Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Ambient particle characteristics by single particle aerosol mass spectrometry at a coastal site in Hong Kong: a case study affected by the sea-land breeze

The sea-land breeze (SLB) circulation plays a vital role in the transport of atmospheric pollutants in coastal cities. In this study, a single particle aerosol mass spectrometer (SPAMS) and combined bulk aerosol instruments were deployed to investigate the ambient particle characteristic at a suburban coastal site in Hong Kong from February 22 to March 10, 2013. Significant SLB circulations were captured from March 6-10, 2013, during the campaign. During the SLB periods, air quality worsened, with PM_2.5 concentrations reaching a peak of 55.6 μg m^-3 and an average value of 42.8 ± 4.5 μg m^-3. A total of 235,894 particles were measured during the SLB stage. Eight major sources were identified by investigating the mixing states of the total particles, including the coal-burning related particles (48.1%), biomass burning particles (6.7%), vehicle emission-related particles (16.4%), sea salt (9.2%), ship emission particles (2.7%), dust/steeling industries (3.7%), waste incineration (6.3%), and road dust (3.9%). It was noteworthy that the PM_2.5 concentrations and particle numbers increased sharply during the transition of land wind to the sea breeze. Meanwhile, the continental sourced pollutants recirculated back to land resulting in a cumulative increase in pollutants. Both individual and bulk measurements support the above results, with high contributions from coal burning, biomass burning, bulk K⁺, and NO₃ ^-, which were probably from the regional transportation from the nearby area. In contrast, the ship and vehicle emissions increased during the SLB period, with a high sulfate concentration partially originating from the ship emission. In this study, field evidence of continental-source pollutants backflow to land with the evolution of sea breeze was observed and helped our current understanding of the effect of SLB on air quality in the coastal city.

Self-feedback LSTM regression model for real-time particle source apportionment

Atmospheric particulate matter pollution has attracted much wider attention globally. In recent years, the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques. Such demands are summarized, in this paper, as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances, such as the existence of secondary source and similar source. In this study, we firstly analyze the possible and potential restraints in single particle source apportionment, then propose a novel three-step self-feedback long short-term memory (SF-LSTM) network for approximating the source contribution. The proposed deep learning neural network includes three modules, as generation, scoring and refining, and regeneration modules. Benefited from the scoring modules, SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment, meanwhile, the regeneration module calculates the source contribution in a non-linear way. The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators (residual sum of squares, stability, sparsity, negativity) for the restraints. Additionally, in short time-resolution analyzing, SF-LSTM provides better results under the restraint of stability.

Single particle mass spectral signatures from vehicle exhaust particles and the source apportionment of on-line PM2.5 by single particle aerosol mass spectrometry

In order to accurately apportion the many distinct types of individual particles observed, it is necessary to characterize fingerprints of individual particles emitted directly from known sources. In this study, single particle mass spectral signatures from vehicle exhaust particles in a tunnel were performed. These data were used to evaluate particle signatures in a real-world PM_2.5 apportionment study. The dominant chemical type originating from average positive and negative mass spectra for vehicle exhaust particles are EC species. Four distinct particle types describe the majority of particles emitted by vehicle exhaust particles in this tunnel. Each particle class is labeled according to the most significant chemical features in both average positive and negative mass spectral signatures, including ECOC, NaK, Metal and PAHs species. A single particle aerosol mass spectrometry (SPAMS) was also employed during the winter of 2013 in Guangzhou to determine both the size and chemical composition of individual atmospheric particles, with vacuum aerodynamic diameter (d_va) in the size range of 0.2-2μm. A total of 487,570 particles were chemically analyzed with positive and negative ion mass spectra and a large set of single particle mass spectra was collected and analyzed in order to identify the speciation. According to the typical tracer ions from different source types and classification by the ART-2a algorithm which uses source fingerprints for apportioning ambient particles, the major sources of single particles were simulated. Coal combustion, vehicle exhaust, and secondary ion were the most abundant particle sources, contributing 28.5%, 17.8%, and 18.2%, respectively. The fraction with vehicle exhaust species particles decreased slightly with particle size in the condensation mode particles.

Characterization of typical metal particles during haze episodes in Shanghai, China

Aerosol particles were collected during three heavy haze episodes at Shanghai in the winter of 2013. Transmission electron microscopy (TEM) coupled with energy dispersive X-ray spectroscopy was used to study the morphology and speciation of typical metal particles at a single-particle level. In addition, time-of-flight aerosol mass spectrometry (ATOFMS) was applied to identify the speciation of the Fe-containing particles. TEM analysis indicated that various metal-containing particles were hosted by sulfates, nitrates, and oxides. Fe-bearing particles mainly originated from vehicle emissions and/or steel production. Pb-, Zn-, and Sb-bearing particles were mainly contributed by anthropogenic sources. Fe-bearing particles were clustered into six groups by ATOFMS: Fe-Carbon, Fe-Inorganic, Fe-Trace metal, Fe-CN, Fe-PO_3, and Fe-NO₃. ATOFMS data suggested that Fe-containing particles corresponded to different origins, including industrial activities, resuspension of dusts, and vehicle emissions. Fe-Carbon and Fe-CN particles displayed significant diurnal variation, and high levels were observed during the morning rush hours. Fe-Inorganic and Fe-Trace metal particle levels peaked at night. Furthermore, Fe-Carbon and Fe-PO₃ were mainly concentrated in the fine particles. Fe-CN, Fe-Inorganic, and Fe-Trace metal exhibited bimodal distribution. The mixing state of the particles revealed that all Fe-bearing particles tended to be mixed with sulfate and nitrate. The data presented herein is essential for elucidating the origin, evolution processes, and health effects of metal-bearing particles.

Characteristics of atmospheric single particles during haze periods in a typical urban area of Beijing: A case study in October, 2014

To investigate the composition and possible sources of particles, especially during heavy haze pollution, a single particle aerosol mass spectrometer (SPAMS) was deployed to measure the changes of single particle species and sizes during October of 2014, in Beijing. A total of 2,871,431 particles with both positive and negative spectra were collected and characterized in combination with the adaptive resonance theory neural network algorithm (ART-2a). Eight types of particles were classified: dust particles (dust, 8.1%), elemental carbon (EC, 29.0%), organic carbon (OC, 18.0%), EC and OC combined particles (ECOC, 9.5%), Na-K containing particles (NaK, 7.9%), K-containing particles (K, 21.8%), organic nitrogen and potassium containing particles (KCN, 2.3%), and metal-containing particles (metal, 3.6%). Three haze pollution events (P1, P2, P3) and one clean period (clean) were analyzed, based on the mass and number concentration of PM2.5 and the back trajectory results from the hybrid single particle Lagrangian integrated trajectory model (Hysplit-4 model). Results showed that EC, OC and K were the major components of single particles during the three haze pollution periods, which showed clearly increased ratios compared with those in the clean period. Results from the mixing state of secondary species of different types of particles showed that sulfate and nitrate were more readily mixed with carbon-containing particles during haze pollution episodes than in clean periods.

Sources and characteristics of fine particles over the Yellow Sea and Bohai Sea using online single particle aerosol mass spectrometer

Marine aerosols over the East China Seas are heavily polluted by continental sources. During the Chinese Comprehensive Ocean Experiment in November 2012, size and mass spectra of individual atmospheric particles in the size range from 0.2 to 2.0 μm were measured on board by a single particle aerosol mass spectrometer (SPAMS). The average hourly particle number (PN) was around 4560±3240 in the South Yellow Sea (SYS), 2900±3970 in the North Yellow Sea (NYS), and 1700±2220 in the Bohai Sea (BS). PN in NYS and BS varied greatly over 3 orders of magnitude, while that in SYS varied slightly. The size distributions were fitted with two log-normal modes. Accumulation mode dominated in NYS and BS, especially during episodic periods. Coarse mode particles played an important role in SYS. Particles were classified using an adaptive resonance theory based neural network algorithm (ART-2a). Six particle types were identified with secondary-containing, aged sea-salt, soot-like, biomass burning, fresh sea-salt, and lead-containing particles accounting for 32%, 21%, 18%, 16%, 4%, and 3% of total PN, respectively. Aerosols in BS were relatively enriched in particles from anthropogenic sources compared to SYS, probably due to emissions from more developed upwind regions and indicating stronger influence of continental outflow on marine environment. Variation of source types depended mainly on origins of transported air masses. This study examined rapid changes in PN, size distribution and source types of fine particles in marine atmospheres. It also demonstrated the effectiveness of high-time-resolution source apportionment by ART-2a.

Characterization of aerosol optical properties, chemical composition and mixing states in the winter season in Shanghai, China

Physical and chemical properties of ambient aerosols at the single particle level were studied in Shanghai from December 22 to 28, 2009. A Cavity-Ring-Down Aerosol Extinction Spectrometer (CRD-AES) and a nephelometer were deployed to measure aerosol light extinction and scattering properties, respectively. An Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) was used to detect single particle sizes and chemical composition. Seven particle types were detected. Air parcels arrived at the sampling site from the vicinity of Shanghai until mid-day of December 25, when they started to originate from North China. The aerosol extinction, scattering, and absorption coefficients all dropped sharply when this cold, clean air arrived. Aerosol particles changed from a highly aged type before this meteorological shift to a relatively fresh type afterwards. The aerosol optical properties were dependent on the wind direction. Aerosols with high extinction coefficient and scattering Ångström exponent (SAE) were observed when the wind blew from the west and northwest, indicating that they were predominantly fine particles. Nitrate and ammonium correlated most strongly with the change in aerosol optical properties. In the elemental carbon/organic carbon (ECOC) particle type, the diurnal trends of single scattering albedo (SSA) and elemental carbon (EC) signal intensity had a negative correlation. We also found a negative correlation (r=-0.87) between high mass-OC particle number fraction and the SSA in a relatively clean period, suggesting that particulate aromatic components might play an important role in light absorption in urban areas.

Comparison of elemental and black carbon measurements during normal and heavy haze periods: implications for research

Studies specifically addressing the elemental carbon (EC)/black carbon (BC) relationship during the transition from clean-normal (CN) air quality to heavy haze (HH) are rare but have important health and climate implications. The present study, in which EC levels are measured using a thermal-optical method and BC levels are measured using an optical method (aethalometer), provides a preliminary insight into this issue. The average daily EC concentration was 3.08 ± 1.10 μg/m(3) during the CN stage but climbed to 11.77 ± 2.01 μg/m(3) during the HH stage. More importantly, the BC/EC ratio averaged 0.92 ± 0.14 during the CN state and increased to 1.88 ± 0.30 during the HH state. This significant increase in BC/EC ratio has been confirmed to result partially from an increase in the in situ light absorption efficiency (σap) due to an enhanced internal mixing of the EC with other species. However, the exact enhancement of σap was unavailable because our monitoring scheme could not acquire the in situ absorption (bap) essential for σap calculation. This reveals a need to perform simultaneous measurement of EC and bap over a time period that includes both the CN and HH stages. In addition, the sensitivity of EC to both anthropogenic emissions and HH conditions implies a need to systematically study how to include EC complex (EC concentration, OC/EC ratio, and σap) as an indicator in air quality observations, in alert systems that assess air quality, and in the governance of emissions and human behaviors.

Images reveal that atmospheric particles can undergo liquid-liquid phase separations

A large fraction of submicron atmospheric aerosol particles contains both organic material and inorganic salts. As the relative humidity cycles in the atmosphere and the water content of the particles correspondingly changes, these mixed particles can undergo a range of phase transitions, possibly including liquid-liquid phase separation. If liquid-liquid phase separation occurs, the gas-particle partitioning of atmospheric semivolatile organic compounds, the scattering and absorption of solar radiation, and the reactive uptake of gas species on atmospheric particles may be affected, with important implications for climate predictions. The actual occurrence of liquid-liquid phase separation within individual atmospheric particles has been considered uncertain, in large part because of the absence of observations for real-world samples. Here, using optical and fluorescence microscopy, we present images that show the coexistence of two noncrystalline phases for real-world samples collected on multiple days in Atlanta, GA as well as for laboratory-generated samples under simulated atmospheric conditions. These results reveal that atmospheric particles can undergo liquid-liquid phase separations. To explore the implications of these findings, we carried out simulations of the Atlanta urban environment and found that liquid-liquid phase separation can result in increased concentrations of gas-phase NO(3) and N(2)O(5) due to decreased particle uptake of N(2)O(5).

Particulate nitrate formation in a highly polluted urban area: a case study by single-particle mass spectrometry in Shanghai

An aerosol time-of-flight mass spectrometer was deployed in August 2007 to characterize the 0.1-2.0 microm diameter particles in Shanghai to examine nitrate-containing particles. About 39% of the mass spectra of single particles contained nitrate ion peaks. The relative intensity of nitrate signals showed a pronounced diurnal profile, peaking in the late night or early morning during highly polluted days, and is closely correlated with the ambient relative humidity (RH). However, during the sampling days with good air quality, the diurnal pattern of nitrate changed by showing much lower signal intensity of nitrate with irregular variation. Poor correlation between the signals of ammonium and nitrate inthe mass spectra excluded the possibility of NH4NO3 as a major form of particulate nitrate, whose formation is favored by high RH and low temperature. The peak intensities of nitrate during the nighttime and high concentrations of O3 and NO2 strongly suggest that the heterogeneous reactions of N2O5 and NO3 onthe aerosol surface dominated the particulate nitrate formation on polluted days.

Source apportionment of lead-containing aerosol particles in Shanghai using single particle mass spectrometry

Lead (Pb) in individual aerosol particles was measured using single particle aerosol mass spectrometer (ATOFMS) in the summer of 2007 in Shanghai, China. Pb was found in 3% of particles with diameters in the range 0.1-2.0 microm. Single particle data were analyzed focusing on the particles with high Pb content which were mostly submicron. Using the ART-2a neural network algorithm, these fine Pb-rich particles were classified into eight main classes by their mass spectral patterns. Based on the size distribution, temporal variation of number density, chemical composition and the correlation between different chemical species for each class, three major emission sources were identified. About 45% of the Pb-rich particles contained organic or elemental carbon and were attributed to the emission from coal combustion; particles with good correlation between Cl and Pb content were mostly attributed to waste incineration. One unique class of particles was identified by strong phosphate and Pb signals, which were assigned to emissions from phosphate industry. Other Pb-rich particles included aged sea salt and particles from metallurgical processes.

Characterisation of indoor airborne particles by using real-time aerosol mass spectrometry

An Aerosol Time-of-Flight Mass Spectrometer (ATOFMS; TSI 3800) was deployed to Athens (Greece) during August 2003. The instrument provides information on a polydisperse aerosol, acquiring precise aerodynamic diameter (+/-1%) within the range 0.3 to 3 mum and individual particle positive and negative mass spectral data in real time. Sampling was carried out indoors and outdoors at an office in a building on a minor road in the city centre and various outdoor and indoor sources were identified. Specific outdoor particles such as dust and carbon particles were detected in indoor air. The generation of particles from indoor sources was studied and several different types of particle were found to be present in environmental tobacco smoke (ETS): three were potassium-rich (with differing proportions of carbon) emitted directly in the exhaled mainstream smoke. Two other types arose mainly when the cigarette was left smouldering on an ash-tray. Another particle type exhibited a strong signal at m/z 84, most likely due to a nicotine fragment. The temporal trend of this specific particle type showed likely condensation of semi-volatile constituents on existing potassium-rich particles. A release of insect repellent in the room was also successfully monitored.

Characterization of fine particle sources in the Great Smoky Mountains area

A source apportionment study to characterize sources of fine particles in the Great Smoky Mountains area was conducted analyzing ambient PM(2.5) (particulate matter< or =2.5 microm in aerodynamic diameter) speciation data collected at a Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring site. A total of 1442 samples collected between March 1988 and December 2003 analyzed for 30 elemental species were analyzed with the application of the positive matrix factorization (PMF). Eight major sources were extracted: summer-high secondary sulfate (55%), carbon-rich secondary sulfate (16%), summer-low secondary sulfate (2%), gasoline vehicle emissions (13%), diesel emissions (1%), airborne soil (6%), industry (5%), and secondary nitrate (2%). The contributions from the carbon-rich secondary sulfate particles are likely a combination of local and regional influences of the biogenic as well as anthropogenic secondary particles. The compositional profiles for gasoline vehicle and diesel emissions are similar to those identified in other US areas. Backward trajectories indicate that the high impacts of airborne soil were likely caused by Asian and Saharan dust storms. This study would assist in the implementation plan development for attaining the air quality standards for PM(2.5), regional haze rule planning, and source-specific community epidemiology.

Single-particle detection efficiencies of aerosol time-of-flight mass spectrometry during the North Atlantic marine boundary layer experiment

During the North Atlantic marine boundary layer experiment (NAMBLEX) sampling campaign at Mace Head, Ireland, both continental and maritime air masses were sampled. Aerosol was characterized both with a TSI 3800 time-of-flight mass spectrometer (ATOFMS) and a MOUDI microorifice impactor, and particle number counts were measured independently with an aerodynamic particle sizer. The data have been analyzed in order to elucidate factors determining the particle detection efficiencies of the ATOFMS. These are broken down according to the efficiency of the inlet system, the hit efficiency on particles which enter the sensing zone of the instrument and the sensitivity of the measured ion signal to the chemical species. A substantial matrix effect depending on the chemical composition of the aerosol sampled at the time was found, which is reflected in variations in the hit efficiency of particles entering the sensing zone of the instrument with the main desorption-ionization laser. This is in addition to the strong inverse power-law dependence of inlet transmission efficiency on particle diameter. The variation in hit efficiency with particle type is likely attributable to differences in the energetics of laser energy absorption, ablation, and ion formation. However, once variations in both inlet transmission and hit efficiencies are taken into account, no additional matrix dependence of ATOFMS response is required to obtain a linear relationship between the ion signal and the concentration of a particular chemical species. The observations show that a constant mass of material is ionized from each particle, irrespective of size. Consequently the integrated ion signal for a given chemical component and particle size class needs to be increased by a factor related to the cube of particle diameter in order to correlate with the airborne mass of that component.

Ozonolysis and photolysis of alkene-terminated self-assembled monolayers on quartz nanoparticles: implications for photochemical aging of organic aerosol particles

Photolysis of alkene-terminated self assembled monolayers (SAM) deposited on Degussa SiO(2) nanoparticles is studied following oxidation of SAM with a gaseous ozone/oxygen mixture. Infrared cavity ring-down spectroscopy is used to observe gas-phase products generated during ozonolysis and subsequent photolysis of SAM in real time. Reactions taking place during ozonolysis transform alkene-terminated SAM into a photochemically active state capable of photolysis in the tropospheric actinic window (lambda > 295 nm). Formaldehyde and formic acid are the observed photolysis products. Photodissociation action spectra of oxidized SAM and the observed pattern of gas-phase products are consistent with the well-established Criegee mechanism of ozonolysis of terminal alkenes. There is strong evidence for the presence of secondary ozonides (1,3,4-trioxalones) and other peroxides on the oxidized SAM surface. The data imply that photolysis plays a role in atmospheric aging of primary and secondary organic aerosol particles.

Sources of fine particles in a rural midwestern U.S. area

Ambient PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter) samples collected at a rural monitoring site in Bondville, IL on every third day using Interagency Monitoring of Protected Visual Environments (IMPROVE) sampler were analyzed through the application of the positive matrix factorization (PMF). The particulate carbon fractions were obtained from the thermal optical reflectance method that divides particulate carbon into four organic carbon, pyrolyzed organic carbon (OP), and three elemental carbon fractions. A total of 257 samples collected between March 2001 and May 2003 analyzed for 35 species were used and eight sources were identified: summer-high secondary sulfate aerosol (40%), secondary nitrate aerosol (32%), gasoline vehicle (9%), OP-high secondary sulfate aerosol (7%), selenium-high secondary sulfate aerosol (4%), airborne soil (4%), aged sea salt (2%), and diesel emissions (2%). The compositional profiles for gasoline vehicle and diesel emissions are similar to those estimated in other U.S. areas. Backward trajectories indicate that the highly elevated airborne soil impacts were likely caused by Asian and Saharan dust storms. Potential source contribution function analyses show the potential source areas and pathways of secondary sulfate aerosols, especially the regional influences of the biogenic as well as anthropogenic secondary aerosol.

Determination of single particle mass spectral signatures from light-duty vehicle emissions

In this study, 28 light-duty gasoline vehicles (LDV) were operated on a chassis dynamometer at the California Air Resources Board Haagen-Smit Facility in El Monte, CA. The mass spectra of individual particles emitted from these vehicles were measured using aerosol time-of-flight mass spectrometry (ATOFMS). A primary goal of this study involves determining representative size-resolved single particle mass spectral signatures that can be used in future ambient particulate matter source apportionment studies. Different cycles were used to simulate urban driving conditions including the federal testing procedure (FTP), unified cycle (UC), and the correction cycle (CC). The vehicles were selected to span a range of catalytic converter (three-way, oxidation, and no catalysts) and engine technologies (vehicles models from 1953 to 2003). Exhaust particles were sampled directly from a dilution and residence chamber system using particle sizing instruments and an ATOFMS equipped with an aerodynamic lens (UF-ATOFMS) analyzing particles between 50 and 300 nm. On the basis of chemical composition, 10 unique chemical types describe the majority of the particles with distinct size and temporal characteristics. In the ultrafine size range (between 50 and 100 nm), three elemental carbon (EC) particle types dominated, all showing distinct EC signatures combined with Ca, phosphate, sulfate, and a lower abundance of organic carbon (OC). The relative fraction of EC particle types decreased as particle size increased with OC particles becoming more prevalent above 100 nm. Depending on the vehicle and cycle, several distinct OC particle types produced distinct ion patterns, including substituted aromatic compounds and polycyclic aromatic hydrocarbons (PAH), coupled with other chemical species including ammonium, EC, nitrate, sulfate, phosphate, V, and Ca. The most likely source of the Ca and phosphate in the particles is attributed to the lubricating oil. Significant variability was observed in the chemical composition of particles emitted within the different car categories as well as for the same car operating under different driving conditions. Two-minute temporal resolution measurements provide information on the chemical classes as they evolved during the FTP cycle. The first two minutes of the cold start produced more than 5 times the number of particles than any other portion of the cycle, with one class of ultrafine particles (EC coupled with Ca, OC, and phosphate) preferentially produced. By number, the three EC with Ca classes (which also contained OC, phosphate, and sulfate) were the most abundant classes produced by the nonsmoking vehicles. The smoker category produced the highest number of particles, with the dominant classes being OC comprised of substituted monoaromatic compounds and PAHs, coupled with Ca and phosphate, thus suggesting used lubricating oil was associated with many of these particles. These studies show, by number, EC particles dominate gasoline emissions in the ultrafine size range particularlyforthe lowest emitting newer vehicles, suggesting the EC signature alone cannot be used as a unique tracer for diesels. This represents the first report of high time- and size-resolved chemical composition data showing the mixing state of nonrefractory elements in particles such as EC for vehicle emissions during dynamometer source testing.

Source apportionment of fine particles in Washington, DC, utilizing temperature-resolved carbon fractions

Integrated ambient particulate matter < or =2.5 microm in aerodynamic diameter (PM2.5) samples were collected at a centrally located urban monitoring site in Washington, DC, on Wednesdays and Saturdays using Interagency Monitoring of Protected Visual Environments samplers. Particulate carbon was analyzed using the thermal optical reflectance method that divides carbon into four organic carbon fractions, pyrolyzed organic carbon, and three elemental carbon fractions. A total of 35 variables measured in 718 samples collected between August 1988 and December 1997 were analyzed. The data were analyzed using Positive Matrix Factorization and 10 sources were identified: sulfate (SO4(2-))-rich secondary aerosol I (43%), gasoline vehicle (21%), SO4(2-)-rich secondary aerosol II (11%), nitrate-rich secondary aerosol (9%), SO4(2-)-rich secondary aerosol III (6%), incinerator (4%), aged sea salt (2%), airborne soil (2%), diesel emissions (2%), and oil combustion (2%). In contrast to a previous study that included only total organic carbon and elemental carbon fractions, motor vehicles were separated into fractions identified as gasoline vehicle and diesel emissions containing carbon fractions whose abundances were different between the two sources. This study indicates that the temperature-resolved carbon fraction data can be utilized to enhance source apportionment, especially with respect to the separation of diesel emissions from gasoline vehicle sources. Conditional probability functions using surface wind data and deduced source contributions aid in the identifications of local sources.