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Wintertime PM2.5 and PM10 Source Apportionment

at Sacramento, California

Nehzat Motallebi a

a

 California Air Resources Board, Research Division, Sacramento, California, USA

Version of record first published: 27 Dec 2011.

To cite this article: Nehzat Motallebi (1999): Wintertime PM2.5 and PM10 Source Apportionment at Sacramento,

California, Journal of the Air & Waste Management Association, 49:9, 25-34

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Motallebi

Volume 49 September 1999 Journal of the Air & Waste Management Association PM-25

ISSN 1047-3289 J. Air & Waste Manage. Assoc. 49: PM-25-34

Copyright 1999 Air & Waste Management Association

TECHNICAL PAPER

Wintertime PM2.5 and PM10 Source Apportionment at

Sacramento, California

Nehzat Motallebi

California Air Resources Board, Research Division, Sacramento, California

ABSTRACT

The chemical mass balance (CMB) model was applied to

winter (November through January) 1991–1996 PM2.5 and

PM10 data from the Sacramento 13th and T Streets site in

order to identify the contributions from major source categories to peak 24-hr ambient PM2.5 and PM10 levels. The

average monthly PM10 monitoring data for the nine-year

period in Sacramento County indicate that elevated concentrations are typical in the winter months. Concentrations on days of highest PM10 are dominated by the PM2.5

fraction. One factor contributing to increased PM2.5 concentrations in the winter is meteorology (cool temperatures, low wind speeds, low inversion layers, and more

humid conditions) that favors the formation of secondary nitrate and sulfate aerosols. Residential wood burning also elevates fine particulate concentrations in the

Sacramento area.

The results of the CMB analysis highlight three key

points. First, the source apportionment results indicate

that primary motor vehicle exhaust and wood smoke are

significant sources of both PM2.5 and PM10 in winter. Second, nitrates, secondarily formed as a result of motorvehicle and other sources of nitrogen oxide (NOx

), are another principal cause of the high PM2.5 and PM10 levels

during the winter months. Third, fugitive dust, whether

it is resuspended soil and dust or agricultural tillage, is

not the major contributor to peak winter PM2.5 and PM10

levels in the Sacramento area.

IMPLICATIONS

Developing a technically defensible PM2.5 control program

requires identifying the contribution of each source type

to the measured PM2.5 concentrations and then estimating the air quality benefits associated with implementing a

suite of emission controls. The present methodologies for

particulate matter (PM) source apportionment are not adequate for all the PM pollution situations in California, and

will need to be complemented by new methods and more

specific source characterizations.

INTRODUCTION

Reducing fine particulate pollution is one of the most difficult environmental challenges facing California because

of the great diversity of sources and chemical species involved. Developing a technically defensible PM2.5 control

program requires identifying the contribution of each

source type to the measured PM2.5 concentrations and then

estimating the air quality benefits associated with implementing a suite of emission controls. Linking sources to

measured air quality uses empirical methods termed

“source apportionment” and “receptor modeling.”

In this paper, the receptor modeling approach is used

to identify the contributions from major source categories to ambient PM2.5 and PM10 levels in the Sacramento

area. Receptor models use the chemical and physical characteristics of gases and particles measured at sources and

receptors to both identify the sources and quantify their

contributions to the pollutants measured at the receptors.

The particle characteristics must be such that (1) they are

present in different proportions in different source emissions (i.e., they provide a means by which to distinguish

sources); and (2) these proportions remain relatively constant for each source type. The chemical mass balance

(CMB) model is a powerful multivariate technique that

can provide a direct and quantitative estimate of source

contributions. This approach provides a means of predicting the impact of control programs. Included are descriptions of the ambient and source profiles used in the receptor model, the model itself, and the model results.

AMBIENT PARTICULATE MATTER DATA

The federal government and the state of California have

established ambient air quality standards for particulate

matter (PM) with aerodynamic diameters of 10 μm or less

(PM10). Both short-term (24-hr) and long-term (annual)

standards have been established to protect public health.

The 24-hr federal and state standards are 150 and 50 μg/

m3

, respectively. Concentrations of PM10 have been measured in Sacramento County since 1986 at seven sampling

sites: Citrus Heights, North Highlands, Sacramento 13th

and T Streets, Del Paso Manor, Earhart Drive, Stockton

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PM-26 Journal of the Air & Waste Management Association Volume 49 September 1999

Boulevard, and Sacramento Branch Center Road (Figures

1a and 1b). Ambient PM10 samples are collected for a 24-hr

sampling period every sixth day. The PM10 seasonal patterns are similar at all seven sites, with higher winter concentrations and lower concentrations during the rest of

the year. To illustrate this point, monthly mean PM10 mass

from the Del Paso Manor site from November 1986 through

December 1997 is presented in Figure 2. This figure illustrates that elevated PM concentrations are typical in the

winter, with lowest average values occurring in the summer months. The most recent violation of the 24-hr federal standard for PM10 occurred on December 25, 1990, with

a concentration of 187 μg/m3

 at the Del Paso Manor site.

No detailed data were available to perform the CMB model

or to conduct a thorough data analysis for this date.

Four types of filter-based aerosol samplers have been

operated at the 13th and T Streets site since 1991. These

include the size-selective inlet high volume (SSI Hi-Vol)

sampler, the California Acid Deposition Monitoring Program (CADMP) sampler,1 the dichotomous (dichot) sampler, and the tapered element oscillating microbalance

(TEOM) sampler, which permits continuous, hourlyaverage measurements of PM10 concentrations. At the Sacramento 13th and T Streets site, data on light scattering

(gathered with a nephelometer) and light absorption (the

coefficient of haze [COH]) are also available. COH units

are defined as that quantity of PM that produces an optical density of 0.01 on the paper tape. A photometer detects the change in the quantity of light transmitted

through the spot as the PM collects on the paper filter

tape and produces an electrical signal proportional to the

optical density. A COH of less than 1.0 represents relatively clean air, while a COH of greater than 2.0 represents air with a relatively high concentration of combustion-caused particles.

In this study, sulfate, nitrate, ammonium, and

water-soluble potassium concentrations were obtained

Figure 2. Monthly average SSI PM10 mass at Del Paso Manor (11/

86–12/97).

Figure 1b. Sacramento Valley Air Basin monitoring stations—Yolo, Sacramento, and Solano counties, July 1997–1998.

Figure 1a. Sacramento Valley Air Basin monitoring stations, July 1997–1998.

a b

3

)

Monthly Average SSI PM10 Mass

Sacramento Del Paso (11/86–12/97)

Please refer to Figure 1b for

detailed Sacramento Valley

Air Basin monitoring

information in Yolo,

Sacramento, and Solano

Counties.

SHASTA

Redding...G,P

 Anderson...G,P Lasson Volcanic NP...G,M

Tuscan Buttes...G,M Red Bluff-Oak...G Red Bluff-Messen...P

TEHAMA

GLENN

BUTTE

Chico-Manzanita...G,P,T,M

Chico-Salem...G,M

Willows...G,P,M

YUBA

COLUSA Gridley...P,M

Colusa-Sunrise...G,P,M Sutter Buttes...G Yuba City...G,P,M PLACER

Lincoln...P

Arbuckle...P,M

SUTTER Pleasant Grove...G,P,M

YOLO

SOLANO

SACRAMENTO

Rocklin...G,P,M

Roseville...G,P,T,M

MONITORING TYPES

G Gaseous

P Particulate

T Toxics

M Meteorological

E

MILES

0 20 40 60

YOLO

Sacramento-Earhart...G,P,M

Woodland...G,P,M

North Highlands...G,P

Folsom...G,P

Sacramento-Del Paso...G,P,M Sacramento-El Cam/Walk...G,P,M

West Sacramento...P

Davis...G,P,M

Sacramento Health Dept...P

Sacramento Branch Center...P

Sloughhouse...G

Sacramento- 13th and T streets...G,P,M

SACRAMENTO

Elk Grove...G,M

Vacaville-Elmira...G,M

Vacaville-Merchant...G,M

SOLANO

MONITORING TYPES

G Gaseous

P Particulate

T Toxics

M Meteorological

E

MILES

0 5 10 15

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Volume 49 September 1999 Journal of the Air & Waste Management Association PM-27

from the CADMP sampler measurements at the Sacramento 13th and T Streets site. Total potassium and sulfur

measured by X-ray fluorescence (XRF) from the dichot

sampler were compared with water-soluble potassium

measured by atomic absorption spectrophotometry and

water-soluble sulfate measured by ion chromatography

from the CADMP sampler. For most cases, the soluble

potassium concentration was less than total potassium

and the sulfate was less than three times the total sulfur

because only a portion of these species are soluble. Nitrate values were converted to secondary ammonium nitrate values by multiplying the nitrate values by the ratio

of the molecular weight of ammonium nitrate to the

molecular weight of nitrate.

The dichot sampler uses a low-flow volume PM10

inlet followed by a virtual impactor, which splits the

air stream in two, and, with different air flow rates, separates particles into two fractions: fine particles (PM2.5,

i.e., particles with aerodynamic diameters of less than

2.5 μm) and coarse particles (those having aerodynamic

diameters of 2.5–10 μm). Each fraction collected by the

dichot sampler is analyzed by XRF spectroscopy for 30

elemental species. Data from the dichot sampler are used

in the CMB source apportionment. The sum of the fine

and coarse fractions provides a measure of total PM10.

The primary source of PM2.5 data in California is from

dichot samplers. Figure 3 shows the monthly average

fine and coarse concentration data as generated from

the dichot sampler from September 1991 through June

1997 at the 13th and T Streets site. PM2.5 concentrations were highest during the winter months. One factor contributing to increased PM2.5 concentrations in

the winter is meteorology (cool temperatures, low wind

speeds, low inversion layers, and more humid conditions) that favors the formation of secondary nitrate

and sulfate aerosols. Residential wood burning also elevates fine particulate concentrations at the 13th and T

Streets site. This will be discussed in greater detail in

the description of the CMB results.

At the 13th and T Streets site, COH and light scatter

(bscat) provide a relative indication of the contributions of

light scattering and light absorption. The COH is a direct

measure of the light-absorbing ability of the particles. Light

absorption is primarily due to elemental carbon from combustion. The bscat roughly measures all scattering by fine

particles. The characteristics of light scattering are extremely sensitive to the size of the scattering particles.

Light scattering by the large particles (>10-μ diameter) is

generally not significant. As particle sizes approach the

range of light wavelengths (0.1–1 μ), they become more

efficient in light scattering. TEOM PM10 levels are associated with high COH and bscat values during winter at the

Sacramento site (Figure 4). To make the graphs more readable, COH values are multiplied by 50.

METEOROLOGICAL DATA

Hourly average values for wind speed, wind direction, temperature, and dew point are obtained from continuous

monitors. Wind speed is measured by a three-cup anemometer. Wind direction is measured by a wind vane

coupled to a precision low-torque potentiometer. Ambient temperature is measured by a thermistor housed in

an aspirated radiation shield. Dew point is measured using a dew cell that consists of a heated, wire-wound bobbin coated with lithium chloride. The data acquisition

system resolves the instantaneous wind speed and wind

direction signals into vector components, averages the

components, and determines the average vector wind

speed and direction.

Regional meteorological conditions are generally influenced by the topography of the Sacramento Valley,

surrounded on three sides by mountain ranges. Wind

speeds and directions reflect the channeling effect of the

mountain ranges. Pollutants are often concentrated in

m3

)

Figure 3. Monthly average dichot coarse and fine mass data at

Sacramento 13th and T Streets (9/91–6/97).

Monthly Average Dichot Mass

Sacramento 13th and T (9/91–6/97)

Figure 4. Monthly average TEOM PM10 mass, light scattering (bscat), and

light absorption (COH) at Sacramento 13th and T Streets (1/94–9/97).

m3

) 10

Monthly Average TEOM PM10, bscat, and COH

Sacramento 13th and T (1/94–9/97)

[COH × 50 and bscat × 10]

bscat

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PM-28 Journal of the Air & Waste Management Association Volume 49 September 1999

the valley “bowl.” Marine air from the San Francisco Bay

area enters the Sacramento Valley through the Carquinez

Straight and the Cordelia Gap in the coastal ranges. This

relatively cool marine air mass often creates an inversion layer that can further trap valley pollution. Inversions occur with great frequency throughout the year in

the Sacramento Valley, and when they are accompanied

by low wind speeds in the winter, pollutant concentration levels can escalate.

Predominant winds are south-southwest during the

spring, summer, and fall. Typically, during the late fall

and winter, air flows experience a significant decrease

in speed, leading to calm conditions. The light winds

and calm conditions lead to high air pollution potential because pollutants can accumulate in the area for

several days before being dispersed. These conditions

have predominantly existed during all of the PM10

exceedances in Sacramento County. Precipitation plays

a role in the suppression of soil and road dust in the

winter, but comes most often in the form of dew. Rain

is common in December, but typically occurs on only a

few days. Since winter PM2.5 is mostly secondary aerosols from emissions combustion products, it is largely

independent of precipitation. As a result, except during

periods of rain, precipitation has little direct effect on

typical PM2.5 concentrations.

CMB RECEPTOR MODEL

The CMB receptor model consists of a least-squares solution to a set of linear equations that expresses a receptor

concentration of a chemical species as a linear sum of

products of source profile species and source contributions. The source profile (the fractional amount of the

species in the emissions from each source type) and the

receptor concentrations, with appropriate uncertainty

estimates, serve as input data to the CMB model. The

output consists of the contributions from each source type

to the ambient PM2.5 or PM10 mass as well as to individual

chemical species concentrations. The model uses input

data uncertainties to estimate the uncertainties of the

source contributions. These procedures are described in

greater detail in an operational manual 2 and in an applications and validation protocol3

 accompanying the U.S.

Environmental Protection Agency (EPA)’s version 7 of the

CMB receptor modeling software.

The basic assumptions of the CMB model are that

(1) compositions of source emissions are constant over

the period of ambient and source sampling; (2) chemical

species do not react with one another (i.e., they add linearly); (3) all sources that may significantly contribute

to the receptor have been identified and their emissions

characterized; (4) the number of source categories is less

than or equal to the number of chemical species; (5)

source profiles are linearly independent (i.e., they are

statistically different); and (6) measurement uncertainties are random, noncorrelated, and normally distributed.

Assumptions 1 through 6 are fairly restrictive and will

never be totally complied with in actual practice. The

CMB model can tolerate deviations from these assumptions, although these deviations increase the stated uncertainties of the source contribution estimates.

Source Profiles

Receptor modeling requires information about the chemical characteristics of the emission sources that are likely

to affect pollutant concentrations at a receptor. For the

CMB receptor model, the information required is the fractional composition of each chemical species in the source

emissions and an estimate of the variability of those compositions. Most of the source profiles used for Sacramento

source apportionment were selected from the California

Air Resources Board (CARB) library.4

 There are many source

emission subtypes within some of these categories which

are too similar in composition to be distinguished by commonly measured chemical species. For example, with current measurements, it is not possible to distinguish contributions from leaded, unleaded, and diesel-fueled vehicle

exhaust contributions based on their vehicle exhaust

chemical composition. It is also not possible to distinguish

among contributions from resuspended road dust, windblown dust, construction activities, and agricultural tilling. CARB studies are underway to develop methods for

distinguishing among these sources.

Mobile source particulate emissions are among the most

difficult to measure with respect to emission rate and chemical composition. This difficulty arises from (1) the large

number of mobile source types (passenger cars, light-duty

trucks, diesel trucks, diesel buses, etc.); (2) the large number of individual emitters within each type; (3) the radical

change in fuel-use characteristics over the past decade (e.g.,

the phaseout of leaded gasoline); (4) the large variety of

undefined (and probably undefinable) operating conditions;

(5) the variety of emission points on each vehicle (tailpipe,

resuspended dust, fuel evaporation, tire wear, and brake

wear); and (6) the fact that many emissions are probably

semi-volatile or secondary particle precursors.

With the limited elemental data available, it is not

possible to differentiate the contribution of each type of

motor vehicles (i.e., catalyst-equipped, non-catalystequipped, and diesel) to the ambient PM2.5 and PM10 concentrations. Therefore, we developed a composite vehicle

profile that includes contributions from the three main

vehicle types. The vehicles are categorized by the fuel that

they burn, independent of vehicle weight. This resulted

in three motor vehicle categories: unleaded, leaded, and

diesel. The ratio that each contributes to the total motor

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Volume 49 September 1999 Journal of the Air & Waste Management Association PM-29

vehicle direct tailpipe particle emissions was determined

from CARB’s emissions inventory results, derived from

the EMFAC7 model (CARB 1993). This ratio reveals that

the PM inventory is dominated (about 88%) by emissions

from diesel-fueled vehicles. The calculated ratio was used

to weigh the contribution that each of the three vehicle

source profiles made to the total composite profile. The

individual motor vehicle exhaust profiles (for vehicles

operated on a dynamometer during the cold-stabilized

cycle of the Federal Test Procedure) were obtained from

the South Coast Air Quality Management District’s source

profiles library.5 The composite profile was used to apportion the motor vehicle contributions.

Five different geochemical profiles were tested, by running a CMB model for each profile, to determine which

profile best described the ambient mass and crustal species aluminum, silicon, iron, calcium, manganese, and

titanium. The Bakersfield Unpaved Road profile explained

ambient concentrations of these species. Several woodand agricultural-burning profiles were also tested. Because

the wood- and agricultural-burning profiles are chemically similar, only a single profile, representing one or both

of these emitters, could be used in the CMB source apportionment for any single sample. The key species in

wood-burning emissions are soluble potassium, chlorine,

and organic carbon. It was found that these species were

best explained with the Bakersfield Cordwood (Majestic

Fireplace) profile. The signature species for crude-oil combustion are vanadium and nickel. The Santa Fe Energy

crude-oil boiler profile explained ambient concentrations

of these species.

Significant quantities of the sulfate and nitrate in

ambient aerosol cannot be entirely accounted for by primary emissions of sulfur dioxide (SO2) and NOx. To account for secondary aerosol contributions to PM2.5 and

PM10 in the CMB calculations, ammonium sulfate and

ammonium nitrate were expressed as purely secondary

source profiles.

CMB RESULTS

The CMB model calculates source contribution estimates

for each ambient sample, which is usually of 24-hr duration for PM2.5 and PM10. We chose to run all the ambient

days in which the PM10 mass measured by the SSI-HiVol

sampler at the Sacramento 13th and T Streets site was

greater than 40 μg/m3

. There were 126 days that met this

criterion during the winters (November through January)

of 1991–1996.

In each of the PM2.5 and PM10 size fractions, 126

samples were used to calculate initial source contribution

estimates. These initial tests were intended to determine

which profiles best explained the data at the Sacramento

site. The CMB performance measures were examined to

determine how well the ambient concentrations were

explained by the source contribution estimates.

The initial source apportionments also examined the

species that would provide the best indication of the presence or absence of sources (fitting species). Many of the

trace elements (such as cobalt, arsenic, selenium, strontium, molybdenum, cadmium, tin, antimony, cesium,

barium, platinum, and mercury) were omitted as fitting

species because we found their concentrations to be less

than or close to their lower quantifiable limits and because we did not find great abundance of these species

in the source profiles. Vanadium and nickel were retained, even though their concentrations were very low,

so that the contribution of residual oil combustion could

be sought. Our final runs used 17 fitting species (aluminum, bromine, calcium, chlorine, chromium, iron, lead,

manganese, nickel, phosphorous, potassium, silicon,

sulfur, strontium, titanium, vanadium, and zinc) for the

PM2.5 and PM10 size fractions. This number of species gave

us the best model results as measured by r2 values and

high values for percent mass explained. If the model

explains 80% of the ambient sample mass and the values of goodness-of-fit (r2 and chi-square) approach unity,

it can be assumed that the model is adequately describing the sample.

The data used in this study generally met all requirements for CMB model applicability, and no unacceptable

stability and consistency problems were found. Only days

where no uncertainty/similarity clusters were observed,

and chi- and r2 values were less than 4.0 and between 0.8

and 1.0, respectively, were used in determining the average values reported. All other model summary statistics

and diagnostics were generally within acceptable limits.

If negative source contributions were obtained from the

model and no uncertainty/similarity clusters were found,

the model was rerun with the source profile corresponding to the most negative source contribution estimate removed. This process was continued until all negative contributions were eliminated (usually elimination of the

single most negative value gave all positive contributions

on the next apportionment run). If uncertainty/similarity clusters were found, the day was eliminated from further consideration. Over 500 initial tests were performed

to determine which profiles best explained the data at

the Sacramento site.

The main source types that contributed to the PM2.5

and PM10 air quality in Sacramento were (1) fugitive dust

from tilling and road construction; (2) wood stoves, fireplaces, and agricultural burning; (3) motor vehicle exhaust

(from cars, trucks, and buses fueled with diesel, leaded gasoline, and unleaded gasoline); and (4) secondary particles.

Other sources that might have contributed to a lesser extent were residual and crude oil combustion and

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PM-30 Journal of the Air & Waste Management Association Volume 49 September 1999

marine-generated aerosols (sodium chloride [NaCl] and

sodium nitrate [NaNO3]). Marine aerosol transported from

the San Francisco Bay area can react with acidic gases, especially nitric acid. This process results in depletion of halogens from the aerosol and formation of alkaline salts.

Meteorological data are used to determine the meteorological parameters that most influence PM2.5 and PM10

air quality, and to quantify values of meteorological parameters for high PM2.5 and PM10 levels. Sacramento temperatures can reach a high of 110 °F during the summer

and remain low during the winter months, when readings

in the 20s °F are recorded. High temperature contributes to

the formation of ozone in the summer, but seasonal PM2.5/

PM10 patterns show that high temperature has a limited

direct effect on PM levels in Sacramento County. It is suggested that low temperatures in the presence of increased

humidity are conductive to the formation of secondary

particles. Secondary pollutant formation is influenced by

a combination of precursor pollutant concentrations and

weather conditions. Conversion of oxides of sulfur to sulfate aerosols is accelerated by the presence of oxidants and

hydroxyl radicals in the air (as during ozone episodes), and

is accelerated even more under humid conditions, when

the conversion can occur inside water droplets. NOX conversion to nitrate is even more sensitive, as formation rates

must compete with dissociation back to gases, so that nitrate is generally a cool-wet (e.g., winter) phenomenon.

Also, as the winter temperature drops, more residents are

likely to utilize wood combustion (fireplaces) for residential heating, increasing recorded PM levels.

Scatter plots (Figures 5a and 5b) show that the wind

directions are generally from the north or the south, consistent with up-valley and down-valley air flows. These

graphs also show that high PM levels are associated with

Figure 5a. Scatter plots of dichot PM10 mass versus temperature, relative humidity, resultant wind speed, and resultant wind direction at the

Sacramento 13th and T Streets site.

Dichot PM10 and Temperature

Sacramento 13th and T, Winter 1991–1996

Dichot PM10 and Relative Humidity

Sacramento 13th and T, Winter 1991–1996

Dichot PM10 and Resultant Wind Speed

Sacramento 13th and T, Winter 1991–1996

Dichot PM10 and Resultant Wind Direction

Sacramento 13th and T, Winter 1991–1996

24-hr Avg. Temperature (°F) 24-hr Avg. Relative Humidity (%)

24-hr Avg. R-Wind Speed (mi/hr) 24-hr Avg. R-Wind Direction

24-hr PM10 Mass (

μg/m3

)

24-hr PM10 Mass (

μg/m3

)

24-hr PM10 Mass (

μg/m3

)

24-hr PM10 Mass (

μg/m3

)

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Volume 49 September 1999 Journal of the Air & Waste Management Association PM-31

cool temperatures and low wind speeds, perhaps because

during winter, wind speeds with lower mixing depths

lead to more buildup of source emissions than the deeper

mixing layers and higher wind speeds of spring and summer. All of the PM10 exceedances of the 24-hr federal and

state standards (150 and 50 μg/m3) experienced in Sacramento County have occurred during winter.

Each of the 126 CMB sample days during the winters

of 1991–1996 were examined based on meteorological

parameters, including vector average wind speed, temperature, and relative humidity. The meteorological class that

leads to higher overall PM concentrations in the Sacramento area was designated as class 1 for this study. It is

typified by wind speeds less than 4 mi/hr, temperature

less than 50 °F, and relative humidity greater than 40%.

During the sampling periods, there were 44 days in which

the meteorological conditions of class 1 were met.

The computed source contributions to 24-hr PM10

concentrations at the 13th and T Streets site are presented

in Figures 6a and 6b. Temporal patterns can easily be observed in histograms of the individual source contributions. A statistical summary of CMB source contributions

is presented in Table 1. The standard deviation is given as

an indication of the variability of the source contributions. These average contributions are plotted as a pie chart

in Figure 7. The major contributors to PM10 were fugitive

dust (12%) (6.4 μg/m3), wood smoke (17%) (8.6 μg/m3),

motor vehicles (23%) (11.8 μg/m3

), and ammonium nitrate (29%) (14.9 μg/m3).

During the colder winter months, one would expect

a lot of wood smoke generation, especially during holidays and weekends. Some of the largest wood smoke contributions occurred on Thursday, December 26, 1991

(19.9 μg/m3

); Thanksgiving Day, Thursday, November 24,

1994 (16.1 μg/m3); and Sunday, December 10, 1994 (18.6

μg/m3). Secondary ammonium sulfate was detectable (2.3

μg/m3) but was not a major contributor to PM10. Crude

oil contributions to PM10 on these dates were negligible.

Dichot PM2.5 and Temperature

Sacramento 13th and T, Winter 1991–1996

Dichot PM2.5 and Relative Humidity

Sacramento 13th and T, Winter 1991–1996

Dichot PM2.5 and Resultant Wind Speed

Sacramento 13th and T, Winter 1991–1996

Dichot PM2.5 and Resultant Wind Direction

Sacramento 13th and T, Winter 1991–1996

.

24-hr Avg. R-Wind Speed (mi/h) 24-hr Avg. R-Wind Direction

Figure 5b. Scatter plots of dichot PM2.5 mass versus temperature, relative humidity, resultant wind speed, and resultant wind direction at the

Sacramento 13th and T Streets site. 24-hr PM2.5 Mass (

μg/m3

)

24-hr PM2.5 Mass (

μg/m3

)

24-hr PM2.5 Mass (

μg/m3

)

24-hr PM2.5 Mass (

μg/m3

)

24-hr Avg. Temperature (°F) 24-hr Avg. Relative Humidity (%)

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PM-32 Journal of the Air & Waste Management Association Volume 49 September 1999

Approximately 15% of the measured PM10 was not explained by the selected source types at the 13th and T

Streets site. Gray et al.6 at Rubidoux and Chow7 at Bakersfield found a similar situation. This unexplained mass

can be due to several factors: (1) not all sources were

included in the CMB run; (2) poor correspondence between the source profiles used in the CMB modeling and

the actual contributing source profiles; (3) volatilization

of the semi-volatile compounds (organics and ammonium nitrate) between sample weighing and ion analysis; and (4) the presence of liquid water associated with

the ionic species in these samples but not included in

the secondary source profiles. This points out some of

the limitations of aerosol apportionment which derive

from the manner in which aerosols have traditionally

been measured, and the importance of having detailed,

locality-specific profiles when using CMB models.

The computed source contributions to 24-hr PM2.5

concentrations at the 13th and T Streets site are presented

in Figures 8a and 8b. A statistical summary of CMB source

contributions is presented in Table 2. These average contributions are plotted as a pie chart in Figure 9. The main

contributors to PM2.5 were wood smoke (18%) (7.1 μg/

m3

), motor vehicles (25%) (9.7μg/m3), and ammonium

nitrate (37%) (14.5 μg/m3). Secondary ammonium sulfate

was detectable (1.8 μg/m3) but not a major contributor to

PM2.5. Fugitive dust contributions (0.5 μg/m3) to PM2.5 were

negligible. Approximately 15% of the measured PM2.5 was

not explained by the selected source types at the 13th

and T Streets site.

CONCLUSIONS

The receptor modeling reported here separates primary

from secondary contributions, associates the secondary

species with the chemical compounds in which they are

contained, and attributes the primary PM2.5 and PM10 to

major source categories. Detecting the sources of specific

Figure 6a. Wintertime PM10 source contribution estimates, 1991–1993.

Figure 6b. Wintertime PM10 source contribution estimates, 1994–1996.

Table 1. Statistical summary of CMB PM10 source apportionments at Sacramento 13th and T Streets, winter 1991–1996.

Average Std. Dev. Minimum Maximum Range

(μg/m3) (μg/m3) (μg/m3) (μg/m3) (%)

Fugitive dust 6.40 0.14 1.08 28.09 4–39

Wood smoke 8.57 0.09 2.92 19.86 11–33

Motor vehicles 11.79 0.10 5.58 27.11 12–36

Secondary ammonium nitrate 14.90 0.21 1.81 39.34 6–46

Secondary ammonium sulfate 2.27 0.02 0.44 4.84 1–11

Crude oil 0.10 0.02 0.02 0.65 0–2

Unexplained 7.50 0.14 0.0 30.59 0–48

Figure 7. Average source contributions to wintertime PM10, 1991–1996.

a

b

PM10 Source Contribution Estimates

Sacramento 13th and T, Winter 1991–1993 Fugitive Dust (12.5%)

Average PM10 mass = 51.4 μg/m3

Avg. CMB Source Estimate to 24-hr PM10

Sacramento 13th and T, Winter 1991–1996

PM10 Source Contribution Estimates

Sacramento 13th and T, Winter 1994–1996

Source Contributions (μg/m3

) Source Contributions (μg/m3

)

Sulfate (4.4%)

Wood Smoke (16.7%)

Motor Vehicle (32.9%)

Nitrate (28.9%)

Unexplained (14.6%)

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Motallebi

Volume 49 September 1999 Journal of the Air & Waste Management Association PM-33

emitters within the stated categories and apportionment of the secondary compounds to the

emitters of their precursors cannot be performed

using the CMB model and the current data set.

The CMB is a simple linear mixing model that

requires conservation of composition between

source and receptor and makes no assumption

about transport or removal processes. Trajectory

and secondary aerosol models should be integrated with CMB receptor modeling to further

identify sources and quantify their contributions.

 The results of the CMB analysis emphasize

three key points. First, fugitive dust, whether it

is resuspended soil and dust or agricultural tillage, is not the main contributor to peak PM10

levels in Sacramento. Second, the source apportionments indicate that primary motor vehicle

exhaust and wood smoke are significant sources

of PM10 in winter. Third, nitrates, secondarily

formed from motor vehicles and other NOx

sources, are another principal cause of the high

PM2.5 and PM10 levels during the winter months.

 In the case of PM (PM2.5 and PM10), which

can exhibit large spatial variation in the timing

and magnitude of airborne mass concentrations,

it is especially important to have confidence that

the particular sources selected for control are also

the ones significantly contributing to the high

pollutant concentrations. This requires highresolution source attribution in terms of space,

time, and source type. The present methodologies for PM source apportionment are not adequate for all of the PM pollution situations in

California, and will need to be complemented

by new methods and more specific source characterizations. The complexity of the PM problem necessitates not only total mass measurements but also compositional analyses, source

Figure 8a. Wintertime PM2.5 source contribution estimates, at Sacramento 13th and

T Streets, winter 1991–1993.

Figure 8b. Wintertime PM2.5 source contribution estimates, at Sacramento 13th and

T Streets, winter 1994–1996.

Table 2. Statistical summary of CMB PM2.5 source apportionments at Sacramento 13th and T Streets, winter 1991–1996.

Average Std. Dev. Minimum Maximum Range

(μg/m3) (μg/m3) (μg/m3) (μg/m3) (%)

Fugitive dust 0.45 0.01 0.07 1.53 0–3

Wood smoke 7.13 0.09 2.80 19.46 15–30

Motor vehicles 9.69 0.08 5.22 20.06 24–43

Secondary ammonium nitrate 14.46 0.18 1.81 32.90 9–51

Secondary ammonium sulfate 1.76 0.02 0.12 4.14 0–14

Crude oil 0.09 0.02 0.04 0.37 0–1

Unexplained 5.98 0.14 0.0 15.39 0–46

PM2.5 Source Contribution Estimates

Sacramento 13th and T, Winter 1991–1993

m3

) m3

)

PM2.5 Source Contribution Estimates

Sacramento 13th and T, Winter 1994–1996

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Motallebi

PM-34 Journal of the Air & Waste Management Association Volume 49 September 1999

About the Author

Dr. Nehzat Motallebi is an air pollution research specialist

at the California Air Resources Board, Research Division,

Sacramento, CA. Motallebi can be reached at nmotalle@

arb.ca.gov.

Figure 9. Average source contributions to wintertime PM2.5, at

Sacramento 13th and T Streets, winter 1991–1996.

apportionment modeling, and the development of control programs suited to the unique nature of the PM problem in each area.

DISCLAIMER

The statements and conclusions in this paper are those

of the author and not necessarily those of the CARB.

The mention of commercial products, their sources, or

their use in connection with material reported herein is

not to be construed as actual or implied endorsement of

such products.

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California’s San Joaquin Valley,” Atmos. Environ. 1992, 26A, 3335–3354.

Avg. CMB Source Estimate to 24-hr PM2.5

Sacramento 13th and T, Winter 1991–1996

Average PM2.5 mass = 39.5 μg/m3

Fugitive Dust (1.2%)

Wood Smoke (18.1%)

Motor Vehicle (24.5%)

Sulfate (4.5%)

Nitrate (36.6%)

Unexplained (15.1%)

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