrate, hydrat ed sulfates, and carbonaceous mass in fe deral reference method fine particulate matter for six eastern U.S. cities. J Air Waste Manag Assoc, 56: 500-11. 098909

Fraser MP; Cass GR; Simoneit BRT (1999). Particulate orga nic compounds emitted from motor vehicle exhaust and in the urban atmosphere. Atmos Environ, 33: 2715-2724. 010819

Fraser MP; Yue ZW; Buzcu B (2003). Source apportionment of fine particulate matter in Houston, TX, using organic molecular markers. Atmos Environ, 37: 2117-2123. 042231

Fruin S; Westerdahl D; Sax T; Sioutas C; Fine PM (2008). Measurements and predictors of on-road ultrafine particle concentrations and associated pollutants in Los Angeles. Atmos Environ, 42: 207-219. 097183

Fuentes M; Raftery AE (2005). Model evaluation and spatial in terpolation by Bayesian combination of observations with outputs from numerical mode ls. Biometrics, 61: 36-45. 087580

Fuentes M; Song HR; Ghosh SK; Holland DM; Davis JM (2006). Spatial association between speciated fine particles and mortality. Biometrics, 62: 855-863. 097647

Gallagher MW; Choularton TW; Morse AP; Fowler D (1988). M easurements of the size de pendence of cloud droplet deposition at a hill site. Q J Roy Meteorol Soc, 114: 1291-1303. 046631

December 2009 3-200Gao S; Ng NL; Keywood M; Varutbangkul V; Bahreini R; Nenes A; He J; Yoo KY; Beauchamp JL; Hodyss RP; Flagan RC; Seinfeld JH (2004). Particle phase acidity and oligom er formation in secondary organic aerosol. Environ Sci Technol, 38: 6582-6589. 156460

Garner JHB; Pagano T; Cowling EB (1989). An evaluation of the role of ozone, acid deposition, and other airborne pollutants in the forests of eastern North America. S outheastern Forest Experiment Station. Asheville, NC. 042085

Garten CT Jr; Hanson PJ (1990). Foliar retention of 15N-n itrate and 15N-ammonium by red maple (Acer rubrum) and white oak (Quercus alba) leaves from simu lated rain. Environ Exp Bot, 30: 333-342. 036803

Gaydos TM; Pinder R; Koo B; Fahey KM; Yarwood G; Pandi s SN (2007). Development and application of a threedimensional aerosol chemical transport m odel, PMCAMx. Atmos Environ, 41: 2594-2611. 139738

Gaydos TM; Stanier CO; Pandis SN (2005). Modeling of in situ ultrafine atmospheric particle formation in the eastern United States. J Geophys Res, 110: D07S12. 191762

Gelencsér A; Mészáros T; Blazsó M; Kiss G; Krivácsy Z; Molnár A; Mészáros E (2000). St ructural Characterisation of Organic Matter in Fine Tropospheric Aerosol by Pyro lysis-Gas Chromatography-Mass Spectrometry. J Atmos Chem, 37: 173-183. 155785

Gelencser A; Varga Z (2005). Evaluation of the atmospheric si gnificance of multiphase reactions in atmospheric secondary organic aerosol formation. Atmos Chem Phys, 5: 2823–2831. 156463

Geller MD; Ntziachristos L; Mamakos A; Samaras Z; Schmitz DA; Froines JR; Sioutas C (2006). Physicochemical and redox characteristics of particulate matter (PM) emitted from gasoline and diesel passenger cars. Atmos Environ,

40: 6988-7004. 139644

Generoso S; Bey I; Attié J-L; Bréon F-M (2007). A satellite- and model-based assessment of the 2003 Russian fires: impact on the arctic region. J Geophys Res, 112: 5302. 155786

Georgopoulos PG; Wang S-W; Vyas VM; Sun Q; Burke J; Ve dantham R; McCurdy T; Ozkaynak H (2005). A source-todose assessment of population exposures to fine PM a nd ozone in Philadelphia, PA, during a summer 1999 episode. J Expo Sci Environ Epidemiol, 15: 439-457. 080269

Gery MW; Whitten GZ; Killus JP; Dodge MC (1989). A photoche mical kinetics mechanism for urban and regional scale computer modeling. J Ge ophys Res, 94: 12,925-12,956. 043039

Giglio L; van der Werf GR; Randerson JT; Collatz GJ; Ka sibhatla P (2006). Global estima tion of burned area using MODIS active fire observations. Atmos Chem Phys, 6: 957–974. 156469

Gillette DA; Hanson KJ (1989). Spatial and temporal variability of dust production caused by wind erosion in the United States. J Geophys Res, 94: 2197-2206. 030212

Gilliam RC; Childs PP; Huber AH; Raman S (2005). Metropolitan- scale transport and dispersion from the New York World Trade Center following September 11, 2001 Part I: an evaluation of the CALMET meteorological model. Pure Appl Geophys, 162: 1981-2003. 056749

Gilliam RC; Huber AH; Raman S (2005). Metropolitan-scale tr ansport and dispersion from the New York World Trade Center following September 11, 2001 Part II: an application of the CALPUFF plume model. Pure Appl Geophys,

162: 2005-2028. 056750

Gilliland F; Avol E; Kinney P; Jerrett M; Dvonch T; Lurmann F; Buckley T; Breysse P; Keeler G; de Villiers T; McConnell R (2005). Air pollution exposure assessment for epidemiolo gic studies of pregnant women and children: lessons

learned from the Centers for Children's Environmental H ealth and Disease Prevention Research. Environ Health Perspect, 113: 1447-54. 098820

Gómez-Perales JE; Colvile RN; Fernández-Bremauntz AA; Gu tiérrez-Avedoy V; Páramo-Figueroa VH; Blanco-Jiménez S; Bueno-López E; Bernabé-Cabanillas R; Mandujano F; Hidalgo-Navarro M; Nieuwenhuijsen MJ (2007). Bus, minibus, metro inter-comparison of co mmuters' exposure to air pollution in Mexico City. Atmos Environ, 41: 890-

901. 138816

Gómez-Perales JE; Colvile RN; Nieuwenhuijsen MJ; Ferná ndez-Bremauntz A; Gutiérrez-Avedoy VJ; Páramo-Figueroa VH; Blanco-Jiménez S; Bueno-López E; Mandujano F; Be rnabé-Cabanillas R (2004). Commuters' exposure to PM2.5, CO, and benzene in public transport in the me tropolitan area of Mexico City. Atmos Environ, 38: 1219-

1229. 054418

December 2009 3-201Gong L; Xu B; Zhu Y (2009). Ultrafine particles deposition in side passenger vehicles . Aerosol Sci Technol, 43: 544-553.

190124

Goriaux M; Jourdain B; Temime B; Besombes JL; Marchand N; Albinet A; Leoz-Garziandia E; Wortham H (2006). Field comparison of particulate PAH measurem ents using a low-flow denuder device and conventional sampling systems. Environ Sci Technol, 40: 6398-6404. 156484

Gouw Jd; Brock C; Atlas E; Bates T; Fehsenfeld F; Goldan P; Holloway J; Kuster W; Le rner B; Matthew B; Middlebrook A; Onasch T; Peltier R; Quinn P; Senff C; Stohl A; Sullivan A; Trainer M; Warneke C; Weber R; Williams E (2008). Sources of particulate matter in the northeastern United States in summer: 1. Direct emissions and secondary formation of organic matter in urban plumes. J Geophys Res, 113: D08301. 191757

Graham B; Guyon P; Taylor PE; Artaxo P; Maenhaut W; Glovsky MM; Flagan RC; Andreae MO (2003). Organic compounds present in the natural Amazonian aerosol: Ch aracterization by gas chromatography-mass spectrometry. J Geophys Res, 108: 4766. 156489

Grell GA; Emeis S; Stockwell WR; Schoenemeyer T; Forkel R; Michalakes J; Knoche R; Seidl W (2000). Application of a multiscale, coupled MM5/chemistry model to the comp lex terrain of the VOTALP valley campaign. Atmos Environ, 34: 1435-1453. 048047

Grover BD; Eatough NL; Eatough DJ; Chow JC; Watson JG; Ambs JL; Meyer MB; Hopke PK; Al-Horr R; Later DW; Wilson WE (2006). Measurement of Both Nonvolatile and Semi-Volatile Fractions of Fine Particulate Matter in Fresno, CA. Aerosol Sci Technol, 40: 811-826. 138080

Grover BD; Kleinman M; Eatough NL; Eatough DJ; Cary RA; Hopke PK; Wilson WE (2008). Measurement of fine particulate matter nonvolatile and semi-volatile organic material with the Sunset Laboratory Carbon Aerosol Monitor. J Air Waste Manag Assoc, 58: 72-77. 156502

Grover BD; Kleinman M; Eatough NL; Eatough DJ; Hopke PK; Long RW; Wilson WE; Meyer MB; Ambs JL (2005). Measurement of total PM25 mass (nonvolatile plus semivol atile) with the Filter Dynamic Measurement System tapered element oscillating microbalance monitor. J Geophys Res, 110: D07S03. 090044

Gulliver J; Briggs DJ (2004). Personal exposure to particulate air pollution in transport microenvironments. Atmos Environ, 38: 1-8. 053238

Gulliver J; Briggs DJ (2005). Time-space modeling of journey-time exposure to tr affic-related air pollution using GIS. Environ Res, 97: 10-25. 191079

Gulliver J; Briggs DJ (2007). Journe y-time exposure to particulate air pol lution. Atmos Environ, 41: 7195-7207. 155814

Gustafsson O; Kruså M; Zencak Z; Sheesley RJ; Granat L; Engström E; Praveen PS; Rao PS; Leck C; Rodhe H (2009). Brown clouds over South Asia: biomass or fossil fuel combustion? Science, 323: 495-498. 192000

Gutiérrez-Dabán A; Fernández-Espinosa AJ ; Ternero-Rodríguez M; Fernández-Álva rez F (2005). Particle-size distribution of polycyclic aromatic hydrocarbons in urban air in southern Spain. Anal Bioanal Chem, 381: 721-736. 155818

Hagler GSW; Baldauf RW; Thoma ED; Long TR; Snow RF; Kins ey JS; Oudejans L; Gullett BK (2009). Ultrafine particles near a major roadway in Raleigh, North Carolina: Downwind attenuation and correlation with traffic-related pollutants. Atmos Environ, 43: 1229-1234. 191185

Hains JC; Chen L-WA; Taubman BF; Doddridge BG; Dickerso n RR (2007). A side-by-side comparison of filter-based

PM25 measurements at a suburban site: a closure study. Atmos Environ, 41: 6167-6184. 091039

Hammond DM; Lalor MM; Jones SL (2007). In-vehicle measurem ent of particle number concentrations on school buses equipped with diesel retrofits . Water Air Soil Pollut, 179: 217-225. 190135

Han Y; Cao J; Chow JC; Watson JG; An Z; Jin Z; Fung K; Liu S (2007). Evaluation of the thermal/optical reflectance method for discrimination between char-and soot-EC. Chemosphere, 69: 569-574. 155823

Hand JL; Kreidenweis SM (2002). A New Method for Retrieving Particle Refractive Index and Effective Density from Aerosol Size Distribution Data. Aerosol Sci Technol, 36: 1012-1026. 155824

Hansen K; Draaijers GPJ; Ivens WPMF; Gundersen P; van Leeuwen NFM (1994). Concentration variations in rain and canopy throughfall collected sequentially during individual rain events. Atmos Environ, 28: 3195-3205. 046634

Harrison D; Shik Park S; Ondov J; Buckley T; Roul Kim S; Jayanty RKM (2004). Highly time resolved fine particle nitrate

measurements at the Baltimore S upersite. Atmos Environ, 38: 5321-5332. 136787

December 2009 3-202Harrison RM; Jones AM (2005). Multisite study of particle numbe r concentrations in urban air. Environ Sci Technol, 39:

6063-6070. 191005

Hasegawa S;Hirabayashi M;Kobayashi S;Moriguchi Y;Kondo Y;Tanabe K;Wakamatsu (2005). Size Distribution and

Characterization of Ultrafine Particles in Roadside Atmo sphere. J Environ Sci Health A Tox Hazard Subst Environ Eng, 39: 2671-2690. 157355

Hays MD; Geron CD; Linna KJ; Smith ND; Schauer JJ (2002). Sp eciation of gas-phase and fine particle emissions from burning of foliar fuels. Environ Sci Technol, 36: 2281-2295. 026104

Hays MD; Lavrich RJ (2007). Developments in direct thermal extraction gas chro matography-mass spectrometry of fine aerosols. Trends Analyt Chem, 26: 88-102. 155831

Heald C; Spracklen D (2009). Atmospheric budget of primary biological aerosol particles from fungal sources. Geophys Res Lett, 36: L09806. 190014

HEI (2009). Traffic-related air pollution: A critical review of the literature on emissions, exposure, and health effects. Health Effects Institute. Boston. Special Report 17. 191009

Henderson R (2005). Clean Air Scientific Advisory Committ ee (CASAC) review of the EPA staff recommendations concerning a Potential Thoracic Coarse PM standard in the Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment of Scientific a nd Technical Information. U.S. Environmental Protection Agency. Washington, DC. 156537

Henderson R (2009). Clean Air Scientific Advisory Committee (CASAC) letter the the Administrator: Consultation on monitoring issues related to the NAAQS for particul ate matter. U.S. Environmental Protection Agency . Washington, DC. EPA-CASAC-09-006. 192001

Henry RC (1997). History and fundamentals of multivariate air quality receptor models. Chemometr Intell Lab Syst, 37:

37-42. 020941

Hering S; Fine PM; Sioutas C; Jaques PA; Ambs JL; Hogref e O; Demerjian KL (2004). Field assessment of the dynamics of particulate nitrate vaporization us ing differential TEOM® and automated nitrate monitors. Atmos Environ, 38:

5183-5192. 155837

Hering SV (2007). Using Regional Data and Building Leakage to Assess Indoor Concentrations of Particles of Outdoor Origin. Aerosol Sci Technol, 41: 639-654. 155839

Hering SV; Stolzenburg MR; Quant FR; Oberreit DR; Keady PB (2005). A Laminar-Flow, Water-Based Condensation Particle Counter (WCPC). Ae rosol Sci Technol, 39: 659-672. 155838

Hermann M; Wehner B; Bischof O; Han HS; Krinke T; Liu W; Zerrath A; Wiedensohler A (2007). Particle counting efficiencies of new TSI condensation par ticle counters. J Aerosol Sci, 38: 674-682. 155840

Herner JD; Aw J; Gao O; Chang DP; Kleeman MJ (2005). Size and composition distribution of airborne particulate matter in Northern California: I - Particulate mass, carbon, and water-soluble ions. J Air Waste Manag Assoc, 55: 30-51.

135983

Ho KF; Cao JJ; Harrison RM; Lee SC (2004). Indoor/outdoor relationships of organic carbon (OC) and elemental carbon (EC) in PM2.5 in roadside environment of Hong Kong. Atmos Environ, 38: 6327-6335. 056804

Hoek G; Beelen R; de Hoogh K; Vienneau D; Gulliver J; Fischer P; Briggs D (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ, 42: 7561-7578. 195851

Hogrefe C; Hao W; Civerolo K; Ku JY; Sistla G; Gaza RS; Sedefian L; Schere K; Gilliland A; Mathur R (2007). Daily Simulation of Ozone and Fine Particulates over New York State: Findings and Challenges. J Appl Meteor Climatol,

46: 961-979. 156561

Hogrefe O; Schwab JJ; Drewnick F; Lala GG; Peters S; Demerjian KL; Rhoads K; Felton HD; Rattigan OV; Husain L; Dutkiewicz VA (2004). Semicontinuous PM2.5 sulfate and nitr ate measurements at an urban and a rural location in New York: PMTACS-NY summer 2001 and 2002 campai gns. J Air Waste Manag Assoc, 54: 1040-1060. 099003

Hopke PK; Ramadan Z; Paatero P; Norris GA; Landis MS; Williams RW; Lewis CW (2003). Receptor modeling of ambient and personal exposure samples: 1998 Baltimore Pa rticulate Matter Epidemiology-Exposure Study. Atmos Environ, 37: 3289-3302. 095544

December 2009 3-203Hosker RP Jr; Lindberg SE (1982). Review: atmospheric depo sition and plant assimilation of gases and particles. Atmos Environ, 16: 889-910. 019118

Hsiao T-C; Chen D-R; Son SY (2009). Development of mini-c yclones as the size-selective inlet of miniature particle detectors. J Aerosol Sci, 40: 481-491. 191001

Hu S; Herner JD; Shafer M; Robertson W; Schauer JJ; Dwyer H; Collins J; Huai T; Ayala A (2009). Metals emitted from heavy-duty diesel vehicles equipped with advanced PM and NO"X emission controls. Atmos Environ, 43: 2950-

2959. 189886

Huang C; Chang C; Chang S; Tsai C; Shih T; Tang D (2005). Use of porous foam as the s ubstrate of an impactor for respirable aerosol sampling. J Aerosol Sci, 36: 1373-1386. 186991

Huang L; Brook JR; Zhang W; Li SM; Graham L; Ernst D; Ch ivulescu A; Lu G (2006). Stab le isotope measurements of carbon fractions (OC/EC) in airborne particulate: A new dimension for source characterization and apportionment. Atmos Environ, 40: 2690-2705. 097654

Huang YL; Batterman S (2000). Selection and evaluation of air pollution exposure indicators based on geographic areas. Sci Total Environ, 253: 127-144. 156572

Huebert BJ; Charlson RJ (2000). Uncertainties in data on organic aerosols. Tellus B Chem Phys Meteorol, 52: 1249-1255.

156577

Huebert BJ; Luke WT; Delany AC; Brost RA (1988). Measur ements of concentrations and dry surface fluxes of atmospheric nitrates in the presence of ammonia. J Geophys Res, 93: 7127-7136. 036569

Husar RB; Tratt DM; Schichtel BA; Falke SR; Li F; Jaffe D; Gasso S; Gill T; Laulainen NS; Lu F; Reheis MC; Chun Y; Westphal D; Holben BN; Gueymard C; McKendry I; Kuring N; Feldman GC; McClain C; Frouin RJ; Merrill J; DuBois D; Vignola F; Murayama T; Nickovic S; Wilson WE; Sassen K; Sugimoto N; Malm WC (2001). Asian dust events of April 1998. J Ge ophys Res, 106: 18,317-18,330. 024947

Hwang IJ; Hopke PK; Pinto JP (2008). Source apportionment a nd spatial distributions of coarse particles during the regional air pollution study. Environ Sci Technol, 42: 3524-3530. 194533

Hwang K-W; Lee J-H; Jeong D-Y; Lee C-H; Bhatnagar A; Park J-M; Kim S-H (2008). Observation of difference in the size distribution of carbon and major inorganic compounds of atmospheric aerosols after the long-range transport between the selected days of winter and summer. Atmos Environ, 42: 1057-1063. 134420

ICRP (1995). Human respiratory tract model for radiological pr otection: a report of a task group of the International Commission on Radiological Protection. Ann ICRP, 24: 1-482. 006988

Isakov V; Irwin JS; Ching J (2007). Using CMAQ for exposur e modeling and characterizing the subgrid variability for exposure estimates. J Appl Me teor Climatol, 46: 1354-1371. 195880

Isakov V; Touma JS; Burke J; Lobdell DT; Palma T; Rosenbaum A; Ozkaynak H (2009). Combining regional- and localscale air quality models with exposure models for use in environmental health studies. J Air Waste Manag Assoc,

59: 461-472. 191192

Isakov V; Touma JS; Khlystov A (2007). A method of assessing air toxics concentrations in urban areas using mobile platform measurements. J Air Waste Manag Assoc, 57: 1286-1295. 156588

Jacob DJ (1999). Introduction to atmospheric chemistry. New Jersey: Princeton University Press. 091122

Jacobson MZ (2002). Atmospheric pollution: history, science, and regulation. New York: Cambridge University Press.

090667

Jacobson MZ; Kittelson DB; Watts WF (2005). Enhanced coagulation due to evaporation and its effect on nanoparticle evolution. Environ Sci Technol, 39: 9486-9492. 191187

Jaffe D; Price H; Parrish D; Goldstein A; Harris J (2003). Increasing background ozone during spring on the west coast of North America. Geophys Res Lett, 30: 1613. 052229

Jansen KL; Larson TV; Koenig JQ; Mar TF; Fields C; Stewar t J; Lippmann M (2005). Associations between health effects and particulate matter and black carbon in subjects with respiratory disease. Environ Health Perspect, 113: 1741-

1746. 082236

Jaques PA; Ambs JL; Grant WL; Sioutas C (2004). Field evalua tion of the differential TEOM monitor for continuous PM 2.5 mass concentrations. Aerosol Sci Technol, 38: 49-59. 155878

December 2009 3-204Jeong CH; Hopke PK; Chalupa D; Utell M (2004). Characteristics of nucleation and growth ev ents of ultrafine particles measured in Rochester, NY. Environ Sci Technol, 38: 1933-1940. 180350

Jerrett M; Arain A; Kanaroglou P; Beckerman B; Potoglou D; Sahsuvaroglu T; Morrison J; Giovis C (2005). A review and evaluation of intraurban air pollution exposure models. J Expo Sci Environ Epidemiol, 15: 185-204. 092864

Jerrett M; Burnett RT; Ma R; Pope CA III; Krewski D; Newbold KB; Thurston G; Shi Y; Finkelstein N; Calle EE; Thun MJ (2005). Spatial analysis of air pollution and mort ality in Los Angeles. Epidemiology, 16: 727-736. 087600

Jimenez JL; Jayne JT; Shi Q; Kolb CE; Worsnop DR; Yourshaw I; Seinfeld JH; Flagan RC; Zhang X; Smith KA (2003). Ambient aerosol sampling using the Aerodyne Aerosol Mass Spectrometer. J Geophys Res, 108: 8425. 156611

Johnson T; Long T; Ollison W (2000). Predic tion of hourly microenvironmental concentrations of fine particles based on measurements obtained from the Baltimore scripted activ ity study. J Expo Sci Environ Epidemiol, 10: 403-411.

001660

Kahn R; Gaitley B; Martonchik J; Diner D; Crean K; Holben B (2005). Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations. J Geophys Res, 110: D10S04. 189961

Kalberer M; Paulsen D; Sax M; Steinbacher M; Dommen J; Pr evot ASH; Fisseha R; Weingartner E; Frankevich V; Zenobi R (2004). Identification of Polymers as Major Components of Atmospheric Organic Aerosols. Science, 303: 1659-

1662. 156619

Kastner-Klein P; Plate EJ (1999). Wind-tunnel study of concen tration fields in street canyons. Atmos Environ, 33: 3973-

3979. 001961

Kaur S; Nieuwenhuijsen M; Colvile R (2005). Personal exposur e of street canyon intersection users to PM2.5, ultrafine particle counts and carbon monoxide in centr al London, UK. Atmos Environ, 39: 3629-3641. 086504

Kaur S; Nieuwenhuijsen MJ; Colvile RN (2005). Pedestrian exposure to air pollution along a major road in Central London, UK. Atmos Environ, 39: 7307-7320. 088175

Keller A; Siegmann HC (2001). The role of condensation a nd coagulation in aerosol monitoring. J Expo Sci Environ Epidemiol, 11: 441-448. 025881

Kelly JM (1988). Annual elemental input/output estimates for two forested watersheds in eastern Tennessee. J Environ Qual, 17: 463-468. 037379

Kenny LC; Merrifield T; Mark D; Gussman R; Thorpe A (2004). The Ddevelopment and Designation Testing of a New USEPA-Approved Fine Particle Inlet: A Study of the USEP A Designation Process. Aerosol Sci Technol, 38: 15-22.

155895

Ketzel M; Wahlin P; Berkowicz R; Palmgren F (2003). Particle and trace gas emission factors under urban driving conditions in Copenhagen based on street and roo f-level observations. Atmos Environ, 37: 2735-2749. 131251

Khlystov A; Stanier C; Pandis SN (2004). An Algorithm for Combining Electrical Mobility and Aerodynamic Size Distributions Data when Measuring Ambient Aerosol. Aerosol Sci Technol, 38: 229-238. 155897

Khlystov A; Stanier CO; Takahama S; Pandis SN (2005). Wate r content of ambient aerosol during the Pittsburgh Air Quality Study. J Geophys Res, 110: D07S10. 156635

Kidwell CB; Ondov JM (2004). Elemental Analysis of Sub-Hour ly Ambient Aerosol Collections. Aerosol Sci Technol, 38:

205-218. 155898

Kim E; Hopke PK; Pinto JP; Wilson WE (2005). Spatial vari ability of fine particle mass, components, and source contributions during the regional air pollution study in St Louis. Environ Sci Technol, 39: 4172-4179. 083181

Kim SY; Sheppard L; Kim H (2009). Health effects of long-term air pollution: influence of exposure prediction methods. Epidemiology, 20: 442-50. 188446

Kinney PL; Aggarwal M; Northridge ME; Janssen NAH; Shepar d P (2000). Airborne concentr ations of PM25 and diesel exhaust particles on Harlem sidewalks: a community-based pilot study. Environ Health Perspect, 108: 213-218.

001774

Kinsey JS; Williams DC; Dong Y; Logan R (2007). Characterization of fine partic le and gaseous emissions during school bus idling. Environ Sci Technol, 41: 4972-4979. 190073

December 2009 3-205Kiss G; Varga B; Galambos I; Ganszky I (2002). Character ization of water-soluble organic matter isolated from atmospheric fine aerosol. J Geophys Res, 107: 8339. 156646

Kittelson DB (1998). Engines and nanoparticles: a review. J Aerosol Sci, 29: 575-588. 051098

Kittelson DB; Schauer JJ; Lawson DR; Watts WF; Johnson JP (2006). On-road and laboratory evaluation of combustion aerosols-Part 2:. Summary of spark ignition engine results. J Aerosol Sci, 37: 931-949. 156648

Kittelson DB; Watts WF; Johnson JP (2006). On-road and labor atory evaluation of combusti on aerosols—Part 1: Summary of diesel engine results. J Aerosol Sci, 37: 913-930. 156649

Kleindienst TE; Edney EO; Lewandowski M; Offenberg JH; Jaoui M (2006). Secondary Organic Carbon and Aerosol Yields from the Irradiations of Isopren e and-Pinene in the Presence of NO x and SO 2. Environ Sci Technol, 40:

3807-3812. 156650

Klepeis NE; Nelson WC; OttWR; Robinson JPTsang AM; Switzer P; Behar JV; Hern SC; Engelmann WH (2001). The National Human Activity Pattern Survey (NHAPS): a resour ce for assessing exposure to environmental pollutants. J Expo Sci Environ Epid emiol, 11: 231-252. 002437

Klouda GA; Filliben JJ; Parish HJ; Chow JC; Watson JG; Cary RA (2005). Reference material 8785: Air particulate matter on filter media. Aerosol Sci Technol, 39: 173-183. 130382

Koenig JQ; Mar TF; Allen RW; Jansen K; Lumley T; Sullivan JH; Trenga CA; Larson T; Liu LJ (2005). Pulmonary effects of indoor- and outdoor-generated particles in children with asthma. Environ Health Perspect, 113: 499-503. 087384

Kokhanovsky A; Breon F; Cacciari A; Carboni E; Diner D; Di Nicolantonio W; Grainger R; Grey W; Höller R; Lee K (2007). Aerosol remote sensing over land: a comparison of satellite retrievals using different algorithms and instruments. Atmos Res, 85: 372-394. 190009

Koutrakis P; Suh HH; Sarnat JA; Brown KW; Coull BA; Sc hwartz J (2005). Characterizat ion of particulate and gas exposures of sensitive subpopulations li ving in Baltimore and Boston. Health Effects Institute. Boston, MA. 131. http://pubs.healtheffects.org/view.php?id=91. 095800

Krewski D; Burnett RT; Goldberg MS; Hoover K; Siemiatycki J; Jerrett M; Abrahamowicz M; White WH (2000). Reanalysis of the Harvard Six Cities study and the Ameri can Cancer Society study of particulate air pollution and mortality. Health Effects Institute. Cambridge , MA.http://pubs.healtheffects.org/view.php?id=6. 012281

Krewski D; Jerrett M; Burnett RT; Ma R; Hughes E; Shi Y; Turner MC; Pope AC III; Thurston G; Calle EE; Thun MJ (2009). Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. Health Effects Institute. Cambridge, MA. Report Nr. 140. 191193

Krieger UK; Rupp S; Hausammann E; Peter T (2007). Simult aneous measurements of PM10 and PM1 using a single TEOM. Aerosol Sci Technol, 41: 975-980. 129657

Kroll JH; Seinfeld JH (2008). Chemistry of secondary organic aerosol: Formation and evolution of low-volatility organics in the atmosphere. Atmos Environ, 42: 3593-3624. 155910

Krudysz MA; Froines JR; Fine PM; Sioutas C (2008). Intra-community spatial variation of size-fractionated PM mass, OC, EC, and trace elements in the Long Beach, CA area. Atmos Environ, 42: 5374-5389. 190064

Kruize H; Hanninen O; Breugelmans O; Lebret E; Jantune n M (2003). Description and demonstration of the EXPOLIS simulation model: two examples of modeling population exposure to particulate matter. J Expo Sci Environ Epidemiol, 13: 87-99. 156661

Kuang C; McMurry PH; McCormick AV; Eisele FL (2008). Dependence of nucleation rates on sulfuric acid vapor concentration in diverse atmospheric locations. J Geophys Res, 113: D10209. 191196

Kuhn T; Biswas S; Sioutas C (2005). Diurnal and seasonal charact eristics of particle volatility and chemical composition in the vicinity of a light-duty vehicle freeway. Atmos Environ, 39: 7154-7166. 129448

Kuhns H; Knipping EM (2005). Development of a United St ates-Mexico emissions inventory for the Big Bend Regional Aerosol and Visibility Observational (BRAVO) Study. J Air Waste Manag Assoc, 55: 677-692. 156663

Kulmala M; Laaksonen A; Pirjola L (1998). Parameterizations for sulfuric acid/water nucleation rates. J Geophys Res, 103:

8301-8307. 129411

December 2009 3-206Kulmala M; Mordas G; Petäjä T; Grönholm T; Aalto PP; Vehkamäki H; Hienola AI; Herrmann E; Sipilä M; Riipinen I (2007). The condensation particle counter battery (CPCB): A new tool to investigate the activation properties of nanoparticles. J Aerosol Sci, 38: 289-304. 155911

Kulmala M; Riipinen I; Sipila M; Manninen HE; Petaja T; Ju nninen H; Maso MD; Mordas G; Mirme A; Vana M; Hirsikko A; Laakso L; Harrison RM; Hanson I; Leung C; Lehtinen KE; Kerminen VM (2007). To ward direct measurement of atmospheric nucleation. Science, 318: 89-92. 097838

Kulmala M; Vehkamaki H; Petajda T; Dal Maso M; Lauri A; Kerminen V-M; Birmili W; McMurry PH (2004). Formation and growth rates of ultrafine atmospheric particles: a review of observations. J Aerosol Sci, 35: 143-176. 089159

Kuo Y; Huang S; Shih T; Chen C; Weng Y; Lin W (2005). Development of a size-selective inlet-simulating ICRP lung deposition fraction. Aerosol Sci Technol, 39: 437-443. 186997

Kurniawan A; Schmidt-Ott A (2006). Monitoring the soot em issions of passing cars. Envi ron Sci Technol, 40: 1911-5.

098823

Lake DA; Tolocka MP; Johnston MV; Wexler AS (2003). Mass Spectrometry of Individual Particles between 50 and 750 nm in Diameter at the Baltimore Supersite. Environ Sci Technol, 37: 3268-3274. 156669

Lake DA; Tolocka MP; Johnston MV; Wexler AS (2004). The ch aracter of single particle sulfate in Baltimore. Atmos Environ, 38: 5311-5320. 088411

Langholz B; Ebi K; Thomas D; Peters J; London S (2002). Traffic density and the risk of childhood leukemia in a Los Angeles case-control study. Ann Epidemiol, 12: 482-487. 191771

Larson T; Gould T; Simpson C; Liu LJ; Claiborn C; Lewtas J (2004). Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington, using positive matr ix factorization. J Air Waste Manag Assoc, 54: 1175-87.

098145

Lee HM (2007). Fabrication of Reference F ilter for Measurements of EC (Elemental Carbon) and OC (Organic Carbon) in Aerosol Particles. Aerosol Sci Technol, 41: 284-294. 155926

Lee JH; Hopke PK; Holsen TM; Lee DW; AJaques P; Sioutas C; Ambs JL (2005). Perform ance evaluation of continuous PM2.5 mass concentration monitors. J Aerosol Sci, 36: 95-109. 155925

Lee JH; Hopke PK; Holsen TM; Polissar AV; Lee DW; Edgert on ES; Ondov JM; Allen G (2005). Measurements of fine particle mass concentrations using continuous and integrat ed monitors in Eastern US Cities. Aerosol Sci Technol,

39: 261-275. 128139

Lee SJ; Demokritou P; Koutrakis P; Delgado-Saborit JM (2006). Development and evaluation of personal respirable particulate sampler (PRPS). Atmos Environ, 40: 212-224. 098249

Leith D; Sommerlatt D; Boundy MG (2007). Passive sampler fo r PM10-2.5 aerosol. J Air Waste Manag Assoc, 57: 332-6.

098241

Levine PM; Gong P; Levicky R; Shepard KL (2009). Real-tim e, multiplexed electrochemical DNA detection using an active complementary metal-oxide-semiconductor biosensor array with integrated sensor electronics. Biosens Bioelectron, 24: 1995-2001. 192091

Levy JI; Bennett DH; Melly SJ; Spengler JD (2003). Influence of traffic patterns on particulate matter and polycyclic aromatic hydrocarbon concentrations in Roxbury, Mass achusetts. J Expo Sci Envir on Epidemiol, 13: 364-371.

052661

Lewne M; Nise G; Lind ML; Gustavsson P (2006). Exposure to particles and nitrogen dioxide among taxi, bus and lorry drivers. Int Arch Occup Environ Health, 79: 220-226. 090556

Lim H-J; Turpin BJ; Edgerton E; Hering SV; Allen G; Maring H; Solomon P (2003). Semi-continuous aerosol carbon measurements: Comparis on of Atlanta supersite measur ements. J Geophys Res, 108: 8419. 037037

Lim MCH; Ayoko GA; Morawska L; Ristovski ZD; Jayaratne ER (2007). The effects of fuel characteristics and engine operating conditions on the elemental composition of emi ssions from heavy duty dies el buses. Fuel, 86: 1831-1839.

155931

Lindberg SE; Lovett GM (1985). Field measurements of particle dry deposition rate s to foliage and inert surfaces in a forest canopy. Environ Sci Technol, 19: 238-244. 036530

December 2009 3-207Lipfert FW; Perry HM Jr; Miller JP; Baty JD; Wyzga RE; Carmody SE (2000). The Washington University-EPRI veterans' cohort mortality study: preliminary re sults. Inhal Toxicol, 4: 41-73. 004087

Lippmann M (2009). Semi-continuous speciation analyses for ambi ent air particulate matter: an urgent need for health effects studies. J Expo Sci Envi ron Epidemiol, 19: 235-247. 190083

Lipsky E; Robinson A (2006). Effects of dilution on fine particle mass and partitioning of semivolatile organics in diesel exhaust and wood smoke. Environ Sci Technol, 40: 155-162. 189891

Little P; Wiffen RD (1977). Emission and deposition of petrol engine exhaust Pb--I deposition of exhaust Pb to plant and soil surfaces. Atmos Environ, 11: 437-447. 070869

Liu L-J; Box M; Kalman D; Kaufman J; Koenig J; Larson T; Lumley T; Sheppard L; Wallace L (2003). Exposure assessment of particulate matter for susceptible populations in Seattle. Environ Health Perspect, 11: 909-918.

073841

Liu XH; Hegg DA; Stoelinga MT (2001). Numerical simulation of new particle formation over the northwest Atlantic using the MM5 mesoscale model coupled with su lfur chemistry. J Ge ophys Res, 106: 9697-9715. 048201

Lokken PR; Wellenius GA; Coull BA; Burger MR; Schlaug G; Suh HH; Mittleman MA (2009). Air Pollution and Risk of

Stroke: Underestimation of Effect Due to Misclassificat ion of Time of Event Onset. Epidemiology, 20: 137-142.

186774

Long CM; Suh HH; Catalano PJ; Koutrakis P (2001). Using time- and size-resolved particulate data to quantify indoor penetration and deposition behavior . Environ Sci Technol, 35: 2089-2099. 011526

Long RW; McClenny WA (2006). Laboratory and field evaluation of instrumentation for the semicontinuous determination of particulate nitrate (and other water-soluble particulate components). J Air Waste Manag Assoc, 56: 294-305.

098214

Lovett GM (1994). Atmospheric deposition of nutrients and pollut ants in North America: an ecological perspective. Ecol Appl, 4: 629-650. 024049

Lowenthal DH; Rogers CF; Saxena P; Watson JG; Chow JC (1995) . Sensitivity of estimated light extinction coefficients to model assumptions and measurement errors. Atmos Environ, 29: 751-766. 045134

Lu R; Turco RP; Jacobson MZ (1997). An integrated air pollution modeling system for urban and regional scales: 1 Structure and performance. J Geophys Res, 102: 6063-6079. 048202

Lu R; Turco RP; Jacobson MZ (1997). An integrated air po llution modeling system for urban and regional scales: 2. Simulations for SCAQS 1987 . J Geophys Res, 102: 6081-6098. 191768

Lunden MM; Kirchstetter TW; Thatcher TL; Hering SV; Brown NJ (2008). Factors affecting th e indoor concentrations of carbonaceous aerosols of outdoor origin. Atmos Environ, 42: 5660-5671. 155949

Lunden MM; Revzan KL; Fischer ML; Thatcher TL; Littlejoh n D; Hering SV; Brown NJ (2 003). The transformation of

outdoor ammonium nitrate aerosols in the indo or environment. Atmo s Environ, 37: 5633-5644. 081201

Lunden MM; Thatcher TL; Hering SV; Brown NJ (2003). The use of time-and chemically resolved particulate data to

characterize the infiltration of outdoor PM2.5 into a resi dence in the San Joaquin Valley. Environ Sci Pol, 37: 4724-

4732. 156718

Mader BT; Schauer JJ; Seinfeld JH; Flagan RC; Yu JZ; Yang H; Lim HJ; Turpin BJ; Deminter JT; Heidemann G (2003). Sampling methods used for the collection of particle- phase organic and elemental carbon during ACE-Asia. Atmos Environ, 37: 1435-1449. 155955

Maheswaran R; Elliott P (2003). Stroke mortality associated with living near main roads in England and wales: a geographical study. Stroke, 34: 2776-2780. 125271

Mahrt L (1998). Stratified atmospheric boundary layers and breakdown of models. Theor Comput Fluid Dynam, 11: 263-

279. 048210

Makar PA; Gravel S; Chirkov V; Strawbridge KB; Froude F; Arnold J; Brook J (2006). Heat flux, urban properties, and regional weather. Atmos Environ, 40: 2750-2766. 155959

Mar TF; Norris GA; Larson TV; Wilson WE; Koenig JQ (2003). Air pollution and cardiovascular mortality in Phoenix, 1995-1997. Health Effects Institute. Cambridge, MA. 042841

December 2009 3-208Maricq MM (2007). Chemical characterization of particulate emi ssions from diesel engines: A review. J Aerosol Sci, 38:

1079-1118. 155973

Martin RV; Chance K; jacob DJ; Kurosu TP; Spurr RJD; Bu csela E; Gleason JF; Palmer PI; Bey I; Fiore AM; Li Q; Yantosca RM; Koelemeijer RBA (2002). An improved retr ieval of tropospheric nitrogen dioxide from GOME. J Geophys Res, 107: 1-21. 089380

Massman WJ; Pederson J; Delany A; Grantz D; Denhartog G; Neumann HH; Oncley SP; Pearson R; Shaw RH (1994). An evaluation of the regional acid deposition model surface module for ozone uptake at 3 sites in the San-Joaquin valley of California. J Geophys Res, 99: 8281-8294. 043681

Mathis U; Mohr M; Forss AM (2005). Comprehensive particle characterization of modern gasoline and diesel passenger cars at low ambient temperatures. Atmos Environ, 39: 107-117. 155970

Mathur R (2008). Estimating the impact of the 2004 Alaskan forest fires on episodic particulate matter pollution over the eastern United States through assimilation of satellite-derived aerosol optical depths in a regional air quality model. J Geophys Res, 113: D17302. 156742

Matsumoto K; Hayano T; Uematsu M (2003). Positive artifact in the measurement of particulate carbonaceous substances using an ambient carbon particulate m onitor. Atmos Environ, 37: 4713-4717. 124293

Matthias-Maser S; Bogs B; Jaenicke R (2000). The size distribu tion of primary biological aerosol particles in cloud water on the mountain Kleiner Feldberg/Taunus (FRG). Atmos Res, 54: 1-13. 155972

McBride SJ; Williams RW; Creason J (2007). Bayesian hierarchical modeling of personal exposure to particulate matter. Atmos Environ, 41: 6143-6155. 124058

McCurdy T; Glen G; Smith L; Lakkadi Y (2000). The National Exposure Research Laborat ory's consolidated human activity database. J Expo Sci Environ Epidemiol, 10: 566-578. 000782

McDonald JD; Eide I; Seagrave J; Zielinska B; Whitn ey K; Lawson DR; Mauderly JL (2004). Relationship between composition and toxicity of motor vehicle emission samples. Environ Health Perspect, 112: 1527-1538. 087458

McKendry IG; Strawbridge KB; O?Neill NT; Macdonald AM; Liu PSK; Leaitch WR; Anlauf KG; Jaegle L; Fairlie TD; Westphal DL (2007). Trans-Pacific transport of Saharan dust to western North America: A case study. J Geophys Res, 112: D01103. 156748

McKenna DS; Konopka P; Grooss J-U; Gunther G; Muller R; Spang R; Offermann D; Orsolini Y (2002). A new chemical Lagrangian model of the stratosphere (CLaMS) 1. form ulation of advection and mixing. J Geophys Res, 107: 4309.

053445

McMurry PH; Fink M; Sakurai H; Stolzenburg MR; Mauldin RL III; Smith J; Eisele F; Moore K; Sjostedt S; Tanner D; Huey LG; Nowak JB; Edgerton E; Voisin D (2005). A criterion for new particle formation in the sulfur-rich Atlanta atmosphere. J Geophys Res, 110: 1-10. 191759

Mebust MR; Eder BK; Binkowski FS; Roselle SJ (2003). M odels-3 community multiscale air quality (CMAQ) model aerosol component 2. Model eval uation. J Geophys Res, 108: 4184. 156749

Meng QY; Turpin BJ; Korn L; Weisel CP; Morandi M; Colome S; Zhang J; Stock T; Spektor D; Winer A; Zhang L; Lee JH; Giovanetti R; Cui W; Kwon J; Alimokhtari S; Shendell D; Jones J; Farrar C; Maberti S (2005). Influence of ambient (outdoor) sources on residential indoor and pers onal PM2.5 concentrations: analyses of RIOPA data. J Expo Sci Environ Epidemiol, 15: 17-28. 058595

Meng QY; Turpin BJ; Lee JH; Polidori A; Weisel CP; Morandi M; Colome S; Zhang JF; Stock T; Winer A (2007). How does infiltration behavior modify the composition of ambient PM2.5 in indoor spaces? An analysis of RIOPA data. Environ Sci Tech, 41: 7315-7321. 194618

Mensink C; De Ridder K; Deutsch F; Lefebre F; Van de Vel K (2008). Examples of scale inter actions in local, urban, and regional air quality modelling. Atmos Res, 89: 351-357. 155980

Mfula AM; Kukadia V; Griffiths RF; Hall DJ (2005). Wind tunnel modelling of urban building exposure to outdoor pollution. Atmos Environ, 39: 2737-2745. 123359

Middha P; Wexler A (2006). Design of a Slot Nanoparticle Virtual Impactor. Aerosol Sci Technol, 40: 737-743. 155982

December 2009 3-209Middlebrook AM; Murphy DM; Lee S-H; Thom son DS; Prather KA; Wenzel RJ; Liu D- Y; Phares DJ; Rhoads KP; Wexler AS; Johnston MV; Jimenez JL; Jayne JT; Worsnop DR; Yourshaw I; Seinfeld JH; Flagan RC (2003). A comparison of particle mass spectrometers during the 1999 Atlanta Supersites project. J Geophys Res, 108: 8424. 042932

Miller FJ; Gardner DE; Graham JA; Lee RE Jr; Wilson WE; B achmann JD (1979). Size considerations for establishing a standard for inhalable particles. J Air Waste Manag Assoc, 29: 610-615. 070577

Miller SL; Anderson MJ; Daly EP; Milfor d JB (2002). Source apportionm ent of exposures to volatile organic compounds I Evaluation of receptor models using simulate d exposure data. Atmos Environ, 36: 3629-3641. 030661

Misra C; Geller MD; Shah P; Sioutas C; Solomon PA (2001). Development and evaluation of a continuous coarse (PM10- PM25) particle monitor. J Air Waste Manag Assoc, 51: 1309-1317. 018998

Misra C; Geller MD; Sioutas C; Solomon PA (2003). Devel opment and evaluation of a PH10 impactor-inlet for a continuous coarse particle monitor. Aerosol Sci Technol, 37: 271-281. 195001

Miyazaki Y; Kondo Y; Takegawa N; Komazaki Y; Fukuda M; Kawamura K; Mochida M; Okuzawa K; Weber RJ (2006). Time-resolved measurements of water-soluble organic carbon in Tokyo. J Geophys Res, 111: D23206. 156767

Molnár P; Johannesson S; Boman J; Barregard L; Sallsten G (2006). Personal exposures and indoor, residential outdoor, and urban background levels of fine particle trace elements in the general popu lation. J Environ Monit, 8: 543-551.

156773

Moolgavkar SH (2000). Air pollution and hospital admissions for diseases of the circulatory system in three US metropolitan areas. J Air Waste Manag Assoc, 50: 1199-1206. 010305

Moore K; Krudysz M; Pakbin P; Hudda N; Sioutas C (2009). Intra-community variability in total particle number concentrations in the San Pedro Harbor Area (Los Angeles, California). Aerosol Sci Technol, 43: 587-603. 191004

Moore KF; Ning Z; Ntziachristos L; Schauer JJ; Sioutas C (2 007). Daily variation in the pr operties of urban ultrafine aerosol-Part I: Physical characterization a nd volatility. Atmos Environ, 41: 8633-8646. 122445

Morawska L; Keogh DU; Thomas SB; Mengersen K (2007). M odality in ambient particle size distributions and its potential as a basis for developing air quali ty regulation. Atmos Environ, 42: 1617-1628. 155990

Morawska L; Ristovski Z; Jayaratne ER; Keogh DU; Ling X (2008). Ambient nano and ultrafine particles from motor vehicle emissions: Characteristics, ambient processing a nd implications on human exposure . Atmos Environ, 42:

8113-8138. 191006

Morawska L; Thomas S; Jamriska M; Johnson G (1999). The m odality of particle size distributions of environmental

aerosols. Atmos Envi ron, 33: 4401-4411. 007609

Mulchandani A; Chen W; Mulchandani P; Rogers KR (2001) . Biosensors for direct determination of organophosphate pesticides . Biosens Bioelectron, 16: 225-230. 191003

Muller K; Spindler G; Maenhaut W; Hitzenberger R; Wieprecht W; Baltensperger U; ten Brink H (2004). INTERCOMP2000, a campaign to assess the comparability of methods in use in Europe for measuring aerosol composition. Atmos E nviron, 38: 6459-6466. 097109

Murahashi T (2003). Determination of mutagenic 3-nitrobenzan throne in diesel exhaust particulate matter by threedimensional high-performance liquid chromatography. Analyst, 128: 42-5. 096539

Murphy BN; Pandis SN (2009). Simulating the formation of semivolatile primary and secondary organic aerosol in a regional chemical transport model . Environ Sci Technol, 43: 4722-4728. 190095

Nash DG; Tomas Baer T; Johnston MV (2006). Aerosol mass sp ectrometry: an introductory re view . Int J Mass Spectrom,

258: 2-12. 199502

Nerriere E; Guegan H; Bordigoni B; Hautemaniere A; Momas I; Ladner J; Target A; Lameloise P; Delmas V; Personnaz MB; Koutrakis P; Zmirou-Navier D (2007). Spatial heterogene ity of personal exposure to airborne metals in French urban areas. Sci Total Environ, 373: 49-56. 156801

Ng NL; Kroll JH; Chan AWH; Chhabra PS; Flagan RC; Seinfeld JH (2007). Secondary organic aerosol formation from mxylene, toluene, and benzene. Atmo s Chem Phys Discuss, 7: 4085-4126. 199528

Ning Z; Geller MD; Moore KF; Sheesley R; Schauer JJ; Sioutas C (2007). Daily Variation in Chemical Characteristics of Urban Ultrafine Aerosols and Inference of Their Sources. Environ Sci Technol, 41: 6000-6006. 156809

December 2009 3-210Ntziachristos L; Ning Z; Geller MD; Sioutas C (2007). Particle concentration and characteristics near a major freeway with heavy-duty diesel traffic. E nviron Sci Technol, 41: 2223-2230. 089164

Ntziachristos L; Samaras Z (2006). Combination of aerosol inst rument data into reduced variables to study the consistency of vehicle exhaust particle measur ements. Atmos Envi ron, 40: 6032-6042. 116722

Odman MT; Boylan JW; Wilkinson JG; Russell AG; Mueller SF; Imhoff RE; Doty KG; Norris WB; McNider RT (2002). SAMI air quality modeling: final report. 092474

Offenberg JH; Lewandowski M; Edney EO; Kleindienst TE; Jaoui M (2007). Investigation of a systematic offset in the measurement of organic carbon with a semicontinuous analyzer. J Air Waste Manag Assoc, 57: 596-9. 098101

Ogulei D; Hopke PK; Zhou L; Patrick Pancras J; Nair N; Ondov JM (2006). Source apporti onment of Baltimore aerosol

from combined size distribution and chemical composition data. Atmo s Environ, 40: 396-410. 119973

Olfert JS; Kulkarni P; Wang J (2008). Measuring aerosol size distributions with the fast integrated mobility spectrometer. J Aerosol Sci, 39: 940-956. 156004

Olivier JGJ; Bouwman AF; Berdowski JJM; Veldt C; Bloos JPJ; Visschedijk AJH; van der Maas CWM; Zandveld PYJ (1999). Sectoral emission inventories of greenhouse gases for 1990 on a per country basis as well as on 1°× 1°. Environ Sci Pol, 2: 241-263. 156829

Olivier JGJ; Bouwman AF; Berdowski JJM; Veldt C; Bloos JPJ; Visschedijk AJH; Zandveld PYJ; Haverlag JL (1996). Description of EDGAR Version 2.0: A set of global em ission inventories of greenhouse gases and ozone-depleting substances for all anthropogenic and most natural sources on a per country basis and on 1 degree x 1 degree grid.

156828

Olson DA; Burke JM (2006). Distributions of PM2.5 sour ce strengths for cooking from the Research Triangle Park particulate matter panel study. Environ Sci Technol, 40: 163-169. 189951

Olson DA; Mcdow SR (2009). Near roadway concentrations of organic source markers. Atmos Environ, 43: 2862-2867.

191188

Olson DA; Norris GA (2005). Sampling ar tifacts in measurement of elemental and organic carbon: Low-volume sampling

in indoor and outdoor environmen ts. Atmos Environ, 39: 5437-5445. 156005

Orsini DA; Ma Y; Sullivan A; Sierau B; Baumann K; Webe r RJ (2003). Refinements to the particle-into-liquid sampler (PILS) for ground and airborne meas urements of water soluble aerosol composition. Atmos Environ, 37: 1243-

1259. 156008

Ott DK; Cyrs W; Peters TM (2008). Passive measurement of coarse particulate matter, PM10-2.5. J Aerosol Sci, 39: 156-

167. 195004

Ott DK; Kumar N; Peters TM (2008). Passive sampling to captu re spatial variability in PM10-25. Atmos Environ, 42: 746-

756. 119394

Ozkaynak H; Xue J; Zhou H; Raizenne M (1996). Associations between daily mortality and motor vehicle pollution in Toronto, Canada. U.S. Environmenta l Protection Agency, Office of Reserch and Development, National Exposure Research Laboratory. Research Triangle Park, NC. EPA/600/R-95/098. 073986

Paatero P; Tapper U (1994). Positive matrix factorization: a non-negative factor m odel with optimal utilization of error estimates of data values. Environmetrics, 5: 111-126. 086998

Paciorek CJ; Yanosky JD; Puett RC; Laden F; Suh HH (2009). Pr actical large-scale spatio-temporal modeling of particulate matter concentrations. Ann Appl Stat, 3: 370-397. 190090

Padro J (1996). Summary of ozone dry deposition velocity measurements and model estimates over vineyard, cotton, grass and deciduous forest in summer. Atmos Environ, 30: 2363-2369. 052446

Pakbin P;Ning Z;Schauer J;Sioutas C (2009). Characterization of Particle Bound Organic Carbon from Diesel Vehicles Equipped with Advanced Emission Control Tec hnologies. Environ Sci Technol, 43: 4679-4686. 189893

Pandian MD; Behar JV; Ott WR; Wallace LA; Wilson AL; Colome S D (1998). Correcting errors in the nationwide data base of residential air exchange rates. J Expo Sci Environ Epidemiol, 8: 577-586. 090552

Pandis SN (2004). Atmospheric aerosol processes. In McMurry PH; Shepard, M; Vickery JS (Ed.), Particulate Matter Science for Policy Makers: A NARSTO Assessment Cambridge, UK: Cambridge University Press. 156838

December 2009 3-211Pang Y; Eatough NL; Modey WK; Eatough DJ (2002). Evaluation of the RAMS continuous monitor for determination of PM2.5 mass including semi-volatile material in Philadelphia, PA. J Air Waste Manag Assoc, 52: 563-572. 030353

Pang Y; Turpin BJ; Gundel LA (2006). On the Importance of Organic Oxygen for Understanding Organic Aerosol Particles. Aerosol Sci Technol, 40: 128-133. 156012

Park J; Mitchell MJ; McHale PJ; Christopher SF; Myers TP (2 003). Interactive effects of changing climate and atmospheric deposition on N and S biogeochemistry in a forested watershed of the Adirondack Mountains, New York State. Global Change Biol, 9: 1602-1619. 156842

Park K; Chow JC; Watson JG; Trimble DL; Doraiswamy P; Arnott WP; Stroud KR; Bowers K; Bode R; Petzold A; Hansen AD (2006). Comparison of continuous and filter-based carbon m easurements at the Fresno supersite. J Air Waste Manag Assoc, 56: 474-91. 098104

Park RJ; Stenchikov GL; Pickering; Dickerson RR; Alle n DJ; Kondragunta S (2001). Regional air pollution and its radiative forcing: studies with a single column chemi cal and radiation transport model. J Geophys Res, 106: 28,751-28,770. 044169

Peltier RE; Hsu S-I; Lall R; Lippmann M (2008). Residual oil com bustion: A major source of airborne nickel in New York City. J Expo Sci Environ Epidemiol, 19: 603-612. 197452

Peltier RE; Lippmann M (2009). Residual oil combustion: 2. Dist ributions of airborne nickel and vanadium within New York City. J Expo Sci Environ Epidemiol, TBD: 1-9. 197455

Peng RD; Dominici F; Pastor-Barriuso R; Zeger SL; Samet JM (2005). Seasonal analyses of air pollution and mortality in 100 US cities. Am J Epidemiol, 161: 585-594. 087463

Petäjä T; Mordas G; Manninen H; Aalto PP; Hmeri K; Ku lmala M (2006). Detection efficiency of a water-based TSI condensation particle counter 3785. Aerosol Sci Technol, 40: 1090-1097. 156021

Peters K; Eiden R (1992). Modelling the dry deposition velocity of aerosol particles to a spruce forest. Atmos Environ, 26:

2555-2564. 045277

Peters TM (2006). Use of the Aerodynamic Particle Sizer to Measure Ambient PM 10-2. 5: The Coarse Fraction of PM 10. J Air Waste Manag Assoc, 56: 411-416. 156860

Pettijohn FJ (1957). Sedimentary rocks. New York: Harper & Brothers. 156862

Phares DJ; Rhoads KP; Johnston MV; Wexler AS (2003). Size-reso lved ultrafine particle compos ition analysis 2. Houston. J Geophys Res, 108: 8420. 156866

Phuleria HC; Geller MD; Fine PM; Sioutas C (2006). Size-Resolved emissions of organic tracers from light-and heavyduty vehicles measured in a California road way tunnel. Environ Sci Technol, 40: 4109-4118. 156867

Phuleria HC; Sheesley RJ; Schauer JJ; Fine PM; Sioutas C (2 007). Roadside measurements of size-segregated particulate organic compounds near gasoline and diesel-dominated freeways in Los Angeles, CA. Atmos Environ, 41: 4653-

4671. 117816

Pierce JR; Adams PJ (2009). Uncertainty in global CCN concentrations from uncertain aer osol nucleation and primary emission rates. Atmos Chem Phys, 9: 1339-1356. 191189

Pirjola L; Parviainen H; Hussein T; Valli A; Haemeri K; Aaa lto P; Virtanen A; Keskinen J; Pakkanen TA; Maekelae T; Hillamo RE (2004). 'Sniffer'; a novel tool for chasing vehi cles and measuring traffic pol lutants. Atmos Environ, 38:

3625-3635. 117564

Polidori A; Arhami M; Sioutas C; Delfino RJ; Allen R (2007). Indoor/outdoor relations hips, trends, and carbonaceous content of fine particulate matter in retirement homes of the Los Angeles Basin. J Air Waste Manag Assoc, 57: 366-

379. 156877

Polidori A; Turpin B; Meng QY; Lee JH; Weisel C; Morandi M; Colome S; Stock T; Winer A; Zhang J; Kwon J; Alimokhtari S; Shendell D; Jones J; Farrar C; Maberti S (2006). Fine organic particulate matter dominates indoorgenerated PM2.5 in RIOPA homes. J Expo Sci Environ Epidemiol, 16: 321-331. 156876

Pope CA III; Burnett RT; Thun MJ; Calle EE; Krewski D; Ito K; Thurston GD (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine pa rticulate air pollution. JAMA, 287: 1132-1141. 024689

December 2009 3-212Pope CA III; Thun MJ; Namboodiri MM; Dockery DW; Evans JS; Speizer FE; Heath CW Jr (1995). Particulate air pollution as a predictor of mortality in a prospective study of US adults. Am J Respir Crit Care Med, 151: 669-674.

045159

Pöschl U (2005). Atmospheric aerosols: co mposition, transformation, climate and health effects. Angew Chem Weinheim Bergstr Ger, 44: 7520-7540. 156882

Pouliot G; Pace TG; Roy B; Pierce T; Mobley D (2008). Development of a biomass bur ning emissions inventory by combining satellite and ground-based information. J of Applied Remote Sensing, 2: 021501-021517. 156883

Price M; Bulpitt S; Meyer MB (2003). A comparison of PM10 m onitors at a Kerbside site in the northeast of England. Atmos Environ, 37: 4425-4434. 098082

Puett RC; Schwartz J; Hart JE; Yanosky JD; Speizer FE; Suh H; Paciorek CJ; Neas LM; Laden F (2008). Chronic particulate exposure, mortality, and coronary heart diseas e in the nurses' health study. Am J Epidemiol, 168: 1161-

1168. 156891

Pun BK; Seigneur C; White W (2003). Day-of-week behavior of atmospheric ozone in three US cities. J Air Waste Manag Assoc, 53: 789-801. 047775

Qian S; Sakurai H; McMurry PH (2007). Ch aracteristics of regional nucleation events in urban East St Louis. Atmos Environ, 41: 4119-4127. 116435

Qin X; Prather KA (2006). Impact of bi omass emissions on particle chemistry dur ing the California Regional Particulate Air Quality Study. Int J Mass Spectrom, 258: 142-150. 156895

Ramachandran G; Adgate JL; Pratt GC; Sexton K (2003) . Characterizing indoor and outdoor 15 minute average PM2.5 concentrations in urban neighborhoods. Aerosol Sci Technol, 37: 33-45. 195017

Rattigan OV; Hogrefe O; Felton HD; Schwab JJ; Roychowdhur y UK; Husain L; Dutkiewicz VA; Demerjian KL (2006). Multi-year urban and rural semi-continuous PM25 sulfate a nd nitrate measurements in New York state: Evaluation and comparison with filter based m easurements. Atmos E nviron, 40: 192-205. 115897

Rees SL; Robinson AL; Khlystov A; Stanier CO; Pandis SNSN (2004). Mass balance closure and the Federal Reference Method for PM2.5 in Pittsburgh, Pennsyl vania. Atmos Environ, 38: 3305-3318. 097164

Reff A; Weisel CP; Zhang J; Morandi M; Stock T; Colome S; Winer A; Turpin BJ; Offenberg JH (2007). A functional group characterization of organic PM2.5 exposure: Result s from the RIOPA study. Atmos Environ, 41: 4585-4598.

156045

Reithmeier C; Sausen R (2002). ATTILA: atmospheric tracer transport in a Lagr angian model. Tellus Dyn Meteorol Oceanogr, 54B: 278-299. 053447

Reponen T; Grinshpun SA; Trakumas S; Martuzevicius D; Wang Z-M; LeMasters G; Lockey JE; Biswas P (2003). Concentration gradient patterns of aer osol particles near interstate highways in the Greater Cincinnati airshed. J Environ Monit, 5: 557-562. 088425

Rice J (2004). Comparison of Integrated Fi lter and Automated Carbon Aerosol Meas urements at Research Triangle Park, North Carolina. Aerosol Sci Technol, 38: 23-36. 156049

Riddle SG; Jakober CA; Robert MA; Cahill TM; Charles MJ; Kleem an MJ (2007). Large PAHs detected in fine particulate matter emitted from light-duty gasoline vehicles. Atmos Environ, 41: 8658-8668. 115272

Rigby M; Toumi R (2008). London air pollution climatology: I ndirect evidence for urban boundary layer height and wind speed enhancement. Atmos Environ, 42: 4932-4947. 156050

Roache PJ (1998). Verification and validation in computationa l science and engineering. Socorro New Mexico: Hermosa Publishing. 156915

Robinson AL; Donahue NM; Rogge WF (2006). Photochemical oxidation and changes in molecular composition of organic aerosol in the regional context. J Geophys Res, 111: D03302. 156918

Robinson AL; Donahue NM; Shrivastava MK; Weitkamp EA; Sage AM; Grieshop AP; Lane TE; Pierce JR; Pandis SN (2007). Rethinking organic aerosols: Semivolatile emissi ons and photochemical aging. Science, 315: 1259-1262.

156053

December 2009 3-213Rossner P; Svecova V; Milcova A; Lnenickova Z; Solansky N; Sram RJ (2008). Seasonal variability of oxidative stress markers in city bus drivers - Part I. Oxidative damage to DNA. Mutat Res Fund Mol Mech Mutagen, 642: 14-20.

156927

Rudich Y; Donahue NM; Mentel TF (2007). Aging of Orga nic Aerosol: Bridging the Gap Between Laboratory and Field Studies. Annu Rev Phys Chem, 58: 321. 156059

Russell A; Dennis R (2000). NARSTO critical review of phot ochemical models and modeling. Atmos Environ, 34: 2283-

2324. 035563

Russell M; Allen DT; Collins DR; Fraser MP (2004). Daily, seasona l and spatial trends in PM25 mass and composition in southeast Texas. Aerosol Sci Technol, 1: 14-26. 082453

Ryan PH; LeMasters GK (2007). A review of land-use regr ession models for characterizing intraurban air pollution exposure. Inhal Toxicol, 19: 127. 156063

Ryan PH; LeMasters GK; Levin L; Burkle J; Biswas P; Hu S; Grinshpun S; Reponen T (2008). A land-use regression model for estimating microenvironmenta l diesel exposure given multiple addresses from birth through childhood. Sci Total Environ, 404: 139-147. 156064

Rynö M; Rantanen L; Papaioannou E; Konstandopoulos AG; Kosk entalo T; Savela K (2006). Comparison of pressurized fluid extraction, Soxhlet extraction and sonication for th e determination of polycyclic aromatic hydrocarbons in urban air and diesel exhaust particulat e matter. J Environ Monit, 8: 488-493. 156065

Saathoff H; Naumann KH; Schnaiter M; Schöck W; Weingartner E; Baltensperger U; Krämer L; Bozoki Z; Pöschl U; Niessner R (2003). Carbon mass determ inations during the AIDA soot aerosol campaign 1999. J Aerosol Sci, 34:

1399-1420. 156066

Sabin LD; Kozawa K; Behrentz E; Winer AM; Fitz DR; Pankrat z DV; Colome SD; Fruin SA (2005). Analysis of real-time variables affecting children's exposure to diesel-related pollutants during school bus commutes in Los Angeles. Atmos Environ, 39: 5243-5254. 087728

Sadezky A; Muckenhuber H; Grothe H; Niessner R; Posc hl U (2005). Raman microspectroscopy of soot and related carbonaceous materials: Spectral analysis and stru ctural information. Carbon N Y, 43: 1731-1742. 097499

Sage AM; Weitkamp EA; Robinson AL; Donahue NM (2008). Evol ving mass spectra of the oxidized component of organic aerosol: results from aerosol mass spectrometer analyses of aged diesel emissions. Atmos Chem Phys, 8: 1139-

1152. 191758

Sahlodin AM; Sotudeh-Gharebagh R; Zhu Y (2007). Modeling of dispersion near roadways based on the vehicle-induced turbulence concept. At mos Environ, 41: 92-102. 114058

Sakurai H; Tobias HJ; Park K; Zarling D; Docherty KS; Kittelson DB; McMurry PH; Ziemann PJ (2003). On-line measurements of diesel nanoparticle compositio n and volatility. Atmos Environ, 37: 1199-1210. 113924

Salminen K; Karlsson V (2003). Comparability of low-volume PM10 sampler with β-attenuation monitor in background air. Atmos Environ, 37: 3707-3712. 156070

Santarpia JL; Li RJ; Collins DR (2004). Direct measurement of the hydration state of ambient aerosol populations. J Geophys Res, 109: D18209. 156944

Sardar SB; Fine PM; Mayo PR; Sioutas C (2005). Size-fractiona ted measurements of ambient ultrafine particle chemical composition in Los Angeles using the Na noMOUDI. Environ Sci Technol, 39: 932-944. 180086

Sardar SB; Solomon PA; Geller MD; Sioutas C (2006). De velopment and evaluation of a high-volume dichotomous sampler for chemical speciation of coarse and fine particles. J Aerosol Sci, 37: 1455-1466. 156071

Sarnat JA; Brown KW; Schwartz J; Coull BA; Koutrakis P (2005). Ambient gas concentrations and personal particulate matter exposures: implications for studying the health effects of particles. Ep idemiology, 16: 385-395. 087531

Sarnat JA; Long CM; Koutrakis P; Coull BA; Schwartz J; Suh HH (2002). Using sulfur as a tracer of outdoor fine particulate matter. Environ Sci Technol, 36: 5305-5314. 037056

Sarnat JA; Schwartz J; Catalano PJ; Suh HH (2001). Gaseous pollutants in particulate matter epidemiology: Confounders or surrogates? Environ Health Perspect, 109: 1053-1061. 019401

Sarnat SE; Coull BA; Ruiz PA; Koutrakis P; Suh HH (2006). The influences of ambient particle composition and size on particle infiltration in Los Angeles, CA residences. J Air Waste Manag Assoc, 56: 186-196. 089166

December 2009 3-214Sarnat SE; Klein M; Sarnat JA; Flanders WD; Waller LA; Mulholland JA; Russell AG; Tolbert PE (2009). An examination of exposure measurement error from air pollutant spatia l variability in time-series studies. J Expo Sci Environ Epidemiol, In Press: 1-12. 180084

Sarnat SE; Suh HH; Coull BA; Schwartz J; Stone PH; Go ld DR (2006). Ambient particulate air pollution and cardiac arrhythmia in a panel of older adults in St eubenville, Ohio. Occup Environ Med, 63: 700-706. 090489

Sawant AA; Na K; Zhu X; Cocker K; Butt S; Song C; Cock er DR III (2004). Characterization of PM25 and selected gasphase compounds at multiple indoor and outdoor sites in Mira Loma, California. At mos Environ, 38: 6269-6278.

056798

Schauer JJ; Kleeman MJ; Cass GR; Simoneit BRT (1999). Me asurement of emissions from air pollution sources 2 C1 through C30 organic compounds from medium duty diesel trucks. Environ Sci Technol, 33: 1578-1587. 010582

Schauer JJ; Kleeman MJ; Cass GR; Simoneit BRT (2002). Me asurement of emissions from air pollution sources 5 C1 - C32 organic compounds from gasoline-powered moto r vehicles. Environ Sci Technol, 36: 1169-1180. 035332

Schauer JJ; Rogge WF; Hildemann LM; Mazurek MA; Cass GR (1996). Source apportionment of airborne particulate matter using organic compounds as tr acers. Atmos Environ, 30: 3837-3855. 051162

Schnelle-Kreis J; Sklorz M; Peters A; Cyrys J; Zimmerma nn R (2005). Analysis of particle-associated semi-volatile aromatic and aliphatic hydrocarbons in urban particulate matter on a daily basis. Atmos Environ, 39: 7702-7714.

112944

Schwab JJ; Felton HD; Rattigan OV; Demerjian KL (2006). New York State urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM: Evaluations and Comparisons with the FRM . J Air Waste Manag Assoc, 56: 372-83. 098449

Schwab JJ; Hogrefe O; Demerjian KL; Ambs JL (2004). La boratory characterization of modified tapered element oscillating microbalance samplers. J Air Waste Manag Assoc, 54: 1254-63. 098450

Schwartz DE; Gong P; Shepard KL (2008). Time-resolved Fö rster-resonance-energy-transf er DNA assay on an active CMOS microarray. Biosens Bioelectron, 24: 383-390. 192094

Schwartz J; Sarnat JA; Coull BA; Wilson WE (2007). Eff ects of exposure measurement error on particle matter epidemiology: a simulation using data from a panel study in Baltimore, MD. J Expo Sci Environ Epidemiol, 17: S2-

S10. 090220

Seagrave JC; McDonald JD; Bedrick E; Edgerton ES; Gigliotti AP; Jansen JJ; Ke L; Naeher LP; Seilkop SK; Zheng M; Mauderley JL (2006). Lung toxicity of ambient particulate matter from southeastern US sites with different contributing sources: relationships between composition and effects. Environ Health Perspect, 114: 1387-93.

091291

Seaman NL (2000). Meteorological modeling for air quality assessments. Atmos Environ, 34: 2231-2259. 035562

Seinfeld JH; Pandis SN (1998). Atmospheric chemistry and phys ics: from air pollution to climate change. New York: John Wiley & Sons. 018352

Sheesley RJ; Schauer JJ; Meiritz M; Deminter JT; Bae M-S; Turner JR (2007). Daily Variation in Particle-Phase Source Tracers in an Urban Atmosphere. Aerosol Sci Technol, 41: 981-993. 112017

Shen S; Jaques PA; Zhu Y; Geller MD; Sioutas C (2002). Eval uation of the SMPS–APS system as a continuous monitor for measuring PM2. 5, PM10 and coarse (PM2. 5- 10) concentrations. Atmos Environ, 36: 3939-3950. 156086

Sheppard L; Slaughter JC; Schildcrout J; Liu L-JS; Lu mley T (2005). Exposure and m easurement contributions to estimates of acute air pollution effects. J Expo Sci Environ Epidemiol, 15: 366-376. 079176

Sheya SA; Glowacki C; Chang MC; Chow JC; Watson JG (2008). Hot filter/impinger and dilution sampling for fine particulate matter characterization from ferrous metal cas ting processes. J Air Waste Manag Assoc, 58: 553-561.

156977

Shi Q; Sakurai H; McMurry PH (2007). Climatology of Regi onal Nucleation Events in Urban East St. Louis. Atmos Environ, 41: 4119-4127 . 191760

Shimpi S; Khalek I; Bougher T; Tennant C (2009). Number co unt measurements of particul ate trap-equipped diesel truck exhaust from engines that meet 2007 US on-highway emission regulations. Presented at A&WMA's 102nd Annual Conference and Exhibition, J une 16-19, 2009, Pittsburgh, PA. 189888

December 2009 3-215Shrivastava MK; Subramanian R; Rogge WF; Robinson AL (2007). Sources of organic aerosol: Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models. Atmos Environ, 41: 9353-9369. 111594

Singh M; Misra C; Sioutas C (2003). Field evaluation of a pe rsonal cascade impactor sampler (PCIS). Atmos Environ, 37:

4781-4793. 156088

Singh M; Phuleria HC; Bowers K; Sioutas C (2006). Seasonal and spatial trends in particle number concentrations and size distributions at the children's health study sites in Southern California. J Expo Sci Environ Epidemiol, 16: 3-18.

190136

Sioutas C; Delfino RJ; Singh M (2005). Exposur e assessment for atmospheric ultrafine particles (UFPs) and implications in epidemiologic research. Environ Health Perspect, 113: 947-955. 088428

Sioutas C; Kim S; Chang M; Terrell LL; Gong H Jr (2000). Field evaluation of a modified DataRAM MIE scattering

monitor for real-time PM25 mass concentration measurements. Atmos E nviron, 34: 4829-4838. 025223

Slowik JG; Cross ES; Han J-H; Davidovits P; Onasch TB; Jayne JT; Williams LR; Canagaratna MR; Worsnop DR; Chakrabarty RK; Moosm; uuml; ller H; Arnott WP; Schwarz JP; Gao R-S; Fahey DW; Kok GL; Petzold A (2007). An Inter-Comparison of Instruments Measuring Black Carbon Content of Soot Particles. Aerosol Sci Technol, 41:

295 - 314. 096177

Smith JN; Dunn MJ; VanReken TM; Iida K; Stolzenburg MR; McMurry PH; Huey LG (2008). Chemical composition of atmospheric nanoparticles formed from nucleation in Tecamac , Mexico: evidence for an important role for organic species in nanoparticle growth. Geophys Res Lett, 35: L04808. 199529

Smith JN; Moore KF; McMurry PH; Eisele FL (2004). Atmos pheric Measurements of Sub- 20 nm Diameter Particle Chemical Composition by Thermal Desorption Chemical Ionization Mass Spectrometry. Aerosol Sci Technol, 38:

100-110. 156090

Smith RL; Spitzner D; Kim Y; Fuentes M (2000). Threshold depende nce of mortality effects for fine and coarse particles in Phoenix, Arizona. J Air Waste Manag Assoc, 50: 1367-1379. 010335

Smith WH; Staskawicz BJ (1977). Removal of atmospheric par ticles by leaves and twigs of urban trees: some preliminary

observations and assessment of research needs. J Environ Manage, 1: 317-330. 046675

So ESP; Chan ATY; Wong AYT (2005). Large-eddy simulations of wind flow and pollutant dispersion in a street canyon. Atmos Environ, 39: 3573-3582. 110746

Solomon P; Baumann K; Edgerton E; Tanner R; Eatough D; Modey W; Marin H; Savoie D; Natarajan S; Meyer MB (2003). Comparison of integrated samplers for mass a nd composition during the 1999 Atlanta supersites project. J Geophys Res, 108: 8423. 156994

Solomon PA; Hopke PK (2008). A Special Issue of JA&WMA Supporting Key Scientific and Policy-and Health-Relevant Findings from EPA's Particulate Matter Supersites Program and Related Studies: An Integration and Synthesis of Results. J Air Waste Manag Assoc, 58: 137. 156997

Solomon PA; Sioutas C (2008). Continuous and semicontinuou s monitoring techniques for particulate matter mass and chemical components: a synthesis of findings from EPA's Pa rticulate Matter Supersites Program and related studies. J Air Waste Manag Assoc, 58: 164-195. 190139

Song CH; Carmichael GR (2001). A three-di mensional modeling investigation of the evolution processes of dust and seasalt particles in east Asia. J Geophys Res, 106: 18,131-18,154. 036064

Stanier CO; Khlystov AY; Chan WR; Mandiro M; Pandis SN (2004). A Method for the In Situ Measurement of Fine Aerosol Water Content of Ambient Aerosols: The Dry-Am bient Aerosol Size Spectrome ter (DAASS). Aerosol Sci Technol, 38: 215 - 228. 095955

Stein AF; Lamb D; Draxler RR (2000). Incorporation of detail ed chemistry into a three-di mensional Lagrangian-Eulerian hybrid model: application to regional tropos pheric ozone. Atmos Environ, 34: 4361-4372. 048341

Stevenson D; Dentener FJ; Schultz MG; Ellingsen K; Van Noije TPC; Wild O; Zeng G; Amann M; Atherton CS; Bell N; Bergmann DJ; Bey I; Butler T; Cofala J; Collins WJ; De rwent RG; Doherty RM; Drevet J; Eskes HJ; Fiore AM; Gauss M; Hauglustaine DA; Horowitz LW; Isaksen ISA; Krol MC; Lamarque J-F; Lawrence MG; Montanaro V; Muller J-F; Pitari G; Prather MJ; Pyle JA; Rast S; Rodriguez JM; Sanderson MG (2006). Multimodel ensemble simulations of present-day and near-future tropospheric ozone. J Geophys Res, 111: D08301. 089222

December 2009 3-216Stockwell WR; Middleton P; Chang JS; Tang X (1990). The second generation Regional Acid Deposition Model chemical mechanism for regional air quality modeling. J Geophys Res, 95: 16,343-16,367. 043095

Stolzenburg MR; Dutcher DD; Kirby BW; Hering SV (2003). Au tomated Measurement of the Size and Concentration of

Airborne Particulate Nitrate. Aerosol Sci Technol, 37: 537-546. 156102

Stolzenburg MR; Hering SV (2000). Method for the automated meas urement of fine particle nitrate in the atmosphere. Environ Sci Technol, 34: 907-914. 013289

Stracquadanio M; Bergamini D; Massaroli E; Trombini C ( 2005). Field evaluation of a passive sampler of polycyclic aromatic hydrocarbons (PAHs) in an urban atmosphere (Bologna, Italy). J Environ Monit, 7: 910-915. 156104

Strand M; Hopke PK; Zhao W; Vedal S; Gelfand E; Rabi novitch N (2007). A study of hea lth effect estimates using competing methods to model personal exposures to ambi ent PM2.5. J Expo Sci Envi ron Epidemiol, 17: 549-558.

157018

Strand M; Vedal S; Rodes C; Dutton SJ; Gelfand EW; Rabinovitch N (2006). Estimating effects of ambient PM2.5 exposure on health using PM2.5 com ponent measurements and regressi on calibration. J Expo Sci Environ Epidemiol, 16: 30-38. 089203

Streets DG; Zhang Q; Wang L; He K; Hao J; Wu Y; Tang Y; Carmichael GR (2006). Revisiting China?s CO emissions after the Transport and Chemical Evolution over the Pacific (TRACE-P) mission: Synthesis of inventories, atmospheric modeling, and observations. J Geophys Res, 111: D14306. 157019

Subramanian R; Khlystov A; Robinson A (2006). Effect of Peak Inert-Mode Te mperature on Elemental Carbon Measured Using Thermal-Optical Analysis. Aerosol Sci Technol, 40: 763-780. 156107

Subramanian R; Khlystov AY; Cabada JC; Robinson AL (2004) . Positive and negative artifacts in particulate organic carbon measurements with denuded and undenuded sample r configurations. Aerosol Sci Technol, 1: 27-48. 081203

Sullivan AP; Peltier RE; Brock CA; de Gouw JA; Holloway JS; Warneke C; Wollny AG; Weber RJ (2006). Airborne

measurements of carbonaceous aerosol soluble in water over northeastern United States: Method development and an investigation into water-soluble orga nic carbon sources. J Ge ophys Res, 111: 1-14. 157030

Sullivan AP; Weber RJ (2006). Chemical char acterization of the ambient organic aerosol soluble in water: 1. Isolation of hydrophobic and hydrophilic fractions with a X AD-8 resin. J Geophys Res, 111: D05314. 157031

Sullivan AP; Weber RJ; Clements AL; Turner JR; Bae MS; Sc hauer JJ (2004). A method for on -line measurement of watersoluble organic carbon in ambient aerosol particles: Re sults from an urban site. Geophys Res Lett, 31: L13105.

157029

Sullivan JH; Hubbard R; Liu SL; Shepherd K; Trenga CA; Koenig JQ; Chandler WL; Kaufman JD (2007). A community study of the effect of particulate matt er on blood measures of inflammation and thrombosis in an elderly population. Environ Health Perspect, 6: 3. 100083

Swartz E; Stockburger L; Gundel LA (2003). Recovery of semivolatile organic compounds during sample preparation: Implications for characterization of airborne pa rticulate matter. Environ Sci Technol, 37: 597-605. 157035

Swietlik D; Faust M (1984). Foliar nutrition of fruit crops. Hortic Rev (Am Soc Hortic Sci), 6: 287-355. 046678

Sørensen M; Loft S; Andersen HV; Raaschou-Nielsen O; Skovgaard LT; Knudsen LE; Nielsen IV; Hertel O (2005). Personal exposure to PM2.5, black smoke and NO2 in Copenhagen: relationship to bedroom and outdoor concentrations covering seasonal variation. J Expo Sci Environ Epidemiol, 15: 413-422. 089428

Taha G; Box GP; Cohen DD; Stelcer E (2007). Black Carbon Measurement using Laser Integrating Plate Method. Aerosol Sci Technol, 41: 266 - 276. 096277

Taylor DA (2002). Dust in the wind. E nviron Health Perspect, 110: A80-A87. 025693

Taylor GE Jr; Hanson PJ; Baldocchi DD (1988). Pollutant deposition to individual leaves and plant canopies: sites of regulation and relationship to injury. In Heck, W. W.; Taylor, O. C.; Tingey, D. T. (Ed.),Assessment of crop loss from air pollutants (pp. 227-257). New York, NY: Elsevier Applied Science. 019289

Temime-Roussel B; Monod A; Massiani C; Wortham H (2004). Evaluation of an annular denuder for atmospheric PAH partitioning studies--2: evaluation of mass and numbe r particle losses. Atmos Environ, 38: 1925-1932. 098530

December 2009 3-217Temime-Roussel B; Monod A; Massiani C; Wortham H (2004). Ev aluation of an annular denuder tubes for atmospheric PAH partitioning studies--1: evaluation of the trapping efficiency of gaseous PAHS. Atmos Environ, 38: 1913-

1924. 098521

ten Brink H; Hoek G; Khlystov A (2005). An approach to moni tor the fraction of elemental carbon in the ultrafine aerosol. Atmos Environ, 39: 6255-6259. 156115

ten Brink H; Maenhaut W; Hitzenberger R; Gnauk T; Spindler G; Even A; Chi X; Bauer H; Puxbaum H; Putaud J-P; Tursic J; Berner A (2004). INTERCOMP2000: the comparability of methods in use in Europe for measuring the carbon content of aerosol. Atmos Environ, 38: 6507-6519. 097110

Tesche TW; Morris R; Tonnesen G; McNally D; Boylan J; Brewer P (2006). CMAQ/CAMx annual 2002 performance evaluation over the eastern US. Atmos Environ, 40: 4906-4919. 157050

Thornburg J; Rodesa CE; Lawless PA; Williams R (2009). Spatia l and Temporal Variability of Outdoor Coarse Particulate

Matter Mass Concentrations Measured with a New Coar se Particle Sampler During the Detroit Exposure and Aerosol Research Study . Atmos Environ, 43: 4251-4258. 190999

Thurston GD; Spengler JD (1985). A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston. Atmos Environ, 19: 9-25. 056074

Tobias HJ; Kooiman PM; Docherty KS; Ziemann PJ (2000). R eal-Time Chemical Analysis of Organic Aerosols Using a Thermal Desorption Particle Beam Mass Spectrometer. Aerosol Sci Technol, 33: 170-190. 156121

Tobias HJ; Ziemann PJ (1999). Compound Identification in Organic Aerosols Using Temp erature-Programmed Thermal Desorption Particle Beam Mass Spectrometry. Anal Chem, 71: 3428-3435. 157053

Tolocka M; Jang PM; Ginter JM; Cox FJ; Kamens RM; Johnst on MV (2004). Formation of oli gomers in secondary organic aerosol. Environ Sci Technol, 38: 1428-1434. 087578

Trijonis JC; Malm WC; Pitchford M; White WH; Charlson R ( 1990). Acidic deposition: State of science and technology. Report 24. Visibility: Existing and historical conditions-causes and effects. Final report. 157058

Tsai CJ; Chang CT; Huang CH (2006). Direct field observation of the relative humidit y effect on the beta-gauge readings. J Air Waste Manag Assoc, 56: 834-40. 098312

Tsai YI; Kuo SC (2006). Development of diffuse reflect ance infrared Fourier transfor m spectroscopy for the rapid

characterization of aerosols. Atmos Environ, 40: 1781-1793. 156127

TSI (2005). Data Merge Software Module for merging and fitting of SMPA and APS Data Files, P/N 1930074, User's Guide, Model 390069, Revision A. St. Paul, MN. A. St Paul, MN: TSI, Incorporated 157196

Tuch TM; Herbarth O; Franck U; Peters A; Wehner B; Wi edensohler A; Heintzenberg J (2006). Weak correlation of ultrafine aerosol particle concentrati ons< 800 nm between two sites within one city. J Expo Sci Environ Epidemiol,

16: 486-491. 157060

Tung TCW; Chao CYH; Burnett J (1999). A methodology to investigate the particulate penetration coefficient through building shell. Atmos Environ, 33: 881-893. 049003

Turpin BJ; Lim H-J (2001). Species contributions to PM25 mass concentrations: revisiting common assumptions for estimating organic mass. Aerosol Sci Technol, 35: 602-610. 017093

Turpin BJ; Weisel CP; Morandi M; Colome S; Stock T; Eise nreich S; Buckley B (2007). Relationships of Indoor, Outdoor, and Personal Air (RIOPA): Part II. Analyses of concentrations of particulate matter species. 157062

U.S. EPA (1982). Air quality criteria for particulate matter and sulfur oxides, Vol 1, 2, 3. U.S. Environmental Protection Agency, Environmental Criteria and Assessment Offi ce. Washington, D.C.. EPA 600/8-82-029a; EPA 600/8-82- 029b; EPA 600/8-82-029c. http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=3000188Z.txt; http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=300018EV.txt; http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=300053KV.txt. 017610

U.S. EPA (1996). Air quality criteria for particulate matter. U. S. Environmental Protection Agency. Research Triangle Park,

NC. EPA/600/P-95/001aF-cF. 079380

U.S. EPA (1998). SLAMS/NAMS/PAMS network review guidance. 093211

U.S. EPA (2004). Air quality criteria for particulate matter. U. S. Environmental Protection Agency. Research Triangle Park,

NC. EPA/600/P-99/002aF-bF. 056905

December 2009 3-218U.S. EPA (2005). Review of the national ambient air quality standards for particulate matter: Policy assessment of scientific and technical information OAQPS staff paper. U.S. Environmental Protection Agency. Washington, DC. EPA/452/R-05-005a. http://www.epa.gov/ttn/naaqs/ standards/pm/data/pmstaffpaper_20051221.pdf . 090209

U.S. EPA (2006). 2002 National Emissions Inventory Data and Documentation. Retrieved 02-DEC-09, from http://www.epa.gov/ttnchie1/net/2002inventory.html. 157070

U.S. EPA (2006). Air quality criteria for ozone and related photochemical oxidants. EPA. DC. 088089

U.S. EPA (2006). Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure. U.S. Environmental Protection Agency. Research Triangle Park, NC. 157071

U.S. EPA (2008). Analysis of particulate matter emissions from light-duty gasoline vehicles in Kansas City. U.S. Environmental Protection Agency. Washington, D.C. . EPA420-R-08-010. 191767

U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria. U.S. Environmental Protection Agency. Research Triangle Park, NC. EPA/600/R-08/071. 157073

U.S. EPA (2008). Integrated science assessment for oxides of nitrogen and sulfur: Ecological criteria. U.S. Environmental Protection Agency. Research Triangle Park, NC. EPA/600/R-08/082F. 157074

U.S. EPA (2008). Integrated Science Assessment for Sulfur Oxides - Health Criteria. U. S. Environmental Protection Agency. Research Triangle Park, NC. EPA/600/R-08/047F. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843. 157075

U.S. EPA (2008). National Air Quality Status and Trends Th rough 2007. U.S. Environmental Protection Agency. Research Triangle Park. EPA-454/R-08-006 . 191190

U.S. EPA (2008). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation (TRIM.Expo/APEX, Version 4.3). Volume 1: User's Guide. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Research Triangle Park, NC. EPA-452/B-08-001b. 191775

U.S. EPA (2008). U.S. EPA's 2008 Report on the Environment (F inal Report). U.S. Environmental Protection Agency. DC.

157076

U.S. EPA (2009). Risk and Exposure Asse ssment for Review of the Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Su lfur Second Draft. U.S. Environmenta l Protection Agency. Washington, D.C.. EPA-452/P-09-004a. http://www.epa.gov/ttnn aaqs/standards/no2so2sec/cr_rea.html. 191774

U.S. EPA (2009). Verified Retrofit Technologies. Retrieved 02-DEC-09, from http://www.epa.gov/otaq/retrofit/index.htm.

189885

UNCEC (2007). Hemispheric Transport of Air Pollut ion 2007. New York: United Nations Publications. 157078

Unsworth MH; Wilshaw JC (1989). Wet, occult and dry deposit ion of pollutants on forests. Agr Forest Meteorol, 47: 221-

238. 046682

VanCuren RA; Cahill TA (2002). Asian aeros ols in North America: Frequency and concentration of fine dust. J Geophys Res, 107: 4804. 157087

Van Aalst RM (1982). Dry deposition of NOx. In Schne ider, T.; Grant, L. (Ed.),Air pollution by nitrogen oxidesAmsterdam, The Netherlands: Elsevier Scientific Publishing Company. 036481

van der Werf GR; Randerson JT; Giglio L; Collatz GJ; Kasibhatla PS; Arellano Jr AF (2006). Interannual variability in global biomass burning emissions from 1997 to 2004. Atmos Chem Phys, 6: 3423–3441. 157084

Vega E; Reyes E; Wellens A; Sanchez G; Chow JC; Watson JG (2003). Comparison of conti nuous and filter based mass measurements in Mexico City . Atmos Environ, 37: 2783-2793. 105974

Venkataraman C; Lyons JM; Friedlander SK (1994). Size distri butions of polycyclic aromatic hydrocarbons and elemental carbon 1 Sampling, measurement met hods, and source characterization. Environ Sci Technol, 28: 555-562. 002475

Venn A; Lewis S; Cooper M; Hubbard R; Hill I; Boddy R; Be ll M; Britton J (2000). Local road traffic activity and the prevalence, severity and persistence of wheeze in school ch ildren: combined cross sectional and longitudinal study. Occup Environ Med, 57: 152-158. 007895

Venn AJ; Lewis SA; Cooper M; Hubbard R; Britton J (2001). Living near a main road and the risk of wheezing illness in children. Am J Respir Crit Care Med, 164: 2177-2180. 023644

December 2009 3-219Veranth JM; Moss TA; Chow JC; Labban R; Nichols WK; Walton JC; Walton JG; Yost GS (2006). Correlation of in vitro cytokine responses with the chemical composition of soil-d erived particulate matter. Environ Health Perspect, 114:

341-349. 087479

Viana M; Chi X; Maenhaut W; Cafmeyer J; Querol X; Al astuey A; Mikuska P; Vecera Z (2006). Influence of Sampling Artefacts on Measured PM, OC, and EC Levels in Carbon aceous Aerosols in an Urban Area. Aerosol Sci Technol,

40: 107-117. 179987
Viana M; Querol X; Alastuey A; Ballester F; Llop S; Esplugues A; FernÃ¡ndez-Patier R; GarcÃ-a dos Santos S; Herce MD (2008). Characterising exposure to PM aerosols for an epidemiological st udy. Atmos Environ, 42: 1552-1568.

156135

Violante FS; Barbieri A; Curti S; Sanguinetti G; Grazios i F; Mattioli S (2006). Urban atmospheric pollution: Personal exposure versus fixed monitoring stati on measurements. Chem osphere, 64: 1722-1729. 156140

Virkkula A; Makela T; Hillamo R; Yli-Tuomi T; Hirsikko A; Hameri K; Koponen IK (2007). A simple procedure for correcting loading effects of aethalometer data. J Air Waste Manag Assoc, 57: 1214-1222. 157098

Voisin D; Smith JN; Sakurai H; McMurry PH; Eisele FL (2003). Thermal Desorption Chemical Ionization Mass Spectrometer for Ultrafine Particle Chemical Composition. Aerosol Sci Technol, 37: 471-475. 156141

Volckens J; Olson DA; Hays MD (2008). Carbonaceous sp ecies emitted from handheld two-stroke engines. Atmos Environ, 42: 1239-1248. 105465

Vyas VM; Christakos G (1997). Spatiotem poral analysis and mapping of sulfate deposition data over Eastern USA. Atmos Environ, 31: 3623-3633. 156142

Wagner J; Leith D (2001). Passive aerosol sampler. Part I: Principle of operation. Aerosol Sci Technol, 34: 186-192.

190153

Wagner J; Leith D (2001). Passive aerosol sampler. Part II: Wind tunnel experiments. Aerosol Sci Technol, 34: 193-201.

190154

Wallace L (2000). Real-time monitoring of particles, PAH, and CO in an occupied townhouse. J Occup Environ Hyg, 15:

39-47. 000803

Wallace L; Williams R (2005). Use of persona l-indoor-outdoor sulfur concentrations to estimate th e infiltration factor and outdoor exposure factor for individual homes and persons. Environ Sci Technol, 39: 1707-1714. 057485

Wallace L; Williams R; Rea A; Croghan C (2006). Continuous weeklong measurements of pe rsonal exposures and indoor concentrations of fine particles fo r 37 health-impaired North Carolina resi dents for up to four seasons. Atmos Environ, 40: 399-414. 088211

Wallace L; Williams R; Suggs J; Jones P (2006). Estimating contri butions of outdoor fine particles to indoor concentrations and personal exposures: effects of house hold characteristics and personal activit ies. U.S. Environmental Protection Agency; National Exposure Research Laboratory. Research Triangle Park, NC. EPA/600/R-06/023. 089190

Wang J; Christopher SA; Nair US; Reid JS; Prins EM; Szykman J; Hand JL (2006). Mesoscale modeling of Central American smoke transport to the United States: 1. "Top-down" assessment of emission strength and diurnal variation impacts. J Geophys Res, 111: D05S17. 157109

Wang LH; Milford JB; Carter WPL (2000). Reactivity estimates for aromatic compounds Part 2 uncertainty in incremental

reactivities. Atmos E nviron, 34: 4349-4360. 048365

Wang LH; Milford JB; Carter WPL (2000). Reactivity estimates for aromatic compounds Part I: uncertainty in chamberderived parameters. Atmos Environ, 34: 4337-4348. 048357

Watson JG; Chen LW; Chow JC; Doraiswamy P; Lowentha l DH (2008). Source apportionment: findings from the U.S. Supersites Program. J Air Waste Manag Assoc, 58: 265-288. 157128

Watson JG; Chow JC (2002). Comparison and evaluation of in si tu and filter carbon measurements at the Fresno Supersite. J Geophys Res, 107: 8341. 037873

Watson JG; Chow JC; Chen LWA (2005). Summary of organi c and elemental carbon/black carbon analysis methods and intercomparisons. Aerosol Air Qual Res, 5: 69–102. 157125

Watson JG; Fujita EM; Chow JC; Zielinska B; Richards LW; Neff W; Dietrich D (1998). Northern front range air quality study Final report. 012257

December 2009 3-220Watson JG; Robinson NF; Chow JC; Henry RC; Kim BM; Pace TG; Meyer EL; Nguyen Q (1990). The USEPA/DRI chemical mass balance receptor model, CMB 70. Environ Software, 5: 38-49. 004848

Weber RJ; Orsini D; Daun Y; Lee Y-N; Kotz PJ; Brechtel F (2 001). A particle-into-liquid collector for rapid measurement of aerosol bulk chemical compositi on. Aerosol Sci Technol, 35: 718-727. 024640

Weber RJ; Orsini D; Duan Y; Baumann K; Kiang CS; Chameides W; Lee YN; Brechtel F; Klotz P; Jongejan P (2003). Intercomparison of near real time monitors of PM2. 5 nitrate and sulfate at the US Environmental Protection Agency Atlanta Supersite. J Geophys Res, 108: 8421. 157129

Weijers EP; Khlystov AY; Kos GPA; Erisman JW (2004). Variabili ty of particulate matter concentrations along roads and motorways determined by a moving measurem ent unit. Atmos Environ, 38: 2993-3002. 104186

Weingartner E; Saathoff H; Schnaiter M; Streit N; Bitnar B; Baltensperger U (2003). Absorption of light by soot particles: determination of the absorption coefficient by m eans of aethalometers. J Aerosol Sci, 34: 1445-1463. 156149

Wenzel RJ; Liu DY; Edgerton ES; Prather KA (2003). Aerosol time-of-flight mass spectrometry during the Atlanta Supersite Experiment: 2. Scaling pr ocedures. J Geophys Res, 108: 8426. 157139

Wesely ML; Eastman JA; Stedman DH; Yalvac ED (1982). An eddy-correlation measurement of NO2 flux to vegetation and comparison to O3 flux. Atmos Environ, 16: 815-820. 036564

Wesely ML; Hicks BB (2000). A review of the current st atus of knowledge on dry deposition. Atmos Environ, 34: 2261-

2282. 025018

Westerdahl D; Fruin S; Sax T; Fine PM; Sioutas C (2005). Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and reside ntial streets in Los Angeles. Atmos Environ, 39: 3597-

3610. 086502

Whitby KT (1978). The physical char acteristics of sulfur aerosols. Atmos Environ, 12: 135-159. 071181

Williams B; Goldstein A; Kreisberg N; Hering S (2006). An In-Situ Instrument for Speci ated Organic Composition of Atmospheric Aerosols: Thermal Desorption Aerosol GC/MS-FID (TAG). Aerosol Sci Technol, 40: 627-638.

156157

Williams R; Rea A; Vette A; Croghan C; Whitaker D; Stevens C; McDow S; Fortmann R; Sheldon L; Wilson H; Thornburg J; Phillips M; Lawless P; Rodes C; Daughtrey H (2008). The design and field implementation of the Detroit exposure and aerosol research study. J E xpo Sci Environ Epidemiol, 19: 643-659. 191201

Williams TC; Shaddix CR; Jensen KA; Suo-Anttila JM (2006). Measurements of the Dimensionless Extinction Coefficient

of Soot Within Laminar Diffusion Flames. Combust Fl ame, 50: 1616-1630. 157148

Wilson JG; Zawar-Reza P (2006). Intraurban-scale dispersion m odelling of particulate matter concentrations: applications for exposure estimates in cohort st udies. Atmos Environ, 40: 1053-1063. 088292

Wilson WE; Brauer M (2006). Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study. J E xpo Sci Environ Epidemiol, 16: 264-274. 088933

Wilson WE; Grover BD; Long RW; Eatough NL; Eatough DJ (2006) . The measurement of fine particulate semivolatile material in urban aerosols. J Air Waste Manag Assoc, 56: 384-387. 091142

Wilson WE; Mage DT; Grant LD (2000). Estimating separately personal exposure to ambient and nonambient particulate matter for epidemiology and risk assessment: why and how. J Air Waste Manag Assoc, 50: 1167-1183. 010288

Wilson WE; Mar TF; Koenig JQ (2007). Influence of exposure error and effect modification by socioeconomic status on the association of acute cardiovascular mortality with part iculate matter in Phoenix. J Expo Sci Environ Epidemiol,

17: S11-S19. 157149

Wilson WE; Stanek J; Han HS; Johnson T; Sakurai H; Pui DY; Turner J; Chen DR; Duthie S (2007). Use of the electrical

aerosol detector as an indicator of the surface area of fine particles deposited in the lung. J Air Waste Manag Assoc,

57: 211-20. 098398

Wilson WE; Suh HH (1997). Fine particles and coarse particles: concentration relationships relevant to epidemiologic studies. J Air Waste Manag Assoc, 47: 1238-1249. 077408

Winkler PM; Steiner G; Vrtala A; Vehkamaki H; Noppel M; Lehtinen KEJ; Reischl GP; Wagner PE; Kulmala M (2008). Heterogeneous Nucleation Experiments Bridging the Scale fro m Molecular Ion Clusters to Nanoparticles. Science,

319: 1374-1377. 156160

December 2009 3-221Womiloju TO; Miller JD; Mayer PM; Brook JR (2003). Methods to determine the biological composition of particulate matter collected from outdoor air. Atmos Environ, 37: 4335-4344. 179954

Wongphatarakul V; Friedlander SK; Pinto JP (1998). A comparat ive study of PM2.5 ambient aer osol chemical databases. Environ Sci Technol, 32: 3926-3934. 049281

Woo K-S; Chen D-R; Pui DYH; McMurry PH (2001). Measurement of Atlanta aerosol size distributions: observations of ultrafine particle events. Aerosol Sci Technol, 34: 75-87. 011702

Wu C-F; Delfino RJ; Floro JN; Quintana; (2005). Exposure assessment and modeling of particulate matter for asthmatic children using personal nephelomete rs. Atmos Environ, 39: 3457-3469. 086397

Wu CF; Delfino RJ; Floro JN; Samimi BS; Quintana PJ; Klei nman MT; Liu LJ (2005). Evaluation and quality control of personal nephelometers in indoor, outdoor and personal envi ronments. J Expo Sci Envir on Epidemiol, 15: 99-110.

157155

Wu CF; Jimenez J; Claiborn C; Gould T; Simpson CD; Larson T; Liu LJS (2006). Agricultural burning smoke in Eastern Washington: Part II. Exposure assess ment. Atmos Environ, 40: 5379-5392. 179950

Wu J; Houston D; Lurmann F; Ong P; Winer A (2009). Exposure of PM2. 5 and EC from diesel and gasoline vehicles in communities near the Ports of Los Angeles and L ong Beach, California. Atmo s Environ, 43: 1962-1971. 191773

Wu J; Lurmann F; Winer A; Lu R; Turco R; (2005). Developm ent of an individual exposure model for application to the

Southern California children's health study. Atmos Environ, 39: 259-273. 058570

Wu S; Mickley L; Jacob D; Rind D; Streets D (2008). Ef fects of 2000-2050 changes in c limate and emissions on global tropospheric ozone and the policy-relevant background surf ace ozone in the United States. J Geophys Res, 113:

D18312. 190039

Xia X; Hopke PK (2006). Seasonal Variation of 2-Methyltetrols in Ambient Air Samples. Environ Sci Technol, 40: 6934-

6937. 179947

Xiaomin X; Zhen H; Jiasong W (2006). The impact of urba n street layout on local atmospheric environment. Build Environ, 41: 1352-1363. 156165

Yang H; Li Q; Yu JZ (2003). Comparison of two methods for the determination of water-soluble organic carbon in atmospheric particles. Atmos Environ, 37: 865-870. 156167

Yanosky JD; Paciorek CJ; Schwartz J; Laden F; Puett R; Suh HH (2008). Spatio-tempor al modeling of chronic PM10 exposure for the Nurses' Health St udy. Atmos Environ, 42(18): 4047-4062. 099467

Yanosky JD; Paciorek CJ; Suh HH (2009). Pr edicting chronic fine and coarse particulate exposures using spatiotemporal

models for the northeastern and midwestern United States. Environ Health Perspect, 117: 522-529. 190114

Yi SM; Ambs JL; Patashnick H; Rupprecht G; Hopke PK ( 2004). Particle Collection Characteristics of a Prototype Electrostatic Precipitator (ESP) for a Differential TEOM System. Aerosol Sci Technol, 38: 46-51. 156169

Yu H; Remer LA; Chin M; Bian H; Kleidman RG; Diehl T (2008) . A satellite-based assessment of transpacific transport of pollution aerosol. J Geophys Res, 113: 1-15. 157168

Yu JZ; Yang H; Zhang H; Lau AKH (2004). Size distributions of water-soluble organic carbon in ambient aerosols and its

size-resolved thermal characteris tics. Atmos Environ, 38: 1061-1071. 156172

Zanis P; Trickl T; Stohl A; Wernli H; Cooper O; Zerefos C; Gaeggeler H; Schnabel C; Tobler L; Kubik PW; Priller A; Scheel HE; Kanter HJ; Cristofanelli P; Forster C; James P; Gerasopoulos E; Delcloo A; Papayannis A; Claude H (2003). Forecast, observation a nd modelling of a deep stratospheric in trusion event over Europe. Atmos Chem Phys, 3: 763-777. 053423

Zanobetti A; Wand MP; Schwartz J; Ryan LM (2000). Generalized additive distributed lag models: quantifying mortality displacement. Biostatistics, 1: 279-292. 004133

Zeger SL; Thomas D; Dominici F; Samet JM; Schwartz J; Dockery D; Cohen A (2000). E xposure measurement error in time-series studies of air pollution: concepts and consequences. Environ Health Perspect, 108: 419-426. 001949

Zeng Y (2006). A comprehensive particulate matter monitoring system and dosimetry-based ambient particulate matter standards. J Air Waste Manag Assoc, 56: 518-29. 098375

December 2009 3-222December 2009 3-223 Zhang K; Wexler A (2008). Modeling urban and regiona l aerosols—Development of the UCD Aerosol Module and implementation in CMAQ model. Atmos Environ, 42: 3166-3178 . 191770

Zhang Q; Jimenez JL; Canagaratna MR; Allan JD; Coe HL Ulbrich I; Alfarra MR; Takami A; Middlebrook AM; Sun YL; Dzepina K; Dunlea E; Docherty K; DeCarlo PF; Salcedo D; Onasch T; Jayne JR; Miyoshi T; Shimono A; Hatakeyama S; Takegawa N; Kondo Y; Schneider J; Drewnick F; Borrmann S; Weimer S; Demerjian K; Williams P; Bower K; Bahreini R; Cottrell L; Griffin RJ; Rau tiainen J; Sun JR; Zhang YM; Worsnop DR (2007). Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes. Geophys Res Lett, 34: L13801. 189998

Zhang Q; Jimenez JL; Canagaratna MR; Jayne JT; Worsnop DR (2005). Time- and size-resolved chemical composition of submicron particles in Pittsburgh: Imp lications for aerosol sources and pr ocesses. J Geophys Res, 110: 1-19.

157185

Zhang X; Zhuang G; Guo J; Yin K; Zhang P (2007). Characteri zation of aerosol over the Northern South China Sea during

two cruises in 2003. Atmo s Environ, 41: 7821-7836. 101119

Zhao W; Hopke PK; Norris G; Williams R; Paatero P (2006). Source apportionment and anal ysis on ambient and personal exposure samples with a combined receptor model and an adaptive blank estimation strategy. Atmos Environ, 40:

3788-3801. 156181

Zhao W; Rabinovitch N; Hopke PK; Gelfand EW (2007). Us e of an expanded receptor model for personal exposure analysis in schoolchildren with asthma. Atmos Environ, 41: 4084-4096. 156182

Zheng M; Cass GR; Schauer JJ; Edgerton ES (2002). Source appor tionment of PM2.5 in the southeastern United States using solvent-extractable organic compounds as tracers. Environ Sci Technol, 36: 2361-2371. 026100

Zheng M; Ke L; Edgerton ES; Schauer JJ; Dong M; Russell AG (2006). Spatial distribution of carbonaceous aerosol in the southeastern United States using molecular markers and carbon isotope data. J Geophys Res, 111: D10S06. 157189

Zhou Y; Levy JI (2007). Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis. BMC Public Health, 7: 89. 098633

Zhu K; Zhang J; Lioy PJ (2007). Evaluation and comparison of continuous fine particulate matter monitors for

measurement of ambient aerosols. J Air Waste Manag Assoc, 57: 1499-506. 098367

Zhu Y; Eiguren-Fernandez A; Hinds WC; Miguel AH (2007). In -Cabin Commuter Exposure to Ultrafine Particles on Los Angeles Freeways. Environ Sci Technol, 41: 2138-2145. 179919

Zhu Y; Hinds WC; Kim S; Shen S; Sioutas C (2002). Study of ultrafine particles near a major highway with heavy-duty

diesel traffic. Atmos Environ, 36: 4323-4335. 041553

Zhu Y; Hinds WC; Shen S; Sioutas C (2004). Seasonal trends of concentration and size distribution of ultrafine particles near major highways in Los Angeles. Aerosol Sci Technol, 38: 5-13. 156184

Zhu Y; Kuhn T; Mayo P; Hinds WC ( 2005). Comparison of Daytime and Nightti me Concentration Profiles and Size

Distributions of Ultrafine Particles near a Major Highway. Environ Sci Technol, 39: 2531-2536. 157191

Zidek JV; Shaddick G; Meloche J; Chatfield C; White R (2007). A framework for pred icting personal exposures to

environmental hazards. Envir on Ecol Stat, 14: 411-431. 190076

Zidek JV; Wong H; Le ND; Burnett R (1996). Causality, m easurement error and multicollinearity in epidemiology. Environmetrics, 7: 441-451. 051879