Evaluation of the IMPROVE Equation for estimating aerosol light extinction.

UNLABELLED: The [revised] IMPROVE Equation for estimating light extinction from aerosol chemical composition was evaluated considering new measurements at U.S. national parks. Compared with light scattering (Bsp) measured at seven IMPROVE sites with nephelometer data from 2003-2012, the [revised] IMPROVE Equation over- and underestimated Bsp in the lower and upper quintiles, respectively, of measured Bsp. Underestimation of the worst visibility cases (upper quintile) was reduced by assuming an organic mass (OM)/organic carbon (OC) ratio of 2.1 and hygroscopic growth of OM, based on results from previous field studies. This assumption, however, tended to overestimate low Bsp even more. Assuming that sulfate was present as ammonium bisulfate rather than as ammonium sulfate uniformly reduced estimated Bsp. The split-mode model of concentration- and size-dependent dry mass scattering efficiencies in the [revised] IMPROVE Equation does not eliminate systematic biases in estimated Bsp. While the new measurements of OM/OC and OM hygroscopicity should be incorporated into future iterations of the IMPROVE Equation, the problem is not well constrained due to a lack of routine measurements of sulfate neutralization and the water-soluble fraction of OM in the IMPROVE network. IMPLICATIONS: Studies in U.S. national parks showed that aerosol organics contain more mass and absorb more water as a function of relative humidity than is currently assumed by the IMPROVE Equation for calculating chemical light extinction. Consideration of these results could significantly shift the apportionment of light extinction to water-soluble organic aerosols and therefore better inform pollution control strategies under the U.S. Environmental Protection Agency Regional Haze Rule.

PM2.5 source apportionment with organic markers in the Southeastern Aerosol Research and Characterization (SEARCH) study.

UNLABELLED: Positive matrix factorization (PMF) and effective variance (EV) solutions to the chemical mass balance (CMB) were applied to PM(2.5) (particulate matter with an aerodynamic diameter <2.5 µm) mass and chemically speciated measurements for samples taken from 2008 to 2010 at the Atlanta, Georgia, and Birmingham, Alabama, sites. Commonly measured PM(2.5) mass, elemental, ionic, and thermal carbon fraction concentrations were supplemented with detailed nonpolar organic speciation by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS). Source contribution estimates were calculated for motor vehicle exhaust, biomass burning, cooking, coal-fired power plants, road dust, vegetative detritus, and secondary sulfates and nitrates for Atlanta. Similar sources were found for Birmingham, with the addition of an industrial source and the separation of biomass burning into open burning and residential wood combustion. EV-CMB results based on conventional species were qualitatively similar to those estimated by PMF-CMB. Secondary ammonium sulfate was the largest contributor, accounting for 27-38% of PM(2.5), followed by biomass burning (21-24%) and motor vehicle exhaust (9-24%) at both sites, with 4-6% of PM(2.5) attributed to coal-fired power plants by EV-CMB. Including organic compounds in the EV-CMB reduced the motor vehicle exhaust and biomass burning contributions at both sites, with a 13-23% deficit for PM(2.5) mass. The PMF-CMB solution showed mixing of sources within the derived factors, both with and without the addition of speciated organics, as is often the case with complex source mixtures such as those at these urban-scale sites. The nonpolar TD-GC/MS compounds can be obtained from existing filter samples and are a useful complement to the elements, ions, and carbon fractions. However, they should be supplemented with other methods, such as TD-GC/MS on derivitized samples, to obtain a wider range of polar compounds such as sterols, sugars, and organic acids. The PMF and EV solutions to the CMB equations are complementary to, rather than replacements for, each other, as comparisons of their results reveal uncertainties that are not otherwise evident. IMPLICATIONS: Organic markers can be measured on currently acquired PM(2.5) filter samples by thermal methods. These markers can complement element, ion, and carbon fraction measurements from long-term speciation networks. Applying the positive matrix factorization and effective variance solutions for the chemical mass balance equations provides useful information on the accuracy of the source contribution estimates. Nonpolar compounds need to be complemented with polar compounds to better apportion cooking and secondary organic aerosol contributors.

Mass reconstruction methods for PM2.5: a review.

Major components of suspended particulate matter (PM) are inorganic ions, organic matter (OM), elemental carbon (EC), geological minerals, salt, non-mineral elements, and water. Since oxygen (O) and hydrogen (H) are not directly measured in chemical speciation networks, more than ten weighting equations have been applied to account for their presence, thereby approximating gravimetric mass. Assumptions for these weights are not the same under all circumstances. OM is estimated from an organic carbon (OC) multiplier (f) that ranges from 1.4 to 1.8 in most studies, but f can be larger for highly polar compounds from biomass burning and secondary organic aerosols. The mineral content of fugitive dust is estimated from elemental markers, while the water-soluble content is accounted for as inorganic ions or salt. Part of the discrepancy between measured and reconstructed PM mass is due to the measurement process, including: (1) organic vapors adsorbed on quartz-fiber filters; (2) evaporation of volatile ammonium nitrate and OM between the weighed Teflon-membrane filter and the nylon-membrane and/or quartz-fiber filters on which ions and carbon are measured; and (3) liquid water retained on soluble constituents during filter weighing. The widely used IMPROVE equations were developed to characterize particle light extinction in U.S. national parks, and variants of this approach have been tested in a large variety of environments. Important factors for improving agreement between measured and reconstructed PM mass are the f multiplier for converting OC to OM and accounting for OC sampling artifacts.

Regional source identification using Lagrangian stochastic particle dispersion and HYSPLIT backward-trajectory models.

The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55-0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30-0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases.

Particulate emission factors for mobile fossil fuel and biomass combustion sources.

PM emission factors (EFs) for gasoline- and diesel-fueled vehicles and biomass combustion were measured in several recent studies. In the Gas/Diesel Split Study (GD-Split), PM(2.5) EFs for heavy-duty diesel vehicles (HDDV) ranged from 0.2 to ~2 g/mile and increased with vehicle age. EFs for HDDV estimated with the U.S. EPA MOBILE 6.2 and California Air Resources Board (ARB) EMFAC2007 models correlated well with measured values. PM(2.5) EFs measured for gasoline vehicles were ~two orders of magnitude lower than those for HDDV and did not correlate with model estimates. In the Kansas City Study, PM(2.5) EFs for gasoline-powered vehicles (e.g., passenger cars and light trucks) were generally <0.03 g/mile and were higher in winter than summer. EMFAC2007 reported higher PM(2.5) EFs than MOBILE 6.2 during winter, but not during summer, and neither model captured the variability of the measured EFs. Total PM EFs for heavy-duty diesel military vehicles ranged from 0.18±0.03 and 1.20±0.12 g/kg fuel, corresponding to 0.3 and 2 g/mile, respectively. These values are comparable to those of on-road HDDV. EFs for biomass burning measured during the Fire Laboratory at Missoula Experiment (FLAME) were compared with EFs from the ARB Emission Estimation System (EES) model. The highest PM(2.5) EFs (76.8±37.5 g/kg) were measured for wet (>50% moisture content) Ponderosa Pine needles. EFs were generally <20 g/kg when moisture content was <20%. The EES model agreed with measured EFs for fuels with low moisture content but underestimated measured EFs for fuel with moisture content >40%. Average EFs for dry chamise, rice straw, and dry grass were within a factor of three of values adopted by ARB in California's San Joaquin Valley (SJV). Discrepancies between measured and modeled emission factors suggest that there may be important uncertainties in current PM(2.5) emission inventories.

Black and organic carbon emission inventories: review and application to California.

Particulate black or elemental carbon (EC) (black carbon [BC]) and organic carbon (OC) affect climate, visibility, and human health. Several "top-down" and "bottom-up" global emission inventories for these components have compiled country-wide emission factors, source profiles, and activity levels that do not necessarily reflect local conditions. Recent estimates of global BC and OC emissions range from 8 to 24 and 33 to 62 Tg (1012 g) per year, respectively. U.S. BC emissions account for 5.6% of the global total emissions. Uncertainties in global BC emission estimates are a factor of 2 or more. The U.S. National Emissions Inventory is well documented, but its major source categories are not easily related to EC- and OC-emitting source subcategories. California's bottom-up emission inventory is easily accessible at many levels of detail and provides an example of how sources can be regrouped for speciated emission rates. PM2.5 (particulate matter with aerodynamic diameters < 2.5 microm) emissions from these categories are associated with EC and OC source profiles to generate California's speciated emissions. A BC inventory for California of 38,731 t/yr was comparable to the 33,281 t/yr estimated from a bottom-up global BC inventory. However, further examination showed substantial differences among subcategories, with the global inventory BC from fossil fuel combustion at two-thirds that from the California inventory and the remainder attributed to biomass burning. Major discrepancies were found for directly emitted OC, with the global inventory estimating more than twice that of the California inventory. Most of the discrepancy was due to differences in open biomass burning (wildfires and agricultural waste) for which carbon emissions are highly variable. BC and OC emissions are sensitive to the availability and variability of existing source profiles, and profiles more specific to fuels and operating conditions are needed to increase emission accuracy.

Evaluation of regional-scale receptor modeling.

The ability of receptor models to estimate regional contributions to fine particulate matter (PM2.5) was assessed with synthetic, speciated datasets at Brigantine National Wildlife Refuge (BRIG) in New Jersey and Great Smoky Mountains National Park (GRSM) in Tennessee. Synthetic PM2.5 chemical concentrations were generated for the summer of 2002 using the Community Multiscale Air Quality (CMAQ) model and chemically speciated PM2.5 source profiles from the U.S. Environmental Protection Agency (EPA)'s SPECIATE and Desert Research Institute's source profile databases. CMAQ estimated the "true" contributions of seven regions in the eastern United States to chemical species concentrations and individual source contributions to primary PM2.5 at both sites. A seven-factor solution by the positive matrix factorization (PMF) receptor model explained approximately 99% of the variability in the data at both sites. At BRIG, PMF captured the first four major contributing sources (including a secondary sulfate factor), although diesel and gasoline vehicle contributions were not separated. However, at GRSM, the resolved factors did not correspond well to major PM2.5 sources. There were no correlations between PMF factors and regional contributions to sulfate at either site. Unmix produced five- and seven-factor solutions, including a secondary sulfate factor, at both sites. Some PMF factors were combined or missing in the Unmix factors. The trajectory mass balance regression (TMBR) model apportioned sulfate concentrations to the seven source regions using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) trajectories based on Meteorological Model Version 5 (MM5) and Eta Data Simulation System (EDAS) meteorological input. The largest estimated sulfate contributions at both sites were from the local regions; this agreed qualitatively with the true regional apportionments. Estimated regional contributions depended on the starting elevation of the trajectories and on the meteorological input data.

Toward effective source apportionment using positive matrix factorization: experiments with simulated PM2.5 data.

To elucidate the relationship between factors resolved by the positive matrix factorization (PMF) receptor model and actual emission sources and to refine the PMF modeling strategy, speciated PM2.5 (particulate matter with aerodynamic diameter < 2.5 microm) data generated from a state-of-the-art chemical transport model for two rural sites in the eastern United States are subjected to PMF analysis. In addition to chi2 and R2 used to infer the quality of fitting, the interpretability of PMF factors with respect to known primary and secondary sources is evaluated using a root mean square difference analysis. For the most part, factors are found to represent imperfect combinations of sources, and the optimal number of factors should be just adequate to explain the input data (e.g., R2 > 0.95). Retaining more factors in the model does not help resolve minor sources, unless temporal resolution of the data is increased, thus allowing more information to be used by the model. If guided with a priori knowledge of source markers and/or special events, rotation of factors leads to more interpretable PMF factors. The choice of uncertainty weighting coefficients greatly influences the PMF modeling results, but it cannot usually be determined for simulated or real-world data. A simple test is recommended to check whether the weighting coefficients are suitable. However, uncertainties in the data divert PMF solutions even when the optimal weighting coefficients and number of factors are in place.

Evaluation of Regional-Scale Receptor Modeling.

The ability of receptor models to estimate regional contributions to fine particulate matter (PM2.5) was assessed with synthetic, speciated datasets at Brigantine National Wildlife Refuge (BRIG) in New Jersey and Great Smoky Mountains National Park (GRSM) in Tennessee. Synthetic PM2.5 chemical concentrations were generated for the summer of 2002 using the Community Multiscale Air Quality (CMAQ) model and chemically speciated PM2.5 source profiles from the U.S. Environmental Protection Agency (EPA)'s SPECIATE and Desert Research Institute's source profile databases. CMAQ estimated the "true" contributions of seven regions in the eastern United States to chemical species concentrations and individual source contributions to primary PM2.5 at both sites. A seven-factor solution by the positive matrix factorization (PMF) receptor model explained approximately 99% of the variability in the data at both sites. At BRIG, PMF captured the first four major contributing sources (including a secondary sul-fate factor), although diesel and gasoline vehicle contributions were not separated. However, at GRSM, the resolved factors did not correspond well to major PM2.5 sources. There were no correlations between PMF factors and regional contributions to sulfate at either site. Unmix produced five- and seven-factor solutions, including a secondary sulfate factor, at both sites. Some PMF factors were combined or missing in the Unmix factors. The trajectory mass balance regression (TMBR) model apportioned sulfate concentrations to the seven source regions using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) trajectories based on Meteorological Model Version 5 (MM5) and Eta Data Simulation System (EDAS) meteorological input. The largest estimated sulfate contributions at both sites were from the local regions; this agreed qualitatively with the true regional apportionments. Estimated regional contributions depended on the starting elevation of the trajectories and on the meteorological input data.

Toward Effective Source Apportionment Using Positive Matrix Factorization: Experiments with Simulated PM2.5 Data.

To elucidate the relationship between factors resolved by the positive matrix factorization (PMF) receptor model and actual emission sources and to refine the PMF modeling strategy, speciated PM2.5 (particulate matter with aerodynamic diameter <2.5 µm) data generated from a state-of-the-art chemical transport model for two rural sites in the eastern United States are subjected to PMF analysis. In addition to χ-2 and R 2 used to infer the quality of fitting, the interpretability of PMF factors with respect to known primary and secondary sources is evaluated using a root mean square difference analysis. For the most part, factors are found to represent imperfect combinations of sources, and the optimal number of factors should be just adequate to explain the input data (e.g., R 2 > 0.95). Retaining more factors in the model does not help resolve minor sources, unless temporal resolution of the data is increased, thus allowing more information to be used by the model. If guided with a priori knowledge of source markers and/or special events, rotation of factors leads to more interpretable PMF factors. The choice of uncertainty weighting coefficients greatly influences the PMF modeling results, but it cannot usually be determined for simulated or real-world data. A simple test is recommended to check whether the weighting coefficients are suitable. However, uncertainties in the data divert PMF solutions even when the optimal weighting coefficients and number of factors are in place.

Source apportionment: findings from the U.S. Supersites Program.

Receptor models are used to identify and quantify source contributions to particulate matter and volatile organic compounds based on measurements of many chemical components at receptor sites. These components are selected based on their consistent appearance in some source types and their absence in others. UNMIX, positive matrix factorization (PMF), and effective variance are different solutions to the chemical mass balance (CMB) receptor model equations and are implemented on available software. In their more general form, the CMB equations allow spatial, temporal, transport, and particle size profiles to be combined with chemical source profiles for improved source resolution. Although UNMIX and PMF do not use source profiles explicitly as input data, they still require measured profiles to justify their derived source factors. The U.S. Supersites Program provided advanced datasets to apply these CMB solutions in different urban areas. Still lacking are better characterization of source emissions, new methods to estimate profile changes between source and receptor, and systematic sensitivity tests of deviations from receptor model assumptions.

Continuous and filter-based measurements of PM 2.5 nitrate and sulfate at the Fresno Supersite.

PM(2.5) nitrate (NO-(3)) and sulfate (SO=(4)) were measured continuously with R&P8400N and R&P8400S instruments, respectively, and compared with filter-based measurements at the Fresno Supersite from October, 2000 through December, 2005. NO-(3) concentrations were higher in winter than summer with a long-term decreasing trend. Correlations between 24-h average continuous and filter-based NO-(3) were greater than 0.96 in 4 out of 5 years. Continuous NO-(3) was generally lower than filter-based NO-(3) although the difference decreased over time, from -52% in 2001 to +13% in 2005. These differences were similar in winter (-23%) and summer (-19%) while the corresponding differences between ambient and instrument temperature were -12 and 0.7 degrees C, respectively. Neither seasonal nor long-term trends in NO-(3) can be explained by variations in ambient temperature, the difference between ambient and instrument temperature, or changes in aerosol chemical composition. There were no seasonal or long-term trends in SO=(4) concentrations, partially due to low concentrations observed in Fresno. Long-term variability in the performance of R&P8400 NO-(3) and SO=(4) instruments suggest that collocation with filter measurements is needed for long-term measurements.

Comparison of particle light scattering and fine particulate matter mass in central California.

Particle light scattering (Bsp) from nephelometers and fine particulate matter (PM2.5) mass determined by filter samplers are compared for summer and winter at 35 locations in and around California's San Joaquin Valley from December 2, 1999 to February 3, 2001. The relationship is described using particle mass scattering efficiency (sigmasp) derived from linear regression of Bsp on PM2.5 that can be applied to estimated PM2.5 from nephelometer data within the 24-hr filter sampling periods and between the every-6th-day sampling frequency. An average of sigmaSp = 4.9 m2/g was found for all of the sites and seasons; however, sigmasp averaged by site type and season provided better PM2.5 estimates. On average, the sigmasp was lower in summer than winter, consistent with lower relative humidities, lower fractions of hygroscopic ammonium nitrate, and higher contributions from fugitive dust. Winter average sigmasp were similar at non-source-dominated sites, ranging from 4.8 m2/g to 5.9 m2/g. The sigmasp was 2.3 m2/g at the roadside, 3.7 m2/g at a dairy farm, and 4.1 m2/g in the Kern County oilfields. Comparison of Bsp from nephelometers with and without a PM2.5 inlet at the Fresno Supersite showed that coarse particles contributed minor amounts to light scattering. This was confirmed by poorer correlations between Bsp and coarse particulate matter measured during a fall sampling period.

Nanoparticle and ultrafine particle events at the Fresno supersite.

Continuous measurements of particle size distributions of 3-407 nm were collected from August 2002 to July 2004 at the Fresno Supersite to understand their number concentrations, size distributions, and formation processes. Measurements for fine particulate matter (PM2.5) mass, sulfate (SO4(2-)), nitrate (NO3-), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (PAHs), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), and meteorological data (wind speed, wind direction, temperature [T], relative humidity [RH], and solar radiation) were used to determine the causes of nanoparticle (3-10 nm) and ultrafine (10-100 nm) particle events. These events were found to be divided into four types: (1) 3- to 10-nm morning nucleation; (2) 10- to 30-nm morning traffic; (3) 10- to 30-nm afternoon photochemical; and (4) 50- to 84-nm evening home heating, including residential wood combustion. Intense examples of the first type (>10(4) number [#]/cm3) were observed on 29 days, nearly always during the summer. The second type of event was observed on more than 73 days and occurred throughout the year. The third type was observed on 36 days, from spring through summer. The fourth type was found on 109 days, all of them during the winter. Although sulfur dioxide (SO2) emissions in Central California are low, the small residual amounts in gasoline and diesel fuel are apparently sufficient to initiate nucleation events. These were measured in the morning, soon after the shallow surface inversion coupled with layers aloft where nucleation probably was initiated. PM2.5 concentrations were poorly correlated with nanoparticle number.

Particulate carbon measurements in California's San Joaquin Valley.

Aerosol carbon sampling methods and biases were evaluated during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) and Fresno Supersite programs. PM2.5 sampling was conducted using Desert Research Institute (DRI) sequential filter samplers (SFS) from December 1999 through February 2001 at two urban sites (Fresno and Bakersfield), one regional transport site (Angiola), and two boundary sites (Bethel Island and Sierra Nevada Foothills) during CRPAQS in the San Joaquin Valley (SJV). Additional filter-based sampling was done in Fresno as part of the US Environmental Protection Agency (EPA) Supersites program. Organic carbon (OC) and elemental carbon (EC) concentrations were higher during winter (December-February) than summer (June-August) and this trend was most pronounced at Fresno and Bakersfield. OC and EC displayed similar diurnal trends during winter and summer at Fresno and during winter at Angiola. The diurnal pattern at Angiola reflected the transport of secondary pollutants to the site. Collocated measurements of OC and EC on undenuded quartz-fiber filters were made at Fresno with the DRI SFS and the Andersen FRM and RAAS samplers. All average differences in OC between samplers were less than their respective measurement uncertainties. Positive and negative OC biases were evaluated at Fresno using the Andersen RAAS sampler with carbon-denuded and undenuded channels with Teflon-membrane and quartz-fiber filter pairs. Differences between the denuded particle OC and that obtained by subtracting the quartz-behind-Teflon or quartz-behind-quartz OC from the undenuded quartz-fiber front filter were less than twice their measurement uncertainties in most cases. Particulate OC in the denuded channel agreed most closely with the difference between undenuded front and backup quartz-fiber OC.

Variations of nanoparticle concentrations at the Fresno Supersite.

Particle size distributions from 3 nm to 2 mum were measured at the Fresno, CA, Supersite from August 25, 2002 through July 31, 2003. Nanoparticle (3-10 nm) concentrations and the ratio of nanoparticle to total particle concentration were inversely related to particle surface areas from 50 to 1000 mum(2) cm(-3). Elevated nanoparticle concentrations were associated with motor vehicle emissions and with photochemical particle production. In contrast with Atlanta, GA, where concentrations of photochemically derived nanoparticles exceeded 10(5) cm(-3), 5-min average nanoparticle concentrations in Fresno never exceeded 24,400 cm(-3). While photochemical particle production occurs in Fresno, evidence of new particle production (i.e., an increase in number concentration with decreasing size below 10 nm) was not observed. This suggests that photochemical particle production may have occurred at a higher altitude followed by mixing to the surface, or that the fresh particle production rate was smaller with respect to the loss rate by coagulation than it was in Atlanta. Lower production rates in Fresno are more consistent with lower concentrations of sulfur precurors and low relative humidity in Fresno than they are in Atlanta.

Loss of PM2.5 nitrate from filter samples in central California.

Evaporative loss of particulate matter (with aerodynamic diameter < 2.5 microm, [PM2.5]) ammonium nitrate from quartz-fiber filters during aerosol sampling was evaluated from December 3, 1999, through February 3, 2001, at two urban (Fresno and Bakersfield) and three nonurban (Bethel Island, Sierra Nevada Foothills, and Angiola) sites in central California. Compared with total particulate nitrate, evaporative nitrate losses ranged from < 10% during cold months to > 80% during warm months. In agreement with theory, evaporative loss from quartz-fiber filters in nitric acid denuded samplers is controlled by the ambient nitric acid-to-particulate nitrate ratio, which is determined mainly by ambient temperature. Accurate estimation of nitrate volatilization requires a detailed thermodynamic model and comprehensive chemical measurements. For the 14-month average of PM2.5 acquired on Teflon-membrane filters, measured PM2.5 mass was 8-16% lower than actual PM2.5 mass owing to nitrate volatilization. For 24-hr samples, measured PM2.5 was as much as 32-44% lower than actual PM2.5 at three California Central Valley locations.

Determination of the organic aerosol mass to organic carbon ratio in IMPROVE samples.

The ratio of organic mass (OM) to organic carbon (OC) in PM(2.5) aerosols at US national parks in the IMPROVE network was estimated experimentally from solvent extraction of sample filters and from the difference between PM(2.5) mass and chemical constituents other than OC (mass balance) in IMPROVE samples from 1988 to 2003. Archived IMPROVE filters from five IMPROVE sites were extracted with dichloromethane (DCM), acetone and water. The extract residues were weighed to determine OM and analyzed for OC by thermal optical reflectance (TOR). On average, successive extracts of DCM, acetone, and water contained 64%, 21%, and 15%, respectively, of the extractable OC, respectively. On average, the non-blank-corrected recovery of the OC initially measured in these samples by TOR was 115+/-42%. OM/OC ratios from the combined DCM and acetone extracts averaged 1.92 and ranged from 1.58 at Indian Gardens, AZ in the Grand Canyon to 2.58 at Mount Rainier, WA. The average OM/OC ratio determined by mass balance was 2.07 across the IMPROVE network. The sensitivity of this ratio to assumptions concerning sulfate neutralization, water uptake by hygroscopic species, soil mass, and nitrate volatilization were evaluated. These results suggest that the value of 1.4 for the OM/OC ratio commonly used for mass and light extinction reconstruction in IMPROVE is too low.

Variation of mass scattering efficiencies in IMPROVE.

The Interagency Monitoring of Protected Visual Environments (IMPROVE) equation used to assess compliance under the U.S. Environmental Protection Agency (EPA) Haze Rule assumes that dry mass scattering efficiencies for aerosol chemical components are constant. However, examination of aerosol size distributions and chemical composition during the Big Bend Regional Aerosol and Visibility Observational Study and the Southeastern Aerosol and Visibility Study suggests that volume and mass scattering efficiencies vary directly with increasing particle light scattering and aerosol mass concentration. This is consistent with the observation that particle distributions were shifted to larger sizes under more polluted conditions and appears to be related to aging of the aerosol during transport to remote locations.

Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study.

Representative PM2.5 and PM10 source emissions were sampled in Texas during the Big Bend Regional Aerosol Visibility and Observa (BRAVO) study. Chemical source profiles for elements, ions, and carbon fractions of 145 samples are reported for paved and unpaved road dust, soil dust, motor vehicle exhaust, vegetative burning, four coal-fired power stations, an oil refinery catalytic cracker, two cement kilns, and residential meat cooking. Several samples were taken from each emitter and source type, and these were averaged by source type, and in source subgroups based on commonality of chemical composition. The standard deviation represents the variability of the chemical mass fractions. BRAVO profiles differed in some respects from profiles measured elsewhere. High calcium abundances in geological dust, high selenium abundances in coal-fired power stations, and high antimony abundances in oil refinery catalytic cracker emissions were found. Abundances of eight thermally evolved carbon fractions [Atmos. Environ. 28 (15) (1994) 2493] differ among combustion sources, and a Monte Carlo simulation demonstrates that these differences are sufficient to differentiate among several carbon-emitters.