### This article was downloaded by: [24.23.173.10] On: 25 January 2013, At: 14:33 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK



# Journal of the Air & Waste Management Association
Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uawm20 

# Wintertime PM

2.5 

# and PM

10 

# 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 PM 2.5 

### and PM

10 

### Source Apportionment at Sacramento, California, Journal of the Air & Waste Management Association, 49:9, 25-34

To link to this article: http://dx.doi.org/10.1080/10473289.1999.10463881

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions
This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

### 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 PM 2.5 

# and PM 10 

# 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 PM 2.5 and
PM 10 data from the Sacramento 13th and T Streets site inorder to identify the contributions from major source categories to peak 24-hr ambient PM 2.5 and PM 10 levels. The average monthly PM 10 monitoring data for the nine-yearperiod in Sacramento County indicate that elevated concentrations are typical in the winter months. Concentrations on days of highest PM 10 are dominated by the PM 2.5 fraction. One factor contributing to increased PM 2.5concentrations in the winter is meteorology (cool temperatures, low wind speeds, low inversion layers, and morehumid 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 PM 2.5 and PM 10in winter. Second, nitrates, secondarily formed as a result of motorvehicle and other sources of nitrogen oxide (NO

x ), are another principal cause of the high PM 2.5 and PM 10 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 PM 2.5 and PM 10 levels in the Sacramento area.

IMPLICATIONS Developing a technically defensible PM 2.5 control program requires identifying the contribution of each source type to the measured PM 2.5 concentrations and then estimating the air quality benefits associated with implementing a suite of emission controls. The present methodologies forparticulate 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. INTRODUCTIONReducing fine particulate pollution is one of the most difficult environmental challenges facing California becauseof the great diversity of sources and chemical species involved. Developing a technically defensible PM 2.5 control program requires identifying the contribution of each source type to the measured PM 2.5 concentrations and thenestimating 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 usedto identify the contributions from major source categories to ambient PM 2.5 and PM 10 levels in the Sacramentoarea. 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 arepresent in different proportions in different source emissions (i.e., they provide a means by which to distinguishsources); 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 sourcecontributions. 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 μ m or less (PM 10 ). 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 μ g/

m 3 , respectively. Concentrations of PM 10have 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

### Motallebi

PM-26 Journal of the Air & Waste Management Association Volume 49 September 1999 Boulevard, and Sacramento Branch Center Road (Figures 1a and 1b). Ambient PM 10 samples are collected for a 24-hr sampling period every sixth day. The PM 10seasonal 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 PM 10 mass from the Del Paso Manor site from November 1986 throughDecember 1997 is presented in Figure 2. This figure illustrates that elevated PM concentrations are typical in thewinter, with lowest average values occurring in the summer months. The most recent violation of the 24-hr federal standard for PM 10 occurred on December 25, 1990, with
a concentration of 187 μ g/m 3 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 PM 10concentrations. 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 theoptical 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 PM 10 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 b3) Monthly Average SSI PM 10 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
Gridley...P,M COLUSA
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

### Motallebi

Volume 49 September 1999 Journal of the Air & Waste Management Association PM-27from 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 sulfurbecause 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 PM 10 inlet followed by a virtual impactor, which splits theair stream in two, and, with different air flow rates, separates particles into two fractions: fine particles (PM 2.5 , i.e., particles with aerodynamic diameters of less than
2.5 μ m) and coarse particles (those having aerodynamic
diameters of 2.5–10 μ m). 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 PM 10 . The primary source of PM 2.5 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. PM 2.5concentrations were highest during the winter months. One factor contributing to increased PM 2.5 concentrations in the winter is meteorology (cool temperatures, low windspeeds, low inversion layers, and more humid conditions) that favors the formation of secondary nitrateand 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 (b
scat ) 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. Lightabsorption is primarily due to elemental carbon from combustion. The b
scat roughly measures all scattering by fineparticles. 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 PM 10levels are associated with high COH and b
scat values during winter at theSacramento site (Figure 4). To make the graphs more readable, COH values are multiplied by 50.

METEOROLOGICAL DATAHourly average values for wind speed, wind direction, temperature, and dew point are obtained from continuousmonitors. Wind speed is measured by a three-cup anemometer. Wind direction is measured by a wind vanecoupled to a precision low-torque potentiometer. Ambient temperature is measured by a thermistor housed inan 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 inm3)

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 PM 10

mass, light scattering (b scat ), and light absorption (COH) at Sacramento 13th and T Streets (1/94–9/97).m3)10 Monthly Average TEOM PM 10 , b
scat , and COH Sacramento 13th and T (1/94–9/97)[COH×50 and bscat×10]



### b
scat

### Motallebi

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. Thisrelatively 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 accompaniedby 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 windsand 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 PM 10 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 w inter PM 2.5 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 PM 2.5 concentrations.

CMB RECEPTOR MODELThe 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 ofproducts 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 PM 2.5 or PM 10 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 2and in an applications and validation protocol 3 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) chemicalspecies 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 arestatistically 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. TheCMB model can tolerate deviations from these assumptions, although these deviations increase the stated uncertainties of the source contribution estimates.

Source ProfilesReceptor modeling requires information about the chemical characteristics of the emission sources that are likely to affect pollutant concentrations at a receptor. For theCMB receptor model, the information required is the fractional composition of each chemical species in the sourceemissions 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 whichare 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 distinguishamong 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 mostdifficult 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-dutytrucks, 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 ofmotor vehicles (i.e., catalyst-equipped, non-catalystequipped, and diesel) to the ambient PM 2.5 and PM 10concentrations. 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

### Motallebi

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. 5The 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 whichprofile best described the ambient mass and crustal species aluminum, silicon, iron, calcium, manganese, and titanium. The Bakersfield Unpaved Road profile explainedambient concentrations of these species. Several woodand agricultural-burning profiles were also tested. Becausethe wood- and agricultural-burning profiles are chemically similar, only a single profile, representing one or bothof 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 (MajesticFireplace) 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 inambient aerosol cannot be entirely accounted for by primary emissions of sulfur dioxide (SO 2 ) and NO

x . To account for secondary aerosol contributions to PM 2.5 and
PM 10 in the CMB calculations, ammonium sulfate and ammonium nitrate were expressed as purely secondary source profiles.

CMB RESULTS The CMB model calculates source contribution estimatesfor each ambient sample, which is usually of 24-hr duration for PM 2.5 and PM 10 . We chose to run all the ambient days in which the PM 10 mass measured by the SSI-HiVol sampler at the Sacramento 13th and T Streets site was greater than 40 μ g/m 3 . There were 126 days that met this criterion during the winters (November through January)
of 1991–1996.
In each of the PM 2.5 and PM 10 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 thespecies that would provide the best indication of the presence or absence of sources (fitting species). Many of thetrace 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 lessthan or close to their lower quantifiable limits and because we did not find great abundance of these speciesin the source profiles. Vanadium and nickel were retained, even though their concentrations were very low,
so that the contribution of residual oil combustion couldbe 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
PM 2.5 and PM 10 size fractions. This number of species gave us the best model results as measured by r 2 values and high values for percent mass explained. If the modelexplains 80% of the ambient sample mass and the values of goodness-of-fit (r 2 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 r 2 values were less than 4.0 and between 0.8and 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 contributionson 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 PM 2.5 and PM 10 air quality in Sacramento were (1) fugitive dustfrom 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

### Motallebi

PM-30 Journal of the Air & Waste Management Association Volume 49 September 1999 marine-generated aerosols (sodium chloride [NaCl] and sodium nitrate [NaNO 3 ]). Marine aerosol transported fromthe 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 PM 2.5 and PM 10air quality, and to quantify values of meteorological parameters for high PM 2.5 and PM 10levels. 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 PM 2.5 /
PM 10 patterns show that high temperature has a limiteddirect 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. NO Xconversion to nitrate is even more sensitive, as formation ratesmust 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 arelikely to utilize wood combustion (fireplaces) for residential heating, increasing recorded PM levels. Scatter plots (Figures 5a and 5b) show that the winddirections 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 PM 10

mass versus temperature, relative humidity, resultant wind speed, and resultant wind direction at the Sacramento 13th and T Streets site. 

### Dichot PM 10 

### and Temperature Sacramento 13th and T, Winter 1991–1996 

### Dichot PM 10 

### and Relative Humidity Sacramento 13th and T, Winter 1991–1996



### Dichot PM 10 

### and Resultant Wind Speed Sacramento 13th and T, Winter 1991–1996 

### Dichot PM 10 

### 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 Direction24-hr PM10Mass (μg/m3)24-hr PM10Mass (μg/m3)24-hr PM10Mass (μg/m3)24-hr PM10Mass (μg/m3)

### Motallebi

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 deepermixing layers and higher wind speeds of spring and summer. All of the PM 10 exceedances of the 24-hr federal and state standards (150 and 50 μ g/m 3) 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 meteorologicalparameters, including vector average wind speed, temperature, and relative humidity. The meteorological class thatleads 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 PM 10 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 asan indication of the variability of the source contributions. These average contributions are plotted as a pie chart in Figure 7. The major contributors to PM 10 were fugitive dust (12%) (6.4 μ g/m 3 ), wood smoke (17%) (8.6 μ g/m 3 ), motor vehicles (23%) (11.8 μ g/m 3), and ammonium nitrate (29%) (14.9 μ g/m 3 ). During the colder winter months, one would expecta 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 μ g/m 3 ); Thanksgiving Day, Thursday, November 24, 1994 (16.1 μ g/m 3 ); and Sunday, December 10, 1994 (18.6

μ g/m 3 ). Secondary ammonium sulfate was detectable (2.3

μ g/m 3 ) but was not a major contributor to PM 10 . Crude
oil contributions to PM 10 on these dates were negligible. 

## Dichot PM 2.5 

## and Temperature

Sacramento 13th and T, Winter 1991–1996 

## Dichot PM 2.5 

## and Relative Humidity

Sacramento 13th and T, Winter 1991–1996



## Dichot PM 2.5 

## and Resultant Wind Speed

Sacramento 13th and T, Winter 1991–1996 

## Dichot PM 2.5 

## 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 PM 2.5 mass versus temperature, relative humidity, resultant wind speed, and resultant wind direction at the Sacramento 13th and T Streets site.24-hr PM2.5Mass (μg/m3)24-hr PM2.5Mass (μg/m3)24-hr PM2.5Mass (μg/m3)24-hr PM2.5Mass (μg/m3) 24-hr Avg. Temperature ( ° F) 24-hr Avg. Relative Humidity (%)

### Motallebi

PM-32 Journal of the Air & Waste Management Association Volume 49 September 1999 Approximately 15% of the measured PM 10was not explained by the selected source types at the 13th and T Streets site. Gray et al. 6 at Rubidoux and Chow 7at Bakersfield found a similar situation. This unexplained mass can be due to several factors: (1) not all sources wereincluded in the CMB run; (2) poor correspondence between the source profiles used in the CMB modeling and the actual contributing source profiles; (3) volatilizationof 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 PM 2.5 concentrations at the 13th and T Streets site are presented in Figures 8a and 8b. A statistical summary of CMB sourcecontributions is presented in Table 2. These average contributions are plotted as a pie chart in Figure 9. The main
contributors to PM 2.5 were wood smoke (18%) (7.1 μ g/

m 3 ), motor vehicles (25%) (9.7 μ g/m 3 ), and ammonium nitrate (37%) (14.5 μ g/m 3 ). Secondary ammonium sulfate was detectable (1.8 μ g/m 3 ) but not a major contributor to
PM 2.5 . Fugitive dust contributions (0.5 μ g/m 3 ) to PM 2.5 were negligible. Approximately 15% of the measured PM 2.5 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 PM 2.5 and PM 10 to major source categories. Detecting the sources of specific Figure 6a. Wintertime PM 10

source contribution estimates, 1991–1993. Figure 6b. Wintertime PM 10

source contribution estimates, 1994–1996.

Table 1. Statistical summary of CMB PM 10

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

Average Std. Dev. Minimum Maximum Range ( μ

g/m 3 ) ( μ

g/m 3 ) ( μ

g/m 3 ) ( μ

g/m 3 ) (%)

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 PM 10 , 1991–1996. 

## a

b PM 10 Source Contribution Estimates Sacramento 13th and T, Winter 1991–1993 Fugitive Dust (12.5%)

Average PM 10 mass = 51.4 μ g/m 3 Avg. CMB Source Estimate to 24-hr PM 10 Sacramento 13th and T, Winter 1991–1996

PM 10 Source Contribution Estimates Sacramento 13th and T, Winter 1994–1996Source Contributions (μg/m3)Source Contributions (μg/m3) Sulfate (4.4%)

Wood Smoke (16.7%)

Motor Vehicle (32.9%) Nitrate (28.9%) Unexplained (14.6%)

### Motallebi

Volume 49 September 1999 Journal of the Air & Waste Management Association PM-33emitters 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. Trajectoryand 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 itis resuspended soil and dust or agricultural tillage, is not the main contributor to peak PM 10levels in Sacramento. Second, the source apportionments indicate that primary motor vehicle exhaust and wood smoke are significant sources
of PM 10 in winter. Third, nitrates, secondarily
formed from motor vehicles and other NO

x sources, are another principal cause of the high
PM 2.5 and PM 10 levels during the winter months. In the case of PM (PM 2.5 and PM 10 ), 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 highpollutant 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 complementedby 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 PM 2.5

source contribution estimates, at Sacramento 13th and T Streets, winter 1991–1993. Figure 8b. Wintertime PM 2.5

source contribution estimates, at Sacramento 13th and T Streets, winter 1994–1996.

Table 2. Statistical summary of CMB PM 2.5

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

Average Std. Dev. Minimum Maximum Range ( μ

g/m 3 ) ( μ

g/m 3 ) ( μ

g/m 3 ) ( μ

g/m 3 ) (%)

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 PM 2.5 Source Contribution Estimates Sacramento 13th and T, Winter 1991–1993m3)m3)

PM 2.5 Source Contribution Estimates Sacramento 13th and T, Winter 1994–1996

### 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 PM 2.5 , 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.

REFERENCES 1. Watson, J.G; Chow, J.C.; Egami, R.T.; Bowen, J.L.; Fraier, C.A.; Gertler, A.W.; Lowenthal, D.H.; Fung, K. Dry Deposition Measurements for the California Acid Deposition Monitoring Program; Prepared for the California Air Resources Board: Sacramento, CA, 1991; Contract No. A6- 076-32. 2. Axetell, K.; Watson, J.G.; Pace, T.G. Receptor Model Technical Series Volume III (revised): CMB User’s Manual (version 6.0) ; U.S. Environmental Protection Agency: Research Triangle Park, NC, 1987; EPA-450/ 4-83-04R. 3. Pace, T.G.; Watson, J.G . Protocol for Applying and Validating the CMB Model ; U.S. Environmental Protection Agency: Research Triangle Park, NC, 1987; EPA 450/4-87-010. 4. Houck, J.E.; Chow, J.C.; Watson, J.G.; Simons, C.A.; Pritchett, L.C.; Goulet, J.M.; Frazier, C.A. Determination of Particle Size Distribution and Chemical Composition of Particulate Matter from Selected Sources in California ; Prepared for the California Air Resources Board, Sacramento, CA, by OMNI Environmental Services Inc., Beaverton, OR, and Desert Research Institute, Reno, NV, 1989; Vol. II (appendices); Contract No. A6-175-32. 5. Peltier, R.G.; Redline, J.R.; Cooper, J.A.; Aquilizan, A.V.; Miller, E.A.; White, J.L. Enhancement of the South Coast Air Basin Source Profile Library for Chemical Mass Balance Receptor Model Applications ; Source Profile Library Supplement Final Report. South Coast Air Quality Management District: El Monte, CA, 1990; Vol. II. 6. Gray, H.A.; Landry, B.; Liu, C.S.; Henry, R.C.; Cooper, J.A.; Sherman, J.R. “Receptor modeling for PM 10 source apportionment in the South Coast Air Basin of California.” In Transactions, PM 10 : Implementation of Standards ; Mathai, C.V.; Stonefield, D.H., Eds.; Air Pollution Control Association: Philadelphia, PA, 1988; pp 399–418. 7. Chow, J.C.; Watson, J.G.; Lowenthal, D.H.; Solomon, P.A.; Magliano, K.L.; Ziman, S.D.; Richards, L.W. “PM 10 source apportionment in California’s San Joaquin Valley,” Atmos. Environ. 1992

, 26A , 3335–3354. Avg. CMB Source Estimate to 24-hr PM 2.5 Sacramento 13th and T, Winter 1991–1996

Average PM 2.5 mass = 39.5 μ g/m 3 Fugitive Dust (1.2%)

Wood Smoke (18.1%)

Motor Vehicle (24.5%) Sulfate (4.5%)

Nitrate (36.6%) Unexplained (15.1%)