Chapter 3. 

3.2.1. 

Source to Exposure 

3.1. Introduction 

This chapter reviews concepts and findings in atmospheric sciences and exposure assessment 

that provide a foundation for the detailed presentation of evidence of CO-related health effects in 

subsequent chapters and for a causality finding regarding climate forcing effects of CO. Section 3.2

provides an overview of the primary and secondary sources of CO as well as the atmospheric 

chemistry involved in the production and removal of CO by oxidation processes. Section 3.3

provides a description of climate forcing caused directly and indirectly by CO. Descriptions of CO 

measurement methods, monitor siting requirements, and monitor locations are presented in Section 

3.4. Ambient CO concentrations and their spatial and temporal variability are characterized in 

Section 3.5. The background concentrations of CO useful for risk and policy assessments informing 

decisions about the NAAQS, referred to as policy-relevant background (PRB) concentrations, are 

also presented in Section 3.5. Factors related to human exposure to ambient CO, and their 

implications for epidemiologic studies, are discussed in Section 3.6. Finally, a summary and 

conclusions of the chapter are presented in Section 3.7. 

3.2. CO Sources, Emissions, and Chemistry 

Direct CO Emissions 

CO is formed primarily by incomplete combustion of carbon-containing fuels and 

photochemical reactions in the atmosphere. In general, any increase in fuel O2 content, burn 

temperature, or mixing time in the combustion zone will tend to decrease production of CO relative 

to CO2. CO emissions from large fossil-fueled power plants are typically very low since the boilers 

at these plants are tuned for highly efficient combustion with the lowest possible fuel consumption. 

Additionally, by allowing time for the furnace flue gases to mix with air and be oxidized by OH to 

CO2 in the hot gas stream before the OH concentrations drop as the flue gases cool, the CO-to-CO2

ratio in these emissions is shifted toward CO2. 

Figure 3-1 lists CO emissions totals in tons segregated by individual source sectors in the U.S. 

for 2002, which is the most recent publicly available CO emissions data meeting EPA’s data quality 

assurance objectives. In the U.S., direct CO emissions data are tracked in the National Emissions 

Inventory (U.S. EPA, 2006, 157070), a composite of data from various sources including industries 

and state, tribal, and local air agencies, and from the Biogenic Emissions Inventory System (BEIS). 

NEI data are collected for all states, the District of Columbia, the U.S. territories of Puerto Rico and 

Virgin Islands, and some of the territories of federally recognized American Indian nations. Different 

data sources use different data collection methods, most of which are based on empirical estimates 

and engineering calculations rather than measurements. Most fuel combustion and industrial sources, 

for example, estimate their CO emissions using EPA-approved emission factors, as do on-road and 

non-road mobile source emitters where models (MOBILE6, MOVES, NONROAD) are available to 

calculate inventories (U.S. EPA, 2006, 157070). The NEI includes fires of anthropogenic and natural 

origin. Anthropogenic fires include structural fires, agricultural fires, prescribed burning, and slash 

burning; forest wildfires are considered to be of natural origin. Estimates of direct CO emission from 

 

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 (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 3-1 
soil are calculated by the EPA using the BEIS model. Although these estimates are generated using 

well-established approaches, uncertainties are inherent in the emission factors and models used to 

represent sources for which emissions have not been directly measured. These uncertainties vary by 

source category, season, and region. Discussion of uncertainties is provided in subsequent 

paragraphs related to mobile sources, the largest source category. 

Nationally, on-road mobile sources in the NEI constituted more than half of total CO 

emissions in 2002, or ~60.6 MT of ~116.8 MT total, which includes anthropogenic and biogenic 

emissions reported in the NEI and the BEIS (http://www.epa.gov/ttnchie1/emch/biogenic). High 

concentrations of CO can often occur in areas of heavy traffic. In metropolitan areas in the U.S., for 

example, as much as 75% of all CO emissions came from on-road vehicle exhaust in the 2002 NEI 

(U.S. EPA, 2006, 157070). When the emissions from incomplete combustion of fuels powering 

non-road mobile sources were included, all mobile sources accounted for ~80% of total CO 

emissions in the U.S. in 2002 (Figure 3-1). 

CO emissions from internal combustion engines vary substantially with ambient temperature 

and operating conditions. Substantial light-duty gasoline vehicle CO emissions occur during the cold 

start before the catalyst is warmed up. Most emission tests such as the Federal Test Procedure (FTP) 

which includes a cold start portion and is used to certify that vehicles meet EPA emission standards, 

are conducted at about 75oF. Lower ambient temperatures result in increased CO emissions because 

spark ignition engines are required to run richer air:fuel ratios for longer periods of time, and also 

because the time before the catalyst is warmed up increases compared to the time for catalyst warmup occurring at 75oF (U.S. EPA, 2006, 199897). Thus, in addition to the vehicle CO emissions 

standards EPA implemented starting with the 1968 model year, EPA has also implemented a cold 

temperature CO emission standard for light-duty gasoline vehicles and trucks at 20oF that phased in 

for 40% of the new fleet in the 1994 model year, 80% for the 1995 model year, and 100% with the 

1996 and succeeding model years. The emission standard of 10 g/mile results in a reduction of about 

20-30% in CO emissions at 20o F (57 FR 3188-31923 July 17, 1992). Increased vehicle CO 

emissions can also occur under conditions such as high rates of acceleration, rapid speed 

fluctuations, heavy-vehicle load demands (such as occur while pulling a trailer or going up a steep 

hill), and use of air-conditioning. Such driving conditions were not originally fully reflected in the 

FTP. EPA has issued a Supplemental Federal Test Procedure (SFTP) to control excess CO emissions 

under these conditions. These regulations were phased in for the 1998-2000 model years (61 FR 

54852-54906 October 22, 1996). Moreover, the gasoline-powered spark ignition engines that 

predominate in light-duty on-road vehicles have higher uncontrolled CO emission rates than other 

combustion sources because they typically operate closer to the stoichiometric air-to-fuel ratio, have 

relatively short residence times at peak combustion temperatures, and have very rapid cooling of 

cylinder exhaust gases. By contrast, the diesel-powered engines that predominate in heavy-duty onroad vehicles and in off-road and non-road fixed combustion sources have much lower engine-out 

CO emissions than do the spark-ignition engines because the diesels typically operate at very high 

air-to-fuel ratios, which promote mixing oxygen and fuel, thus improving carbon burn. 

January 2010 3-2 
60,600,000

22,700,000

5,820,000

18,500,000

3,550,000

1,590,000

1,270,000

987,000

657,000

490,000

357,000

284,000

118,000

25,600

1,660

0 20,000,000 40,000,000 60,000,000 80,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

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

Figure 3-1. CO emissions (tons) in the U.S. by source sector in 2002 from the NEI and the 

BEIS. The “fires” category has been extracted from Tier 3 miscellaneous 

categories of the NEI, and biogenic fires are those attributed to forest wildfires. 

The “soils” category comprises the BEIS data. The “roadway vehicles” and “nonroad vehicles” categories have been renamed here for clarity. 

Figure 3-2 shows present and historical CO emissions from the traditionally inventoried 

anthropogenic source categories: (1) fuel combustion, which includes emissions from coal-, gas-, 

and oil-fired power plants and industrial, commercial, and institutional sources, as well as residential 

heaters (e.g., wood-burning stoves) and boilers; (2) industrial processes, which include chemical 

production, petroleum refining, metals production, and industrial processes other than fuel 

combustion; (3) on-road vehicles, which include cars, trucks, buses, and motorcycles; and (4) nonroad vehicles and engines, such as farm and construction equipment, boats, ships, snowmobiles, 

aircraft, locomotive, and the two-stroke engines found in lawnmowers, chainsaws, and other small 

gasoline-powered equipment. Using these NEI data, trends in the national CO emissions can be 

computed and compared over time. So, for example, the national-scale estimated anthropogenic CO 

emissions decreased 35% between 1990 and 2002. The trend in Figure 3-2 demonstrates that 

controls in the on-road vehicle sector have produced nearly all the national-level CO reductions 

since 1990. (Data are presented here for 1990 and from 1996-2002 because only 1990 data have 

been updated to be comparable to the more recent inventories made since 1996.) 

January 2010 3-3 
Source: U.S. EPA (2008, 157076) 

Figure 3-2. Trends in anthropogenic CO emissions (MT) in the U.S. by source category for 

1990 and 1996-2002. 

With the exception of this downward trend resulting from emissions controls, anthropogenic 

CO emissions demonstrate less interannual variability than biogenic emissions (Bergamaschi et al., 

2000, 192377). Several recent reports using both ambient concentrations and fuel-based emissions 

estimates have explored this annual-to-decadal emissions decrease in anthropogenic CO in finer 

detail; they include Harley et al. (2001, 193922; 2005, 088154), Parrish et al. (2002, 052472), 

Parrish (2006, 090352), Pollack et al. (2004, 184461), and Mobley et al. (2005, 194008). The 

consistent conclusion from those investigations has been that annual average U.S. on-road vehicle 

CO emissions have decreased at a rate of ~5% per year since the early 1990s. This can be seen from 

Figure 3-2 as well. Additional analyses by Harley et al. (2005, 088154) and Parrish (2006, 090352) 

were also consistent with the suggestion in Pollack et al. (2004, 184461) that the EPA MOBILE6 

vehicle emissions model (http://www.epa.gov/otaq/m6.htm) now overestimates vehicle CO 

emissions by a factor of ~2. Field measurements by Bishop and Stedman (2008, 194670) were in 

accord with Parrish’s (2006, 090352) findings that the measured trends of CO and NOX

concentrations from mobile sources in the U.S. indicated that modeled CO emission estimates were 

substantially too high. Hudman et al. (2008, 191253) found that the NEI overestimated 

anthropogenic CO emissions by 60% for the eastern U.S. during the period July 1-August 15, 2004 

using aircraft observations of CO from the International Consortium for Atmospheric Research on 

Transport and Transformation (ICARTT) campaign (Fehsenfeld et al., 2006, 190531) and results 

from a tropospheric chemistry model (GEOS-Chem)(Figure 3-3). 

January 2010 3-4 
Source: Reprinted with Permission of the Amercian Geophysical Union from Hudman et al. (2008, 191253) 

Figure 3-3. Surface air CO concentrations at Chebogue Point during the ICARTT campaign. 

Observations (black) are compared to model results using the 1999 NEI 

anthropogenic emissions (green) and with these CO emissions reduced by 60% 

(blue). Yellow bands are periods of U.S. outflow diagnosed by Millet et al. (2006, 

195106). Overestimation near day 200 is due to model misplacement of a large 

Alaskan/Canadian biomass burning plume. 

Improvements in emissions technologies not correctly represented in MOBILE emissions 

models have been suggested as one cause for this discrepancy. For example, Pokharel et al. (2002, 

052473; 2003, 053740) demonstrated substantial decrements in the CO fraction of tailpipe exhaust in 

several U.S. cities, and Burgard et al. (2006, 193222) documented improvements in emissions from 

heavy-duty on-road diesel engines. The Motor Vehicle Emission Simulator (MOVES) model has 

been designed to address some of the largest errors in the MOBILE model. It was released in final 

form in December 2009 (http://www.epa.gov/otaq/models/moves/index.htm). 

Estimates of non-anthropogenic CO emissions are made using the BEIS model with data from 

the Biogenic Emissions Landcover Database (BELD) and annual meteorological data. National 

biogenic emissions, excluding fires, were estimated to contribute 5%, or ~5.8 MT, of total CO 

emissions from all sources in 2002. Biogenic wildfires in 2002 added another 12%, or ~14.1 MT, to 

the national CO emissions total and were responsible for 76.1% of all CO emissions estimates from 

fires. This is shown in Figure 3-1 using the NEI and BEIS data. Geogenic emissions of CO, also 

included in this inventory, include volcanic gases released from molten rock in the Earth’s mantle. 

Mixing ratios of dissolved CO in this rock vary in a range from 0.01 to 2% as a function of the rock 

stratum surrounding the volcano and other geologic conditions. This high variability and infrequent 

though often violent release mean geogenic CO measurements are very difficult to make with 

precision, though on non-local scales the magnitude of their contribution is small relative to 

anthropogenic sources. Photodecomposition of organic matter in oceans, rivers, lakes, and other 

surface waters, and from soil surfaces also releases CO (Goldstein and Galbally, 2007, 193247). 

However, soils can act as a CO source or a sink depending on soil moisture, UV flux reaching the 

soil surface, and soil temperature (Conrad and Seiler, 1985, 029520). Soil uptake of CO is driven by 

anaerobic bacteria (Inman et al., 1971, 010972). Emissions of CO from soils appear to occur by 

abiotic processes, such as thermodecomposition or photodecomposition of organic matter. In 

general, warm and moist conditions found in most soils favor CO uptake, whereas hot and dry 

conditions found in deserts and some savannas favor the release of CO (King, 1999, 002828). 

Biomass burning consists of wildfires and the intentional burning of vegetation to clear new 

land for agriculture and population resettlement; to control the growth of unwanted plants on pasture 

land; to manage forest resources with prescribed burning; to dispose of agricultural and domestic 

waste; and as fuel for cooking, heating, and water sterilization. Globally, most wildfires may be 

ignited directly as the result of human activities, leaving only 10-30% initiated by lightning 

January 2010 3-5 
(Andreae, 1991, 078147). However, because fire management practices suppress natural wildfires, 

the buildup of fire fuels increases the susceptibility of forests to more severe but less frequent fires in 

the future. Thus there is considerable uncertainty in attributing the fraction of wildfire emissions to 

human activities because the emissions from naturally occurring fires that would have been present 

in the absence of fire suppression practices are not known. 

Biomass burning also exhibits strong seasonality and interannual variability (van der Werf et 

al., 2006, 157084), with most biomass burned during the local dry season. This is true for both 

prescribed burns and wildfire. The unusually warm and dry weather in central Alaska and western 

Yukon in the summer of 2004, for example, contributed to the burning of 11 million acres there. 

These fires, the largest on record for this region, produced CO emissions easily tracked by the 

Measurement of Pollution in the Troposphere (MOPITT) instrument on NASA’s Terra satellite 

(Figure 3-4). The high CO concentration measured by MOPITT coincided with the surface location 

of fires tracked using aerosol plumes identified by the Moderate Resolution Imaging 

Spectroradiometer (MODIS) also on Terra. Subsequent modeling by Pfister et al. (2005, 093009) 

showed that the CO contribution from these fires in July 2004 was 33.1 (± 5.5) MT that summer, or 

in the range of the total U.S. anthropogenic CO emissions during the same time. The smoldering 

phase of combustion yields higher CO emissions than the flaming phase. Using controlled 

combustion chamber experiments, Lobert et al. (1991, 029473) found that with a wide variety of 

vegetation types, on average, 84% of the CO from biomass fires was produced during the smoldering 

phase and 16% during the flaming phase of combustion. 

CO emissions data for EPA’s 10 administrative Regions in the U.S., depicted in Figure 3-5, 

show a more nuanced view of the national concentrations and trends described above. Net 

anthropogenic CO emissions were estimated to have declined in all EPA Regions between 1990 and 

2002, with the largest decrease (10.8 MT) occurring in Region 9 and the smallest (1.3 MT) in 

Region 10. 

At state and local levels, CO emissions from on-road mobile sources or from fires can 

dominate in different locations across the U.S. Figure 3-6 illustrates this variability with CO statelevel emissions totals and selected county totals in 2002 for Colorado (Annex A includes analogous 

data for Alaska, Utah, Massachusetts, Georgia, California, and Alabama). In Colorado, emissions 

from fires and on-road vehicles were nearly equal: ~0.9 MT from fires and ~1.1 MT from on-road 

vehicles. Emissions sources varied strongly across counties, however, with urban Denver County 

dominated by on-road vehicle emissions at 71% and rural Garfield County dominated by fire 

emissions at 67%. 

January 2010 3-6 
Source: Reprinted with Permission of the American Meteorological Society from Fishman et al. (2008, 193927) 

Figure 3-4. CO concentrations centered at ~3,000 m above sea level measured by the 

MOPITT sensor on the Terra satellite for the period July 15-23, 2004, during 

intense wildfires in Alaska and the Yukon. 

Source: U.S. EPA (2008, 157076) 

Figure 3-5. Trends in subnational CO emissions in the 10 U.S. EPA Regions for 1990 and 

1996-2002. 

January 2010 3-7 
Colorado 

35

0

180

4

1,400

3,500

7,300

1,300

21,000

530

86,000

140,000

970,000

400,000

1,100,000

0 500,000 1,000,000 1,500,000

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OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

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FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Denver County

0

130,000

42,000

36

2,900

3

340

42

390

180

99

0

0

760

0

0 50,000 100,000 150,000

SOLVENT UTILIZATION

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OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

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FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Garfield County

0

0

0

0

75

11

45

0

2,200

0

3,200

3,700

65,000

4,600

20,000

0 25,000 50,000 75,000

SOLVENT UTILIZATION

MISCELLANEOUS

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OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

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FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Denver

County

Garfield 

County

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

Figure 3-6. CO emissions density map and distributions for the state of Colorado and for 

selected counties in Colorado in 2002, from the NEI and the BEIS. The “fires” 

category has been extracted from Tier 3 miscellaneous categories of the NEI, and 

biogenic fires are those attributed to wildfires. The “soils” category comprises 

the BEIS data. The “roadway vehicles” and “non-road vehicles” categories have 

been renamed here for clarity. 

January 2010 3-8 
3.2.2. Secondary CO Emissions and Associated Chemistry 

Oxidation of anthropogenic and biogenic VOCs constitute important secondary sources of CO. 

For example, Hudman et al. (2008, 191253) determined that oxidation of isoprene and other biogenic 

VOCs contributed 9.1 MT of atmospheric CO (with isoprenes contributing 7.1 MT), and oxidation 

of anthropogenic VOCs contributed another 2.0 MT of CO emissions during the period July 1-

August 15, 2004, for the eastern U.S. In contrast, direct anthropogenic CO emissions were estimated 

to be 5.1 MT for this time period and location. Hence, secondary biogenic formation was found to be 

a more important source of CO emissions than direct anthropogenic activities for the study period. 

Hudman et al. (2008, 191253) noted that biogenic CO emissions were highest in the southeastern 

U.S., where isoprene emissions are also greatest. These estimates were obtained using aircraft 

measurements from the ICARTT campaign (Fehsenfeld et al., 2006, 190531) and estimates from the 

GEOS-Chem model (Bey et al., 2001, 051218), configured as described by Hudman et al.(2007, 

089474). 

Secondary CO production occurs by photooxidation of methane (CH4) and other VOCs, 

including nonmethane hydrocarbons (NMHCs) in the atmosphere and organic molecules in surface 

waters and soils. CH4 oxidation is summarized in this reaction sequence: 

CH4 + OH → CH3 + H2O

CH3 + O2 (+ M) → CH3O2 (+ M) 

CH3O2 + NO → CH3O + NO2

CH3O2 + HO2 → CH3OOH + O2

CH3O + O2 → CH2O + HO2

CH2O + hν → H2 + CO

or CH2O + hν → HCO + H

or CH2O + OH → HCO + H2O

HCO + O2 → CO + HO2

Reaction 3-1 

where M is a reaction mediator that is neither created nor destroyed and stabilizes the reaction 

product. 

Photolysis of formaldehyde (CH2O) proceeds by two pathways. The first produces molecular 

hydrogen (H2) and CO with a reaction yield of 55% in conditions of clear skies and low zenith 

angles; the second yields a hydrogen radical (H) and the formyl radical (HCO). HCO then reacts 

with O2 to form hydroperoxy radical (HO2; OH and HO2 together are termed HOX) and CO. 

Reaction of methyl peroxy radical (CH3O2) with HO2 radicals to form methyl hydroperoxide 

(CH3OOH) is also operative, especially in low oxides of nitrogen (NO+NO2=NOX) conditions. 

Heterogeneous removal of the partially water-soluble intermediate products, such as CH3OOH and 

CH2O, will decrease CO yields from CH4 oxidation. 

While oxidation of CH2O nearly always produces CO and some small quantities of formic acid 

(CH2O2) in the reaction of CH2O with HO2 (not shown here), oxidation of acetaldehyde (CH3CHO) 

does not always yield two CO molecules. Reaction of CH3CHO with OH can yield acetyl radicals 

(CH3CO) which then will participate with O2 in a termolecular recombination reaction to form 

peroxyacyl radicals, which then can react with nitric oxide (NO) to form CH3 and CO2; or the 

peroxyacyl radicals can react with NO2 to form peroxyacetyl nitrate (PAN), CH3CO3NO2. In this 

way, one carbon atom is oxidized directly to CO2 without passing through CO. The yield of CO from 

these pathways depends on the OH concentration and the photolysis rate of CH3CHO, as well as on 

the abundance of NO, since peroxyacyl radicals also will react with other odd hydrogen radicals like 

HO2. 

Estimating the CO yield from oxidation of hydrocarbons (HCs) larger than CH4 requires 

computing the yields of CH2O, CH3CHO, CH3CO, and analogous radicals from oxidation of the 

parent molecules. Moreover, the extent of heterogeneous removal of soluble intermediate products 

also affects oxidation of more complex HCs. However, the detailed gas-phase kinetics for many HCs 

January 2010 3-9 
with more than a few carbons is still unknown. This is especially the case for several important 

classes of VOCs, including the aromatics, biogenic HCs including isoprene, and their intermediate 

oxidation products like epoxides, nitrates, and carbonyls. Mass-balance analyses performed on 

irradiated smog chamber mixtures of aromatic HCs indicate that only about one-half of the carbon is 

in the form of compounds that can be identified. In addition, reactions like the oxidation of terpenes 

that produce condensable products are also significant because these reactions produce secondary 

organic aerosols, thereby reducing the potential yield of CO. The CO yield from oxidation of CH4, 

for example, is ~0.9 on a per carbon basis (Kanakidou and Crutzen, 1999, 011760). Yields from 

other compounds range from <0.1 for anthropogenic alkanes (Altshuller, 1991, 192375) to ~0.9 for 

ethane; yields from other compounds are given in Table 3-1 taken from Kanakidou and Crutzen 

(1999, 011760). 

Table 3-1. Literature values for CO yields from hydrocarbons in per carbon units, except as noted. 

Specific hydrocarbons are noted in parentheses. 

Reference CO Yields 

Zimmerman et al. (1978, 010758) 0.3 (hydrocarbons) 

Brewer et al. (1984, 194402) 0.22-0.27 (isoprene) 

Hanst et al. (1980, 011988) According to chamber experiments, CO and CO2 yield: 

 ~0.85 (ethylene) 

 ~0.90 (ethane) 

 ~0.80 (propane) 

 ~0.58 (n-butane) 

 ~0.73 (isoprene) 

 ~0.30 (alpha-pinene) 

Crutzen (1987, 002848) 0.9 of CH4

Kanakidou et al. (1991, 029701) 0.39 (C2H6 and C3H8) 

Jacob and Wofsy (1990, 029668) @ low NOX: 0.2 (isoprene) 

@ high NOX: 0.6 (isoprene) 

Crutzen et al. (1985, 194403) =0.8 (isoprene + OH) 

Kirchhoff and Marinho (1990, 194406) Isoprene oxidation may form 10 ppbv CO/d over the Amazon (3 km deep boundary layer)

Altshuller (1991, 192375) Conversion factors of 19 (C2-C6) anthropogenic alkenes vary between 0.010 and 0.075 

Manning et al. (1997, 194401) CH4 in the SH: 0.7 

Kanakidou and Crutzen (1999, 011760) Annual tropospheric mean conversion factors: 

CH4: 0.9 

 Isoprene: 0.4 

Other nonmethane hydrocarbons: 0.7 

Source: Adapted with Permission of Elsevier Ltd. from Kanakidou and Crutzen (1999, 011760)

The major pathway for removal of CO from the atmosphere is reaction with OH to produce 

CO2 and H radicals that rapidly combine with O2 to form HO2 radicals, with a rate constant at 1 atm 

in air of ~2.4×10-13 cm3

/molecule/s (Finlayson-Pitts and Pitts, 2000, 055565). The mean tropospheric 

photochemical lifetime (τ) of CO in the northern hemisphere is ~57 days (Khalil and Rasmussen, 

1990, 012352; Thompson and Cicerone, 1986, 019374). Owing to variation in atmospheric water 

vapor, OH concentration, and insolation, shorter τ are found nearer the tropics and longer ones at 

higher latitudes. During winter at high latitudes, CO has nearly no photochemical reactivity on urban 

and regional scales. Because the CO τ is shorter than the ~1 yr characteristic time scale for mixing 

between the hemispheres and because northern hemisphere CO emissions are higher due to 

anthropogenic activity (Khalil and Rasmussen, 1990, 012443), a large gradient in concentrations 

January 2010 3-10 
exists between the hemispheres (Edwards et al., 2004, 199889). In addition, the CO τ at high 

latitudes is long enough to result in much smaller gradients between 30° latitude and the pole of 

either hemisphere. The typical residence time of CO in urban areas when assuming a diel-average 

OH concentration of 3×106

/cm3 in urban areas is ~16 days, so CO will not typically be destroyed in 

urban areas where it is emitted and will likely be mixed on continental and larger scales. OH 

concentrations are orders of magnitude lower in indoor environments, and so CO will generally not 

be affected by indoor air reactions. 

3.3. CO Climate Forcing Effects 

Recent data do not alter the current well-established understanding of the role of urban and 

regional CO in continental and global-scale chemistry outlined in the 2000 CO AQCD (U.S. EPA, 

2000, 000907) and subsequently confirmed in the recent global assessments of climate change by the 

Intergovernmental Panel on Climate Change (IPCC) (2001, 156587; 2007, 092765). CO is a weak 

direct contributor to greenhouse warming because its fundamental absorption band near 4.63 μm is 

far from the spectral maximum of Earth’s longwave radiation at ~10 μm. Sinha and Toumi (1996, 

193747) estimated the direct RF of CO computed for all-sky conditions at the tropopause, which is 

the IPCC’s preferred form for the calculation (Forster et al., 2007, 092936), to be 0.024 W/m2

 based 

on an assumed change in CO mean global concentration from 25 to 100 ppb since preindustrial 

times. The direct RF value similarly projected by Sinha and Toumi (1996, 193747) if the mean 

global background concentration were to increase from 25 to 290 ppb was 0.057 W/m2. 

However, because reaction with CO is the major sink for OH on a global scale, increased 

concentrations of CO can lead to increased concentrations of other trace gases whose loss processes 

also involve OH chemistry. Some of those trace gases, CH4 and O3 for example, absorb infrared 

radiation from the Earth’s surface and contribute to the greenhouse effect directly. Others, including 

hydrochlorofluorocarbons (HCFCs), methyl chloride, and methyl bromide, can deplete stratospheric 

O3, increasing the surface-incident UV flux. 

This indirect effect of CO on stratospheric O3 concentrations is opposite in sign to the effect of 

CO on O3 in the troposphere where CO reacts in a manner similar to other VOCs in the presence of 

NOX and UV to create O3; see the detailed description of O3 formation from VOCs and NOX in the 

2006 O3 ISA (U.S. EPA, 2006, 088089). Because CO’s chemical lifetime is longer than those of the 

VOCs most important for O3 formation on urban and regional scales and because CO oxidation has 

one-to-one stoichiometry (whereby one molecule of CO converts only one molecule of NO to NO2), 

CO has a significantly lower O3 forming potential than other VOCs in the troposphere. Carter (1998, 

192380) computed a maximum incremental reactivity for CO of 0.06 g O3 for 1 g CO, as compared 

to reactivities of total on-road vehicle exhaust emissions typically in the range of 3 to 4 g O3 per 

g VOC. However, because the total mass of CO emissions is substantially greater than those of the 

other VOCs with higher carbon numbers and faster reactivities, CO can contribute significantly to O3

formation even though its photochemical processing is slow. Using data from instrumented models, 

including that of Jeffries (1995, 003055), the NRC (1999, 010614) estimated, for example, that CO 

can contribute 15-25% of the total O3 forming potential of gasoline exhaust emissions, although this 

estimate shows strong regionality. The contribution of CO to urban and regional O3 concentration is 

often <10% owing to its very slow reactivity on these scales and to locally variable radical 

concentration ratios. 

Emissions of CO and the other O3 precursors, nonmethane VOCs (NMVOCs) and NOX, affect 

the oxidizing capacity of the atmosphere largely by perturbing HOX concentrations. From a climate 

perspective, this HOX perturbation chiefly affects the CH4 τ and production of O3 in the troposphere. 

Changes in the concentration of O3 and hence in its RF occur mainly in the time of a few months. 

However, Prather (1996, 193195) showed that changes in CH4 concentration and its RF extend to the 

“primary mode” timescale of troposphere chemistry of about 14 yr (Derwent et al., 2001, 047912; 

Wild et al., 2001, 193196). The primary mode timescale of CH4 is in part determined by the positive 

feedbacks in the CH4-OH-CO system in which even low concentration additions of CH4 produce 

additional CO through oxidation by OH. That additional CO then further decreases atmospheric OH 

concentrations when OH oxidizes it to CO2. The resulting decreased OH concentration then further 

increases the CH4 τ (Daniel and Solomon, 1998, 193235; Isaksen and Hov, 1987, 019490). 

Atmospheric CH4 concentrations since 1750 have increased by more than a factor of 2, giving an RF 

January 2010 3-11 
of ~0.6 W/m2

 (Forster et al., 2007, 092936). Roughly 25% of the global mean tropospheric CO is 

produced by CH4 oxidation (Wuebbles and Hayhoe, 2002, 044159). Using a 2-D global model on a 

coarse grid, Wang and Prinn (1999, 011758) showed that increasing CO and CH4 concentrations 

leading to decreased OH concentrations can extend the CO τ as well as the CH4 τ. Wang and Prinn 

(1999, 011758) varied the CO emissions and other model inputs and parameters in a matrix of 

simulations that showed, with increased or even constant 20th century CO concentrations, the CO τ

was increased by more than 50% in 100 yr. However, Wang and Prinn (1999, 011758) stated that 

their simulation omitted NMHCs and therefore likely underestimated CO concentrations while 

under- or overestimating hydroxyl radical concentrations. Likewise, low spatial resolution of the 

model likely incurred additional error in the solution. 

CH4 is long-lived and, in general, well mixed in the atmosphere. However, the reaction of CH4

and OH, and hence the CH4 τ, is governed by the behavior and location of emissions of the shortlived gases, including CO, VOCs, and NOX. This produces high regional variability and uncertainty 

in the concentrations and RFs from CO and its related climate forcing gases (Berntsen et al., 2006, 

193244; Fuglestvedt et al., 1999, 047431). NOX, for example, can produce effects on the combined 

indirect RF opposite in direction to those of CH4 since under most global background conditions an 

increase in NOX increases the global average OH concentration and decreases CH4 τ and RF 

(Berntsen et al., 2005, 193241; Wild et al., 2001, 193196). Wild et al. (2001, 193196) also showed 

that emissions changes in CO have effects opposite in sign to those of NOX because increases in CO 

act to depress OH concentrations and that the combined effect of CO and NOX emissions yields a 

positive RF. The results of this study underscore the need to consider the combined effects of 

pollutants emitted from similar sources. 

Using the 3-D global chemistry model MOZART-2 (Horowitz et al., 2003, 057770), Naik 

et al. (2005, 193194) simulated changes in global tropospheric O3 concentrations and RF resulting 

from differing reductions in emissions of NOX alone or a combination of NOX, CO, and NMHCs in 

nine regions of the Earth. For the reductions in Europe, North America, and Southeast Asia, reducing 

CO and NMHCs in addition to reducing NOX lowered the spatial inhomogeneity of the O3

concentration and RF because CO has a longer lifetime than NOX. 

Wild et al. (2001, 193196) used the University of California-Irvine chemical transport model 

(CTM) (Wild and Prather, 2000, 052402) driven by the NASA GISS II general circulation model 

(Rind and Lerner, 1996, 193750) to compute changes in O3 concentrations and RF from regional 

emissions of NOX and CO. Changes in O3 and CH4 result from increases in global surface NOX

emissions alone and run for 10-yr periods produced negative net RFs, ranging from -0.2 W/m2

 in 

East Asia to -0.5 W/m2 in the Tropics owing to the long-term interdependencies in the CO-CH4-NOX

system described above. When global CO emissions were increased by an 11 MT pulse for 1 yr 

together with the same 1-yr pulsed NOX surface emissions and run again for a 10-yr period, the 

global net RF rose to 1.7 W/m2 with an estimated 20% uncertainty based on the spatial variability 

and short-term reactivity of O3 (Wild et al., 2001, 193196). 

Determining whether several species’ τ and RF will increase or decrease in response to pulsed 

or step-wise emissions of the short-lived O3 precursor species (NMVOC, CO, and NOX) is 

complicated by its global location with respect to the O3 production response surface. See the 

description of the O3 production response surface and its dependence on NOX and radical 

concentrations in the 2008 NOX ISA (U.S. EPA, 2008, 157073) for additional details. Fiore et al. 

(2002, 051221; 2008, 193749) found that O3 is closely coupled with CH4 and that their relationship 

is influenced by regional variation in NOX concentrations. Using the weighted average results from 

12 3-D global chemistry models exercised for the IPCC Third Assessment Report (2001, 156587), 

Wigley et al. (2002, 047883) confirmed that increases in CO and VOC emissions increased the O3

RF both directly and indirectly through the CH4 effects described above. Furthermore, Wigley et al. 

(2002, 047883) demonstrated that NOX emissions produced a mix of direct and indirect increases in 

RF, mostly dominated by the direct effects for all modeled scenarios. Wigley et al. (2002, 047883) 

concluded that tropospheric O3 RF influences were larger than CH4 influences and that the shortlived reactive gases produced 60-80% of that forcing, with the remainder coming from CH4. Given 

these chemical interdependencies, calculations of an indirect RF for any of these short-lived O3

precursor species are most often made for all of the most important ones together. 

Figure 3-7 illustrates model estimates of the combined RF of increased short-lived O3

precursor species relative to long-lived GHGs, aerosols, and other changes (Forster et al., 2007, 

092936). The combined effect of increased CH4, CO, NMVOC, and NOX emissions for the period 

1750-2005 has produced tropospheric O3 concentrations associated with a net RF of ~0.4 W/m2

 with 

January 2010 3-12 
±50% uncertainty based on Shindell et al. (2005, 080129). Indirect effects of CO through the GHGs 

O3, CH4, and CO2 were estimated to contribute RF ~0.2 W/m2

, which is more than a factor of 2 

larger than the indirect effect of the shorter-lived NMVOCs on the same three GHGs (Forster et al., 

2007, 092936). Of the indirect effects on these three GHGs from CO emissions, the O3-related 

component was the largest, accounting for approximately one-half of the forcing (Forster et al., 

2007, 092936). In comparison, CO2 contributed a direct forcing of 1.6 ± 0.2 W/m2

 over this time 

period. 

Integrated RF estimates over longer time horizons may indicate the future climate effects of 

present-day emissions. Modeled integrated 20-yr and 100-yr time horizon RFs are presented by 

Forster et al. (2007, 092936) in Figure 3-8 for year 2000 emissions of short-lived and long-lived 

GHGs. The integrated RF for CO was estimated to be ~0.2 W/m2

⋅yr with ~50% uncertainty. It can be 

seen that the integrated RF of CO2 is much smaller for the 20-yr horizon because the lifetime of CO2

perturbations is roughly 150 yr. As a result, the RF related to short-lived CO is ~25% of that for CO2

for the 20-yr horizon (~0.7 W/m2

⋅yr) but only ~7% of that for longer-lived CO2 over a 100-yr time 

horizon (~2.4 W/m2

⋅yr). This indirect forcing is just slightly lower than the RF of year 2000 black 

carbon emissions from fossil fuel and biomass burning on the same horizons according to this 

assessment. 

It is also possible to compute individual contributions to the integral RF from CO based on 

separate emissions sectors. Unger et al. (2009, 193238) used the NASA GISS model for Physical 

Understanding of Composition-Climate Interactions and Impacts (G-PUCCINI) (Shindell et al., 

2006, 193751). Unger et al. (2009, 193238) divided the 1995 global anthropogenic CO emissions 

total of 933.3 MT/yr into sectors for on-road transport (ORT) and power generation (PG), and then 

separated contributions from each of these sectors for the U.S. and other large geographic regions of 

the Earth. ORT CO emissions in the U.S. were 84.1 MT/yr; PG CO emissions were 0.55 MT/yr out 

of the total U.S. anthropogenic CO emissions of 112.5 MT/yr. Unger et al. (2009, 193238) concluded 

from analysis of 7-yr runs that the CO indirect CH4 effects (that is, the CO effects through CH4

changes as described above) in the 1995 emissions run were -0.004 W/m2

 for the global ORT and 

-0.022 W/m2 for the global PG. In the U.S., the indirect CH4 RF was positive at +0.009 W/m2

because the positive effects on CH4 τ from the CO emissions dominated over the negative effects 

from NOX through OH. This RF fraction from indirect CH4 is approximately the same as the direct 

O3 RF from ORT in the U.S., 0.010 W/m2. Because the PG sector emits NOX but less CO relative to 

the ORT, the indirect CH4 RF from the U.S. PG was not dominated by the positive CO effects and 

remained a net negative at -0.006 W/m2

 (Unger et al., 2009, 193238). The authors acknowledged 

some uncertainty in this relationship related to the influence of NOX emissions on CO 

transformation, but no quantification of uncertainty in these modeled estimates was provided. 

These gross emissions sectors can also be subdivided to demonstrate more clearly the 

localized chemical interdependencies of the CO-CH4-NOX system. Fuglestvedt et al. (2008, 193242) 

used the Oslo CTM2 model to simulate effects from all emissions and changes in all transportation 

subsectors from 1850-2000. Fuglestvedt et al. (2008, 193242) found that global transport has been 

responsible for ~15% of the total anthropogenic CO2 RF and ~15% of the total anthropogenic O3 RF. 

Of the total O3 RF, the largest contributor was the shipping sector at 0.03 W/m2

, because its high 

NOX-to-CO and NOX-to-VOC ratios produced OH increases and hence CH4 decreases in regions of 

naturally low NOX. For the shipping segment of the transport sector, the high NOX emissions there 

reduced the CH4 τ but increased O3. The global mean effect from these two was small and still 

smaller than the direct negative effect from SO4

2- aerosols. In the on-road segment of global 

transportation, emissions of CO and VOCs together with NOX produce an O3 RF larger than the 

negative RF from CH4. 

January 2010 3-13 
Source: Reprinted with Permission of Cambridge University Press from Forster et al. (2007, 092936) 

Figure 3-7. Components of RF in 2005 resulting from emissions since 1750. (S) and (T) 

indicate stratospheric and tropospheric changes, respectively. 

January 2010 3-14 
Source: Reprinted with Permission of Cambridge University Press from Forster (2007, 092936) 

Figure 3-8. Integrated RF of year 2000 emissions over 20-yr and 100-yr time horizons. The 

values provided refer to global annual emissions, but effects are expected to vary 

regionally for short-lived gases. 

Caution is warranted in interpreting RF estimates. RF values are global model calculations 

using the assumption that global climate sensitivities are equal for all forcing mechanisms, whether 

CO2, SO4

2- and other aerosols, or the short-lived gases like CO (Berntsen et al., 2005, 193241; 

Berntsen et al., 2006, 193244). That assumption is under challenge now by GCM results using 

regionalized RF values separately for different forcing mechanisms and with CO2, O3, and solar 

input changes (Joshi et al., 2003, 193752). Joshi et al. (2003, 193752) found that global climate 

system sensitivities from non-CO2 RF varied by ±30% compared to CO2 RF. Other GCM 

experiments by Lelieveld et al. (2002, 190361), Rotstayn and Penner (2001, 193754), Menon et al. 

(2002, 155978), and Kristjansson (2002, 045282) have indicated that regionally changing RF can 

induce changes in large-scale circulation patterns that control the regionalized cycles of flooding and 

drought through disruptions in regional temperature and hydrologic cycles. However, such 

regionalized patterns resulting from GCM experiments are so uncertain and so widely variable 

across models that even the sign of these regionalized changes can vary with model type and any of 

January 2010 3-15 
the models’ unconstrained assumptions (Berntsen et al., 2006, 193244). Even with such uncertainty 

and variability, though, the consensus of the climate community is that the climate effects of changes 

to emissions of the long- and especially the short-lived pollutants, including CO, very likely depend 

on location. 

Global warming potential (GWP) is a widely used relative measure of the potential effect of 

different emissions on climate, usually defined as the time-integrated RF from an instantaneous 

pulsed release of 1 kg of a trace gas relative to the effects from a pulsed release of 1 kg of CO2. 

Because the greenhouse warming effects from CO are nearly completely indirect and because CO 

concentrations are spatially heterogeneous, neither the IPCC nor EPA computes direct GWPs for 

CO, just as they do not for tropospheric O3, NO, NO2, or VOCs (U.S. EPA, 2008, 184463). IPCC 

does estimate indirect GWP for CO using the integrated indirect RF (Forster et al., 2007, 092936). 

However, the indirect GWP values evaluated and summarized by IPCC are global and cannot reflect 

effects of localized emissions or emissions changes, making the values for the short-lived species 

NMVOC, CO, and NOX more uncertain than the values for the long-lived, well-mixed species as a 

result of the OH chemistry described above. Moreover, urban- and regional-scale oxidation of CO to 

CO2 under current atmospheric conditions proceeds very slowly, and IPCC considers production of 

CO2 through this pathway to be double counting of CO effects (Forster et al., 2007, 092936). 

Variation in the CO GWP range of estimates can be attributed to the unusually large heterogeneity in 

model type and form, pulsed or stepped emissions increase, time-horizon unit, and integral or 

differential indirect effects in several combinations (with or without NOX emissions changes, 

including or excluding CO2 effects). 

Even with such variability in methods and tools, the CO GWPs have been largely in agreement 

for approximately 10 yr. The IPCC estimated the indirect GWP of CO to be 1.9 (Forster et al., 2007, 

092936). Daniel and Solomon (1998, 193235) used a global box model for changes through CH4 and 

O3 effects from pulsed CO emissions and estimated a CO GWP exclusive of the effect through CO2

to be between 1 and 4.4. Using the STOCHEM CTM, Derwent et al. (2001, 047912) estimated a 

pulsed emissions CO GWP, again exclusive of effects through CO2, to be 1.5. Johnson and Derwent 

(1996, 193192) had previously computed and integrated GWP of 2.1 for the CH4 and O3 effect from 

a step-wise emissions change using a 2-D and a 100-yr time horizon. Derwent et al. (2001, 047912) 

and Collins et al. (2002, 044156) subsequently differentiated that integral for each effect and 

reported GWP for step-wise CO emissions changes on a 100-yr time horizon of 1.0, 0.6, and 1.6 

through the effects on CH4, O3, and CO2, respectively. Berntsen et al. (2005, 193241) used the model 

LMDz v3.3 (Hauglustaine et al., 2004, 193191) to compute 100-yr GWP values for pulsed CO 

emissions through all indirect effects to be 1.9 as resolved for Europe and 2.4 for Asia, 

demonstrating the strong regionality in the indirect effects from these short-lived precursors. Most 

recently, Shindell et al. (2009, 201599) compared GWPs separately for CO, CH4, and NOX with and 

without interactions with aerosols. When including direct and indirect radiative effects related to 

interaction of CO with aerosols, the GWP for CO was estimated to rise from a range of 1-3 to a 

range of 3-8. 

3.4. Ambient Measurements 

3.4.1. Ambient Measurement Instruments 

For enforcement of the air quality standards set forth under the Clean Air Act, EPA has 

established provisions in the Code of Federal Regulations (CFR) under which analytical methods can 

be designated as federal reference methods or federal equivalent methods (FRM or FEM, 

respectively). Measurements for determinations of NAAQS compliance must be made with FRMs or 

FEMs. As of August 2009, 20 automated FRMs and no FEMs had been approved for CO 

(http://www.epa.gov/ttn/amtic/criteria.html).

All EPA FRMs for CO operate on the principle of nondispersive infrared (NDIR) detection 

and can include the gas filter correlation (GFC) methodology. NDIR is an automated and continuous 

method based on the specific absorption of infrared radiation by the CO molecule. Most 

commercially available analyzers incorporate a gas filter to minimize interferences from other gases 

and operate near atmospheric pressure. NDIR is based on the physics of CO’s characteristic infrared 

January 2010 3-16 
absorption near 4.63 μm. NDIR methods have several practical advantages over other techniques for 

CO detection in that they are not sensitive to flow rate changes, require no wet chemicals, are 

reasonably independent of ambient air temperature changes, are sensitive over wide concentration 

ranges, and have fast response times. An extensive and comprehensive review of NDIR, GFC, and 

alternative, non-FRM techniques for CO detection, including tunable diode laser spectroscopy, gas 

chromatography, mercury liberation, and resonance fluorescence, was made for the 2000 CO AQCD 

(U.S. EPA, 2000, 000907), and the reader is directed there for additional information. The 

description here is limited to a brief outline of the FRM NDIR and GFC techniques. 

GFC spectroscopy analyzers are used most frequently now in documenting compliance with 

ambient air standards. A GFC monitor has all of the advantages of an NDIR instrument and the 

additional advantages of smaller size, no interference from CO2, and very small interference from 

water vapor. During operation, air flows continuously through a sample cell. Radiation from the 

infrared source is directed by optical transfer elements through two main optical subsystems: (1) the 

rotating gas filter; and (2) the optical multipass (sample) cell. The beam exits the sample cell through 

an interference filter, which limits the spectral passband to a few of the strongest CO absorption 

lines. Detection of the transmitted radiation occurs at the infrared detector. The gas correlation cell is 

constructed with two compartments, one filled with 0.5 atm CO, and a second with pure nitrogen gas 

(N2). Radiation transmitted through the CO is completely attenuated at the wavelengths where CO 

absorbs strongly. The radiation transmitted through the N2 is reduced by coating the exit window of 

the cell with a neutral attenuator so that the amounts of radiation transmitted by the two cells are 

made approximately equal in the passband that reaches the detector. In operation, radiation passes 

alternately through the two cells as they are rotated to establish a signal modulation frequency. If CO 

is present in the sample, the radiation transmitted through the CO is not appreciably changed, 

whereas that through the N2 cell is changed. This imbalance is linearly related to CO concentrations 

in ambient air. 

Specifications for CO monitoring are designed to help states demonstrate whether they have 

met compliance criteria; operational parameters required under 40 CFR 53 are provided in Table 3-2. Given the 1-h level of the NAAQS of 35 ppm and the 8-h level of the NAAQS of 9 ppm, a 1.0 ppm 

limit of detection (LOD) is sufficient for demonstration of compliance, where the LOD is set at three 

times an instrument’s noise level when analyzing a zero air sample to ensure that reported signals are 

in response to actual ambient CO concentrations. However, with ambient CO levels now routinely at 

or below 1 ppm, there is greater uncertainty in the monitoring data because a large percentage is 

below the LOD. For this reason, a new generation of ambient CO monitors has been designed for 

measurements below 0.5 ppm, with LOD = 0.04 ppm. Additionally, CO measurements at 

concentrations below 0.5 ppm are needed to support additional objectives, such as validating the 

inputs to CTMs, improving estimates of low-concentration CO exposure, and assessing differences 

between CO levels in urban and rural areas, because background CO concentrations are on the order 

of 0.1 ppm. Effective LOD is influenced by instrumental noise and drift and by the amount of water 

vapor in the air. Recent improvements in the instruments’ optical components and dehumidification 

of the air stream help to reduce the amount of noise and drift in the CO measurements. Newer GFC 

instruments have been designed for automatic zeroing to minimize drift (U.S. EPA, 2000, 000907). 

January 2010 3-17 
Table 3-2. Performance specifications for analytical detection of CO, based on 40 CFR Part 53. 

Parameter Specification 

Range 0-50 ppm 

Noise 0.5 ppm 

LOD 1.0 ppm 

Interference equivalent 

Each interfering substance ±1.0 ppm 

Total interfering substances 1.5 ppm 

Zero drift 

12 h ±1.0 ppm 

24 h ±1.0 ppm 

Span drift, 24 h 

20% of upper range limit ±10.0% 

80% of upper range limit ±2.5% 

Lag time 10 min 

Rise time 5 min 

Fall time 5 min 

Precision 

20% of upper range limit 0.5 ppm 

80% of upper range limit 0.5 ppm 

 

Currently, 24 models of CO monitors are in use; the models are listed in Annex A, Table A-1. 

Among them, 20 are older NDIR instruments listed to have an LOD of 0.5 ppm, and 4 are GFC 

instruments listed to have an LOD of 0.04 ppm. States do not routinely report the operational LOD, 

precision, and accuracy of the monitors to EPA’s Air Quality System (AQS). When the monitored 

value is below the LOD, some states report the raw monitored data, while others report the 

concentration as 50% of the LOD (0.25 ppm for high-LOD instruments and 0.02 ppm for low-LOD 

instruments) when reported data are below the LOD. Among several of the older instruments still in 

use (Federal Reference Method codes 008, 012, 018, 033, 041, 050, 051, and 054), performance 

testing has shown effective LODs of 0.62-1.05 ppm, with 24-h drift ranging from 0.044-0.25 ppm 

and precision ranging from 0.022-0.067 ppm at 20% of the upper range limit of the instrument 

(Michie et al., 1983, 194043). Among newer GFC instruments, manufacturer-declared LODs range 

from 0.02-0.04 ppm, with 24-h zero drift varying between 0.5% within 1 ppm and 0.1 ppm and 

precision varying from 0.5% to 0.1 ppm. 

Comparison of older and newer monitors with LOD = 0.5 ppm and 0.04 ppm, respectively, 

calls attention to several data quality issues with the older monitors; Figure 3-9 illustrates this point 

with data from two co-located monitors with LODs of 0.5 ppm and 0.04 ppm, in Charlotte, NC. 

First, the data appearing below the LOD of 0.5 ppm for the older monitor comprise 58% of the data 

obtained by that monitor. In contrast, no data from the 0.04 ppm LOD monitor are reported below 

0.04 ppm. Below 0.5 ppm, observations obtained with the older monitor are on average more than 

five times higher than those from the newer monitor. Second, the data from the older monitor are 

reported in units of 0.1 ppm, as seen in the lower resolution of the data with respect to the x-axis. 

Last, it is possible from the data that the older monitor exhibits some upward drift, since newer 

models have automatic zeroing functions. Above 0.5 ppm, the slope of the scatterplot is 0.95, 

suggesting that readings from the older monitor are on average 5% higher than those from the newer 

monitor. The median data are 0.4 ppm for the older monitor and 0.24 ppm for the newer monitor. 

However the mean from the older monitor is 0.5 ppm, in contrast with 0.330 ppm for the newer 

monitor. The 99th percentile is 1.8 ppm for the older monitor, in contrast with the newer monitor, 

whose 99th percentile level is 1.485 ppm. 

January 2010 3-18 
0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5 2 2.5 3 3.5 4

Method 054 CO Concentration (ppm)

Method 593 CO Concentration (ppm)

Below Method 054 LOD

Above Method 054 LOD

Figure 3-9. Scatterplot comparing data from co-located monitors in Charlotte, NC. Data from 

Method 054 are from an older model (Thermo Electron Model 48C, Waltham, MA) 

with LOD = 0.5 ppm, while data from Method 593 are from a newer instrument 

(Teledyne API Model 300EU, San Diego, CA) with LOD = 0.04 ppm. Above the 

Method 054 LOD, the methods vary linearly as: [Method 593] = 0.95[Method 054] - 

0.20 (R2 = 0.88, n = 6990); below the Method 054 LOD, the regression changes to 

[Method 593] = 0.19[Method 054] + 0.16 (R2 = 0.07, n = 9856). 

3.4.2. 

3.4.2.1.

Ambient Sampling Network Design 

Monitor Siting Requirements 

Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient 

monitoring requirements (71 FR 61236, October 17, 2006). This action was made to allow for 

reductions in measurements of CO and some other pollutants (SO2, NO2, and Pb) where measured 

levels were well below the applicable NAAQS and air quality problems were not expected. CO 

monitoring activities have been maintained at some State and Local Air Monitoring Stations 

(SLAMS), and these measurements of CO using FRM are required to continue until discontinuation 

is approved by the EPA Regional Administrator. CO monitors are typically sited at the following 

spatial scales (40 CFR Part 58 Appendix D): 

 Microscale: Data represent concentrations within a 100 m radius of the monitor. For CO, 

microscale monitors are sited 2-10 m from a roadway. Measurements are intended to 

represent the near-road or street canyon environment. 

January 2010 3-19 
 Middle scale: Data represent concentrations averaged over areas defined by 100-500 m 

radii. Measurements are intended to represent several city blocks. 

 Neighborhood scale: Data represent concentrations averaged over areas defined by 

0.5-4.0 km radii. Measurements are intended to represent extended portions of a city. 

In 2007, there were 376 CO monitors reporting values to the EPA Air Quality System (AQS) 

database. Where CO monitoring is ongoing, 40 CFR Part 58 requires at least one CO monitor to 

capture maximum levels in a given region. This requirement is met with a monitor situated at the 

CFR-defined microscale distance from the side of a roadway for CO. Microscale monitor locations 

also have sample inlets mounted at 3 ± 0.5 m above ground level, unlike the monitors sampling for 

larger scales, whose inlet heights can vary between 2 and 15 m. For the CFR-defined neighborhood 

scale monitoring, the minimum monitor distance from a major roadway is directly related to the 

average daily traffic counts on that roadway to ensure that measurements are not substantially 

influenced by any one roadway. For example, the minimum distance of a neighborhood scale CO 

monitor from a roadway with an average daily traffic count of 15,000 vehicles per day is 25 m, while 

the minimum distance is 135 m for a roadway with an average daily traffic count of 50,000 vehicles 

per day. Occasionally, CO monitors are sited at urban (covering areas of 4-50 km) or regional 

(covering areas of tens to hundreds of km) scale. More detail on siting requirements can be found in 

40 CFR Part 58 Appendices D and E. 

In addition to monitoring for determining compliance with the NAAQS, EPA is currently in 

the process of implementing plans for a new network of multipollutant stations called National Core 

(NCore) that is intended to meet multiple monitoring objectives. A subset of the SLAMS network, 

NCore stations are intended to address integrated air quality management needs to support long-term 

trends analysis, model evaluation, health and ecosystem studies, as well as the more traditional 

objectives of NAAQS compliance and Air Quality Index reporting. The complete NCore network, 

required to be fully implemented by January 1, 2011, will consist of approximately 60 urban and 20 

rural stations and will include some existing SLAMS sites that have been modified for the additional 

measurements. Each state will contain at least one NCore station, and 46 of the states plus 

Washington, DC, will have at least one urban station. CO will be measured using 0.04 ppm LOD 

monitors at all sites, as will SO2, NO, and NOY

1

; surface meteorology will also be measured at 

NCore sites. The advantage to the NCore strategy is that time-resolved, simultaneous measurements 

of multiple pollutants will be obtained at each site. The disadvantage is that the NCore network will 

be sparse, and so spatial variability will be difficult to ascertain from the data obtained. 

3.4.2.2.

 

Spatial and Temporal Coverage 

Figure 3-10 depicts the distribution of the 376 regulatory CO monitors operating in the U.S. in 

2007. Data from 291 of the 376 CO monitors operating year-round at 290 sites in the years 

2005-2007 met the data completeness criteria for inclusion in the multiyear ambient data analyses 

for this assessment. Completeness criteria require that data be collected for 75% of the hours in a 

day, 75% of the days in a quarter, and 3 complete quarters for all 3 yr; criteria for Region 10 were 

relaxed to 2 complete quarters because it contains Alaska. The greatest density of monitors is in the 

CSAs for Los Angeles, and San Francisco, CA; and along the Mid-Atlantic seaboard. Monitors are 

also located in regions where biomass burning is more prevalent, such as Anchorage, AK, but not all 

of these monitors report values from all seasons of all years. The number of monitors per sampling 

scale is provided in Table 3-3, and locations of monitors with nearby roadway types and traffic 

counts are provided in Annex A, Tables A-2 through A-7, for each monitoring scale. Twenty-four 

percent of the monitors meeting completeness criteria are categorized as “Null”, meaning that no 

scale has been identified for those monitors. Furthermore, given the overlap between scales 

regarding the type of road at which the monitor is sited, it is possible that scale has been 

misclassified for some of the monitors. 

1

 NCore sites must measure, at a minimum, PM2.5 particle mass using continuous and integrated/filter-based samplers, speciated PM2.5, 

PM10-2.5 particle mass, speciated PM10–2.5, O3, SO2, CO, NO/NOY, wind speed, wind direction, relative humidity, and ambient temperature. 

January 2010 3-20 
Table 3-3. Counts of CO monitors by sampling scale meeting 75% completeness criteria for use in 

the U.S. during 2005-2007. 

Monitoring Scale Count 

Microscale 57 

Middle Scale 31 

Neighborhood Scale 119 

Urban Scale 11 

Regional Scale 2 

Null 71 

Figure 3-10 also shows the locations of the newer 0.04 ppm LOD CO monitors throughout the 

U.S in 2007, indicated by blue and purple triangles. The newer monitors included in the analysis are 

located in: Baton Rouge, LA; Boston, MA; Charlotte, NC; Dallas, TX; Decatur, GA; Houston, TX; 

Portland, OR; Presque Isle, ME; San Jose, CA; and rural locations within Georgia and South 

Carolina. Other 0.04 ppm LOD monitors not meeting completeness criteria for the 2005-2007 

analysis were located in: Beltsville, MD; Cedar Rapids, IA; Davenport, IA; Des Moines, IA; 

Nederland, TX; Northbrook, IL; Plant City, FL; Seattle, WA; Thomaston, CT; Tulsa, OK; Westport, 

CT; and rural locations in Maryland and Wisconsin. A listing of 0.04 ppm and 0.5 ppm LOD 

monitors meeting completeness criteria by state for 2005-2007 is provided in Annex A, Table A-8. 

Eleven metropolitan regions were chosen for closer investigation of monitor siting based on 

their relevance to the health studies assessed in subsequent chapters of this ISA and to demonstrate 

specific points about geospatial distributions of CO emissions and concentrations. These regions 

were: Anchorage, AK; Atlanta, GA; Boston, MA; Denver, CO; Houston, TX; Los Angeles, CA; New 

York City, NY; Phoenix, AZ; Pittsburgh, PA; Seattle, WA; and St. Louis, MO. Core-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 As an example, Figure 3-11 through Figure 3-14 display CO 

monitor density with respect to population density (for total population and elderly adults aged 65 

and over) for the Denver and Los Angeles CSAs (Annex A, Figures A-7 through A-22 show 

analogous plots for the other nine metropolitan regions). Figure 3-18 and Figure 3-21 and additional 

figures in Annex A show the locations of CO monitors for the 11 CSAs/CBSAs in relation to major 

roadways, including interstate highways, U.S. highways, state highways, and other major roadways 

required for traffic network connectivity. In the examples shown for Denver and Los Angeles, the 

monitors were typically located near high population density neighborhoods within the CSA. The 

Los Angeles CSA monitors appear to be distributed fairly evenly across the city of Los Angeles, 

while the Denver CSA had three monitors in the city center and two in the suburbs of the Denver 

CSA. Regional background sites were not included on the maps unless they lay within the 

CSA/CBSA. 

 

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 Anchorage and Phoenix, which are not contained within a CSA. Therefore, the smaller CBSA definition was used for these metropolitan 

areas. 

January 2010 3-21 
Figure 3-10. Map of CO monitor locations in the U.S. in 2007. Locations are indicated with 

triangles: blue and red triangles show locations of the sites used in data analysis 

for this assessment; purple and green triangles are at locations with monitors 

which did not meet the data completeness requirements for analysis; blue lines 

mark the boundaries of the 11 CSAs/CBSAs used in the data analysis for this 

assessment. 

January 2010 3-22 
Figure 3-11. Map of CO monitor locations with respect to population density in the Denver, CO 

CSA, total population. The circles indicate 5 km buffers around the monitors. 

January 2010 3-23 
Figure 3-12. Map of CO monitor locations with respect to population density in the Denver, CO 

CSA, age 65 and older. The circles indicate 5 km buffers around the monitors. 

January 2010 3-24 
Figure 3-13. Map of CO monitor locations with respect to population density in the Los 

Angeles, CA CSA, total population. The circles indicate 5 km buffers around the 

monitors. 

January 2010 3-25 
Figure 3-14. Map of CO monitor locations with respect to population density in the Los 

Angeles, CA CSA, age 65 and older. The circles indicate 5 km buffers around the 

monitors. 

January 2010 3-26 
Ambient monitors for CO and other criteria pollutants are located to monitor compliance 

rather than population exposures. However, CO monitors submitting data to the AQS are often used 

for exposure assessment. For this reason, data are presented here to assess population density in the 

vicinity of CO monitors. Table 3-4 and Table 3-5 show the population density around CO monitors 

for the total population and for elderly adults aged 65 and over for each CSA/CBSA. The percentage 

of population within specific radii of the monitors for each city was, for the most part, similar 

between the total and elderly populations. In the cases of Anchorage, Denver, Phoenix, and St. Louis 

however, the percentage of the elderly population within given radii of the monitors was 

considerably different compared with the total population. Between-city disparities in population 

density were larger. Los Angeles, with 85%, and Denver, with 68%, had the largest proportion of the 

total population within 15 km of a monitor. Seattle, with 18%, had the lowest population coverage in 

large part because ambient CO concentrations there require only a single CO monitor. For the elderly 

population, Los Angeles, at 83%, Anchorage, at 73%, and Denver, at 70%, had the greatest 

population coverage within 15 km of a monitor; Seattle, at 18%, again had the lowest coverage. 

Proximity to monitoring stations is considered further in Sections 3.5 and 3.6 regarding spatial 

variability within cities. In combination, these data illustrate that population coverage varies by 

monitor and across cities. 

Table 3-4. Proximity to CO monitors for the total population by city. 

Region Total CSA/ 

CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km 

N N % N % N % N % 

Anchorage, AK 352,225 5,391 1.53 131,608 37.36 212,834 60.43 239,842 68.09 

Atlanta, GA 5,316,742 5,480 0.10 149,772 2.82 672,701 12.65 1,444,986 27.18 

Boston, MA 7,502,707 95,732 1.28 1,180,054 15.73 2,432,846 32.43 3,418,353 45.56 

Denver, CO 2,952,039 26,096 0.88 497,598 16.86 1,091,444 36.97 1,720,360 58.28 

Houston, TX 5,503,320 29,068 0.53 599,796 10.90 1,669,117 30.33 2,506,830 45.55 

Los Angeles, CA 17,655,319 202,340 1.15 4,064,309 23.02 11,928,427 67.56 15,074,972 85.38 

New York, NY 22,050,940 201,350 0.91 3,711,369 16.83 8,385,801 38.03 12,454,837 56.48 

Phoenix, AZ 3,818,147 47,478 1.24 503,433 13.19 1,033,102 27.06 1,581,887 41.43 

Pittsburgh, PA 2,515,383 29,136 1.16 369,965 14.71 895,252 35.59 1,359,596 54.05 

Seattle, WA 3,962,434 4,814 0.12 94,649 2.39 279,976 7.07 699,490 17.65 

St. Louis, MO 2,869,955 16,638 0.58 255,499 8.90 886,412 30.89 1,303,636 45.42 

 

January 2010 3-27 
Table 3-5. Proximity to CO monitors for adults aged 65 and older by city. 

Region Total CSA/ 

CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km 

N N % N % N % N % 

Anchorage, AK 17,742 361 2.03 8,986 50.65 12,038 67.85 12,990 73.22 

Atlanta, GA 362,201 423 0.12 12,758 3.52 54,148 14.95 111,232 30.71 

Boston, MA 945,790 8,272 0.87 131,198 13.87 297,392 31.44 430,502 45.52 

Denver, CO 232,974 2,541 1.09 42,760 18.35 102,783 44.12 163,682 70.26 

Houston, TX 377,586 1,703 0.45 42,312 11.21 130,567 34.58 182,049 48.21 

Los Angeles, CA 1,626,663 17,974 1.10 380,079 23.37 1,069,188 65.73 1,355,461 83.33 

New York, NY 2,710,675 29,534 1.09 427,601 15.77 940,121 34.68 1,429,215 52.73 

Phoenix, AZ 388,150 2,877 0.74 35,839 9.23 77,244 19.90 125,300 32.28 

Pittsburgh, PA 449,544 5,383 1.20 66,967 14.90 166,440 37.02 255,220 56.77 

Seattle, WA 390,372 556 0.14 12,142 3.11 31,036 7.95 69,858 17.90 

St. Louis, MO 358,747 3,203 0.89 42,890 11.96 127,274 35.48 184,491 51.43 

 

3.5. Environmental Concentrations 

3.5.1. 

3.5.1.1.

Spatial Variability 

National Scale 

The current NAAQS designates that the level of the NAAQS is not to be exceeded more than 

once per year at a given monitoring site. Figure 3-15 and Figure 3-16 show the second-highest 1-h 

and second-highest 8-h county-average CO concentrations, respectively, over the U.S. along with 

estimates of the fraction of the U.S. total population exposed to those concentrations. Although 93% 

of the U.S. counties are not represented in AQS reporting, based on their population densities and 

proximity to sources, those counties are not expected to have higher concentrations than the ones 

analyzed here in the absence of extreme events such as wildfires. Continuous hourly averages are 

reported from U.S. monitoring stations. One-hour (1-h) and 8-h CO data were available for 243 

counties and autonomous cities or municipalities (e.g., Anchorage, AK, Washington, DC) where CO 

monitors met the 75% data completeness criteria used in this analysis for the years 2005-2007. In 

2007, no monitored location reported a second-highest 1-h CO concentration above 35 ppm (Figure 

3-15). Moreover, only two monitored locations, one in Weber County, UT and the other in Jefferson 

County, AL (including Birmingham, AL), reported second-highest 1-h CO concentrations between 

15.1 and 35.0 ppm. Figure 3-16 shows that only 5 counties reported second-highest 8-h CO 

concentrations above 5.0 ppm: Jefferson County, AL; Imperial County, CA; Weber County, UT; 

Philadelphia County., PA; and Anchorage Municipality, AK. 

January 2010 3-28 
Figure 3-15. County-level map of second-highest 1-h avg CO concentrations in the U.S. in 

2007. The bar on the left shows the total U.S. population living in counties with 

CO concentrations in the range indicated. Note that approximately 150 million 

people live in counties with no CO monitors. 

January 2010 3-29 
Figure 3-16. County-level map of second-highest 8-h avg CO concentrations in the U.S. in 

2007. The bar on the left shows the total U.S. population living in counties with 

CO concentrations in the range indicated. Note that approximately 150 million 

people live in counties with no CO monitors. 

January 2010 3-30 
Table 3-6. Distribution of 1-h avg CO concentration (ppm) derived from AQS data. 

Percentiles 

 N Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 7,180,700 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 39.0 

2005 2,391,962 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.3 22.3 

2006 2,402,153 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 35.3 

2007 2,386,585 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.1 1.9 39.0 

Winter (December - February) 1,752,340 0.6 0.0 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.6 2.7 20.0 

Spring (March - May) 1,826,167 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.0 1.7 35.3 

Summer (June - August) 1,811,082 0.4 0.0 0.0 0.0 0.0 0.2 0.3 0.5 0.7 0.9 1.5 39.0 

Fall (September - November) 1,791,111 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.2 24.1 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.0 1.5 

2005 285 0.5 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 0.9 1.3 1.6 

2006 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.2 1.4 

2007 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.1 1.5 

Winter (December - February) 285 0.6 0.0 0.0 0.2 0.2 0.4 0.5 0.7 0.9 1.1 1.5 1.6 

Spring (March - May) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.0 1.6 

Summer (June - August) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1.1 1.5 

Fall (September - November) 285 0.5 0.0 0.0 0.1 0.2 0.4 0.4 0.6 0.8 0.9 1.1 1.5 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 25,672 1.1 0.0 0.1 0.2 0.3 0.5 0.7 1.3 2.3 3.1 5.0 13.1 

Atlanta 76,683 0.5 0.0 0.0 0.2 0.2 0.3 0.4 0.6 0.8 1.1 1.6 10.8 

Boston 171,975 0.4 0.0 0.0 0.0 0.1 0.2 0.4 0.5 0.7 0.9 1.4 10.0 

Denver 129,038 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 1.0 1.3 2.2 9.3 

Houston 123,925 0.3 0.0 0.0 0.0 0.0 0.2 0.3 0.4 0.6 0.8 1.4 4.6 

Los Angeles 592,960 0.5 0.0 0.0 0.0 0.1 0.2 0.3 0.6 1.0 1.4 2.3 8.4 

New York 226,673 0.5 0.0 0.0 0.1 0.1 0.3 0.5 0.6 0.9 1.1 1.6 5.8 

Phoenix 127,477 0.8 0.0 0.0 0.1 0.2 0.3 0.5 1.0 1.9 2.5 3.6 7.8 

Pittsburgh 179,758 0.3 0.0 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.2 6.7 

Seattle 25,818 0.8 0.0 0.1 0.2 0.3 0.4 0.6 0.9 1.3 1.6 2.5 5.9 

St. Louis 77,142 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.9 1.4 5.7 

Not in the 11 cities 5,449,251 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 39.0 

a

CO monitoring is only available for quarters 1 and 4; since monitoring data are not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

January 2010 3-31 
Table 3-6 contains the distribution of hourly CO measurements reported to AQS for 

2005-2007. All monitoring locations meeting the 75% data completeness criteria have been included 

in this table. Several monitors in EPA Region 10, including four in Alaska, did not meet the data 

completeness criteria since CO reporting was only required during the first and fourth quarters of 

each year at these sites. Anchorage was included in the table, however, for an approximate 

comparison with the other CSAs and CBSAs reporting year-round measurements to AQS. 

Anchorage and other partial-year monitors were not, however, included in the national statistics 

shown in the table. AQS site number 371190041, located in Charlotte, NC, was the only site with colocated monitors both meeting the data completeness criteria and, therefore, the nationwide data in 

the table was derived from 286 monitors located at 285 sites. In Section 3.5.1.3, the nationwide 1-h 

avg statistics shown in Table 3-6 (along with the nationwide 24-h avg, 1-h daily max and 8-h daily 

max statistics) are further divided by monitoring scale (microscale, middle scale, etc.) to address 

issues relating to the near-road environment. 

The nationwide mean, median, and interquartile range for 1-h measurements reported for 

2005-2007 were 0.5, 0.4 and 0.4 ppm, respectively, and these statistics did not change by more than 

0.1 ppm over the 3-yr period. More than 50% of the data nationwide were below the LOD for the 

majority of monitors in use. The largest recorded second-highest 1-h concentration, 26.3 ppm, for 

this period was reported in 2006 in Birmingham, AL (AQS site ID: 010736004). The highest 1-h 

concentration, 39 ppm, between 2005 and 2007, was reported in Ogden, UT (AQS site ID: 

490570006) on August 28, 2007. An annual outdoor barbeque festival held in Ogden on that day 

resulted in a period of elevated CO concentrations. The seasonally stratified concentrations in Table 

3-6 are generally highest in the winter (December-February) and fall (September-November) and 

decrease on average during the spring (March-May) and summer (June-August). 

Nationwide statistics pooled by site are listed in the center rows of Table 3-6 and illustrate the 

distribution of the site average CO concentrations recorded at the 285 monitoring sites for 

2005-2007 (Figure 3-10). The site reporting the highest 3-yr pooled 1-h avg CO concentration, 

1.5 ppm, was located in San Juan, Puerto Rico (AQS site ID: 721270003). The 11 individual 

CSAs/CBSAs discussed earlier are included in the table, none of which reported concentrations 

above the value of the 1-h NAAQS. Four of the 11 cities (Boston, Houston, Pittsburgh and St. Louis) 

had 95th percentile 1-h CO concentrations below 1 ppm; the 95th percentile concentrations for the 

remaining cities were below 3.1 ppm. Lack of year-round monitoring in Anchorage prevented a 

direct comparison with the other metropolitan regions. However, Anchorage exhibited a 1-h CO 

distribution shifted higher in concentration when compared to the U.S. average during fall or winter. 

The 99th percentile 1-h avg concentration in Anchorage was 5.0 ppm; the other selected cities with 

year-round monitoring had 99th percentile concentrations ranging from 0.9 ppm to 2.5 ppm. 

January 2010 3-32 
Table 3-7. Distribution of 24-h avg CO concentration (ppm) derived from AQS data. 

Percentiles 

 N Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 303,843 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.7 7.0 

2005 101,184 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.8 5.8 

2006 101,652 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.6 7.0 

2007 101,007 0.4 0.0 0.0 0.0 0.1 0.2 0.4 0.5 0.8 1.0 1.6 6.9 

Winter (December-February) 74,144 0.6 0.0 0.0 0.1 0.2 0.3 0.5 0.7 1.1 1.3 2.0 7.0 

Spring (March - May) 77,317 0.4 0.0 0.0 0.0 0.1 0.2 0.4 0.5 0.7 0.9 1.4 6.4 

Summer (June - August) 76,562 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.7 0.8 1.3 6.9 

Fall (September - November) 75,820 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.7 5.8 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.0 1.5 

2005 285 0.5 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 0.9 1.3 1.6 

2006 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.2 1.4 

2007 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.1 1.5 

Winter (December - 

February) 285 0.6 0.0 0.0 0.2 0.2 0.4 0.5 0.7 0.9 1.1 1.5 1.6 

Spring (March - May) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.0 1.6 

Summer (June - August) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1.1 1.5 

Fall (September - November) 285 0.5 0.0 0.0 0.1 0.2 0.4 0.4 0.6 0.8 0.9 1.1 1.5 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 1,074 1.1 0.0 0.2 0.2 0.4 0.6 0.9 1.4 1.9 2.4 3.3 4.6 

Atlanta 3,229 0.5 0.0 0.1 0.2 0.2 0.3 0.4 0.6 0.8 0.9 1.2 1.6 

Boston 7,446 0.4 0.0 0.0 0.1 0.1 0.3 0.4 0.5 0.7 0.8 1.1 2.2 

Denver 5,363 0.5 0.0 0.1 0.2 0.2 0.3 0.5 0.6 0.9 1.1 1.5 2.3 

Houston 5,188 0.3 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.9 1.9 

Los Angeles 25,803 0.5 0.0 0.0 0.1 0.1 0.2 0.4 0.6 1.0 1.2 1.7 3.8 

New York 9,513 0.8 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 1.0 1.3 2.5 

Phoenix 5,348 0.8 0.0 0.1 0.2 0.3 0.4 0.6 1.1 1.6 1.9 2.5 3.4 

Pittsburgh 7,497 0.3 0.0 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.7 1.0 1.9 

Seattle 1,079 0.8 0.1 0.2 0.3 0.4 0.5 0.7 0.9 1.2 1.4 1.8 2.4 

St. Louis 3,216 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.8 1.0 1.9 

Not in the 11 cities 230,161 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.1 1.6 7.0 

a

CO monitoring is only available for quarters 1 and 4; since monitoring data are not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

Table 3-7 contains the distribution of 24-h avg CO concentrations derived from the 1-h 

concentrations reported to AQS and summarized in Table 3-6. The nationwide mean, median, and 

interquartile range for 24-h avg values during 2005-2007 were 0.5, 0.4 and 0.3 ppm, respectively. 

These were similar to those for the 1-h values and showed more than half the data falling below the 

LOD for the majority of monitors in the field. The maximum 24-h avg concentration in these years, 

7 ppm, was reported in Birmingham, AL (AQS site ID: 010736004). The 99th percentile 24-h avg 

January 2010 3-33 
concentrations ranged from 0.9 ppm to 2.5 ppm in the selected cities with year-round monitoring; 

Anchorage had a 99th percentile concentration of 3.3 ppm. 

Table 3-8. Distribution of 1-h daily max CO concentration (ppm) derived from AQS data. 

Percentiles 

 N Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 303,843 0.9 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.8 2.4 3.8 39.0 

2005 101,184 1.0 0.0 0.0 0.2 0.3 0.5 0.8 1.3 2.0 2.6 4.1 22.3 

2006 101,652 0.9 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.9 2.4 3.9 35.3 

2007 101,007 0.8 0.0 0.0 0.1 0.2 0.4 0.7 1.1 1.7 2.1 3.4 39.0 

Winter (December - February) 74,144 1.2 0.0 0.0 0.2 0.3 0.5 0.9 1.6 2.5 3.1 4.7 20.0 

Spring (March - May) 77,317 0.8 0.0 0.0 0.1 0.3 0.4 0.7 1.0 1.6 2.0 3.0 35.3 

Summer (June - August) 76,562 0.7 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.3 1.6 2.5 39.0 

Fall (September - November) 75,820 1.0 0.0 0.0 0.2 0.3 0.5 0.8 1.3 2.0 2.5 3.8 24.1 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.9 0.1 0.1 0.3 0.5 0.6 0.8 1.1 1.5 1.7 2.3 3.9 

2005 285 1.0 0.1 0.1 0.4 0.5 0.7 0.9 1.2 1.6 2.0 2.5 3.7 

2006 285 0.9 0.1 0.1 0.3 0.5 0.6 0.9 1.1 1.6 1.8 2.3 4.8 

2007 285 0.8 0.1 0.1 0.3 0.4 0.6 0.8 1.0 1.4 1.6 2.0 3.1 

Winter (December - February) 285 1.2 0.0 0.1 0.4 0.6 0.8 1.0 1.5 2.1 2.5 3.4 4.1 

Spring (March - May) 285 0.8 0.1 0.1 0.3 0.4 0.6 0.8 1.0 1.3 1.5 2.1 4.0 

Summer (June - August) 285 0.7 0.0 0.1 0.2 0.3 0.5 0.6 0.8 1.1 1.3 2.2 3.3 

Fall (September - November) 285 1.0 0.1 0.1 0.3 0.5 0.7 0.9 1.2 1.7 2.0 2.4 4.1 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 1,074 2.6 0.0 0.3 0.6 0.8 1.3 2.2 3.5 5.0 6.1 7.6 13.1 

Atlanta 3,229 0.8 0.0 0.2 0.3 0.3 0.4 0.7 1.1 1.4 1.7 2.2 10.8 

Boston 7,446 0.7 0.0 0.1 0.2 0.3 0.4 0.6 0.9 1.2 1.6 2.6 10.0 

Denver 5,363 1.2 0.1 0.2 0.4 0.5 0.7 1.0 1.5 2.2 2.7 3.9 9.3 

Houston 5,188 0.7 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.3 1.7 2.6 4.6 

Los Angeles 25,803 1.0 0.0 0.1 0.2 0.3 0.5 0.8 1.3 2.0 2.6 4.0 8.4 

New York 9,513 0.9 0.0 0.1 0.2 0.4 0.6 0.8 1.1 1.5 1.8 2.5 5.8 

Phoenix 5,348 1.9 0.0 0.3 0.5 0.6 0.9 1.6 2.5 3.5 4.1 5.3 7.8 

Pittsburgh 7,497 0.6 0.0 0.0 0.0 0.1 0.2 0.5 0.8 1.1 1.4 2.0 6.7 

Seattle 1,079 1.5 0.2 0.4 0.5 0.7 0.9 1.3 1.8 2.4 2.9 4.3 5.9 

St. Louis 3,216 0.8 0.0 0.1 0.3 0.4 0.5 0.6 0.9 1.3 1.7 2.7 5.7 

Not in the 11 cities 230,161 0.9 0.0 0.0 0.1 0.2 0.4 0.7 1.2 1.8 2.4 3.8 39.0 

a

CO monitoring is only available for quarters 1 and 4; since monitoring data are not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

Table 3-8 contains the distribution of 1-h daily max CO concentrations derived from 1-h 

values reported to AQS for all monitors meeting the inclusion criteria described earlier. The 

nationwide mean, median, and interquartile range for 1-h daily max concentrations reported for 

2005-2007 were 0.9, 0.7 and 0.8 ppm, respectively. Roughly one-third of the 1-h daily max data fall 

below the LOD for the majority of CO monitors reporting to AQS. The 99th percentile 1-h daily max 

January 2010 3-34 
concentrations ranged from 2.0 ppm to 5.3 ppm in the selected cities with year-round monitoring; 

Anchorage had a 99th percentile concentration of 7.6 ppm. 

Table 3-9. Distribution of 8-h daily max CO concentration (ppm) derived from AQS data. 

Percentiles 

 N Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 303,843 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.7 2.6 10.9 

2005 101,184 0.7 0.0 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.8 2.8 9.7 

2006 101,652 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.7 2.6 9.8 

2007 101,007 0.6 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.2 1.5 2.3 10.9 

Winter (December - February) 74,144 0.9 0.0 0.3 0.3 0.3 0.4 0.7 1.1 1.7 2.1 3.2 9.8 

Spring (March - May) 77,317 0.6 0.0 0.3 0.3 0.3 0.3 0.5 0.7 1.1 1.3 2.0 9.6 

Summer (June - August) 76,562 0.5 0.0 0.3 0.3 0.3 0.3 0.4 0.6 0.9 1.1 1.7 10.9 

Fall (September - November) 75,820 0.7 0.0 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.8 2.7 9.0 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.7 2.1 

2005 285 0.7 0.3 0.3 0.3 0.4 0.5 0.6 0.9 1.1 1.4 1.9 2.2 

2006 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.8 1.1 1.2 1.8 2.4 

2007 285 0.6 0.2 0.3 0.3 0.4 0.5 0.6 0.7 1.0 1.1 1.6 2.0 

Winter (December - February) 285 0.9 0.2 0.3 0.4 0.4 0.6 0.8 1.1 1.4 1.7 2.4 2.6 

Spring (March - May) 285 0.6 0.2 0.3 0.3 0.4 0.4 0.5 0.7 0.9 1.1 1.6 2.2 

Summer (June - August) 285 0.5 0.2 0.3 0.3 0.3 0.4 0.5 0.6 0.8 0.9 1.5 2.0 

Fall (September - November) 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.9 1.2 1.3 1.8 2.2 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 1,074 1.7 0.3 0.3 0.4 0.6 0.9 1.5 2.3 3.3 3.9 5.0 6.5 

Atlanta 3,229 0.6 0.0 0.2 0.2 0.3 0.4 0.5 0.8 1.1 1.3 1.7 2.5 

Boston 7,446 0.6 0.3 0.3 0.3 0.3 0.3 0.5 0.7 0.9 1.1 1.8 5.8 

Denver 5,363 0.8 0.3 0.3 0.3 0.3 0.5 0.7 1.0 1.4 1.8 2.4 3.4 

Houston 5,188 0.5 0.3 0.3 0.3 0.3 0.3 0.4 0.6 0.9 1.1 1.7 3.3 

Los Angeles 25,803 0.7 0.3 0.3 0.3 0.3 0.3 0.6 0.9 1.5 1.8 2.7 6.2 

New York 9,513 0.7 0.3 0.3 0.3 0.3 0.4 0.6 0.9 1.2 1.4 1.8 3.0 

Phoenix 5,348 1.3 0.3 0.3 0.3 0.4 0.6 1.0 1.8 2.5 3.0 3.8 5.8 

Pittsburgh 7,497 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.6 0.9 1.0 1.5 3.7 

Seattle 1,079 1.1 0.3 0.3 0.4 0.5 0.7 1.0 1.4 1.8 2.2 3.2 4.0 

St. Louis 3,216 0.6 0.3 0.3 0.3 0.3 0.3 0.5 0.7 0.9 1.2 1.9 4.2 

Not in the 11 cities 230,161 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.6 2.5 10.9 

a

CO monitoring is only available for quarters 1 and 4; since monitoring data is not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

Table 3-9 contains the distribution of 8-h daily max concentrations derived from the 1-h CO 

concentrations reported to AQS. This was done by first calculating the average concentration for 

each successive 8-h period, thereby producing 24 8-h avg per day. The maximum of these values for 

a given monitor within a given day (midnight-to-midnight) was used as the 8-h daily max statistic 

for that monitor and day. The nationwide mean, median, and interquartile range for 8-h daily max 

January 2010 3-35 
concentrations reported for 2005-2007 were 0.7, 0.5, and 0.5 ppm, respectively. Half of the 8-h daily 

max concentrations fell below the LOD for the majority of CO monitors in the field. The highest 8-h 

daily max concentration, 10.9 ppm, was recorded at a monitor located 5 mi north of Newkirk, OK 

(AQS site ID: 400719010). The 99th percentile 8-h daily max concentrations ranged from 1.5 ppm to 

3.8 ppm in the selected cities with year-round monitoring; Anchorage had a 99th percentile 8-h daily 

max concentration of 5.0 ppm. 

Spring

daily avg v

Winter

s. d

 daily

s. d

aily max 1h 

daily avg vs. max 8h 

daily max 1h v aily max 8h 

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0

Fall 

daily avg daily max 1h 

daily avg vs. ly max 8h 

daily max 1h . daily max 8h 

 vs.

 dai

 vs

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0

daily avg vs. daily max 1h 

daily avg vs. daily max 8h 

daily max 1h vs. daily max 8h 

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0

Summer

daily avg vs. daily max 1h 

daily avg vs. daily max 8h 

daily max 1h vs. daily max 8h 

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0







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Figure 3-17. Seasonal plots showing the variability in correlations between 24-h avg CO 

concentration with 1-h daily max and 8-h daily max CO concentrations and 

between 1-h daily max and 8-h daily max CO concentrations. Red bars denote the 

median, green stars denote the arithmetic mean, the box incorporates the IQR 

and the whiskers extend to the 5th and 95th percentiles. Correlations outside the 

5th and 95th percentiles are shown as individual points. 

Table 3-7 through Table 3-9 show distributions of CO data based on the 24-h avg, 1-h daily 

max and 8-h daily max concentration. The current standards are based on 1-h and 8-h calculations. 

While the nationwide concentrations vary in absolute magnitude based on these three statistics, the 

shape of the distributions are quite similar up to the 99th percentile. The relative increase from the 

99th percentile to the max for the 1-h daily max is larger than for the 24-h or 8-h daily max. This is 

to be expected since this statistic is more sensitive to short-term (less than 8 h) increases in CO 

concentration. Box plots showing the range in Pearson correlation coefficients (r) between the 

different statistics are shown in Figure 3-17. Included are the correlation of the 24-h avg with the 1-h 

daily max and 8-h daily max, as well as the correlation between the 1-h daily max and 8-h daily max, 

all calculated using the same 2005-2007 data set stratified by season. Correlations are generally quite 

high across all seasons and all comparisons, with median r > 0.8. Correlations are higher on average 

in the wintertime compared to the summertime for the two comparisons involving the 1-h daily max 

January 2010 3-36 
statistic. The correlations between the 24-h avg and the 8-h daily max are the highest in all seasons, 

which is in agreement with the distributional similarities shown in the preceding tables. 

3.5.1.2. Urban Scale 

This section describes urban variability in CO concentrations reported to AQS at the individual 

CSA/CBSA level. Denver, CO, and Los Angeles, CA, were selected for this assessment to illustrate 

the variability in CO concentrations measured across contrasting metropolitan regions. Information 

on the other nine cities evaluated for this assessment is included in Appendix A. Maps of the Denver 

CSA and the Los Angeles CSA shown in Figure 3-18 and Figure 3-21, respectively, illustrate the 

location of all CO monitors meeting the inclusion criteria described earlier. Letters on the maps 

identify the individual monitor locations and correspond with the letters provided in the 

accompanying concentration box plots (Figure 3-19 and Figure 3-22) and pair-wise monitor 

comparison tables (Table 3-10 and Table 3-11). The box plots for each monitor include the hourly 

CO concentration median and interquartile range with whiskers extending from the 5th to the 95th 

percentile. Data from 2005-2007 were used to generate the box plots, which are stratified by season 

as follows: 1 = winter (December-February), 2 = spring (March-May), 3 = summer (June-August), 

and 4 = fall (September-November). The comparison tables include the Pearson correlation 

coefficient (r), the 90th percentile of the absolute difference in concentrations (P90) in ppm, the 

coefficient of divergence (COD) and the straight-line distance between monitor pairs (d) in km. The 

COD provides an indication of the variability across the monitoring sites within each CSA/CBSA 

and is defined as follows: 

2

1

1

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− =

p

i ikij

ikij

jk XX

XX

p

COD

Equation 3-1 

where Xij and Xik represent the observed hourly concentrations for time period i at sites j and k, and p

is the number of paired hourly 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. Pearson correlation is also plotted as a function of distance for Denver and Los 

Angeles in Figure 3-20 and Figure 3-23, respectively. Similar maps, box plots, and comparison 

tables for the nine remaining CSAs/CBSAs are included in Annex A. 

The information contained in these figures and tables should be used with some caution since 

many of the reported concentrations for the years 2005-2007 are near or below the monitors’ stated 

LOD. Because ambient concentrations are now in large part very near or below the 0.5 ppm LOD for 

the majority of FRMs and the coarsely reported measurement resolution is 0.1 ppm, the comparison 

statistics shown in these tables might be biased to exhibit specious heterogeneity in the box plots. 

January 2010 3-37 
Figure 3-18. Map of CO monitor locations and major highways for Denver, CO. 

January 2010 3-38 
A B C D E 

Site ID 08-031-0002 08-031-

0019 

08-013-

0009 

08-123-

0010 

08-001-

3001 

Mean 0.65 0.52 0.42 0.55 0.52 

SD 0.42 0.46 0.38 0.46 0.36 

Obs 25959 25552 25559 26048 25920 

Scale Micro Micro Micro Neighborhood 

Neighborhood 

concentration (ppm)

0

1

2

3

season

1234 1234 1234 1234 1234

Figure 3-19. Box plots illustrating the distribution of 2005-2007 hourly CO concentrations in 

Denver, CO. The data are stratified by season along the x-axis where 1 = winter, 

2 = spring, 3 = summer, and 4 = fall. The box plots show the median and 

interquartile range with whiskers extending from the 5th to the 95th percentile. 

Identifiers and statistics for each site are shown at the top of the figure. 

January 2010 3-39 
Table 3-10. Table of intersampler comparison statistics, as defined in the text, including Pearson r, 

P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to AQS for 

2005-2007 in Denver, CO. The table is grouped and identified by monitoring scale. 

Micro Neighborhood 

A B C D E 

A 1.00 0.76 0.46 0.45 0.59 

0.0 0.5 0.7 0.7 0.6 

0.00 0.34 0.44 0.36 0.29 

0 1.3 46.9 78.3 10.1 

B 1.00 0.49 0.46 0.64 

0.0 0.7 0.7 0.5 

0.00 0.47 0.42 0.37 

0 47.0 79.0 10.9 

C 1.00 0.54 0.53 

0.0 0.6 0.6 

0.00 0.43 0.43 

Micro 

0 44.6 38.5 

D 1.00 0.52 

0.0 0.6 

0.00 0.34 

Legend 0 68.2 

E r 1.00 

P90 0.0 

COD 0.00 

Neighborhood 

d 0 

January 2010 3-40 
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 9

Distance (km)

Correlation

0

Figure 3-20. Intersampler correlation versus distance for monitors located within the Denver 

CSA. 

January 2010 3-41 
Figure 3-21. Map of CO monitor locations and major highways for Los Angeles, CA. 

January 2010 3-42 
A B C D E F G H 

Site ID 06-065-

1003 

06-059-

1003 

06-037-

9033 

06-037-

1301 

06-071-

9004 

06-065-

9001 

06-037-

5005 

06-059-

0007 

Mean 0.67 0.31 0.23 0.98 0.53 0.29 0.24 0.42 

SD 0.42 0.47 0.29 0.89 0.38 0.20 0.37 0.46 

Obs 24885 24760 24135 24825 24844 24792 24965 24264 

Scale Micro Middle Middle Middle Middle Neighborhood 

Neighborhood Urban 

concentration (ppm)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

season

1234 1234 1234 1234 1234 1234 1234 1234

Figure 3-22. Box plots illustrating the distribution of 2005-2007 hourly CO concentrations in 

Los Angeles, CA. The data are stratified by season along the x-axis where 

1 = winter, 2 = spring, 3 = summer, and 4 = fall. The box plots show the median 

and interquartile range with whiskers extending from the 5th to the 95th 

percentile. Identifiers and statistics for each site are shown at the top of the 

figure (monitors without scale designations in AQS are labeled Null). Part 1 of 3 

of Figure 3-22. See the next two pages for parts 2 and 3 of Figure 3-22. 

January 2010 3-43 
I J K L M N O P 

Site ID 06-037-

0002 

06-071-

0001 

06-037-

1002 

06-059-

5001 

06-037-

4002 

06-037-

1103 

06-059-

2022 

06-065-

5001 

Mean 0.42 0.17 0.66 0.62 0.69 0.56 0.26 0.25 

SD 0.27 0.17 0.59 0.55 0.56 0.50 0.25 0.14 

Obs 2,5001 24105 24892 24705 24259 24645 24831 24938 

Scale Null Null Null Null Null Null Null Null 

concentration (ppm)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

season

1234 1234 1234 1234 1234 1234 1234 1234

Part 2 of 3 for Figure 3-22 

January 2010 3-44 
Q R S T U V W X 

Site ID 06-037-

2005 

06-037-

1701 

06-037-

1201 

06-065-

8001 

06-037-

6012 

06-071-

1004 

06-071-

0306 06-037-0113 

Mean 0.72 0.69 0.57 0.60 0.30 0.59 0.30 0.41 

SD 0.48 0.45 0.54 0.46 0.25 0.32 0.28 0.36 

Obs 24804 24912 24281 24778 24860 24767 24796 24916 

Scale Null Null Null Null Null Null Null Null 

concentration (ppm)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

season

1234 1234 1234 1234 1234 1234 1234 1234

Part 3 of 3 for Figure 3-22 

January 2010 3-45 
Table 3-11. Table of intersampler comparison statistics, as defined in the text, including Pearson r, 

P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to AQS for 

2005-2007 in Los Angeles, CA. The table is grouped and identified by monitoring 

scale (monitors without scale designations in AQS are labeled Null). 

Micro Middle Neighbor

-hood 

Urban Null 

A B C D E F G H I J K L M N O P Q R S T U V W X 

A 1.00 0.56 0.56 0.54 0.73 0.72 0.45 0.62 0.54 0.35 0.70 0.66 0.46 0.62 0.61 0.48 0.53 0.78 0.73 0.67 0.54 0.70 0.55 0.57 

0.0 0.8 0.9 1.1 0.5 0.7 0.9 0.7 0.7 1.0 0.6 0.6 0.8 0.6 0.8 0.9 0.7 0.4 0.6 0.6 0.8 0.5 0.8 0.7 

Micro 

0.00 0.66 0.67 0.30 0.30 0.46 0.73 0.46 0.33 0.68 0.29 0.24 0.39 0.37 0.58 0.47 0.35 0.19 0.29 0.37 0.53 0.20 0.54 0.42 

0 57.1 104.6 74.8 21.3 30.5 95.0 51.3 52.6 110.6 88.2 51.0 74.1 77.4 43.3 80.1 70.1 35.0 108.0 6.1 114.5 27.4 62.8 98.1 

B 1.00 0.55 0.67 0.50 0.46 0.60 0.75 0.14 0.28 0.70 0.72 0.64 0.58 0.62 0.34 0.50 0.60 0.57 0.40 0.25 0.36 0.55 0.47 

0.0 0.6 1.3 0.7 0.5 0.5 0.5 0.7 0.7 0.9 0.7 0.9 0.8 0.5 0.6 0.9 0.9 0.8 0.9 0.7 0.8 0.6 0.7 

0.00 0.66 0.70 0.64 0.59 0.69 0.55 0.64 0.69 0.63 0.62 0.64 0.63 0.56 0.59 0.68 0.66 0.62 0.67 0.67 0.65 0.62 0.59 

0 112.0 38.6 76.9 55.1 55.8 17.3 51.2 158.5 66.3 27.9 29.5 51.6 23.7 129.6 54.0 46.3 80.7 59.3 96.2 54.9 107.5 64.4 

C 1.00 0.55 0.50 0.50 0.39 0.56 0.27 0.45 0.59 0.63 0.43 0.53 0.51 0.41 0.45 0.61 0.53 0.41 0.40 0.49 0.67 0.42 

0.0 1.6 0.7 0.4 0.6 0.7 0.6 0.4 1.1 0.9 1.1 0.9 0.4 0.4 1.0 0.9 0.9 0.9 0.5 0.7 0.4 0.7 

0.00 0.72 0.64 0.57 0.74 0.60 0.62 0.62 0.65 0.64 0.69 0.63 0.61 0.56 0.70 0.66 0.62 0.67 0.62 0.65 0.57 0.60 

 0 82.5 100.4 132.5 84.4 94.7 62.2 104.0 57.4 84.2 94.0 67.5 122.7 171.9 59.6 75.4 64.0 99.2 48.4 77.9 75.4 74.9 

D 1.00 0.44 0.39 0.63 0.71 0.21 0.33 0.70 0.78 0.70 0.74 0.57 0.28 0.53 0.65 0.49 0.35 0.23 0.39 0.51 0.50 

 0.0 1.3 1.6 1.5 1.1 1.5 1.7 0.9 0.9 1.0 1.0 1.5 1.7 1.0 1.0 1.2 1.2 1.6 1.3 1.5 1.3 

 0.00 0.42 0.56 0.76 0.51 0.44 0.73 0.35 0.30 0.39 0.41 0.65 0.56 0.39 0.29 0.41 0.45 0.60 0.35 0.61 0.50 

 0 88.6 86.0 20.4 27.4 35.0 152.6 29.1 23.8 11.8 15.3 59.5 154.5 23.8 45.0 42.1 73.7 58.2 57.0 103.4 26.4 

E 1.00 0.69 0.43 0.55 0.48 0.31 0.65 0.55 0.39 0.58 0.51 0.46 0.52 0.68 0.71 0.64 0.50 0.64 0.51 0.53 

 0.0 0.6 0.8 0.6 0.6 0.8 0.7 0.7 0.9 0.6 0.6 0.7 0.7 0.6 0.6 0.6 0.6 0.5 0.6 0.6 

 0.00 0.42 0.72 0.46 0.35 0.65 0.35 0.33 0.46 0.39 0.56 0.43 0.41 0.31 0.32 0.41 0.51 0.29 0.52 0.41 

Middle 

 0 48.0 108.0 68.5 59.9 90.2 96.3 65.7 90.1 87.9 64.6 73.3 78.6 44.2 116.3 17.7 119.3 32.7 44.9 109.1

F 1.00 0.43 0.53 0.56 0.30 0.58 0.55 0.36 0.53 0.51 0.49 0.47 0.69 0.66 0.66 0.56 0.68 0.49 0.55 

0.0 0.5 0.6 0.4 0.4 1.0 0.8 1.1 0.8 0.3 0.3 0.9 0.8 0.8 0.8 0.3 0.6 0.4 0.5 

0.00 0.70 0.42 0.38 0.58 0.46 0.43 0.54 0.46 0.50 0.32 0.53 0.46 0.40 0.49 0.47 0.43 0.47 0.39 

0 106.1 58.7 74.8 137.8 106.5 63.7 81.1 93.4 32.4 75.7 89.2 58.1 125.1 36.6 135.3 54.8 92.3 112.0 

G 1.00 0.58 0.19 0.18 0.64 0.59 0.59 0.59 0.42 0.26 0.40 0.51 0.52 0.43 0.24 0.27 0.41 0.59 

0.0 0.6 0.7 0.6 1.0 0.8 1.0 0.8 0.5 0.5 1.0 1.0 0.9 0.9 0.6 0.9 0.6 0.6 

Neighborhood 

0.00 0.71 0.72 0.75 0.72 0.72 0.75 0.73 0.73 0.70 0.75 0.73 0.72 0.74 0.73 0.73 0.72 0.69 

0 47.4 51.0 166.0 27.0 44.2 26.4 22.7 78.3 174.8 34.4 63.9 29.1 93.7 48.7 75.8 118.6 11.4 

H 1.00 0.29 0.31 0.72 0.81 0.63 0.70 0.67 0.37 0.54 0.69 0.61 0.49 0.29 0.47 0.54 0.59 

0.0 0.6 0.8 0.7 0.5 0.8 0.6 0.5 0.7 0.8 0.7 0.7 0.8 0.7 0.7 0.6 0.5 

Legend r 

P90 

COD 

d 0.00 0.43 0.62 0.41 0.38 0.48 0.40 0.46 0.41 0.52 0.44 0.41 0.49 0.53 0.45 0.50 0.39 

Urban 

 0 33.9 144.7 51.8 10.5 23.2 37.3 32.9 129.2 37.7 31.4 68.3 51.7 81.8 41.6 93.7 53.7 

I 1.00 0.17 0.43 0.34 0.17 0.46 0.42 0.33 0.41 0.57 0.44 0.47 0.43 0.61 0.24 0.45 

0.0 0.6 1.0 0.8 1.1 0.8 0.5 0.5 0.8 0.8 0.7 0.8 0.5 0.5 0.6 0.5 

0.00 0.62 0.35 0.33 0.47 0.38 0.52 0.36 0.43 0.33 0.33 0.43 0.47 0.30 0.50 0.38 

0 117.7 36.5 23.6 42.4 29.0 60.6 131.4 18.7 17.7 56.5 49.2 61.9 27.4 68.4 50.0 

J 1.00 0.35 0.34 0.24 0.34 0.33 0.29 0.31 0.40 0.31 0.19 0.25 0.36 0.43 0.25 

0.0 1.2 1.0 1.3 1.0 0.4 0.3 1.1 1.1 1.0 1.1 0.5 0.8 0.4 0.7 

0.00 0.67 0.66 0.70 0.66 0.63 0.55 0.71 0.68 0.64 0.69 0.62 0.66 0.59 0.62 

0 142.7 137.1 159.7 143.4 152.3 123.6 131.7 113.3 158.2 105.4 148.8 103.7 51.0 161.1

K 1.00 0.75 0.62 0.84 0.69 0.40 0.67 0.78 0.74 0.52 0.39 0.59 0.58 0.69 

0.0 0.6 0.8 0.5 1.0 1.2 0.7 0.5 0.6 0.8 1.1 0.7 1.0 0.8 

0.00 0.26 0.41 0.29 0.56 0.46 0.39 0.26 0.28 0.42 0.52 0.29 0.53 0.38 

0 43.6 40.8 14.7 84.6 167.7 18.1 53.5 20.0 85.3 30.1 63.8 97.8 18.9 

L 1.00 0.62 0.74 0.67 0.40 0.58 0.77 0.61 0.50 0.34 0.54 0.58 0.59 

0.0 0.7 0.5 0.8 1.0 0.7 0.5 0.7 0.8 0.9 0.6 0.8 0.6 

0.00 0.37 0.31 0.54 0.42 0.37 0.20 0.29 0.38 0.52 0.25 0.51 0.37 

0 24.6 29.8 41.5 130.6 28.1 24.3 61.5 50.2 73.4 35.8 86.4 48.5 

M 1.00 0.60 0.48 0.24 0.41 0.52 0.44 0.31 0.15 0.28 0.43 0.43 

0.0 0.8 1.1 1.2 0.9 0.8 0.9 1.0 1.2 0.9 1.1 0.9 

0.00 0.45 0.58 0.53 0.46 0.38 0.44 0.48 0.60 0.41 0.59 0.48 

0 27.1 52.1 152.4 34.7 48.6 52.3 74.0 69.4 60.3 109.6 35.2 

N 1.00 0.63 0.32 0.67 0.77 0.64 0.49 0.34 0.57 0.51 0.71 

0.0 0.8 1.0 0.7 0.5 0.7 0.8 0.9 0.6 0.8 0.6 

0.00 0.55 0.46 0.44 0.33 0.34 0.45 0.52 0.36 0.53 0.38 

0 70.2 157.4 11.7 43.8 31.8 75.1 44.7 55.2 95.9 21.2 

O 1.00 0.44 0.55 0.70 0.55 0.43 0.28 0.57 0.45 0.57 

0.0 0.3 0.9 0.9 0.8 0.9 0.5 0.7 0.4 0.6 

0.00 0.47 0.62 0.58 0.54 0.59 0.59 0.56 0.56 0.50 

Null 

0 107.9 69.5 48.9 101.2 47.5 114.6 52.6 102.5 85.9 

January 2010 3-46 
Micro Middle Neighbor

-hood 

Urban Null 

P 1.00 0.39 0.47 0.42 0.40 0.40 0.47 0.38 0.35 

0.0 1.0 1.0 0.9 0.9 0.4 0.7 0.4 0.6 

0.00 0.54 0.47 0.40 0.50 0.45 0.43 0.44 0.38 

0 149.6 114.2 187.6 82.4 192.2 104.2 102.8 178.1

Q 1.00 0.65 0.54 0.39 0.32 0.53 0.46 0.49 

0.0 0.6 0.8 0.8 1.0 0.7 0.9 0.8 

0.00 0.34 0.42 0.46 0.58 0.35 0.59 0.49 

0 35.4 38.0 67.2 46.2 46.0 84.3 31.6 

R 1.00 0.70 0.60 0.47 0.78 0.58 0.63 

0.0 0.6 0.7 0.9 0.5 0.9 0.7 

0.00 0.30 0.38 0.53 0.18 0.54 0.41 

0 73.4 31.8 79.6 12.0 62.4 65.0 

S 1.00 0.62 0.53 0.58 0.55 0.64 

0.0 0.7 0.8 0.6 0.8 0.7 

0.00 0.40 0.49 0.30 0.50 0.34 

0 105.2 20.4 83.8 115.6 17.8 

T 1.00 0.46 0.54 0.38 0.55 

0.0 0.9 0.6 0.9 0.7 

0.00 0.56 0.37 0.58 0.46 

0 110.8 22.8 57.0 96.1 

U 1.00 0.53 0.39 0.36 

0.0 0.6 0.5 0.6 

0.00 0.51 0.54 0.50 

0 88.3 110.7 37.4 

V 1.00 0.47 0.50 

0.0 0.7 0.6 

0.00 0.52 0.40 

0 52.7 76.4 

W 1.00 0.41 

0.0 0.6 

0.00 0.50 

0 115.3 

X 1.00 

0.0 

0.00 

Null 

0 

 

January 2010 3-47 
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250

Distance (km)

Correlation

 Figure 3-23. Intersampler correlation versus distance for monitors located within the Los 

Angeles CSA. 

The Denver CSA in Figure 3-18 incorporates an area of 33,723 km2 with a maximum straightline distance between CO monitors of 79 km. Of the five CO monitors meeting the inclusion criteria, 

three were sited for microscale monitoring and two were sited for neighborhood scale monitoring. 

Sites A and B are located in downtown Denver while Site E is located in an industrial region north of 

town and surrounded on three sides by three heavily-traveled interstate highways. Sites C and D are 

located in two smaller towns (Longmont and Greeley, respectively) north of Denver. The means and 

seasonal patterns shown in Figure 3-19 are similar for all five monitors within this CSA. The highest 

annual mean concentration (0.7 ppm) was observed at Site A, a downtown microscale monitor, while 

the lowest annual mean concentration (0.4 ppm) was observed at Site C, a microscale monitor in 

Longmont. The step-wise nature of the box plots is attributed to the 0.1 ppm resolution of the CO 

monitors used in the Denver CSA. Because these monitors have LOD of 0.5 ppm, it is also likely 

that the means and statistical distributions are biased as well. 

The Los Angeles CSA in Figure 3-21 incorporates an area of 88,054 km2

 and a maximum 

straight-line distance between monitors of 192 km, making it more than twice the size of the Denver 

CSA. Of the 11 CSAs/CBSAs investigated, Los Angeles had the largest number of CO monitors (N 

= 24) meeting the inclusion criteria. One monitor was sited for microscale, four for middle scale, two 

for neighborhood scale, and one for urban scale. The remaining 16 monitors did not contain a siting 

classification in AQS. The monitors were evenly distributed around the Los Angeles and Riverside 

areas, with outlying monitors in Santa Clarita (Site U), Lancaster (Site C), Victorville (Site W), 

Barstow (Site J) and Palm Springs (Site P). A large amount of variability is present in the means and 

seasonal patterns displayed in Figure 3-22. Generally speaking, lower annual mean concentrations 

(<0.3 ppm) were measured in the outlying towns including those listed above as well as Lake 

Elsinore (Site F) and Mission Viejo (Site O). In addition, a neighborhood scale upwind background 

site (Site G) located on the grounds of the Los Angeles International Airport and 1.5 km from the 

January 2010 3-48 
Pacific Ocean reported a relatively low mean annual concentration of 0.2 ppm. The highest annual 

mean concentration (1.0 ppm) was observed at Site D, a middle scale maximum concentration site 

located 25 m from a busy surface street and adjacent to the Imperial Shopping Mall. This site is also 

180 m from a major highway intersection and 350 m from Interstate 105. The step-wise nature of the 

box plots is attributed to the 0.1 ppm resolution of the CO monitors used in the Los Angeles CSA. 

Because these monitors have LOD of 0.5 ppm, it is also likely that the means and statistical 

distributions are biased as well. 

The pair-wise comparisons for measurements at the monitors in each of the 11 CSAs/CBSAs 

included in this analysis reveal a wide range of response between monitors in each city and among 

the cities judged against each other (Table 3-10, Table 3-11 and Annex Tables A-9 through A-16). 

While this wide range is produced by the interactions of many physical and chemical elements, the 

location of each monitor and the uniqueness of its immediate surroundings can often explain much 

of the agreement or lack thereof. 

For the monitor comparisons within the Denver CSA (Table 3-10 and Figure 3-20), the 

correlations tend to be inversely related to the monitor separation distance, with the highest 

correlation (r = 0.76) for the two downtown Denver monitors (Sites A and B) separated by 1.3 km 

and the lowest correlations (r ≤ 0.46) between the downtown Denver monitors and the Greeley 

monitor (Site D) located roughly 80 km north. While Sites A and B have a high correlation, the 

comparative magnitudes of the concentrations measured at these two sites, as determined by the P90 

and COD, is comparable to comparisons with much less proximal monitors. This is likely caused by 

the location of these two monitors on opposite sides of downtown Denver, as illustrated by the aerial 

view of monitors A and B in Figure 3-24. While there is no prevailing wind direction in Denver, the 

wind comes from the south-southwest with a slightly higher frequency than other directions, making 

Site A downwind of the urban core more frequently than Site B. Assuming traffic within the urban 

core is a major source of CO, this would explain the higher mean concentrations measured at Site A 

relative to Site B despite their close proximity. 

Greater variability in the pair-wise comparison statistics is observed in the Los Angeles CSA 

compared to the Denver CSA, partially due to the greater number of monitors spread over a larger 

area. Factors other than the distance between monitors, however, can contribute substantially to 

concentration disparities observed between monitors. To illustrate this point, Site S (located in 

Reseda, a suburb in the Simi Valley northwest of Los Angeles) correlates well (r = 0.73) with Site A 

(located 108 km to the southeast in Riverside). In fact, Site S correlates well (r > 0.62) with Sites A, 

E, F and T, all east of Los Angeles and all over 100 km away. Site S is located in a densely populated 

urban area with a mixture of commercial and residential land whereas the other four sites are located 

in less densely populated regions with commercial, residential and undeveloped land. Sites S and T 

contain no monitoring scale information in AQS, but Sites A, E and F are classified as microscale, 

middle scale and neighborhood scale, respectively. In contrast to the above example, Sites I and Q 

are located only 19 km apart in Azusa and Pasadena, respectively, and they correlate less well 

(r = 0.41). While these two locations are relatively close in proximity with similar topography, the 

siting of the two monitors is quite different. Site I in Azusa is located 700 m from I-210 in a mixed 

use community containing warehouses, small industry, housing and a gravel operation (Figure 3-25) 

while Site Q in Pasadena is located between a large residential neighborhood and the California 

Institute of Technology campus (Figure 3-26). Neither of these sites has monitoring scale 

designations reported in AQS. The contrasting CO emission sources surrounding these two monitors 

result in disparate concentrations with poor correlations despite their close proximity. Topography 

and micrometeorology can also play an important role in the correlation between monitors. For 

example, Sites C and P are isolated from the other sites in the Los Angeles CSA by the San Gabriel 

Mountains and the San Bernardino Mountains, respectively, resulting in lower than average 

concentrations (Figure 3-22) and relatively low pair-wise correlations (Table 3-11) for these two 

sites. This analysis demonstrates that agreement between monitors on an urban scale is a complex 

function of monitor siting, location relative to sources, geography, and micrometeorology. 

January 2010 3-49 
Figure 3-24. Aerial view of the location of CO monitors A and B (marked by the red pins) in 

Denver, CO, depicting their proximity to the urban core. Scale: 1 cm = 145 m. 

January 2010 3-50 
Figure 3-25. Aerial view of the location of CO monitor I (marked by the red pin) in Azusa, CA 

(Los Angeles CSA), depicting its proximity to mixed use land. 

Scale: 1 cm = 145 m. 

January 2010 3-51 
 

Figure 3-26. Aerial view of the location of CO monitor Q (marked by the red pin) in Pasadena, 

CA (Los Angeles CSA), depicting its proximity to a residential neighborhood. 

Scale: 1 cm = 145 m. 

January 2010 3-52 
3.5.1.3. Micro- to Neighborhood Scale and the Near-Road Environment 

Table 3-12 shows the 2005-2007 nationwide distributional data for all hourly, 1-h daily max, 

1-h daily avg, and 8-h daily max CO concentrations broken down by spatial sampling scale. The 

different sampling scales included in the table (microscale, middle scale, neighborhood scale and 

urban scale) were defined in Section 3.4.2.1. While monitors classified under all four scales are used 

for highest concentration monitoring and regulatory compliance, individual monitors are classified 

by spatial scale to be used for addressing more particular monitoring objectives. Microscale, middle 

scale, and neighborhood scale monitors are used to quantify source impacts while neighborhood 

scale and urban scale monitors are used for population-oriented monitoring (40 CFR Part 58 

Appendix D). For CO, traffic is the major source in an urban setting and therefore microscale data 

are sited “to represent distributions within street canyons, over sidewalks, and near major roadways” 

with at least one monitor sited to capture maximum concentrations, while middle scale monitors are 

sited to represent “air quality along a commercially developed street or shopping plaza, freeway 

corridors, parking lots and feeder streets” (40 CFR Part 58 Appendix D). The data used to create 

Table 3-12 were subject to the same 75% completeness criteria described in Section 3.5.1.1. More 

than 50% of the reported hourly data fell below the reported LOD (reported as 0.5 ppm for the 

majority of monitors reporting to AQS). 

Table 3-12. National distribution of all hourly observations, 1-h daily max, 1-h daily average, and 8-h 

daily max concentration (ppm) derived from AQS data, based on monitor scale 

designations, 2005-2007. 

PERCENTILES 

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

ALL HOURLY 

Microscale 1,428,745 0.6 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.1 1.4 2.2 19.6 

Middle Scale 771,941 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.3 18.9 

Neighborhood Scale 2,878,993 0.4 0.0 0.0 0.0 0.0 0.2 0.3 0.5 0.8 1.1 2.1 35.3 

Urban Scale 279,311 0.3 0.0 0.0 0.0 0.0 0.1 0.3 0.5 0.7 0.9 1.6 10.8 

1-H DAILY MAX 

Microscale 59,905 1.2 0.0 0.2 0.3 0.4 0.7 1.0 1.5 2.1 2.5 3.9 19.6 

Middle Scale 32,659 1.0 0.0 0.1 0.2 0.3 0.5 0.8 1.2 2.0 2.5 4.0 18.9 

Neighborhood Scale 121,328 0.9 0.0 0.0 0.1 0.2 0.4 0.6 1.1 1.8 2.4 4.0 35.3 

Urban Scale 11,784 0.7 0.0 0.0 0.0 0.1 0.3 0.5 0.9 1.3 1.8 3.1 10.8 

1-H DAILY AVERAGE 

Microscale 59,905 0.6 0.0 0.0 0.1 0.2 0.4 0.5 0.8 1.0 1.2 1.7 4.0 

Middle Scale 32,659 0.5 0.0 0.0 0.1 0.1 0.3 0.4 0.6 0.9 1.2 1.9 5.5 

Neighborhood Scale 121,328 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.0 1.6 7.0 

Urban Scale 11,784 0.3 0.0 0.0 0.0 0.0 0.2 0.3 0.5 0.7 0.8 1.2 2.5 

8-H DAILY MAX 

Microscale 59,905 0.8 0.3 0.3 0.3 0.3 0.5 0.7 1.1 1.5 1.8 2.6 5.8 

Middle Scale 32,659 0.7 0.1 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.9 2.8 6.2 

Neighborhood Scale 121,328 0.6 0.0 0.3 0.3 0.3 0.3 0.4 0.8 1.2 1.6 2.7 10.9 

Urban Scale 11,784 0.5 0.0 0.2 0.3 0.3 0.3 0.4 0.7 1.0 1.3 2.1 4.0 

January 2010 3-53 
The median hourly CO concentration across the U.S. obtained at microscale monitors was 

25% higher than at middle scale and 67% higher than at neighborhood scale. However, 

measurements at or below the median hourly concentration were almost entirely below the LOD for 

all scales, thereby limiting the usefulness of hourly median comparisons. The upper percentiles (90% 

and above), however, were all above the LOD and reveal consistently lower hourly concentrations 

for the urban scale monitors relative to the other monitors. For example, the 99th percentile of 

reported hourly values was 2.2, 2.3, and 2.1 ppm for microscale, middle scale and neighborhood 

scale, respectively, compared to 1.6 ppm for urban scale. Similar patterns were present in the 1-h 

daily max, 1-h daily average, and 8-h daily max distributions. Overall, the urban scale nationwide 

distributions tended to have lower concentrations relative to neighborhood scale, middle scale and 

microscale distributions (Table 3-12). 

Distributions categorized by spatial scale and CSA/CBSA are provided in Figure 3-27 for 

hourly data and in Figure 3-28 for 1-h daily max data for the select CSAs/CBSAs where data were 

available at multiple scales (not all scales were reported by each CSA/CBSA studied). Tables A-17 

through A-26 of Annex A contain tabular distributions for all CSAs/CBSAs except Anchorage. On a 

city-by-city basis, there was considerable variability when comparing distributions at the available 

spatial scales. With a few exceptions, however, the distribution of microscale and middle scale 

monitors tended to be higher than those obtained from neighborhood and urban scale monitors. For 

example, in CSAs/CBSAs containing both microscale and neighborhood scale monitors (Boston, 

Denver, Houston, Los Angeles, New York and Phoenix), median hourly concentrations at monitors 

sited for microscale were 20-40% higher than for middle scale and 0-150% greater than those sited 

for neighborhood scales. At the 99th percentile, microscale concentrations ranged from 31% less 

than to 59% greater than middle scale concentrations and from 14% less than to 67% greater than 

neighborhood scale. For most cities, the median hourly data are near or below the 0.5 ppm LOD 

reported for most monitors in use. In general, these data suggest that CO concentrations measured 

with monitors sited at micro- and middle scales, typically near roads, were somewhat elevated 

compared with neighborhood and urban scale monitor locations. However, the magnitude of these 

differences varies by city and is difficult to discern given the predominance of CO concentrations 

near or below the LOD. 

Despite differences in concentrations observed at different scales (Figure 3-27 and Figure 

3-28), intersampler correlations do not follow a distinct trend with respect to spatial monitoring scale 

(Table 3-10 and Table 3-11). For instance, intersampler correlation in Denver ranged from 0.46 to 

0.76 among microscale monitors and was 0.52 for the correlation between the two neighborhood 

scale monitors (no monitors in Denver reporting to the AQS are sited at middle scale). Intersampler 

correlation in Los Angeles ranged from 0.44 to 0.73 for middle scale, and the one pair of 

neighborhood scale monitors had a correlation of 0.43. Only one monitor was sited each at 

microscale and urban scale, and 16 of the 24 CO monitors in Los Angeles are not declared to sample 

at any spatial scale (scale designation = “null”). In Denver, the distribution of hourly CO data 

obtained at microscale was nearly identical to that obtained at neighborhood scale. In Los Angeles, 

the microscale data was typically higher than middle, neighborhood, or urban scale data except at the 

upper end of the distribution, where middle scale data were higher for both hourly and 1-h daily max 

data (Figure 3-27 and Figure 3-28). 

January 2010 3-54 
Atlanta

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Boston

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Denver

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Houston

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Los Angeles

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

New York City

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Phoenix

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Pittsburgh

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Figure 3-27. Distribution of hourly CO concentration data by city and 

monitoring scale. For comparison purposes, the y-axis has 

been scaled to the city-specific 99th percentile 

concentration. Note that Anchorage, Seattle, and St. Louis 

CSAs are not included here because these cities do not have 

monitors sited at different scales.

microscale

middle scale

neighborhood scale

urban scale

January 2010 3-55 
Atlanta

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Boston

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Denver

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Houston

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Los Angeles

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

New York City

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Phoenix

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Pittsburgh

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

microscale

middle scale

neighborhood scale

urban scale

Figure 3-28. Distribution of 1-h daily max CO concentration data by 

city and monitoring scale. For comparison purposes, the 

y-axis has been scaled to the city-specific 99th 

percentile concentration. Note that Anchorage, Seattle, 

and St. Louis CSAs are not included here because these 

cities do not have monitors sited at different scales.

January 2010 3-56 
The microscale and middle scale CO data reported here are consistent with hourly 

concentrations reported in the literature for the near-road environment within the U.S. Baldauf et al. 

(2008, 190239) reported CO concentrations obtained 20 m from an interstate highway in Raleigh, 

NC, to have a median around 0.25 ppm and with maximum concentration <4.0 ppm. Zhu et al. 

(2002, 041553) reported CO concentration of 1.9-2.6 ppm at a distance of 17 m from an interstate 

highway in Los Angeles, with concentration decreasing exponentially with distance from the 

highway. Zhu et al. (2002, 041553) observed on-road CO concentrations to be approximately 10 

times higher than at an upwind monitoring site, as shown in Figure 3-29. Concentrations continued 

to decrease and were still two times higher than upwind levels at a monitoring site 300 m away. 

Baldauf et al. (2008, 190239) also reported a drop in concentration at a monitoring site 300 m from 

the road compared with the 20 m site. Figure 3-30 illustrates the distribution of measurements taken 

throughout a day. In this plot, the near-road (20 m distance) CO concentrations tended to be 

significantly higher than those obtained at 300 m, and the daily variability in the CO concentration 

time series was greater at the 20 m site than at the 300 m site. The ratio of 20 m to 200 m 

concentrations was higher for the Zhu et al. (2002, 041553) paper. This was likely due to the fact that 

the 300 m site was always downwind in Zhu et al. (2002, 041553), whereas winds were more 

variable in Baldauf et al. (2008, 190239). Other near-road measurements reported in the literature are 

similar to those from the Zhu et al. (2002, 041553) and Baldauf et al. (2008, 190239) studies. Chang 

et al. (2000, 001276) reported near-road ambient CO measurements obtained in downtown Baltimore 

(distance to road not specified) in the range of 0.5-1.3 ppm. Riediker et al. (2003, 043761) reported 

measurements of CO concentration obtained near one of four heavily-trafficked roads in Wake 

County, NC, to average 1.1 ppm (range: 0.4-1.7 ppm). Neighborhood scale measurements reported 

in the literature were also consistent with if not slightly lower than those reported by AQS. Gentner 

et al. (2009, 194034) reported CO concentrations ranging from roughly 0.4-0.9 ppm in Riverside, 

CA, 1 km east of an interstate highway. Singh et al. (2006, 190136) reported 24-h avg CO 

concentrations, obtained with a 0.04 ppm LOD CO monitor in Long Beach, CA, within 0.5 km and 

1.5 km of two interstate highways, to range from 0.2-1.4 ppm. 

Source: Reprinted with Permission of Elsevier Ltd. from Zhu et al. (2002, 041553) 

Figure 3-29. Relative concentrations of CO and copollutants at various distances from the 

I-710 freeway in Los Angeles. 

January 2010 3-57 
Source: Reprinted with Permission of Air and Waste Management Association from Baldauf et al. (2008, 190239) 

Figure 3-30. CO concentration time series 20 m and 300 m from the I-440 highway in Raleigh, 

NC. Symbols denote the mean concentration, and whiskers denote standard 

deviations. 

Determinants of spatial variability in ambient CO concentration include roadway density, 

traffic counts, meteorology, and natural and urban topography. Mobile sources are the largest single 

source of CO, and their abundance and density affect the magnitude of CO production. Rodes et al. 

(1998, 010611) compared traffic volume, roadway type, and concentrations of CO and several 

copollutants in Los Angeles and Sacramento, CA, in a study of on-road traffic emissions. They noted 

that there was little difference in CO concentration between arterial roads and freeways for Los 

Angeles. Rodes et al. (1998, 010611) found that traffic was also much more congested throughout 

Los Angeles. This finding was not surprising given that Los Angeles is a much larger city with 

substantially higher traffic volumes than Sacramento. Under similar wind conditions, morning 

concentrations were much higher in Los Angeles than Sacramento. Rodes et al. (1998, 010611) 

observed that high afternoon winds ventilate Los Angeles, but Sacramento is not as well ventilated. 

As a result, Sacramento has nearly the same concentrations as Los Angeles in the afternoon. This 

observation is consistent with measurements by Gentner et al. (2009, 194034), showing that CO 

concentrations varied inversely with wind speed. 

Measured on-road and road-side CO concentrations may also relate to the traffic volume. 

Among the 291 active sites where monitors met completeness criteria during 2005-2007, 57 were 

declared by state agencies as microscale with average annual daily traffic (AADT) counts on the 

nearby roads ranging from 500 vehicles per day at one site in Denver, CO to 133,855 vehicles per 

day in Tampa, FL with a geometric mean of 17,462 vehicles per day and a geometric standard 

deviation of 2.5 (Table A-2 of Annex A). Within a geometric standard deviation, the data range from 

6,576-40,000 vehicles per day. Only two monitors were sited at roads with 100,000 vehicles per day 

or more. In contrast, the site where Zhu et al. (2002, 041553) collected data had 160,000-178,000 

vehicles per day in 2001 (CalTrans, 2009, 194036). Microscale sites near roads in the mid-range of 

the traffic count data may record data that are not substantially different from those obtained from 

neighborhood scale measurements, as indicated in Table 3-12. Likewise, with little microscale data 

at roads with AADT of more than 100,000 vehicles per day, there is still much uncertainty regarding 

the magnitude of concentrations in the near-road environment. 

Field measurements, computational modeling, and wind tunnel experiments have shown that 

roadway design, roadside structures and vegetation, and on-road traffic levels can affect 

concentrations of CO and other pollutant concentrations near roadways. Field measurements 

reported by Baldauf et al. (2008, 191017) indicated that noise barriers could reduce near-road 

pollutant concentrations by as much as 50%, although this effect was highly dependent on 

January 2010 3-58 
meteorological conditions; these results are illustrated in Figure 3-31. This study also showed that 

the presence of mature vegetation further reduced concentrations and flattened the concentration 

gradient away from the road. Urban dispersion and wind-field modeling by Bowker et al. (2007, 

149997) also demonstrated the influence of noise barriers and vegetation on the concentrations and 

spatial variability of nonreactive pollutants emitted from traffic sources. Heist et al. (2009, 194037) 

ran wind tunnel experiments using a model of a road with different roadside features and a tracer gas 

line source emitted from the simulated road to study how concentrations of gaseous traffic emissions 

vary spatially in the near-road environment. They demonstrated that noise barriers and roadway 

design characteristics, such as the presence of embankments and elevated roadway segments, can 

alter airflow and contaminant dispersion patterns in the near-road environment. For example, their 

results indicated that roadway design having below-grade sections of road and embankments 

reduced concentrations away from the road. These results showed similar concentrations as those of 

Zhu et al. (2002, 041553), both for roadway segments at-grade with no obstructions to air flow and 

for elevated roadway segments with different road fill conditions. Additionally, Khare et al. (2005, 

194016) illustrated in a wind tunnel study that vertical dispersion of a nonreactive gas increased with 

increasing simulated traffic volume; this effect was also sensitive to changes in approaching wind 

direction. These studies taken together suggest that localized turbulence induced by roadside 

structures, roadway design, and traffic provide some mixing and resulting dilution of the CO 

concentration in the near-road environment; the extent of mixing effects varies by meteorological 

conditions and the specific roadway design and traffic loading. 

 Source: Reprinted with Permission of Elsevier Ltd. from Baldauf et al. (2008, 191017) 

Figure 3-31. CO concentration profile 10 m from I-440 in Raleigh, NC, behind a noise barrier 

and in open terrain. 

The geometry of urban street canyons has a profound effect on the distribution of CO 

concentrations on a microscale. A number of studies have performed computational and wind tunnel 

modeling of street canyons using nonreactive tracers and demonstrated the potential variability in 

concentration within a canyon (e.g., 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). Because CO is a pollutant with very low reactivity on urban and regional scales, results 

from these models are directly relevant to CO concentration distributions in street canyons. 

Parameters influencing street canyon dispersion include canyon height to width ratio (H/W), source 

positioning, wind speed and direction, building shape, and upstream configuration of buildings. 

Figure 3-32 shows dimensionless concentrations obtained from wind tunnel and computational fluid 

dynamics simulations of tracer gas 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 

(downwind) canyon wall were four times those of the windward (upwind) wall near ground level. 

January 2010 3-59 
When the canyon height was twice the street width (typical of higher-density cities) 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. These results suggest that the 

magnitude of microscale CO concentrations may vary by factors of three or four times at different 

locations within a street canyon and are heavily influenced by wind speed and street canyon 

topography. The relationship between in-canyon concentration and wind speed and turbulence is 

well established with concentration varying inversely with the magnitude of wind speed and 

turbulence (Britter and Hanna, 2003, 090295). When studying the effect of wind direction on street 

canyon concentration levels for a continuous “line source” of traffic exhaust, concentration levels 

were at local maxima under two conditions: wind perpendicular to or parallel to the street canyon. 

Wind gusts at the turbulence interface at the top of the canyon or traffic-based turbulence can also 

cause dilution of the exhaust concentration within the canyon (Kastner-Klein et al., 2000, 194035). 

Source: Reprinted with Permission of Elsevier Ltd. from Xiaomin et al. (2006, 156165) 

Figure 3-32. Dimensionless tracer gas concentration on the windward and leeward sides of 

the canyon plotted against the elevation of the measurement (Z) scaled by 

building height (H) under two different H/W and wind speed conditions. Shown 

are measurements obtained in a wind tunnel (symbols) and model simulations 

using computational fluid dynamics (lines). 

Street canyon field studies support the computational and wind tunnel modeling results 

described above. In a multisite survey of curbside CO concentration in London, U.K., Croxford and 

Penn (1998, 087176) observed up to threefold differences in concentration related to the side of the 

January 2010 3-60 
street on which the monitor was positioned relative to the wind direction, with H/W varying between 

0.7 and 1.7 depending on position within the canyon. Bogo et al. (2001, 192378) measured CO 

concentrations in a street canyon with H/W of 1 in Buenos Aires, Argentina, using a continuous CO 

monitor. Similar to the Xiaomin et al. (2006, 156165) simulation results for H/W of 1, Bogo et al. 

(2001, 192378) observed slightly higher leeward concentrations than windward concentrations 

within the canyon, where recirculating airflow inside the canyon causes pollutants to collect in 

higher concentration on one side. However, for the case of a deep street canyon (H/W of 5.7) in 

Naples, Italy, Murena et al. (2008, 194038) observed that the concentrations on two sides of the 

canyon differed by <15%, with wind direction varying between 10° and 80° from the street axis. 

Doran et al. (2003, 143352) measured CO concentration in a street canyon in Phoenix, AZ, during 

the morning hours and observed that CO concentration decreases with elevation above the ground if 

turbulent mixing is small, but that the difference between ground level and 39th-floor (50 m AGL) 

measurements of CO concentration decreases when turbulent mixing increases (with maximum 

measurements at any elevation not exceeding 2 ppm). As shown in Figure 3-33, the larger difference 

in concentration as a function of turbulent mixing can occur when there are meteorologically stable 

conditions in the lower boundary layer. These results support findings from the modeling studies that 

CO concentration can vary by several times within a street canyon and is greatly influenced by local 

meteorology and building topography. 

Source: Reprinted with Permission of Elsevier Ltd. From Doran et al. (2003, 143352) 

Figure 3-33. Normalized difference between CO measurements taken at ground level and from 

the 39th floor of a building in a Phoenix, AZ street canyon as a function of bulk 

Richardson number (Ri). Bulk Ri is a dimensionless number that describes the 

ratio of potential to kinetic energy, and it is used here as a measure of stability 

within the street canyon, with greater Ri corresponding to greater stability and 

values near or less than zero indicating greater mixing. 

Research by Kaur and Nieuwenhuijsen (2009, 194014) and Carslaw et al. (2007, 148210) 

suggests that CO exposures are related to traffic volume and fleet mix in the street-canyon 

environment. Kaur and Nieuwenhuijsen (2009, 194014) used multiple linear regression to model CO 

concentration data from central London as a function of mode of transport (broken down by vehicle 

type), traffic count, wind speed, and temperature. They added each variable successively and found 

traffic count, temperature, wind speed, and walking to be significant parameters in the model, with 

traffic count being the strongest determinant. Analysis of variance showed variability in traffic count 

to explain 78% of the variability in CO levels for these data, and variability in mode of transport 

explained 6% of the variability. Likewise, Carslaw et al. (2007, 148210) used a generalized additive 

model to determine how CO concentration data (log-transformed) obtained in central London varied 

as a function of light- and heavy-duty traffic counts, along-street and cross-street components of 

January 2010 3-61 
wind, temperature, year, and Julian day. Light-duty vehicle count was a more important determinant 

of CO concentration than heavy-duty (i.e., diesel) vehicle count in this study. They found that the CO 

declined steadily with year and that wind was the most significant covariate. In addition to showing 

meteorology to be an important determinant of concentration, these modeling exercises also suggest 

a linear or log-linear relationship between concentration and traffic. 

3.5.2. 

3.5.2.1. 

Temporal Variability 

Multiyear Trends 

Figure 3-34 (top) shows ambient CO concentrations in ppm from 1980 to 2006 based on 

continuous measurements averaged over 8-h time segments. Figure 3-34 (bottom) depicts trends in 

the annual second-highest 8-h CO concentrations for 144 sites in 102 counties nationwide having 

data either in the SLAMS network or from other special purpose monitors. 

The 2006 annual second highest 8-h CO concentration averaged across 144 monitoring sites 

nationwide was 75% below that for 1980 and is the lowest recorded during the past 27 yr (Figure 

3-34 [top]). Since 1992, more than 90% of these sites have reported second highest CO 

concentrations below the 8-h NAAQS of 9 ppm. The mean annual second highest 8-h ambient CO 

concentration has been below 5 ppm since 2004. The downward trend in CO concentrations in the 

1990s parallels the downward trend observed in CO emissions, attributed largely to decreased 

mobile source emissions. In addition, of the 144 sites used to determine this trend, from a total of 

375 monitoring sites operating in 2006, the number reporting second-highest 8-h CO concentrations 

above the level of the NAAQS declined to zero over the same period (Figure 3-34 [bottom]). 

Consistent with the nationwide trends in emissions and concentrations, CO concentrations in 

all 10 EPA Regions have steadily decreased since 1980, with reductions over this period ranging 

from 68% in Region 7 to 85% in Region 1 (Figure 3-35). This is also consistent with declining 

emissions seen in many regions of the U.S., shown in Figure 3-5. 

January 2010 3-62 
Source: U.S. EPA (2008, 157076) 

Figure 3-34. (Top) Trends in ambient CO in the U.S., 1980-2006, reported as the annual second 

highest 8-h concentrations (ppm) for the mean, median, 10% and 90% values. 

(Bottom) Trends in ambient CO in the U.S., 1980-2006, reported as the number of 

trend sites (y-axis) with annual second highest 8-h concentrations 

above the level of the NAAQS (9 ppm). 

January 2010 3-63 
Source: U.S. EPA (2008, 157076) 

Figure 3-35. Trends in ambient CO in the U.S., 1980-2005, reported as the annual second 

highest 8-h concentrations (ppm) for the EPA Regions 1 through 10, along with a 

depiction of the geographic extent of those Regions. 

January 2010 3-64 
3.5.2.2. Hourly Variation 

Weekday and weekend diel variation for the mean, median, 5th, 10th, 90th, and 95th 

percentiles of hourly CO concentration over 2005-2007 are shown in Figure 3-36 and Figure 3-37, respectively, for the 11 CSAs and CBSAs examined in this assessment. 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 compared to the 

actual daily temporal behavior on any specific day measured by an individual monitor. However, 

these figures are useful for comparing the general hourly variation in CO concentrations across cities 

and by day of the week (i.e., weekday versus weekend). The weekday data showed that the 

Anchorage mean, median, 5th and 10th percentile CO concentration curves exhibit pronounced 

morning and evening rush hour peak CO levels. Boston, Denver, Houston, Los Angeles, Phoenix, 

Pittsburgh, and St. Louis all exhibited similar trends, although the magnitude of the concentrations 

shown was roughly twice as high for Anchorage as the other cities. The curves had less overall 

variability for Boston, Pittsburgh, and St. Louis. The Atlanta plot shows that the median 

concentration was fairly constant throughout the 24-h period, with a slightly elevated mean during 

the morning hours. The 90th and 95th percentile curves exhibit stronger morning and evening CO 

concentration peaks. New York City shows fairly constant CO mean and median concentration 

throughout the day, with slight elevations throughout the morning rush hour and a slight trough 

between 1:00 and 5:00 a.m. The Seattle plot shows a daytime plateau beginning around 5:00 a.m. 

and lasting until roughly 10:00 p.m., with higher concentrations during morning and afternoon rush 

hour. Differences in hourly variation among the 11 CSAs and CBSAs reflect city-to-city variation in 

source characteristics and meteorology. For instance, the rush hour peaks in many cities likely 

correspond to increased mobile source emissions during those periods. Local meteorology and 

topography, which influence mixing heights, can also affect hourly variation in CO concentration. 

Figure 3-37 illustrates weekend diel trends for the 11 CSAs and CBSAs considered in this 

assessment. For Anchorage during the period 2005-2007, the mean and median concentration curves 

peaked during the morning and evening hours. A daytime concentration trough is evident. The 90th 

and 95th percentiles of concentration were similar but more pronounced. The shape of this plot is 

also characteristic of Atlanta, Boston, Denver, Houston, Los Angeles, Phoenix, Pittsburgh, Seattle, 

and St. Louis, although the Anchorage CO concentrations are nearly 100% higher than 

concentrations in the other cities. The weekend diel plot for New York City shows that the mean and 

median CO concentrations remain fairly constant throughout the day, with a slight reduction between 

2:00 and 7:00 a.m. The 90th and 95th percentile curves illustrate more diel variation. 

January 2010 3-65 
Figure 3-36. Diel plot generated from weekday hourly CO data (ppm) for the 11 CSAs and 

CBSAs, 2005-2007. Included are the number of monitor days (N) and the median, 

mean, 5th, 10th, 90th and 95th percentiles of composite CO concentrations 

plotted by time of day. Note that the y-axis of the Anchorage and Phoenix plots 

are scaled to 5.8 ppm while the other plots are scaled to 3.0 ppm. 

January 2010 3-66 
Figure 3-37. Diel plot generated from weekend hourly CO data (ppm) for the 11 CSAs and 

CBSAs, 2005-2007. Included are the number of monitor days (N) and the median, 

mean, 5th, 10th, 90th and 95th percentiles of composite CO concentrations 

plotted by time of day. Note that the y-axis of the Anchorage and Phoenix plots 

are scaled to 5.8 ppm while the other plots are scaled to 3.0 ppm. 

January 2010 3-67 
3.5.3. Associations with Copollutants 

Associations between hourly CO and other copollutants, including SO2, NO2, O3, PM10, and 

PM2.5 are provided in box plots in Figure 3-38 using AQS data across the U.S. AQS data were 

obtained from all available co-located monitors across the U.S. after application of the 75% 

completeness criteria described earlier in Section 3.5.1.1. Pearson correlation coefficients (r) were 

calculated using 2005-2007 data stratified by season. Correlation plots analogous to Figure 3-38 for 

select individual cities are provided in Annex A, Figures A-43 to A-48. 

Spring

SO2 

NO2 

O3 

PM10 

PM2.5 

correlation (r) with CO

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Fall 

SO2 

NO2 

O3 

PM10 

PM2.5 

correlation (r) with CO

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Winter

SO2

NO2

O3

PM10

PM2.5

correlation (r) with CO

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Summer

SO2

NO2

O3

PM10

PM2.5

correlation (r) with CO

-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 3-38. Seasonal plots showing the variability in correlations between hourly CO 

concentration and co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations. 

Red bars denote the median, green stars denote the arithmetic mean, the box 

incorporates the IQR, and the whiskers extend to the 5th and 95th percentiles. 

Correlations outside the 5th and 95th percentiles are shown as individual points. 

In all cases, a wide range of correlations existed between CO and copollutants as illustrated in 

Figure 3-38. The mean and median correlation between CO and copollutants were positive for NO2, PM10, and PM2.5; near zero for SO2; and negative for O3. These findings reflect common combustion 

sources for CO, NO2, and PM. CO is highly correlated with NO2 and PM2.5 because they are both 

emitted directly during incomplete combustion and because secondary nitrate PM comes from NOX, which is largely produced from mobile sources. Among those copollutants with positive 

associations, NO2 had the highest mean and median correlations, followed by PM2.5 and PM10

(correlations vary by season). The IQR of correlations with SO2 spanned from positive to negative 

for all seasons; SO2 would not be expected to correlate well with CO because SO2 emanates 

primarily from industrial sources. Correlations between CO and O3 were almost entirely negative for 

January 2010 3-68 
winter, when CO emissions tend to be high and O3 formation is low. During the other three seasons, 

most of the CO-O3 correlations were also negative. Given the role of CO in O3 chemistry, crosscorrelation functions were also computed by season for the CO-O3 relationship (Annex A, Figure 

A-50). The data showed negative correlations at all lags with minima at zero lag for winter, spring, 

and fall. During the summer, a small positive peak correlation (r = 0.071) was centered at a lag 

of -8 h and a minima occurred at a lag of 1 h, r = -0.272. It is not known whether the positive lagged 

correlations in summertime are related to interaction of CO with O3 through chemistry, coinciding 

independent effects such as peak times for rush hour CO emissions and afternoon O3 formation, or 

some combination of interactive and independent effects. However, given the very low magnitude of 

these correlations, it is clear that many other factors influence the O3 and CO time series. 

Within and between individual metropolitan areas, the distribution of copollutant correlations 

varied substantially. Figure 3-39 and Figure 3-40 illustrate the correlations between CO and 

copollutants for Denver, CO, and Los Angeles, CA, to exemplify these differences. Copollutant 

correlation plots are also shown for other cities in Annex A, Figures A-44 through A-49. For 

instance, correlations between CO and copollutants are all positive for SO2, NO2, PM10, and PM2.5

and are all negative for O3 in Denver. In contrast, the correlations in Los Angeles span from negative 

to positive for O3, PM10, and PM2.5, in various seasons. The larger span of correlations for Los 

Angeles in comparison with Denver could result from several factors. For example, more variation 

in meteorology, topography, or source distribution with respect to monitor placement in Los Angeles 

may cause the distribution of copollutant correlations to be wider. In addition, fewer co-located 

monitors in Denver compared with Los Angeles may be causing some of the observed differences. 

Figure 3-39. Seasonal plots showing the variability in correlations between hourly CO 

concentration and co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations 

for Denver, CO. 

January 2010 3-69 
Figure 3-40. Seasonal plots showing the variability in correlations between hourly CO 

concentration and co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations 

for Los Angeles, CA. 

Several recent studies reported correlations between ambient CO and other pollutants. 

Reported relationships were generally consistent with the correlations shown above using AQS data. 

Sarnat et al. (2001, 019401) reported significant positive Spearman’s correlations between CO and 

NO2 (r = 0.76) and PM2.5 (r = 0.69) and significant negative correlations between CO and O3

(r = -0.67) in Baltimore (concentration averaging periods not specified). Correlation of CO with SO2

was insignificant (r = -0.12). The Sarnat et al. (2001, 019401) study focused on correlations of 

ambient and personal PM2.5 with gaseous copollutants, so seasonal information is only available for 

the correlation between PM2.5 and CO. High correlation of ambient CO with NO2 is expected given 

that both are closely related to mobile source combustion emissions. Sarnat et al (2005, 087531) also 

reported significant year-round association between CO and PM2.5 and significant associations 

between CO and SO4

2- aerosols. Kim et al. (2006, 089820) measured CO, NO2, and PM2.5 at ambient 

fixed sites in Toronto, Canada, and found associations, averaged over monitoring stations, of CO 

with PM2.5 (Spearman’s r = 0.38, nonsignificant) and of CO with NO2 (r = 0.72, significant). Tolbert 

et al. (2007, 090316) reported correlations between multiple pollutants in Atlanta and also showed 

the highest Spearman’s correlation for CO with NO2 (r = 0.70). CO was also reported to have fairly 

high correlation with PM2.5 elemental carbon (EC) (r = 0.66), PM2.5 organic carbon (OC) (r = 0.59), 

and PM2.5 total carbon (TC) (r = 0.63). Correlations were reported to be much lower for CO with O3

(r = 0.27) and PM2.5 SO4

2- (r = 0.14). The higher correlations of CO with EC, OC, and TC are likely 

related to the fact that CO and carbonaceous PM are both emitted by mobile sources. Gentner et al. 

(2009, 194034) analyzed the relationship between ambient CO and VOC concentrations, serving as 

markers of gasoline vehicle emissions in Riverside, CA. Correlations of CO with two compounds, 

n-butane and isopentane, are shown in Figure 3-41 for summer and fall. Higher concentrations of 

n-butane per unit of CO were observed for fall, as well as higher correlation (fall: r = 0.88; summer: 

r = 0.65). For isopentane, the slopes of regression are much closer for fall and summer, with higher 

correlations between isopentane and CO (fall: r = 0.93; summer: r = 0.86). Gentner et al. (2009, 

194034) noted that isopentane vapor fraction was higher in summer than winter and that the n-butane 

vapor fraction increases in winter. This reflects the higher volatility of n-butane compared with 

isopentane. In this work, Gentner et al. (2009, 194034) used emissions modeling to estimate that 

overall VOC emissions from gasoline varies with CO emissions with a ratio of 0.086 with a 

correlation of r = 0.80 in summer. Gentner et al. (2009, 194034) suggest that the near-road slope of 

January 2010 3-70 
ambient VOC to CO concentration might be influenced by upwind CO concentration and secondary 

CO production by oxidation of VOCs. 

Source: Reprinted with Permission of ACS from Gentner et al. (2009, 194034) 

Figure 3-41. Linear regression of n-butane and isopentane concentration as a function of CO 

concentration, Riverside, CA. 

January 2010 3-71 
3.5.4. 

3.5.4.1.

Policy-Relevant Background 

Background concentrations of pollutants used for informing policy decisions about national 

standards in the U.S. are commonly referred to at EPA as PRB concentrations. In this assessment, 

PRB concentrations include contributions from natural sources everywhere in the world and from 

anthropogenic sources outside the U.S., Canada, and Mexico. 

Surface-Based Determinations 

For this assessment, PRB concentrations of CO were determined from the extensive and longrunning network of remote-site baseline CO measurements conducted by NOAA’s Earth System 

Research Laboratory (ESRL), Global Monitoring Division (GMD), as part of their Carbon Cycle 

Greenhouse Gases Group (CCGG) Cooperative Air Sampling Network (CASN); see 

http://www.esrl.noaa.gov/gmd/ccgg/iadv. Surface-based CO measurements have been made for more 

than 10 yr with exceptionally high sensitivity and selectivity at locations significantly away from 

local sources. In this assessment, for example, CO data through December 2007 are available with 

extensive quality assurance and control information from the worldwide network of 72 stations 

active in December 2008. ESRL GMD uses the highly sensitive gas chromatography-mercury 

liberation photometric detection technique with precision to 1 ppb in 50 ppb or 2 ppb in 200 ppb and 

accuracy to 1.5 ppb in 500 ppb or 2 ppb in 200 ppb. 

In order to smooth interannually changing meteorological and emissions effects, data from 

2005-2007 at 12 remote sites in the U.S. were used to determine PRB. A map of these sites is shown 

in Figure 3-42; they are: Cold Bay, AK; Barrow, AK; Shemya Island, AK; Cape Kumukahi, HI; 

Mauna Loa, HI; Trinidad Head, CA; Point Arena, CA; Wendover, UT; Niwot Ridge, CO; Park Falls, 

WI; Southern Great Plains, OK; and Key Biscayne, FL. These sites are affected by anthropogenic 

emissions in North America to varying degrees. Average concentrations for each month and for each 

of the 3 yr are shown for each site in Figure 3-43. All sites demonstrate the well-known seasonality 

in background CO with minima in the summer and fall and maxima in the winter and spring in the 

Northern Hemisphere. Northern Hemisphere summer-time minima are related in large measure to 

the enhanced photochemical reaction of CO with OH, as described in Section 3.2.2. Analysis for 

North American PRB is made here by segregating the three Alaska sites (owing to their high 

latitude) and the two Hawaii sites (owing to their distance from the continent) and treating the 

remaining seven sites as being more representative of the CONUS surface-level background 

concentrations. Outside the defined CONUS domain used here, the 3-yr avg CO PRB in Alaska 

ranged from 127 to 135 ppb with an average of 130 ppb, and from 95.3 to 103.1 ppb with an average 

of 99.2 ppb in Hawaii. Over the CONUS domain the 3-yr avg CO PRB concentration ranged from 

118 to 146 ppb with an average of 132 ppb. 

January 2010 3-72 
Figure 3-42. Map of the baseline monitor sites used in this assessment to compute PRB 

concentrations. 

January 2010 3-73 
Figure 3-43. Monthly (circles) and annual (squares) average CO concentrations (ppb), 

2005-2007. Cold Bay, AK; Barrow, AK; Shemya Island, AK; Cape Kumukahi, HI; 

Wendover, UT; Niwot Ridge, CO; Mauna Loa, HI; Trinidad Head, CA; 

Point Arena, CA; Park Falls, WI; Southern Great Plains, OK; and 

Key Biscayne, FL. 

3.5.4.2. Limitations of Other Possible Methods 

The significance of CO for surface-level air quality and for its indirect climate forcing effects 

through CH4, O3, and CO2 as described previously in this chapter, and its long τ relative to that of 

other primarily urban and regional pollutants make it an important species for measurement and 

evaluation on multiple spatial, temporal, and chemical scales. 

In addition to the ESRL GMD surface network used in this assessment’s determination of CO 

PRB, CO concentrations away from local sources can be measured from space. So, for example, CO 

has been observed from space by the Measurement of Air Pollution from Satellites (MAPS) 

instrument on Space Shuttle orbiter flights for three 10-day missions in 1984 and 1994 (Connors et 

al., 1994, 193755) and by the Measurement of Pollution in the Troposphere (MOPITT) on the Terra 

satellite since 2000 (Emmons et al., 2004, 193756). Surface spatial coverage with both space-based 

instruments was limited by the common problems of cloud cover, high surface albedo and 

emissivities, and image swath pattern and timing, with the result that much of the CONUS, for 

example, was missed some of the time. In addition, all of these satellite measurements were limited 

though somewhat differently in the vertical resolution of their total column CO concentration values. 

January 2010 3-74 
For a determination of a PRB-equivalent background concentration for 2008, the MAPS data 

would be of no use, except for comparisons on temporal trends, and even that is limited by the very 

few observations from MAPS. MOPITT data might seem more useful were it not for MOPITT’s 

very low precision and accuracy in the lowest few kilometers above the Earth’s surface of its 

integrated total column CO measurement by thermal infrared radiances (Shindell et al., 2005, 

193746). MOPITT CO profile sensitivities are so low at the surface that retrievals at the 850 hPa 

level, the lowest reported, do not capture the surface concentration accurately but actually represent 

a broad and deep vertical slice of the lower troposphere with an integral concentration that often 

peaks well above 850 hPa (Shindell et al., 2006, 091028). Error analysis by Emmons et al. (2004, 

193756), reported in Shindell et al. (2006, 091028) revealed that MOPITT concentration error in the 

lower troposphere was 7% and had greater bias over cleaner sites, which are of most interest when 

estimating a CONUS PRB. 

Since the integrated total column measurements of CO from space-borne instruments are 

dominated by CO in the mid- and upper troposphere, comparisons to surface measurements are 

subject to appreciable error. Using a subset of seven to nine of the ESRL GMD network stations in 

North America, for example, to compare to the MAPS and MOPITT data, Shindell et al. (2005, 

193746) found that the satellite data showed an increase of between 3 and 13 ppb CO while the 

surface data at these locations showed a decrease of 20 ppb in the years 2000-2002 relative to 1994. 

Mean global concentrations of CO were apparently decreasing before 2000, but that trend has now 

mostly ended (Duncan and Logan, 2008, 194042), so that the integrated column CO total measured 

from space may have indicated a false trend. 

CO concentrations can also be predicted with numerical CTMs on regional, continental, and 

global scales. Hence it would, in principle, be possible to predict CO PRB concentrations for the 

CONUS. The chief limitation to this method comes from the highly uncertain emissions of CO 

worldwide needed to drive the global CTMs, which in turn set the boundary conditions and chemical 

flow fields for the finer-scale models that might be used to compute PRB. Interannual variability in 

CO emissions from global biomass burning is very high, and the emissions source strength of this 

signal is a very strong component of the CONUS PRB given the CO τ of ~57 d. The long τ means 

that PRB monitoring sites are subject to contamination by regional pollution. Estimates of total 

global CO emissions used in recent forward and inverse model experiments range from 

<1,100 MT/yr to >3,300 MT/yr (Shindell et al., 2005, 193746). 

A comprehensive evaluation of 26 state-of-the-science atmospheric chemistry models 

exercised for present-day and future CO simulations was performed and reported by Shindell et al. 

(2006, 091028). They found substantial under-prediction of CO in the extra-tropical Northern 

Hemisphere compared to satellite and local surface observations and large variability among the 

models as well, even when using identical CH4 abundances and CO emissions. In North America, for 

example, the multimodel average underestimated the observations of lower troposphere CO by 60 

ppb or more, or by ~50% or more of the measured background concentration at many of the ESRL 

GMD sites. The Pearson r values for the multimodel average against MOPITT data globally for 

2000-2001 was 0.84 ± 0.08 for April at 850 hPa (as near to the surface as tested) but only 0.55 ± 0.11 

in October (Shindell et al., 2006, 091028). Shindell et al. (2006, 091028) proposed several reasons 

why this could contribute to pervasive underprediction: (1) the models do not adequately simulate 

CO build-up during the wintertime periods of lower OH flux; (2) the models have no seasonal CH4

cycle with build-up in the Northern Hemisphere winter; and (3) variability in the models’ OH 

concentrations, which accounted for ~80% of the CO intermodel variance (Shindell et al., 2006, 

091028). 

All of the above methods have their own advantages and disadvantages. The levels determined 

by the ESRL/GMD network show the temporal and spatial variability of CO levels. Although these 

sites are subject to North American pollution sources to varying degrees, these data could be used 

provided this caveat is borne in mind. Resulting errors in estimating excess risks will be very small 

because the concentrations are only a small fraction of the CO NAAQS. 

January 2010 3-75 
3.6. 

3.6.1. 

Issues in Exposure Assessment 

Summary of Findings from 2000 CO AQCD 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) describes the results of studies completed 

prior to 1999 on personal exposures and microenvironmental concentrations of CO. Although these 

studies may no longer be representative of current exposure levels due to declining ambient CO 

concentrations, the personal-microenvironmental-ambient relationships are still instructive. Time 

spent commuting, particularly in cars, was a major contributor to personal CO exposures. Many 

studies measured in-vehicle concentrations of CO and found elevated concentrations compared to 

fixed-site monitors. Roadside CO monitors were elevated compared to ambient levels and equal to or 

lower than in-vehicle levels (Ott et al., 1994, 076546; Rodes et al., 1998, 010611). A small portion of 

the CO concentrations inside a vehicle cabin comes from the vehicle itself, while a substantial 

fraction comes from roadway mobile source emissions entering the cabin via air exchange. Studies 

summarized in the 2000 CO AQCD found that in-vehicle CO concentrations were generally two to 

five times higher than ambient CO concentrations obtained at fixed-site monitors within the cities 

studied. High-traffic volumes contributed to increased in-vehicle concentrations. 

Prior to the 2000 CO AQCD, it was well known that CO levels in residences may be elevated 

above ambient due to nonambient indoor sources, such as cooking, space heating, and smoking. 

Separation of indoor CO into ambient and nonambient components was found to be important for 

determining the effect of ambient CO concentrations, although this had not been done successfully in 

studies conducted to date. Two large studies performed in Denver, CO, and Washington, DC, in the 

early 1980s found that fixed-site monitor concentrations were higher than personal exposures for 

those with low-level exposures, while fixed-site monitor concentrations were lower than exposures 

for those with high-level exposures (Akland et al., 1985, 011618; Johnson, 1984, 024652). 

Nonambient sources contributing to high-total exposures likely obscured this relationship. In 

Denver, gas stove operation, passive smoking, and attached garages increased residential indoor 

exposure by 2.6, 1.6, and 0.4 ppm, respectively, compared to individuals without those sources 

present. Categorical analyses found significantly higher personal exposures on high-ambient 

concentration days than on low-ambient concentration days, suggesting that personal exposures are 

related to ambient levels. Nonambient exposures tend to obscure the relationship between ambient 

CO concentrations, as measured at ambient monitors, and total personal CO exposure. 

3.6.2. General Exposure Concepts 

A theoretical model of personal exposure is presented to highlight measurable quantities and 

the uncertainties that exist in this framework. An individual’s time-integrated total exposure to CO 

can be described based on a compartmentalization of the person’s activities throughout a given time 

period: 

∫ = CE dt T j

Equation 3-2 

where ET = total (T) exposure over a time-period of interest, Cj = airborne CO concentration at 

microenvironment j, and dt = portion of the time-period spent in microenvironment j. Equation 3-2

can be decomposed into a model that accounts for exposure to CO of ambient (Ea) and nonambient 

(Ena) origin of the form: 

= + EEE naaT

Equation 3-3 

Examples of ambient CO sources include industrial and mobile source emissions, biomass 

combustion, and agricultural processes. Examples of nonambient sources include environmental 

tobacco smoke (ETS), cooking, and home heating. CO concentrations generated by ambient and 

January 2010 3-76 
nonambient sources are subject to spatial and temporal variability that can affect estimates of 

exposure and resulting health effects. Exposure factors affecting interpretation of epidemiologic 

studies are discussed in detail in Section 3.6.8. 

This assessment focuses on the ambient component of exposure because this is more relevant 

to the NAAQS review. Ea can be expressed in terms of 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 3-4 

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 3-4 is subject to the constraint Σfo + 

Σfi = 1 to reflect the total exposure over a specified time period, 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/or recreational facilities (movies, restaurants, 

bars). “Outdoor” exposure can occur in parks or yards, on sidewalks, and on bicycles or motorcycles. 

Finf is a function of the building air exchange characteristics. Assuming steady state ventilation 

conditions, the infiltration factor is a function of the penetration (P) of CO, the air exchange rate (a) 

of the microenvironment, and the rate of CO loss (k) in the microenvironment; Finf = Pa/(a+k). 

Given that k Æ 0 for CO, Finf reduces to P. Studies of CO infiltration are reviewed in Section 3.6.5.1. 

In epidemiologic studies, 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 different microenvironments. If an epidemiologic 

study employs only Ca, then the assumed model of an individual’s exposure to ambient CO, first 

given in Equation 3-4, is re-expressed solely as a function of Ca: 

( ) oa += ∑ CPffE ai

Equation 3-5 

Meteorology, strength of CO sources, spatial variability of CO concentration, proximity of the 

study population to sources of CO, design of the epidemiologic study, and other factors determine 

whether or not Equation 3-5 is a reasonable approximation for Equation 3-4. Errors and uncertainties 

inherent in use of Equation 3-5 in lieu of Equation 3-4 are described in Section 3.6.8 with respect to 

implications for interpreting epidemiologic studies. Epidemiologic studies often use concentration 

measured at a central site monitor to represent ambient concentration; thus α, the ratio between 

personal exposure to ambient CO and the ambient concentration of CO, is defined as: 

a

a

C

E

α =

 Equation 3-6 

Combination of Equation 3-5 and Equation 3-6 yield: 

+= ∑ Pff α o i

Equation 3-7 

α varies between 0 and 1. If a person’s exposure occurs in a single microenvironment, the ambient 

component of a microenvironmental CO concentration can be represented as the product of the 

ambient concentration and P. Wallace et al. (2006, 089190) note that time-activity data and 

corresponding estimates of P for each microenvironmental exposure are needed to compute an 

individual’s α with accuracy. If local sources and sinks exist and are significant but not captured by 

central site monitors, then the ambient component of the local outdoor concentration may be 

January 2010 3-77 
estimated using dispersion models, land use regression models, receptor models, fine scale CTMs or 

some combination of these techniques. These techniques are described in Section 3.6.3. 

3.6.3. 

3.6.3.1. 

Exposure Modeling 

Stochastic Population-Based Time-Weighted Microenvironmental Exposure 

Models 

Population-based methods, such as the Air Pollution Exposure (APEX) and Stochastic Human 

Exposure and Dose Simulation (SHEDS) models, involve stochastic treatment of the model inputs 

(Burke et al., 2001, 014050; U.S. EPA, 2009, 194009). These are described in detail in the 2008 NOX

ISA (U.S. EPA, 2008, 157073), in Annex AX 3.6.1. Stochastic models utilize distributions of 

pollutant-related and individual-level variables, such as ambient and local CO concentration source 

contributions and breathing rate respectively, to compute the distribution of individual exposures 

across the modeled population. The models also have the capability to estimate received dose 

through a dosimetry model. Using distributions of input parameters in the model framework rather 

than point estimates allows the models to incorporate uncertainty and variability explicitly 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. For example, Bruinen de Bruin et al. (2004, 190943) utilized the EXPOLIS (exposure in 

polis, or cities) model to predict CO population exposures in Milan, Italy, based on subjects’ timeactivity data broken into 15-min intervals. The simulation results showed that the U.S. 8-h NAAQS 

level was exceeded in 1 case out of 1,000. The model also showed that exposures exceeded 20 ppm 

in 1 case out of 100,000. The results were not shown to be very sensitive to the number of 

microenvironments (e.g., outdoors, indoors, in-vehicle) included in the 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 indoor residences; other indoor locations, such as schools, 

offices, and public buildings; and vehicles. 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 biometric characteristics 

(U.S. EPA, 2009, 194010). Breathing rates are calculated for each activity based on exertion level, 

and the corresponding received dose is then computed. For APEX, the CO dosimetry algorithm 

calculates venous COHb levels using the nonlinear CFK model, as described in Chapter 4. 

(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 benchmark exposure levels. The models also 

consider the nonambient 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 the 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) are illustrated in Figure 3-44 (Klepeis et al., 2001, 002437), which 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 

January 2010 3-78 
activity patterns only for subgroups such as children or the elderly. Population exposures can be 

estimated using CO concentration data in each microenvironment. 

Source: Reprinted with Permission of Nature from Klepeis et al. (2001, 002437). 

Figure 3-44. Distribution of time that the sample population spends in various environments, 

from the NHAPS. 

Compartmental models, such as the Indoor Air Model (INDAIR), can be used to assess 

exposure to infiltrated ambient air pollutants in a deterministic or probabilistic framework 

(Dimitroulopoulou et al., 2001, 014737). To examine indoor concentrations of ambient CO, 

Dimitroulopoulou et al. (2006, 090302) used the probabilistic formulation of the INDAIR model to 

examine indoor exposure to ambient CO, along with NOX and PM for a given distribution of 

background CO levels, meteorology, residential air exchange rate, and residential room dimensions. 

They found that 24-h avg CO concentration increased from 1.86 ppm outdoors to 1.90-1.93 ppm 

indoors in the absence of nonambient sources, and that indoor 24-h avg CO concentration could 

increase to 1.93-2.00 ppm in the presence of smoking and to 1.98-2.32 ppm in the presence of gas 

cooking. Similarity between the outdoor and nonsource indoor concentrations was attributed to the 

lack of CO loss mechanisms. In the Reducing Urban Pollution Exposure from Road Transport 

(RUPERT) study, Bell et al. (2004, 192376) presented methodology to use the probabilistic form of 

INDAIR for development of personal exposure frequency distributions of CO, NOX, and PM, based 

on time spent in residential, transportation, school, office, and recreational environments, with inputs 

from transportation source categories (Chen et al., 2008, 193986). 

3.6.3.2. Using Spatial Models to Estimate Exposure 

Another set of approaches to improve exposure estimates in urban areas involves construction 

of a concentration surface over a geographic area, with concentration between monitors estimated 

using a model to compensate for missing data. The calculated CO concentration surface can then be 

used to estimate exposures outside residences, schools, workplaces, roadways, or other locations of 

January 2010 3-79 
interest. This technique does not estimate exposure directly because it does not account for activity 

patterns or concentrations in different microenvironments. There are two main types of approaches: 

spatial interpolation of measured concentrations, and regression models using land use, roadway 

characteristics, and other variables to predict concentrations at receptors in the domain. Rigorous 

first-principle models, such as dispersion models and CTMs, can also be used for this type of 

application, but are less suitable because they have intensive resource requirements and are typically 

applied over larger domains. 

The STEMS model provides an example of an integrated-exposure modeling approach using a 

range of spatial inputs. STEMS maps exposures based on inputs for traffic levels, atmospheric 

dispersion, background concentrations, and geography. Gulliver and Briggs (2005, 191079) tested 

the STEMS model for CO and observed some correlation between modeled and measured CO 

concentrations (R2

 = 0.41), which was consistent with results for PM10 and NOX. Exposures were 

estimated from the predicted ambient CO concentration using a term similar to α that varied 

depending on whether the individual was walking or in a vehicle. Gulliver and Briggs (2005, 

191079) noted that a limitation to modeling CO is the scarcity of background CO data obtained at 

rural sites. For this reason, they assumed a constant value obtained from estimates made over the 

North Atlantic Ocean. Although the authors only presented detailed results for a model of PM10

based on traffic and meteorology in Northampton, U.K., they found that the majority of variation on 

a given day in modeled exposure among school children was due to differences in travel routes. 

Variation across days was also influenced by background and meteorological conditions. Similar 

results can be expected for CO based on the tendency for variation of the CO concentration profile 

on the neighborhood and microscales (Jerrett et al., 2005, 092864). Flachsbart (1999, 015857) tested 

numerous meteorological, traffic, and background CO input variables in a regression approach to 

predicting CO exposure among individuals while traveling in a vehicle. This work showed travel 

time and average speed of on-road vehicles to be important determinants of CO exposure in a 

vehicle. Results from individual models of this nature can be pooled to develop a distribution for 

examination of population effects or for comparison with population exposure models. 

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 a model such as California Line Source Dispersion Model (CALINE), the American 

Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD), 

CALPUFF (long-range plume transport model created by the California Air Resources Board), or the 

Operational Street Pollution Model (OSPM) for estimation of street-level ambient CO exposure 

(e.g., Abdul-Wahab, 2004, 194011; Delfino et al., 2009, 190254; Zhou and Levy, 2008, 190091). 

CALINE, CALPUFF, and AERMOD utilize Gaussian dispersion models to describe pollutant 

transport, while OSPM is a semi-empirical model of airflow and pollutant transport within an infinite 

street canyon. Delfino et al. (2009, 190254) used CALINE (version 4) to model exposure in the nearroad environment for estimation of relative risks of asthma hospitalizations as a function of increases 

in ambient CO and NOX concentrations. The concentration at each subject’s home was computed 

with the dispersion model, and then the data were aggregated to estimate a population risk. Zhou and 

Levy (2008, 190091) used results from an OSPM simulation to compute intake fraction, defined as 

the fraction of emissions that are inhaled or ingested, for ambient CO and other copollutants. 

Daytime activity patterns were modeled using both CHAD and the American Community Survey 

(http://www.census.gov/acs/www) to model commuting behaviors that would affect both mobilesource emissions and population-based exposures. With an individualized exposure approach, the 

model is deterministic. However, population exposures were estimated by performing repeated 

simulations using various housing characteristics and then computing the probability distribution 

function for exposure. When comparing street-canyon exposure computed by OSPM with near-road 

exposure computed simply with a Gaussian dispersion model, Zhou and Levy (2008, 190091) 

estimated that the street-canyon exposures would be three times greater than those in the general 

community. Isakov et al. (2009, 191192) developed a methodology to link a chemical transport 

model, used to compute regional scale spatiotemporally-varying concentration in an urban area, with 

stochastic population-exposure models to predict annual and seasonal variation in population 

exposure within urban microenvironments. Although this approach was demonstrated for PM2.5, it is 

January 2010 3-80 
similar to the one used by Zhou and Levy (2008, 190091) for linking ambient CO concentrations 

with population activity pattern data to associate the spatial-concentration field with personal 

exposure to ambient CO. 

Land Use Regression Models 

Land use regression (LUR) models have also been developed to estimate pollution levels as a 

function of several land use factors, such as land use designation, traffic counts, home heating usage, 

point source strength, and population density (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 the land use factors. The regression 

is then used for predicting concentration at multiple locations based on the independent variables at 

those particular 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). Like deterministic-dispersion models, LUR can be performed over wide areas to 

develop a probability density 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. 

A GIS platform is typically used to organize the independent variable data and map the results 

from an LUR simulation. 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 estimated at 23 monitors and then applied to simulate PM2.5

concentration at 18,000 points in the simulation domain, and an inverse distance weighting (IDW) 

kriging method was applied to interpolate the predicted concentration. In New York City, the LUR 

was estimated at 49 monitors for a 3-yr model and at 36 monitors for a model of winter 2000 and 

then applied to simulate PM2.5 concentration at 5,600 locations in the 28-county domain; 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 concentrations where monitors 

are not located. Yanosky et al. (2008, 099467) described an approach to estimate PM 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). 

Marshall et al. (2008, 193983) compared four spatial interpolation techniques for estimation of 

CO concentrations in Vancouver, BC. The investigators assigned a daily average CO concentration 

to each of the 51,560 postal-code centroids using one of the following techniques: (1) the 

concentration from the nearest monitor within 10 km; (2) the average of all monitors within 10 km; 

(3) the IDW average of all monitors in the area; and (4) the IDW average of the three closest 

monitors within 50 km. Method 1 (the nearest-monitor approach) and Method 4 (IDW-50 km) had 

similar mean and median estimated annual average concentrations, although the 10th-90th percentile 

range was smaller for IDW-50. This is consistent with the averaging of extreme values inherent in 

IDW methods. The Pearson correlation coefficient between the two methods was 0.88. Methods 2 

and 3 were considered sub-optimal and were excluded from further analysis. In the case of Method 

2, a single downtown high-concentration monitor skewed the results in the vicinity, partially as a 

result of the asymmetric layout of the coastal city of Vancouver. Method 3 was too spatially 

homogenous because it assigned most locations a concentration near the regional average, except for 

locations immediately adjacent to a monitoring site. LUR results were also reported in this study for 

January 2010 3-81 
NO and NO2 and indicated that LUR’s higher spatial precision reflects neighborhood-scale effects 

from nearby land use but may not account for urban-scale variation. Brauer et al. (2008, 156292) 

also evaluated LUR and IDW-based spatial-interpolation models and suggested that LUR is 

appropriate for directly-emitted pollutants with high spatial variability; Brauer et al. (2008, 156292) 

used NO and BC as examples, but CO emitted from mobile sources would also fall in that category. 

These results highlight the variation in local concentration estimates with choice of estimation 

technique. 

Originally, LUR was used to model NO2 dispersion. It has been adapted for modeling 

exposures to other pollutants, although application of LUR to CO exposures has been performed in 

only a few studies. Findings related to other pollutants are provided because they are instructive in 

how LUR can be used to predict CO concentrations. Carslaw et al. (2007, 148210) used multipleregression modeling to examine the effects of traffic volumes, wind components, temperature, and 

time on concentrations of CO, NOX, NO2 (O3 was also a predictive variable for NO2), benzene, and 

butadiene at a single site. These results were used for forecasting concentrations at that site, but the 

study lacked the spatial resolution to predict concentrations at alternate sites. Cassidy et al. (2007, 

199975) applied LUR to analyze the effect of wind, temperature, traffic volume, roadway size, 

number of stories of surrounding buildings, other sources of pollution, terrain, and time of day on 

concentrations of CO, PM2.5, and PM10 at 30 street-level sites within Baguio City, Philippines. In this 

work, they found traffic volume was the only significant predictor of CO during rush-hour periods, 

while winds significantly predicted early morning concentrations of CO, PM2.5, and PM10. The 

model was not used for spatial interpolation in this case. Brauer et al. (2003, 155702) used LUR to 

analyze PM2.5 exposure at 40-42 sites each within 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 and used GIS to apply the model at home addresses of asthmatic 

subjects to estimate exposures. Ryan et al. (2008, 156064) reported on an LUR model developed 

from monitor and land-use data and then applied at the locations of children to assess their exposure 

to traffic-derived EC for the Cincinnati Allergy and Air Pollution Study. Ryan et al. (2008, 156064) 

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. Although these 

studies differed in the number of sites and in the pollutants of focus, they are instructive in 

considering how LUR can be employed for estimating CO exposure. 

3.6.4. Personal Exposure Monitors for CO 

Portable monitors for measuring personal CO exposure include the Langan and Draeger 

monitors, both of which use electrochemical oxidation-reduction techniques (Langan, 1992, 

046120). These monitors continuously log CO concentrations, making them suitable for use in 

personal monitoring studies. Electrochemical CO sensors typically have an LOD of 1 ppm and a 

90% sensor response time (or the time required for the sensor to register 90% of a step change in CO 

concentration) of 20-60 s. The 2000 CO AQCD (U.S. EPA, 2000, 000907) provided detail on design 

updates of electrochemical CO sensors made during the 1990s. Commercially available personal CO 

exposure monitors are not designed to detect concentrations below 1 ppm. Electrochemical personal 

CO monitors are also typically sensitive to temperature changes, so that data correction is normally 

required. 

3.6.5. 

3.6.5.1. 

Indoor Exposure to CO 

Infiltration of Ambient CO 

CO is a relatively inert gas, making the indoor decay rate negligible compared to typical air 

exchange rates (~1/h). In the absence of indoor sources, this would lead to an indoor-outdoor 

concentration ratio (I/O) of approximately 1. For this reason, few studies have calculated I/O for CO. 

Polidori et al. (2007, 156877) calculated I/O of 0.94-1.21 for two retirement communities in the Los 

Angeles area. The authors suggested that similarity between I/O for CO and NOX can be attributed to 

lack of indoor sources of either gas. Chaloulakou and Mavroidis (2002, 026050) reported I/O using 

CO measurements in the absence of indoor sources in a school building in Athens, Greece, and 

January 2010 3-82 
found that I/O varied with season. During the summer, median I/O was reported to be 0.57 on 

weekdays, 0.91 on Saturdays, and 0.81 on Sundays. In winter, median I/O was reported to be 0.82 

during weekdays, 0.90 on Saturdays, and 0.74 on Sundays. In a related study, Chaloulakou et al. 

(2003, 190945) reported the median I/O over all days as 0.8 for the same school and 0.9 for an 

Athens office building with no ETS (the presence of other sources was not clearly stated but 

assumed zero). However, observed indoor values are often greater than outdoor concentrations in the 

presence of indoor sources. A recent study in the U.K. reported I/O of 3.9-4.3 in homes with gas 

cookers (Dimitroulopoulou et al., 2006, 090302), which is consistent with previous studies. A 

multipollutant study conducted in 2000-2001 attempted to measure I/O for CO and calculated 

residential infiltration factors, but low CO concentrations resulted in a large number of 

measurements below the LOD (Williams et al., 2003, 053335). Ni Riain et al. (2003, 053792) 

examined the effects of mechanical ventilation and wind speed on I/O. In this study, the authors 

measured indoor and outdoor concentrations at two buildings located on a six-lane highway in 

central London with natural and mechanical ventilation. Ni Riain et al. (2003, 053792) found that 

outdoor concentrations for each building and ventilation condition ranged from 1.5 ± 0.1 ppm to 1.9 

± 0.1 ppm. Ni Riain et al. (2003, 053792) reported cumulative I/O approaching 0.9 within 30 min of 

sampling for the mechanical ventilation case and cumulative I/O varying between 0.65 and 0.8 for 

more than 70 h of sampling for the natural ventilation case. Ni Riain et al. (2003, 053792) found that 

wind speed and direction influenced the variation in I/O. 

Indoor air flow may affect CO exposure in the absence of indoor sources. Milner et al. (2006, 

123100) compared hourly CO concentration time series from different parts of a building (with a 

mix of natural and mechanical ventilation) located near a busy road and intersection in central 

London, U.K. They found that, within a given floor, CO concentration is greater in rooms that are 

closer to busy roads or an intersection. They noted that the correlation coefficient between indoor 

and outdoor CO concentrations also decreased within the building with distance from the road; the 

correlation coefficients were reported to be 0.80 for two time series obtained in rooms near the road, 

while they were reported to range between 0.46 and 0.55 on the sides of the building furthest from 

the road. The magnitude of the difference between CO concentrations in different rooms located 

nearer or further from the roads also depended on wind direction. Milner et al. (2006, 123100) noted 

that I/O tended to decrease with increasing wind speed, but Chaloulakou et al. (2003, 190945) also 

noted that indoor CO concentration varied inversely with wind speed. Chaloulakou et al. (2003, 

190945) attributed their observation to reduced concentrations related to dilution effects. Milner 

et al. (2006, 123100) stated that this relationship could be due to dilution of CO or to the tendency of 

people to keep windows closed on windy days. Additionally, CO concentrations were higher on 

lower floors of the building and varied over a given day throughout the building. These findings 

suggest that differences in exposure can occur within the same building as a result of differences in 

air exchange related to access to windows, mechanical ventilation, and outdoor meteorological 

conditions. 

3.6.5.2. Exposure to Nonambient CO 

Several papers have investigated the microenvironmental sources of total personal CO 

exposure. The CDC conducted a survey of ED visits for nonfatal CO poisoning, CO exposure, or 

potential CO exposure, and found that home heating was the largest known source of CO exposure, 

prompting 16.4% of CO-related ED visits, followed by motor vehicle exhaust exposure accounting 

for 8.1% of ED visits (Annest et al., 2008, 190236). Alm et al. (2000, 192374; 2001, 020237) studied 

factors that contributed to elevated CO exposures among preschool children and found that presence 

of a gas stove at home, ETS, natural ventilation, and living in a high-rise building all contributed to 

increased CO exposures. Time-activity diaries were linked to personal CO exposures in the 

EXPOLIS study. Here, Georgoulis et al. (2002, 025563) observed that geometric mean exposure 

among smokers ranged from 0.33 ppm in Helsinki, Finland, to 3.2 ppm in Athens, Greece, while 

among nonsmokers it ranged from 0.36 ppm in Helsinki to 1.7 ppm in Milan and ambient CO 

concentration ranged from 0.42 ppm in Helsinki to 3.2 ppm in Athens. Bruinen de Bruin (2004, 

190943) found, for a panel of 46 subjects in Milan, that indoor CO concentrations were 3.4 ppm in 

the presence of gas cooking and ETS, compared with 2.9 ppm in the presence of ETS only, 2.4 ppm 

in the presence of gas cooking only, and 1.8 ppm in the absence of indoor CO sources. Scotto di 

Marco et al. (2005, 144054) reported that average indoor CO increased in the presence of ETS from 

0.96-1.2 ppm for the home indoor environment and from 1.0-1.4 ppm for the work indoor 

January 2010 3-83 
environment. CO concentrations were measured to decrease from 1.5 to 1.3 ppm in other (not home 

or work) indoor environments, but those locations included garages, restaurants, and bars and could 

have been differently influenced by CO from cooking, indoor automobiles, or other sources. 

Personal CO concentrations can also be much more variable than ambient measurements. 

Figure 3-45 shows hourly versus personal CO concentration data obtained by Chang et al. (2000, 

001276) for a 1998-1999 multipollutant sampling campaign in Baltimore, MD. Personal exposures 

were obtained in five separate microenvironments in this study. A high degree of scatter is evident in 

this figure, which suggests that these personal exposures are influenced by both ambient and 

nonambient sources of CO. Figure 3-46 is a box plot of the personal-to-ambient CO concentration 

ratio for the same five microenvironments. Wide variability is seen in these plots, particularly during 

the summer. Much of that variability could be due to the influence of nonambient sources, which 

would then result in poor correlation between total personal exposure and ambient concentration. 

Source: Reprinted with Permission of the Air and Waste Management Association from Chang et al. (2000, 001276) 

Figure 3-45. Hourly personal versus ambient CO concentrations obtained in Baltimore, MD, 

during summer of 1998 in five settings: indoor residence, indoor other, outdoor 

near road, outdoor away from road, and in vehicle. 

January 2010 3-84 
Source: Adapted with Permission of the Air and Waste Management Association from Chang et al. (2000, 001276) 

Figure 3-46. Box plots of the ratio of personal to ambient concentrations obtained in 

Baltimore, MD, during summer of 1998 and winter of 1999 in five settings: indoor 

residence, indoor other, outdoor near road, outdoor away from road, and in 

vehicle. The grey line shows the mean, and the black mid-line shows the median. 

S = summer; W = winter. 

Vehicle self-pollution, defined by Behrentz et al. (2004, 155682) as the fraction of a vehicle’s 

own exhaust entering the vehicle microenvironment, is another potential nonambient source of CO 

exposure. This has been studied using inert tracer gases to evaluate exposures of children riding 

school buses. Behrentz et al. (2004, 155682) used sulfur hexafluoride (SF6) tracer gas emitted from 

school bus engines to determine the proportion of in-vehicle pollution related to self-pollution. 

Based on the SF6 concentration, they calculated that 0.04-0.29% of the bus cabin air contained 

exhaust for high-emitting diesel engines, 0.01-0.03% for “regular” diesel buses, 0.02-0.04% for 

buses fitted with a particle trap, and 0.03-0.04% for buses running on compressed natural gas. SF6

concentrations were higher when bus windows were closed. 

3.6.6. 

3.6.6.1. 

Exposure Assessment Studies at Different Spatial Scales 

Neighborhood to Urban Scale Studies of Ambient CO Exposure 

Although several multipollutant exposure studies have been conducted recently in the U.S., 

(e.g., Sarnat et al., 2006, 089784), most have not included CO in the suite of pollutants, possibly due 

to high detection limits in personal monitors. A few studies conducted in Europe and Canada 

measured personal-ambient relationships for CO. This section summarizes CO exposure assessment 

studies that compare personal exposure measurements with ambient concentration measurements for 

the purpose of examining how well these measures correspond. 

The EXPOLIS study (Georgoulis et al., 2002, 025563) found that 48-h personal exposures 

were significantly correlated with ambient concentrations in each of five European cities (Athens, 

Basel, Helsinki, Milan, and Prague). Controlling for source terms, including ETS, traffic, and natural 

gas appliances, regression coefficients between personal exposure and ambient concentration ranged 

from 0.28 in Milan to 1.99 in Helsinki and were all statistically significant (p ≤ 0.01 for all cities 

except Prague, where p = 0.05). The regression coefficient for Helsinki (>1) likely reflects 

nonambient sources that were not controlled in the study. The ambient concentration was the only 

variable that was statistically significantly associated with 48-h personal exposure for all five cities 

January 2010 3-85 
in this study, with correlations between personal CO exposure and ambient CO concentration 

ranging from 0.33 to 0.77. Georgoulis et al. (2002, 025563) reported that CO exposure in traffic 

ranged from 0.99 ppm in Helsinki to 4.2 ppm in Athens, while ambient CO concentration ranged 

from 0.42 ppm in Helsinki to 3.2 ppm in Athens. As part of this study, personal CO exposure was 

measured for a panel of 50 office workers in Milan (Bruinen de Bruin et al., 2004, 190943). Average 

measured 1-h personal exposures were 7.3 ppm in comparison with 5.0 ppm for fixed site 1-h 

measurements. Average 8-h (3.3 ppm) and 24-h (2.1 ppm) CO concentrations were the same for 

personal and fixed-site measurements. Percentage of time exposed, exposures, and percentage of 

exposure from the Bruinen de Bruin et al. (2004, 190943) study, in the absence of nonambient CO 

from ETS and gas cooking, are shown in Table 3-13. The largest percentage of time-weighted CO 

exposure was attributed to home indoor exposure in the absence of indoor sources, while the highest 

exposure levels were observed during transit; Scotto di Marco et al. (2005, 144054) found similar 

results. Bruinen de Bruin et al. (2004, 190943) and Scotto di Marco et al. (2005, 144054) found that 

mobile source emissions were important contributors to personal exposure, as described in Section 

3.6.6.2. 

Table 3-13. Percentage of time exposed to ambient CO (adjusted to reflect the absence of 

nonambient CO from ETS and gas cooking), average CO exposures, and percentage of 

exposure estimated for the population. 

Percent of time exposed 

(%) Exposure (ppm) Percent of exposure (%) 

INDOORS 89.6 81.1 

Home 56.5 1.8 49.4 

Work 29.1 1.9 26.8 

Other 4.1 2.5 4.9 

OUTDOORS 1.8 2.1 

Home 0.2 2.3 0.2 

Work 0.6 2.1 0.6 

Other 1.0 2.6 1.2 

IN-TRANSIT 8.5 16.8 

Walking 3.0 3.0 4.4 

Train/metro 0.7 3.0 1.0 

Bus/tram 2.0 3.8 3.7 

Motorbike 0.2 4.5 0.4 

Car/taxi 2.6 5.7 7.2 

Source: Reprinted with Permission of Nature from Bruinen de Bruin et al. (2004, 190943) 

. 

EXPOLIS also looked at the special case of children’s exposure to CO because children 

generally do not produce CO in their daily activities and have no occupational exposures. Alm et al. 

(2000, 192374; 2001, 020237) reported higher personal exposures than ambient concentrations for 

children aged 3-6 yr in Helsinki. Their mean 1-h daily max exposure was 5.2 ppm, compared to 

1.4 ppm measured at a fixed-site monitor. For the average of 8-h and 24-h daily max concentrations, 

the corresponding values were 2.9 ppm and 2.1 ppm for personal exposure and 0.8 and 0.6 ppm, 

respectively, for fixed site measurements. The Spearman rank correlation, although statistically 

significant, was relatively low (r = 0.15) between individual 24-h avg exposure and the ambient 

monitor. The correlation improved when the average exposure of children measured on the same day 

(r = 0.33, 3-6 children) or the same week (r = 0.55, 10-23 children) was compared to the monitor 

data. A regression model using questionnaire data found that parental smoking status, parental 

education, and presence of a gas stove explained only 12% of the variability in the 8-h max 

January 2010 3-86 
exposures, indicating that other factors, such as time spent outdoors and proximity to roadways, are 

likely to be important in determining personal exposure. 

Kim et al. (2006, 089820) reported mean CO concentrations of 1.4 ppm for a panel of 28 

cardiac-compromised individuals in Toronto, Canada. Corresponding fixed-site monitor mean 

concentrations ranged from 0.5 to 1.4 ppm, with an overall mean of 1.0 ppm. The observed higher 

personal exposures may have been due to both indoor sources and proximity to roadways when 

outdoors. Personal-ambient Spearman correlations ranged from -0.65 to 0.93, with a median of 

r = 0.31, indicating that while moderate correlations are observed overall, inter-individual 

differences based on time spent in different microenvironments have a strong influence on the 

observed correlation. Lai et al. (2004, 056811) measured relationships between personal CO 

exposure and microenvironmental (home indoor, home outdoor, and work indoor) concentrations in 

Oxford, U.K. The highest personal exposures were associated with smoking, cooking, and 

transportation, while low correlations were observed between personal and indoor residential 

concentrations, further indicating the importance of indoor sources and the need to separate ambient 

contributions to personal exposure from total personal exposure. 

The studies presented above present mixed results regarding the association between ambient 

CO concentration measurements and personal CO exposures. Some personal CO measurements have 

been reported to be higher than ambient concentrations, while others are similar. Additionally, 

correlation between ambient CO concentration and personal exposure has varied in the literature. 

Nonambient (described in Section 3.6.5) and in-transit sources (described in Section 3.6.6.2) have 

been identified as important contributors to personal exposure. These observations raise questions 

about where and when ambient CO concentration can be used as a surrogate for personal CO 

exposure; these concepts are explored further in Section 3.6.8. 

3.6.6.2. Microscale Studies of Ambient CO Exposure: Near-Road and On-Road 

Exposures 

The 2007 American Housing Survey (AHS) (U.S. Census Bureau, 2008, 194013) reports that 

17.9 million occupied homes nationwide (16.1%) are within ~90 m (300 ft) of a “4-or-more-lane 

highway, railroad, or airport” and so are exposed to the near-road environment. Within city centers, 

6.2 million occupied homes (19.7% of those living in city centers) are within approximately 90 m of 

a highway, railroad, or airport; whereas in rural areas outside designated Metropolitan Statistical 

Areas (MSA), 1.4 million occupied homes (9.2% of those in rural areas outside MSAs) are near a 

highway, railroad, or airport. Those data can be put into context for exposure assessment in the nearroad environment; Section 3.5.1.3 describes near-road studies in which ambient CO was measured 

within the vicinity of a road and microscale AQS data obtained in the near-road environment. The 

AQS data suggest some spatial variability (20-40% difference between microscale and middle scale 

monitors, with the hourly microscale concentration having a median of 0.5 ppm and a 99th percentile 

value of 2.2 ppm), which was much lower than that reported by Zhu et al. (2002, 041553) for the 

near-road environment, in which the average concentration at 17 m from the road was 2.3 ppm 

(range 1.9-2.6 ppm) and a factor of about 12.5 lower for the monitoring site located 300 m from the 

road. The larger discrepancy observed between the Zhu et al. (2002, 041553) data and the AQS data 

might be attributed to the fact that the sampling equipment used by Zhu et al. (2002, 041553) were 

downwind of the freeway for the entire sampling period, while the hourly AQS data represents a 

range of wind speeds and directions that vary across different monitoring sites. For those living in 

the 16.1% of occupied homes situated in the near-road environment (within ~90 m), median hourly 

CO concentrations are typically higher than those further from the road, but the magnitude of the 

outdoor concentration is still in most circumstances measured to be below 2.2 ppm. 

Kaur and Nieuwenhuijsen (2009, 194014) and Carslaw et al. (2007, 148210) suggest that CO 

exposures are related to traffic volume and fleet mix in the street-canyon environment. In this 

research, Kaur and Nieuwenhuijsen (2009, 194014) developed a multiple linear regression of CO as 

a function of mode of traffic, broken down by vehicle type, wind speed, temperature, and traffic 

count for data obtained in central London as part of the DAPPLE study of traffic-related pollution. 

They added each variable successively and found traffic count, temperature, wind speed, and 

walking to be significant parameters in the model, with traffic count being the strongest determinant. 

Analysis of variance showed variability in traffic count to explain 78% of the variability in CO levels 

for these data, and variability in mode of transport explained 6% of the variability. Likewise, 

Carslaw et al. (2007, 148210) used a generalized additive model to determine how CO concentration 

January 2010 3-87 
(log-transformed) varies as a function of year, the along-street and cross-street components of wind, 

temperature, Julian day, light and heavy traffic counts, and temperature for data obtained in central 

London. Light-duty vehicle count was a more important determinant of CO concentration than was 

heavy-duty (i.e., diesel) vehicle count in this study, which is not surprising because gasolinepowered vehicles are known to emit more CO than diesel engines. They found that the CO 

concentration declined steadily with year and that wind was the most significant covariate. The 

decline in CO concentration with year, adjusted for all other covariates, was usually significantly 

different than the simple relationship between concentration and year, but the adjusted and 

unadjusted trends were similar. In addition to showing meteorology to be an important determinant 

of concentration, these modeling exercises also suggest a linear or log-linear relationship between 

concentration and traffic count. 

Findings regarding meteorology are consistent with in-vehicle CO concentration studies. 

Gómez-Perales et al. (2007, 138816) also noted that meteorology can impact in-vehicle exposures, 

with evening increases in wind speed causing a 50% reduction in CO exposures among bus and 

minibus commuters. Alm et al. (1999, 047196) made a similar observation in a study of urban 

commuters’ exposure within a vehicle. These observations are sensible given the influence of 

meteorology on near-road concentrations shown by Baldauf et al. (2008, 190239) and Gokhale and 

Khare et al (2007, 194015). 

A number of studies have focused on transit-time CO exposure, which can occur while in a 

vehicle or cycling (on-road) or while walking (near-road). Chang et al. (2000, 001276) showed that 

personal exposures in vehicles were on average 2.8 times higher than ambient concentrations during 

the summer and 4.1 times higher than ambient concentrations in the winter (Figure 3-46). For the 

other four microenvironments tested, the average ratio of personal exposure to ambient concentration 

was ~1. Kaur et al. (2005, 086504) found that transit time exposures in London, U.K., were 

significantly higher than measurements made at a fixed-site background monitor away from traffic 

(0.3 ± 0.1 ppm) for car riders (1.3 ± 0.2 ppm), taxi riders (1.1 ± 0.1 ppm), bicyclers (1.1 ± 0.2 ppm), 

walkers (0.9 ± 0.2 ppm), and bus riders (0.8 ± 0.1 ppm). Curbside measurements (1.5 ± 0.7 ppm) in 

this study were slightly higher than car riders’ exposures. Duci et al. (2003, 044199) found that 

average in-transit exposures in Athens, Greece, were highest for cars (winter: 21.4 ± 4 ppm), 

followed by pedestrians (winter: 11.5 ± 2.6 ppm; summer: 10.1 ± 1.7 ppm), buses (winter: 

10.4 ± 2.9 ppm; summer: 9.4 ± 3.6 ppm), trolleys (winter: 9.6 ± 1.9 ppm; summer: 8.2 ± 3 ppm), and 

rail transit (winter: 4 ± 0.6 ppm; summer: 3.4 ± 0.7 ppm). Duci et al. (2003, 044199) did not provide 

fixed-site CO concentrations but stated that in-transit exposures were higher in each case. GómezPerales et al. (2004, 054418) measured CO exposures on buses, minibuses, and metro cars in Mexico 

City, Mexico, to be 12 ppm, 15 ppm, and 7 ppm, respectively. These values are much higher than 

CONUS measurements and those presented by Kaur et al. (2005, 086504), but the relative difference 

between the minibus and bus exposures in the Gómez-Perales et al. study are similar to those seen 

for the taxi-to-bus or car-to-bus comparisons in Kaur et al. (2005, 086504). These studies indicate 

that on-road exposures might be influenced by vehicle type, but that city-to-city differences are 

likely larger than differences between different modes of transport. 

Additional analyses from the EXPOLIS study indicated that on-road mobile source emissions 

were the most important source of CO exposure for non-ETS-exposed subjects (Bruinen de Bruin et 

al., 2004, 190943; Scotto Di Marco et al., 2005, 144054). Scotto di Marco et al. (2005, 144054) 

found that, for a panel of 201 adult Helsinki, Finland, residents (aged 25-55 yr), subjects spent 8.1% 

(1.9 h) of their time in transit, which accounted for 12.6% of their total exposure (range of means = 

0.96 ppm on a train – 2.8 ppm in a car). Similarly, in a panel study of 50 office workers, Bruinen de 

Bruin et al. (2004, 190943) found that, in the absence of nonambient sources, the subjects spent 

8.5% (2 h) of their time in transit, which accounted for 16.8% of their total exposure, with 2.6% of 

time spent in a car or taxi accounting for 7.2% of exposure (mean = 5.7 ppm). Commuting time was 

an important predictor of exposure, such that subjects living in low CO concentration suburban areas 

and commuting to work experienced higher exposures than urban residents with short commute 

times. According to the 2007 AHS (U.S. Census Bureau, 2008, 194013), 110.1 million U.S. workers 

(87.8% of those working) commute to work in automobiles. 32.8% of U.S. workers work at home or 

commute less than 15 min to work, 32.1% commute 15-29 min to work, 15.1% commute 30-44 min 

to work, 5.7% commute 45-59 min to work, and 5.0% commute 1 h or longer to work. 

Vehicle ventilation can be an important determinant of in-vehicle concentrations. A study from 

Abi Esber and El Fadel (2008, 190939) in Beirut, Lebanon, is presented because the authors 

observed in-vehicle CO concentration time series under a range of ventilation conditions, although 

January 2010 3-88 
the in-vehicle CO concentrations measured are substantially higher than those typically observed in 

the U.S. Abi Esber and El Fadel (2008, 190939) reported results from CO concentration 

measurements taken directly outside and within an automobile in Beirut, Lebanon, during the 

morning commute period of 7:30-9:30 a.m under three different ventilation conditions. Figure 3-47

shows that the time series for the cabin and outdoor CO samples are very similar for the fresh air 

scenario. However, for the recirculating air ventilation scenario, the in-vehicle concentration 

increases and then reaches a plateau at a higher level. Abi Esber et al. (2007, 190941) stated that 

unaccounted sources of CO cause the build-up of in-cabin CO concentrations when the ventilation is 

set to recirculation mode. The correspondence between in-vehicle and outside-vehicle concentrations 

for the fresh air ventilation experiments, and lack thereof for the recirculation mode ventilation 

experiments, observed by Abi Esber and El Fadel (2008, 190939) suggests that in-vehicle 

concentrations of ambient CO are affected by mode of ventilation. 

Source: Reprinted with Permission of Elsevier Ltd. from Abi Esber and El Fadel (2008, 190939) 

Figure 3-47. Comparison of in-vehicle (solid line) and outside-the-vehicle (dotted line) results 

for (left) driving with windows closed and air conditioner in recirculating air 

mode, and (right) driving with windows closed and air conditioner in fresh air 

mode. 

Substantial variability can occur over time within a vehicle. Riediker et al. (2003, 043761) 

measured CO concentrations inside highway patrol cars during shifts. Troopers recorded in a timeactivity diary the ventilation settings of their cars and exit/entry from the vehicle, and the air 

conditioning was typically set to recirculation mode during the shifts. Riediker et al. (2003, 043761) 

found that CO concentrations (mean ± SD: 2.6 ± 1.1 ppm) were higher than ambient monitor 

concentrations (0.8 ± 0.3 ppm). They were also higher than roadside CO concentrations 

(1.1 ± 0.3 ppm), indicating that either the vehicle itself contributes to in-cabin CO or on-road 

concentrations are higher than roadside concentrations or both. Riediker et al. (2003, 043761) noted 

that within-shift variability was higher than between-shift variability, which underscores the 

variability in police officers’ activities during a given shift. Data were not segregated by ventilation 

settings, although the police officers typically operated the air conditioning continually because the 

study was performed during the summer. Alm et al. (1999, 047196) reported in-vehicle CO 

concentrations of 5.7 ppm in the morning and 3.1 ppm in the afternoon commute for Kuopio, 

Finland. These data indicate that within-shift variability observed by Riediker et al. (2003, 043761) 

might be related to time of day. Likewise, Rodes et al. (1998, 010611) reported CO concentrations in 

vehicles in Sacramento and Los Angeles under different driving conditions (arterial, freeway, highoccupancy-vehicle freeway lane, and “maximum” conditions at rush-hour and nonrush-hour times). 

They measured peak in-vehicle concentrations spanning 7-67 ppm on a freeway during rush hour, 

although the mean for each scenario was <6 ppm. In comparison, the peak roadside concentration 

ranged from 3 to 11 ppm and the peak ambient CO concentration was 1.3 ppm at the time of the 

measurements. The Rodes et al. (1998, 010611) data agree with results from the Riediker et al. 

January 2010 3-89 
(2003, 043761) and Alm et al. (1999, 047196) studies showing that substantial variability in CO 

concentration inside the cabin of a vehicle can occur during the course of a commute. 

In their review of roadway exposures to CO and PM, Kaur et al. (2007, 190070) listed a 

number of factors that may influence near-road or on-road exposure. Vertical CO concentration 

gradients have been documented in which concentrations decreased with height; lower breathing 

zone height among children may make them more likely to be exposed to higher CO tailpipe 

emissions. With respect to transportation, Kaur et al. (2007, 190070) suggested that vehicle 

ventilation, speed, position in traffic, and start/stop activity influence in-vehicle exposures. Abi Esber 

and El Fadel (2008, 190939) and Riediker et al. (2003, 043761) illustrated the effect of vehicle 

ventilation on in-vehicle concentrations. The influence of vehicle speed and start/stop activity is 

consistent with the turbulence research of Khare et al. (2005, 194016) and Gokhale and Khare (2007, 

194015) that suggested an increase in traffic volume and vehicle movement acts to dilute the on-road 

concentration of CO discussed in Section 3.5.1.3. 

3.6.7. Association between Personal CO Exposure and Copollutants 

Since incomplete combustion is the primary source of ambient CO in urban areas, exposure to 

ambient CO is accompanied by exposure to other combustion-related pollutants, such as NOX, PM, 

and VOCs. Thus, ambient CO is often considered a surrogate for exposure to traffic-generated 

pollutants. However, the specific mix of CO with NOX and PM depends on the source; for example, 

the mixture generated by gasoline engines differs from that produced by natural gas combustion. 

Correlations between ambient CO and ambient PM2.5, PM10, NO2, SO2, and O3 from AQS data and 

the peer-reviewed literature were presented in Section 3.5.3. Nationwide, ambient CO was most 

highly correlated with ambient NO2, followed by PM2.5 and PM10. Correlations between CO and 

PM2.5 were not consistently positive on a national basis; correlations spanned from negative to 

positive for ambient CO with ambient SO2 and ambient PM10, and ambient CO was negatively 

correlated with ambient O3. The correlation between ambient CO and specific ambient VOCs 

depends on parameters such as ambient temperature and the volatility of a specific compound. 

Relationships between personal CO exposures and copollutants were reported less frequently 

in the literature, but results from these studies were consistent with the findings cited above. In a 

study of personal exposures to CO, PM2.5, and ultrafine PM in a street canyon, Kaur et al. (2005, 

086504) found low Pearson’s correlation of total personal CO exposure with personal PM2.5

exposure (r = 0.23). Personal CO exposure had much better correlation with personal ultrafine 

particle (UFP) exposure (r = 0.68). Chang et al. (2000, 001276) reported correlations of personal CO 

exposure with personal PM2.5, personal toluene, and personal benzene exposures in Baltimore, MD, 

at five locations, labeled indoor residential, indoor nonresidential, outdoors near roadway, outdoors 

away from road, and in vehicle. Much variability was observed in the correlations for different 

locations and seasons (winter versus summer). In general, the correlations of personal CO with 

personal VOCs tended to be stronger in the winter. Chang et al. (2000, 001276) suggested that lower 

wintertime indoor air exchange rates could increase exposure to nonambient CO and VOC sources, 

such as ETS, and hence increase correlations between personal exposure of CO to VOCs. Significant 

associations of CO with benzene and toluene were also observed in vehicle microenvironments. 

3.6.8. Implications for Epidemiology 

Exposure error can be an important contributor to variability in epidemiologic study results. 

Community time-series studies may involve thousands or 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). Community-averaged concentration is typically used as a surrogate for ambient exposure in 

community time-series studies. Exposures and health effects are spatially aggregated over the time 

intervals of interest because community time-series studies are designed to examine health effects 

and their potential causes at the community level (e.g., Bell et al., 2009, 194033). 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., Rosenlund et al., 2006, 089796). In addition, panel studies, which consist of 

a relatively small sample (typically tens) of study participants followed over a period of days to 

months, have been used to examine the health effects associated with exposure to ambient 

January 2010 3-90 
concentrations of air pollutants. Panel studies include time-activity diary studies (Akland et al., 1985, 

011618; Bruinen de Bruin et al., 2004, 190942; Scotto Di Marco et al., 2005, 144054). These studies 

may apply a microenvironmental model to represent exposure to an air pollutant. 

The importance of exposure misclassification varies with study design and is dependent on the 

spatial and temporal aspects of the design. For example, the use of a community-averaged CO 

concentration in a community time-series epidemiologic study may not allow for adequate 

examination of the role of spatial variability. Other factors that could influence exposure estimates 

include spatial and temporal variability related to source strength, topography of the natural and built 

environment, and meteorology; measurement errors; use of ambient CO concentration as a surrogate 

for ambient CO exposure; and the presence of CO in a mixture of combustion-related pollutants. The 

following sections will consider various sources of error and how they affect the interpretation of 

results from epidemiologic studies of different designs. 

3.6.8.1. Measurement Error 

Measurement Error at Community-Based Ambient Monitors and Exposure 

Assessment 

Because CO concentrations measured with community-based ambient monitors are often used 

as surrogates for ambient CO exposure in epidemiology studies, the limitations of the 

instrumentation are important to consider. As stated in Section 3.4.2, among the 291 monitors 

meeting completeness criteria for 2005-2007, only 8 were monitors with LOD = 0.04 ppm; the other 

monitors had LOD of 0.5 ppm. Among the nationwide AQS data for 2005-2007 from these 291 

monitors, more than 50% of the hourly CO concentration data were below the LOD of the 

instrumentation. Data below the LOD adds uncertainty to the association between CO exposure and 

health effects estimates. Additionally, many of the monitors are not sited for a specific measurement 

scale, and a given scale classification can represent a range of CO source conditions, as described in 

Section 3.5.1.3. These factors also contribute uncertainty in interpretation of measurements. 

Instrumental measurement error, other than that related to high LOD, is not expected to bias 

health effect estimates substantially in most circumstances. Because there will be some random 

component to instrumental measurement error, the correlation of the measured CO concentration 

with the true CO concentration will likely be <1. When analyzing the effect of instrument error for 

measuring nonreactive ambient pollutants, Zeger et al. (2000, 001949) stated that the instrument 

error for ambient measurements “is close to the Berkson type.” In the Berkson error model, the 

measured-exposure estimate is used instead of the true exposure, based on the assumption that the 

average measurement is the average of the true exposure. It is generally expected that the health 

effects estimate will not be biased by using measured values with error but may have more 

uncertainty than would an estimate based on the true-average exposure. In order for instrument error 

to cause substantial bias in health effects estimates, the error term (the difference between the true 

concentrations and the measured concentrations) must be strongly correlated with the measured 

concentrations. 

Measurement Error for Personal Exposure Monitors 

Personal electrochemical CO monitors are subject to interference and drift and have a 

relatively high LOD (~1 ppm) relative to current ambient concentrations. Previous studies in the 

1980s and 1990s, when ambient levels were higher, involved successful deployment of these 

monitors, but more recent exposure studies have avoided personal CO measurements because there 

are now a high percentage of nondetects. The lack of a suitable personal monitor for measuring lowlevel exposures (<1 ppm) has hampered field studies assessing personal exposure to ambient CO. 

Chang et al. (2001, 019216) evaluated the Langan CO monitor as part of an air quality sampling 

manifold. At relatively high (0.4-3.0 ppm) CO concentrations, the instrument correlated well (R2

 = 

0.93) with a reference NDIR CO monitor, with the Langan underestimating the CO concentration by 

41%. When ambient levels fell consistently below that level, coefficient of determination (R2

) 

between the Langan and reference monitor fell to R2

 = 0.40 in summer and R2 = 0.59 in winter, with 

January 2010 3-91 
the arithmetic average concentration underestimated by 47% in summer and by 63% in winter. 

Chang et al. (2001, 019216) pointed out the need for frequent instrument zeroing to minimize 

instrument drift. Abi Esber and El Fadel (2007, 190940) evaluated a similar personal electrochemical 

CO sensor, the GEMTM 2000, by comparing measured concentrations with those obtained through 

co-located grab-bag sampling in a vehicle cabin. Differences between the GEMTM 2000 and the 

reference samples were fairly low during weekday driving (differences = 2.1-10.6%). Differences on 

Sundays, when traffic was significantly lower than during weekdays, were dependent on vehicle 

ventilation conditions, with better agreement when vehicle ventilation allowed for higher cabin CO 

concentrations (differences = 3.4-5.6%). But the electrochemical sensor did not compare well with 

reference values when concentrations were low (differences = 20-71%). In general, it is difficult to 

separate the large instrumental measurement error seen at concentrations below instrument LOD 

from variation related to nonambient CO sources. This large variation in personal measurements can 

result in high levels of classical measurement error (Sheppard et al., 2005, 079176). 

3.6.8.2. Exposure Issues Related to Nonambient CO 

The focus of the ISA is on ambient CO because that is relevant to the NAAQS. Uncertainty 

related to nonambient CO exposure may make it difficult to distinguish the effect of ambient CO on 

health effects. Wallace and Ziegenfus (1985, 011656) used NHANES II (1976-1980) data to evaluate 

the relationship between COHb levels and ambient CO concentration in 20 U.S. cities. They found a 

significant slope of 0.066% per 1 ppm increase of CO concentration. However, there was much 

scatter in the data, and variability in ambient CO concentration only accounted for 3% of the 

variation in COHb. The authors attributed this scatter to variability in nonambient sources such as 

gas cooking and ETS. This finding illustrates the importance of considering the relative role of 

ambient and nonambient CO in total personal exposure. 

Ambient and nonambient CO are chemically identical and so exert the same health effects. At 

the same time, ambient and nonambient sources are distinct and not correlated with each other 

(Wilson and Suh, 1997, 077408) and so would not confound the association between ambient CO 

exposure and the health effect (see also Sheppard et al., 2005, 079176). Zeger and Diggle (2001, 

026017) noted that, because ambient and nonambient CO exposures are uncorrelated, in a health 

effects model the regression coefficient of ambient concentration should be equal to the product of α

(the ratio of ambient exposure to ambient concentration) and the regression coefficient obtained 

when average personal exposure is used. The confidence intervals around the estimate obtained 

using total personal exposure would be wider because nonambient CO concentrations add variability. 

This is true even for the case when the chemical compound is the same for the ambient and 

nonambient pollutants, as in the case of CO. Likewise, Sheppard et al. (2005, 079176) simulated 

ambient and nonambient exposures to a nonreactive pollutant and observed that nonambient 

exposure has no effect on the association between ambient exposure and health outcomes for the 

case where ambient and nonambient concentrations were independent. Hence, the bias that will be 

introduced to epidemiologic models by using ambient CO concentration instead of personal 

exposure to ambient CO is given by the average α. Random variations in daily values of α would not 

change the health effects estimate but would also widen the confidence intervals around the health 

effect estimate. 

3.6.8.3. Spatial Variability 

CO concentration is known to be spatially heterogeneous, as evidenced by the near-road and 

in-vehicle studies cited in Sections 3.5.1.3 and 3.6.6.2, as well as the intraurban correlations 

provided in Section 3.5.1.2 and Tables A-9 through A-16 of Annex A. Results from Zhu et al. (2002, 

041553), which showed a large CO concentration gradient in the near-road environment, support the 

contention that CO exposures for those living in the near-road environment but far from a monitor 

might be underestimated. Conversely, exposures for those living away from roads might be 

overestimated by near-road CO concentration measurements. Exposure error may occur if the 

ambient CO concentration measured at the central site monitor is used as an ambient exposure 

surrogate and differs from the actual ambient CO concentration outside a subject’s residence and/or 

worksite (in the absence of indoor CO sources). Averaging data from a large number of samplers will 

dampen intersampler variability, and use of multiple monitors over smaller land areas may allow for 

January 2010 3-92 
more variability to be incorporated into an epidemiologic analysis. This is consistent with 

conclusions presented in the 2000 AQCD (U.S. EPA, 2000, 000907). 

Community exposure may not be well represented when monitors cover large areas with 

several subcommunities having different sources and topographies. The intersampler correlations of 

AQS data from monitors, presented in Section 3.5.1.2, reflect how well the time series of 

concentration data correspond across metropolitan areas. Overall, the data show moderate site-to-site 

correlation; for example, in the Los Angeles CSA the mean of the correlation was 0.50, and within 

one standard deviation of the mean, the range of correlations was 0.36-0.65. Bell et al. (2009, 

194033) tested the association between monitor density and 1-h max CO effect estimates for CVD 

hospitalizations for 126 U.S. counties and found an 8% increase in effect estimate size (95% CI: -7% 

to - 24%) with an IQR decrease in area covered by the monitor. This difference was not statistically 

significant but suggested that the magnitude of the effect estimate might be related to monitor 

coverage. Sarnat et al. (2009, 180084) studied the spatial variability of CO, along with NO2, O3, and 

PM2.5, in the Atlanta, GA, metropolitan area and how spatial variability affects interpretation of 

epidemiologic results, using time-series data for circulatory disease ED visits. Sensitivity to spatial 

variability was examined at slightly greater than neighborhood scale (8 km) in this study. 

Interestingly, Sarnat et al. (2009, 180084) found that relative risk varied with distance between the 

monitor and study population when comparing urban to rural locations, but distance of the study 

population to the monitor was not an important factor when comparing urban population groups. 

This suggests that, even for spatially heterogeneous CO, urban scale measures may produce results 

comparable to neighborhood-scale exposures in some circumstances. This may be due to 

comparability of sites throughout a city, for example, as a result of similar traffic patterns. However, 

Sarnat et al. (2009, 180084) caution that, because their study was limited to 8 km radii, it is not 

possible to interpret this work with respect to near-road and on-road microscale exposures. 

3.6.8.4. Temporal Variability 

Temporal Correlation 

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. At the same time if monitors are well correlated across a metropolitan area, even if the 

magnitude of concentration varies over space, time series analyses should provide comparable 

results across larger spatial areas. Such temporal correlation resulted in the small variation in relative 

risk estimates within the metropolitan region in Sarnat et al. (2009, 180084), where peak rush-hour 

times were similar throughout the city, in comparison with the rural area where temporal driving 

patterns were different. Burnett and Goldberg (2003, 042798) found that community time-series 

epidemiologic study 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. Note that for this study, 

Burnett and Goldberg (2003, 042798) analyzed mortality related to PM exposure, but the results are 

not specific to a given pollutant or health effect and thus are generalized here for time-series 

analysis. Dominici et al. (2000, 005828) noted that ensuring correlation between ambient and 

community average exposure time-series air pollutant data is made difficult by limitations in 

availability and duration of detailed ambient concentration and exposure time-series data, resulting 

in a source of uncertainty. If sufficient data are available and the time series of concentration data 

adequately represent population dynamics, then high temporal correlation between sampling sites 

should limit bias in health effects estimates, even if the magnitude of the concentrations differ. 

January 2010 3-93 
Seasonality 

Community time-series epidemiologic 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). Sheppard et al. (2005, 079176) examined the role of seasonality on 

epidemiologic models. They found that α for the population will vary seasonally. This makes sense 

because α is a function of the amount of time spent indoors and outdoors and of indoor ventilation. 

Given that use of ambient CO concentration instead of ambient CO exposure biases the coefficient 

used in epidemiologic models by α, Sheppard et al. (2005, 079176) found that seasonal trends 

causing a change in α would contribute additional positive or negative bias, depending on the season 

and region of the country. However, several studies discussed in Chapter 5 investigated seasonal 

effects. No consistent seasonal pattern across health outcomes was observed in these studies. 

3.6.8.5. CO Exposure in Copollutant Mixtures 

Because CO exposures most often occur together with exposure to other combustion-related 

pollutants, especially in traffic, interpretation of health studies using ambient CO data can be a 

challenge, as discussed further in Chapter 5. Ambient CO concentrations from AQS data (Section 

3.5.3) have been shown to be correlated with ambient concentrations of NO2 and VOCs, and 

personal CO exposures have been correlated with personal PM and VOC exposures (Section 3.6.7). 

Correlation between factors is one condition for confounding, so it is possible that coexistence of CO 

with NO2 or VOCs could confound estimates of the health effects of ambient CO concentrations, and 

CO concentration could potentially confound estimates of the health effects of NO2 or VOCs. For 

this to be true, both CO and the copollutant would have to be correlated with the health outcome of 

interest. The moderately high correlations between ambient CO and copollutants make it difficult to 

discern the extent to which CO and other compounds are associated with a given health effect. 

It is also possible that the factor of interest may be the multipollutant mixture emitted from onroad or other combustion processes. The HEI Report on Traffic Related Pollutants (HEI, 2009, 

191009) suggests that ambient CO, NO2, and benzene could all be considered as surrogates for 

mobile source-related pollution, but none are ideal surrogates for mobile-source pollution because 

ambient CO concentration tends to decrease rapidly with distance from the source (e.g., Baldauf et 

al., 2008, 190239; Zhu et al., 2002, 041553), NO2 is reactive and benzene is volatile. Additionally, 

PM components of mobile source emissions change rapidly in size and composition from secondary 

formation and other atmospheric processing. Given that the mixture of mobile source-related 

emissions changes rapidly as a result of these factors, the ratio of CO to other components of mobilesource emissions also changes. Hence, even if CO is itself stable within the mixture of copollutants, 

the dynamic evolution of the mixture may change the representativeness of CO as an indicator of 

that mixture over time. Additionally, reductions in CO emissions over the past 30 yr have brought 

ambient CO concentrations down substantially, with more than half of hourly measurements below 

the LOD for most instruments (Section 3.5.1.1). Furthermore, CO and other copollutants found in 

mobile-source emissions have multiple anthropogenic and biogenic sources and, as a result, are 

difficult to attribute solely to mobile source pollution (Section 3.2). For all of these reasons, the 

representativeness of CO as an indicator of the multipollutant mixture of mobile-source emissions 

has not been clearly determined. 

3.6.8.6. Conclusions 

This section presents considerations for exposure assessment and the exposure errors and 

uncertainties that can potentially affect health effects estimates. These issues can be categorized into 

the following areas: measurement, nonambient sources, spatial variability, temporal variability, and 

CO in copollutant mixtures. Potential influences of each of these sources on health effect estimates 

derived from panel, time-series, and longitudinal epidemiologic studies are described above. 

Additionally, error sources have the potential to interact with each other. For example, CO 

concentrations have been shown to decrease rapidly with distance from a highway, and so spatial 

variability is an important issue in assessing CO exposure. Exposure error may occur if the ambient 

CO concentration measured at the central site monitor is used as an ambient exposure surrogate and 

differs from the actual ambient CO concentration outside a subject’s residence and/or worksite. 

January 2010 3-94 
However in time-series epidemiologic studies, spatial variability will only be an important source of 

error if the time series of CO concentration at different locations are not well correlated in time. The 

spatial variability of CO, in mixture with the dynamically changing group of mobile source 

pollutants, adds to the difficulty of quantifying the health effects related specifically to CO compared 

with those related to other combustion-related copollutants. 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 CO 

with copollutants are examples of sources of uncertainty that could widen confidence intervals and 

so reduce the statistical significance of health effects estimates. 

3.7. Summary and Conclusions 

3.7.1. CO Sources, Emissions, and Chemistry 

In the U.S., on-road mobile sources constituted more than half, or ~61 MT out of ~117 MT, of 

total CO emissions in the 2002 NEI and BEIS, which are the most recent publicly available CO 

emission datasets meeting EPA’s data quality assurance objectives. In metropolitan areas in the U.S., 

for example, as much as 75% of all CO emissions can come from on-road vehicle exhaust 

(U.S. EPA, 2006, 157070). The majority of these on-road CO emissions derive from gasolinepowered vehicles since the O2 content, pressure, and temperature required for diesel fuel ignition do 

not produce large quantities of CO. Anthropogenic CO emissions are estimated to have decreased 

35% between 1990 and 2002. On-road vehicle sector emissions controls have produced nearly all 

these national-level CO reductions. Nationally, biogenic emissions, excluding fires, were estimated 

to contribute ~5%, or ~5.8 MT, of total CO emissions from all sources in 2002, and fires in 2002 

added another 16%, or ~18.5 MT, to the national CO emissions total. Although these estimates are 

generated using well-established approaches, uncertainties inherent in the emission factors and 

models used to represent sources for which emissions have not been directly measured and vary by 

source category, season, and region. 

In addition to being emitted directly by incomplete combustion, CO is produced by 

photooxidation of CH4 and other VOCs in the atmosphere, including NMHCs. Estimating the CO 

yield from oxidation of HCs larger than CH4 requires computing the yields of several intermediate 

products and reactants from oxidation of the parent molecules. The major pathway for removal of 

CO from the atmosphere is reaction with OH to produce CO2 and HO2. The mean photochemical 

lifetime (τ) of CO in the northern hemisphere is ~57 days. During winter at high latitudes, CO has 

nearly no photochemical reactivity on urban and regional scales. 

3.7.2. Climate Forcing Effects Related to CO 

Recent data do not alter the current well-established understanding of the role of urban and 

regional CO in continental- and global-scale chemistry outlined in the 2000 CO AQCD (U.S. EPA, 

2000, 000907) and subsequently confirmed in the recent global assessments of climate change by the 

Intergovernmental Panel on Climate Change (IPCC, 2001, 156587; IPCC, 2007, 092765). CO is a 

weak direct contributor to RF and greenhouse warming. Sinha and Toumi (1996, 193747) estimated 

the direct RF of CO computed for all-sky conditions at the tropopause to be 0.024 W/m2

 based on an 

assumed change in CO mean global concentrations from 25 to 100 ppb since preindustrial times. The 

direct RF attributed to CO over this time-frame is ~1.5% of the direct RF for CO2 estimated by the 

IPCC (Forster et al., 2007, 092936). 

More importantly, CO can indirectly cause increased RF because it reacts with tropospheric 

OH and thus can increase the lifetime of trace gases in the atmosphere including the GHGs CH4 and 

O3. Additionally, the major pathway for removal of CO from the atmosphere is reaction with OH to 

produce CO2. CH4, O3, and CO2 absorb infrared radiation from the Earth’s surface and contribute to 

the greenhouse effect. Indirect RF attributed to 1750-2005 emissions of CO through changes in 

concentration of the GHGs O3, CH4, and CO2 was estimated by Forster et al. (2007, 092936) to be 

~0.2 W/m2

 or ~12% of the direct RF of CO2 (Figure 3-7). The future direct and indirect integrated 

January 2010 3-95 
RF for year 2000 emissions of CO was estimated to be ~0.2 W/m2

⋅yr with ~50% uncertainty over 

both 20-yr and 100-yr time horizons (Figure 3-8). The RF related to short-lived CO is ~25% of that 

for CO2 for a 20-yr time horizon, but only ~7% of that for longer-lived CO2 over a 100-yr time 

horizon. Overall, the evidence reviewed in this assessment is sufficient to conclude that a causal 

relationship exists between current atmospheric concentrations of CO and effects on 

climate. 

3.7.3. 

3.7.4. 

Ambient CO Measurements 

As of August 2009, 24 automated FRMs and no FEMs had been approved for monitoring CO. 

All EPA FRMs for CO operate on the principle of nondispersive infrared (NDIR) detection and can 

include gas filter correlation (GFC). Current specifications for CO monitoring are designed to help 

states demonstrate whether they have met compliance criteria, with requirements for an LOD of 

1 ppm. The reported LOD for 20 of the 24 FRMs is 0.5 ppm, and four models of FRMs are in 

operation with an LOD of 0.04 ppm. FRMs with higher LOD also are limited to a precision of 

0.1 ppm and are more subject to drift compared with newer monitors with automatic drift correction 

options. 

For 2005-2007, there were 291 CO monitors meeting the 75% completeness requirements and 

reporting values year-round to the AQS in the 50 states, plus the District of Columbia, Puerto Rico, 

and the Virgin Islands. 57 monitors across the U.S. have been sited at microscale to capture 

near-road concentrations, 31 have been sited at middle scale, and 119 are sited for neighborhood 

scale monitoring; among the remaining 84 monitors, states did not declare the spatial scale of 

monitoring for 71 monitors, and 13 are sited for monitoring urban or regional scale. For CO, traffic 

is the major source in an urban setting and therefore microscale data are sited “to represent 

distributions within street canyons, over sidewalks, and near major roadways” while middle scale 

monitors are sited to represent “air quality along a commercially developed street or shopping plaza, 

freeway corridors, parking lots and feeder streets” (40 CFR Part 58 Appendix D). At middle and 

neighborhood scales, required minimum monitor distance from a road is directly related to the road’s 

average daily traffic count to capture community averages. Ambient monitors for CO and other 

criteria pollutants are located to monitor compliance rather than population exposures. However, 

AQS monitors are often used for exposure assessment. When comparing CO monitor location with 

population density, it was observed that population coverage varies both within and between cities. 

Environmental CO Concentrations 

CO concentration data for 1-h and 8-h intervals were available for 243 counties and 

autonomous cities or municipalities that maintained active CO monitoring stations meeting the 75% 

completeness criteria for the years 2005-2007. There were no violations of the 1-h or 8-h NAAQS in 

those years. The nationwide mean, median, and interquartile range for 1-h measurements reported 

between 2005 and 2007 were 0.5, 0.4, and 0.4 ppm, respectively, and these statistics did not change 

by more than 0.1 ppm for each year of the 3-yr period. More than 50% of the data nationwide were 

below the LOD for the majority of monitors in use. The nationwide mean, median, and interquartile 

range for 8-h daily max concentrations, reported between 2005 and 2007, were 0.7, 0.5, and 

0.5 ppm, respectively. Half of the 8-h daily max concentrations fell below the LOD for the majority 

of CO monitors in the field. The 2006 annual second highest 8-h CO concentration, averaged across 

144 monitoring sites nationwide, was 75% below that for 1980 and is the lowest concentration 

recorded during the past 27 yr. The mean annual second highest 8-h ambient CO concentration has 

been below 5 ppm since 2004. The downward trend in CO concentrations in the 1990s parallels the 

downward trend observed in CO emissions and can be attributed largely to decreased mobile source 

emissions. 

The correlation structures for measurements at the monitors in each of the 11 CSAs/CBSAs 

examined for this assessment reveal a wide range of responses between monitors in each city and 

among the cities. While this wide range is produced by the interactions of many physical and 

chemical elements, the location of each monitor and the uniqueness of its immediate surroundings 

can often explain much of the agreement or lack thereof. CO concentrations can be elevated near 

roadways and decrease with increasing distance from the road. Anchorage, AK, had concentrations 

roughly twice those of the other metropolitan areas. Most of the CSAs/CBSAs examined here had 

January 2010 3-96 
diel concentration curves with pronounced morning and evening rush hour peak CO levels, although 

diel CO concentrations had less variability for New York City, Atlanta, and Seattle than for the other 

eight cities. For most metropolitan areas examined here, concentrations were generally highest in the 

winter (December-February) and fall (September-November) and decreased, on average, during the 

spring (March-May) and summer (June-August). Measurements near or below the 0.5 ppm LOD for 

most instruments, coupled with the coarsely reported measurement resolution of 0.1 ppm, can 

artificially influence the comparison statistics shown in the tables and result in apparent 

heterogeneity in the box plots (Figure 3-19 and Figure 3-22). 

CO measurements obtained at different monitoring scales were compared to assess spatial 

variability of CO concentration. The median hourly CO concentration across the U.S. obtained at 

microscale monitors was 25% higher than at middle scale and 67% higher than at neighborhood 

scale. The microscale and middle scale CO data reported here are consistent with hourly 

concentrations reported in the literature for the near-road environment within the United States, with 

CO concentration decaying with downwind distance from the road. Determinants of spatial 

variability of ambient CO concentration within the near-road environment include roadway density, 

traffic counts, meteorology, and natural and urban topography. 

In all cases, a wide range of correlations existed between CO and copollutants computed from 

AQS data. The mean and median correlations between CO and copollutants were positive for NO2, PM10, and PM2.5; near zero for SO2; and negative for O3. These findings might reflect common 

combustion sources for CO, NO2, and PM. Among those copollutants with positive associations, 

NO2 had the highest mean and median correlations, followed by PM2.5 and PM10. Within and 

between individual metropolitan areas, the distribution of copollutant correlations varied 

substantially. Studies in the literature also found fairly high correlations of CO with EC and certain 

VOCs. 

This assessment has used data from 2005-2007 at 12 remote sites as part of the international 

CCGG CASN in the CONUS, Alaska, and Hawaii to determine PRB. All sites demonstrate the wellknown seasonality in background CO, with minima in the summer and fall and maxima in the winter 

and spring. The 3-yr avg CO PRB in Alaska was 130 ppb; in Hawaii it was 99 ppb; and over the 

CONUS it was 132 ppb. 

3.7.5. Exposure Assessment and Implications for Epidemiology 

Very few recent exposure assessment studies involve ambient CO concentration data. The 

studies of personal exposure to ambient CO presented here generally found that the largest 

percentage of time in which an individual is exposed to ambient CO occurs indoors, but the highest 

ambient CO exposure levels occur in transit. In-vehicle CO concentrations are typically reported to 

be between 2 and 5 times higher than ambient concentrations, although peak in-vehicle 

concentrations more than an order of magnitude higher than corresponding ambient monitor 

concentrations have also been reported. Among commuters, exposures were higher for those 

traveling in automobiles in comparison with those traveling on buses and motorbikes and with those 

cycling or walking. Ambient CO exposure in automobiles has been demonstrated to vary with 

vehicle ventilation settings, and a very small portion of that exposure is thought to come from the 

vehicle in which the exposed person travels. High near-road CO concentrations can be important for 

those living in the near-road environment because virtually all of ambient CO infiltrates indoors. 

Hence, indoor exposure to ambient CO is determined by the CO concentration outside the building. 

Residents of the 17.9 million occupied homes located within approximately 90 m of a highway, 

railroad, or airport may be exposed to elevated ambient CO levels. However, CO concentration in 

the near-road environment has been shown to decrease sharply with downwind distance from a 

highway, wind direction, emission source strength (e.g., number of vehicles on a highway). Natural 

and urban topography also influence localized ambient CO levels. 

Recent exposure assessment studies support one of the main conclusions of the 2000 CO 

AQCD, that central-site ambient CO monitors may overestimate or underestimate individuals’ 

personal exposure to ambient CO because ambient CO concentration is spatially variable, 

particularly when analyzing exposures in the near-road environment. Exposure error may occur if the 

ambient CO concentration measured at the central-site monitor is used as an ambient exposure 

surrogate and differs from the actual ambient CO concentration outside a subject’s residence and/or 

worksite. For example, measurement at a “hot spot” could skew community exposure estimates 

upwards, and likewise measurement at a location with few nearby CO sources could skew exposure 

January 2010 3-97 
estimates downwards. Correlations across CO monitors can vary widely from within and between 

cities across the U.S. as a function of natural and urban topography, meteorology, and strength and 

proximity to sources. Typically, intersampler correlation ranges from 0.35 to 0.65 for monitors sited 

at different scales within a metropolitan area, although it can be greater than 0.8 in some areas. 

Health effects estimates from time-series epidemiologic studies are not biased by spatial variability 

in CO concentrations if concentrations at different locations are correlated in time. Additionally, 

exposure assessment is complicated by the existence of CO in multipollutant mixtures emitted by 

combustion processes. Because ambient CO exists in a mixture with volatile and reactive pollutants, 

the correlation between exposure to ambient CO and copollutants can vary substantially over time 

and across locations. For this reason, it is difficult to quantify the effects related specifically to CO 

exposure compared with those related to another combustion-related pollutant or mix of pollutants. 

In most circumstances, exposure error tends to bias a health effect estimate downward. Spatial and 

temporal variability not fully captured by ambient monitors and correlation of CO with copollutants 

are examples of sources of uncertainty that could widen confidence intervals of health effects 

estimates. 

January 2010 3-98 
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