Measurement of Both Nonvolatile and Semi-Volatile Fractions of Fine Particulate Matter in Fresno,CA

An intensive sampling campaign was performed in Fresno, CA during December 2003 measuring fine particulate matter including both the semi-volatile and nonvolatile fractions of the aerosol. Both the newly developed R&P FDMS Monitor and a PC-BOSS have been shown to measure total PM _2.5 concentrations including semi-volatile nitrate and organic material. Good agreement was observed between the PC-BOSS and the R&P FDMS Monitor in this study with linear regression analysis resulting in a zero-intercept slope of 1.00 ± 0.02 and an R ² = 0.93. Several real-time measuring systems including the R&P Differential TEOM, the Met One BAMS, and a GRIMM Monitor were also employed and comparisons of total PM _2.5 mass were made with the R&P FDMS Monitor. Agreement among these various monitors was generally good. However, differences were sometimes seen. Reasons for observed differences in the real-time mass measurement systems are explained by the composition and complexity of the measured aerosol, most importantly the composition of semi-volatile material. A newly automated ion chromatographic system developed by Dionex was also field tested and compared to both R&P 8400N Nitrate and integrated PC-BOSS inorganic species measurements. Sulfate and nitrate determined by the Dionex and PC-BOSS systems agreed. However, nitrate measured by the 8400N was low during fog events compared to the other two systems.     

One– and Three–Hour PM2.5 Characterization,Speciation, and Source Apportionment Using Continuous and Integrated Samplers

Ammonium nitrate and semivolatile organic compounds (SVOC) are significant components of fine particles in many urban atmospheres. These components, however, are not properly measured by current EPA accepted methods, such as the R&P TEOM monitor, due to loss of semivolatile material (SVM) from particles in the heated environment of the filter during sampling. The accurate determination of semivolatile material is important due to the possible effects of these species on human health, visibility, and global climate change. The concentration and composition of fine particulate material were determined using a combination of continuous and integrated samplers at the Brigham Young University–EPA Environmental Monitoring for Public Access and Community Tracking (BYU–EPA EMPACT) monitoring site in Salt Lake City, Utah over a six–day sampling period (30 January to 4 February) during the winter of 2001. Continuous samples were collected using a RAMS (total PM_2.5 mass), a TEOM monitor (nonvolatile PM_2.5 mass), an Aethalometer (elemental carbon), a TSI CPC (particle count), and a Nephelometer (light scattering by particles, b_sp). Fine particle composition and mass were determined on a three–hour basis using the PC–BOSS diffusion denuder sampler. Total PM_2.5 mass–determined with the RAMS agreed with constructed mass determined from the chemical composition measured in collocated PC–BOSS–integrated samples. Results from this study indicate that semivolatile material (ammonium nitrate and semivolatile organic compounds) is a significant component of fine particle mass. Semivolatile organic compounds were the major contributor to light scattering during the six–day sampling period. Semivolatile nitrate, but not organic material, was suggested to be hygroscopic by the nephelometric data. The majority of the SVM observed appeared to be secondary material formed from photochemical reactions of the organic and NO_x emissions from mobile sources and wood smoke combustion.     

Apportionment of Ambient Primary and Secondary PM2.5 During a 2001 Summer Intensive Study at the NETL Pittsburgh Site Using PMF2 and EPA UNMIX

Apportionment of primary and secondary pollutants during a July 2001 intensive study at the National Energy Technology Laboratory is reported. PM_2.5 was apportioned into primary and secondary contributions using PMF2, and results were compared with apportionment based on UNMIX 2.3. Input to PMF2 included PM_2.5 mass data from four per 24 hour PC-BOSS filters and TEOM, NO_x, NO₂, O₃, non-volatile, semi-volatile, and volatile organic material, elemental carbon, sulfate, and PIXE determined trace metals. Nine factors were identified in the PMF analysis. Six factors were associated with primary particles from crustal, mobile (gasoline and diesel), and three local sources high in trace metals. Three factors were associated with secondary sources. Two were associated with local emissions dominated by organic material, one was dominated by transported ammonium sulfate. UNMIX was able to identify the two major mobile sources, major local secondary source and transported secondary source. The three major sources of PM_2.5 were identified as secondary transported material (dominated by ammonium sulfate) from west and southwest (46%), secondary material formed during mid-day photochemical processes (21%), and primary emissions from diesel (10%) and gasoline (8%) mobile sources. The other five sources accounted for the remaining 15% of the PM_2.5. These findings are consistent with the majority of secondary ammonium sulfate in the Pittsburgh area resulting from distant transport, and so decoupled from local activity involving organic pollutants in the metropolitan area. In contrast, the major local secondary sources were dominated by organic material.     

Twenty Four-Hour PC-BOSS Air-Monitoring Results from the NETL Fine-Particulate Sampling Site in Pittsburgh,Pennsylvania: An Annual Perspective

The concentration and composition of PM _2.5 from May to September of 2000 and monthly trends in ambient fine-particulate material concentrations from October 1999 through December 2000 at the National Energy Technology Laboratory's airmonitoring site in Pittsburgh are reported. Twenty four-hour integrated samples were collected using the Particle Concentrator-Brigham Young University Organic Sampling System (PC-BOSS), a multichannel integrated diffusion denuder sampler designed for routine determination of the chemical composition of ambient particulate matter. The fine-particulate pollutants determined were sulfate estimated as ammonium sulfate, nonvolatile organic material, semivolatile organic material lost from particles during sampling, elemental carbon, nitrate estimated as ammonium nitrate, including ammonium nitrate lost from particles during sampling and elemental content determined by PIXE (for a limited number of samples). Episodes with elevated sulfate and organic material (both semivolatile and nonvolatile) concentrations were seen throughout this period. For the purpose of this discussion, an episode was defined as all times when 3 h average TEOM monitor PM _2.5 concentrations exceeded 30 μg/m³. The use of estimated back-trajectories indicated that during the periods for which these elevated concentrations were observed, pollutants were transported predominantly from the Southwest from the Ohio River Valley to the sampling site. For days when fine particulate episodes occurred, back-trajectory computations were derived for time intervals for which PM _2.5 TEOM concentrations exceeded 30 μg/m³. However, for nonepisode days, back trajectories were computed over a 24 h period. Average PC-BOSS–constructed PM _2.5 concentration (including semivolatile components lost from particles during sampling) for the period from October 1999 through December 2000 was 19 μg/m ³ , excluding crustal material concentration.     

Source Investigation for Ambient PM 2.5 in Indianapolis,IN

The objectives of this study were to identify the sources of the PM _2.5 in Indianapolis, Indiana and estimate their contributions to the total PM _2.5 mass concentrations by analyzing the data from the samples collected at the EPA Speciation Trends Networks (STN) site in Indianapolis, Indiana. Both positive matrix factorization (PMF2) and an expanded factor analysis model were applied. The two methods obtained essentially identical source profiles and contributions, so the results of the simpler method, PMF, are described in the formal text of this paper in detail while the corresponding results provided by the expanded factor analysis model are presented in the supplemental material for this paper. The seven resolved sources are secondary sulfate (40.2%), secondary nitrate (21.9%), gasoline emission (16.6%), diesel emission (7.9%), airborne soil (5.3%), Fe-related industries (4.4%), and Cu-related industries (2.5%). The comparison between two models suggests that PMF coupled with subsequent data analysis methods (such as CPF, PSCF, seasonal variation analysis, and weekday/weekend variation analysis) yields the results that are comparable to those of the expanded factor analysis. The results suggest that such studies of STN data can be used to assist in the development of State Implementation Plans (SIPs) for PM _2.5 .     

An Intercomparison of Measurement Methods for Carbonaceous Aerosol in the Ambient Air in New York City

Measurement methods for fine carbonaceous aerosol were compared under field sampling conditions in Flushing, New York during the period of January and early February 2004. In-situ 5- to 60-minute average PM _2.5 organic carbon (OC), elemental carbon (EC), and black carbon (BC) concentrations were obtained by the following methods: Sunset Laboratory field OC/EC analyzer, Rupprecht and Patashnick (R&P) series 5400 ambient carbon particulate monitor, Aerodyne aerosol mass spectrometer (AMS) for total organic matter (OM), and a two-wavelength AE-20 Aethalometer. Twenty-four hour averaged PM _2.5 filter measurements for OC and EC were also made with a Speciation Trends Network (STN) sampler. The diurnal variations in OC/EC/BC concentrations peaked during the morning and afternoon rush hours indicating the dominant influence of vehicle emissions. BC/EC slopes are found to range between 0.86 and 1.23 with reasonably high correlations (r > 0.75). Low mixing heights and absence of significant transported carbonaceous aerosol are indicated by the measurements. Strong correlations are observed between BC and thermal EC as measured by the Sunset instrument and between Sunset BC and Aethalometer BC. Reasonable correlations are observed among collocated OC/EC measurements by the various instruments.     

Major Source Categories for PM2.5 in Pittsburgh using PMF and UNMIX

An objective of the Pittsburgh Air Quality Study was to determine the major sources of PM_2.5 in the Pittsburgh region. Daily 24-hour averaged filter-based data were collected for 13 months, starting in July 2001, including sulfate and nitrate data from IC analysis, trace element data from ICP-MS analysis, and organic and elemental carbon from the thermal optical transmittance (TOT) method and the NIOSH thermal evolution protocol. These data were used in two source-receptor models, Unmix and PMF. Unmix, which is limited to a maximum number of seven factors, resolved six source factors, including crustal material, a regional transport factor, secondary nitrate, an iron, zinc and manganese factor, specialty steel production and processing, and cadmium. PMF, which has no limit to the number of factors, apportioned the PM_2.5 mass into ten factors, including crustal material, secondary sulfate, primary OC and EC, secondary nitrate, an iron, zinc and manganese factor, specialty steel production and processing, cadmium, selenium, lead, and a gallium-rich factor. The Unmix and PMF common factors agree reasonably well, both in composition and contributions to PM_2.5. To further identify and apportion the sources of PM_2.5, specific OC compounds that are known markers of some sources were added to the PMF analysis. The results were similar to the original solution, except that the primary OC and EC factor split into two factors. One factor was associated with vehicles as identified by the hopanes, PAH's, and other OC compounds. The other factor had strong correlations with the OC and EC ambient data as well as wood smoke markers such as levoglucosan, syringols, and resin acids.     

Chemical Mass Closure and Chemical Characteristics of Ambient Ultrafine Particles and other PM Fractions

Ambient ultrafine particles (UPs or PM _0.1 ), PM _2.5 and PM ₁₀ were investigated at the roadside of Syuefu road in Hsinchu city and in the Syueshan highway tunnel in Taipei, Taiwan. A SMPS (TSI Model 3936), three Dichotomous samplers (Andersen Model SA-241), and three MOUDIs (MSP Model 110) were collocated to determine the PM number and mass concentrations simultaneously. The filter samples were further analyzed for organic carbon (OC), element carbon (EC), water-soluble ions, and trace elements. The OC artifact was studied and quantified using the quartz behind quartz (QBQ) method for all PM fractions. Taking into account the OC artifact, chemical mass closure (ratio of the reconstructed chemical mass to the gravimetrical mass) of PM _0.1 , PM _2.5 , and PM ₁₀ was then calculated and found to be good. The chemical analysis results of UPs at both sites showed that UPs in the present tunnel was mostly contributed from the vehicle emissions while UPs at the roadside was mainly influenced by urban sources.     

PM1.0 and PM2.5 Characteristics in the Roadside Environment of Hong Kong

Daily mass concentrations of PM _1.0 (particles less than 1.0 μm in diameter), PM _2.5 (particles less than 2.5 μm in diameter), organic carbon (OC), and elemental carbon (EC) were measured from January through May 2004 at a heavily trafficked sampling site in Hong Kong (PU). The average concentrations for PM _1.0 and PM _2.5 were 35.9 ± 12.4 μ g cm ^{? 3} and 52.3 ± 18.3 μ g cm ^{? 3} . Carbonaceous aerosols were the dominant species in fine particles, accounting for ～ 45.7% of PM _1.0 and ～ 44.4% of PM _2.5 . During the study period, seven fine-particle episodes occurred, due to the influence of long-range transport of air masses from mainland China. PM _1.0 and PM _2.5 responded in similar ways; i.e., with elevated mass and OC concentrations in those episode days. During the sampling period, PM _1.0 OC and EC generally behaved similarly to the carbonaceous aerosols in PM _2.5 , regardless of seasonal variations and influence by regional pollutions. The low and relatively constant OC/EC ratios in PM _1.0 and PM _2.5 indicated that vehicular emissions were major sources of carbonaceous aerosols. PM _1.0 and PM _2.5 had the same dominant sources of vehicular emissions in winter, while in spring PM _2.5 was more influenced by PM _{1 ? 2.5} (particles 1–2.5 μ m in diameter) that did not form from vehicle exhausts. Therefore, PM _1.0 was a better indicator for vehicular emissions at the Roadside Station.     

PM2.5 and PM10 Mass Measurements in California's San Joaquin Valley

PM _2.5 and PM ₁₀ mass measurements from different sampling systems and locations within California's San Joaquin Valley (SJV) are compared to determine how well mass concentrations from a unified data set can be used to address issues such as compliance with particulate matter (PM) standards, temporal and spatial variations, and model predictions. Pairwise comparisons were conducted among 20 samplers, including four Federal Reference Method (FRM) units, battery-powered MiniVols, sequential filter samplers, dichotomous samplers, Micro-Orifice Uniform Deposit Impactors (MOUDIs), beta attenuation monitors (BAMs), tapered element oscillating microbalances (TEOMs), and nephelometers. The differences between FRM samplers were less than 10 and 20% for 70 and 92% of the pairwise comparisons, respectively. The TEOM, operating at 50°C in this study, measured less than the other samplers, consistent with other comparisons in nitrate-rich atmospheres. PM _2.5 mass measured continuously with the BAM was highly correlated with filter-based PM _2.5 although the absolute bias was greater than 20% in 45% of the cases. Light scattering (B _sp ) was also highly correlated with filter-based PM _2.5 at most sites, with mass scattering efficiencies varying by 10 and 20% for B _sp measured with Radiance Research nephelometers with and without PM _2.5 size-selective inlets, respectively. Collocating continuous monitors with filter samplers was shown to be useful for evaluating short-term variability and identifying outliers in the filter-based measurements. Comparability among different PM samplers used in CRPAQS is sufficient to evaluate spatial gradients larger than about 15% when the data are pooled together for spatial and temporal analysis and comparison with models.     

Chemical Characteristics of Fine Particles (PM1) from Xi'an,China

Daily mass concentrations of water-soluble inorganic (WS-i) ions, organic carbon (OC), and elemental carbon (EC) were determined for fine particulate matter (PM₁, particles < 1.0 μm in diameter) collected at Xi'an, China. The annual mean PM₁ mass concentration was 127.3 ± 62.1 μg m^–3: WS-i ions accounted for ～38% of the PM₁ mass; carbonaceous aerosol was ～30%; and an unidentified fraction, probably mostly mineral dust, was ～32%. WS-i ions and carbonaceous aerosol were the dominant species in winter and autumn, whereas the unidentified fraction had stronger influences in spring and summer. Ion balance calculations indicate that PM₁ was more acidic than PM_2.5 from the same site. PM₁ mass, sulfate and nitrate concentrations followed the order winter > spring > autumn > summer, but OC and EC levels were higher in autumn than spring. Annual mean OC and EC concentrations were 21.0 ± 12.0 μg m^?3 and 5.1 ± 2.7 μg m^–3 with high OC/EC ratios, presumably reflecting emissions from coal combustion and biomass burning. Secondary organic carbon, estimated from the minimum OC/EC ratios, comprised 28.9% of the OC. Positive matrix factorization (PMF) analysis indicates that secondary aerosol and combustion emissions were the major sources for PM₁.     

The 2003/2004 Libby,Montana PM2.5 Source Apportionment Research Study

Except for areas in California, Libby, Montana is the only designated EPA nonattainment area for fine particulate matter (PM _2.5 ) in the mid and western states. During the winter of 2003/2004, PM _2.5 speciated data (mass, elements, ions, organic/elemental carbon) were collected every six days from November 11, 2003 through February 27, 2004. Using a Chemical Mass Balance computer model (Version 8.0), these data were used to apportion the sources of PM _2.5 in the Libby valley. In support of the source apportionment program, a comprehensive evaluation of the particulate matter associated organic compounds (including polar organics, phenolics, polycyclic aromatic hydrocarbons, and ¹⁴ C) present in the airshed was also conducted.

CMB modeling results revealed that emissions from residential wood combustion was the major source of PM _2.5 throughout the winter months in Libby, contributing an average of 82% of the measured PM _2.5 . Levoglucosan, a well-known chemical marker for wood smoke, had the highest measured concentrations of any of the 95 polar organic compounds quantified from the fine fraction, accounting for over 15.5% of the measured organic carbon fraction. Other semi-volatile organic compounds with high measured concentrations during the program were four phenolic compounds commonly found in wood smoke, including phenol, 2-methylphenol ( o -cresol), 4-methylphenol ( p -cresol), and 2,4-dimethylphenol. Results from ¹⁴ C analysis indicate that as much as 82% of the measured ¹⁴ C results from a wood smoke source. These indicators support modeling results that residential wood combustion was the major source of PM _2.5 in Libby, Montana throughout the winter months.     

Variability of Particle Number,Black Carbon,and PM10, PM2.5, and PM1 Levels and Speciation: Influence of Road Traffic Emissions on Urban Air Quality

Measurements of particle number concentration (N), black carbon (BC), and PM ₁₀ , PM _2.5 , and PM ₁ levels and speciation were carried out at an urban background monitoring site in Barcelona. Daily variability of all aerosol monitoring parameters was highly influenced by road traffic emissions and meteorology. The levels of N, BC, PM _X , CO, NO, and NO ₂ increased during traffic rush hours, reflecting exhaust, and non-exhaust traffic emissions and then decreased by the effect of breezes and the reduction of traffic intensity. PM _2.5–10 levels did not decrease during the day as a result of dust resuspension by traffic and wind. N showed a second peak, registered in the afternoon and parallel to O ₃ levels and solar radiation intensity, that may be attributed to photochemical nucleation of precursor gases. An increasing trend was observed for PM ₁ levels from 1999 to 2006, related to the increase in the traffic flow and the diesel fleet in Barcelona. PM composition was highly influenced by road traffic emissions, with exhaust emissions being an important source of PM ₁ and dust resuspension processes of PM _2.5–10 , respectively.     

Characterization of Fine Particulate Emissions from Casting Processes

Fine particle (PM_2.5) emission rates and compositions from gray iron metal casting foundry were characterized for No-Bake molds poured at the Research Foundry located at Technikon, LLC (McClellan, CA). For each mold, PM_2.5 was collected for chemical analysis, and particle size distributions were measured by an Electrical Low Pressure Impactor (ELPI) to understand PM emissions during different part of the casting process. Molds prepared with phenolic urethane binders were poured with Class 30 gray cast iron at 1,427–1,480°C. PM_2.5 was collected from the pouring, cooling, and shakeout processes for each mold. Most of the PM_2.5 mass emitted from these processes was composed of carbonaceous compounds, including 37–67% organic carbon (OC) and 17–30% elemental carbon (EC). Oxides of aluminum (Al), silicon (Si), calcium (Ca), and iron (Fe) constituted 8–20% of PM_2.5 mass, and trace elements (e.g., K, Ti, Mn, Cu, Zn, and Pb) contributed 3–6%. Chemical abundances in PM were different between pouring and shakeout for each discrete mold. PM_2.5 mass emissions from pouring were 15–25% of the total from each discrete mold. Ultrafine particles (< 0.1 μm) contributed less than 1% of PM_2.5 mass, but nearly all of the particle numbers. Different mechanisms for pouring and shakeout result in variations in chemical abundances and particle size distributions. The highest PM_2.5 mass and number concentrations were observed when shakeout started. PM_2.5 size distributions in mass concentration during shakeout contained particles in the tail of coarse particles (1.6–2.5 μm) and a vapor condensation mode (0.65–1.6 μm). Flame conditions, vaporization, thermal decomposition of organic materials, and the variability of mold breakup during shakeout affect PM emission rates. A detailed chemical speciation for size-segregated PM samples at different process points needs to be conducted at full-scale foundries to obtain emission factors and source profiles applicable to emission inventories, source receptor modeling, and implementation of emission standards.     

Performance evaluation of continuous PM2.5 mass concentration monitors

${\mathrm{PM}}_{2.5}$ mass concentrations were measured on a short time interval basis using the continuous ambient mass monitor (CAMM) and real-time ambient mass sampler (RAMS) along with a differential tapered element oscillating microbalance (TEOM) in Rubidoux CA, and intercomparisons were made among these samplers to evaluate their measurement performance of ${\mathrm{PM}}_{2.5}$ mass. Nephelometers were used as an additional sampler to assist in the between-sampler comparisons. Based on the correlation with particulate nitrate concentrations and particulate mass, the differential TEOM showed better measurement of semivolatile species in ${\mathrm{PM}}_{2.5}$ than either the CAMM or the RAMS. The RAMS typically measured more mass than the CAMM. The results suggest that the differential TEOM was better reflecting the ${\mathrm{PM}}_{2.5}$ mass concentrations, with less impact from the loss of semivolatile components. Particulate ammonium nitrate losses estimated from the paired Harvard-EPA annular denuder system and a commercial federal reference method (FRM) sampler were equivalent to the ${\mathrm{PM}}_{2.5}$ mass difference between the paired differential TEOM and FRM samplers. It suggests that volatilization of semivolatile ammonium nitrate was substantial at Rubidoux, and thus, the differential TEOM performed better in measurement of the actual ${\mathrm{PM}}_{2.5}$ mass concentrations, including semivolatile and non-volatile PM, with less loss of semivolatile materials from the filter.     

Reconstructing Primary and Secondary Components of PM2.5 Composition for an Urban Atmosphere

Total 360 samples (of 8 h each) of PM_2.5 were collected from six sampling sites for summer and winter seasons in Kanpur city, India. The collected PM_2.5 mass was subjected to chemical speciation for: (1) ionic species (NH⁺ ₄, SO^2– ₄, NO^– ₃, and Cl^–), (2) carbon contents (EC and OC), and (3) elemental contents (Ca, Mg, Na, K, Al, Si, Fe, Ti, Mn, V, Cr, Ni, Zn, Cd, Pb, Cu, As, and Se). Primary and secondary components of PM_2.5 were assessed from speciation results. The influence of marine source to PM_2.5 was negligible, whereas the contribution of crustal dust was significant (10% in summer and 7% in winter). A mass reconstruction approach for PM_2.5 could distinctly establish primary and secondary components of measured PM_2.5 as: (1) Primary component (27% in summer and 24% in winter): crustal, elemental carbon, and organic mass, (2) Secondary component (45% in summer and 50% in winter): inorganic and organic mass, and (3) others: unidentified mass (27% in summer and 26% in winter). The secondary inorganic component was about 34% in summer (NH⁺ ₄: 9%; SO^2– ₄: 16%; NO^– ₃: 9%) and 32% in winter (NH⁺ ₄: 8%; SO²⁺ ₄: 13%; NO^– ₃: 11%). The secondary organic component was 12% in summer and 18% in winter. In conclusion, secondary aerosol formation (inorganic and organic) accounted for significant mass of PM _2.5 (about 50%) and any particulate control strategy should also include control of primary precursor gases.     

A Novel Optical Instrument for Estimating Size Segregated Aerosol Mass Concentration in Real Time

A novel optical instrument has been developed that estimates size segregated aerosol mass concentration (i.e., PM ₁₀ , PM ₄ , PM _2.5 , and PM ₁ ) over a wide concentration range (0.001–150 mg/m ³ ) in real time. This instrument combines photometric measurement of the particle cloud and optical sizing of single particles in a single optical system. The photometric signal is calibrated to approximate the PM _2.5 fraction of the particulate mass, the size range over which the photometric signal is most sensitive. The electrical pulse heights generated by light scattering from particles larger than 1 micron are calibrated to approximate the aerodynamic diameter of an aerosol of given physical properties, from which the aerosol mass distribution can be inferred. By combining the photometric and optical pulse measurements, this instrument can estimate aerosol mass concentrations higher than typical single particle counting instruments while providing size information and more accurate mass concentration information than traditional photometers. Experiments have shown that this instrument can be calibrated to measure aerosols with very different properties and yet achieve reasonable accuracy.     

Effects of Dilution Sampling on Fine Particle Emissions from Pulverized Coal Combustion

A dilution sampler was used to examine the effects of dilution ratio and residence time on fine-particle emissions from a pilot-scale pulverized coal combustor. Measurements include the particle size distribution from 0.003 to 2.5 μm, PM_2.5 mass, and PM_2.5 composition (OC/EC, major ions, and elemental). Heated filter samples were also collected simultaneously at stack temperatures in order to compare the dilution sampler measurements with standard stack sampling methodologies. Measurements were made both before and after the bag house, the particle control device used on the coal combustor, and while firing three different coal types and one coal–biomass blend. The PM_2.5 mass emission rates measured using the dilution sampler agreed to within experimental uncertainty with those measured with the hot-filter sampler. Relative to the heated filter sample, dilution did increase the PM_2.5 mass fraction of selenium for all fuels tested, as well as ammonium and sulfate for selected fuels. However, the additional particulate mass created by gas-to-particle conversion of these species is within the uncertainty of the gravimetric analysis used to determine the overall mass emission rate. The enrichment of PM_2.5 selenium caused by dilution did not vary with dilution ratio and residence time. The enrichment of PM_2.5 sulfate and ammonium varied with fuel composition and dilution ratio but not residence time. For example, ammonium was only enriched in diluted acidic aerosol samples. A comparison of the PM_2.5 emission profiles for each of the fuels tested underscores how differences in PM_2.5 composition are related to the fuel ash composition. When sampling after the bag house, the particle size distribution and total particle number emission rate did not depend on residence time and dilution ratio because of the much lower particle number concentrations in diluted sample and the absence of nucleation. These results provide new insight into the effects of dilution sampling on measurements of fine particle emissions, providing important data for the ongoing effort of the EPA and ASTM to define a standardized dilution sampling methodology for characterizing emissions from stationary combustion sources.     

Application of PSCF and CPF to PMF-Modeled Sources of PM2.5 in Pittsburgh

Ambient PM _2.5 composition data in Pittsburgh, PA have been used with Positive Matrix Factorization (PMF) to determine the major sources of PM _2.5 sampled. This paper describes the use of the potential source contribution function (PSCF) with the PMF-modeled source contributions to locate the sources in a grid of 0.1° × 0.1° cells. The domain extends from the Pittsburgh Supersite at 40.44°N, 79.94°W over the range 35°–50° north latitude and 75°–90° west longitude. Six-hour back trajectories have been obtained from HYSPLIT four times each day for the 13 months of the study for use with PSCF. Using the results, higher probability locations are compared with known locations of specific source types, based on information from the EPA Toxic Release Inventory (TRI) and the EPA AIRS Database. PSCF results for several sources are compared to the conditional probability function (CPF) analysis, which uses 15-minute wind direction data to determine the most probable direction of a source. Using PSCF and CPF together aids in interpretation of potential source regions. The selenium and sulfate factor source locations are regional, while the lead, cadmium, and specialty steel factor source locations are local. The gallium-rich and Fe, Mn, and Zn factor source locations are potentially both local and regional. The nitrate, vehicle emissions and road dust, wood combustion, vegetative detritus and cooking, and crustal material factor CPF and PSCF results were inconclusive as sources of these factors exist in all directions from the site and therefore one would not expect a clear probability field in any one direction.     

A Shelter to Protect a Passive Sampler for Coarse Particulate Matter,PM10 − 2.5

This work designed and tested a shelter to protect a passive sampler for measuring coarse particulate matter, PM _{10 ? 2.5} . The shelter protects the sampler from precipitation and reduces the effects of wind on the deposition of particles to its collection surface. Six shelters were tested in a wind tunnel at three wind speeds: 2, 8, and 24 km hr ^?1 . Shelter performance was expressed as the ratio of PM _{10 ? 2.5} measured with the passive samplers to that measured with a filter-based dichotomous sampler. For most shelters, the PM _{10 ? 2.5} ratio averaged across wind speeds was well above one (2.4 to 8.5) and was generally dependent on wind speed. However, the PM _{10 ? 2.5} ratio for one shelter, the Flat Plates shelter, was 1.04 with substantially less effect on particle deposition from wind speed. Eight week-long field tests were conducted to compare PM _{10 ? 2.5} measured with a passive sampler installed in a Flat Plates shelter to that measured with a collocated filter-based dichotomous sampler. In these tests, the mean PM _{10 ? 2.5} ratio was 1.29. The linear relationship between PM _{10 ? 2.5} measured passively to that measured with the filter-based sampler had a Pearson correlation coefficient of 0.97 and was not significantly affected by the addition of weekly mean wind speed (p = 0.35). Although temperature was significant in this regression model (p = 0.02), it only improved the relationship marginally. The passive sampler in a Flat Plates shelter offers an inexpensive means to assess ambient PM _{10 ? 2.5} without on-site measurement of wind speed.