Effects of sulfur dioxide and oxides of nitrogen emission reductions on fine particulate matter mass concentrations: regional comparisons

Two thermodynamic equilibrium models were applied to estimate changes in mean airborne fine particle (PM2.5) mass concentrations that could result from changes in ambient concentrations of sulfate, nitric acid, or ammonia in the southeastern United States, the midwestern United States, and central California. Pronounced regional differences were found. Southeastern sites exhibited the lowest current mean concentrations of nitrate, and the smallest predicted responses of PM2.5 nitrate and mass concentrations to reductions of nitric acid, which is the principal reaction product of the oxidation of nitrogen dioxide (NO2) and the primary gas-phase precursor of fine particulate nitrate. Weak responses of PM2.5 nitrate and mass concentrations to changes in nitric acid levels occurred even if sulfate concentrations were half of current levels. The midwestern sites showed higher levels of fine particulate nitrate, characterized by cold-season maxima, and were projected to show decreases in overall PM levels following decreases of either sulfate or nitric acid. For some midwestern sites, predicted PM2.5 nitrate concentrations increased as modeled sulfate levels declined, but sulfate reductions always reduced the predicted fine PM mass concentrations; PM2.5 nitrate concentrations became more sensitive to reductions of nitric acid as modeled sulfate concentrations were decreased. The California sites currently have the highest mean concentrations of fine PM nitrate and the lowest mean concentrations of fine PM sulfate. Both the estimated PM2.5 nitrate and fine mass concentrations decreased in response to modeled reductions of nitric acid at all California sites. The results indicate important regional differences in expected PM2.5 mass concentration responses to changes in sulfate and nitrate precursors. Analyses of ambient data, such as described here, can be a key part of weight of evidence (WOE) demonstrations for PM2.5 attainment plans. Acquisition of the data may require special sampling efforts, especially for PM2.5 precursor concentration data.     

Source apportionment of fine particulate matter in the southeastern United States

Particulate matter (PM) less than 2.5 microm in size (PM2.5) source apportionment by chemical mass balance receptor modeling was performed to enhance regional characterization of source impacts in the southeastern United States. Secondary particles, such as NH4HSO4, (NH4)2SO4, NH4NO3, and secondary organic carbon (OC) (SOC), formed by atmospheric photochemical reactions, contribute the majority (>50%) of ambient PM2.5 with strong seasonality. Source apportionment results indicate that motor vehicle and biomass burning are the two main primary sources in the southeast, showing relatively more motor vehicle source impacts rather than biomass burning source impacts in populated urban areas and vice versa in less urbanized areas. Spatial distributions of primary source impacts show that each primary source has distinctively different spatial source impacts. Results also find impacts from shipping activities along the coast. Spatiotemporal correlations indicate that secondary particles are more regionally distributed, as are biomass burning and dust, whereas impacts of other primary sources are more local.     

Simulating the fine and coarse inorganic particulate matter concentrations in a polluted megacity

A three dimensional chemical transport model (PMCAMx) is applied to the Mexico City Metropolitan Area (MCMA) in order to simulate the chemical composition and mass of the major PM₁ (fine) and PM_1–10 (coarse) inorganic components and determine the effect of mineral dust on their formation. The aerosol thermodynamic model ISORROPIA-II is used to explicitly simulate the effect of Ca, Mg, and K from dust on semi-volatile partitioning and water uptake. The hybrid approach is applied to simulate the inorganic components, assuming that the smallest particles are in thermodynamic equilibrium, while describing the mass transfer to and from the larger ones. The official MCMA 2004 emissions inventory with improved dust and NaCl emissions is used. The comparison between the model predictions and measurements during a week of April of 2003 at Centro Nacional de Investigacion y Capacitacion Ambiental (CENICA) “Supersite” shows that the model reproduces reasonably well the fine mode composition and its diurnal variation. Sulfate predicted levels are relatively uniform in the area (approximately 3 μg m^?3), while ammonium nitrate peaks in Mexico City (approximately 7 μg m^?3) and its concentration rapidly decreases due to dilution and evaporation away from the urban area. In areas of high dust concentrations, the associated alkalinity is predicted to increase the concentration of nitrate, chloride and ammonium in the coarse mode by up to 2 μg m^?3 (a factor of 10), 0.4 μg m^?3, and 0.6 μg m^?3 (75%), respectively. The predicted ammonium nitrate levels inside Mexico City for this period are sensitive to the physical state (solid versus liquid) of the particles during periods with RH less than 50%.     

A reanalysis of fine particulate matter air pollution versus life expectancy in the United States

A reduction in population exposure to fine particulate matter air pollution (PM2.5) has been associated with improvements in life expectancy. This article presents a reanalysis of this relationship and comments on the results from a study on the reduction of ambient air PM_2.5 concentrations versus life expectancy in metropolitan areas of the United States. The results of the reanalysis show that the statistical significance of the correlation is lost after removing one of the metropolitan areas from the regression analysis, suggesting that the results may not be suitable for a meaningful and reliable inference.

Implications: The observed loss of statistical significance in the correlation between the reduction of ambient air PM_2.5 concentrations and life expectancy in metropolitan areas of the United States, after removing one of the metropolitan areas from the regression analysis, may raise concern for the policymakers in decisions regarding further reductions in permitted levels of air pollution emissions.     

Trends in speciated fine particulate matter and visibility across monitoring networks in the Southeastern United States

Trends in fine particulate matter <2.5 microm in diameter (PM2.5) and visibility in the Southeastern United States were evaluated for sites in the Interagency Monitoring of Protected Visual Environments, Speciated Trends Network, and Southeastern Aerosol Research and Characterization Study networks. These analyses are part of the technical assessment by Visibility Improvement-State and Tribal Association of the Southeast (VISTAS), the regional planning organization for the southeastern states, in support of State Implementation Plans for the regional haze rule. At all of the VISTAS IMPROVE sites, ammonium sulfate and organic carbon (OC) are the largest and second largest contributors, respectively, to light extinction on both the 20% haziest and 20% clearest days. Ammonium nitrate, elemental carbon (EC), soils, and coarse particles make comparatively small contributions to PM2.5 mass and light extinction on most days at the Class I areas. At Southern Appalachian sites, the 20% haziest days occur primarily in the late spring to fall, whereas at coastal sites, the 20% haziest days can occur through out the year. Levels of ammonium sulfate in Class I areas are similar to those in nearby urban areas and are generally higher at the interior sites than the coastal sites. Concentrations of OC, ammonium nitrate, and, sometimes, EC, tend to be higher in the urban areas than in nearby Class I areas, although differences in measurement methods complicate comparisons between networks. Results support regional controls of sulfur dioxide for both regional haze and PM2.5 implementation and suggest that controls of local sources of OC, EC, or nitrogen oxides might also be considered for urban areas that are not attaining the annual National Ambient Air Quality Standard for PM2.5.     

Compilation and assessment of recent positive matrix factorization and UNMIX receptor model studies on fine particulate matter source apportionment for the eastern United States

In 1997, the U.S. Environmental Protection Agency (EPA) revised its particulate matter standards to include an annual standard for fine particulate matter (PM2.5; 15 microg/m3) and a 24-hr standard (65 microg/m3). The 24-hr standard was lowered to 35 microg/m3 in 2006 in an effort to further reduce overall ambient PM2.5 concentrations. Identifying and quantifying sources of particulate matter affecting a particular location through source apportionment methods is now an important component of the information available to decision makers when evaluating the new standards. This literature compilation summarizes a subset of the source apportionment research and general findings on fine particulate matter in the eastern half of the United States using Positive Matrix Factorization. The results between studies are generally comparable when comparable datasets are used; however, methodologies vary considerably. Commonly identified source categories include: secondary sulfate/coal burning (sometimes over 50% of total mass), secondary organic carbon/mobile sources, crustal sources, biomass burning, nitrate, various industrial processes, and sea salt. The source apportionment tools and methodologies have passed the proof-of-concept stage and are now being used to understand the ambient composition of particulate matter for sites across the United States and the spatial relationship of sources to the receptor. Recommendations are made for further and standardized method development for source apportionment studies, and specific research areas of interest for the eastern United States are proposed.     

Application of a microscale emission factor model for particulate matter to calculate vehicle-generated contributions to fine particulate emissions

This paper discusses the evaluation and application of a new generation of particulate matter (PM) emission factor model (MicroFacPM). MicroFacPM that was evaluated in Tuscarora Mountain Tunnel, Pennsylvania Turnpike, PA shows good agreement between measured and modeled emissions. MicroFacPM application is presented to the vehicle traffic on the main approach road to the Ambassador Bridge, which is one of the most important international border entry points in North America, connecting Detroit, MI, with Windsor, Ontario, Canada. An increase in border security has forced heavy-duty diesel vehicles to line up for several kilometers through the city of Windsor causing concern about elevated concentrations of ambient PM. MicroFacPM has been developed to model vehicle-generated PM (fine [PM2.5] and coarse < or = 10 microm [PM10]) from the on-road vehicle fleet, which in this case includes traffic at very low speeds (10 km/h). The Windsor case study gives vehicle generated PM2.5 sources and their breakdown by vehicle age and class. It shows that the primary sources of vehicle-generated PM2.5 emissions are the late-model heavy-duty diesel vehicles. We also applied CALINE4 and AERMOD in conjunction with MicroFacPM, using Canadian traffic and climate conditions, to describe the vehicle-generated PM2.5 dispersion near this roadway during the month of May in 2003.     

Analysis and apportionment of organic carbon and fine particulate matter sources at multiple sites in the midwestern United States

Speciated fine particulate matter (PM2.5) data collected as part of the Speciation Trends Network at four sites in the Midwest (Detroit, MI; Cincinnati, OH; Indianapolis, IN; and Northbrook, IL) and as part of the Interagency Monitoring of Protected Visual Environments program at the rural Bondville, IL, site were analyzed to understand sources contributing to organic carbon (OC) and PM2.5 mass. Positive matrix factorization (PMF) was applied to available data collected from January 2002 through March 2005, and seven to nine factors were identified at each site. Common factors at all of the sites included mobile (gasoline)/secondary organic aerosols with high OC, diesel with a high elemental carbon/OC ratio (only at the urban sites), secondary sulfate, secondary nitrate, soil, and biomass burning. Identified industrial factors included copper smelting (Northbrook, Indianapolis, and Bondville), steel/manufacturing with iron (Northbrook), industrial zinc (Northbrook, Cincinnati, Indianapolis, and Detroit), metal plating with chromium and nickel (Detroit, Indianapolis, and Bondville), mixed industrial with copper and iron (Cincinnati), and limestone with calcium and iron (Bondville). PMF results, on average, accounted for 96% of the measured PM2.5 mass at each site; residuals were consistently within tolerance (+/-3), and goodness-of-fit (Q) was acceptable. Potential source contribution function analysis helped identify regional and local impacts of the identified source types. Secondary sulfate and soil factors showed regional characteristics at each site, whereas industrial sources typically appeared to be locally influenced. These regional factors contributed approximately one third of the total PM2.5 mass, on average, whereas local mobile and industrial sources contributed to the remaining mass. Mobile sources were a major contributor (55-76% at the urban sites) to OC mass, generally with at least twice as much mass from nondiesel sources as from diesel. Regional OC associated with secondary sulfate and soil was generally low.     

Characterization of spatially homogeneous regions based on temporal patterns of fine particulate matter in the continental United States

Statistical analyses of time-series or spatial data have been widely used to investigate the behavior of ambient air pollutants. Because air pollution data are generally collected in a wide area of interest over a relatively long period, such analyses should take into account both spatial and temporal characteristics. The objective of this study is 2-fold: (1) to identify an efficient way to characterize the spatial variations of fine particulate matter (PM2.5) concentrations based solely upon their temporal patterns, and (2) to analyze the temporal and seasonal patterns of PM2.5 concentrations in spatially homogenous regions. This study used 24-hr average PM2.5 concentrations measured every third day during a period between 2001 and 2005 at 522 monitoring sites in the continental United States. A k-means clustering algorithm using the correlation distance was used to investigate the similarity in patterns between temporal profiles observed at the monitoring sites. A k-means clustering analysis produced six clusters of sites with distinct temporal patterns that were able to identify and characterize spatially homogeneous regions of the United States. The study also presents a rotated principal component analysis (RPCA) that has been used for characterizing spatial patterns of air pollution and discusses the difference between the clustering algorithm and RPCA.     

Impact of biogenic emission uncertainties on the simulated response of ozone and fine particulate matter to anthropogenic emission reductions

The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1-0.25 microg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1-2% of the value of the annual PM2.5 NAAQS of 15 microg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions.     

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.     

Effects of changes in sulfate,ammonia, and nitric acid on particulate nitrate concentrations in the southeastern United States

A thermodynamic equilibrium model, Simulating Composition of Atmospheric Particles at Equilibrium (SCAPE2), was used to investigate the response of fine particulate NO3(-) to changes in concentrations of HNO3, NH3, and SO4(2-) in the southeastern United States. The data consisted of daily, 24-hr time resolution measurements from the Aerosol Research Inhalation Epidemiology Study (ARIES) Jefferson Street (Atlanta) site and five other sites of the Southeastern Aerosol Research and Characterization Project (SEARCH). Reductions of total NH3 (gas-phase NH3 plus particulate NH4(+)), total NO3(-) (HNO3 plus particulate NO3(-)), SO4(2-), or combined total NO3(-) (HNO3 plus particulate NO3(-)) with SO4(2-) were used to estimate the effects of changing emission levels. The conversion of SO2 to SO4(2-) and NO2 to HNO3 involves additional nonlinear reactions not incorporated into the model. For all sites, fine particulate NO3(-) concentrations decreased in response to reductions of either NH3 or total NO3(-), but the particulate NO3(-) decreases were greater for the NH3 reductions than for the total NO3(-) reductions. Particulate NO3(-) concentrations increased in response to reductions of SO4(2-). For the combined reduction (total NO3(-) plus SO4(2-)), the resulting particulate NO3(-) concentrations were on average no different than the base-case NO3(-) levels. Measurements of fine particulate NO3(-) and HNO3 support the modeling conclusions and indicate that particulate NO3(-) formation is limited by the availability of NH3 at most times at all SEARCH sites.     

Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change

We applied a multiple linear regression (MLR) model to study the correlations of total PM_2.5 and its components with meteorological variables using an 11-year (1998–2008) observational record over the contiguous US. The data were deseasonalized and detrended to focus on synoptic-scale correlations. We find that daily variation in meteorology as described by the MLR can explain up to 50% of PM_2.5 variability with temperature, relative humidity (RH), precipitation, and circulation all being important predictors. Temperature is positively correlated with sulfate, organic carbon (OC) and elemental carbon (EC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. RH is positively correlated with sulfate and nitrate, but negatively with OC and EC. Precipitation is strongly negatively correlated with all PM_2.5 components. We find that PM_2.5 concentrations are on average 2.6 μg m^?3 higher on stagnant vs. non-stagnant days. Our observed correlations provide a test for chemical transport models used to simulate the sensitivity of PM_2.5 to climate change. They point to the importance of adequately representing the temperature dependence of agricultural, biogenic and wildfire emissions in these models.     

Development of an empirical model to estimate real-world fine particulate matter emission factors: the traffic air quality model

The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM2.5) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds < 50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flow-speeds > 80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.     

Spatial patterns of mobile source particulate matter emissions-to-exposure relationships across the United States

Assessing the public health benefits from air pollution control measures is assisted by understanding the relationship between mobile source emissions and subsequent fine particulate matter (PM_2.5) exposure. Since this relationship varies by location, we characterized its magnitude and geographic distribution using the intake fraction (iF) concept. We considered emissions of primary PM_2.5 as well as particle precursors SO₂ and NO_x from each of 3080 counties in the US. We modeled the relationship between these emissions and total US population exposure to PM_2.5, making use of a source–receptor matrix developed for health risk assessment. For primary PM_2.5, we found a median iF of 1.2 per million, with a range of 0.12–25. Half of the total exposure was reached by a median distance of 150 km from the county where mobile source emissions originated, though this spatial extent varied across counties from within the county borders to 1800 km away. For secondary ammonium sulfate from SO₂ emissions, the median iF was 0.41 per million (range: 0.050–10), versus 0.068 per million for secondary ammonium nitrate from NO_x emissions (range: 0.00092–1.3). The median distance to half of the total exposure was greater for secondary PM_2.5 (450 km for sulfate, 390 km for nitrate). Regression analyses using exhaustive population predictors explained much of the variation in primary PM_2.5 iF (R²=0.83) as well as secondary sulfate and nitrate iF (R²=0.74 and 0.60), with greater near-source contribution for primary than for secondary PM_2.5. We conclude that long-range dispersion models with coarse geographic resolution are appropriate for risk assessments of secondary PM_2.5 or primary PM_2.5 emitted from mobile sources in rural areas, but that more resolved dispersion models are warranted for primary PM_2.5 in urban areas due to the substantial contribution of near-source populations.     

Transport of atmospheric fine particulate matter: part 2--findings from recent field programs on the intraurban variability in fine particulate matter

Air quality field data, collected as part of the fine particulate matter Supersites Program and other field measurements programs, have been used to assess the degree of intraurban variability for various physical and chemical properties of ambient fine particulate matter. Spatial patterns vary from nearly homogeneous to quite heterogeneous, depending on the city, parameter of interest, and the approach or method used to define spatial variability. Secondary formation, which is often regional in nature, drives fine particulate matter mass and the relevant chemical components toward high intraurban spatial homogeneity. Those particulate matter components that are dominated by primary emissions within the urban area, such as black carbon and several trace elements, tend to exhibit greater spatial heterogeneity. A variety of study designs and data analysis approaches have been used to characterize intraurban variability. High temporal correlation does not imply spatial homogeneity. For example, there can be high temporal correlation but with spatial heterogeneity manifested as smooth spatial gradients, often emanating from areas of high emissions such as the urban core or industrial zones.     

Evaluation of multisectional and two-section particulate matter photochemical grid models in the Western United States

Version 4.10s of the comprehensive air-quality model with extensions (CAMx) photochemical grid model has been developed, which includes two options for representing particulate matter (PM) size distribution: (1) a two-section representation that consists of fine (PM2.5) and coarse (PM2.5-10) modes that has no interactions between the sections and assumes all of the secondary PM is fine; and (2) a multisectional representation that divides the PM size distribution into N sections (e.g., N = 10) and simulates the mass transfer between sections because of coagulation, accumulation, evaporation, and other processes. The model was applied to Southern California using the two-section and multisection representation of PM size distribution, and we found that allowing secondary PM to grow into the coarse mode had a substantial effect on PM concentration estimates. CAMx was then applied to the Western United States for the 1996 annual period with a 36-km grid resolution using both the two-section and multisection PM representation. The Community Multiscale Air Quality (CMAQ) and Regional Modeling for Aerosol and Deposition (REMSAD) models were also applied to the 1996 annual period. Similar model performance was exhibited by the four models across the Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network monitoring networks. All four of the models exhibited fairly low annual bias for secondary PM sulfate and nitrate but with a winter overestimation and summer underestimation bias. The CAMx multisectional model estimated that coarse mode secondary sulfate and nitrate typically contribute <10% of the total sulfate and nitrate when averaged across the more rural IMPROVE monitoring network.     

Secondary particulate matter in the United States: insights from the Particulate Matter Supersites Program and related studies

Secondary aerosols comprise a major fraction of fine particulate matter (PM2.5) in all parts of the country, during all seasons, and times of day. The most abundant secondary species include sulfate, nitrate, ammonium, and secondary organic aerosols (SOAs). The relative abundance of each species varies in space and time as a function of meteorology, source emissions strength and type, thermodynamics, and atmospheric processing. Transport of secondary aerosols from upwind locations can contribute significantly at downwind receptor sites, especially regionally in the eastern United States, and across a given urbanized area, such as in Los Angeles. Processes governing the formation of the inorganic secondary species (sulfate, nitrate, and ammonium) are fairly well understood, although the occurrence of nucleation bursts initiated with the formation of ultrafine sulfuric acid particles observed regionally on clean days in the eastern United States was unexpected. Because of the complex nature of organic material in air, much is still to be learned about the sources, formation, and even spatial and temporal distributions of SOAs. For example, a considerable fraction of ambient organic PM is oxidized organic species, many of which still need to be identified, quantified, and their sources and formation mechanisms determined. Furthermore, significant uncertainty (approaching 50% or more) is associated with estimating the SOA fraction of organic material in air with current methods. This review summarizes the findings of the Supersites Program and related studies addressing secondary particulate matter (PM), including spatial and temporal variations of secondary PM and its precursor species, data and methods for determining the primary and secondary fractions of PM mass, and findings on the anthropogenic and natural fractions of secondary PM.     

Atmospheric conversion of sulfur dioxide to particulate sulfate and nitrogen dioxide to particulate nitrate and gaseous nitric acid in an urban area

Sulfur dioxide, nitrogen dioxide, particulate sulfate and nitrate, gaseous nitric acid, ozone and meteorological parameters (temperature and relative humidity) were measured during the winter season (1999-2000) and summer season (2000) in an urban area (Dokki, Giza, Egypt). The average particulate nitrate concentrations were 6.20 and 9.80 microg m(-3), while the average gaseous nitric acid concentrations were 1.14 and 6.70 microg m(-3) in the winter and summer seasons, respectively. The average sulfate concentrations were 15.32 microg m(-3) during the winter and 25.10 microg m(-3) during the summer season. The highest average concentration ratio of gaseous nitric acid to total nitrate was found during the summer season. Particulate sulfate and nitrate and gaseous nitric acid concentrations were relatively higher in the daytime than those in the nighttime. Sulfur conversion ratio (Fs) and nitrogen conversion ratio (Fn) defined in the text were calculated from the field measurement data. Sulfur conversion ratio (Fs) and nitrogen conversion ratio (Fn) in the summer were about 2.22 and 2.97 times higher than those in the winter season, respectively. Moreover, sulfur conversion ratio (Fs) and nitrogen conversion ratio (Fn) were higher in the daytime than those in the nighttime during the both seasons. The sulfur conversion ratio (Fs) increases with increasing ozone concentration and relative humidity. This indicates that the droplet phase reactions and gas phase reactions are important for the oxidation of SO2 to sulfate. Moreover, the nitrogen conversion ratio (Fn) increases with increasing ozone concentration, and the gas phase reactions are important and predominant for the oxidation of NO2 to nitrate.     

Improving source apportionment of fine particles in the eastern United States utilizing temperature-resolved carbon fractions

The objectives of this study were to examine the use of carbon fractions to identify particulate matter (PM) sources, especially traffic-related carbonaceous particle sources, and to estimate their contributions to the particle mass concentrations. In recent studies, positive matrix factorization (PMF) was applied to ambient fine PM (PM2.5) compositional data sets of 24-hr integrated samples including eight individual carbon fractions collected at three monitoring sites in the eastern United States: Atlanta, GA, Washington, DC, and Brigantine, NJ. Particulate carbon was analyzed using the Interagency Monitoring of Protected Visual Environments/Thermal Optical Reflectance method that divides carbon into four organic carbons (OC): pyrolized OC and three elemental carbon (EC) fractions. In contrast to earlier PMF studies that included only the total OC and EC concentrations, gasoline emissions could be distinguished from diesel emissions based on the differences in the abundances of the carbon fractions between the two sources. The compositional profiles for these two major source types show similarities among the three sites. Temperature-resolved carbon fractions also enhanced separations of carbon-rich secondary sulfate aerosols. Potential source contribution function analyses show the potential source areas and pathways of sulfate-rich secondary aerosols, especially the regional influences of the biogenic, as well as anthropogenic secondary aerosol. This study indicates that temperature-resolved carbon fractions can be used to enhance the source apportionment of ambient PM2.5.