Characterization of visibility impacts related to fine particulate matter in Canada

Canada has recently established standards for the management of particulate matter (PM) air quality. National networks currently measure PM mass concentrations and chemical speciation. Methods used in the U.S. IMPROVE network are applied to the 1994--2000 Canadian fine PM data to obtain a regional reconstruction of the visibility based on particle composition. Nationally, the greatest light extinction occurs in the Windsor-Quebec City corridor. Variations in the dominant chemical species responsible for the reduction in visibility are presented for regions across the country. In most regions, sulfate and nitrate contribute most greatly to reduced visibility. The visibility implications of achieving the Canada-Wide Standard (CWS) across the country are evaluated, with the greatest improvement in visibility associated with achieving the CWS in southern Ontario. Elsewhere in the country, achieving the CWS will actually result in deteriorating air quality. Improving current estimates of visibility requires higher spatially and temporally resolved measurements of organic and elemental carbon fractions and particulate nitrate.     

Measurement, analysis, and modeling of fine particulate matter in eastern North Carolina

An analysis of fine particulate data in eastern North Carolina was conducted to investigate the impact of the hog industry and its emissions of ammonia into the atmosphere. The fine particulate data are simulated using ISORROPIA, an equilibrium thermodynamic model that simulates the gas and aerosol equilibrium of inorganic atmospheric species. The observational data analyses show that the major constituents of fine particulate matter (PM2.5) are organic carbon, elemental carbon, sulfate, nitrate, and ammonium. The observed PM2.5 concentration is positively correlated with temperature but anticorrelated with wind speed. The correlation between PM2.5 and wind direction at some locations suggests an impact of ammonia emissions from hog facilities on PM2.5 formation. The modeled results are in good agreement with observations, with slightly better agreement at urban sites than at rural sites. The predicted total inorganic particulate matter (PM) concentrations are within 5% of the observed values under conditions with median initial total PM species concentrations, median relative humidity (RH), and median temperature. Ambient conditions with high PM precursor concentrations, low temperature, and high RH appear to favor the formation of secondary PM.     

patterns and trends in levels of suspended particulate matter

The National Air Surveillance Network (NASN) has collected samples of suspended particulate matter since 1957. These data values are graphically summarized by the application of Whittaker-Henderson Type A curve-smoothing formulas to 10 years of data. Fifty-eight urban sites and 20 nonurban sites are studied by this technique, which permits an intuitive grasp of the underlying cyclical patterns as well as long-term trends in nationwide levels of suspended particulate matter. Seasonal patterns are evident for many urban and nonurban sites, although sharp contrasts in seasonal characteristics exist between the two types of sites. Long-term levels tend slightly downward at many urban locations, but the opposite effect is observed at many nonurban sites.     

Trends and sources of particulate matter in the Superstition Wilderness using air trajectory and aerosol cluster analysis

Ambient aerosols adversely affect human health and visibility and impact climate. Identification of sources of particulate matter and its precursors is necessary for developing control strategies. The goal of this research is to utilize long-term speciated particulate matter data and back-trajectory cluster analyses to determine trends and sources of particulate matter in the Superstition Wilderness, a rural area east of Phoenix, Arizona. Twenty-four hour back-trajectories were calculated for every hour of every 24-h particulate matter sample obtained by IMPROVE from 1991 to 2004. Days that included back-trajectories with considerable spatial variance were excluded from further analyses. To minimize uncertainties inherent in single trajectories, all calculated trajectories for each sampling day were averaged to represent the air mass sampled during that day. Cluster analysis of trajectories identified four unique regions, including a region with Phoenix, a region with copper smelters, and one with coal-fired power plants. Yearly averages of sulfate, nitrate, soil, and carbon concentrations were calculated for each region. Statistically significant trends in species concentrations by region and independent of region and differences in concentrations between regions were examined.Sulfate concentrations from the region with smelters were higher than other regions but decreased during the study period. Emissions data from the smelters indicate that much of the sulfate from the region was due to the smelters. The overall 2.2% year⁻¹ decrease in sulfate concentrations at TNM is likely due to decreased emissions from the copper smelters. A 3.6% year⁻¹ increase in nitrate concentrations was driven largely by increasing NO_x concentrations from Phoenix and to a lesser extent the region southwest of the site which includes Tucson and suburban/urban areas between Phoenix and Tucson. Soil concentrations were higher from regions with deserts than the region without desert. This method could not identify trends or source regions of carbonaceous aerosols at this site.     

Identification of weather patterns impacting 24-h average fine particulate matter pollution

Methods are presented to extract intra-seasonal meteorological patterns at three scales to explain 24-h fine particulate matter (PM_2.5) pollution events: evolving large-scale meteorological scenarios, synoptic regimes driving diurnal variability near the surface, and localized meteorological triggers. The methods were applied to understand how winter weather conditions impacted PM_2.5 around the San Francisco Bay Area (SFBA). Analyzing data across 12 winters (November–March) ensured robust characterization of the SFBA conditions. SFBA 24-h PM_2.5 exceedances (35 μg m^?3) required several simultaneous characteristics: a ridge of aloft high pressure moving over SFBA, providing weak surface pressure gradients over Central California; persistent easterly flows through SFBA extending vertically to around the 925-hPa pressure level; orographically channeled winds resulting from stability; enhanced nocturnal cooling under clear-sky conditions providing for enhanced drainage flows off the Central California slopes; and at least two consecutive days of these conditions.     

Correlation of ambient inhalable bioaerosols with particulate matter and ozone: a two-year study

In this study, we have examined the relationships between the concentrations of ambient inhalable airborne fungi and pollen with PM10, PM2.5, ozone, organic carbon, selected trace metals (cadmium, copper, lead, and zinc), temperature, and relative humidity. The database was collected in Cincinnati, Ohio, USA, during two consecutive years. Measurements of all environmental variables were performed at the same site continuously 5 days a week except during winter months. The airborne concentrations of biological and non-biological pollutants ranged as follows: total fungi: 184-16 979 spores m(-3); total pollen: 0-6692 pollen m(-3); PM10: 6.70-65.38 microg m(-3); PM2.5: 5.04-45.02 microg m(-3); and ozone: 2.54-64.17 ppb. Higher levels of total inhalable fungi and particulate matter were found during fall and summer months. In contrast, total pollen concentration showed elevated levels in spring. Peak concentrations of ozone were observed during summer and beginning of fall. Our study concluded that several types of inhalable airborne fungi and pollen, particulate matter, and ozone could be positively correlated as a result of the atmospheric temperature influence.     

Evaluation and comparison of continuous fine particulate matter monitors for measurement of ambient aerosols

To provide a scientific basis for the selection and use of continuous monitors for exposure and/or health effects studies, and for compliance and episode measurements at strategic locations in the State of New Jersey, we evaluated the performance of seven continuous fine particulate matter (PM2.5) monitors in the present study. Gravimetric samplers, as reference methods, were collocated with realtime instruments in both laboratory and field tests. The results of intercomparison of real-time monitors showed that the two nephelometers used in this study correlated extremely well (r2 approximately 0.97), and two tapered element oscillating monitors (TEOM 1400 and TEOM filter dynamics measurement system [FDMS]) correlated well (r2 > 0.85), whereas two beta gauges displayed a weaker correlation (r2 < 0.6). During a summertime controlled (laboratory) evaluation, the measurements made with the gravimetric method correlated well with the 24-hr integrated measurements made with the real-time monitors. The SidePak nephelometer overestimated the particle concentration by a factor of approximately 3.4 compared with the gravimetric method. During a summertime field evaluation, the TEOM FDMS monitor reported approximately 30% higher mass concentration than the Federal Reference Method (FRM); and the difference could be explained by the loss of semi-volatile materials from the FRM sampler. Results also demonstrated that 24-hr average PM2.5 mass concentrations measured by beta gauges and TEOM (50 degrees C) in winter correlated well with the integrated gravimetric method. Seasonal differences were observed in the performance of the TEOM (50 degrees C) monitor in measuring the particle mass attributed to the higher semi-volatile material loss in the winter weather. In applying the realtime particulate matter monitoring data into Air Quality Index (AQI) reporting, the Conroy method and the 8-hr end-hour average method were both found to be suitable.     

Characterization of major chemical components of fine particulate matter in North Carolina

This paper presents measurements of daily sampling of fine particulate matter (PM2.5) and its major chemical components at three urban and one rural locations in North Carolina during 2002. At both urban and rural sites, the major insoluble component of PM2.5 is organic matter, and the major soluble components are sulfate (SO4(2-)), ammonium (NH4(+)), and nitrate (NO3(-)). NH4(+) is neutralized mainly by SO4(2-) rather than by NO3(-), except in winter when SO4(2-) concentration is relatively low, whereas NO3(-) concentration is high. The equivalent ratio of NH4(+) to the sum of SO4(2-) and NO3(-) is < 1, suggesting that SO4(2-) and NO3(-) are not completely neutralized by NH4(+). At both rural and urban sites, SO4(2-) concentration displays a maximum in summer and a minimum in winter, whereas NO3(-) displays an opposite seasonal trend. Mass ratio of NO3(-) to SO4(2-) is consistently < 1 at all sites, suggesting that stationary source emissions may play an important role in PM2.5 formation in those areas. Organic carbon and elemental carbon are well correlated at three urban sites although they are poorly correlated at the agriculture site. Other than the daily samples, hourly samples were measured at one urban site. PM2.5 mass concentrations display a peak in early morning, and a second peak in late afternoon. Back trajectory analysis shows that air masses with lower PM2.5 mass content mainly originate from the marine environment or from a continental environment but with a strong subsidence from the upper troposphere. Air masses with high PM2.5 mass concentrations are largely from continental sources. Our study of fine particulate matter and its chemical composition in North Carolina provides crucial information that may be used to determine the efficacy of the new National Ambient Air Quality Standard (NAAQS) for PM fine. Moreover, the gas-to-particle conversion processes provide improved prediction of long-range transport of pollutants and air quality.     

Nonlinear analysis and prediction of coarse particulate matter concentration in ambient air

This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the applicability of the chaos theory. First, the autocorrelation function and Fourier power spectrum were used to analyze the behavior of the time-series. The dynamics of the time-series was additionally studied through correlation integral analysis and phase space reconstruction. The nonlinear predictions were then obtained using local polynomial approximation based on the reconstructed phase space. The results were then compared with the autoregressive model. The results of nonlinear analysis indicated the presence of chaotic character in the PM10 time-series. It was also observed that the nonlinear local approximation outperforms the autoregressive model, because the observed relative error of prediction for the autoregressive model was greater than the local approximation model. The invariant measures of nonlinear dynamics computed for the predicted time-series using the two models also supported the same findings.     

Source identification of personal exposure to fine particulate matter using organic tracers

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m^?3. Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction.The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations (r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak (r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.     

Analysis of trace elements and ions in ambient fine particulate matter at three elementary schools in Ohio

The results from a chemical characterization study of fine particulate matter (PM2.5) measured at three elementary schools in Central and Southeast Ohio is presented here. PM2.5 aerosol samples were collected from outdoor monitors and indoor samplers at each monitoring location during the period of February 1, 1999, through August 31, 2000. The locations included a rural elementary school in Athens, OH, and two urban schools within Columbus, OH. The trace metal and ionic concentrations in the collected samples were analyzed using an X-ray fluorescence spectrophotometer and ion chromatography unit, respectively. Sulfate ion was found to be the largest component present in the samples at all three of the sites. Other abundant components included nitrate, chloride, ammonium, and sodium ions, as well as calcium, silicon, and iron. The average PM2.5 concentrations showed similar temporal variations among the three sites within the study region. PM2.5 and its major component, sulfate ion, showed strong seasonal variations with maximum concentrations observed during the summer at all three of the sites. The indoor environment was found to be more contaminated during the spring months (March through May) at New Albany (a suburb of Columbus, OH) and East Athens (rural Ohio area). Potential source contribution function analysis showed that particulate matter levels at the monitoring sites were affected by transport from adjoining urban areas and industrial complexes located along the Ohio River Valley. A preliminary outdoor source apportionment using the principal component analysis (PCA) technique was performed. The results from the PCA suggest that the study region was primarily impacted by industrial, fossil fuel combustion, and geological sources. The 2002 emissions inventory data for PM2.5 compiled by Ohio Environmental Protection Agency also showed impacts of similar source types, and this was used to validate the PCA analysis.     

Characterization of fine particulate matter produced by combustion of residual fuel oil

Combustion experiments were carried out on four different residual fuel oils in a 732-kW boiler. PM emission samples were separated aerodynamically by a cyclone into fractions that were nominally less than and greater than 2.5 microns in diameter. However, examination of several of the samples by computer-controlled scanning electron microscopy (CCSEM) revealed that part of the PM2.5 fraction consists of carbonaceous cenospheres and vesicular particles that range up to 10 microns in diameter. X-ray absorption fine structure (XAFS) spectroscopy data were obtained at the S, V, Ni, Fe, Cu, Zn, and As K-edges and at the Pb L-edge. Deconvolution of the X-ray absorption near edge structure (XANES) region of the S spectra established that the dominant molecular forms of S present were sulfate (26-84% of total S) and thiophene (13-39% of total S). Sulfate was greater in the PM2.5 samples than in the PM2.5+ samples. Inorganic sulfides and elemental sulfur were present in lower percentages. The Ni XANES spectra from all of the samples agreed fairly well with that of NiSO4, while most of the V spectra closely resembled that of vanadyl sulfate (VO.SO4.xH2O). The other metals investigated (i.e., Fe, Cu, Zn, and Pb) also were present predominantly as sulfates. Arsenic was present as an arsenate (As+5). X-ray diffraction patterns of the PM2.5 fraction exhibit sharp lines due to sulfate compounds (Zn, V, Ni, Ca, etc.) superimposed on broad peaks due to amorphous carbons. All of the samples contain a significant organic component, with the loss on ignition (LOI) ranging from 64 to 87% for the PM2.5 fraction and from 88 to 97% for the PM2.5+ fraction. Based on 13C nuclear magnetic resonance (NMR) analysis, the carbon is predominantly condensed in graphitic structures. Aliphatic structure was detected in only one of seven samples examined.     

Extending the Kolmogorov-Zurbenko filter: application to ozone, particulate matter, and meteorological trends

Tropospheric ozone (O3) and particulate matter (PM) are pollutants of great concern to air quality managers. Federal standards for these pollutants have been promulgated in recent years because of the known adverse effects of the pollutants on human health, the environment, and visibility. Local meteorological conditions exert a strong influence over day-to-day variations in pollutant concentrations; therefore, the meteorological signal must be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for the future. Although the Kolmogorov-Zurbenko (KZ) filter has been widely used for this type of trend separation in O3 studies in the eastern United States, this article aims to extend the method in three key ways. First, whereas the KZ filter is known as a useful tool for O3 analysis, this study also evaluates its effectiveness when applied to PM. Second, the method was applied to Tucson, AZ, a city in the semi-arid southwestern United States (Southwest), to evaluate the appropriateness of the method in a region with weaker synoptic weather controls on air quality than the eastern United States. Third, additional forms of output were developed and tailored to be more applicable to decision-makers' needs through a partnership between academic researchers and air quality planners and managers. Results of the study indicate that the KZ filter is a useful method for examining emissions-related PM trends, resulting in small, but potentially significant, differences after adjustment. For the Tucson situation with weaker synoptic controls, the KZ method identified mixing height as a more important variable than has been found in other cities.     

Apportionment of ambient primary and secondary fine particulate matter at the Pittsburgh National Energy Laboratory particulate matter characterization site using positive matrix factorization and a potential source contributions function analysis

Fine particulate matter (PM2.5) concentrations associated with 202 24-hr samples collected at the National Energy Technology Laboratory (NETL) particulate matter (PM) characterization site in south Pittsburgh from October 1999 through September 2001 were used to apportion PM2.5 into primary and secondary contributions using Positive Matrix Factorization (PMF2). Input included the concentrations of PM2.5 mass determined with a Federal Reference Method (FRM) sampler, semi-volatile PM2.5 organic material, elemental carbon (EC), and trace element components of PM2.5. A total of 11 factors were identified. The results of potential source contributions function (PSCF) analysis using PMF2 factors and HYSPLIT-calculated back-trajectories were used to identify those factors associated with specific meteorological transport conditions. The 11 factors were identified as being associated with emissions from various specific regions and facilities including crustal material, gasoline combustion, diesel combustion, and three nearby sources high in trace metals. Three sources associated with transport from coal-fired power plants to the southeast, a combination of point sources to the northwest, and a steel mill and associated sources to the west were identified. In addition, two secondary-material-dominated sources were identified, one was associated with secondary products of local emissions and one was dominated by secondary ammonium sulfate transported to the NETL site from the west and southwest. Of these 11 factors, the four largest contributors to PM2.5 were the secondary transported material (dominated by ammonium sulfate) (47%), local secondary material (19%), diesel combustion emissions (10%), and gasoline combustion emissions (8%). The other seven factors accounted for the remaining 16% of the PM2.5 mass. The findings are consistent with the major source of PM2.5 in the Pittsburgh area being dominated by ammonium sulfate from distant transport and so decoupled from local activity emitting organic pollutants in the metropolitan area. In contrast, the major local secondary sources are dominated by organic material.     

Determination of levoglucosan in atmospheric fine particulate matter

A microanalytical method suitable for the quantitative determination of the sugar anhydride levoglucosan in low-volume samples of atmospheric fine particulate matter (PM) has been developed and validated. The method incorporates two sugar anhydrides as quality control standards. The recovery standard sedoheptulosan (2,7-anhydro-beta-D-altro-heptulopyranose) in 20 microL solvent is added onto samples of the atmospheric fine PM and aged for 1 hr before ultrasonic extraction with ethylacetate/ triethylamine. The extract is reduced in volume, an internal standard is added (1,5-anhydro-D-mannitol), and a portion of the extract is derivatized with 10% by volume N-trimethylsilylimidazole. The derivatized extract is analyzed by gas chromatography/mass spectrometry (GC/MS). The recovery of levoglucosan using this procedure was 69 +/- 6% from five filters amended with 2 microg levoglucosan, and the reproducibility of the assay is 9%. The limit of detection is approximately 0.1 microg/mL, which is equivalent to approximately 3.5 ng/m3 for a 10 L/min sampler or approximately 8.7 ng/m3 for a 4 L/min personal sampler (assuming 24-hr integrated samples). We demonstrated that levoglucosan concentrations in collocated samples (expressed as ng/m3) were identical irrespective of whether samples were collected by PM with aerodynamic diameter < or = 2.5 microm or PM with aerodynamic diameter < or = 10 microm impactors. It was also demonstrated that X-ray fluorescence analysis of samples of atmospheric PM, before levoglucosan determinations, did not alter the levels of levoglucosan.     

Feasibility of using low-cost portable particle monitors for measurement of fine and coarse particulate matter in urban ambient air

Exposure to ambient particulate matter (PM) is known as a significant risk factor for mortality and morbidity due to cardiorespiratory causes. Owing to increased interest in assessing personal and community exposures to PM, we evaluated the feasibility of employing a low-cost portable direct-reading instrument for measurement of ambient air PM exposure. A Dylos DC 1700 PM sensor was collocated with a Grimm 11-R in an urban residential area of Houston Texas. The 1-min averages of particle number concentrations for sizes between 0.5 and 2.5 µm (small size) and sizes larger than 2.5 µm (large size) from a DC 1700 were compared with the 1-min averages of PM_2.5 (aerodynamic size less than 2.5 µm) and coarse PM (aerodynamic size between 2.5 and 10 µm) concentrations from a Grimm 11-R. We used a linear regression equation to convert DC 1700 number concentrations to mass concentrations, utilizing measurements from the Grimm 11-R. The estimated average DC 1700 PM_2.5 concentration (13.2 ± 13.7 µg/m³) was similar to the average measured Grimm 11-R PM_2.5 concentration (11.3 ± 15.1 µg/m³). The overall correlation (r²) for PM_2.5 between the DC 1700 and Grimm 11-R was 0.778. The estimated average coarse PM concentration from the DC 1700 (5.6 ± 12.1 µg/m³) was also similar to that measured with the Grimm 11-R (4.8 ± 16.5 µg/m³) with an r² of 0.481. The effects of relative humidity and particle size on the association between the DC 1700 and the Grimm 11-R results were also examined. The calculated PM mass concentrations from the DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.

Implications: The performance of a low-cost particulate matter (PM) sensor was evaluated in an urban residential area. Both PM_2.5 and coarse PM (PM_10-2.5) mass concentrations were estimated using a DC1700 PM sensor. The calculated PM mass concentrations from the number concentrations of DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.     

Future impacts of distributed power generation on ambient ozone and particulate matter concentrations in the San Joaquin Valley of California

Distributed power generation-electricity generation that is produced by many small stationary power generators distributed throughout an urban air basin-has the potential to supply a significant portion of electricity in future years. As a result, distributed generation may lead to increased pollutant emissions within an urban air basin, which could adversely affect air quality. However, the use of combined heating and power with distributed generation may reduce the energy consumption for space heating and air conditioning, resulting in a net decrease of pollutant and greenhouse gas emissions. This work used a systematic approach based on land-use geographical information system data to determine the spatial and temporal distribution of distributed generation emissions in the San Joaquin Valley Air Basin of California and simulated the potential air quality impacts using state-of-the-art three-dimensional computer models. The evaluation of the potential market penetration of distributed generation focuses on the year 2023. In general, the air quality impacts of distributed generation were found to be small due to the restrictive 2007 California Air Resources Board air emission standards applied to all distributed generation units and due to the use of combined heating and power. Results suggest that if distributed generation units were allowed to emit at the current Best Available Control Technology standards (which are less restrictive than the 2007 California Air Resources Board standards), air quality impacts of distributed generation could compromise compliance with the federal 8-hr average ozone standard in the region.     

Nitro-PAH in ambient particulate matter in the atmosphere of Athens

Nitrated polynuclear aromatic hydrocarbons (NPAH) with a molecular mass of 247 Daltons were found in soot collected in downtown Athens during a campaign performed in 1996. In particular, 2-nitrofluoranthene (2-NFa) and 2-nitropyrene (2-NPy), which are mainly related to photo-induced chemical processes occurring in the atmosphere, were more abundant than 1-nitropyrene (1-NPy) usually associated to motor vehicle exhaust.     

Comparison of source apportionments of fine particulate matter at two San Jose speciation trends network sites

In this study, the chemical composition of fine particulate matter samples collected at U.S. Environmental Protection Agency Speciation Trends Network sites in San Jose, CA, from February 2000 to February 2005 were analyzed. A San Jose site was initially established at 4th Street and then subsequently moved to Jackson Street in mid-2002. These sites are approximately 1 km apart. There were no known major changes in the nature of the sources in the area over this period. The study used positive matrix factorization model to extract the source profiles and their mass contributions and to compare the results for the congruence of the source apportionments between these two nearby sites. In the case of the 4th Street site, the average mass was apportioned to wood combustion (32.1 +/- 2.5%), secondary nitrate (22.3 +/- 2%), secondary sulfate (10.7 +/- 0.6%), fresh sea salt (7.7 +/- 0.9%), gasoline vehicles (7.3 +/- 0.5%), aged sea salt (6.8 +/- 0.4%), road dust (6.7 +/- 0.7%), diesel emissions (3.9 +/- 0.3%), and a Ni-related industrial source (2.5 +/- 0.4%). At the Jackson Street site, the average mass was apportioned to wood combustion (33.6 +/- 2.6%), secondary nitrate (20.3 +/- 1.9%), secondary sulfate (13.9 +/- 0.9%), aged sea salt (12.4 +/- 0.7%), gasoline vehicle (8.3 +/- 0.6%), fresh sea salt (5.3 +/- 0.5%), diesel emission (3.2 +/- 0.3%), road dust (1.9 +/- 0.1%), and Ni-related industrial source (1.3 +/- 0.1%). Conditional probability function analysis was used to help identify local sources. These results suggested that moving the sampling site a short distance had little effect on the nature of the resolved source types although some differences in their quantitative impacts were obtained in the positive matrix factorization analyses.