Aerosol Mass Spectral Profiles from NAMaSTE Field-Sampled South Asian Combustion Sources.

Unit mass resolution mass spectral profiles of nonrefractory submicron aerosol were retrieved from undersampled atmospheric emission sources common to South Asia using a "mini" aerosol mass spectrometer. Emission sources including wood- and dung-fueled cookstoves, agricultural residue burning, garbage burning, engine exhaust, and coal-fired brick kilns were sampled during the 2015 Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) campaign. High-resolution peak fitting estimates of the mass spectra were used to characterize ions found within each source profile and help identify mass spectral signatures unique to aerosol emissions from the investigated source types. The first aerosol mass spectral profiles of dung burning, charcoal burning, garbage burning, and brick kilns are provided in this work. The online aerosol mass spectra show that organics were generally the dominant component of the nonrefractory aerosol. However, inorganic aerosol components including ammonium and chloride were significant in dung- and charcoal-fired cookstove emissions and sulfate compounds were major components of the coal-fired brick kiln emissions. Organic mass spectra from both the charcoal burning and zigzag brick kiln were dominated by nitrogen-containing ions thought to be from the electron ionization of amines and amides contained in the emissions. The mixed garbage burning emissions profiles were dominated by plastic combustion with very low fractions of organic markers associated with biomass burning. The plastic burning emissions were associated with enhanced organic signal at mass-to-charge (m/z) 104 and m/z 166, which could be useful fragment ion indicators for garbage burning in ambient aerosol profiles. Finally, a framework for the identification of emission sources using the unit mass resolution organic mass fractions at m/z 55 (f 55), m/z 57 (f 57), and m/z 60 (f 60) is proposed in this work. Plotting the ratio of f 55 to f 57 versus f 60 is found to be effective for the identification of emissions by the fuel type and even useful in separating emissions of similar source types. Although the sample size was limited, these results give further context to the aerosol and gas-phase emission factors presented in other NAMaSTE works and provide a critical reference for future aerosol composition measurements in South Asia.

Emission factors and emission inventory of diesel vehicles in Nepal.

A comprehensive emission inventory of the transport sector through fuel-based emission factors (EFs) was developed for the first time in Nepal. This study estimates air pollutants emission from diesel vehicles between the years 1989 and 2018 based on national statistical data, average vehicle kilometers travelled, fuel mileage, and measurement-based EFs for each vehicle category during idle and moving conditions. The consumption of diesel by vehicle category was also estimated and total consumption was compared with national sales data. The Monte Carlo was used to estimate uncertainties. Nationally, total diesel consumption was estimated as 892,770 kL (85-115%) in 2017/18, 13.4 times higher than 1989/90. Ratnoze1 and Microaeth were used to conduct the tail pipe emission measurements. The fuel-based EFs of CO2, CO, BC, and PM2.5 were calculated through the carbon mass balance method. Of all diesel vehicles measured (n = 29) during idling, the average EFs were estimated as CO2 2600 (99-101%), CO 33.3 (44-156%), BC 0.6 (25-101%), and PM2.5 5.2 (0-235%) in unit of g L-1. For moving conditions (n = 5), the average EFs were estimated to be CO2 2476 (90-110%), CO 97.3 (0-232%), BC 1.7 (46-110%), and PM2.5 20.7 (0-255%), all in g L-1. Multiplying fuel consumption by EFs, national air pollutant emissions were estimated as 2214 (90-110%) to 2781(85-115%) for CO2, 27.7 (42-158%) to 88.8 (0-232%) for CO, 0.51 (23-177%) to 3.55 (46-110%) for BC and 3.42 (0-236%) to 23.47 (0-255%) for PM2.5 in 2017/18 in unit of Gg. This paper recommends revising national vehicle mass emission standards based on the findings of this study and including and enhancing sustainable low-carbon transport through amendment of transport policy.

A model-ready emission inventory for crop residue open burning in the context of Nepal.

Open burning of crop residue is an important source of air pollution which is poorly characterized in South Asia. Currently, the gridded inventory reported by Global Fire Emissions Database for biomass burning including open burning of crop residue are of coarse resolution (0.25° × 0.25°), and may not be appropriate for a simulation for Nepal. This study develops a comprehensive high resolution (1 km × 1 km) gridded model-ready emissions inventory for Nepal to understand the spatial characteristics of air pollutant emissions from open burning. We estimate the national air pollutant emissions from crop residue burned between the years 2003 and 2017. The best available data on agricultural production, residue consumption patterns, agricultural burning parameters and emission factors were derived from secondary sources. The Monte Carlo method was used to estimate uncertainties. The mass of crop residue burned in 2016/17 was 2908 Gg (61-139%), which was 22% of the dry matter generated that year. By multiplying the burned crop residue mass by emission factors, the air pollutant emissions were estimated as 4140 for CO2 (56-144%), 154 for CO (4-196%), 6.5 for CH4 (7-193%), 1.2 for SO2 (60-140%), 24.5 for PM2.5 (30-170%), 8.6 for OC (38-162%), 2.2 for BC (-1-201%), 7 for NOx (54-146%), 22.5 for NMVOC (8-192%) and 2.7 for NH3 (3-197%) in unit of Gg yr-1. More than 80% of air pollutants were generated during the months of February to May from the open burning of crop residue. The findings of this paper indicate that substantial reduction in open field burning would dramatically improve air quality in both the Terai region and other parts of Nepal and help reduce negative health impacts associated with the open burning of residue such as premature deaths, respiratory disease, and cardiovascular disease.

Estimating emissions from open burning of municipal solid waste in municipalities of Nepal.

Open burning of municipal solid waste (MSW) is a poorly-characterized and frequently-underestimated source of air pollution in developing countries. This paper estimates the quantity of MSW that was burned in five erstwhile municipalities of the Kathmandu valley, Nepal. A household survey, a transect walk survey, an experiment to measure the fraction of waste that is combustible, a survey on fraction of population burning waste outside their houses, and a survey of the fraction of MSW burned at dump sites were performed in this study, whereas burning/oxidation efficiency, municipal populations, MSW generation rates, and emission factors were derived from the literature. The total mass of MSW burned during 2016 is estimated to be 7400 tons (i.e., 20 tons/day), which was of 3% of the total MSW generated in the valley municipalities that year. This exceeds Government estimates by a factor of three. Multiplying the burned MSW mass by emission factors, the air pollutant emissions are estimated as PM2.5 55 tons (OC 42 tons and EC 1.4 tons), PM10 60 tons, BC 25 tons, CO2 11,900 tons, CH4 30 tons, SO2 5.0 tons, NOx 19.2 tons, CO 630 tons, NMVOC 112 tons, and NH3 5.7 tons per year. Open burning of MSW can trigger health impacts such as acute and chronic respiratory disease, heart diseases, and allergic hypersensitivity, in addition to impacts on local climate. Improved waste-segregation practices at the source and waste-collection systems throughout the valley are needed to mitigate this pollution source and its effects.

Predicting the effects of nanoscale cerium additives in diesel fuel on regional-scale air quality.

Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM2.5) emissions and alter the emissions of carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbon (HC) species, including several hazardous air pollutants (HAPs). To predict their net effect on regional air quality, we review the emissions literature and develop a multipollutant inventory for a hypothetical scenario in which nCe additives are used in all on-road and nonroad diesel vehicles. We apply the Community Multiscale Air Quality (CMAQ) model to a domain covering the eastern U.S. for a summer and a winter period. Model calculations suggest modest decreases of average PM2.5 concentrations and relatively larger decreases in particulate elemental carbon. The nCe additives also have an effect on 8 h maximum ozone in summer. Variable effects on HAPs are predicted. The total U.S. emissions of fine-particulate cerium are estimated to increase 25-fold and result in elevated levels of airborne cerium (up to 22 ng/m3), which might adversely impact human health and the environment.

Near-road modeling and measurement of cerium-containing particles generated by nanoparticle diesel fuel additive use.

Cerium oxide nanoparticles (nCe) are used as a fuel-borne catalyst in diesel engines to reduce particulate emissions, yet the environmental and human health impacts of the exhaust particles are not well understood. To bridge the gap between emission measurements and ambient impacts, size-resolved measurements of particle composition and mass concentration have been performed in Newcastle-upon-Tyne, United Kingdom, where buses have used an nCe additive since 2005. These observations show that the noncrustal cerium fraction thought to be associated with the use of nCe has a mass concentration ∼ 0.3 ng m(-3) with a size distribution peaking at 100-320 nm in aerodynamic diameter. Simulations with a near-roadway multicomponent sectional aerosol dynamic model predict that the use of nCe additives increases the number concentration of nuclei mode particles (<50 nm in diameter) while decreasing the total mass concentration. The near-road model predicts a downwind mass size distribution of cerium-containing particles peaking at 150 nm in aerodynamic diameter, a value similar to that measured for noncrustal cerium in Newcastle. This work shows that both the emission and atmospheric transformation of cerium-containing particles needs to be taken into account by regional modelers, exposure scientists, and policymakers when determining potential environmental and human health impacts.

Diagnostic air quality model evaluation of source-specific primary and secondary fine particulate carbon.

Ambient measurements of 78 source-specific tracers of primary and secondary carbonaceous fine particulate matter collected at four midwestern United States locations over a full year (March 2004-February 2005) provided an unprecedented opportunity to diagnostically evaluate the results of a numerical air quality model. Previous analyses of these measurements demonstrated excellent mass closure for the variety of contributing sources. In this study, a carbon-apportionment version of the Community Multiscale Air Quality (CMAQ) model was used to track primary organic and elemental carbon emissions from 15 independent sources such as mobile sources and biomass burning in addition to four precursor-specific classes of secondary organic aerosol (SOA) originating from isoprene, terpenes, aromatics, and sesquiterpenes. Conversion of the source-resolved model output into organic tracer concentrations yielded a total of 2416 data pairs for comparison with observations. While emission source contributions to the total model bias varied by season and measurement location, the largest absolute bias of -0.55 µgC/m(3) was attributed to insufficient isoprene SOA in the summertime CMAQ simulation. Biomass combustion was responsible for the second largest summertime model bias (-0.46 µgC/m(3) on average). Several instances of compensating errors were also evident; model underpredictions in some sectors were masked by overpredictions in others.

Public health impacts of secondary particulate formation from aromatic hydrocarbons in gasoline.

BACKGROUND: Aromatic hydrocarbons emitted from gasoline-powered vehicles contribute to the formation of secondary organic aerosol (SOA), which increases the atmospheric mass concentration of fine particles (PM2.5). Here we estimate the public health burden associated with exposures to the subset of PM2.5 that originates from vehicle emissions of aromatics under business as usual conditions. METHODS: The PM2.5 contribution from gasoline aromatics is estimated using the Community Multiscale Air Quality (CMAQ) modeling system and the results are compared to ambient measurements from the literature. Marginal PM2.5 annualized concentration changes are used to calculate premature mortalities using concentration-response functions, with a value of mortality reduction approach used to monetize the social cost of mortality impacts. Morbidity impacts are qualitatively discussed. RESULTS: Modeled aromatic SOA concentrations from CMAQ fall short of ambient measurements by approximately a factor of two nationwide, with strong regional differences. After accounting for this model bias, the estimated public health impacts from exposure to PM2.5 originating from aromatic hydrocarbons in gasoline lead to a central estimate of approximately 3800 predicted premature mortalities nationwide, with estimates ranging from 1800 to over 4700 depending on the specific concentration-response function used. These impacts are associated with total social costs of $28.2B, and range from $13.6B to $34.9B in 2006$. CONCLUSIONS: These preliminary quantitative estimates indicate particulates from vehicular emissions of aromatic hydrocarbons demonstrate a nontrivial public health burden. The results provide a baseline from which to evaluate potential public health impacts of changes in gasoline composition.

Simulating the degree of oxidation in atmospheric organic particles.

Modeled ratios of organic mass to organic carbon (OM/OC) and oxygen to carbon (n(O)/n(C)) in organic particulate matter are presented across the US for the first time and evaluated extensively against ambient measurements. The base model configuration systematically underestimates OM/OC ratios during winter and summer months. Model performance is greatly improved by applying source-specific OM/OC ratios to the primary organic aerosol (POA) emissions and incorporating a new parametrization to simulate oxidative aging of POA in the atmosphere. These model improvements enable simulation of urban-scale gradients in OM/OC with values in urban areas as much as 0.4 lower than in the surrounding regions. Modeled OM/OC and n(O)/n(C) ratios in January range from 1.4 to 2.0 and 0.2 to 0.6, respectively. In July, modeled OM/OC and n(O)/n(C) ratios range from 1.4 to 2.2 and 0.2 to 0.8, respectively. Improved model performance during winter is attributed entirely to our application of source-specific OM/OC ratios to the inventory. During summer, our treatment of oxidative aging also contributes to improved performance. Advancements described in this paper are codified in the latest public release of the Community Multiscale Air Quality model, CMAQv5.0.

Model representation of secondary organic aerosol in CMAQv4.7.

Numerous scientific upgrades to the representation of secondary organic aerosol (SOA) are incorporated into the Community Multiscale Air Quality (CMAQ) modeling system. Additions include several recently identified SOA precursors: benzene, isoprene, and sesquiterpenes; and pathways: in-cloud oxidation of glyoxal and methylglyoxal, particle-phase oligomerization, and acid enhancement of isoprene SOA. NO(x)-dependent aromatic SOA yields are also added along with new empirical measurements of the enthalpies of vaporization and organic mass-to-carbon ratios. For the first time, these SOA precursors, pathways and empirical parameters are included simultaneously in an air quality model for an annual simulation spanning the continental U.S. Comparisons of CMAQ-modeled secondary organic carbon (OC(sec)) with semiempirical estimates screened from 165 routine monitoring sites across the U.S. indicate the new SOA module substantially improves model performance. The most notable improvement occurs in the central and southeastern U.S. where the regionally averaged temporal correlations (r) between modeled and semiempirical OC(sec) increase from 0.5 to 0.8 and 0.3 to 0.8, respectively, when the new SOA module is employed. Wintertime OC(sec) results improve in all regions of the continental U.S. and the seasonal and regional patterns of biogenic SOA are better represented.

To what extent can biogenic SOA be controlled?

The implicit assumption that biogenic secondary organic aerosol (SOA) is natural and can not be controlled hinders effective air quality management. Anthropogenic pollution facilitates transformation of naturally emitted volatile organic compounds (VOCs) to the particle phase, enhancing the ambient concentrations of biogenic secondary organic aerosol (SOA). It is therefore conceivable that some portion of ambient biogenic SOA can be removed by controlling emissions of anthropogenic pollutants. Direct measurement of the controllable fraction of biogenic SOA is not possible, but can be estimated through 3-dimensional photochemical air quality modeling. To examine this in detail, 22 CMAQ model simulations were conducted over the continental U.S. (August 15 to September 4, 2003). The relative contributions of five emitted pollution classes (i.e., NO(x), NH(3), SO(x), reactive non methane carbon (RNMC) and primary carbonaceous particulate matter (PCM)) on biogenic SOA were estimated by removing anthropogenic emissions of these pollutants, one at a time and all together. Model results demonstrate a strong influence of anthropogenic emissions on predicted biogenic SOA concentrations, suggesting more than 50% of biogenic SOA in the eastern U.S. can be controlled. Because biogenic SOA is substantially enhanced by controllable emissions, classification of SOA as biogenic or anthropogenic based solely on VOC origin is not sufficient to describe the controllable fraction.

A review of selected engineered nanoparticles in the atmosphere: sources, transformations, and techniques for sampling and analysis.

A state-of-the-science review was undertaken to identify and assess sampling and analysis methods to detect and quantify selected nanomaterials (NMs) in the ambient atmosphere. The review is restricted to five types of NMs of interest to the Office of Research and Development Nanomaterial Research Strategy (U.S. Environmental Protection Agency): cerium oxide, titanium dioxide, carbon nanostructures (carbon nanotubes and fullerenes), zero-valent iron, and silver nanoparticles. One purpose was determining the extent to which present-day ultrafine sampling and analysis methods may be sufficient for identifying and possibly quantifying engineered NMs (ENMs) in ambient air. Conventional sampling methods for ultrafines appear to require modifications. For cerium and titanium, background levels from natural sources make measurement of ENMs difficult to quantify. In cases where field studies have been performed, identification from bulk analysis samples have been made. Further development of methods is needed to identify these NMs, especially in specific size fractions of ambient aerosols.

Emissions inventory of PM2.5 trace elements across the United States.

This paper presents the first National Emissions Inventory (NEI) of fine particulate matter (PM2.5) that includes the full suite of PM2.5 trace elements (atomic number > 10) measured at ambient monitoring sites across the U.S. PM2.5 emissions in the NEI were organized and aggregated into a set of 84 source categories for which chemical speciation profiles are available (e.g., Unpaved Road Dust Agricultural Soil, Wildfires). Emission estimates for ten metals classified as Hazardous Air Pollutants (HAP) were refined using data from a recent HAP NEI. All emissions were spatially gridded, and U.S. emissions maps for dozens of trace elements (e.g., Fe, Ti) are presented for the first time. Nationally, the trace elements emitted in the highest quantities are silicon (3.8 x 10(5) ton/yr), aluminum (1.4 x 10(5) ton/yr), and calcium (1.3 x 10(5) ton/yr). Our chemical characterization of the PM2.5 inventory shows that most of the previously unspeciated emissions are comprised of crustal elements, potassium, sodium, chlorine, and metal-bound oxygen. This work also reveals that the largest PM2.5 sources lacking specific speciation data are off-road diesel-powered mobile equipment, road construction dust, marine vessels, gasoline-powered boats, and railroad locomotives.

Seasonal and regional variations of primary and secondary organic aerosols over the continental United States: semi-empirical estimates and model evaluation.

Seasonal and regional variations of primary (OC(pri)) and secondary (OC(sec)) organic carbon aerosols across the continental United States for the year 2001 were examined by a semi-empirical technique using observed OC and elemental carbon (EC) data from 142 routine monitoring sites in mostly rural locations across the country, coupled with the primary OC/EC ratios, obtained from a chemical transport model (i.e., Community Multiscale Air Quality (CMAQ) model). This application yields the first non-mechanistic estimates of the spatial and temporal variations in OC(pri) and OC(sec) over an entire year on a continental scale. There is significant seasonal and regional variability in the relative contributions of OC(pri) and OC(sec) to OC. Over the continental United States, the median OC(sec) concentrations are 0.13, 0.36, 0.63, 0.44, and 0.42 mictrog C m(-3) in winter (DJF), spring (MAM), summer (JJA), fall (SON), and annual, respectively, making 21, 44, 51, 42, and 43% contributions to OC, respectively. OC(pri) exceeds OC(sec) in all seasons except summer. Regional analysis shows that the southeastern region has the highest concentration of OC(pri) (annual median = 1.35 microg C m(-3)), whereas the central region has the highest concentration of OC(sec) (annual median = 0.76 microg C m(-3)). The mechanistic OC(sec) estimates from the CMAQ model were compared against the independently derived semi-empirical OC(sec) estimates. The results indicate that the mechanistic model reproduced the monthly medians of the semi-empirical OC(sec) estimates well over the northeast, southeast, midwest, and central regions in all months except the summer months (June, July, and August), during which the modeled regional monthly medians were consistently lower than the semi-empirical estimates. This indicates that the CMAQ model is missing OC(sec) formation pathways that are important in the summer.

Diagnostic model evaluation for carbonaceous PM2.5 using organic markers measured in the southeastern U.S.

Summertime concentrations of fine particulate carbon in the southeastern United States are consistently underestimated by air quality models. In an effort to understand the cause of this error, the Community Multiscale Air Quality model is instrumented to track primary organic and elemental carbon contributions from fifteen different source categories. The model results are speciated using published source profiles and compared with ambient measurements of 100 organic markers collected at eight sites in the Southeast during the 1999 summer. Results indicate that modeled contributions from vehicle exhaust and biomass combustion, the two largest sources of carbon in the emission inventory, are unbiased across the region. In Atlanta, good model performance for total carbon (TC) is attributed to compensating errors: overestimation of vehicle emissions with underestimations of other sources. In Birmingham, 35% of the TC underestimation can be explained by deficiencies in primary sources. Cigarette smoke and vegetative detritus are not in the inventory, but contribute less than 3% of the TC at each site. After the model results are adjusted for source-specific errors using the organic-marker measurements, an average of 1.6 microgC m(-3) remain unexplained. This corresponds to 26-38% of ambient TC concentrations at urban sites and up to 56% at rural sites. The most likely sources of unexplained carbon are discussed.

Receptor modeling of ambient particulate matter data using positive matrix factorization: review of existing methods.

Methods for apportioning sources of ambient particulate matter (PM) using the positive matrix factorization (PMF) algorithm are reviewed. Numerous procedural decisions must be made and algorithmic parameters selected when analyzing PM data with PMF. However, few publications document enough of these details for readers to evaluate, reproduce, or compare results between different studies. For example, few studies document why some species were used and others not used in the modeling, how the number of factors was selected, or how much uncertainty exists in the solutions. More thorough documentation will aid the development of standard protocols for analyzing PM data with PMF and will reveal more clearly where research is needed to help future analysts select from the various possible procedures and parameters available in PMF. For example, research likely is needed to determine optimal approaches for handling data below detection limits, ways to apportion PM mass among sources identified by PMF, and ways to estimate uncertainties in the solution. The review closes with recommendations for documenting the methodological details of future PMF analyses.

Comparison of two methods for obtaining quantitative mass concentrations from aerosol time-of-flight mass spectrometry measurements.

Aerosol time-of-flight mass spectrometry (ATOFMS) measurements provide continuous information on the aerodynamic size and chemical composition of individual particles. In this work, we compare two approaches for converting unscaled ATOFMS measurements into quantitative particle mass concentrations using (1) reference mass concentrations from a co-located micro-orifice uniform deposit impactor (MOUDI) with an accurate estimate of instrument busy time and (2) reference number concentrations from a co-located aerodynamic particle sizer (APS). Aerodynamic-diameter-dependent scaling factors are used for both methods to account for particle transmission efficiencies through the ATOFMS inlet. Scaling with APS data retains the high-resolution characteristics of the ambient aerosol because the scaling functions are specific for each hourly time period and account for a maximum in the ATOFMS transmission efficiency curve for larger-sized particles. Scaled mass concentrations obtained from both methods are compared with co-located PM(2.5) measurements for evaluation purposes. When compared against mass concentrations from a beta attenuation monitor (BAM), the MOUDI-scaled ATOFMS mass concentrations show correlations of 0.79 at Fresno, and the APS-scaled results show correlations of 0.91 at Angiola. Applying composition-dependent density corrections leads to a slope of nearly 1 with 0 intercept between the APS-scaled absolute mass concentration values and BAM mass measurements. This paper provides details on the methodologies used to convert ATOFMS data into continuous, quantitative, and size-resolved mass concentrations that will ultimately be used to provide a quantitative estimate of the number and mass concentrations of particles from different sources.

A field-based approach for deterimining ATOFMS instrument sensitities to ammonium and nitrate.

Aerosol time-of-flight mass spectrometry (ATOFMS) instruments measure the size and chemical composition of individual particles in real-time. ATOFMS chemical composition measurements are difficult to quantify, largely because the instrument sensitivities to different chemical species in mixed ambient aerosols are unknown. In this paper, we develop a field-based approach for determining ATOFMS instrument sensitivities to ammonium and nitrate in size-segregated atmospheric aerosols, using tandem ATOFMS-impactor sampling. ATOFMS measurements are compared with collocated impactor measurements taken at Riverside, CA, in September 1996, August 1997, and October 1997. This is the first comparison of ion signal intensities from a single-particle instrument with quantitative measurements of atmospheric aerosol chemical composition. The comparison reveals that ATOFMS instrument sensitvities to both NH4+ and NO3- decline with increasing particle aerodynamic diameter over a 0.32-1.8 microm calibration range. The stability of this particle size dependence is tested overthe broad range of fine particle concentrations (PM1.8) = 17.6 +/- 2.0-127.8 +/- 1.8 microg m(-3)), ambient temperatures (23-35 degrees C), and relative humidity conditions (21-69%), encountered during the field experiments. This paper describes a potentially generalizable methodology for increasing the temporal and size resolution of atmospheric aerosol chemical composition measurements, using tandem ATOFMS-impactor sampling.

Evaluation of an air quality model for the size and composition of source-oriented particle classes.

Air quality model predictions of the size and composition of atmospheric particle classes are evaluated by comparison with aerosol time-of-flight mass spectrometry (ATOFMS) measurements of single-particle size and composition at Long Beach and Riverside, CA, during September 1996. The air quality model tracks the physical diameter, chemical composition, and atmospheric concentration of thousands of representative particles from different emissions classes as they are transported from sources to receptors while undergoing atmospheric chemical reactions. In the model, each representative particle interacts with a common gas phase but otherwise evolves separately from all other particles. The model calculations yield an aerosol population, in which particles of a given size may exhibit different chemical compositions. ATOFMS data are adjusted according to the known particle detection efficiencies of the ATOFMS instruments, and model predictions are modified to simulate the chemical sensitivities and compositional detection limits of the ATOFMS instruments. This permits a direct, semiquantitative comparison between the air quality model predictions and the single-particle ATOFMS measurements to be made. The air quality model accurately predicts the fraction of atmospheric particles containing sodium, ammonium, nitrate, carbon, and mineral dust, across all particle sizes measured by ATOFMS at the Long Beach site, and in the coarse particle size range (Da > or = 1.8 microm) atthe Riverside site. Given thatthis model evaluation is very likely the most stringent test of any aerosol air quality model to date, the model predictions show impressive agreement with the single-particle ATOFMS measurements.