Source Apportionment of Visibility Impairment Using a Three

Environ. Sci. Technol. 2004, 38, 1089-1101

Source Apportionment of Visibility Impairment Using a Three-Dimensional Source-Oriented Air Quality Model QI YING, MITCHELL MYSLIWIEC, AND MICHAEL J. KLEEMAN* Department of Civil and Environmental Engineering, University of California, Davis, One Shields Avenue, Davis, California 95616

A three-dimensional source-oriented Eulerian air quality model is developed that can predict source contributions to the visibility reduction. Particulate matter and precursor gases from 14 different sources (crustal material, paved road dust, diesel engines, meat cooking, noncatalyst-equipped gasoline engines, catalyst-equipped gasoline engines, highsulfur fuel, sea salt, refrigerant losses, residential production, animals, soil and fertilizer application, other anthropogenic sources, and background sources) are tracked though a mathematical simulation of emission, chemical reaction, gas-to-particle conversion, transport, and deposition. A visibility model based on Mie theory is modified to use the calculated source contributions to airborne particulate matter size and composition as well as gas-phase pollutant concentrations to quantify total source contributions to visibility impairment. The combined air qualityvisibility model is applied to predict source contributions to visibility reduction in southern California for a typical air pollution episode (September 23-25, 1996). The model successfully predicts a severe visibility reduction in the eastern portion of the South Coast Air Basin where the average daytime visibility is measured to be less than 10 km. In the relatively clean coastal portion of the domain, the model successfully predicts that the average daytime visibility is greater than 65 km. Transportation-related sources directly account for approximately 50% of the visibility reduction (diesel engines ∼15-20%, catalyst-equipped gasoline engines ∼10-20%, noncatalyst-equipped gasoline engines ∼3-5%, crustal and paved road dust ∼5%) in the region with the most severe visibility impairment. Ammonia emissions from animal sources account for approximately 10-15% of the visibility reduction.

Introduction Visual air quality (visibility) is a valuable natural resource both in terms of asthetic value and economic benefits (1, 2). Ammendments to the 1977 Clean Air Act seek to protect visibility from man-made pollution in Class I Federal Areas (large national parks and wildness areas) within the United States. Despite the long-standing goal of improved visibility, measurements made between 1970 and 1998 show that visual range has not improved significantly in the eastern United * Corresponding author phone: (530)752-8386; fax: (530)752-7872; e-mail: [email protected]. 10.1021/es0349305 CCC: $27.50 Published on Web 01/10/2004

 2004 American Chemical Society

States and has shown only slight improvement (10-15%) in the western United States over the past 30 years (3). One of the primary reasons for the persistence of the visibility problem is a lack of methods that can quantitatively show how different sources contribute to visibility impairment. Atmospheric visibility impairment results from the scattering and absorption of light by gases and suspended particles. Previous studies have shown that secondary particulate matter (formed in the atmosphere from the reaction of precursor gases) contributes significantly to visibility impairment in polluted class 1 areas (3). Receptororiented statistical methods for source apportionment are not capable of identifying the source-origin of secondary particulate matter, and so the source-origin of the visibility problem cannot be easily determined. A new approach is needed if the sources of visibility impairment are to be clearly identified. Recently, a source-oriented mechanistic air quality model has been developed that can directly calculate source contributions to primary and secondary particulate matter (4). Particles and precursor gases released from different sources are tracked separately through a simulation of atmospheric transport, chemical reaction, and phase change. Information about particle size, number concentration, and chemical composition is retained so that scattering and absorption can be calculated. The results produced by this model directly show the contribution that major sources make to primary and secondary airborne particulate matter. This information makes it possible to perform a direct source apportionment of visibility impairment. The purpose of the current study is to describe the extension of the Lagrangian technique for source apportionment of secondary particulate matter to a full 3D Eulerian simulation in southern California during a 3-day episode (September 23-25, 1996). This is the first time that a mechanistic source apportionment technique of secondary particulate matter has been coupled in a complex 3D Eulerian air quality model and used to directly apportion visibility to emission sources. Southern California has one of the longestrecognized and best-characterized visibility problems in the world (5-8), making it an ideal location for the evaluation of new visibility source apportionment techniques. In the following sections, the formulation of the 3D Eulerian sourceoriented air quality model and the downstream visibility model are described. The results of the model predictions are then compared to observations, and a total source apportionment of visibility reduction is performed.

Background The reduction of visual range and the discoloration of the sky are both caused by the scattering and absorption of light due to gases and suspended particles. In a pristine atmosphere, visibility is only limited by light scattering due to gas molecules (Rayleigh scattering), resulting in a visual range of approximately 300 km. In polluted areas, anthropogenic pollutants significantly reduce the visual range. Past analyses have shown that light scattering associated with sulfate, ammonium ion, nitrate, and carbon are the chief sources of visibility impairment (9). Particle-phase elemental carbon and gas-phase NO2 are the most important sources of light absorption, which can be a significant fraction of the total extinction (10). Visibility is usually quantified by the visual range, light extinction coefficient, or deciviews. Visual range (Lv) is defined as the greatest distance at which an observer can distinguish an object from its background, providing an intuitive way to VOL. 38, NO. 4, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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quantitatively represent atmospheric visibility. Visual range is linked to the light extinction coefficient (bext) through the classical Koschmieder equation:

Lv )

Kc 3.912 ) bext bext

(1)

where Kc is the Koschmieder constant, which equals 3.912 when a contrast threshold of 2% is assumed. Experiments have shown that a perceptible change in atmospheric visibility is strongly dependent on the baseline visibility. It is desirable to transform the nonlinear relationship between perceptible visibility change and baseline visibility to a more useful linear form in which the relative change in the visibility index represents the same perceptible changes in visibility to human observers. Pitchford and Malm (11) developed a visual index, deciview (dv), which is linear with perceived changes in visibility:

(

dv ) 10 × ln

bext

0.01 km-1

)

(2)

where 0.01 km-1 is the approximate extinction coefficient due to Rayleigh scattering in a pristine atmosphere. The deciview scale is near zero for a near-pristine atmosphere and increases as visibility impairment increases. Henry (12) performed a color matching experiment in a natural environment and found that the main effect of atmospheric haze was to decrease the perceived colorfulness of an image viewed by a human observer. Henry defined the just-noticeable differences (JNDs) in atmospheric haze as a decrease in extinction that would produce a 95% probability of a measurable increase in colorfulness of an object. Statistical methods have been used to relate the light extinction coefficient to atmospheric aerosol constituents and to identify the important emission sources that contribute to the visibility impairment problem. For example, multilinear regression techniques have been used to determine the relationship between particle chemical constituents (such as elemental carbon, sulfate, and nitrate) and light scattering and absorption coefficients (13, 14). The chemical mass balance (CMB) method has been used to link the visibility degradation components identified in the multi-linear regression studies to their respective sources (14, 15). The ability of the CMB method to determine the source contribution to light extinction coefficients is limited because a large fraction of the visibility reduction is typically associated with secondary particulate matter that cannot be traced back to a source using the CMB method (16). Furthermore, the contribution that aerosol water content makes to light scattering is usually estimated by applying empirical (9) or semiempirical (17) aerosol growth functions that cannot be related to a source. Particle water content can also be predicted from aerosol chemical composition and environmental relative humidity information using thermodynamic software packages (18). Chan et al. (14) estimated an upper limit for the contribution of aerosol water to light scattering by taking the difference between the scattering coefficients measured from particles in ambient and heated air. Airborne particulate matter concentrations predicted by mechanistic air quality model simulations have been used to estimate visibility in some recent studies. Seigneur et al. (19) used a reactive plume model and a light extinction model based on Mie theory to estimate the effect a reduction in power plant SO2 emissions on visibility. Binkoski and Roselle (20) incorporated an aerosol component into the Models-3 Community Multiscale Air Quality (CMAQ) model and used parameterized methods to estimate light extinction by airborne particles. Source contributions to visibility impairment could not be calculated in these previous studies since 1090

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no information about the emissions source of each particle was kept in the model simulations. Middleton (21) performed a visibility source apportionment using the Denver Air Quality Model (DAQM) to predict aerosol concentrations and a parameterized equation to predict the associated light extinction coefficient. Emissions from specific sources were set to zero, and the predicted change in visibility was analyzed. This source apportionment method did not explicitly treat aerosol dynamics or the time evolution of multiple aerosol size categories, and it did not account for nonlinear interactions between different emissions sources. Airport visual range observations can be used to estimate the light extinction coefficients when the appropriate contrast threshold is specified. Previous researchers have shown that the standard contrast threshold of 2% (giving a Koschmieder constant of 3.912) would overestimate extinction coefficients by more than a factor of 2 (22). This overestimation results because the classical Koschmieder equation assumes that the a black object is observed in front of a white background while airport visual range is frequently estimated using targets with different colors such as buildings and water towers. Further variability is introduced because the contrast of the viewed object against the background is a function of solar zenith angle, and observers may have a threshold contrast value greater than 2%. In several studies (22-24), Koschmieder-type equations were derived from direct scattering or extinction coefficient measurements and reported airport visual ranges. Griffing (23) derived the relationship Lv ) 1.7/ bscat using aerosol scattering measurements and visibility measurements from the Raleigh-Durham Airport. Dzubay et al. (24) derived Lv ) (1.63 ( 0.21)/bext for Houston, TX, while Ozkaynak et al. (22) derived Lv ) (1.8 ( 0.4)/bext using the measured extinction coefficients and the airport visual range observations in 12 large U.S. cities including Los Angeles. In this study, visual range observations made at airports will be converted to light extinction coefficients and compared with the coefficients predicted by the visibility model. Visibility reduction has been studied in California for many years. Measurements made in the Los Angeles area show that daylight visual range can decrease to 4.8 km or less (58). Kleeman et al. (25) used a source-oriented trajectory model to study the effect of emission control programs on visibility in southern California. Their analyses have shown that most of the light extinction is due to secondary nitrate, sulfate, and ammonium ions that condensed on primary particles with diameters in the size range of 0.1-1.0 µm. The emission sources of the gaseous precursors for the secondary nitrate, sulfate, and ammonia could not be identified in that study because source information of gaseous pollutants was not retained during the model simulation.

Model Description Source-Oriented Mechanistic Air Quality Model. The Eulerian source-oriented mechanistic air quality model used in this study is a further development of the source-oriented air quality model described by Kleeman and Cass (26), which is based on the CIT photochemical airshed model (27, 28). The source-oriented 3D Eulerian air quality model tracks the size, chemical composition, and number concentration of primary particles emitted from 10 different source categories: crustal material, paved road dust, food cooking, diesel engines, catalyst-equipped gasoline engines, noncatalyst-equipped gasoline engines, high sulfur-content fuel, other anthropogenic sources, sea salt, and background sulfate particles. The model includes a complete description of atmospheric transport, deposition, and chemical transformation processes to describe the detailed evolution of airborne particles released from different sources.

semivolatile organic compounds in the gas phase, and the formation of secondary organic aerosol. For the current modeling episode, the contribution of secondary organics to total light extinction is so small (Figure 6n) that no effort has been made to apportion the SOA light extinction to different emission sources of VOC precursors. Additional details about the source apportionment algorithm are described by Mysliwiec and Kleeman (4). Visibility Model. Visibility impairment can be quantified by the light extinction coefficient (bext):

bext ) bsg + bag + bsp + bap

FIGURE 1. Source apportionment of secondary particulate matter. (a) Emission of primary PM and precursor gases. (b) Formation of secondary particulate matter on primary particle cores. Figure 1 illustrates a simple system with two emission sources of primary particles and precursor gases that can form secondary particulate matter. Precursor gases can form secondary particulate matter on the primary particle cores released from any source. In the current study, primary and secondary particulate matter in the atmosphere are attributed to the emission source of the original primary particle or precursor gas. Secondary particulate matter is apportioned to contributing sources by tracking precursor emissions through the atmospheric chemical reaction system. Individual chemical reactions that involve precursor gases are expanded to separately track the reactants from different sources. As a simple example, consider a system with two NO emission sources (A and B). One possible reaction pathway for the formation of nitric acid (HNO3) from each source can be described by the following equation set:

NOA + RO2 f NO2A + RO NOB + RO2 f NO2B + RO NO2A + OH f HNO3A NO2B + OH f HNO3B

(R1)

where RO2 represents a peroxy-type radical and OH represents the hydroxyl radical. The additive nature of the chemical reactions ensures that the sum of nitric acid formation rates from all sources equals the rate of formation that would be calculated in the absence of source apportionment calculations. In the current study, the SAPRC-90 photochemical mechanism (29) is expanded to track the formation of NH3, HNO3, and H2SO4 from different sources. The aerosol composition is predicted using a dynamic gas-to-particle transfer model that calculates the rate of HNO3, H2SO4, NH3, HCl, and H2O condensation onto (or evaporation from) the particle surface. All these species undergo gas-to-particle conversion based on their vapor pressure over the particle surface and the concentration in the ambient air. The heterogeneous pathway of gas-phase N2O5 reacting with H2O on particle surfaces to form nitrate in the aerosol phase and the formation of sodium nitrate have also been considered. The thermodynamic package AIM (30) is used to predict the aerosol composition and the surface equilibrium vapor pressure. The AIM module has been modified to track the source origin of the gas-to-particle conversion products that form from the condensation of individual semivolatile compounds (4). The air quality model used in the current study simulates VOC emissions, the formation of secondary

(3)

where bsg and bsp are scattering coefficients associated with atmospheric gases and particles, respectively; bag and bap are absorption coefficients associated with atmospheric gases and particles, respectively. A larger light extinction coefficient indicates increased visibility impairment. Light scattering and absorption coefficients for spherical particles are calculated using Mie theory (31, 32). Elemental carbon is considered to be the only light absorbing species in the particle. The Rayleigh scattering of UV radiation by atmospheric gases is calculated using the procedure described by Penndorf (33). The absorption of light by NO2 is calculated using the absorption cross-sections measured by Vandaele et al. (34). The assumption regarding the distribution of aerosol chemical components within each airborne particle affects the calculated aerosol scattering and extinction coefficients. Two alternative configurations of aerosol chemical component distribution are investigated in the current visibility study: homogeneous and core-and-shell. In the homogeneous aerosol configuration, all of the particle chemical components are evenly distributed in the airborne particles, while in the core-and-shell configuration, insoluble components such as elemental carbon form a core that is surrounded by a shell of water-soluble species. The sensitivity of the predicted extinction coefficient to the assumption about aerosol chemical component distribution is discussed in the Uncertainty Analysis section. The complex refractive index for a multicomponent aerosol is calculated using the volume averaging method described by Stelson (35). Numerical packages developed by Bohren and Huffman (31) and Toon and Ackerman (32) are used to calculate the dimensionless scattering and extinction coefficients for the homogeneous and core-and-shell configurations, respectively. The aerosol extinction coefficient (ba,ext) and the aerosol scattering coefficient (ba,scat) are calculated using the following equations: n

ba,ext )

m

∑∑πr

2 i,jNi,jQe,i,j

(4)

i)1 j)1 n

ba,scat )

m

∑∑πr

2 i,jNi,jQs,i,j

(5)

i)1 j)1

where subscript i refers to emissions source for primary particles, subscript j refers to size, n is the number of primary particle source categories, and m is the number of particle sizes. N and r are the number concentration and radius of particles, respectively. Qs and Qe are the dimensionless scattering and extinction coefficients, respectively. The additive nature of the scattering and extinction coefficients makes them suitable parameters for the source apportionment of visibility impairment. Total Source Apportionment of Visibility. The total light extinction coefficient is calculated by summing the aerosol extinction coefficients of all primary particles sources (eq 4), the gas scattering coefficient, and the NO2 gas absorption VOL. 38, NO. 4, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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coefficient. White (36) suggested that the most meaningful definition of the light extinction contributed by an aerosol species is the decrease in the light extinction coefficient that would result if that species was removed. In the current study, the extinction coefficient associated with an emission source is defined as the decrease in the light extinction coefficient that would result from the removal of all the gas-phase and particle-phase components from that emission source. The particle-phase components from each emission source include the primary particles directly emitted from that source, the inorganic secondary particulate matter (nitrate, sulfate and ammonia) formed by the gas-phase precursors emitted from that source, and the particle water associated with each aerosol component from that source. The airborne particle number concentration and particle size are related by the particle mass concentration. Removing an aerosol component could reduce the size of the airborne particles and/or could reduce the number concentration of the airborne particles. Two methods have been used in previous studies to calculate the aerosol extinction coefficient after the removal of an aerosol component (17, 18). In the first method, the particle number concentration is held constant and the particle size is reduced when an aerosol component is removed (interactive method). In the second method, the particle size is held constant and the particle number concentration is reduced (noninteractive method). It has been shown (18) that the source-apportioned scattering coefficients calculated using noninteractive method are nearly additive (the scattering coefficient reconstructed by summing individual scattering coefficients is close to the total scattering coefficient calculated directly using eq 5). The reconstructed total scattering coefficients calculated using interactive method were found to be up to 28% higher than the total scattering coefficients calculated directly. Since the source-oriented model differentiates the primary particle core and secondary particulate matter that forms on it, a more realistic approach (mixed method) can be used to estimate the source contribution to aerosol extinction coefficients. When aerosol components within primary particle cores are removed, the particle number concentration for that primary particle source is reduced but the particle size is held constant. When secondary particulate components are removed, the particle size is reduced but the particle number concentration is held constant. The sensitivity of the source apportionment results to the selection of interactive, noninteractive, or mixed method is discussed in the Uncertainty Analsysis section. The removal of the aerosol components from an emission source effectively leads to the removal of some particulate water associated with those components. Particulate water is apportioned to each aerosol component based on the ZSR equation (37): Ne

W)

nk

∑ m (RH,T) k)1

(6)

k

where W is the equilibrium aerosol water content (kg/m3 air), Ne is the total number of electrolyte species in the aerosol, nk is the concentration of electrolyte k (mol/m3 air), mk is the equilibrium molality (mol/kg H2O) of the electrolyte at a specified relative humidity (RH) and temperature (T). The ZSR equation can be used to predict the relative equilibrium water content associated with each electrolyte in the aerosol:

ekw )

nk/mk(RH,T) W

(7)

where ekw is the relative contribution of electrolyte k to the aerosol water content. In the present study, the electrolyte 1092

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concentrations are calculated using the aerosol inorganic module (30, 38), and the equilibrium molalities at a specific temperature and relative humidity are calculated based on the experiments conducted by Tang and Munkelwitz (39). The relative contributions of components Na+, NH4+, Cl-, SO42-, and NO3- to total aerosol water content are calculated using the following equation set (8): + 1 NaNO3 1 NaCl 2 Na2SO4 + ew + ew CNa w ) ew 2 2 3 + 1 1 2 4 4Cl 2NO3 4)2SO4 CNH ) eNH + eNH + e(NH w 2 w 2 w 3 w 1 NaCl 1 NH4Cl HCl CCl + ew w ) ew + ew 2 2 21 2SO4 1 (NH4)2SO4 4 2SO4 CSO ) eNa + ew + eH w w 3 w 3 1 1 3 3 3 4NO3 CNO ) eNaNO + eHNO + eNH w w 2 w 2 w

(8)

Since the source origin of the particulate sodium, ammonium ion, chloride, sulfate, and nitrate is retained during the model simulation, the relative source contribution to aerosol water content (Sjw) can be calculated using eq 9: Nc

Sjw )

∑XC

j i i w

(9)

i)1

where j is the source, i represents the aerosol component (sodium, ammonium ion, sulfate, or nitrate), Nc is the number of components, Ciw is the relative contribution to aerosol water content associated with component i, and Xji is the fraction of component i associated with source j.

Model Application The source-oriented air quality model, the visibility model, and the total source apportionment method described above are applied to study the total source contribution to visibility impairment in South Coast Air Basin (SCAB) of California during the period September 23-25, 1996. The horizontal grid resolution used in the current study is 5 km with seven vertical layers ending at 35, 100, 200, 400, 600, 800, and 1000 m. The hourly input meteorology fields needed for air quality calculations are interpolated from hourly observation of wind speed, wind direction, temperature, relative humidity, and radiation intensity measured at various meteorological sites operated by the California Air Resources Board (CARB) and the National Climatic Data Center (NCDC) in the modeling domain. The diagnostic interpolation scheme (40) used to interpolate values between observation sites in the present study has been successfully used previously in numerous air quality studies in Los Angeles [see, for example, McRae et al. (27); Eldering and Cass (41); Kleeman and Cass (26)]. Initial conditions for gas and particulate matter species are based on pollutant concentrations measured throughout the domain. Continuous fields were created from discrete measurements using the interpolation scheme described by Goodin et al. (42). Boundary conditions are specified based on the measurements made at Santa Catalina Island and San Nicolas Island on the upwind side of the modeling domain (26). The raw emissions inventories provided by the Source Coast Air Quality Management District for the study period are processed using a detailed emissions model that applies source-specific profiles for volatile organic compounds (VOCs) and particulate matter (PM) (38, 41). In the current study, gas-phase emissions of NO, NO2, SO2, and H2SO4 are

FIGURE 2. Calculated surface daytime average PM2.5 mass concentrations (panel a) and visual range (panel b) in the South Coast Air Basin on September 25, 1996. separated into five source categories (diesel exhaust, noncatalyst-equipped gasoline engines, catalyst-equipped gasoline engines, high-sulfur fuel combustion, and other sources). Gas-phase emissions of NH3 are separated into six source categories (commercial NH3 refrigerant losses, residential NH3 sources, animal NH3 sources, catalyst-equipped gasoline engines, soil and fertilizer NH3 sources, and other sources). Residential ammonia sources include ammonia released from domestic animal waste, household ammonia use, human perspiration, and human respiration. The ammonia emitted into the atmosphere is a semivolatile species that undergoes gas-to-particle conversion to form secondary ammonium ion in the particle phase that can contribute to visibility degradation. The air quality model developed in this study is capable of independently tracking the formation of ammonium ion from different NH3 sources along with the light extinction associated with that ammonium ion. Table 1 summarizes the emissions from each general source category in the SCAB on September 24, 1996.

TABLE 1. Summary of Primary Particulate Matter, NOx, SOx, and NH3 Emissions in the South Coast Air Basin on September 24, 1996 source category

-1 primary PM gas (ton day ) (ton day-1) SOx NOx NH3

crustal material paved road dust diesel engines meat cooking noncatalyst-equipped gasoline catalyst-equipped gasoline high-sulfur fuel refrigerant losses residential NH3 source animal NH3 source soil and fertilizer NH3 source other anthropogenic sources sea salt

312.0 158.0 19.8 8.8 19.9 4.5 6.3 0.0 0.0 0.0 0.0 41.1 2.5

0.0 0.0 0.0 0.0 44.7 320.7 0.0 0.0 0.6 39.0 3.4 281.5 16.9 44.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.7 87.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 29.0 0.0 0.4 24.9 82.8 33.3 24.8 0.0

total

572.9

77.3 772.9 195.2

Results Comparison to Measured Values. Figure 2a shows the model predicted average daytime [0700-1700 Pacific Standard Time (PST)] PM2.5 mass concentrations for the SCAB on September 25, 1996. Water associated with the airborne particles is included in the PM2.5 mass prediction. The daytime average PM2.5 concentration peaks in the region northeast of Riverside.

The maximum concentration of 150 µg m-3 is composed mainly of secondary ammonium nitrate aerosol that forms downwind of the dairy feedlots (large sources of ammonia) located to the west of Riverside. Figure 2b shows the average daytime visual range predicted using the classical Koschmieder equation with a contrast threshold of 2%. The VOL. 38, NO. 4, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Observed and predicted light extinction coefficients (550 nm) for daytime hours at Riverside (panel a), Ontario (panel b), Burbank (panel c), and Camarillo (panel d) on September 25, 1996. visibility model predicts that the visual range is reduced to less than 10 km in the region of the SCAB that experiences the highest particulate matter concentrations. In the relatively clean costal region of the domain, the average daytime visibility is predicted to be greater than 65 km. Figure 3a-d shows the predicted light extinction coefficients (at wavelength 550 nm) at Riverside, Ontario, Burbank, and Camarillo from 0700 to 1700 PST on September 25, 1996. Also shown in Figure 3a-d is the extinction coefficient (bext) estimated from hourly airport visual range (Lv) observations using the relationship Lv ) (1.8 ( 0.4)/bext (22). The error bars on the extinction coefficients reflect the upper and lower limits of the constant (1.4 and 2.2, respectively) used in the equation. The four locations are chosen because they represent a wide range of visibility conditions from the relatively clean western coastal region to the highly polluted eastern regions in the study domain. The model successfully predicts the time variation of the extinction coefficients at Riverside, Ontario, and Burbank for almost all the daytime hours. At Riverside (Figure 3a), the extinction coefficient decreases significantly in the morning hours and then remains relatively constant in the afternoon. At Ontario (Figure 3b), the extinction coefficient increases from 0700 to 0800 PST and then remains at this higher level before decreasing in the afternoon. At Burbank (Figure 3c), both the observed and the predicted extinction coefficients decrease slightly from the morning to the afternoon, but model predictions are lower than observed values. At Camarillo (Figure 3d), predictions match observations for the majority of day except for the period between 0700 and 1094

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1000 PST when observed values are slightly greater than model predictions. The extinction coefficient depends on aerosol number concentration, size distribution, and the refractive index calculated from the aerosol chemical composition for each size section. The extinction coefficient also depends on the absorption cross-section of light-absorbing gas species (NO2 and O3) and their concentrations in the atmosphere. The ability of the model to correctly predict the extinction coefficients at different hours of the day and different aerosol loading conditions depends on the ability of the model to correctly predict the number concentration, size distribution, and chemical composition of the airborne particulate matter in addition to correctly predicting NO2 and O3 concentrations. It can be seen from the formulation of the source apportionment algorithm in the present study that the total gas and aerosol concentration predicted by the air quality model will not change when precursor gases are split into different source categories. As a result, the total model predictions for gas and aerosol species used in the current study are within 3% of the model predictions described by Kleeman and Cass (26) (minor differences result from refinements to emissions inventories, boundary conditions, and wind fields). A previous comparison between model predictions and measurements of gas-phase and particle-phase species shows strong agreement at multiple locations and times (26). Thus the model used in the current study has been rigorously compared to measurements of gas- and particle-phase concentrations, and so it provides a strong foundation for visibility source apportionment calculations. The general agreement between

FIGURE 4. Predicted daytime average source contributions to particle size distribution (panels a and c) and extinction coefficient distribution (panels b and d) at Ontario and Camarillo on September 25, 1996. the model extinction predictions and the observations under different visibility conditions indicates that the coupled air quality-visibility model successfully represents the major atmospheric processes during the study period. Apportionment of Light Extinction Coefficient. Figure 4 shows that the calculated primary and secondary source contributions to particle mass and light extinction coefficients (at wavelength 550 nm) as a function of particle size at Ontario and Camarillo averaged between 0700 and 1700 PST on September 25, 1996. Figure 4a shows that diesel engines and catalyst-equipped gasoline engines are the most significant contributors to PM2.5 at Ontario while crustal material, paved road dust, and sea salt particles are the major sources for coarse particles. Figure 4c shows that PM concentrations are low at Camarillo. Most of the airborne particulate matter is associated with background sulfate particles and sea salt with smaller contributions from diesel engines and paved road dust. Figure 4b,d illustrates primary and secondary source contributions to the predicted extinction coefficients at Ontario and Camarillo as a function of particle diameter. Particles with diameter similar to the wavelength of the incident light extinguish that light most effectively, and so particles larger than 1.5 µm do not contribute significantly to visibility reduction. The majority of the extinction at Camarillo is associated with background sulfate particles. Major sources of extinction at Ontario include diesel engines, catalyst-equipped gasoline engines, background sulfate particles, and animals. Aerosol Component Contribution to Light Extinction. Figure 5 shows the predicted ground-level light extinction

coefficients averaged between 0700 and 1700 PST on September 25, 1996, for major aerosol chemical components. Figure 5a shows that nitrate contributes significantly to the light extinction in the inland regions. The predicted extinction coefficients due to nitrate range from approximately 0.03 km-1 in the relatively clean coastal regions to 0.20 km-1 in the region downwind of Ontario and north of Riverside. The relative contribution that nitrate makes to the light extinction coefficient is approximately 10% in the coastal regions and over 30% in the polluted inland regions. Figure 5b shows that ammonium ion also contributes significantly to light extinction. The maximum ammonium ion extinction coefficient of 0.11 km-1 occurs north of Riverside. The relative contribution that ammonium ion makes to light extinction is approximately 15-20% over the land and 20% over the ocean. Figure 5c shows that the predicted light extinction coefficients associated with sulfate do not change significantly throughout the domain. The extinction coefficients range from approximately 0.01 to 0.02 km-1. The relative contribution that sulfate makes to the light extinction coefficient is approximately 25% over the ocean and 5% over the polluted inland region. Figure 5d shows the regional distribution of the light extinction coefficient associated with primary and secondary organics. The maximum extinction coefficient occurs in the northeast and southeast portion of the modeling domain with values of approximately 0.03 km-1. The relative contribution that organics make to the light extinction is approximately 5% over the ocean and 8-10% over the inland region. Figure 5e shows that the predicted maximum extinction coefficient associated with elemental carbon is VOL. 38, NO. 4, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 5. Predicted daytime average aerosol extinction coefficients associated with major aerosol components in the South Coast Air Basin on September 25, 1996 (units are km-1). Note that scales for each panel are different. approximately 0.2 km-1 in the northeast part of the modeling domain. The relative contribution that elemental carbon makes to light extinction is approximately 4-9%, with higher values occurring between Burbank and Long Beach. Figure 5f shows that liquid water is an important contributor to light extinction in current study. The predicted maximum extinction coefficient associated with water is approximately 0.19 km-1. The relative contribution that water makes to light extinction is 25-35% in most parts of the modeling domain. Source Apportionment of Light Extinction. Figure 6 shows the predicted ground-level light extinction coefficients averaged between 0700 and 1700 PST on September 25, 1996, for each emission source listed in Table 1. The contribution that each emission source makes to light extinction includes the contribution of primary particles and secondary particulate matter formed by precursor gases emitted from that source. The contribution of liquid water to light extinction coefficients is distributed to each emission source using the ZSR method described in the Total Source Apportionment of Visibility section. Figure 6a shows that the predicted extinction coefficients due to crustal material are typically 0.01 km-1 in the eastern portion of the modeling domain where most of the unpaved road travel occurs. The relative contribution from this source is approximately 1-4%. Figure 6b shows that the extinction coefficients predicted to be associated with paved road dust are on the same order of magnitude as the extinction coefficients associated with crustal material but with a broader distribution in the modeling domain. The relative contribution from paved road dust is approximately 3-5%. Figure 6c shows that diesel engines contribute 1096

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significantly to the light extinction coefficient. The extinction coefficient associated with diesel engines is approximately 0.03 km-1 in the clean coastal region and 0.15 km-1 downwind of Ontario and north of Riverside. The relative contribution to light extinction associated with diesel engines is 15-20%. Figure 6d shows that meat cooking is not a significant source of light extinction in the study region. The relative contribution from meat cooking to light extinction is less than 2% throughout the modeling domain. Figure 6e shows that the predicted light extinction coefficients associated with noncatalyst-equipped gasoline engines are approximately 0.010.03 km-1 in the modeling domain. The spatial distribution of the extinction coefficients associated with noncatalystequipped gasoline engines is similar to the spatial distribution of extinction coefficients associated with diesel engines. The relative contribution from noncatalyst-equipped gasoline engines is approximately 3-5%. Figure 6f shows that catalystequipped gasoline engines also contribute significantly to light extinction in the modeling domain. The extinction coefficients associated with this source are within the range of 0.03-0.15 km-1. The spatial distribution of the extinction coefficients is similar to the spatial distribution of extinction coefficients associated with of diesel engines. The relative contribution to light extinction associated with catalystequipped gasoline engines is approximately 10-20%. Figure 6g shows that the light extinction coefficients associated with sea salt are relatively small. Sea salt particles contribute about 10-15% of the overall extinction coefficients over the ocean and less than 5% over the land. Figure 6h shows that the extinction coefficients associated with background sulfate

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FIGURE 6. Predicted source contributions to daytime average extinction coefficients in the South Coast Air Basin on September 25, 1996 (units are km-1). Note that the scales for each panel are different. particles are 0.04-0.07 km-1. The background sulfate particles account for 60% of the total light extinction coefficient over the ocean. The relative contribution is approximately 1020% at inland locations. Figure 6i shows that secondary ammonium ion associated with animals is another significant source of light extinction in the localized regions east of Ontario. The predicted light extinction coefficient associated with animal sources is approximately 0.04-0.09 km-1. The highest relative contribution from this source (26%) occurs northeast of Riverside. Figure 6j shows a clear plume of extinction associated with high-sulfur fuel combustion that originates from Long Beach and that is advected to the downwind portion of the domain. The relative contribution to light extinction coefficients is approximately 2-4%. Figure 6k,l shows the contribution to light extinction coefficients caused by ammonium ion associated with refrigerant losses and residential production of NH3 gas. The spatial distributions of the light extinction associated with the two sources are similar with the highest extinction coefficients occurring northwest of Ontario. The contribution to the total light extinction coefficient associated with refrigerant losses is negligible. The predicted extinction coefficient associated with residential NH3 production is in the range of 0.0050.015 km-1, which accounts for 4-8% of the total light extinction. Figure 6m shows the light extinction coefficient associated with soil and fertilizer NH3 is highest in the 1098

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northeast portion of the modeling domain where most agriculture activities occur. The maximum extinction coefficient is approximately 0.01 km-1, which accounts for approximately 1.5% of the total extinction in the region. Figure 6n shows that secondary organics do not contribute significantly to light extinction in the current study. Figure 6o shows that the light extinction coefficient associated with all the other anthropogenic sources combined is approximately 0.02-0.06 km-1 with a spatial distribution similar to that of the extinction coefficient associated with high-sulfur fuel combustion. The relative contribution to light extinction coefficients associated with this lumped source is highest (approximately 15%) near Long Beach and south of Ontario. Another source of light extinction is the Rayleigh scattering by air molecules in the atmosphere. The average extinction coefficient due to Rayleigh scattering is approximately 0.011 km-1 with a relative contribution of approximately 18% over the ocean and less than 5% at the inland locations. Figure 7 shows the detailed source apportionment of extinction coefficients at Ontario and Camarillo. Camarillo is a relatively clean costal site while Ontario is a polluted inland site that has higher airborne particulate matter concentrations. Figure 7a shows that the daytime average total light scattering coefficient of 0.37km-1 at Ontario is mainly associated with diesel engines (20%), catalystequipped engines (18%), background sulfate particles (20%),

FIGURE 7. Detailed source apportionment of daytime average total scattering coefficients, secondary PM scattering coefficients, total absorption coefficients, and NO2 absorption coefficients at Ontario and Camarillo on September 25, 1996. and animal sources (6%). Figure 7b shows the scattering due to secondary particulate matter (secondary scattering) contributes approximately 68% of the total light scattering at Ontario. Secondary particulate matter associated with diesel engines and catalyst-equipped gasoline engines account for 29.2% and 28.7% of the secondary light scattering, respectively. Figure 7c shows that the daytime-average absorption coefficient (0.0235 km-1) at Ontario is mainly due

to diesel engines (52%) and catalyst-equipped gasoline engines (19%). Figure 7d shows the absorption coefficient associated with NO2 released from different sources; 44% of the total absorption at Ontario is due to NO2 absorption (0.01 km-1). The two largest sources of NO2 are catalyst-equipped gasoline engines (39.8%) and diesel exhaust (36.3%). It can be seen from Figure 7a-d that transportation related sources (crustal material, paved road dust, diesel engines, catalystVOL. 38, NO. 4, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 8. Relative contribution from transportation sources to total daytime average light extinction coefficients in the South Coast Air Basin on September 25, 1996. equipped gasoline engines, and noncatalyst-equipped gasoline engines) dominate both scattering and extinction at Ontario on September 25, 1996. Figure 7e shows that the total scattering coefficient at Camarillo is about 26% of the total scattering coefficient at Ontario during the study period. Background sulfate particles (50%) and Rayleigh scattering (11%) account for the majority of the light scattering. Figure 7f shows that secondary particulate matter accounts for 24% of the total scattering at this location. Secondary particulate matter associated with catalyst-equipped engines and diesel engines account for 17% and 30% of the secondary light scattering, respectively. Figure 7g shows that the daytime average absorption coefficient (0.008 km-1) is mainly associated with diesel engines (47%) and catalyst-equipped gasoline engines (13%). Figure 7h shows that 41% of the light absorption at Camarillo is due to NO2 absorption. The two largest sources of NO2 are catalyst-equipped gasoline engines (30%) and diesel engines (33%). Table 2 shows source contributions to the total light extinction coefficient at the location with the greatest visibility impairment (480 km Easting, 3785 km Northing) in the current study averaged between 0700 and 1700 PST on September 25, 1996. The extinction coefficient at this location is 0.633 km-1, which corresponds to a visual range of 6.2 km based on the standard Koschmieder equation. Diesel engines (24.2%), catalyst-equipped engines (22.9%), animals (14.6%), and background sulfate particles (13.0%) are the most significant sources to the visibility impairment at this location. Uncertainty Analysis. The visual ranges shown in the Comparison to Measured Values section are calculated from predicted extinction coefficients assuming a homogeneous distribution of aerosol chemical components. The extinction coefficients calculated using the core-and-shell particle configuration (see Total Source Apportionment of Visibility section) differ from the extinction coefficients that are calculated assuming a homogeneous distribution by less than 1.0% within the modeling domain. The extinction coefficients can be apportioned to individual sources using the noninteractive, interactive, or mixed methods described in the Source Apportionment of Visibility section. Calculations that assume a homogeneous aerosol chemical component distribution and use the interactive method to calculate source 1100

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TABLE 2. Source Contribution to Visibility Reduction at 480 km Easting, 3785 km Northing Averaged between 0700 and 1700 PST on September 25, 1996 source category

extinction coefficient (km-1)

relative contribution (%)

crustal material paved road dust diesel engines meat cooking noncatalyst-equipped gasoline catalys- equipped gasoline high-sulfur fuel refrigerant losses residential NH3 source animal NH3 source soil and fertilizer NH3 source secondary organics other anthropogenic sources sea salt background sources air scattering

0.017 0.011 0.153 0.005 0.030 0.145 0.016 0.000 0.008 0.093 0.007 0.005 0.048 0.002 0.082 0.011

2.7 1.7 24.2 0.8 4.7 22.9 2.6 0.0 1.3 14.6 1.1 0.7 7.6 0.3 13.0 1.7

total

0.633

100.0

contribution predict higher extinction coefficients for each source than calculations that use the noninteractive method. The maximum increase in the reconstructed total extinction coefficient is approximately 15% in the region where particulate matter concentrations are highest. The maximum increase in the extinction coefficient associated with diesel engines and catalyst-equipped gasoline engines is approximately 4%. Although the extinction coefficients for individual sources predicted by the interactive method are higher, the relative contribution that each source makes to the total extinction coefficient predicted using the interactive method differs by less than 0.5% from the relative contributions predicted by the noninteractive method. Extinction coefficients calculated using the mixed method (see the Total Source Apportionment of Visibility section) are generally higher than the coefficients calculated using the noninteractive method. The maximum increase in the reconstructed total extinction coefficient calculated using the mixed method is approximately 12%. The maximum increase in the extinc-

tion coefficient associated with diesel engines and catalystequipped gasoline engine sources is approximately 3% and 4%, respectively. Once again, the relative contribution that each source makes to the total extinction coefficient predicted by the mixed method changes by less than 1%. The results in this study show that the predicted visual range and the relative source contribution to visibility impairment are not sensitive to the model assumptions of the particle representation (homogeneous vs core-and-shell) and extinction coefficient apportionment methods (interactive, noninteractive, or mixed method).

Discussion Figure 8 shows the relative contributions that transportationrelated sources (crustal material, paved road dust, diesel engines, catalyst-equipped gasoline engines, and noncatalystequipped gasoline engines) make to light extinction in the SCAB averaged between 0700 and 1700 PST on September 25, 1996. Crustal sources are included in this analysis because unpaved road dust accounts for the majority of the emissions in this category. The transportation contribution to visibility impairment ranges from approximately 30% in the relatively clean coastal region to over 50% in the relatively polluted downwind region of the study domain. The analysis shows that transportation related activities are one of the major sources of visibility impairment in the SCAB under the conditions studied.

Acknowledgments This research was supported by a grant from the Chevron Products Company. The statements, opinions, findings, and conclusions of this paper are those of the authors and do not necessarily represent the views of the Chevron Products Company.

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Received for review August 25, 2003. Revised manuscript received November 20, 2003. Accepted November 25, 2003. ES0349305

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Source Apportionment of Visibility Impairment Using a Three

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