Tracking petroleum refinery emission events using lanthanum and lanthanides as elemental markers for PM2.5

Fine particulate matter levels at four air sampling stations in the Houston, TX area are apportioned to quantify the impact of emissions from a local refinery during a reported emission event. Through quantification of lanthanum and lanthanides using a recently developed analytical technique, the impacts of emissions from fluidized-bed catalytic cracking (FCC) units are quantitatively tracked across the Houston region. The results show a significant (33-106-fold) increase in contributions of FCC emissions to PM2.5 compared with background levels associated with routine operation. This impact from industrial emissions to ambient air quality occurs simultaneously with a larger, regional haze episode that lead to elevated PM2.5 concentrations throughout the entire region. By focusing on detailed chemical analysis of unique maker metals (lanthanum and lanthanides), the impact of emissions from the FCC unit was tracked from the local refinery that reported the emission event to a site approximately 50 km downwind, illustrating the strength of the analytical method to isolate an important source during a regional haze episode not related to the emission event. While this source apportionment technique could separate contributions from FCC emissions, improved time-resolved sampling is proposed to more precisely quantify the impacts of transient emission events on ambient PM2.5.     

Atmospheric outflow of anthropogenic semivolatile organic compounds from East Asia in spring 2004

To estimate the emissions of anthropogenic semivolatile organic compounds (SOCs) from East Asia and to identify unique SOC molecular markers in Asian air masses, high-volume air samples were collected on the island of Okinawa, Japan between 22 March and 2 May 2004. Contributions from different source regions (China, Japan, the Koreas, Russia, and ocean/local) were estimated by use of source region impact factors (SRIFs). Elevated concentrations of hexachlorobenzene (HCB), hexachlorcyclohexanes (HCHs), dichlorodiphenyltrichloroethanes (DDTs), and particulate-phase polycyclic aromatic hydrocarbons (PAHs) were attributed to air masses from China. A large proportion of the variation in the current-use pesticides, gas-phase PAHs, and polychlorinated biphenyl (PCB) concentrations was explained by meteorology. Chlordanes showed a technical mixture profile and similar concentrations regardless of source region. alpha/gamma HCH and trans/cis chlordane ratios did not vary significantly with different source regions and had regional averages of 2.5 +/- 1.0 and 1.2 +/- 0.3, respectively. Particulate-phase PAH concentrations were significantly correlated (p value < 0.05) with other incomplete combustion byproduct concentrations, including elemental mercury (Hg0), CO, NOx, black carbon, submicrometer aerosols, and SO2. By use of measured PAH, CO, and black carbon concentrations and estimated CO and black carbon emission inventories, the emission of six carcinogenic particulate-phase PAHs was estimated to be 1518-4179 metric tons/year for Asia and 778-1728 metric tons/year for China, respectively. These results confirm that East Asian outflow contains significant emissions of carcinogenic particulate-phase PAHs.     

Size-resolved emissions of organic tracers from light- and heavy-duty vehicles measured in a California roadway tunnel

Individual organic compounds found in particulate emissions from vehicles have proven useful in source apportionment of ambient particulate matter. Species of interest include the hopanes, originating in lube oil, and selected PAHs generated via combustion. Most efforts to date have focused on emissions and apportionment PM10 or PM2.5 However, examining how these compounds are segregated by particle size in both emissions and ambient samples will help efforts to apportion size-resolved PM, especially ultrafine particles which have been shown to be more potent toxicologically. To this end, high volume size-resolved (coarse, accumulation, and ultrafine) PM samples were collected inside the Caldecott tunnel in Orinda, California to determine the relative emission factors for these compounds in different size ranges. Sampling occurred in two bores, one off-limits to heavy-duty diesel vehicles, which allows determination of the different emissions profiles for diesel and gasoline vehicles. Although tunnel measurements do not measure emissions over a full engine duty cycle, they do provide an average emissions profile over thousands of vehicles that can be considered characteristic of "freeway" emissions. Results include size-fractionated emission rates for hopanes, PAHs, elemental carbon, and other potential organic markers apportioned to diesel and gasoline vehicles. The results are compared to previously conducted PM2.5 emissions testing using dynamometer facilities and othertunnel environments.     

How does infiltration behavior modify the composition of ambient PM2.5 in indoor spaces? An analysis of RIOPA data

The indoor environment is an important venue for exposure to fine particulate matter (PM2.5) of ambient (outdoor) origin. In this work, paired indoor and outdoor PM2.5 species concentrations from three geographically distinct cities (Houston, TX, Los Angeles County, CA, and Elizabeth, NJ) were analyzed using positive matrix factorization (PMF) and demonstrate that the composition and source contributions of ambient PM2.5 are substantially modified by outdoor-to-indoor transport. Our results suggest that predictions of "indoor PM2.5 of ambient origin" are improved when ambient PM2.5 is treated as a combination of four distinct particle types with differing infiltration behavior (primary combustion, secondary sulfate and organics, secondary nitrate, and mechanically generated PM) rather than as a "single internally mixed entity". Study-wide average infiltration factors (i.e., fraction of ambient PM2.5 found indoors) for Relationship of Indoor, Outdoor, and Personal Air (RIOPA) study homes were 0.51, 0.78, and 0.04 (consistent with P = 0.6, 0.9, and 0.09; k = 0.2, 0.1, and 0.6 h(-1)) for PM2.5 associated with primary combustion, secondary formation (excluding nitrate), and mechanical generation, respectively. Modification of the composition, properties, and source contributions of ambient PM2.5 in indoor environments has important implications for exposure mitigation strategies, development of health hypotheses, and evaluation of exposure error in epidemiological studies that use ambient central-site PM2.5 as a surrogate for PM2.5 exposure.     

Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St. Louis

Community time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. If the day-to-day variations in 24-hour integrated concentrations differ substantially across an urban area (i.e., daily measurements at monitors at different locations are not highly correlated), then there is a significant potential for exposure misclassification in community time-series epidemiology. If the annual average concentration differs across an urban area, then there is a potential for exposure misclassification in epidemiologic studies that use annual averages (or multi-year averages) as an index of exposure across different cities. The spatial variability in PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter), its elemental components, and the contributions from each source category at 10 monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study (RAPS) based on the Regional Air Monitoring System (RAMS) conducted between 1975 and 1977. Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. For example, the contributions from incinerators show a difference of a factor of 12.5 between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and contributions from source categories can be highly heterogeneous within a given airshed and thus, there is the potential for exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures.     

Source contributions to atmospheric gases and particulate matter in the southeastern United States

A new approach for determining the contributions of emission sources to concentrations of particulate matter and gases is developed using the chemical mass balance (CMB) method and the U.S. EPA's National Emission Inventory (NEI). The approach apportions combined gas-phase and condensed-phase concentrations of individual compounds as well as PM(2.5) mass. Because the NEI is used to provide source emission profiles for CMB analysis, the method generates information on the consistency of the NEI with ambient monitoring data. The method also tracks secondary species to primary source emissions, permitting a more complete accounting of the impact of aggregated source types on PM(2.5) mass concentrations. An example application is presented using four years of monitoring data collected at eight sites in the Southeastern Aerosol Research and Characterization (SEARCH) network. Including both primary and secondary species, area sources contributed 2.0-3.7 μg m(-3) (13-26%), point sources contributed 3.0-4.6 μg m(-3) (22-33%), and mobile sources contributed 1.0-6.0 μg m(-3) (9-42%) to mean PM(2.5) mass concentrations. Whereas the NEI generally accounts for the ambient concentrations of gases and particles, certain anomalies are identified, especially related to carbonaceous compounds and dust.     

Quantifying PM2.5 source contributions for the San Joaquin Valley with multivariate receptor models

UNMIX and Positive Matrix Factorization (PMF) solutions to the Chemical Mass Balance (CMB) equations were applied to chemically speciated PM2.5 measurements from 23 sites in California's San Joaquin Valley to estimate source contributions. Six and seven factors were determined by UNMIX for the low_PM2.5 period (February to October) and high_PM2.5 period (November to January), respectively. PMF resolved eightfactors for each period that corresponded with the UNMIX factors in chemical profiles and time series. These factors are attributed to marine sea salt, fugitive dust, agriculture-dairy, cooking, secondary aerosol, motor vehicle, and residential wood combustion (RWC) emissions, with secondary aerosol and RWC accounting for over 70% of PM2.5 mass during the high_PM2.5 period. A zinc factor was only resolved by PMF. The contribution from motor vehicles was between 10 and 25% with higher percentages occurring in summer. The PMF model was further evaluated by examining (1) site-specific residuals between the measured and calculated concentrations, (2) comparability of motor vehicle and RWC factors against source profiles obtained from recent emission tests, (3) edges in bi-plots of key indicator species, and (4) spatiotemporal variations of the factors' strengths. These evaluations support the compliance with model assumptions and give a higher confidence level to source apportionment results for the high_PM2.5 period.     

Diagnosis of aged prescribed burning plumes impacting an urban area

An unanticipated wind shift led to the advection of plumes from two prescribed burning sites that impacted Atlanta, GA, producing a heavy smoke event late in the afternoon on February 28, 2007. Observed PM2.5 concentrations increased to over 140 microg/m3 and O3 concentrations up to 30 ppb in a couple of hours, despite the late hour in February when photochemistry is less vigorous. A detailed investigation of PM2.5 chemical composition and source apportionment analysis showed that the increase in PM2.5 mass was driven mainly by organic carbon (OC). However, both results from source apportionment and an observed nonlinear relationship between OC and PM2.5 potassium (K) indicate that the increased OC was not due solely to primary emissions. Most of the OC was water-soluble organic carbon (WSOC) and was dominated by hydrophobic compounds. The data are consistent with large enhancements in isoprenoid (isoprene and monoterpenes) and other volatile organic compounds emitted from prescribed burning that led to both significant O3 and secondary organic aerosol (SOA) production. Formation of oligomers from oxidation products of isoprenoid compounds or condensation of volatile organic compounds (VOCs) with multiple functional groups emitted during prescribed burning appears to be a major component of the secondary organic contributor of the SOA. The results from this study imply that enhanced emissions due to the fire itself and elevated temperature in the burning region should be considered in air quality models (e.g., receptor and emission-based models) to assess impacts of prescribed burning emissions on ambient air quality.     

Source apportionment of polycyclic aromatic hydrocarbons in the urban atmosphere: a comparison of three methods

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants in urban atmospheres. Several PAHs are known carcinogens or are the precursors to carcinogenic daughter compounds. Understanding the contributions of the various emission sources is critical to appropriately managing PAH levels in the environment. The sources of PAHs to ambient air in Baltimore, MD, were determined by using three source apportionment methods, principal component analysis with multiple linear regression, UNMIX, and positive matrix factorization. Determining the source apportionment through multiple techniques mitigates weaknesses in individual methods and strengthens the overlapping conclusions. Overall source contributions compare well among methods. Vehicles, both diesel and gasoline, contribute on average 16-26%, coal 28-36%, oil 15-23%, and wood/other having the greatest disparity of 23-35% of the total (gas- plus particle-phase) PAHs. Seasonal trends were found for both coal and oil. Coal was the dominate PAH source during the summer while oil dominated during the winter. Positive matrix factorization was the only method to segregate diesel from gasoline sources. These methods indicate the number and relative strength of PAH sources to the ambient urban atmosphere. As with all source apportionment techniques, these methods require the user to objectively interpret the resulting source profiles.     

Determination of methoxyphenols in ambient atmospheric particulate matter: tracers for wood combustion

Combustion of wood and other biomass fuels produces source-specific organic compounds arising from pyrolysis of lignin, including substantial amounts of 4-substituted methoxylated phenolic compounds (methoxyphenols). These compounds have been used as atmospheric markers to determine the contribution of wood smoke to ambient atmospheric fine particulate matter (PM). However, reliable quantification of methoxyphenols represents an analytical challenge because these compounds are polar, semi-volatile, and somewhat reactive. We reportherein an improved gas chromatographic-mass spectrometric (GC/MS) method for the sensitive and reliable determination of methoxyphenols in low-volume ambient PM samples. Deuterated standard compounds are added to the environmental samples prior to extraction to determine analyte recoveries in each sample. Analytical figures of merit for the assay, as applied to ambient PM2.5 and PM10 samples are as follows: recovery = 63-100%; precision = 2-6%; analytical limit of detection (S/N 2) = 0.002 microg/mL; limit of quantitation = 0.07-0.45 ng/m3 (assuming a 14 m3 sample). The improved method was applied to ambient PM samples collected between 1999 and 2000 in Seattle, WA. Particle-bound methoxyphenol concentrations in the range <0.1 to 22 ng/m3 were observed and the methoxyphenols were present almost exclusively in the fine (PM2.5) size fraction. We also demonstrated that XRF analysis of samples of atmospheric PM collected on Teflon filters significantly reduced the levels of methoxyphenols measured in the PM samples in subsequent assay of the same filters. Therefore, XRF analysis of filters, commonly undertaken to obtain trace element concentrations for use in source apportionment analyses, would preclude the subsequent analysis of those filters for methoxyphenols and other similarly semivolatile or reactive organic chemicals.     

Iron solubility related to particle sulfur content in source emission and ambient fine particles

The chemical factors influencing iron solubility (soluble iron/total iron) were investigated in source emission (e.g., biomass burning, coal fly ash, mineral dust, and mobile exhaust) and ambient (Atlanta, GA) fine particles (PM2.5). Chemical properties (speciation and mixing state) of iron-containing particles were characterized using X-ray absorption near edge structure (XANES) spectroscopy and micro-X-ray fluorescence measurements. Bulk iron solubility (soluble iron/total iron) of the samples was quantified by leaching experiments. Major differences were observed in iron solubility in source emission samples, ranging from low solubility (<1%, mineral dust and coal fly ash) up to 75% (mobile exhaust and biomass burning emissions). Differences in iron solubility did not correspond to silicon content or Fe(II) content. However, source emission and ambient samples with high iron solubility corresponded to the sulfur content observed in single particles. A similar correspondence between bulk iron solubility and bulk sulfate content in a series of Atlanta PM2.5 fine particle samples (N = 358) further supported this trend. In addition, results of linear combination fitting experiments show the presence of iron sulfates in several high iron solubility source emission and ambient PM2.5 samples. These results suggest that the sulfate content (related to the presence of iron sulfates and/or acid-processing mechanisms by H(2)SO(4)) of iron-containing particles is an important proxy for iron solubility.     

Particulate and trace gas emissions from open burning of wheat straw and corn stover in China

Field measurements were conducted to determine particulate emissions and trace gas emissions, including CO2, CO, CH4, NMHCs, NOx, NH3, N2O, and SO2, from open burning of wheat straw and maize stover, two major agricultural residues in China. The headfire ignition technique was adopted, and sampling was performed downwind from the agricultural fire. Particulate matter (PM) and gas emission factors were determined using the carbon mass-balance method. Particle mass size distributions show a prominent accumulation mode peak at 0.26-0.38 microm. Submicron particles dominate PM emissions. Most measured chemical species measured show a similar size distribution as PM. Chemical composition analysis indicates that PM2.5 is largely composed of carbon, K, and Cl. PM2.5 emission factors of wheat straw and maize stover are 7.6 +/- 4.1 g/kg and 11.7 +/- 1.0 g/kg, respectively, It also indicates that 12.1-24.2% of N in biomass is released as nitrogen-based trace gases and 11.0-24.9% of fuel S is emitted as SO2.     

Lubricating oil and fuel contributions to particulate matter emissions from light-duty gasoline and heavy-duty diesel vehicles

Size-resolved particulate matter emissions from heavy-duty diesel vehicles (HDDVs) and light-duty gasoline vehicles (LDGVs) operated under realistic driving cycles were analyzed for elemental carbon (EC), organic carbon (OC), hopanes, steranes, and polycyclic aromatic hydrocarbons. Measured hopane and sterane size distributions did not match the total carbon size distribution in most cases, suggesting that lubricating oil was not the dominant source of particulate carbon in the vehicle exhaust. A regression analysis using 17alpha(H)-21beta(H)-29-norhopane as a tracer for lubricating oil and benzo[ghi/perylene as a tracer for gasoline showed that gasoline fuel and lubricating oil both make significant contributions to particulate EC and OC emissions from LDGVs. A similar regression analysis performed using 17alpha(H)-21beta(H)-29-norhopane as a tracer for lubricating oil and flouranthene as a tracerfor diesel fuel was able to explain the size distribution of particulate EC and OC emissions from HDDVs. The analysis showed that EC emitted from all HDDVs operated under relatively high load conditions was dominated by diesel fuel contributions with little EC attributed to lubricating oil. Particulate OC emitted from HDDVs was more evenly apportioned between fuel and oil contributions. EC emitted from LDGVs operated underfuel-rich conditions was dominated by gasoline fuel contributions. OC emitted from visibly smoking LDGVs was mostly associated with lubricating oil, but OC emitted from all other categories of LDGVs was dominated by gasoline fuel. The current study clearly illustrates that fuel and lubricating oil make separate and distinct contributions to particulate matter emissions from motor vehicles. These particles should be tracked separately during ambient source apportionment studies since the atmospheric evolution and ultimate health effects of these particles may be different. The source profiles for fuel and lubricating oil contributions to EC and OC emissions derived in this study provide a foundation for future source apportionment calculations.     

Characterization of saccharides in size-fractionated ambient particulate matter and aerosol sources: the contribution of primary biological aerosol particles (PBAPs) and soil to ambient particulate matter

Size-fractionated (equivalent to ambient PM2.5 and PM10) local soil, plant, and spore samples were collected in the Sonoran Desert near Phoenix, AZ and measured for saccharide content with the goal of characterizing ambient particulate matter sources including soil and primary biological aerosol particles (PBAPs) from plants and fungi. Different saccharide compositions were observed among soil, plant, and spore samples and between PM2.5 and PM10 fractions. The total measured nonlevoglucosan saccharide content relative to PM mass in ambient aerosols collected in a Phoenix suburb (Higley) was much higher compared to the local soil samples but much lower compared to the PBAP. The enrichment of saccharides from two saccharide-dominated PM source factors resolved by a positive matrix factorization model is also higher than the saccharide content in the size-fractionated local soil samples, but lower than that measured in the size-segregated PBAP samples. This indicates that ambient concentration of particulate saccharides at Higley was dominated by contributions from PBAPs directly injected into the atmosphere from plants and spores rather than from soil and associated biota. Our results also suggest the contribution to the fine size fraction of ambient PM from the primary biologically derived sources may be greater than previously acknowledged.     

n-alkane profiles of engine lubricating oil and particulate matter by molecular sieve extraction

As part of the Canadian Atmospheric Fine Particle Research Program to obtain reliable primary source emission profiles, a molecular sieve method was developed to reliably determine n-alkanes in lubricating oils, vehicle emissions, and mobile source dominated ambient particulate matter (PM). This work was also initiated to better calculate carbon preference index values (CPI: the ratio of the sums of odd over even n-alkanes), a parameter for estimating anthropogenic versus biogenic contributions in PM. n-Alkanes in lubricating oil and mobile source dominated PM are difficult to identify and quantify by gas chromatography due to the presence of similar components that cannot be fully resolved. This results in a hump, the unresolved complex mixture (UCM) that leads to incorrect n-alkane concentrations and CPI values. The sieve method yielded better chromatography, unambiguous identification of n-alkanes and allowed examination of differences between n-alkane profiles in light (LDV) and heavy duty vehicle (HDV) lubricating oils that would have been otherwise difficult. These profile differences made it possible to relate the LDV profile to that of the PM samples collected during a tunnel study in August 2001 near Vancouver (British Columbia, Canada). The n-alkane PM data revealed that longer sampling times result in a negative artifact, i.e., the desorption of the more volatile n-alkanes from the filters. Furthermore, the sieve procedure yielded n-alkane data that allowed calculation of accurate CPI values for lubricating oils and PM samples. Finally, this method may prove helpful in estimating the respective diesel and gasoline contributions to ambient PM.     

Determination of the sources of indoor PM2.5 in Amsterdam and Helsinki

Daily PM2.5 samples were repeatedly collected (1-8 times) in the homes of elderly nonsmoking individuals with coronary heart disease in Amsterdam, The Netherlands (33 individuals) and Helsinki, Finland (44 individuals). Sources of indoor PM2.5 were evaluated using a two-way multilinear engine model. Because the indoor elemental data lacked a traffic marker, separation of traffic related PM was attempted by combining the indoor data with fixed site outdoor data that also contained NO. Six outdoor sources, including long-range transport (LRT), urban mixture, oil combustion, traffic, sea-salt, and soil were identified, and three indoor sources were resolved: resuspension, potassium-rich and copper-rich sources. The average contribution of the indoor factors was 6% (1.1 microg m(-3)) and 22% (2.4 microg m(-3)) in Amsterdam and Helsinki, respectively. The highest longitudinal correlations between source-specific outdoor and indoor PM2.5 concentrations were found for LRT and urban mixture; the median R was above 0.6 for most sources. The longitudinal correlations were lower in Helsinki than in Amsterdam. Indoor-generated PM2.5 was not related to ambient concentrations. We conclude that using outdoor and indoor data together improved the source apportionment of indoor PM2.5. The results support the use of fixed site outdoor measurements in epidemiological time-series studies on outdoor air pollution.     

Identification of particulate matter sources on an hourly time-scale in a wood burning community

Particulate matter (PM) sources at two different sites in a rural town in New Zealand were investigated on an hourly time-scale. Streaker samplers were used to collect hourly, size-segregated PM(10-2.5) and PM(2.5) samples that were analyzed for elemental content using ion beam analysis techniques. Black carbon concentrations were determined using light reflection and PM(10) concentrations were recorded using colocated continuous PM monitors. PM(10) concentrations at both sites displayed a diurnal pattern, with hourly PM(10) concentration maxima in the evening (7 pm-midnight) and in the morning (7-9 am). One of the monitoring sites experienced consistently higher average PM(10) concentrations during every hour and analysis indicated that katabatic flows across the urban area contributed to the increased concentrations observed. Source apportionment using positive matrix factorization on the hourly data revealed four primary PM(10) sources for each site: biomass burning, motor vehicles, marine aerosol and crustal matter. Biomass burning was the most dominant source at both sites and was responsible for both the evening and morning PM(10) concentration peaks. The use of elemental speciation combined with PM(10) concentrations for source apportionment on an hourly time-scale has never been reported and provides unique and useful information on PM sources for air quality management.     

Source apportionment of molecular markers and organic aerosol--1. Polycyclic aromatic hydrocarbons and methodology for data visualization

Individual organic compounds often referred to as molecular markers are used in conjunction with the chemical mass balance (CMB) model to apportion sources of primary organic aerosol. This paper presents a methodology to visualize molecular marker data; it allows comparison of ambient data and source profiles and allows assessment of chemical stability and aging. The method is intended to complement traditional quantitative source apportionment analysis. The core of the technique involves construction of plots of ratios of species concentrations (ratio-ratio plots) in which source profiles appear as points connected by linear mixing lines. The approach is illustrated using data collected over a 1-year period in Pittsburgh, Pennsylvania. The analysis considers for elemental carbon and a number of high molecular weight polycyclic aromatic hydrocarbons (PAHs) commonly used as molecular markers in CMB: benzo(b+j+k)fluoranthene, benzo(e)pyrene, benzo[g,h,i]perylene, coronene, and indeno(1,2,3-cd)pyrene. In Pittsburgh, the ambient concentrations of these PAHs are higher than in other cities in the United States; they are also strongly correlated consistent with a single, dominant source. Both ratio-ratio plots and CMB analysis indicate that this source is metallurgical coke production. Although emissions from coke production dominate ambient PAH concentrations, on most study days they contributed little fine particle mass. Ratio-ratio plots are then used to investigate the feasibility of using PAHs to help differentiate between gasoline and diesel vehicle emissions. Ambient concentrations of these large PAHs provide little information on the gasoline-diesel split because of the strong influence of local emissions from coke production combined with evidence of photochemical decay of PAHs in the regional air mass. Decay of PAHs will bias estimates of the gasoline-diesel split toward diesel emissions.     

Fine particle emissions from on-road vehicles in the Zhujiang Tunnel, China

Little is known about the characteristics of particulate matter emissions from vehicles in China, although such information is critical in source apportionment modeling, emission inventories, and health effect studies. In this paper, we report a comprehensive characterization of PM2.5 emissions in the Zhujiang Tunnel in the Pearl River Delta region of China. The chemical speciation included elemental carbon, organic carbon, inorganic ions, trace elements, and organic compounds. The emission factors of individual species and their relative distributions were obtained for a mixed fleet of heavy-duty vehicles (19.8%) and light-duty vehicles (80.2%). In addition, separate emission factors of PM2.5 mass, elemental carbon, and organic matter for heavy-duty vehicles and light-duty vehicles also were derived. As compared to the results of other tunnel studies previously conducted, we found that the abundances and distributions of the trace elements in PM2.5 emissions were more varied. In contrast, the characteristics of the trace organic compounds in the PM2.5 emissions in our study were consistent with characteristics found in other tunnel studies and dynamometer tests. Our results suggested that vehicular PM2.5 emissions of organic compounds are less influenced by the geographic area and fleet composition and thereby are more suitable for use in aerosol source apportionment modeling implemented across extensive regions.     

Source apportionment of molecular markers and organic aerosol. 3. Food cooking emissions

The chemical mass balance model is applied to a large dataset of organic molecular marker concentrations to apportion ambient organic aerosol to food cooking emissions in Pittsburgh, Pennsylvania. Ambient concentrations of key cooking markers such as palmitoleic acid, oleic acid, and cholesterol are well correlated, which implies the existence of well-defined source profiles. However, significant inconsistencies exist between the ambient data and published source profiles. Most notably, the ambient ratio of palmitoleic-acid-to-oleic-acid is more than a factor of 10 greater than essentially all published source profiles. This problem is not unique to Pittsburgh. The reason for this discrepancy is not known but it means that both acids cannot be fit simultaneously by CMB. CMB analysis is performed using three different combinations of food cooking source profiles and molecular markers. Although all three solutions have high statistical quality, the amount of OC apportioned to food cooking emissions varies by a factor of 9. Differences in fitting species and source profile marker-to-organic-carbon ratios cause most of the large systematic biases between the different solutions. The best CMB model includes two alkanoic acids as fitting species in addition to other cooking markers, which helps constrain the source contribution estimates. It also includes two meat cooking source profiles to account for the variability in the ambient data. This model apportions 320+/-140 ng-C m(-3) or 10% of the study average ambient organic carbon to food cooking emissions. Although these results illustrate the significant challenges created by source profile variability, the strong correlations in the ambient dataset underscore the significant promise that molecular markers hold for source apportionment analysis.