Chapter 3. Source to Human 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 developments in monitoring methodologies, recent national 

and local measurements and trends in PM concentrations as a function of size range and 

composition, advances in receptor and chemistry-transport modeling, revised estimates of policyrelevant background PM, and recent work on exposure assessment. 

The chapter and its associated annex material are organized as follows: Section 3.2 presents an 

overview of basic information related to the size distribution 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 monitors and networks in the U.S. are covered in 

Section 3.4; supplementary material on these topics is contained 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 results is given in Annex A, Section A.3. Policy 

relevant background concentrations of PM, i.e., those concentrations defined to result from natural 

sources everywhere in the world together with anthropogenic 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 SO2, CO, H2CO and O3, which are well-defined chemical 

entities, atmospheric PM varies in size, shape, and 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., PM2.5, PM10-2.5 and PM10). The subscripts after PM 

 

Note: Hyperlinks to the reference citations throughout this document 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 diameter1

 (dae) in micrometers (μm) of 50% cut points of sampling devices. 

For example, EPA defines PM10 as particles collected by a sampler with an upper 50% cut point of 

10 μm dae and a specific, fairly sharp, penetration curve, as defined in the Code of Federal 

Regulations (40 CFR Part 58). PM2.5 is defined in an analogous way. Ultrafine particles (UFPs), 

defined here as particles with a diameter ≤ 0.1 μm (typically based on physical size, thermal 

diffusivity, or electrical mobility), 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 modes (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 most often associated with the PM2.5 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 PM10-2.5, which does not include the low-end tail of the coarse particle mode. 

With high relative humidity, larger particles in the 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 this way because particle 

number is most highly concentrated in the ultrafine (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/cm3) 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 increase in 

importance in determining a particle’s motion and air can no longer be considered a continuum. Aerodynamic diameter is frequently 

reported down to ~ 0.1 μm where the assumptions used in its derivation no longer hold. A useful metric for characterizing particles <~ 

0.5 μm is the mobility diameter defined as the diameter of a particle having the same diffusivity or electrical mobility in air as the 

particle of interest. In the region between ~ 0.5–1.0 μm, aerodynamic 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 particles 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 PM10 as an indicator of thoracic particles was 

based in large part on dosimetry (U.S. EPA, 1996, 079380). However, the selection of PM2.5 to 

characterize respirable particles was driven mainly by considerations related to measurement 

techniques available at the time rather than dosimetry. Currently, cut points other than 2.5 μm are 

attainable and frequently put into use. For example, 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, PM2.5 is used as an indicator of respirable particles, 

PM10-2.5 is used as an indicator of the thoracic component of coarse particles that is sometimes 

referred to as thoracic coarse (noting that it excludes some coarse particles below 2.5 μm and above 

10 μm), and PM10 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 properties, lifetimes in the atmosphere (τ) and distances over 

which they can travel. Whereas particles in the smaller size modes are formed mainly by combustion 

processes and by nucleation and condensation of gases, coarse particles are generated mainly by 

mechanical activity, such as by the action of wind 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 ambient 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 H2SO4, NH3 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 

HNO3/HCl/SO2 reactions with coarse particles 

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

CaCO3, CaSO4, NaCl, sea salt 

Bacteria, pollen, mold, fungal spores, plant 

and animal debris 

Solubility Not well characterized Largely soluble, hygroscopic, and deliquescent 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 NO2, SO2, 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 3-4 
Source: 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 background of each image and contributes to 

the carbon peak in each spectrum. 

Particles appear in a wide variety of shapes such as spheres, ellipsoids, cubes, and irregular or 

fractal geometries. This is one reason why a standard metric such as aerodynamic diameter is useful 

for describing the mechanical properties of the particles. The shape of particles is important for 

determining the optical properties of the particles. The directionality of sunlight scattered by certain 

shapes of particles, such as plates, also depends strongly on their physical orientation while 

suspended. The shape of particles also affects the surface area of the particles in contact with the 

surface it is deposited on, including cell membranes. 

 Images of six types of individual particles obtained using scanning electron microscope 

(SEM) and their corresponding x-ray spectra showing 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 metal 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 cubic (image 5); this particular particle could be 

marine sea salt due to the proximity to the ocean 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 collapse of the pollen grain (image 6). Changes in 

composition and possibly morphology could occur during the sampling, collection and analysis of 

aerosol samples. For example, particles may be coated 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 Deposition of Primary and 

Secondary PM 

PM is composed of both primary (derived directly from emissions) or secondary (derived from 

atmospheric reactions involving gaseous precursors) components. Table 3-4 summarizes 

anthropogenic and natural sources for the major primary 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 transportation-related sources include direct emissions of 

primary PM and secondary PM precursors from highway 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 combustion from material that has volatilized in 

combustion chambers and then recondensed before emission to the atmosphere. Some ambient PM2.5

forms in the atmosphere from photochemical reactions involving precursor gases. Included in this 

category is the formation of new UFPs by (1) homogeneous nucleation of precursor gases and (2) the 

condensation of gases on pre-existing particles. Biological material also exists in the fine fraction 

including many types of microorganisms, especially viruses and bacteria and fragments of pollens 

and fungal spores. PM10-2.5 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 and 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 precursors can occur over distances of hundreds of 

kilometers. PM10-2.5 has a shorter lifetime in the atmosphere, so its effects tend to be more localized. 

However, intercontinental transport of dust from African and Asian deserts occurs and some of this 

material is in the PM10-2.5 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 atmospheric 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 Natural Anthropogenic Natural Anthropogenic 

Sulfate (SO4

2–) Sea spray Fossil fuel 

combustion Sea spray — 

Oxidation of reduced 

sulfur gases emitted 

by the oceans and 

wetlands and SO2

and H2S emitted by 

volcanism and forest 

fires 

Oxidation of SO2

emitted from fossil fuel 

combustion 

Nitrate (NO3

−

) — Mobile source 

exhaust — — 

Oxidation of NOX

produced by soils, 

forest fires, and 

lightning 

Oxidation of NOX

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 (NH4

+

) — Mobile source 

exhaust — — Emissions of NH3

from wild animals, 

and undisturbed soil 

Emissions of NH3 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 magnitude. 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 (NH4

+

) ions, 

which play a major role in regulating the pH of particles, are derived from emissions of NH3 gas. 

Source categories for NH3 have been divided into emissions from undisturbed soils (natural) and 

emissions that are related to human activities (e.g., fertilized lands, domestic and farm animal waste). 

There is ongoing debate about characterizing emissions 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 occurrence 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. national average emissions of primary PM2.5, PM10 and gaseous precursor species (SO2, 

NOX, NH3 and VOCs) from different source categories are shown in Figure 3-3. Note that the entries 

refer mainly to anthropogenic sources, with little information about natural sources. However, for 

categories such as VOCs, the contribution from biogenic 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 (SO2 or 

SO3) 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 emissions test data with 

updated methods for measuring dust emissions. Also, the spatial and temporal characterization of 

wildfire emissions has improved since 2002 by integrating satellite-derived fire detection and stateof-the-art fuels characterization and consumption models (Pouliot et al., 2008, 156883). Emission 

measurements from high-temperature 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 the 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 techniques 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 accurately 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 precursors to secondary PM formation including 

SO2, NOX, NH3 and VOCs are considerably larger than those for primary PM sources. However, 

translating the emissions of precursors into production 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 (CO2) 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, SO2, NOX and NH3 are 

converted to secondary PM as SO4

2-, NO3-

 and NH4

+

. The ratios of molecular weights of PM 

products (SO4

2-, NO3-

 and NH4

+

) to gaseous precursors (SO2, NOX and NH3) are 1.5, 1.35, and 1.07, 

respectively. Estimating a conversion factor for carbon in VOCs is less straightforward. The 

oxidation of VOCs leads to the formation of secondary OC in PM. As the result of atmospheric 

transformations, nitrogen and oxygen are added to the 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 PM2.5, PM10

and gaseous precursor species SO2, NOX, NH3 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 

Precursors 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 nature, formed as the result of atmospheric reactions 

involving both inorganic and organic gaseous precursors. The major atmospheric chemical 

transformations leading to the formation of particulate nitrate (pNO3) and particulate sulfate (pSO4) 

are relatively well understood; whereas those involving 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 kinetic 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 basic mechanism of the gas and aqueous phase oxidation of NO2 and SO2 has long been 

studied and can be found in numerous texts on atmospheric 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 NOX and SOX 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 perceptions 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 speciation and water solubility of organic aerosols 

have noted the dominant contribution of oxygenated 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 northeastern U.S. cities, de Gouw et al. (2008, 

191757) reported that 40-70% of measured organic mass was water soluble and estimated that 

approximately 37% of SOA is attributable to aromatic precursors, based on PM yields estimated for 

NOX-limited conditions. However, the remaining mass 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 NOX are not dominant, suggesting that transport of less reactive compounds (e.g., 

benzene) out of source regions with high NOX 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 measured in ambient air resembles mass spectra 

measured in irradiated diesel exhaust 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, volatile emissions can be photochemically oxidized to 

more non-volatile compounds which then condense, forming oxidized SOA. Braun (2009, 189997) 

suggested that the weathering of diesel 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 underscore the importance of accurately describing the 

phase distribution of semivolatile organic compounds emitted by combustion sources under 

atmospheric conditions, and of atmospheric photochemical reactions in modifying the composition 

of emissions. 

Until a few years ago, the oxidation of terpenes and aromatic compounds were considered as 

sources of SOA, and the oxidation of isoprene was 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 quantities 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 summer, 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 terpenes. 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. 

Other detailed work has focused on the formation 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 samples could contain mixtures of SOA from 

different sources at different stages of processing, 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, ·NO2, O3 of alkanes, 

aromatics, alkenes, 

olefins, etc

and ·OH, O3, NO2

Gas/particle partitioning

Nucleation, sorption, condensation/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. Organic 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 O3. 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 O3. Various terpenoid compounds are used in a number of household products and 

can be oxidized by ozone that has infiltrated from outdoors. The oxidation of terpenoid compounds 

indoors produces UFPs as described in the 2006 O3 AQCD (U.S. EPA, 2006, 088089). 

3.3.2.3. Formation of New Particles 

In addition to being emitted by high temperature combustion sources, new particles can form 

by nucleation of atmospheric gases. New particle formation has been observed in environments 

ranging from relatively unpolluted marine and continental 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 

H2SO4 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 H2SO4 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 small compared to classical thermodynamic binary 

nucleation theory involving H2SO4 and water vapor, in which the nucleation rate is given by H2SO4

vapor to at least the 10th power (Kulmala et al., 1998, 129411). H2SO4 vapor is produced by the gas 

phase oxidation of SO2 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 O3 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. Emissions from Gasoline Fueled Engines 

PM emitted from gasoline fueled engines is a mix of OC, EC and small quantities of trace 

metals and sulfates, with OC constituting anywhere 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 additive. 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 about the composition of motor vehicle 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 Maricq (2007, 155973) presents a conceptual model of diesel PM as a mix of 

nucleation-mode SO4

2– and hydrocarbons from unspent fuel and soot embedded with trace metals on 

which SO4

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 comparison, 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 exhaust gas recirculation (mostly for control of 

NOX), 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 composition of the much lower mass of PM emitted with this new technology. PM 

samples collected over a composite type test consisted of 53% sulfate, 30% OC, 13% EC and 4% 

other components, including metals. A substantial fraction of sulfur is converted to sulfate over the 

diesel particulate filter resulting in the higher fractional 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 total 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 exhaust aftertreatment devices, including DPFs, 

DOCs, and selective catalytic reduction (SCR) systems to reduce emissions (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 NOX control. Biswas et al. (2008, 189969) examined PM size distribution and 

composition (including semi-volatiles 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 under cruise conditions. Biswas et al. (2009, 189880) 

examined four heavy-duty diesel vehicles with various retrofits showing, in general, large reductions 

in PM but, in some cases, somewhat higher emissions (or smaller decreases than expected) for EC 

and OC. 

In general, under light load conditions such as idle, 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 model 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 NOX

control such as would be used for the 2010 diesel NOX standard. This standard will also result in 

reductions for NOX 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 significantly 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 normal operating temperatures for full emission 

reductions. During the period of catalyst heating, particles will be trapped in the DPF, but volatile 

components can pass through. As a result of this particle-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 and dry deposition are important processes for removing PM and other pollutants from the 

atmosphere on urban, regional, and global scales. The 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/m3

) 

times a deposition velocity (Vd) (m/s). Therefore, deposition has the units of mass per unit time per 

unit area, or flux. The general approach used to estimate Vd for gases or very small particles is the 

resistance-in-series method represented by Equation 3-1: 

d = + + cba )RRR/(1V

Equation 3-1 

where Ra, Rb, and Rc 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 resistances are empirically derived and can vary as a 

function of wind speed, solar radiation, plant characteristics, 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 surface depends on concentrations at the surface itself. 

The approach is also modified somewhat for aerosols where Rb and Rc are replaced with a surface Vd

to account for gravitational settling. 

Source: Courtesy of T. Pierce, USEPA / ORD / 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 difficulties in representing dry deposition over 

December 2009 3-15 
varying terrain where horizontal advection plays a significant role in determining the magnitude of 

Ra and difficulties in adequately determining Vd 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 where 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 Deposition 

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 concentrations. 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 forest canopies as compared to the shorter shrub and 

herbaceous canopies. Following wet deposition, humidity 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 particles from foliar surfaces, reducing direct foliar 

effects and combining the associated chemical burden 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 composition of the rainwater that reaches the soil and the pollutant burden 

that is taken-up by plants. Once in the soil, these 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 enhance foliar uptake through the hydrating of some 

previously dry-deposited particles (U.S. EPA, 2004, 056905) 

Dry Deposition 

Dry particulate deposition, especially of heavy metals, 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- and 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 chemical 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 plant and soil surfaces affects all exposed surfaces. 

Larger particles >5 μm diameter are dry-deposited mainly by gravitational sedimentation and inertial 

impaction. Smaller particles, especially those with diameters between 0.2 and 2.0 μm, are not readily 

December 2009 3-16 
dry-deposited and may travel long distances in the 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 concentrations in the atmosphere and even more variable 

and uncertain estimates of Vd 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 relatively 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 precipitation 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 Vd 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 areas, 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 for estimating dry deposition of particles are 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, and 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 particles 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 translocation 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 established 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 NOX and SOX deposition is inadequate knowledge of 

the mechanisms and factors governing particle dry deposition to diverse surfaces (U.S. EPA, 2004, 

056905). 

Collection and analysis of stem flow and throughfall can also provide useful estimates of 

particulate deposition when compared to directly sampled 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 covariance, eddy accumulation, or flux gradient 

protocols for quantifying dry deposition. These techniques require measurements of particulate 

concentrations and of atmospheric transport processes. They are currently well developed for ideal 

conditions of flat, homogeneous, and extensive landscapes 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 vertical 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, vertical 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 counting) 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 size range of ~0.1-1.0 μm where Vd 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 Vd 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 μm 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 depositing 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 increases the probability of collision. 

Empirical estimates of Vd 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 streamlining of foliar obstacles, turbulence structure near 

surfaces, and various phoretic transport mechanisms are not well characterized (U.S. EPA, 2004, 

056905). The discrepancy between estimated and predicted values of Vd 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 μm exhibit variable Vd between 0.5 and 

1.1 cm/s depending on friction velocities, whereas a minimum particle Vd of 0.03 cm/s exists for 

particles in the size range 0.1 to 1.0 μm; (2) the Vd 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 3-18 
Source: U.S. EPA (2004, 056905). 

Figure 3-6. The relationship between particle diameter and Vd 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 the theoretical distribution 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 μm) are influenced strongly by both particle size and 

sedimentation velocity, and deposition is relatively independent of size. 

Leaf Surface Effects on Vd

The chemical composition of a particle is not usually considered to be a primary determinant 

of its Vd. Rather, the plant leaf surface has an important influence on the Vd of particles, and 

therefore on the flux of dry deposition to the terrestrial environment. Relevant leaf surface properties 

include stickiness, microscale roughness, and cross-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 the 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 particles 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-deposition (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-section of the needles relative to the larger leaf 

laminae of broadleaves allowing for greater penetration of wind into conifer canopies than broadleaf 

ones (U.S. EPA, 2004, 056905). 

December 2009 3-19 
Canopy Surface Effects on Vd

Surface roughness increases particulate deposition, and Vd 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 Vd. The upwind leading edges of forests, hedgerows, and 

individual plants are primary sites of coarse particle deposition. Impaction at high wind speed and 

the sedimentation that follows the reduction in wind 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 vegetated 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 aerosol 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 

Ambient Measurement Techniques 

1.1. PM Mass 

Federal reference methods (FRMs) and federal 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 PM2.5 and PM10, and measurement methods for 

PM10-2.5. Also included are detailed descriptions of the WINS impactor, virtual and cascade 

multistage impactors for PM10-2.5 measurement, high-volume and low-volume PM10 samplers, and 

real-time or continuous methods for PM2.5 and PM10 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. 

In this section, FRMs and FEMs for PM2.5, PM10-2.5, and PM10 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 PM2.5, PM10-2.5, and PM10 are specified in CFR 40 Part 

50, Appendices L, O and J, respectively. The FRM for PM2.5 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 aspects (e.g., flow control) by performance 

specifications (U.S. EPA, 2004, 056905). The PM10 FRM is performance-based; particles are 

inertially separated with a penetration efficiency of 50% at 10 ± 0.5 μm 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 collected on the filter with diminishing collection efficiency as 

particle size increases. Likewise, particles smaller than 10 μm in aerodynamic diameter are 

collected on the filter with increasing collection efficiency as particle size decreases. The FRM for 

PM10-2.5 concentration is computed as the numeric difference between concurrent and co-located 

PM10 and PM2.5 concentrations obtained from low-volume FRM samplers of the same make and 

model. It should be noted that while the FRM for PM2.5 and PM10-2.5 reports data under local 

conditions, the FRM for PM10 reports data corrected to standard temperature (298 K) and pressure 

(101.3 kPa) (STP). 

A very sharp cut cyclone (VSCC) was approved in 2004 as a Class II PM2.5 FEM method 

(Kenny et al., 2004, 155895). The VSCC provides superior performance over long sampling periods 

under heavy loading and was also incorporated as an optional second-stage separator for the PM2.5

FRM (71 FR 61214, October 17, 2006). In 2006, EPA 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 performance 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 and sampling locations. As a result of these new 

performance criteria, EPA has recently approved two filter-based PM2.5 and two filter-based PM10-2.5

Class II FEMs based on the virtual impactor techniques (dichotomous sampler), and five PM2.5 and 

one PM10-2.5 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/files/ambient/criteria/reference-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 variation (CV) <5%. For different co-located FRMs, 

the regression slope of one sampler on another is commonly close to unity with R2

 >0.95. The PM2.5

and PM10 concentrations measured by dichotomous samplers were within 10% of the FRM methods, 

and the differences can be attributed to the sampling 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 monitors 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 NOX and SOX – 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 PM10 “oversample certain agricultural and other source emissions.” These claims are 

based on the erroneous assumption that the "true" PM10 concentration is what would be given by a 

PM10 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 PM2.5 and PM10, as defined 

in the CFR (40 CFR Part 58), include both a 50% cut-point and a penetration curve. For PM10, 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 inlet 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 

PM10 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 PM10 sampler correctly and 

intentionally collects particles greater than 10 μm. 

For PM2.5 and PM10, it has long been known that FRMs are subject to sampling artifacts 

including particle bounce on heavily-loaded impaction substrates and the loss of semi-volatile 

components of PM (e.g., NH4NO3, 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 can measure both semi-volatile and nonvolatile PM, 

FRMs reported PM2.5 or PM10 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+ PM2.5 FRMs operating in 2007 were 

scheduled to sample every day). Under this operating condition, the concentration-response 

relationship in air pollution health studies (especially 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 PM2.5 or PM10 and 

several new methods for measurement of PM10-2.5. 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 PM2.5 or PM10 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 air is reheated to 30°C) in 6-min intervals. As 

clean air flows over the sample filter, the semivolatile PM on the sample filter is evaporated. Thus, 

the instrument provides direct measurements of the nonvolatile particle mass and incorporates an 

adjustment for the semivolatile NH4NO3 and organic material. In a comparison between the TEOM, 

FDMS-TEOM, and FRM mass, the PM2.5 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 PM10-2.5

Methods developed to measure PM10-2.5 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 PM10-2.5 mass 

concentrations were within 10%. The high-volume 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 PM10-2.5

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 PM10-2.5

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 PM10-2.5 FRM (slopes = 0.88-1.17, and 

R2>0.95) (Solomon and Sioutas, 2008, 190139). 

Case et al. (2008, 155149) evaluated a cascade PM sampler designed to collect PM10-2.5 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 PM10-2.5 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 diameter. 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 

1σ of concentrations measured with PM10-2.5 FRM samplers. Ott et al. (2008, 119394) reported the 

precision of the sampler was 11.6% (CV), and the detection limit was 2.3 μg/m3 for a 5-day sample. 

3.4.1.2. PM Speciation 

The following sections describe recent developments regarding measurement techniques to 

ascertain quantities of particle-bound water, cations and anions, elemental composition, carbon, and 

organic species. 

Particle-Bound Water 

Particle-bound water is an important component 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 distribution 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, measured 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 may 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 SO4

2–, NO3–

, NH4

+

, Cl-, Na+, and K+ still 

relies primarily on filter-based collection, water based extraction and ion chromatography (IC) based 

chemical speciation and quantification. In addition, 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 integrated sampling methods for SO4

2–, NO3–

, and 

NH4

+

 have been detailed in the 2008 SOX - Health ISA (U.S. EPA, 2008, 157075) and the 2008 NOX

and SOX - Ecological ISA (U.S. EPA, 2008, 157074). 

Recent developments in multiple ion measurements have focused on the coupling of IC and a 

sample dissolution system, represented by the Particle 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 with 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 collected droplets are then introduced into the IC for 

analysis. The AIM system can provide hourly concentrations 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 and will be detailed later in Section 3.4.1.3. Note that 

measurement and analytical specifications for ions other than SO4

2– and NO3–

 are listed in Annex A, 

Table A-9. 

Sulfate 

Methods used for continuous (sampling interval of minutes) measurements of SO4

2– include 

Aerosol Mass Spectrometry (AMS) (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 SO4

2– to SO2 for detection by a SO2 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 R2 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 SO4

2– monitors. 

The Thermo 5020 particulate sulfate analyzer (based 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 methods and analytical specifications for sampling SO4

2–. 

Nitrate 

In addition to the nylon filter-based method and the new developments mentioned for SO4

2–, 

methods based on flash volatilization-chemiluminescence analysis and catalytic 

conversion-chemiluminescence analysis have also been developed for continuous NO3

–

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 NOX. For the catalytic conversion-chemiluminescence 

analysis system (Weber et al., 2003, 157129), NO3

–

 was measured by conversion of particle NO3

–

into NO, and then detected with the chemiluminescence method. Field and lab comparisons were 

conducted to compare the different instruments mentioned above. Although the R&P 8400N ambient 

particulate NO3

–

 monitor, which is based on the Stolzenburg flash vaporization technique, could 

provide 10-min resolution data and showed excellent 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 NO3

–

 concentrations ~30% lower than the denuder-filter 

systems in both the Baltimore supersite and the multiyear 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 

NO3

–

 information. AMS did not always agree well with the denuder-filter 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 

NO3

–

. Therefore, the disagreement between the AMS and 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 NO3

–

. 

Ammonium 

Several continuous and semi-continuous instruments can be used to monitor ambient 

ammonium concentrations (Al-Horr et al., 2003, 153951; Bae, 2007, 155669) including many listed 

above for SO4

2– and NO3–

. 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 absorption photometer. Bae et al. (2007, 155669) reported the 

inter-sampler coefficients of determination (R2

) 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 NO3

–

 and SO4

2–, including NH4+

. 

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). 

Recent 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 seven 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 analyses 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. Aspects 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 place 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 PM2.5 NAAQS and provides data for PM2.5 mass, SO4

2–, NO3–

, NH4

+

, 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 networks. 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 procedures (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 measurement 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 addressed 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/m3

 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 correction 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 different 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 plateaus; (4) residence time at each plateau; (5) 

optical pyrolysis monitoring configuration and wavelength; (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) location 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 transmission 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 applied to the in-situ/semi-continuous methods, since 

December 2009 3-26 
most of these methods are also based on thermal-optical 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 operational basis, as there are no standard reference 

materials. Initial efforts have been made to produce OC/EC reference materials 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 developed 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 measurement 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 CO2. CO2 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 instruments are subject to interferences by particle 

scattering, interactions with the filter substrate, particle loading on filters, and other pollutants (e.g., 

NO2). 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, 13C, in OC and EC 

with a thermal optical transmission analyzer coupled with gas chromatography-isotope ratio mass 

spectrometer (TOT-GC-IRMS). The ratio of 13C/12C 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 (HPLC) as well as application of thermal 

desorption (TD) techniques are helping to expand 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 infrared 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 for better OMM quantification, have increased its 

December 2009 3-27 
application in organic aerosol characterization 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 (Swartz 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 extraction 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 Atlanta, 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/SO4

2–, Na/K/SO42–, soot/hydrocarbon, and mineral 

particle types) were characterized by these instruments during the studies mentioned above. NO3

–

and SO4

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 these 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 

Instruments for measuring UPFs developed during the past decade permit measurement of size 

distributions of particles down to 3 nm in diameter with mobility particle 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 (d50) 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 surface 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 particle 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 (with respect to hazard and odor) as butanol or 

n-propanol. The performance of this CPC was reported 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 condenser (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 scanners (FIMS) (Olfert et al., 2008, 

156004). 

3.4.1.5. PM Size Distribution 

Along with 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 distribution. An APS incorporating very sharp cut points 

between 0.1 and 10 μm 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 distributions 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 

Instruments sensing back scattered solar radiation on satellites have made it possible to derive 

information about tropospheric aerosol properties on the 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 depth (AOD). Depending on the wavelengths sampled 

and the spectral resolution of the instruments, information about the composition of particles of 

diameter <2 μm and particles of diameter >2μm can be obtained. Data from two main instruments, 

the moderate resolution imaging spectroradiometer (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 wavelengths through the visible to the near infrared at 

one viewing direction; and MISR measures the intensity at four wavelengths (from the visible to the 

near IR) and the same ground pixel at nine viewing 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 satellite, 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 properties 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 better 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) slightly 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 PM2.5 to aerosol optical depths. 

The detailed comparisons of surface measurement and satellite measurements are given in Chapter 9. 

3.4.2. 

3.4.

Ambient Network Design 

2.1. Monitor Siting Requirements 

The EPA Air 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 limited dataset of hazardous air pollutants. In 2007, 

there were 4,693 PM10 monitors and 2,194 PM2.5 monitors reporting values to the AQS. Where 

SLAMS PM10 and PM2.5 monitoring is required, at least one of the sites must be a maximum 

concentration site for that specific area. The appropriate spatial scales for PM2.5, PM10-2.5, and PM10

monitoring differ given the contrasting spatial gradients of coarse PM relative to fine. The relevant 

scales for each size classification are 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. 

PM2.5 

The minimum number of PM2.5 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 PM2.5 monitors ranges from 0 to 3 in any given 

metropolitan statistical area. Continuous PM2.5 monitors must be operated in no fewer than one-half 

of the minimum required sites in each area. Most PM2.5 monitoring in urban areas should be 

representative of a neighborhood scale (for trends and compliance with standards). Urban or regional 

scale sites are located to characterize regional transport of PM2.5. In certain instances where 

population-oriented micro- or middle-scale PM2.5 monitoring are determined by the Regional 

Administrator to represent many such locations throughout a metropolitan area, these smaller scales 

can be considered to represent community-wide air quality. PM2.5 measurements are obtained at local 

temperature and pressure across the NAMS/SLAMS networks (40 CFR Part 58). 

PM2.5 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. 

PM10-2.5

PM10-2.5 monitoring has not been required at SLAMS sites, but will be required at NCore1

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 PM10-2.5 to assess the variation in 

coarse particle concentrations that would be expected across populated areas that are in proximity to 

large emissions sources. PM10-2.5 chemical speciation monitoring and analyses will also be required 

at NCore sites by January 1, 2011. EPA has already approved FRMs and FEMs for PM10-2.5 mass; 

however, methods for PM10-2.5 speciation are still being developed (Henderson, 2009, 192001). PM10-

2.5 measurements are obtained at local temperature and pressure by recalculating the co-located PM10

for local conditions. 

PM10

As for PM2.5, the minimum number of PM10 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 PM10 monitors ranges from 0 to 8 in any given 

metropolitan statistical area. Except for some circumstances where microscale (<100 m, for 

maximum PM10 exposure) monitoring may be appropriate, the most important scales to characterize 

the emissions of PM10 effectively from both mobile and stationary sources are the middle scale (for 

short-term public exposure) and neighborhood scale (for trends and compliance with standards). 

PM10 measurements are obtained at standard temperature and pressure across the NAMS/SLAMS 

networks (40 CFR Part 58). 

3.4.2.2. 

 

Spatial and Temporal Coverage 

Locations of PM2.5 and PM10 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 health 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, Pittsburgh, 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 which counties, and hence which 

monitors, to include for each metropolitan region.1 Figure 3-7 and Figure 3-8 display PM2.5 and 

PM10 monitor density, respectively, with respect to population density in Boston. Annex A includes 

similar information for all fifteen metropolitan regions (Figure A-1 through Figure A-30). 

 

1

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

exception of Los Angeles, Riverside and Phoenix. Los Angeles and Riverside are contained within the same CSA, so the smaller CBSA 

definition was used to delineate these two cities. Phoenix is not contained within a CSA, so the smaller CBSA definition was used for 

this city as well. 

December 2009 3-32 
Figure 3-7. PM2.5 monitor distribution in comparison with population density, Boston CSA. 

December 2009 3-33 
Figure 3-8. PM10 monitor distribution in comparison with population density, Boston CSA. 

December 2009 3-34 
Table 3-3. Proximity to PM2.5 and PM10 monitors for total populationa

 by city. 

Proximity to PM Monitorsb

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

N N % N % N % N % 

PROXIMITY TO PM2.5 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 

Detroit 5,553,465 54,997 0.99 1,174,733 21.15 2,791,555 50.27 3,845,190 69.24 

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 PM10 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 PM2.5 and PM10 monitors for the total 

population for each CSA/CBSA individually. Population totals within various distances of PM 

monitors were calculated assuming equal internal 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 PM2.5, 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 PM2.5 monitors. For PM10, 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 PM10 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 PM2.5 network more closely 

samples near population centers in the Boston CSA compared with the PM10 network shown in 

Figure 3-8, although both PM2.5 and PM10 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 Age 

Table 3-4 breaks down the population density around PM2.5 and PM10 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 detail 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 across the child age groups when compared to total 

population. In the cases of Denver, Detroit, Phoenix, Riverside, and St. Louis for PM2.5 and 

Birmingham, Denver, Riverside, and St. Louis for PM10, 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 percentage of the population within 15 km of a 

monitor was similar for all age groups for both PM2.5 and PM10. Between-city disparities in elderly 

population density within a sampler radius were larger. For PM2.5, 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 PM2.5 monitors. For PM10, 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 

PM10 monitor. These differences may reflect overall density of the samplers within a given city, with 

PM2.5 monitors more numerous than PM10 monitors in most of the CSAs/CBSAs, and retirement and 

settlement trends among elderly adults.

December 2009 3-36 
Table 3-4. Proximity to PM2.5 and PM10 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 Monitorsb

Age 

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

N N % N % N % N % 

PROXIMITY TO PM2.5 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 PM10 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-identified as white or black and having a 

Hispanic or non-Hispanic origin within 1, 5, 10, or 15 km distances from PM2.5 and PM10 monitors 

cumulatively across the fifteen CSAs/CBSAs. For PM2.5, 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 were within that same distance (63% and 67%, 

respectively), across the fifteen CSAs/CBSAs studied. For PM10, 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 blacks 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 PM2.5 monitor most likely represents the fact that 

more PM2.5 monitors are currently deployed compared with PM10 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 PM10 monitor than the corresponding 

white and non-Hispanic populations (0.5% each). Likewise, 2.5% of the black population and 2.8% 

of the Hispanic population reside within 1 km of a PM2.5 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 PM2.5 and PM10 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 PM2.5 and PM10 monitors for adults age 65 yr and oldera

 by city. 

Proximity to PM Monitorsb

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

N N % N % N % N % 

PROXIMITY TO PM2.5 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 

Boston 945,790 18,821 1.99 224,628 23.75 438,920 46.41 606,231 64.10 

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 PM10 MONITORS 

Atlanta 362,201 2,115 0.58 35,448 9.79 93,903 25.93 139,240 38.44 

Birmingham 145,905 3,663 2.51 35,628 24.42 66,839 45.81 86,299 59.15 

Boston 945,790 6,852 0.72 124,911 13.21 262,854 27.79 385,046 40.71 

Chicago 1,018,983 7,619 0.75 107,540 10.55 291,705 28.63 441,771 43.35 

Denver 232,974 3,675 1.58 43,658 18.74 107,548 46.16 168,447 72.30 

Detroit 626,216 1,555 0.25 41,833 6.68 99,680 15.92 167,760 26.79 

Houston 377,586 2,085 0.55 57,413 15.21 166,715 44.15 219,615 58.16 

Los Angeles 1,207,436 4,693 0.39 126,696 10.49 422,725 35.01 810,078 67.09 

New York 2,710,675 2,463 0.09 37,580 1.39 80,222 2.96 104,951 3.87 

Philadelphia 834,110 2,740 0.33 49,413 5.92 154,535 18.53 322,700 38.69 

Phoenix 388,150 8,605 2.22 119,306 30.74 267,456 68.91 348,464 89.78 

Pittsburgh 449,544 13,302 2.96 133,285 29.65 243,723 54.22 314,941 70.06 

Riverside 342,334 4,181 1.22 65,499 19.13 182,615 53.34 236,900 69.20 

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

St. Louis 358,747 4,316 1.20 55,833 15.56 117,743 32.82 172,535 48.09 

+

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 PM2.5 and PM10 monitors based on the population 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 Monitorsb

Race or 

Hispanic 

Origin 

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

N N % N % N % N % 

PROXIMITY TO PM2.5 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 PM10 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 PM2.5 and PM10 monitor cumulatively across the 15 CSAs/CBSAs. For 

PM2.5, 80% of the population below poverty level and 77% of the population with less than high 

school education are within 15 km of a monitor 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 PM10, 

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, whereas the percentage of those above the poverty 

line (39%), those with a high school or more education (38%), and those with a college education 

(35%) were slightly less. Higher percentages of individual SES subpopulations within a given 

distance of PM2.5 monitors relative to PM10 monitors likely reflect the fact that more PM2.5 monitors 

are currently deployed within the 15 CSAs/CBSAs studied compared with PM10 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 PM10 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 PM2.5

monitor. However, the populations of low SES groups are more represented at the neighborhood 

scale than those for higher SES groups. For example, only about 1.5-1.7% of those above the 

poverty line, those with a high school or more education, or those with a college education are within 

1 km of a PM2.5 monitor. Moreover, it is notable that at any scale shown in Table 3-7 and for both 

PM2.5 and PM10, 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 PM2.5 and PM10 monitors based on the population 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 

morea. The figures presented here are cumulative for the 15 CSAs/CBSAs 

examined in Chapter 3. 

Proximity to PM Monitorsb

Total 

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

SES 

N N % N % N % N % 

PROXIMITY TO PM2.5 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 PM10 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 from 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 PM2.5

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 PM2.5 in eastern cities was generally more highly correlated 

across monitoring sites than in western cities. The higher spatial correlations in the eastern cities 

resulted from the more regionally dispersed sources of PM2.5 in the East. Although PM2.5

concentrations at sites within an urban area can be highly correlated, significant differences in 

concentrations can occur on any given day. The ratio of PM2.5 to PM10 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 PM2.5 between eastern and western cities were 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 PM10-2.5, 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 SO4

2–). Spatial variability will affect estimates of communityscale human exposure and caution should be exercised in extrapolating conclusions from one area to 

another, particularly on a regional scale. 

For this PM ISA, the PM2.5 monitoring network has been active for 8 or 9 years depending on 

location. Observations and analyses based on PM2.5 measurements reported to AQS are included in 

this chapter. Furthermore, by selecting locations where PM10 and PM2.5 measurements are 

co-located, information about the spatiotemporal distribution of the PM10-2.5 size fraction is 

investigated. Given the form of the current standard and the relative abundance of PM10 monitors in 

the AQS network, PM10 mass concentrations are also included in this section with the understanding 

that PM10 includes mass contributions from the smaller size fractions and therefore overlaps with 

PM2.5, PM10-2.5 and UFP mass concentrations. Although compliance 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 PM2.5, PM10-2.5 and PM10 data utilized in this section comes 

from the AQS. Based on the population and exposure requirements 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 reported here. Furthermore, biases exist for some 

PM constituents (and hence, total mass) owing to volatilization losses of NO3

–

 and other 

semi-volatile compounds and, conversely, retention of particle-bound water with hygroscopic 

species. The magnitude of these effects 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, 

NO2, O3 and SO2 are covered in Section 3.5.3. 

3.5.1. Spatial Distribution 

Spatial scales of interest for PM range from global and continental scales (>1000 km) down to 

micro scale (~5-100 m). Variation in PM concentration depends on the spatial scale and magnitude 

of PM sources, formation and removal mechanisms, and transport and dispersion of PM. These 

different sources and processes can cause substantial variation in particle size distribution and 

chemistry. This section addresses the spatial variability of PM by focusing primarily on AQS data 

across three different scales: variability across the U.S., urban-scale variability 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. 

PM2.5

Figure 3-9 shows the 3-yr mean of the 24-h PM2.5 concentrations by county across the U.S. for 

2005-2007. The data used in generating this map are from FRM or FRM-like1

 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 PM2.5 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 PM2.5 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 

PM2.5 concentration in excess of 20 μg/m3

. Average concentrations between 18 and 20 μg/m3 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 PM2.5 concehntration data associated with the parameter code “88502 - Acceptable PM2.5 AQI and Speciation Mass” 

in the AQS. These data were collected by continuous instruments which 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 have 

individually decided that the continuous instruments 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 data include 

statistical adjustments by the state/local monitoring agency based 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 PM2.5 data and whether their raw or adjusted continuous PM2.5 data 

should be associated with parameter code 88502 were informed by non-binding EPA guidance issued in 2006 (Technical Note on 

Reporting PM2.5 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 PM2.5 concentration by county derived from FRM or FRM-like 

data, 2005-2007. The population bar shows the number of people residing within 

counties that reported county-wide average concentrations within the specified 

ranges. 

Table 3-8 contains summary statistics for PM2.5 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 provides 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 PM2.5 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/m3

, equivalent to the mean of the individual 24-h avg. The maximum annual average PM2.5

concentration calculated from 24-h FRM data over these 3 yr was 23 μg/m3

 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 becomes 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/m3

 higher than the 24-h avg at the 

95th percentile and 7 μg/m3

 higher at the 99th percentile. This deviation between 1-h and 24-h 

averaging times is a result of short duration spikes in PM2.5 mass lasting long enough to influence the 

upper percentiles of the 1-h distribution but not necessarily 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/m3

 was reported by a 

monitor in Boise, ID (AQS monitor ID: 160010011) on July 4, 2007. Nine of the top 12 1-h PM2.5

concentrations reported across the country also occurred on July 4th, implicating fireworks as the 

common source for these high values. 

Table 3-8. PM2.5 distributions derived from AQS data (concentration in μg/m3). 

Percentiles 

n Mean 

1 5 10 25 50 75 90 95 99 

Max 

2005-2007 PM2.5 FOR DIFFERENT AVERAGING PERIODS 

 Annual avga (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 

PM2.5 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 PM2.5 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 PM2.5 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, winter (December-February) was slightly higher 

than the other seasons. This is consistent with wintertime stagnation events resulting in short-term 

elevated PM2.5 concentrations. Of the 15 CSAs/CBSAs investigated, the highest mean of 24-h PM2.5

concentrations was reported for Riverside (17 μg/m3

), Birmingham (16 μg/m3) and Pittsburgh 

(16 μg/m3

); the lowest was reported for Denver (9 μg/m3) and Seattle (9 μg/m3). 

PM10-2.5

Since PM10-2.5 is not routinely measured and reported to AQS, co-located PM10 and PM2.5

measurements from the AQS network were used to investigate the spatial distribution in PM10-2.5. Only low-volume FRM or FRM-like samplers were considered in calculating PM10-2.5 to avoid 

complications with vastly different sampling protocols (e.g., flow rates) between the independent 

PM10 and PM2.5 measurements. The same 11+ days per quarter completeness criterion discussed 

above was applied to the PM10 and PM2.5 measurements. The PM2.5 concentrations are reported to 

AQS at local conditions whereas the PM10 concentrations are reported at standard conditions. 

Therefore, prior to calculating PM10-2.5 by subtraction, the PM10 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 PM10-2.5 concentration by 

county across the U.S. for 2005-2007. There is considerably less coverage for PM10-2.5 than for PM2.5

or PM10 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-location criteria in all 3 yr (2005-2007), and 

therefore the available measurements do not provide sufficient information to adequately 

characterize regional-scale coarse PM spatial concentration distributions. 

Table 3-9 contains summary statistics for PM10-2.5 for the period 2005-2007 similar to those 

reported in Table 3-8 for PM2.5. 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 PM10-2.5 than 

for PM2.5, 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 eastern cities including Atlanta, Boston, Chicago and 

New York all had a higher fraction in the fine mode with the greatest ratio of fine to thoracic coarse 

in Chicago (14 μg/m3

 PM2.5, 5 μg/m3 PM10-2.5, ratio = 2.8). In contrast, Denver (9 μg/m3 PM2.5, 

20 μg/m3 PM10-2.5, ratio = 0.45) and Phoenix (10 μg/m3 PM2.5, 22 μg/m3 PM10-2.5, ratio = 0.45) had a 

higher fraction in the thoracic coarse mode. Given the limited information available from AQS for 

PM10-2.5 and the current NAAQS for PM10, the next section characterizes the more prevalent PM10

data, acknowledging that PM10 incorporates both thoracic coarse and fine particles. 

December 2009 3-45 
Figure 3-10. Three-yr avg 24-h PM10-2.5 concentration by county derived from co-located low 

volume FRM PM10 and PM2.5 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. PM10-2.5 distributions derived from AQS data (concentration in μg/m3

). 

Percentiles 

n Mean 

5 10 25 50 75 90 95 99 

Max 

1 

2005-2007 PM10-2.5 FOR DIFFERENT AVERAGING PERIODS 

 Annual avga (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 

PM10-2.5 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 PM10-2.5 IN INDIVIDUAL CSAS/CBSAS USING 24-H AVG LOW VOLUME FRM DATAb

 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 PM10 and FRM-like PM2.5 monitors available for Birmingham, Detroit, Houston, Los Angeles, Philadelphia, Pittsburgh, Riverside, Seattle or St. Louis. 

PM10

Figure 3-11 shows the 3-yr mean of the 24-h PM10 concentrations by county across the U.S. 

for 2005-2007. Both FRM and FEM PM10 data reported to AQS were included and the same 

11+ days per quarter completeness criterion described above for PM2.5 was applied. The highest 

3-yr avg for PM10 (>50 μg/m3

) 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 PM10

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 PM10 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 PM10 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. PM10 distributions derived from AQS data (concentration in μg/m3

). 

Percentiles 

n Mean 

1 5 10 25 50 75 90 95 99 

Max 

2005-2007 PM10 FOR DIFFERENT AVERAGING PERIODS 

 Annual avga (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 

PM10 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 PM10 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 PM10 concentration calculated from 24-h FRM data over these 

three years was 85 μg/m3

 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 PM10

concentrations in Table 3-10 exceed 1,000 μg/m3

, 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 PM10 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 PM2.5 observations. Of the 15 CSAs/CBSAs investigated, the highest mean 

of 24-h PM10 concentrations was reported for Phoenix (52 μg/m3

), considerably higher than the 

means for the other CSAs/CBSAs investigated. The lowest was reported for Boston (17 μg/m3

) with 

New York, Philadelphia and Seattle only slightly higher (19 μg/m3). 

On average using the 2005-2007 data for PM2.5 in Table 3-8 and PM10 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 PM2.5/PM10 ratios of 

0.19 and 0.32, respectively). In contrast, a larger fraction is present in the fine mode in Philadelphia 

(0.74), New York (0.68) and Pittsburgh (0.67). Comparisons of PM2.5 to PM10 as reported to AQS 

should be used with caution, however, since PM2.5 concentrations are reported for local conditions 

while PM10 concentrations are converted to STP before reporting. Nevertheless, these findings are 

consistent with those in Table 3-9 for PM10-2.5 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 

PM2.5 to PM10 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 polluted 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 104 per cm3 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 and ~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 cm3

. 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 UFPs 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 PM2.5 is collected routinely at CSN network sites so the majority of this section on PM 

constituents is devoted to PM2.5 composition. PM10-2.5 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, 

SO4

2–, NO3-

, and NH4

+

 mass from PM2.5 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 NO3

-

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

December 2009 3-50 
high PM2.5 OC mass concentration with annual average concentrations greater than 5 μg/m3

 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 chemical 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. will 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 PM2.5 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 metropolitan 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/m3

). 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 PM2.5 SO4

2– mass concentration which shows that SO42– is 

more prevalent in the eastern U.S. owing to the strong west-to-east gradient in SO2 emissions. This 

gradient is magnified in the summer months when more sunlight is available for photochemical 

formation of SO4

2–. In contrast, PM2.5 NO3–

 mass concentration in Figure 3-15 is highest in the west, 

particularly in California. There are also elevated concentrations of NO3

–

 in the upper Midwest. The 

seasonal plots show generally higher NO3

–

 in the wintertime as a result of temperature driven 

partitioning. Exceptions exist in Los Angeles and Riverside where high NO3

–

 readings appear yearround. The PM2.5 NH4

+

 mass concentration maps in Figure 3-16 shows spatial patterns related to 

both SO4

2– and NO3–

 resulting from its presence in both (NH4)2SO4 and NH4NO3. Figure A-31 

through Figure A-36 in Annex A show similar U.S. concentration maps for PM2.5 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 PM2.5 mass was derived using the measured sulfate, adjusted NO3

–

, derived water, 

inferred carbonaceous mass approach (SANDWICH) (Frank, 2006, 098909). This approach uses the 

measured FRM PM2.5 mass and co-located CSN chemical constituents to perform a mass balancebased estimation of the PM2.5 mass fraction attributed to SO4

2–, NO3–

, EC, OCM, and crustal 

material. SO4

2– and NO3–

 include associated NH4

+

 mass and estimated particle-bound water. 

Furthermore, NO3

–

 is assumed to be fully neutralized as NH4NO3 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 PM2.5 mass. The SANDWICH method takes into account passive 

collection of semi-volatile or handling-related mass on the FRM filters in the mass balance 

calculation. The magnitude of this artifact is assigned a nominal value of 0.5 μg/m3

, which is derived 

from limited analysis of FRM field blanks. Other constituents such as salt and other metallic oxides, 

however, are not included in these calculations and 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 discussed 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 PM2.5 OC concentrations measured at CSN sites across the 

U.S., 2005-2007. 

December 2009 3-52 
Figure 3-13. Three-yr avg 24-h PM2.5 EC concentrations measured at CSN sites across the 

U.S., 2005-2007. 

December 2009 3-53 
Figure 3-14. Three-yr avg 24-h PM2.5 SO4

2– concentrations measured at CSN sites across the 

U.S., 2005-2007. 

December 2009 3-54 
Figure 3-15. Three-yr avg 24-h PM2.5 NO3

–

 concentrations measured at CSN sites across the 

U.S., 2005-2007. 

December 2009 3-55 
Figure 3-16. Three-yr avg 24-h PM2.5 NH4

+

 concentrations measured at CSN sites across the 

U.S., 2005-2007. 

December 2009 3-56 
Figure 3-17 shows the PM2.5 compositional breakdown for the 15 CSAs/CBSAs. All available 

monitoring sites with co-located FRM PM2.5 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 PM2.5 constituents. This resulted in a varying 

number of sites (ranging from one to seven, as indicated in the caption to Figure 3-17) used to create 

the averages shown in the figure. Variability in PM2.5 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 PM2.5 speciation estimates for 2005-2007 derived using the 

SANDWICH method. For the following 15 CSAs/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). SO4

2–

and NO3

–

 estimates include NH4

+

 and particle bound water and the circle area is 

scaled in proportion to FRM PM2.5 mass as indicated in the legend. 

On an annual average basis, SO4

2– is a dominant PM component in the eastern U.S. cities. For 

the presented cities, this includes everything east of Houston where the SO4

2– fraction of PM2.5

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. SO4

2–, NO3–

 and crustal material are also important in many of the included western cities. In 

the west, fractional SO4

2– ranges from 18% in Denver to 32% in Los Angeles while fractional NO3–

is relatively large in Riverside (22%), Los Angeles (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 PM2.5 (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 PM2.5 speciation estimates for 2005-2007 derived 

using the SANDWICH method. For the following 15 CSAs/CBSAs: Atlanta, GA; 

Birmingham, AL; Boston, MA; Chicago, IL; Denver, CO; Detroit, MI; Houston, TX; 

Los Angeles, CA; New York City, NY; Philadelphia, PA; Phoenix, AZ; Pittsburgh, 

PA; Riverside, CA; Seattle, WA; and St. Louis, MO. SO4

2– and NO3–

 estimates 

include NH4

+

 and particle bound water and the circle area is scaled in proportion 

to FRM PM2.5 mass as indicated in the legend. 

The seasonal variation in PM2.5 composition across the 15 CSAs/CBSAs is shown in Figure 

3-18 where the seasons are defined as before. SO4

2– dominates in most metropolitan areas in the 

summertime, while NO3

–

 becomes important in the colder wintertime months. Notable exceptions 

include Denver, Phoenix, Riverside, and Seattle where summertime SO4

2– makes up a smaller 

fraction of the PM2.5 mass compared with other regions. Likewise, NO3

–

 is less pronounced in the 

wintertime in Atlanta, Birmingham, Houston, Phoenix, and Seattle compared with other regions. Los 

Angeles and Riverside exhibit elevated NO3

–

 from fall through spring. Crustal material is a 

substantial summertime component in Houston (26%), and is generally low elsewhere in the East in 

all seasons. In the West, crustal material represents a substantial component year-round in Phoenix 

and Denver. 

The only PM size fraction routinely collected at CSN network sites is PM2.5, resulting in less 

available information on speciated PM10-2.5. Edgerton et al. (2005, 088686; 2009, 180385) published 

speciated measurements for PM2.5 and PM10-2.5 obtained using dichotomous samplers from four 

locations included in the Southeastern Aerosol Research and Characterization (SEARCH) study: 

Yorkville, GA, Centreville, AL, Birmingham, AL and 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 PM2.5 and PM10-2.5 included SO4

2–, NO3-

, NH4

+

, and major metal oxides (MMO). In addition, 

OC and either black carbon (BC) or EC were reported for PM2.5 over the entire study period and for 

PM10-2.5 for a subset of samples extending from April 2003 to April 2004. 

For the Atlanta and Birmingham SEARCH sites, the annual average NO3

-

 mass fraction was 

approximately equal for PM2.5 (5.6% and 5.0%, respectively, for Atlanta and Birmingham) and PM10-

2.5 (4.9% and 3.3%). Likewise, the OC mass fraction was approximately equal for PM2.5 (26% and 

26%) and PM10-2.5 (24% and 27%). MMO contributed an order of magnitude smaller mass fraction to 

PM2.5 (2.6% and 4.7%) than PM10-2.5 (38% and 35%). In contrast, SO4

2– contributed an order of 

December 2009 3-58 
magnitude greater mass fraction to PM2.5 (25.1% and 24.1%) than PM10-2.5 (2.8 and 2.1%). BC also 

contributed a larger mass fraction of PM2.5 (8.6% and 10.5%) than EC did for PM10-2.5 (2.9% and 

2.4%). Based on these findings, MMO are present primarily in the thoracic coarse mode, while SO4

2–

and EC/BC are present primarily in the fine mode. NO3

–

 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 UFPs 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 SO4

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 are 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% SO4

2-, 0-19% 

NO3

–

, 0-9% NH4

+

, 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 Riverside. 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 the mass of UFPs in their samples. Sizable 

contributions from Si (0-4%) and Al (6-14%) were also found. MOUDIs are used to collect sizesegregated filter samples in the UFP compositional analyses described above. Coarse particle bounce 

is a concern when using MOUDIs and further studies, 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 SO4

2– were also 

present in the UFP samples, but at much smaller mass fractions. EC was present year-round, 

whereas SO4

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) and 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 pollens 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 contribution 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 PM10

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/m3

) in PM10-2.1 (2.6 μg/m3). The contribution to PM2.1 was estimated to be 

about 10% that in PM10-2.1. Womiloju et al. (2003, 179954) estimated that fungal spores contribute 

14-22% of OC in PM2.5 in and around Toronto. 

Edgerton et al. (2009, 180385) found that PBAP contributed 60-70% of OC (average 

~1.7 μg/m3

) in PM10-2.5 at an urban and a rural site in Alabama in fall of 2000 and spring of 2001. 

The percentage contributions were similar at both 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 

PM2.5

Data from the 15 CSAs/CBSAs were used to investigate urban-scale variability in PM 

reported to AQS. PM2.5 has a longer residence time in the atmosphere compared to PM10-2.5 resulting 

from a slower Vd. As a result, PM2.5 exhibits increased spatial homogeneity with relatively less 

localized influence from point sources. Maps of PM2.5 monitor locations and box plots of seasonal 

PM2.5 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 PM2.5 concentration is quite uniformly distributed across the monitors. Los 

Angeles has one monitor (Site I) that reported noticeably less PM2.5 in all four seasons than the rest 

of the monitors in the region. This monitor is located at Lancaster CA, separated from the rest of the 

Los Angeles region by the San Gabriel Mountains. In general, however, PM2.5 varies approximately 

the same magnitude between monitors as it does between seasons for the 15 selected cities. 

Table 3-11 through Table 3-13 contain pair-wise monitor site comparison statistics for PM2.5 in 

Boston, Pittsburgh, and Los Angeles, respectively. Tables A-20 through A-34 in Annex A contain the 

same statistics for PM2.5 measured within all 15 of the CSAs/CBSAs investigated. Comparison 

statistics shown include the Pearson correlation coefficient (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 Xij and Xik 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 PM2.5 concentrations in Boston range from 0.61 to 0.97 in 

Table 3-11. The lowest correlation in this CSA was between Site A located in Fall River, MA, 1 km 

from the Narragansett Bay and Site L located in Nashua, NH, on the bank of the Merrimack River, 

120 km north. The highest correlation was between Sites P and R, located less than a kilometer apart 

in Providence, RI. 

In Pittsburgh, 24-h PM2.5 correlations range from 0.65 to 0.97. The lowest correlation in this 

CSA was between Sites B and D, located diametrically 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 PM2.5 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 meteorology and topography can be important as well. 

Figure 3-19. Locations of PM2.5 monitors and major highways, Boston, MA. 

December 2009 3-61 
concentration (μg/m 3

)

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 PM2.5 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). 

December 2009 3-62 
Table 3-11. Inter-sampler comparison statistics for each pair of 24-h PM2.5 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 3-63 
Site 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 PM2.5 monitors and major highways, Pittsburgh, PA. 

December 2009 3-65 
Figure 3-22. Seasonal distribution of 24-h avg PM2.5 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 PM2.5 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 PM2.5 monitors and major highways, Los Angeles, CA. 

December 2009 3-68 
Figure 3-24. Seasonal distribution of 24-h avg PM2.5 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 PM2.5 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 PM2.5 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 different topography and climatology as well as different 

PM2.5 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 (R2

 = 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 contrast, 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 (R2

 = 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-sampler correlation vs. distance plot for Pittsburgh 

PM2.5 resembles the one from Boston (with R2

 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 (R2

 = 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 PM2.5 concentration 

profile within the Los Angeles region when compared 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 while 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 3-70 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure 3-25. Inter-sampler correlations for 24-h PM2.5 as a function of distance between 

monitors in Boston, MA. 

0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

1

0

Figure 3-26. Inter-sampler correlations for 24-h PM2.5 as a function of distance between 

monitors in Pittsburgh, PA. 

December 2009 3-71 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

 Figure 3-27. Inter-sampler correlations for 24-h PM2.5 as a function of distance between 

monitors in Los Angeles, CA. 

PM10-2.5

Given the limited number of co-located low-volume FRM PM10 and FRM PM2.5 monitors, 

only a very limited investigation into the intra-urban spatial variability of PM10-2.5 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 PM10-2.5 according to the data 

completeness and monitor specification requirements discussed earlier. Figure 3-28 contains box 

plots of PM10-2.5 for one or two available sites per CSA/CBSA providing adequate PM10-2.5

concentration data. For Boston, the correlation between the two sites for PM10-2.5 was 0.45 compared 

with 0.73 for PM2.5 alone and 0.84 for PM10 alone (using the same two monitoring sites). For New 

York, the correlation was slightly higher for the two sites: 0.74 for PM10-2.5 compared with 0.93 for 

PM2.5 alone and 0.82 for PM10 alone. The COD for PM10-2.5 also increases in both cities compared 

with PM2.5 and PM10 alone, suggesting less spatial homogeneity for thoracic coarse particles 

compared with fine particles. Wilson and Suh (1997, 077408) reported PM10-2.5 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 PM2.5 (r = 0.90) and PM10 (r = 0.87) from the 

same study. Thornburg et al. (2009, 190999) also reported a high degree of spatial variability in 

PM10-2.5 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 PM10-2.5 concentrations, resulting in a 

higher degree of spatial variability in PM10-2.5 relative to PM2.5 or PM10. 

December 2009 3-72 
Figure 3-28. Seasonal distribution of 24-h avg PM10-2.5 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. 

PM10

PM10 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 Vd and 

resulting short atmospheric lifetime of the coarse-mode particles making up much of PM10 mass. As 

a result, local emission sources often dominate PM10 annual average mass at certain monitors. Data 

from the 15 CSAs/CBSAs were used to investigate urban variability in PM10 reported to the AQS 

database. 

Maps of PM10 monitor locations and box plots of seasonal PM10 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 PM2.5 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 PM10 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 range in concentrations measured at different sites. 

Inter-monitor variation in PM10 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 PM10 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 area 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, which 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 monitor 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 PM10 monitors and major highways, Boston, MA. 

December 2009 3-74 
Figure 3-30. Seasonal distribution of 24-h avg PM10 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 PM10 monitors reporting to AQS 

for Boston, MA. 

Site A B C D E F G H 

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 PM10 monitors and major highways, Pittsburgh, PA. 

December 2009 3-76 
Figure 3-32. Seasonal distribution of 24-h avg PM10 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 PM10 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 3-78 
J 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 PM10 monitors and major highways, Los Angeles, CA. 

December 2009 3-80 
Figure 3-34. Seasonal distribution of 24-h avg PM10 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 PM10 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 PM10 measurements obtained in Boston, Pittsburgh and Los Angeles. 

Annex A contains similar plots for all 15 CSAs/CBSAs under investigation beginning with Figure A84. In each plot, substantially more scatter is observed when compared to those for PM2.5 (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 correlation and distance, with average inter-sampler 

correlation remaining higher than 80% when samplers are 44 km apart (R2

 = 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 SO4

2–, which is a regional pollutant and is a major component of PM2.5 and PM10

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 (R2

 = 0.28) than observed in the Boston data. As seen for the Pittsburgh 

PM10 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 sites 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 stable conditions in the planetary boundary layer 

cause localized events of elevated concentration. When those four sites are removed, scatter 

decreases greatly (R2

 = 0.56). The Los Angeles data exhibit a much steeper slope, with average 

inter-sampler correlation remaining higher than 80% when samplers are only 30 km apart 

(R2 = 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 PM10 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 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

1

0

Figure 3-35. Inter-sampler correlations for 24-h PM10 as a function of distance between 

monitors in Boston, MA. 

December 2009 3-82 
1

0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure 3-36. Inter-sampler correlations for 24-h PM10 as a function of distance between 

monitors in Pittsburgh, PA. 

0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

1

0

Figure 3-37. Inter-sampler correlations for 24-h PM10 as a function of distance between 

monitors in Los Angeles, CA. 

December 2009 3-83 
UFPs 

Relatively few studies compare UFP measurements 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 aerosol 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 with 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 (mobility 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 suggests a high degree of spatial heterogeneity in 

UFPs driven primarily by differences in nearby 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 therefore UFP concentrations can exhibit substantial 

variability in space and time. This is to be expected 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 distance 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 PM2.5 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 pie charts for each monitor are included in Figures 

A-127 through A-141 in Annex A and provide an indication of the urban-scale spatial variability in 

PM2.5 composition. In the instances where multiple monitors were available, there was a fair degree 

of spatial homogeneity in PM2.5 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 exhibits some spatial variation in NO3

–

 during the winter, the 

season with the highest measured PM2.5 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 PM2.5 samples when compared 

with surrounding areas (Peltier and Lippmann, 2009, 197455; Peltier et al., 2008, 197452). In 

Phoenix, high winter PM2.5 mass is site specific and appears to be associated with high OC; the 

crustal component also varies and is inversely proportional to total measured mass. 

3.5.1.3. Neighborhood-Scale Variability 

Neighborhood scale spatial variability in the particle concentration profile is affected by land 

and building topography, meteorology, particle size distribution, particle composition, and particle 

volatility. Population density at the neighborhood scale is also an important determinant of the 

spatial distribution of PM concentration because population density impacts source prevalence, 

source magnitude, topographical-driven 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 speed 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 transport 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 background wind speed existed, concentrations on the 

leeward canyon wall were four times those of the 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 barriers, 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 micro- and neighborhood-scale variation related to 

urban topography may have a significant impact on pollutant concentrations at this scale. 

December 2009 3-85 
Source : Reprinted with Permission of Elsevier 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. 

PM2.5 and PM10 

Knowledge of neighborhood-scale variability is important for interpreting data from PM2.5 and 

PM10 community monitors. Figure 3-40 shows data derived from the 15 CSAs/CBSAs for PM2.5 and 

PM10 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). PM2.5 data exhibit a flatter 

slope, with average correlation maintained at 93% within 4 km (R2

 = 0.22). There is more scatter and 

variability among the PM10 data, with an average correlation of 70% within 4 km (R2

 = 0.03). The 

degree of variability in PM10 compared with PM2.5 relates to transport and dispersion of the PM10-2.5

component of PM10 compared with PM2.5. 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 classes, considering the low computed R2

 values for each of these 

curves. 

December 2009 3-86 
RPM2.5 = -0.0163D + 1

R2

 = 0.2178

RPM10 = -0.0748D + 1

R2

 = 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 PM2.5 and PM10 as a function of distance 

between monitors for samplers located within 4 km (neighborhood scale). 

Isakov et al. (2007, 156588) compared PM2.5 concentrations from a central monitoring site in 

Wilmington, DE with PM0.3 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 PM2.5 shown in Figure 3-40. 

PM10-2.5 

Neighborhood-scale variability in PM10-2.5 was investigated by Chen et al. (2007, 147318) in 

the Raleigh/Durham area of NC. The average correlation between 26 residential monitors located 

throughout the region and a centrally located monitor representing a maximum inter-sampler range 

of 60 km was found to be 0.75 for PM10-2.5 compared with 0.92 and 0.94 for PM2.5 and PM10, 

respectively. Based on this study, neighborhood-scale variability is greater for PM10-2.5 than for PM2.5

or PM10, 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 substantial differences in the daily concentration time 

series at each site. Such variability reflects diversity 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 concentration 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 range 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 Raleigh, 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 dilutes 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 increases. 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 the 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 Elsevier 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), and particle number concentration 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 fraction of the mass of particles emitted from the vehicles 

are in the nuclei mode (i.e., smaller than accumulation 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 dominated 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, the UFPs may grow into the accumulation mode 

by coagulation or condensation. In addition to Gaussian 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 nucleation 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 particle transport and dispersion. Depressed road 

sections, where the road bed is below the surrounding 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 resulted 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 sites located within 3-6 km of each other in the 

Long Beach, CA area. Inter-site R2

 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 was 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 PM2.5 and PM10 samples in 

rural neighborhoods and higher concentrations of combustion-derived PM2.5 and PM10, such as EC 

and NO3

–

, 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 Seville, 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 than 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 center in Seville, Spain (Gutiérrez-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 marker 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 PM2.5 concentrations and PAH species at the mouth of the Caldecott Tunnel in Orinda, CA 

and found that the two size classes were highly correlated (R2

 = 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 

PM2.5 sampled at the tunnel mouth in the latter study represented secondary PM2.5 that grew from 

UFP emissions trapped within the tunnel. 

1

10

100

1000

10000

<0.6 1.3–0.6 2.7–1.3 4.9–2.7 10–4.9 >10

Size bin (μm)

Concentration (pg m-3)

HTC

HTP

LTC

LTP

LTIP

Source: Adapted with Permission of Springer-Verlag 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 3-90 
1

10

100

1000

10000

<0.6 1.3–0.6 2.7–1.3 4.9–2.7 10–4.9 >10

Size bin (μm)

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-Verlag 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 

Temporal variability is another important factor in characterizing PM. This section addresses 

trends as well as seasonal and hourly variability. Trends in PM2.5 and PM10 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 particularly important to understanding the behavior of 

PM concentrations in reference to sources, human activity patterns and exposure. Hourly patterns are 

investigated using AQS data on a national basis for PM2.5 and PM10. Data for UFPs and PM 

constituents are presented where available. 

2.1. Regional Trends 

This section summarizes available information on trends in PM mass and composition. Mass 

concentration trends are based on AQS data and incorporate 9 years (1999-2007) of PM2.5 data and 

20 years (1988-2007) of PM10 data. Composition trends are based on six years of available CSN data 

(2002-2007). Several monitoring sites were excluded from the following trend analyses to provide a 

consistent basis for comparison over the desired years of monitoring. This included exclusion of sites 

when there was no corresponding site in later or earlier 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 observed at any individual monitor or geographical 

location within the specified region. 

December 2009 3-91 
PM2.5 

Figure 3-44 shows trends in U.S. ambient 24-h PM2.5 concentrations from 1999-2007. In the 

period 2005-2007, the 3-yr avg of the 98th percentile of 24-h PM2.5 concentrations fell 10% relative 

to the 1999-2001 period (see Figure 3-44A). The number of sites reporting values greater than the 

24-h NAAQS was shown to decline 40% in Figure 3-44B. Figure 3-44C illustrates the downward 

trend in the 98th percentile of 24-h PM2.5 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 PM2.5 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 PM2.5 NAAQS. The seasonally 

weighted 3-yr avg PM2.5 concentrations for the years 2005-2007 were at the lowest since national 

monitoring began in 1999 (Figure 3-45A). 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 PM2.5 NAAQS fell 56% over these same periods in Figure 

3-45B. Figure 3-45C illustrates the annual trends in PM2.5 by U.S. EPA region. Declines were the 

greatest in Region 9 again where annual PM2.5 concentrations fell 20% from the 1999-2001 

averaging period to the 2005-2007 averaging period. 

December 2009 3-92 
Source: U.S. EPA (2008, 157076). 

Figure 3-45. Ambient annual PM2.5 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. 

PM10

Figure 3-46 shows trends in U.S. ambient 24-h PM10 concentrations from 1988-2007. In 2007, 

the U.S. national average second highest PM10 concentration was 37% lower than in 1988 (Figure 

3-46A). Of 281 sites used in this trend analysis, the number reporting concentrations above the 24-h 

PM10 NAAQS (150 μg/m3

) fell from 23 in 1988 to 5 in 2007 with a max of 29 in 1989 (Figure 

3-46B). Figure 3-46C shows trends in the second highest 24-h PM10 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 3-93 
Source: U.S. EPA (2008, 157076). 

Figure 3-46. Ambient 24-h PM10 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 PM2.5 composition from 

FRM mass measurements and CSN bulk composition measurements was used to evaluate trends in 

PM2.5 constituents. Figure 3-47 includes stacked bar charts of PM2.5 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, seasonality, 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 parts 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 warm (May-September)–were considered in the 

figure since many PM2.5 components exhibit strong seasonal dependence. 

December 2009 3-94 
Source: U.S. EPA (2008, 191190). 

Figure 3-47. Regional and seasonal trends in annual PM2.5 compostion from 2002 to 2007 

derived using the SANDWICH method. Data from the 42 monitoring locations 

shown on the map were stratified by region and season including cool months 

(October-April) and warm months (May-September). SO4

2– and NO3–

 estimates 

include NH4

+

 and particle bound water. 

Most of the components showed little discernable trend over the 6-yr period. SO4

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 SO4

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 NO3

-

 in Southern California during both cool and warm 

December 2009 3-95 
months. A smaller decline in NO3

-

 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 degree of uncertainty remains regarding PM2.5 compositional 

trends. However, with the exception of NO3

-

 concentrations in Southern California, no major 

changes in PM2.5 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 PM2.5 mass begins to level 

off after 2002. 

3.5.2.2. Seasonal Variations 

Many of the figures and tables presented in the 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 (September-November). Figures A-142 through A-156 

in Annex A show bar charts of PM2.5 composition by individual month, illustrating intra-annual 

variability on a finer time scale. The same 15 CSAs/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., temperature) are changing most rapidly, driving fluctuation in 

PM2.5 composition on relatively short timescales in many cities. For example, the NO3

–

 mass in Los 

Angeles (Figure A-129) and Riverside (Figure A-134) can vary from a small fraction to the most 

prevalent fraction of PM2.5 mass in a month’s time based on the 3-yr aggregate data. Therefore, 

selecting a different delineation point for the seasons can have an influence on the seasonal 

composition analysis, specifically for constituents that fluctuate rapidly (e.g., NO3

–

). 

Relatively little is known about the seasonal 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 inverse 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 thereafter, suggesting a role for photochemistry in 

addition to meteorological changes in the formation 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 3-96 
Source: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 produce 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 PM2.5 and PM10 measurements are conducted at many sites using beta gauge or TEOM 

monitors. Many of the hourly measurements for PM10 have FRM or FEM status. All available hourly 

data from FRM, FEM and FRM-like monitors in the 15 CSAs/CBSAs discussed earlier were used to 

investigate diel variation in PM. Of the 15 CSAs/CBSAs, Atlanta, Chicago, Pittsburgh, Seattle and 

St. Louis had qualifying hourly PM2.5 and PM10 data available. Houston and New York had only 

qualifying PM2.5 data. Denver, Detroit, Los Angeles, Philadelphia, Phoenix, and Riverside had only 

qualifying hourly PM10 data. Birmingham and Boston had no qualifying hourly PM2.5 or PM10 data. 

Diel plots for PM2.5 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 PM2.5 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 PM2.5

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 PM2.5 concentration peak is broader than the morning peak and extends to 

overnight hours, reflecting the concentration increase caused by a drop in boundary layer height at 

night. Figure 3-49 compares the two-peak diel distribution in PM2.5 for Seattle with the one-peak 

distribution in PM2.5 for Pittsburgh Since these figures represent 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 PM2.5 data (μg/m3) 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 PM10 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 PM10 starting at approximately 6:00 a.m. on 

weekdays, corresponding with the start of the morning 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 PM10 peak concentration, which drops off during the day and 

reappears in the evening. In contrast, Chicago shows a less pronounced peak with the PM10

concentration remaining elevated throughout the day. Figure 3-50 shows the diel plots of PM10 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 PM10 data (μg/m3) 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 originates from a combination of evening rush-hour 

traffic, decreased atmospheric dilution and formation 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 peaking 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 and EC were investigated by Bae et al. (2004, 156243) 

in the urban St. Louis atmosphere. OC diel patterns 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 peaks 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 

Associations between different PM size fractions and between PM and other copollutants 

including SO2, NO2, CO and O3 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. Pearson correlation coefficients (r) were calculated using 2005-2007 

data. The results are displayed graphically in Figure 3-52 for correlations with PM2.5 mass 

concentration and Figure 3-53 for correlations with PM10 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., PM2.5/PM10) 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 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

PM10 (daily avg)

PM10-2.5 (daily avg)

SO2 (daily avg)

NO2 (daily avg)

CO (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

Summer

PM10 (daily avg)

PM10-2.5 (daily avg)

SO2 (daily avg)

NO2 (daily avg)

CO (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













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Figure 3-52. Distribution of correlations between 24-h avg PM2.5 and co-located 24-h avg PM10, 

PM10-2.5, SO2, NO2 and CO and daily max 8-h avg O3 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 3-101 
Spring

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 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

PM2.5 (daily avg)

PM10-2.5 (daily avg)

SO2 (daily avg)

NO2 (daily avg)

CO (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

Summer

PM2.5 (daily avg)

PM10-2.5 (daily avg)

SO2 (daily avg)

NO2 (daily avg)

CO (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













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Figure 3-53. Distribution of correlations between 24-h avg PM10 and co-located 24-h avg PM2.5, 

PM10-2.5, SO2, NO2 and CO and daily max 8-h avg O3 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). 

For both PM2.5 and PM10 national composite copollutant correlations, there is considerable 

spread in the observed correlations in all four seasons. On average, PM2.5 and PM10 correlate with 

each other better than with the gaseous copollutants. The correlations between PM2.5 and PM10 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 PM10-2.5

because only data from low-volume FRM/FRM-like samplers were used to calculate PM10-2.5. The 

available data suggest a stronger correlation between PM10 and PM10-2.5 than between PM2.5 and 

PM10-2.5 on a national basis. 

Correlations among copollutants for individual CSAs/CBSAs are included in Annex A, 

Figure A-175 through Figure A-188 for PM2.5 and Figure A-189 through Figure A-202 for PM10. Each data point in these figures represents a co-located 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, correlations between 24-h average PM2.5 and PM10 concentrations measured at 

the five co-located monitor pairs in Boston between 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 PM10-2.5 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 PM2.5 and PM10-2.5 varied substantially by CSA/CBSA and season 

and no general patterns were observed in the limited data set analyzed here. In contrast, correlations 

between PM10 and PM10-2.5 did show some trends by location and season and were greater in all 

locations than correlations between PM2.5 and PM10-2.5. The highest correlations between PM10 and 

December 2009 3-102 
PM10-2.5 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 PM10 and PM10-2.5

(0.30≤ r ≤0.88), particularly during the fall (0.30≤ r ≤0.56). The lowest correlations between PM10

and PM10-2.5 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 monitor locations in each city. In the four eastern 

CSAs/CBSAs investigated, correlations between PM10 and PM10-2.5 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 PM10 and PM10-2.5 were greater 

than those for PM2.5 and PM10-2.5. Correlations between PM10 and PM10-2.5 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 PM10-2.5 than the criteria used in 

this analysis for the individual CSAs/CBSAs, the results are generally consistent: higher 

correlations between PM10 and PM10-2.5 mass concentrations compared with PM2.5 and PM10-2.5 mass 

concentrations in all regions and higher correlations between PM10 and PM10-2.5 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 SO2. NO2 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 O3 and 24-h avg PM showed the highest degree of 

seasonal variability with positive correlations on average in summer (0.56 for PM2.5 and 0.39 for 

PM10) and negative correlations on average in winter (-0.30 for PM2.5 and -0.18 for PM10). During 

the transition seasons, spring and fall, correlations were mixed but on average were still positive. 

PM2.5 is both primary and secondary in origin, whereas O3 is only secondary. Photochemical 

production of O3 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 PM2.5

components, NO and NO2) in many urban areas are elevated in winter as the result of heating 

emissions, cold starts and low mixing heights. O3 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 O3 and PM is frequently observed in the winter. During summer, both O3 and secondary 

PM2.5 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 correlations between 24-h avg PM (both PM2.5 and PM10) and 24-h avg O3

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 O3 or the 

selection of different monitoring networks and/or time periods. 

For the PM2.5 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 O3 (including Birmingham, Boston, Chicago, Denver, Houston, Los Angeles, 

Philadelphia, Phoenix, Pittsburgh, Riverside and 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 PM2.5 and O3 in the summer (June 

19-August 23, 1998) and winter (February 2-March 13, 1999), respectively. For PM10, the cityspecific correlations with max 8-h avg O3 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 PM10 and daily maximum 8-h avg O3 while Denver, Detroit, Houston, Los Angeles and 

Phoenix showed negative wintertime correlations. The remaining seven CSAs/CBSAs had 

insufficient data. These copollutant correlations illustrate the importance of considering seasonality 

when assessing temporal relationships between air pollutants, particularly PM and O3. 

December 2009 3-103 
3.6. 

3.6.1. 

3.6.1.1. 

Mathematical Modeling of PM 

There are two main classes of models used to study atmospheric PM, receptor models and 

CTMs. Receptor models are statistical models whereas 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 extensively 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 transformations, and deposition. Monitoring 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 

Methods for analyzing the composition of ambient 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 outcomes have been extensively studied. Some health 

studies, described in Section 6.6, have used source apportionment modeling to evaluate relationships 

between health outcomes and PM (mainly PM2.5) 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 publications 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 emissions 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, hopanes, 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 other 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 been known to be significant constituents of the 

atmospheric aerosol, but not many studies have evaluated their contributions, largely because of the 

lack of suitable tracers and the additional equipment 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 PM2.5

and PM10-2.5 as fungal spores were expected to be major 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 PM2.5 and PM10-2.5. They estimated 24-h avg 

contributions of ~ 40% to OC in PM10-2.5 during spring and summer in Vienna, with a smaller 

contribution to PM2.5. 

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 application of CMB-based receptor models with 

detailed organic marker compounds is the photochemical stability of those species. Robinson et al. 

(2006, 156918) reported evidence of significant summertime photooxidation of hopanes and longchain alkenoic acids, low-volatility compounds often 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 marker species mass complicates the interpretation 

of model results, as long-range transport and photochemistry may result in the loss of markers for 

distant sources. Furthermore, photochemical breakdown 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 also may affect the polarity and volatility of 

these compounds. 

In other methods, various forms of factor analysis are used that rely on the varying mix of 

species present in ambient observations of compositional data to derive the source contributions. 

Standard factor analytic approaches such as Principal 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 apportion 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 contributions 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 source with time. Source profiles can also be altered 

between emission and receptor locations resulting 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 unless 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 time dependence of the 

composition of emissions from particular sources, the 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 information 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 simplest 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 uncertainties 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 mathematical 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 have 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 handle well. However, since PLS is a variance based 

approach, it shares the same shortcomings discussed 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 PM2.5 is most often produced mainly 

by fossil fuel combustion. Fugitive dust, found mainly in the PM10-2.5 size range, represents the 

largest source of measured ambient PM10 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 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, 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 

PM10 have appeared in the 2004 PM AQCD (U.S. EPA, 2004, 056905) and using PMF for PM2.5 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 PM2.5 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 PM2.5 and PM10, many of which are in the 

western U.S. 

December 2009 3-107 
Source: Reprinted with Permission of Air & Waste Management Association from Engel-Cox and Weber (2007, 156419). 

Figure 3-55. Source category contributions to PM2.5 at a number of sites in the East derived 

using PMF. 

Spatial Variability in Source Contributions 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 error in epidemiologic studies relating health 

endpoints to sources. Arguments similar to those for using ambient concentrations as surrogates for 

personal exposures apply here. Studies for PM2.5 (Kim et al., 2005, 083181; Wongphatarakul et al., 

1998, 049281) indicate that intra-urban variability increases in the following order: regional sources 

(e.g., secondary SO4

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 PM10-2.5

(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 3-108 
Source: Reprinted with Permission of ACS from Kim et al. (2005, 083181) 

Figure 3-56. Pearson correlation coefficients for source category contributions to PM2.5

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 PM10-2.5 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 are the prime tools used to compute the interactions among atmospheric pollutants and 

their transformation products, the production of secondary aerosols, the evolution of particle size 

distribution, and transport and deposition of pollutants. CTMs are driven by emissions inventories 

for primary species such as NOX, SOX, NH3, VOCs, and primary PM, and by meteorological fields 

produced by other numerical prediction models. Values 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 meteorological analyses, meaning that they are not modified 

December 2009 3-109 
by radiatively active species generated by the air 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 prevented their wide-spread application. 

More recently, new, integrated models of meteorology 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) understand 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 divided into biogenic from vegetation, microbes, and 

animals, and abiotic from biomass burning, lightning, 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. Thus, 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 species 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 lifetime 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 spatial 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 introduce additional uncertainty. Because the chemical 

production and loss terms in the continuity equations 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 modeled, 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 the 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 terms for transport, chemical production and 

destruction, and emissions and deposition (if relevant), in each grid cell. Chemical processes are 

simulated with ordinary differential equations, and 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 which 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 following 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 their 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 computational intensity of Eulerian models depend 

strongly on the size of the horizontal and vertical 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 NOX and SOX – 

Ecological Criteria (U.S. EPA, 2008, 157074). 

3.6.2.1. Global Scale 

Global-scale CTMs are used to address issues associated with climate change and 

stratospheric O3 depletion to characterize long-range air pollution transport, and to provide boundary 

conditions for the regional-scale models. The CTMs 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. The 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 processes 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 major regional-scale air-related modeling efforts at EPA use the Community Multi-scale 

Air Quality modeling system (CMAQ) (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 O3 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, there 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 used for separate meteorological, emissions, and 

photochemical models, there is no reason a priori why the resolution of each grid should match, and 

the gains realized from increased resolution in one model will be wasted in the transition to another 

model. The use of finer horizontal resolution in CTMs 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 variable 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, current CTMs do not adequately resolve fine-scale features such as 

the nocturnal low-level jet in part because little 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 initial 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 O3 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 O3 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 (Stockwell 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 incorporate 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 models 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 O3, NO2, and 

aerosol fields (Binkowski et al., 2007, 090563). 

Spatial and temporal characterizations of anthropogenic 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, NH3, and NOX. 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 temporal variability of emissions is lacking, so longterm annual averages are used in short-term, episodic simulations. Annual emissions estimates are 

often modified by the emissions model to produce emissions more characteristic of the time of day 

and season. Significant errors in emissions can occur 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 km2, is usually too coarse to 

resolve spatial variations on the neighborhood scale. 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 fields 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/env/air/pages/calinesw.htm. The CALINE family of 

models is not supported by the California Department 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/scram001/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 there 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 incorporate 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 designed to treat the dispersion of emissions from 

roads, and like AERMOD does not include production of secondary pollutants. The distinction 

between a steady-state and time varying model could 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 diffusion, and wet and dry deposition. NOX in these systems has 

long been known to (1) act nonlinearly in the production of O3 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. 

NOY species emitted and transformed from emissions control the production and fate of both 

O3 and aerosols by sustaining or suppressing OH cycling. Correctly characterizing the interrelated 

December 2009 3-113 
NOY and OH dynamics for O3 formation and fate in the polluted troposphere depends on new 

techniques using combinations of several NOY 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 relevant 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) revealed that its performance was very similar to 

that of CMAQ’s standard modal module; hence, model 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-UCD domain zoomed over Tampa Bay, FL. 

December 2009 3-115 
0

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), 

NO2 (middle), and total NOX (bottom) concentrations for May 1-31, 2002. Green 

squares = 8 km solution, red diamonds = 2 km solution, blue circles = 

observations. 

December 2009 3-116 
0

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 3-117 
0

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 measured observations of isoprene concentrations 

at Sydney, FL for May 1-31, 2002. Green squares = 8 km solution, blue circles = 

observations. 

December 2009 3-118 
0

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)

PM 2.5 (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 PM2.5 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 NOX concentrations in the model, most likely from on-road emissions, affected 

NOX 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 PM2.5 concentrations and trends over the whole month also replicated the 

observed concentrations well (Figure 3-63). 

Predicted Chemistry for Nitrates and Related Compounds 

Particulate NO3

–

 (pNO3

–

) plays a crucial and complex role in the health of aquatic and 

estuarine ecosystems and human drinking water systems. Gas-phase NO3

–

 replacement of Cl– on sea 

salt particles is often favored thermodynamically and the Vd of the coarse pNO3

–

 formed through this 

replacement is more than an order of magnitude greater than for fine pNO3

–

. Over open bodies of salt 

water such as the Gulf of Mexico and Tampa Bay, FL, pNO3

–

 from this reaction dominates dry 

deposition and is estimated to be of the same order as pNO3

–

 wet deposition. 

However, total NO3

–

 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 pNO3

–

 is derived mostly from gas-phase HNO3 and will 

interact with Na+

, NH4

+

, Cl–, and SO4

2–, all these species and the physical parameters governing their 

creation, transport, transformation, and fate must be accurately replicated to predict pNO3

–

 with high 

fidelity. This has historically been a difficult problem for numerical process models, owing in large 

part to the pervasive dearth of reliable ambient measurements of NO3

–

 in its various forms. 

Normalized mean error (NME) for the large-scale Eulerian CTM-predicted pNO3

–

 has typically been 

on the order of a factor of 3 greater than the NME for particulate SO4

2– (pSO42–) (Odman et al., 2002, 

092474; Pun et al., 2003, 047775). 

SO4

2−

, NH4

+

, 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-water 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 SO4

2– and NH4+

 (0.2 to 1.0 μm), and Na+ and Cl– 

(2.0-10.0 μm). Cl– concentrations were greatly overpredicted during dark hours, but were nearer to 

observed values during the photochemical day. CMAQ-UCD performance for HNO3 and NH3 are 

shown in Figure 3-64 and Figure 3-65, respectively. 

Figure 3-66 shows that CMAQ-UCD systematically underpredicted the hourly time series of 

measured pNO3

–

 concentrations at the Sydney supersite, the only location with discrete pNO3

–

 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 pNO3

–

 concentrations were nearly all underpredicted. 

Since pNO3

–

 is derived in large part from gas-phase HNO3, its underprediction may be due to 

an underprediction of HNO3 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 

HNO3 underprediction in both the 8 km and 2 km solutions, except for four days of very large peak 

overpredictions. This pattern of underpredictions was 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 and inappropriate N limitation in the model. 

A time series molar equivalent ratio of HNO3 to total NO3 depicts which phase stores the NO3

–

and how that storage ratio changes over time. Figure 3-67 shows that at Sydney, FL, CMAQ-UCD 

stored too much NO3

–

 in the gas phase as HNO3 (and recall that the daytime HNO3 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 NO3

–

 form 

between the model and measurements as Figure 3-68 shows in the stacked bar percentage plots of 

fine and coarse pNO3

–

 together with gas-phase HNO3. 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 between 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 pNO3

–

, even though the total NO3

–

 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

6

HNO3 (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

6

HNO3 (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 HNO3

–

 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 3-121 
0

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 NH3 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 3-122 
0

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 pNO3

–

 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 3-123 
0.0

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 HNO3 to total NO3 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 3-124 
MOUDI 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 5/31

Contribution 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, 2002

Contribution 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, 2002

Contribution to Total NO3

< 2.5 um 2.5 - 10 um HNO3

Figure 3-68. CMAQ-UCD predicted size and chemical-form fractions of total NO3

–

 for days in 

May 2002 with measured observations. Measured concentrations (top panel); 

8 km solution (middle panel); 2 km solution (bottom panel). Red bars = pNO3

–

 <2.5 

μm; blue bars = pNO3

–

 2.5-10 μm; green bars = HNO3. 

December 2009 3-125 
While inorganic aerosol anion totals were dominated by NO3

–

 in the coarse fraction and by 

SO4

2– in the fine fraction, there was sufficient NHX (NHX = NH3 + NH4+

) at Sydney, FL, to form fine 

aerosol NH4NO3 in some circumstances. Figure 3-65 depicts the hourly mass concentration of NH3

at Sydney, FL, showing again the strong similarity of the 8 km and 2 km solutions. Each solution, 

however, underpredicted the measured NH3 concentrations consistently, and especially for the nine 

very large excursions of 10-20 μg/m3

 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 NO3 and for NO3

present as gas-phase HNO3. In addition, the model was biased low for the HOX radical reservoir 

species CH2O and H2O2 (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 size 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. Global CTM Performance 

The wet and 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 Vd 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 pNO3

–

 and pSO4

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 3-126 
Source: Adapted with Permission of American Geophysical Union from Dentener et al. (2006, 088434). 

Figure 3-69. Scatter plot of total nitrate (HNO3 plus pNO3

–

) wet deposition (mg N/m2/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 SO4

2– wet deposition (mg S/m2

/yr) of the model mean versus 

measurements for the National Atmospheric 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 deposition 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 configuration 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 western 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 totals 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 following descriptive statistics: correlation, root 

mean square error, normalized mean bias, and normalized mean error. 

Summertime pSO4

2– concentrations are well predicted by CMAQ, to within a factor of 2 at 

nearly every point, and with R2

 >0.8 across all three networks (Figure 3-72). This result tracks the 

generally well-predicted SO4

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 pSO4

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 3-128 
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 pNO3

–

 (Figure 3-73) and total NO3 (HNO3+pNO3

–

) (Figure 3-74) concentrations 

are predicted less well by CMAQ, but NO3 is a pervasively difficult species to measure and model. 

Still, at the CASTNet nodes where the total NO3 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 R2

 >0.8. These CMAQ-predicted concentrations, coupled with modeled cloud 

and precipitation fields produce wet deposition fields for SO4

2– and NO3−

 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 (HNO3 + total pNO3

–

). 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 deposition. Each data point represents an annual 

average paired observation and CMAQ prediction at a particular NADP site. Solid 

lines indicate the factor of 2 around the 1:1 line shown between them. 

December 2009 3-132 
Figure 3-76. 12-km EUS annual nitrate wet deposition. 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 patterns 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 

NH3 and SO4

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. 

200 2003200

NADP Ammonium Ion Wet Deposition

CMAQ Ammonium Ion Wet Deposition

Figure 3-78. Comparison of CMAQ-predicted and NADP-measured NH4

+

 wet deposition : (top 

left) CMAQ prediction; (bottom left) NADP-measurements; (right) regression and 

smoothed median line through CMAQ predictions and NADP measurements with 

sites in the Chesapeake Bay watershed highlighted. 

December 2009 3-134 
Kenansville 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 and lines) and 12-h measured (blue symbols and 

lines) NH3 and SO4

2– surface concentrations at high and low concentration grid 

cells in North Carolina in July 2004. (top left) High concentration NH3 in 

Kenansville; (top right) high concentration SO4

2– in Kenansville; (bottom left) low 

concentration NH3 in Raleigh; (bottom right) low concentration SO4

2– in Raleigh. 

Deposition velocities are difficult to estimate for reasons described in Section 3.3.3. Recent 

work in EPA’s Atmospheric Modeling and Analysis Division with CMAQ showed that the original 

Vd for NH3 was very likely too high and should be nearer to the values for SO2 deposition, or even 

lower over some land use surface types. A sensitivity study with the model was performed to test the 

effects of changing Vd for NH3 on the fraction of NH3 available for transport away from grid cells 

with high emissions concentrations. Comparisons were made for the surface grid cells and total 

column NH3 concentrations. 

In the highest emissions grid cells during June 2002, the surface NHX budget was dominated 

by turbulent transport or vertical mixing moving a majority of the surface NH3 emissions up and 

away from the surface into the mixed layer. Figure 3-80 depicts the NHX budget under the base case 

(Base Vd) and the sensitivity case (SO2 Vd) for which the NH3 Vd was set equal to the SO2 Vd. Lower 

NH3 Vd decreased NHX deposition to the surface from 15 to 8%, leaving more NHX 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 pNH4

+

 and an even smaller fraction wetdeposited to the surface. The total column analyses for NH3 and NHX are shown in Figure 3-81. 

December 2009 3-135 
Free Troposphere 

Gas to 

Particle

Mixed Layer

Surface 

38m 

(−2 km)

NH3 

Emissions

Dry Deposition 

Vertical 

Diffusion 

Horizontal 

Advection 

NH3 Layer 1 Analysis 

Top of Model – 16 km 

8-15%

22-20%

69-64% 

0.04% 

 Right Number: BaseVd 

Left Number: SO2Vd 

Figure 3-80. Surface grid cell (layer 1) analysis of the sensitivity of NHX deposition and 

transport to the change in NH3 Vd in CMAQ. 

Free Troposphere 

Mixed Layer (−2 km) 

Top of Model – 16 km 

NH3 

Emissions

Dry Deposition 

Horizontal 

Advection 

NHX Column Analysis 

Wet Deposition 

8-15% 

0.58-0.57% 

91-84% 

Right Number: BaseVd 

Left Number: SO2Vd 

Gas to Particle 

Conversion 

(unusually 

low) 

NH3 

Emissions 

Dry Deposition 

Horizontal 

Advection 

NH3 Column Analysis 

Wet Deposition 

8-15% 

2.1-2.2% 

0.55-0.54% 

89-82% 

Figure 3-81. Total column analysis for NH3 (left) and NHX (right) showing modeled NH3

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 

NH3 emissions. In these simulations, CMAQ deposited ~25% of the NH3 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 the high concentration, immediately surrounding 

grid cells, was significantly affected by the change in NH3 Vd 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 Vd sensitivity 

tested here from ~180 km in the base case to ~400 km in the case using the lower, more realistic Vd

for NH3. The areal extent of this difference in range of influence is mapped in Figure 3-83. 

June 2002 NHx Range 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

1.0

6

305478

102126150174198222246270294318342366390414438462486

Distance from Center (km)

Fraction of Emissions

Wet+Dry Dep BaseVd Advection BaseVd

Wet+Dry Dep SO2Vd Advection SO2Vd

BaseVd

−180km

SO2Vd 

−400km 

Figure 3-82. Range of influence (where 50% of emitted NH3 deposits) from the high 

concentration Sampson County grid cell in the June 2002 CMAQ simulation of Vd

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

SO2Vd 

Figure 3-83. Areal extent of the change in NHX 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 Vd. 

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 referred to as policy-relevant background (PRB) 

concentrations. PRB concentrations have historically 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 the world and from anthropogenic sources outside 

continental North America. Background concentrations 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 international 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 

Contributions to PRB concentrations of PM include both primary and secondary natural and 

anthropogenic components. Natural sources include wind erosion of natural surfaces (Gillette and 

Hanson, 1989, 030212); volcanic production of SO4

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 large extent on the presence of anthropogenic NOX. 

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 PM10

concentration at 25 reporting stations throughout the northwestern U.S. reached 65 μg/m3

 during an 

episode in the last week in April 1998, compared to an average of 10-25 μg/m3

 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 PM2.5, PM10-2.5, and PM10 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 contributions 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 consists of infrequent, sporadic contributions from 

natural high-concentration events occurring over shorter 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 well 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 the 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, O3, total Hg, and aerosol backscatter associated with air coming from Asia. The 

ITCT-2K2 campaign also found evidence for the oxidation of SO2 to H2SO4 during trans-Pacific 

transport of Asian emissions. If particulate SO4

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 directly 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 satellite assimilated wind fields to estimate the 

transport of PM from Asia. Three-dimensional, global-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 highly problematic. Heald and Spracklen (2009, 

190014) estimated the contribution of fungal spores to PM2.5 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/m3

 in the desert 

Southwest to ~ 0.5 μg/m3 in the more humid Southeast. It should be noted that these are model 

derived estimates that still require evaluation against 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 

Estimates 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 Assessment 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 changes in relations among different size fractions. 

For example, PM10-2.5 might be expected to dominate under certain conditions in the absence of 

primary and secondary PM2.5 from anthropogenic sources. Different 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 influenced 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/m3

. 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 concentrations of PM2.5 and PM10

(μg/m3) from Trijonis et al. (1990, 157058). Estimates of PM10-2.5 were obtained by 

subtraction. 

PM2.5 PM10 PM10-2.5

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 PM2.5 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, PM2.5

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 PM2.5 concentrations (μg/m3

) 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 mixing 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 PM2.5 concentrations at Underhill, VT 

and about 12% at Brigantine, NJ, i.e., values between 1 and 2 μg/m3

. These sites were chosen 

because they were outside of urban areas making it easier to separate pollution from background 

components. However, some contribution of regional 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 out components mainly thought to be emitted by 

regional pollution sources such as SO4

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 PM2.5 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/m3

 in the Central West (ID, MT, WY, ND, SD, CO, UT, NZ, AZ) to 5.2 μg/m3 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 anthropogenic 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 provide 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 contributions from these sources. Numerical models 

are also capable of supplying estimates of PRB concentrations at much higher spatial and temporal 

resolution than can be obtained by relying on measurements obtained at even the most remote 

monitoring sites. In this approach, the monitoring data are used to evaluate the CTM’s performance. 

For this assessment, the global-scale circulation 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 annual 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 set 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 Atmospheric 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 NOX 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, biogenics, and fire updates; MM5, version 3.7.4, was 

used with the Asymmetric Convective Mixing, version 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 SO4, often a major 

determinant of surface-layer PM concentrations, were well-predicted by CMAQ at a 12-km grid 

spacing, to within a factor of 2 at nearly every point of comparison and with R2

 >0.8 across all three 

national networks (CASTNet, IMPROVE and CSN); a more detailed description is included in the 

2008 NOXSOX ISA (U.S. EPA, 2008, 157074). This result for CMAQ, version 4.6.1, for 2002 tracks 

the generally well-predicted SO4

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 SO4

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 SO4

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 NO3

–

 (Figure 3-86) and total NO3 (HNO3 + particulate NO3

–

) (Figure 

3-87) concentrations are predicted as well by CMAQ; however, NO3

–

 is a pervasively difficult 

species to measure and model. Still, at the CASTNet nodes where the total NO3

–

 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 R2

 >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 inside continental North America. 

December 2009 3-144 
Figure 3-86 12-km EUS Winter NO3

-

 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 (HNO3 + total particulate NO3

–

). 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/m3

. However, the base case simulation also 

greatly over-predicts PM2.5 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 Redwoods site, but may not have affected the site. 

However, wildfires indicated by MODIS would have affected other areas either close to these sites 

or could have affected other locations in between the IMPROVE sites. The simulated monthly 

average PRB concentrations in the east/midwest range from a minimum of 0.6 μg/m3

 at Acadia 

National Park (NP) in July to 3.7 μg/m3

 at Voyageurs NP in July. However, most values are 

<1 μg/m3. The monthly average PRB concentrations calculated for the West tend to be lower than for 

the East and range from 0.2 μg/m3

 at Bridger and Glacier NPs in January and February, respectively, 

to 8.7 μg/m3

 at Redwoods NP in November. Excluding values at Redwoods NP which greatly exceed 

measurements, the highest monthly average concentration was 3.7 μg/m3 at Voyageurs NP in the 

East/Midwest and 2.4 μg/m3

 at Gila NP in the West. 

Figure 3-88. Monthly average of PM2.5 concentrations measured at IMPROVE sites in the East 

and Midwest for 2004. Also shown are distributions of PM2.5 concentrations 

calculated by CMAQ for the base case and for PRB. 

December 2009 3-146 
Figure 3-89. Monthly average of PM2.5 concentrations measured at IMPROVE sites in the West 

for 2004. Also shown are distributions of PM2.5 concentrations calculated by 

CMAQ for the base case and for PRB. 

December 2009 3-147 
Figure 3-90. Distribution of PM2.5 concentrations measured at IMPROVE sites in the East and 

Midwest for 2004. Also shown are distributions of PM2.5 concentrations 

calculated by CMAQ for the base case and for PRB. 

December 2009 3-148 
Figure 3-91. Distribution of PM2.5 concentrations measured at IMPROVE sites in the West for 

2004. Also shown are distributions of PM2.5 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 falling within 35%. In the West, CMAQ’s prediction 

of PM2.5 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 PM2.5 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 PM2.5 concentrations at IMPROVE sites. 

Table 3-20. Annual and quarterly mean PM2.5 concentrations (μg/m3) 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 seasonal variation in the base case PM2.5

concentrations across regions of the CONUS, while Table 3-23 shows CMAQ predictions of the 

seasonal variation in regional PRB PM2.5 concentrations. Highest base case PM2.5 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 PM2.5 concentrations were highest on an annual basis in the Southeast, and peaking 

during the winter. In the summer, PRB PM2.5 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 related to hot and dry conditions such as wildfires and 

dust suspension. 

December 2009 3-150 
Table 3-21. Annual and quarterly mean PM2.5 concentrations (μg/m3

) 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 PM2.5 concentrations 

(μg/m3) 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 PM2.5 concentrations (μg/m3

) 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 copollutants, 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 uncertainties exist for each approach. Understanding of 

exposure errors is important because exposure error can potentially 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 effects 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 pooled 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), individual, 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 context 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 patterns, 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 broken down by region of the country, illustrates 

this variability. For example, Table A-58 presents 24-h personal PM2.5 exposures that range from 

roughly 1-55 μg/m3 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 the 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. Finally, implications of exposure assessment issues 

for epidemiologic studies are presented in Section 3.8.6. 

3.8.1. General Exposure Concepts 

A theoretical model of personal exposure is presented 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 compartmentalization of the person’s activities during a given time 

period: 

∫ T = j

dtCE

EEE naaT

Equation 3-3 

where ET = total exposure over a time period of interest, Cj = 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 

(Ea) and nonambient (Ena) 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, biomass combustion, and secondary formation. 

Examples of nonambient sources include smoking, cooking, home heating, cleaning, and indoor air 

chemistry. PM concentrations generated by ambient and nonambient sources are subject to spatial 

and temporal variability that can affect estimates of exposure and resulting health effects. Exposure 

factors affecting interpretation of epidemiology are discussed in detail in Section 3.8.6. 

December 2009 3-153 
Source: 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. Ea 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): 

a ∑ oo += ∑ CFfCfE ,inf, ioii

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 adjacent to a given indoor microenvironment i, and Finf,i = 

infiltration factor for indoor microenvironment i. Equation 3-5 is subject to the constraint Σfo + 

Σfi = 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, enclosed 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. 

Finf represents the equilibrium fraction of the PM concentration outside the microenvironment 

that penetrates inside the microenvironment and 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: Finf = Pa/(a+k). Determination of Ea can be complicated by PM loss through 

chemical and physical processes in microenvironments, 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 studies of the effects of ambient pollutants on 

human health, the association between Ea and concentrations from a central site monitor, Ca, is more 

relevant than the association between ET and Ca because nonambient PM is uncorrelated with Ca, as 

discussed in Section 3.8.4. In ecologic studies of large panels or cohorts, Ca is often used in lieu of 

outdoor microenvironmental data to represent these exposures based on the availability of data. Thus 

it is often assumed that Co = Ca and that the fraction of time spent outdoors can be expressed 

cumulatively as fo; 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 Ca. 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): 

( ) oa += ∑ inf, CFffE aii

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: 

α o += ∑ Fff inf,ii

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 concentration can be represented as the product of the 

ambient concentration and Finf. Wallace et al. (2006, 089190) note that time-activity data and 

corresponding estimates of Finf 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 purpose 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 techniques 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 advancements 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 personal exposure sampling methodology consists 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 scattering aerosol detection instruments, such as the 

personal DataRam (pDR) and SidePak personal aerosol monitor have seen some use in personal 

PM2.5, PM10, and PM1 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 and therefore caused artificial concentration peaks. 

For this reason, Williams et al. (2008, 191201) determined that the humidity 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-μm cut 

point and therefore can characterize respirable particles well. The Case et al. (2008, 155149) twostage cascade impactor separated PM10-2.5 from PM2.5 for personal monitoring and sampled within ± 

20% of a reference method. Hsiao et al. (2009, 191001) developed a mini-cyclone with a 1 μm or 

0.3 μm cut point for sampling accumulation mode and UFPs. The Personal Cascade Impactor 

Sampler (PCIS) has the capability to sample down to a cut point of 250 nm (Singh et al., 2003, 

156088). For PM2.5, 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 PM2.5 species compared with the MOUDI was generally higher: 11% for 

SO4

2–, 22% for NO3–

, 19% for EC, and 94% for OC. Mass was overestimated by 3%, 16%, and 31% 

for PM1-0.5, PM0.5-0.25, and PM0.25, respectively, when compared with the SMPS-APS. Similarly, Case 

et al. (2008, 155149) found a mass difference ranging from -11 to +10% for PM10-2.5 with the 

Personal Respirable Particulate Sampler (PRPS), and Lee et al. (2006, 098249) found a mass 

difference of -6 to 0% for PM2.5 and -6 to -1% for PM10 when comparing results from this device 

with those from the PEM. Leith et al. (2007, 098241) redesigned the Wagner-Leith passive sampler 

for measuring PM10-2.5. In this work, the difference between a PM10-2.5 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 development 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-nephelometer recording 

PM and endotoxin exposure, a complementary metal-oxide-semiconductor (CMOS) fitting in the 

nose to measure allergen PM, and a micro-thermofluidic 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 organophosphate 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 Microenvironmental Exposure Monitoring 

Instrumentation 

The majority of developments since the 2004 PM AQCD (U.S. EPA, 2004, 056905) regarding 

microenvironmental PM characterization have involved real-time instrumentation in the UFP size 

range. Because these methods are also used for ambient 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, SO2, NO2, O3). 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 exposure 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 detection 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 section describes a variety of techniques used to model PM exposure. Many of these 

methods are used in combination to link ambient 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, and errors and uncertainties of these approaches 

are described in Section 3.8.6.2. 

Time-Weighted Microenvironmental Models 

An individual’s exposure is dictated by his or her activity patterns, as modeled by fo and fi 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: 

= + + CfCfCfE iiaoaoohT

Equation 3-9 

where foh = fraction of time spent outdoors at home, foa = fraction of time spent outdoors away from 

home, Co = PM concentration outside the home, and Ci = indoor PM concentration. In Equation 3-9, 

ET can be calculated based on time-activity diary data and time-resolved PM concentration 

measurements. ET 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 concentrations 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 PM2.5 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 microenvironment using multivariate logistic models of the 

indoor school, indoor home, and outdoor microenvironments. They found that (1) the city of 

residence was a significant predictor of being indoors 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 fi and fo 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 considering the amount of outdoor PM infiltrated 

indoors. This version also incorporates Co and Ca: 

December 2009 3-157 
= + + infCFfCfCfE oiaoaooha

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 exposures to be a function of that measured at a 

community monitor. Factors influencing the contribution of Ea to ET may include sample population 

characteristics, location of a site for microenvironmental monitoring, seasonal trends in PM 

concentration, and regional differences affecting 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 1998 and 1999, to compute total personal exposure 

based on time-weighted microenvironmental exposures for each panel subject: 

= ∑ + ∑

k k

T β kkii β CfMECfMEE kkoo

Equation 3-11 

where ME = microenvironment (indoor or outdoor), β = regression coefficient reflecting the 

accuracy of the exposure estimate for a given microenvironment, fk = fraction of time performing an 

activity k, and Ck = 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 PM2.5 exposure in comparison with using 24-h ambient PM2.5 data, but the use of hourly 

ambient data was comparable to personal microenvironmental data in estimating exposure. When 

using ambient concentration data, β reflected infiltration for the indoor microenvironmental 

estimates for a sample population. Using a similar 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 ambient and nonambient exposures separately because 

ambient PM personal exposure was well correlated with PM concentration at central site monitors, 

while nonambient PM exposure was not. 

3.8.3.2. Stochastic Population Exposure Models 

Population-based methods, such as the Air Pollution Exposure (APEX), Stochastic Human 

Exposure and Dose Simulation (SHEDS), and EXPOLIS (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 NOX 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 contributions and breathing rate, respectively, to 

compute the distribution of individual exposures across the modeled population. Using distributions 

of input parameters in the model framework rather 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 dosimetry 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 determined 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 variables 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 pollutants 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 sample 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 population spends in various environments, from the 

National Human Activity Pattern Survey. 

December 2009 3-159 
Stochastic and deterministic methods are often 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 individual risk assessment (Georgopoulos et al., 2005, 

080269). In this formulation, CMAQ was used to predict 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 estimates of exposure are related to dose and metabolic 

distributions to estimate risk of specific health impacts. 

3.8.3.3. Dispersion Models 

Dispersion models have been used both for direct 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 exposure 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 PM10 dispersion and potential for 

exposure in Christchurch, New Zealand. Gulliver and Briggs (2005, 191079) used the Atmospheric 

Dispersion Modeling System (ADMS) to model dispersion 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 specific 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 distribution 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 models have also been developed to describe 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). The regression is then computed for multiple 

locations based on the independent variables at locations 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 NO2 dispersion, but it was 

adapted for PM2.5 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 PM2.5 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 performed over wide areas to develop a posterior 

probability distribution function of exposure at the urban scale. However, Hoek et al. (2008, 195851) 

warn of several limitations of LUR, including distinguishing real associations between pollutants and 

covariates from those of correlated copollutants, limitations in spatial resolution from monitor data, 

applicability of the LUR model under changing 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 PM2.5 concentrations for the 

New York City and Los Angeles metropolitan areas. 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 concentration. 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 PM2.5 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 included distance to nearest road for different PM size 

fractions, urban land use, population density, point source 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 PM2.5, PM10-2.5, and PM10 exposures for the Nurse’s Health Study and provided 

estimates of concentration at approximately 70,000 nodes with PM2.5 and/or PM10 data input from 

more than 900 AQS sites with good validation of the PM2.5 and PM10 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 PM10 concentration. STEMS is a 

multipronged model that links traffic emissions estimates, 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 hourly point estimates of PM concentration. Based 

on the time-activity data, the concentration estimates were then used to calculate exposures to trafficrelated PM10 along a commuting path while an individual is in transit. PM10 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 included 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 covariate in GIS-based regression models. For 

instance, Baxter et al. (2007, 092725) predicted indoor exposure to PM2.5, EC, and NOx 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 PM2.5

were largely influenced by regional ambient PM2.5 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 located 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 PM2.5 because PM2.5 concentrations are more homogeneously distributed. Studies of 

microscale to neighborhood scale dispersion more commonly include data on UF and thoracic coarse 

PM in addition to PM2.5 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 variability within and across regions of the country with 

respect to indoor exposures and ambient concentrations. 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 variation can be attributed to a number of factors, 

including PM size distribution, scope and magnitude 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 exposure estimation are linked to the spatial scale of 

concentration measurements because pollutant transport and dispersion varies over different spatial 

scales as a function of the many factors mentioned 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-Road 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 site monitors. Only studies where samples were 

obtained outdoors and compared with a community-based ambient monitoring site were included 

because indoor microenvironments have penetration losses that affect the comparability of the 

results. Note that some of these studies included enclosed transportation microenvironments (e.g., 

cars, buses, subways), but all studies examined personal exposure in the outdoor microscale 

environment. Also note that studies must be reviewed cautiously because most used different 

instrumentation for personal, microenvironmental, 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 PM10 was significantly higher than fixed community-based ambient 

PM10 measurements in downtown Bologna, Italy. Likewise, the Kaur et al. (2005, 088175), Kaur 

et al. (2005, 086504), and Adams et al. (2001, 019350) studies showed PM2.5 measurements to be 

significantly higher than fixed community-based ambient PM2.5 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 PM2.5 concentrations were not significantly 

different from ambient PM2.5 measurements; this is more consistent with the urban-scale 

homogeneity in concentration of PM2.5. 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 sources 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 raining; particle surface area was 3.7 times larger in 

clear weather; and, black carbon concentration was 1.7 times higher in clear weather. However, 

SMPS data reported for rainy day particle number concentrations 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 elevated 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 consideration, 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 those routinely exposed to outdoor traffic emissions 

(e.g., police, bus drivers) compared with control subjects. Particle-bound PAH exposure can also 

vary with vehicle operation. For example, Kinsey et al. (2007, 190073) estimated from continuous 

idling and restart school bus operating conditions (without 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 particle-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-road personal exposures with fixed site ambient 

concentrations. 

Reference 

and Site 

Ambient 

monitors Personal monitors Microenvironment, other variables Ambient v. Personal Association Primary Findings 

Violante et al. 

(2006, 

156140) 

Bologna, Italy 

Fixed PM10 and 

benzene monitoring 

station (method not 

specified). 

Active pump with PM10

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/m3

Fixed: 43.56 ± 24.10 μg/m3

 (p<0.0001) 

Fixed PM10 correlated with 

multivariate model of traffic and 

meteorology, but not personal 

PM10; relationship between 

benzene and PM10 not 

explored. 

Kaur et al. 

(2005, 

086504) 

London, U.K. 

Fixed TEOM for 

PM2.5 and fixed CO 

monitor at ambient 

and curbside sites. 

High flow personal 

samplers for PM2.5, 

P-Trak monitors for UFP, 

Langan T15 and T15v 

for CO. 

Exposures stratified by 

mode of transport 

(walk, cycle, bus, car, 

taxi). 

Average PM2.5 sampled by TEOM was 3 

times lower than average personal PM2.5

sample, and 8 times lower than maximum 

personal PM2.5 sample. 

PM2.5 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 

PM2.5 and UFP not significantly 

different from those on bus, 

car, or taxi. 

Kaur et al. 

(2005, 

088175) 

London, U.K. 

Fixed TEOM for 

PM2.5 and fixed CO 

monitor at ambient 

and curbside sites. 

High flow personal 

samplers for PM2.5

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 PM2.5: slope = 0.29, 

R = 0.6; personal PM2.5 measurements 

were >2 times background levels and 

more than 15 μg/m3

 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 

PM2.5 and fixed CO 

monitor at ambient 

and curbside sites. 

High flow personal 

samplers for PM2.5. 

Exposures stratified by 

mode of transport 

(cycle, bus, car, 

subway). 

Median values: (μg/m3) 

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 

PM2.5; 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/m3) 

PM2.5

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) 

PM2.5 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 PM2.5, PM10, 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 comparable 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 PM10 (Diapouli et al., 2008, 190119). Curbside measurements of UFPs 

and PM2.5 obtained at a fixed site in the Kaur et al. (2005, 088175; 2005, 086504) studies were 

generally lower than exposures during transit, including during walking and cycling. In contrast, the 

Adams et al. (2001, 019350) study demonstrated that fixed site PM2.5 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 commuting 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, where deposited PM on the seats and dashboard can be 

resuspended and then inhaled or ingested. UFP deposition 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 recirculation mode. They estimated 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 PM2.5 exposure on school buses, Adar et al. (2008, 191200) found that PM2.5 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 particle number concentration measured 

for 0.02-1 μm particles when comparing buses using clean diesel or retrofits compared with nonretrofitted buses. Although not tested here for other 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 exhaust entering the vehicle microenvironment. 

Behrentz et al. (2004, 155682) tested self-pollution with school buses using SF6 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 related to self-pollution. Adar et al. (2008, 191200) 

estimated that 35.5% of PM2.5 mass on school buses was from self-pollution. These findings are 

important for exposure estimation when partitioning local and ambient sources of pollution during 

transport in vehicles. 

3.8.4.2. Ambient PM Exposure Estimates from Central Site Monitoring Data 

The following 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 infiltration 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 between health effects and community-averaged, 

rather than individual, exposure. 

Indoor or other nonambient sources could significantly 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 

SO4

2– could be used as a tracer of the ambient PM2.5 infiltration rate. Sarnat et al. (2002, 037056) 

also noted that it is reasonable to assume that the size distribution of ambient SO4

2– particles is 

sufficiently similar to the size distribution of ambient PM2.5, and therefore that the ambient SO4

2– to 

personal SO4

2– ratio is an acceptable surrogate for the ratio of the ambient PM2.5 exposure to the 

ambient PM2.5 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 SO4

2–. Other concerns in using SO42– as a tracer for PM2.5 arise because SO42– tends to be 

concentrated in the accumulation mode and thus it might not capture any coarse PM found in the 

upper end of the PM2.5 distribution, which can include larger particles 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 PM2.5 particles. Their study took place in 

Denver, where indoor sources of Fe were small. 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 PM2.5 across urban areas. Volatilization of nitrate or organic compounds after 

infiltration of PM2.5 indoors could lead to bias in exposure estimates (Sarnat et al., 2006, 089166). 

This could be a large problem in areas in which PM contains a large semi-volatile component. 

Figure 3-94 shows total exposure to SO4

2– as a function of measured ambient SO42–

concentration. Figure 3-95 shows estimated ambient exposure to PM2.5 as a function of measured 

ambient PM2.5 concentration, where ambient personal exposure is calculated from the ambient 

exposure factor for SO4

2–. Close agreement between these figures can be observed. Figure 3-96

shows total exposure to PM2.5 as a function of measured ambient PM2.5 concentration. However, the 

total exposure to PM2.5 shows virtually no association with ambient PM2.5 because it contains 

nonambient contributions to PM2.5. 

Source: Reprinted with Permission of Nature Publishing Group from Wilson and Brauer (2006, 088933) 

Figure 3-94. Total exposure to SO4

2– as a function of measured ambient SO42– 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 PM2.5 as a function of measured ambient PM2.5

concentration, from the Vancouver study (Vancouver, British Columbia, AprilSeptember 1998, with 16 non-smoking subjects aged 54-86 yr). 

December 2009 3-166 
Source: Reprinted with Permission of Nature Publishing Group from Wilson and Brauer (2006, 088933) 

Figure 3-96. Total exposure to PM2.5 as a function of measured ambient PM2.5 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 PM2.5 is well correlated with measured ambient PM2.5

concentration with zero intercept, implying that nonambient sources were minor. This technique 

works well in areas where SO4

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 PM2.5, and it was 0.001 for personal 

nonambient PM2.5 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 SO4

2- exposure was strongly associated 

with ambient SO4

2- concentration (r = 0.96, 120 > N > 100). Koutrakis et al. (2005, 095800) reported 

the median Spearman correlation coefficients between personal SO4

2- exposure and ambient SO42-

concentration were above 0.60 during both winter and 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 SO4

2- factor and the ambient SO42- factor was 0.93 (ranging from 0.56 to 

0.98 for different subjects), while the median Pearson 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 SO4

2– component of PM2.5 as an 

indicator for personal exposure to ambient PM2.5 may apply in areas where SO4

2– is a minor 

component of PM2.5 or in the absence of significant nonambient sources of SO4

2– (Sarnat et al., 2001, 

019401). 

Source apportionment techniques could also be used, in principle, to derive ambient personal 

PM2.5 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 (NH4NO3

–

 and

(NH4)2SO4, secondary SO4

2-, OC, motor vehicle exhaust) would constitute an estimate of the 

personal ambient PM2.5 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 Towson, MD. Hopke et al. (2003, 095544) found three sources: SO4

2–, 

unknown (perhaps combustion related, according to the authors), and soil, jointly contributing 46%, 

13%, and 4% of PM2.5 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 SO4

2- factor. This study also 

December 2009 3-167 
determined that a few minor indoor and personal activity sources contributed <10% of the ambient 

SO4

2- source to personal exposures. 

Wilson and Brauer (2006, 088933) presented an adaptation of the SO4

2– method for estimating 

exposure to PM10-2.5. α is computed based on the SO4

2– method as the ratio of exposure to ambient 

SO4

2– (as measured by a personal monitor) to ambient SO42– concentration. Then, knowing an 

individual subjects’ time diary and the penetration and loss properties of SO4

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 PM10-2.5 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 PM10-2.5 exposure factor and the ambient PM10-2.5 exposure 

if PM10-2.5 concentration is measured; in the Wilson and Brauer (2006, 088933) paper, PM10-2.5 was 

estimated from ambient PM10 and PM2.5 concentrations. Given that PM10-2.5 deposits more readily 

and therefore disperses over a shorter distance than PM2.5, it is possible that use of ambient PM10-2.5

concentration may incur more error than in using this method for PM2.5. Ebelt et al. (2005, 056907) 

observed in a Vancouver, Canada panel study that the correlation between ambient PM10-2.5 exposure 

and ambient PM10 exposure (r = 0.72) was lower than the correlation between ambient PM2.5

exposure and ambient PM10 exposure (r = 0.92). This is attributed to both a smaller Finf for PM10-2.5 

and PM2.5 comprising a greater fraction of the PM10 for the Vancouver study. In this study, PM10-2.5

mass concentration was calculated from the difference between ambient PM10 and PM2.5 mass 

concentration. 

Wilson and Brauer (2006, 088933) state that their methodology for computing the ambient 

exposure factor based on the PM2.5 SO4

2– method can be applied to PM in the 0.1-0.5 μm size range. 

Little SO4

2– mass is found below 0.1 μm, so the SO42– 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 microscale 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 saturation study of Los Angeles. Moore et al. (2009, 

191004) and Harrison and Jones (2005, 191005) also found that UFPs and PM2.5 measurements were 

poorly correlated at the monitoring sites. 

3.8.4.3. Infiltration 

Finf varies 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, Finf 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 Finf a more accurate descriptor of infiltration than 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 Finf, including window opening, 

age of an indoor microenvironment, number of occupants, 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 the PM through chemical reactions on the particle 

surface, agglomeration, growth, and evaporation given that Finf depends on particle size (Keller and 

Siegmann, 2001, 025881). Finf 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. Finf and I/O are 

listed where available, although it is recognized that I/O is not as meaningful a descriptor but 

provides an approximation of Finf. A number of studies have examined the impact of season on PM infiltration. Season is 

important because it affects the ventilation practices 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 Finf for PM2.5 at 44 residences in Seattle. The mean Finf was calculated 

using light scattering measurements in a recursive mass balance method with no species data. For 

all sampling days, Finf (± 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 Finf 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 PM2.5 was generated outdoors. This study 

provides important data on the distribution of residential Finf 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 Finf of 0.61 ± 0.27 for 13 homes during summer and 0.27 ± 0.18 for 19 homes during 

winter in Canada for PM2.5 from forest fires and wood smoke. 

Likewise, location could impact residential ventilation practices and infiltration. Using the 

SO4

2- method for estimating PM2.5 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 SO4

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 reflects regional use of air conditioning. Sarnat 

et al. (2006, 089166) noted differences in PM2.5 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 μm 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 between 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 μm 

particles. Particles smaller than 80 nm also were reported to have lower infiltration factors. This 

demonstrates the size dependence of PM infiltration, 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 μm. The maximum infiltration was 

observed around the accumulation mode (0.1-0.5 μm), with I/O = 0.7-0.8. Reduced infiltration was 

observed for coarse-mode PM (0.1-0.2 for Dp = 5-10 μm) and, to a lesser extent, UFPs (0.5-0.7 for 

Dp = 0.02-0.1 μm). This is consistent with increased removal mechanisms for those size fractions. 

Deposition is caused by settling for coarse-mode particles. Deposition of UFP can occur by diffusion 

leading to agglomeration into larger particles and subsequent settling, as well as losses to walls. 

December 2009 3-169 
Source: Reprinted with Permission of Air & Waste Management Association from Sarnat et al. (2006, 089166). 

Figure 3-97. Finf as a function of particle size. 

3.8.5. Multicomponent and Multipollutant PM Exposures 

3.8.5.1. Exposure 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 SO4

2–, NO3–

, or carbonaceous aerosols (EC, OC, particle-bound PAHs), 

while others measured concentrations of trace elements 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 determine 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 SO4

2– provides the largest ambient 

contribution to personal and indoor exposures. These studies took place in Baltimore, MD and 

Raleigh/Chapel Hill, NC. In a source apportionment 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 PM2.5 among residents in Denver 

and also observed lower contributions from secondary SO4

2– in comparison with motor vehicle 

emissions and secondary NO3

–

. This suggests that personal exposure to SO4

2– in parts of the West is 

lower than in the Mid-Atlantic. These observations 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 pregnant subjects in an early childhood epidemiologic 

study. Viana et al. (2008, 156135) analyzed PM2.5 and PM10 samples for several species along 

urban-to-rural gradients centered in Valencia, Spain and found gradients for both size fractions in 

anthropogenically-generated SO4

2–, OC, EC, NO3-

, Fe, and NH4

+

, but not in mineral species. 

December 2009 3-170 
Combined, these findings suggest urban- and regional-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 sources 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 

SO4

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 SO4

2– but not NO3–

 (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 results 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 ambient 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, source composition behavior cannot be generalized for 

characterizing exposures and resulting health effects across multiple locations or times. 

December 2009 3-171 
Source: Reprinted with Permission of Elsevier Ltd. from Reff et al. (2007, 156045) 

Figure 3-98. Apportionment of aliphatic carbon, carbonyl, and SO4

2– components of outdoor, 

indoor, and personal PM2.5 samples, for Los Angeles (top), Elizabeth (center), and 

Houston (bottom). 

December 2009 3-172 
Source: 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 secondary combustion (bottom). 

December 2009 3-173 
Differential Infiltration Related to PM Composition 

A number of chemical factors influence the tendency for differential infiltration in PM. 

Lunden et al. (2003, 156718) studied infiltration of BC and OC aerosols and found that Finf can vary 

substantially as a function of gas transport properties 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 aerosol infiltration differed substantially from NO3

–

 and 

SO4

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 generated 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 SO4

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 PM2.5, total carbon, OC, and BC in an unoccupied 

house and found the lowest ratio for PM2.5 (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 PM2.5 I/O ratio to indoor loss of NH4NO3 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 occupied 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 

PM2.5 components from the Baxter et al. (2007, 092726) study found Finf of 0.95 ± 0.07 for S and 

0.60 ± 0.04 for V, the two components identified 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 could 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. NO3

–

, a prevalent PM component year-round in the western U.S. and during 

winter throughout the mid-western and northeastern states, has a decreased Finf due to volatilization 

of NO3

–

 indoors. Sarnat et al. (2006, 089166) calculated Finf values for NO3

–

, PM2.5, and BC, and 

found the values to increase in that order. NO3

–

 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 PM2.5 (median = 0.48, 

IQR = 0.39-0.57) reflected its composition as a mixture of those two components (among others). 

Indoor volatilization of NO3

–

 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 mechanical generation accounts for 2%. Meng et al. 

(2007, 194618) noted that secondary formation processes 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 and OC size distributions and infiltration 

factors reflect low vapor pressure secondary organic 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, and coarse PM are presented in Table A-65. Sarnat 

et al. (2001, 019401) found significant associations between personal exposure to PM2.5 and ambient 

concentrations of O3, NO2, CO (significant only for winter), and SO2 in a panel study conducted in 

Baltimore. Personal exposures to PM2.5 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 PM2.5, PM10, SO4

2–, NO2, and O3. They found that personal exposure to ambient 

PM2.5 was significantly associated with ambient concentrations of PM2.5, NO2, and O3 (O3 in an 

inverse relationship). They also reported that personal exposure to SO4

2– was significantly positively 

associated with ambient PM2.5 and O3 concentrations. 

There is evidence that associations between ambient gases and personal exposure to PM2.5 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 

PM2.5 and ambient concentrations of O3, NO2, and SO2 measured at community-based monitors for 

groups of healthy senior citizens and school children in Boston during the summer. In this study, 

significant associations between personal exposure to ambient PM2.5 and personal O3 exposures were 

observed in summer and between personal PM2.5 and personal NO2 in winter and summer, unlike the 

Baltimore study in which only summertime personal PM2.5 and personal NO2 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 O3

with personal PM2.5, SO4

2–, and EC in the summer. Low but significant associations between ambient 

SO2 and personal PM2.5, and between ambient NO2 and personal EC, were also observed. In the fall, 

December 2009 3-175 
ambient O3 had a weak but significant association with personal EC, and SO2 had a weak but 

significant association with personal SO4

2–. Ambient NO2 was also significantly associated with 

personal PM2.5, SO4

2–, and EC with somewhat higher coefficient of determination (R2

 = 0.25-0.49) in 

the fall. 

3.8.6. Implications of Exposure Assessment Issues for Interpretation of 

Epidemiologic Studies 

Environmental epidemiologic study designs vary by many factors, including study sample 

size, measurement time interval, study duration, monitor type, and spatial distribution of the study 

sample. A panel epidemiology study consists of a relatively small sample (typically tens) of study 

participants followed over a period of days to months. 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 time 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 estimates in PM epidemiologic studies include source 

characteristics, particle size distribution, and particle composition. Potential issues that could 

influence estimates of PM exposure include measurement, modeling, spatial variability, temporal 

variability, use of surrogates for PM exposure, and compositional differences. These are described in 

detail in the following sections. 

3.8.6.1. Measurement Error 

Measurement 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 sampling volatile PM may therefore lead to lack of 

association with health endpoints in time-series and longitudinal studies. In multicity longitudinal 

studies, where PM composition and associated artifacts may vary across cities, the cumulative 

influence of such artifacts on exposure estimates is more difficult to predict. 

Measurement Error for Personal 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 interpreting 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 using 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 models are used in lieu of or to supplement measurements of ambient PM exposure or 

community-based ambient PM concentration, it is important 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 PM2.5

concentration of outdoor origin and to modeled infiltration factor. Hering (2007, 155839) compared 

infiltration model results for PM-based EC, NO3

–

, and SO4

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 SO4

2–, 

and negligible improvement in model results for indoor NO3

–

. This illustrates the impact of 

differential infiltration discussed in Sections 3.8.4 and 3.8.5. 

December 2009 3-177 
Source: 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 exposure assessment is performed. 

For any spatial interpolation models, grid resolution 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 the 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 SO4

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 pollutants are more regionally dispersed, lower spatial 

resolution would be required to attain a converged solution of the spatial concentration field. 

However, higher spatial resolution in the simulation 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 PM2.5 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 PM2.5 at monitor locations as a function of the mean concentration and spatial 

and random errors. The 6 levels of spatial resolution 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 prediction 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 spatial 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 PM10

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 concentrations 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 application 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 PM2.5 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 PM2.5 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 Angeles, 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 the 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, spatial 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 PM2.5 exposure because spatial variability in PM2.5 concentration 

can be low over urban to regional scales in comparison with more spatially variable PM10-2.5 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 exposure 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 community-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 Angeles also suggests that exposure error would occur 

from using one or a few UFP monitors. In an example using AQS data, PM10 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 PM10 data for several monitor pairs are below 0.4 (Table A23 in Annex A). In another example from AQS data, PM2.5 and PM10 monitor locations in the 

Riverside CBSA are shown to correspond more closely with higher population density areas (Figures 

A-25 and A-26 in Annex A), where urban scale intersampler correlations for both PM2.5 and PM10 are 

below 0.4 for several monitor pairs. For most cities, intersampler correlation is much higher for 

PM2.5 than for PM10. 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 PM2.5 and O3 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 NO2 among the entire population 

studied. Considering results reported in the literature along with inter-sampler correlations (reported 

in Annex A, Section A.2 for PM2.5 and PM10, 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 characteristics 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 exposure 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 PM10 and benzene compares with that measured at a 

community-based monitor. This study found that personal exposures to PM10 were significantly 

higher than at the community-based monitor, although the authors were not able to demonstrate 

significant effects of meteorology or traffic on those exposures. Nerriere et al. (2007, 156801) 

observed spatial heterogeneity of personal exposures to metals in PM2.5 and PM10, with higher levels 

found near high-traffic and industrial areas. In a Bayesian hierarchical model analysis of personal 

exposure and ambient PM2.5 data from the pilot Baltimore Epidemiology-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 epidemiology, 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-average 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 represent 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. Likewise, 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 with several sub-communities 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 SES 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 zip 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 epidemiologic studies. For all metropolitan areas 

investigated in this assessment, the PM10 data have significantly more scatter than PM2.5. 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, especially when different monitors are used on different 

days to construct city-wide averages. 

Epidemiologic studies of long-term exposure rely on differences among communities in longterm average ambient concentrations. If exposure errors 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 the exposure indicator for each spatial area. This could 

bias the slope up or down. The following epidemiologic 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-term 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 County and found effects that were both significant 

and largest in magnitude compared to those reported for other long-term cohort studies. Krewski et 

al. (2009, 191193) suggested that significant associations between cardiovascular health effects 

estimates and PM2.5 observed in Los Angeles but not in New York City were related to spatial 

homogeneity of PM2.5 concentration in New York City. The Nurses’ Health Study examined 

associations of mortality with PM10 and found higher and more significant associations when using 

estimated concentrations at subjects’ individual 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, because the likelihood of exposure error increases in 

this situation. Reducing the exposure error by using 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 community time-series epidemiologic studies can 

include those averaged over several monitors, a single 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 across members of the population, they will exhibit 

smooth temporal variation. For this study, mortality 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 detailed 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 Ca. 

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 briefly 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 PM2.5 and PM10, 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 authors 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 regional 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 PM2.5 and cardiovascular 

morbidity (Bell et al., 2009, 191007). Third, infectious diseases are more prevalent during winter and 

so may influence health outcomes. However, it would 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 examine 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 down to the day-level. In studies of the effects of 

short-term PM2.5 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 PM2.5 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 PM2.5 concentration data were also 

available. 

December 2009 3-182 
3.8.6.5. Use of Surrogates for PM Exposure 

Surrogates for Infiltration Tracers 

In panel studies, a tracer can be used for PM that infiltrates indoors, such as sulfate as a 

surrogate for PM2.5, 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 SO4

2– particles are typically 

smaller than other PM contributing to measurable PM2.5 mass, Finf may be biased by using this term 

to describe PM2.5 infiltration. Other concerns in using SO4

2– as a tracer for PM2.5 arise because SO42–

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 PM2.5 particle size distribution. 

Volatilization of ammonium nitrate or organic compounds after infiltration of PM2.5 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 may 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 distribution 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 personal exposure and use four exposure surrogates in 

panel epidemiologic studies: Ca, ET, Ea, and Ena (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 Ca instead of Ea and 2) 

assuming Ea and Ena have the same effects on health outcomes. There was essentially no association 

of the effect with ET or Ena. Wilson and Brauer (2006, 088933) noted that exposure to nonambient 

PM will not affect the relationship between Ca and Ea, but “the difference between ambient 

concentration and ambient exposure will bias the relative risk derived from epidemiologic studies.” 

Strand et al. (2006, 089203) also noted that inclusion of nonambient PM2.5 would not be expected to 

change health effect estimates because ambient and nonambient PM2.5 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 important, not the correlation of each individual’s 

daily exposure with the daily community-average 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 would introduce large classical measurement errors 

related to high variability in Ena. 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 Ca for Ea

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 differences in climate or housing stock, will cause city-tocity differences in the health effect endpoint estimate obtained from the study using Ca 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 multipollutant 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, O3 may be an indicator of photochemical oxidation products 

including organic PM. SO2 may be an indicator of Ni emissions from smelters, V from oil fired 

power plants, or As, Se or Hg from coal-fired power plants. In another example, the HEI Report on 

traffic-related health effects (2009, 191009) lists CO, NO2, PM2.5 and PM10 mass, UFP count, EC, 

benzene, and traffic metrics (e.g., count, fuel consumption) all as potential surrogates for traffic or 

for the mix of all PM and gaseous pollutants in traffic because all of these pollutants are found in 

mobile source emissions. Furthermore, in a multipollutant 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 copollutants 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 

Differences between the composition of ambient PM and the ambient PM that has infiltrated 

indoors may affect exposure estimates. Numerous differential infiltration studies related to indooroutdoor changes in size distribution 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 Finf, perhaps because metals exist more in the coarse range, while S has 

Finf close to unity. Epidemiologic studies cited in Section 6.6 indicate that significant associations 

between health effects estimates and PM trace metal 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 PM2.5, PM10-2.5, and PM10. 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 PM10 and 

especially with PM10-2.5 became larger and more significant. Similar peaks in PM10 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 section 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 differences. Potential influences of each of these 

sources on health effects estimates derived from panel, 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 diesel-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 PM2.5 concentration data, in part because PM2.5 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 variability 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 

Concentrations and Sources of Atmospheric PM 

This section 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 and 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 atmosphere 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 SO2 and NOX and reactions of acidic products with NH3

and organic compounds. Developments in the chemistry of formation of SOA indicate that oligomers 

are likely a major component of OC in aerosol samples. 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 nucleation-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 SO4

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 secondary organic aerosol formation and their oxidation 

results in more oxidized forms of SOA than previously considered, both in near source urban 

environments and further downwind. 

Measurement Techniques 

The federal reference methods for PM2.5 and PM10 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 have been developed to measure real-time PM2.5 or 

PM10 mass concentrations (e.g., FDMS-TEOM). New FRMs and FEMs are available for PM10-2.5 and 

various methods (dichotomous samplers, cascade impactors, and passive sampling techniques) are 

under evaluation to improve PM10-2.5 measurements. Techniques are available to characterize UFP 

mass, surface area, and number concentrations. Continuous and semi-continuous measurement 

techniques are also available for PM species, such as PILS for multiple ion analysis and AMS for 

multiple 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 PM2.5 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 other 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 influenced by region-specific sources and meteorological 

and topographic conditions. 

Spatial Variability across the U.S. 

County-scale, 24-h avg concentration data for PM2.5 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 

PM2.5 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 distribution and importance in recent health effect 

studies, the highest mean 24-h PM2.5 concentrations were reported for Riverside (17 μg/m3

), 

Birmingham (16 μg/m3

) and Pittsburgh (16 μg/m3); the lowest were reported for Denver (9 μg/m3) 

and Seattle (9 μg/m3

). 

Since PM10-2.5 is not routinely measured and reported to AQS, co-located low-volume PM10

and PM2.5 measurements from the AQS network were used to investigate the spatial distribution in 

PM10-2.5. 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 PM10-2.5 in urban areas. 

Only 6 of the 15 CSAs/CBSAs chosen for closer investigation had sufficient data for calculating 

PM10-2.5. In general, in the eastern metropolitan areas including Atlanta, Boston, Chicago and New 

York, most of the mass of PM10 was in the PM2.5 size fraction, with the highest ratio of PM2.5 to 

PM10-2.5 in Chicago (14 μg/m3

 PM2.5, 5 μg/m3 PM10-2.5, ratio = 2.8). In contrast, Denver (9 μg/m3

PM2.5, 20 μg/m3

 PM10-2.5, ratio = 0.45) and Phoenix (10 μg/m3 PM2.5, 22 μg/m3 PM10-2.5, ratio = 0.45) 

contained most of PM10 in the thoracic coarse mode. 

Given the limited information available from AQS for PM10-2.5 and the current National 

Ambient Air Quality Standard for PM10, analyses were performed on the more prevalent PM10 data 

acknowledging that PM10 incorporates both thoracic coarse and fine particles. The highest reported 

3-yr avg PM10 concentrations (>51 μg/m3

) occurred in two counties in southern California and five 

counties in southern Arizona and central New Mexico. The lowest reported annual average PM10

concentrations (≤ 20 μg/m3) were within 114 counties distributed fairly uniformly across the U.S. Of 

the 15 CSAs/CBSAs investigated, the highest mean 24-h PM10 concentrations was reported for 

Phoenix (52 μg/m3

), considerably higher than the means for the other CSAs/CBSAs investigated. 

The lowest was reported for Boston (17 μg/m3) with New York, Philadelphia and Seattle only 

slightly higher (19 μg/m3

). 

Spatial variability in PM2.5 components obtained from the CSN varied considerably by 

species. The highest annual average OC concentrations (>5 μg/m3) 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 between spring and fall. Of the 15 CSAs/CBSAs investigated, OC was 

the dominant PM2.5 component on an annual basis in the western cities, ranging from 34% of PM2.5

mass in Los Angeles to 58% in Seattle. EC exhibited 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/m3

 were present in Los Angeles, Pittsburgh, New York and El Paso. Concentrations of SO4

2–

were higher in the eastern U.S. resulting from higher SO2 emissions in the East compared with the 

West. There is also considerable seasonal variability with higher SO4

2– concentrations in the summer 

months when the oxidation of SO2 proceeds at a faster rate than during the winter. Of the 15 

December 2009 3-186 
CSAs/CBSAs selected, sulfate was the dominant PM2.5 component on an annual basis in the eastern 

cities, ranging from 42% of PM2.5 mass in Chicago to 56% in Pittsburgh. NO3

–

 concentrations were 

highest in California, with annual averages >4 μg/m3

 at many monitoring locations. There were also 

elevated concentrations of NO3

–

 in the Midwest (>2 μg/m3), with wintertime concentrations 

exceeding 4 μg/m3

. In general, NO3

–

 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 NO3

–

; 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 NO3

–

 readings appeared year-round. Crustal 

material constituted a substantial fraction of PM2.5 year-round in Phoenix (28%) and Denver (16%), 

and during the summer in Houston (26%). 

Clearly there are variations in both PM2.5 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 poorly characterized on a broad scale, making it 

difficult to draw general conclusions regarding PM2.5 mass and composition across all cities within a 

given geographic region. 

Spatial Variability on the Urban and Neighborhood Scales 

In general, PM2.5 has a longer atmospheric lifetime than PM10-2.5 because larger particles have 

a higher gravitational settling velocity. For PM2.5, 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 Riverside 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 PM10-2.5 using co-located, low volume FRM monitors. More abundant 

PM10 data, however, showed larger declines in inter-monitor correlations as a function of distance 

relative to PM2.5. Atlanta, Boston, Denver, Los Angeles, New York City, Philadelphia, Phoenix, 

Pittsburgh and Riverside all showed an average correlation 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 correlation of 0.75 at 6 km or less monitor separation). 

Furthermore, correlations between PM10 concentrations exhibited substantially more scatter relative 

to PM2.5. Shorter atmospheric lifetimes for PM10 can result in local emission sources dominating 

PM10 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 PM10-2.5

fraction relative to the PM2.5 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 density are also important determinants of the 

spatial distribution of PM concentrations. Inter-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 PM2.5 than for PM10. The average correlation was 0.93 for PM2.5, 

but it dropped to 0.70 for PM10. 

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 the 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 PM2.5 concentrations from 1999 (the beginning of nationwide monitoring 

for PM2.5) to 2007 was observed in all 10 EPA Regions, with the 3-yr avg of the 98th percentile of 

24-h PM2.5 concentrations dropping 10% over this time period. Similar trends in PM10 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 metropolitan areas, diel variation showed peaks that 

differ by PM size fraction and region. For PM2.5, 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 PM2.5 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. PM2.5 concentrations in Pittsburgh 

remained elevated throughout the night, obscuring the morning peak. For PM10, 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 

Correlations between PM size fractions and between PM and gaseous copollutants including 

SO2, NO2, CO and O3 varied both seasonally and spatially between and within metropolitan areas. 

On average, PM10 and PM2.5 were correlated with each other better than with the gaseous 

copollutants. Correlations between PM10 and PM10-2.5 were greater in all locations than correlations 

between PM2.5 and PM10-2.5. Correlations between PM10 and PM10-2.5 were particularly high in 

Denver and Phoenix (r > 0.88 in all seasons). There was relatively little seasonal variability in the 

mean correlation between PM in both size fractions and SO2 and NO2. CO, however, showed higher 

correlations with PM10 and PM2.5 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 NO2 and CO, 

then higher correlations with these gaseous copollutants are to be expected. Increased atmospheric 

stability in colder months would also reinforce these associations. The correlation between daily 

maximum 8-h avg O3 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 PM2.5 and O3. 

Source Contributions to PM 

Results of receptor modeling calculations indicate that PM2.5 is produced mainly by 

combustion of fossil fuel, either by stationary sources or by transportation. It is apparent that a 

relatively small number of source categories, compared 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 NOX emitted mainly from transportation and 

EGUs), and primary mobile source categories constitute most of PM2.5 (and PM10) in the East. 

Fugitive dust, found mainly in the PM10-2.5 size range, represents the largest source of ambient PM10

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%. A comparison of source 

apportionment techniques indicated that the same major source categories of PM2.5 were consistently 

identified by several independent groups working with the same data sets. Soil-, sulfate-, residual 

oil-, and salt-associated mass were most clearly identified 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 PM2.5 indicate that intra-urban variability increases in the 

following order: regional (e.g., secondary SO4

2– from EGUs) < area (e.g., on-road mobile sources) 

< point (e.g., stacks) sources. Only one study was available for PM10-2.5, indicating a similar 

ordering, but without a regional component (resulting from the short lifetime of PM10-2.5 compared to 

transport times on the regional scale). 

3.9.1.7. 

3.9.2. 

3.9.2.1. 

Policy-Relevant Background 

The background concentrations of PM that are useful for risk and policy assessments 

informing decisions about the NAAQS are referred to as policy-relevant background (PRB) 

concentrations. PRB concentrations have historically 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 the world and from anthropogenic sources outside 

continental North America. Background concentrations 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 international agreements may lead to alternative 

determinations of which PM source contributions should be considered by EPA as part of PRB. 

Contributions to PRB concentrations of PM include 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 PM2.5 were estimated to 

be less than 1 μg/m3 on an annual basis, with maximum daily average values in a range from 3.1 to 

20 μg/m3

 and having a peak of 63 μg/m3 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 section summarizes the findings from the recent exposure assessment literature, which 

include the assessment of exposure to ambient PM, infiltration of ambient PM to indoor 

environments, and source apportionment of PM exposure. 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 microenvironmental models to define total or ambient PM 

exposure. Time-activity diaries or global positioning systems have been employed to capture the 

time-basis for those models. Stochastic population 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 individual exposures at the intra-urban scale to 

examine exposure to pollution surrogates, such as traffic counts, land use, or topographic variables. 

Source proximity and kriging have also been applied. 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 meteorological 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 adaptation 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 confounding 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 include new models of cascade impactors and cyclones 

to sample in the UF size range and miniature monitors for species detection. Additionally, new work 

on microenvironmental modeling using mobile platforms 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 microenvironmental PM monitors currently available. 

3.9.2.2. Spatial Scales of PM Exposure Assessment 

Assessing population-level exposure at the urban scale 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 division of the community by terrain features or 

source locations into several sub-communities that differ in the temporal pattern of pollution. 

Concentrations of SO4

2– and some components of SOA measured at central-site monitors are 

expected to be uniform in urban areas given the regional nature of their sources. However, this is not 

true for primary components like EC whose sources are strongly spatially variable in urban areas. 

Given that roughly 90% of an individual’s day is spent indoors, assessment of exposure to infiltrated 

ambient SO4

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 PM2.5 exposure. 

This technique has also been applied to assess PM10-2.5 exposure but likely with more error than for 

PM2.5 because PM10-2.5 is more highly spatially variable than PM2.5. Source apportionment techniques 

have also been applied to assess urban-scale PM2.5 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 PM10-2.5 and for UFPs, both of which are more 

highly spatially variable than PM2.5. Particle chemistry and source behavior also contribute to spatial 

heterogeneity of PM concentration. Some studies, conducted mainly in Europe, have found personal 

PM2.5 and PM10 exposures for pedestrians in street canyons to be higher than ambient concentrations 

measured by urban background ambient monitors. 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 located at background, central urban, road side, or 

near-residential sites might not reflect peak exposures to individuals who commute. 

PM infiltration factors, Finf, depend on particle size, chemical composition, season, and region 

of the country. Infiltration can best be modeled dynamically based on a distribution of air exchange 

and deposition PM loss rates rather than being represented 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 part 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 temperature and humidity conditions affect the transport, 

dispersion, and size distribution of PM. Residential 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 SO4

2– to personal exposure is higher for the East 

(16-46%) compared with the West (~4%) and that motor vehicle emissions and secondary NO3

-

 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 SO4

2– indicate that personal SO42– 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 aliphatic 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, fossil 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 O3, NO2, 

and SO2 are associated with the ambient component of personal exposure to total PM2.5 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 PM2.5 exposure is mixed. It is likely that associations between ambient gases and 

personal exposure to PM2.5 of ambient origin exist, but they are complex and vary by season and 

location. 

Implications for Epidemiologic Studies 

The importance 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, modeling, 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 estimates 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 estimates of exposure because 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. Additionally, 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. PM2.5 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 PM10 monitoring sites than between PM2.5 monitoring sites. Likewise, 

studies have shown UFPs to be more spatially variable across urban areas. Even if PM2.5, PM10-2.5, 

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 country and with size fraction. Current information 

suggests that UFPs, PM10-2.5, and many PM components are more spatially variable than PM2.5. These 

factors should be considered in using data obtained 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 concentration, 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 concentration gives the change in health effects 

resulting from a change in ambient PM concentration 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 increase 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 interval 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 PM2.5 concentration as a surrogate 

for the community average personal exposure to ambient PM2.5 is not expected to change the 

principal conclusions from PM2.5 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 PM2.5 could be estimated using various tracer and source apportionment 

techniques and that it is highly correlated with ambient concentrations of PM2.5. These studies also 

show that the non-ambient component of personal exposure to PM2.5 is basically uncorrelated with 

ambient PM2.5 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 differential losses of UF or coarse PM from diffusion, 

evaporation of semi-volatile PM, or impaction. The 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. 

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