Photochemical model performance for PM2.5 sulfate,nitrate, ammonium,and precursor species SO2, HNO3, and NH3 at background monitor locations in the central and eastern United States

Health studies have shown premature death is statistically associated with exposure to particulate matter <2.5 μm in diameter (PM2.5). The United States Environmental Protection Agency requires all States with PM2.5 non-attainment counties or with sources contributing to visibility impairment at Class I areas to submit an emissions control plan. These emission control plans will likely focus on reducing emissions of sulfur oxides and nitrogen oxides, which form two of the largest chemical components of PM2.5 in the eastern United States: ammonium sulfate and ammonium nitrate. Emission control strategies are simulated using three-dimensional Eulerian photochemical transport models.A monitor study was established using one urban (Detroit) and nine rural locations in the central and eastern United States to simultaneously measure PM2.5 sulfate ion (SO₄²⁻), nitrate ion (NO₃⁻), ammonium ion (NH₄⁺), and precursor species sulfur dioxide (SO₂), nitric acid (HNO₃), and ammonia (NH₃). This monitor study provides a unique opportunity to assess how well the modeling system predicts the spatial and temporal variability of important precursor species and co-located PM2.5 ions, which is not well characterized in the central and eastern United States.The modeling system performs well at estimating the PM2.5 species, but does not perform quite as well for the precursor species. Ammonia is under-predicted in the coldest months, nitric acid tends to be over-predicted in the summer months, and sulfur dioxide appears to be systematically over-predicted. Several indicators of PM2.5 ammonium sulfate and ammonium nitrate formation and chemical composition are estimated with the ambient data and photochemical model output. PM2.5 sulfate ion is usually not fully neutralized to ammonium sulfate in ambient measurements and is usually fully neutralized in model estimates. The model and ambient estimates agree that the ammonia study monitors tend to be nitric acid limited for PM2.5 nitrate formation. Regulatory strategies in this part of the country should focus on reductions in NO_X rather than ammonia to control PM2.5 ammonium nitrate.     

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains. Part I: hourly and seasonal ozone

Recently, a comprehensive air quality modeling system was developed as part of the Southern Appalachians Mountains Initiative (SAMI) with the ability to simulate meteorology, emissions, ozone, size- and composition-resolved particulate matter, and pollutant deposition fluxes. As part of SAMI, the RAMS/EMS-95/URM-1ATM modeling system was used to evaluate potential emission control strategies to reduce atmospheric pollutant levels at Class I areas located in the Southern Appalachians Mountains. This article discusses the details of the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. The daily mean normalized bias and error for 1-hr and 8-hr ozone were within U.S. Environment Protection Agency guidance criteria for urban-scale modeling. The model typically showed a systematic overestimation for low ozone levels and an underestimation for high levels. Because SAMI was primarily interested in simulating the growing season ozone levels in Class I areas, daily and seasonal cumulative ozone exposure, as characterized by the W126 index, were also evaluated. The daily ozone W126 performance was not as good as the hourly ozone performance; however, the seasonal ozone W126 scaled up from daily values was within 17% of the observations at two typical Class I areas of the SAMI region. The overall ozone performance of the model was deemed acceptable for the purposes of SAMI's assessment.     

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind     

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.     

Integrated Assessment Modeling of Atmospheric Pollutants in the Southern Appalachian Mountains. Part I: Hourly and Seasonal Ozone

Abstract

Recently, a comprehensive air quality modeling system was developed as part of the Southern Appalachians Mountains Initiative (SAMI) with the ability to simulate meteorology, emissions, ozone, size- and composition-resolved particulate matter, and pollutant deposition fluxes. As part of SAMI, the RAMS/EMS-95/URM-1ATM modeling system was used to evaluate potential emission control strategies to reduce atmospheric pollutant levels at Class I areas located in the Southern Appalachians Mountains. This article discusses the details of the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. The daily mean normalized bias and error for 1-hr and 8-hr ozone were within U.S. Environment Protection Agency guidance criteria for urban-scale modeling. The model typically showed a systematic overestimation for low ozone levels and an underestimation for high levels. Because SAMI was primarily interested in simulating the growing season ozone levels in Class I areas, daily and seasonal cumulative ozone exposure, as characterized by the W126 index, were also evaluated. The daily ozone W126 performance was not as good as the hourly ozone performance; however, the seasonal ozone W126 scaled up from daily values was within 17% of the observations at two typical Class I areas of the SAMI region. The overall ozone performance of the model was deemed acceptable for the purposes of SAMI’s assessment.     

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

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

Particulate composition characteristics under different ambient air quality conditions

Particulate compositions including elemental carbon (EC), organic carbon (OC), water-soluble ionic species, and elemental compositions were investigated during the period from 2004 to 2006 in southern Taiwan. The correlation between the pollutant standard index (PSI) of ambient air quality and the various particle compositions was also addressed in this study. PSI revealed a correlation with fine (r = 0.74) and coarse (r = 0.80) particulate matter (PM). PSI manifested a significant correlation with the amount of analyzed ionic species (r approximately 0.80) in coarse and fine particles and a moderate correlation with carbon content (r = 0.63) in fine particles; however, it showed no correlation with elemental content. Although the ambient air quality ranged from good to moderate, the ionic species including chloride (Cl-), nitrate (NO3-), sulfate (SO4(2-)), sodium (Na+), ammonium (NH4+), magnesium (Mg2+), and calcium (Ca2+) increased significantly (1.5-3.7 times for Daliao and 1.8-6.9 times for Tzouying) in coarse PM. For fine particles, NO3-, SO4(2-), NH4+, and potassium (K+) also increased significantly (1.3-2.4 times for Daliao and 2.8-9.6 times for Tzouying) when the air quality went from good to moderate. For meteorological parameters, temperature evidenced a slightly negative correlation with PM concentration and PSI value, which implied a high PM concentration in the low-temperature condition. This reflects the high frequency of PM episodes in winter and spring in southern Taiwan. In addition, the mixing height increase from 980 to 1450 m corresponds to the air quality condition changing from unhealthy to good.     

Application and evaluation of two air quality models for particulate matter for a southeastern U.S. episode

The Models-3 Community Multiscale Air Quality (CMAQ) Modeling System and the Particulate Matter Comprehensive Air Quality Model with extensions (PMCAMx) were applied to simulate the period June 29-July 10, 1999, of the Southern Oxidants Study episode with two nested horizontal grid sizes: a coarse resolution of 32 km and a fine resolution of 8 km. The predicted spatial variations of ozone (O3), particulate matter with an aerodynamic diameter less than or equal to 2.5 microm (PM2.5), and particulate matter with an aerodynamic diameter less than or equal to 10 microm (PM10) by both models are similar in rural areas but differ from one another significantly over some urban/suburban areas in the eastern and southern United States, where PMCAMx tends to predict higher values of O3 and PM than CMAQ. Both models tend to predict O3 values that are higher than those observed. For observed O3 values above 60 ppb, O3 performance meets the U.S. Environmental Protection Agency's criteria for CMAQ with both grids and for PMCAMx with the fine grid only. It becomes unsatisfactory for PMCAMx and marginally satisfactory for CMAQ for observed O3 values above 40 ppb. Both models predict similar amounts of sulfate (SO4(2-)) and organic matter, and both predict SO4(2-) to be the largest contributor to PM2.5. PMCAMx generally predicts higher amounts of ammonium (NH4+), nitrate (NO3-), and black carbon (BC) than does CMAQ. PM performance for CMAQ is generally consistent with that of other PM models, whereas PMCAMx predicts higher concentrations of NO3-, NH4+, and BC than observed, which degrades its performance. For PM10 and PM2.5 predictions over the southeastern U.S. domain, the ranges of mean normalized gross errors (MNGEs) and mean normalized bias are 37-43% and -33-4% for CMAQ and 50-59% and 7-30% for PMCAMx. Both models predict the largest MNGEs for NO3- (98-104% for CMAQ 138-338% for PMCAMx). The inaccurate NO3- predictions by both models may be caused by the inaccuracies in the ammonia emission inventory and the uncertainties in the gas/particle partitioning under some conditions. In addition to these uncertainties, the significant PM overpredictions by PMCAMx may be attributed to the lack of wet removal for PM and a likely underprediction in the vertical mixing during the daytime.     

Characterization of chemical species in atmospheric aerosols in a metropolitan basin

Ambient PM10 aerosol samples were collected from Taiwan's Taichung metropolitan basin between October 1997 and January 1998, and their chemical characteristics studied. The average mass concentration of PM10 was 109.0 +/- 54.1 microg/m3. Carbonaceous materials, sulfate, nitrate, and ammonium were the most important contributors to the PM10 component. On average, 64% of the PM10 was made up of fine particles. During PM10 episodes, average wind speed was 0.7 m/s and relative humidity was high, 83% on average, probably giving rise to stagnation of air pollutants and their entrapment close to the surface. With relative humidity < 70%, NO3-, NH4+, SO4(2-), carbonaceous materials, and PM10 mass showed high correlation with maximum hourly average ozone (O3M). Variation in atmospheric humidity may affect the gas-to-particle interactions of S and N species. The most significant contribution to PM10 in the Taichung urban basin was from the photochemical formation of secondary aerosols and carbonaceous materials in the atmospheric environment.     

Source apportionment methods applied to the determination of the origin of ambient aerosols that affect visibility in forested areas

An aerosol characterization, visibility, and receptor modeling study was conducted in the Shenandoah Valley, VA between 14 July and 15 August 1980. The objectives of this study were to: (1) determine the origin of the ambient particles, (2) determine the major chemical species contributing to the light extinction coefficient, (3) evaluate analytical methods to characterize aerosols and (4) provide data for comparison with chemical composition of aerosols collected in the Great Smoky Mountains and in the Abastumani Mountains of Georgian Soviet Socialist Republic. The average sulfate concentrations measured in fine particles (<2.5μm) at these three locations were: 12.0μgm⁻³ at Great Smoky Mountains; 13.6 μg m⁻³ at Shenandoah Valley, and 4.6 μg m⁻³ at Abastumani Mountains; the fractions of sulfate in the fine particle mass concentrations at each site were 0.50,0.50 and 0.38, respectively. For the two studies in the United States, the fine particle sulfate during sulfate maxima was mostly in the form of ammonium acid sulfate. Factor analysis of the fine aerosol composition measured in the Shenandoah Valley yielded a persistent factor containing large loadings on mass, SO²⁻₄, S, NH⁺₄, H⁺, Se and total nitrate (sum of particulate nitrate and nitric acid), which is characteristic of coal-fired sources. This factor analysis grouping along with additional emissions information suggests that coal-fired power plants are the principal source of sulfate and nitrate.     

Response of inorganic fine particulate matter to emission changes of sulfur dioxide and ammonia: the eastern United States as a case study

A three-dimensional chemical transport model (PMCAMx) was used to investigate changes in fine particle (PM2.5) concentrations in response to changes in sulfur dioxide (SO2) and ammonia (NH3) emissions during July 2001 and January 2002 in the eastern United States. A uniform 50% reduction in SO2 emissions was predicted to produce an average decrease of PM2.5 concentrations by 26% during July but only 6% during January. A 50% reduction of NH3 emissions leads to an average 4 and 9% decrease in PM2.5 in July and January, respectively. During the summer, the highest concentration of sulfate is in South Indiana (12.8 microg x m(-3)), and the 50% reduction of SO2 emissions results in a 5.7 microg x m(-3) (44%) sulfate decrease over this area. During winter, the SO2 emissions reduction results in a 1.5 microg x m(-3) (29%) decrease of the peak sulfate levels (5.2 microg x m(-3)) over Southeast Georgia. The maximum nitrate and ammonium concentrations are predicted to be over the Midwest (1.9 (-3)g x m(-3) in Ohio and 5.3 microg x m(-3) in South Indiana, respectively) in the summer whereas in the winter these concentrations are higher over the Northeast (3 microg x m(-3) of nitrate in Connecticut and 2.7 microg x m(-3) of ammonium in New York). The 50% NH3 emissions reduction is more effective for controlling nitrate, compared with SO2 reductions, producing a 1.1 microg x m(-3) nitrate decrease over Ohio in July and a 1.2 microg x m(-3) decrease over Connecticut in January. Ammonium decreases significantly when either SO2 or NH3 emissions are decreased. However, the SO2 control strategy has better results in July when ammonium decreases, up to 2 microg x m(-3) (37%), are predicted in South Indiana. The NH3 control strategy has better results in January (ammonium decreases up to 0.4 microg x m(-3) in New York). The spatial and temporal characteristics of the effectiveness of these emission control strategies during the summer and winter seasons are discussed.     

Aerosol ion characteristics during the Big Bend Regional Aerosol and Visibility Observational study

The ionic compositions of particulate matter with aerodynamic diameter < or = 2.5 microm (PM2.5) and size-resolved aerosol particles were measured in Big Bend National Park, Texas, during the 1999 Big Bend Regional Aerosol and Visibility Observational study. The ionic composition of PM2.5 aerosol was dominated by sulfate (SO4(2-)) and ammonium (NH4+). Daily average SO4(2-) and NH4+ concentrations were strongly correlated (R2 = 0.94). The molar ratio of NH4+ to SO4(2-) averaged 1.54, consistent with concurrent measurements of aerosol acidity. The aerosol was observed to be comprised of a submicron fine mode consisting primarily of ammoniated SO4(2-) and a coarse particle mode containing nitrate (NO3-). The NO3- appears to be primarily associated with sea salt particles where chloride has been replaced by NO3-, although formation of calcium nitrate (Ca(NO3)2) is important, too, on several days. Size-resolved aerosol composition results reveal that a size cut in particulate matter with aerodynamic diameter < or = 1 microm would have provided a much better separation of fine and coarse aerosol modes than the standard PM2.5 size cut utilized for the study. Although considerable nitric acid exists in the gas phase at Big Bend, the aerosol is sufficiently acidic and temperatures sufficiently high that even significant future reductions in PM2.5 SO4(2-) are unlikely to be offset by formation of particulate ammonium nitrate in summer or fall.     

Current status of air quality models for particulate matter.

The current status of the mathematical modeling of atmospheric particulate matter (PM) is reviewed in this paper. Simulating PM requires treating various processes, including the formation of condensable species, the gas/ particle partitioning of condensable compounds, and in some cases, the evolution of the particle size distribution. The algorithms available to simulate these processes are reviewed and discussed. Eleven 3-dimensional (3-D) Eulerian air quality models for PM are reviewed in terms of their formulation and past applications. Results of past performance evaluations of 3-D Eulerian PM models are presented. Currently, 24-hr average PM2.5 concentrations appear to be predicted within 50% for urban-scale domains. However, there are compensating errors among individual particulate species. The lowest errors tend to be associated with SO4(2-), while NO3-, black carbon (BC), and organic carbon (OC) typically show larger errors due to uncertainties in emissions inventories and the prediction of the secondary OC fraction. Further improvements and performance evaluations are recommended.     

Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2--a case study

We use the fractional aerosol optical depth (AOD) values derived from Multiangle Imaging Spectroradiometer (MISR) aerosol component measurements, along with aerosol transport model constraints, to estimate ground-level concentrations of fine particulate matter (PM2.5) mass and its major constituents in the continental United States. Regression models using fractional AODs predict PM2.5 mass and sulfate (SO4) concentrations in both the eastern and western United States, and nitrate (NO3) concentrations in the western United States reasonably well, compared with the available ground-level U.S. Environment Protection Agency (EPA) measurements. These models show substantially improved predictive power when compared with similar models using total-column AOD as a single predictor, especially in the western United States. The relative contributions of the MISR aerosol components in these regression models are used to estimate size distributions of EPA PM2.5 species. This method captures the overall shapes of the size distributions of PM2.5 mass and SO4 particles in the east and west, and NO3 particles in the west. However, the estimated PM2.5 and SO4 mode diameters are smaller than those previously reported by monitoring studies conducted at ground level. This is likely due to the satellite sampling bias caused by the inability to retrieve aerosols through cloud cover, and the impact of particle hygroscopicity on measured particle size distributions at ground level.     

Long-term assessment of particulate matter using CHIMERE model

Particulate matter (PM) and aerosols have became a critical pollutant and object of several research applications, due to their increasing levels, especially in urban areas, causing air pollution problems and thus effects on human health. The main purpose of this study is to perform a first long-term air quality assessment for Portugal, regarding aerosols and PM pollution. The CHIMERE chemistry-transport model, forced by the MM5 meteorological fields, was applied over Portugal for 2001 year, with 10 km horizontal resolution, using an emission inventory obtained from a spatial top-down disaggregation of the 2001 national inventory database. The evaluation model exercise shows a model trend to overestimate particulate pollution episodes (peaks) at urban sites, especially in winter season. This could be due to an underprediction of the winter model vertical mixing and also to an overestimation of PM emissions. Simulated inorganic components (ammonium and sulfate) and secondary organic aerosols (SOA) were compared to measurements taken at Aveiro (northwest coast of Portugal). An underestimation of the three components was verified. However, the model is able to predict their seasonal variation. Nevertheless, as a first approach, and despite the complex topography and coastal location of Portugal affected by sea salt natural aerosols emissions, the results obtained show that the model reproduces the PM levels, temporal evolution, and spatial patterns. The concentration maps reveal that the areas with high PM values are covered by the air quality monitoring network.     

Chemical characterization of extractable water soluble matter associated with PM10 from Mexico City during 2000

We report the chemical composition of PM10-associated water-soluble species in Mexico City during the second semester of 2000. PM10 samples were collected at four ambient air quality monitoring sites in Mexico City. We determined soluble ions (chloride, nitrate, sulfate, ammonium, sodium, potassium), ionizable transition metals (Zn, Fe, Ti, Pb, Mn, V, Ni, Cr, Cu) and soluble protein. The higher PM(10) levels were observed in Xalostoc (45-174 microg m(-3)) and the lowest in Pedregal (19-54 microg m(-3)). The highest SO2 average concentrations were observed in Tlalnepantla, NO2 in Merced and O3 and NO(x) in Pedregal. The concentration range of soluble sulfate was 6.7-7.9 and 19-25.5 microg m(-3) for ammonium, and 14.8-29.19 for soluble V and 3.2-7.7 ng m(-3) for Ni, suggesting a higher contribution of combustion sources. PM-associated soluble protein levels varied between 0.038 and 0.169 mg m(-3), representing a readily inhalable constituent that could contribute to adverse outcomes. The higher levels for most parameters studied were observed during the cold dry season, particularly in December. A richer content of soluble metals was observed when they were expressed by mass/mass units rather than by air volume units. Significant correlations between Ni-V, Ni-SO4(-2), V-SO4(-2), V-SO2, Ni-SO2 suggest the same type of emission source. The variable soluble metal and ion concentrations were strongly influenced by the seasonal meteoclimatic conditions and the differential contribution of emission sources. Our data support the idea that PM10 mass concentration by itself does not provide a clear understanding of a local PM air pollution problem.     

The Southeastern Aerosol Research and Characterization Study, part 3: continuous measurements of fine particulate matter mass and composition

Deployment of continuous analyzers in the Southeastern Aerosol Research and Characterization Study (SEARCH) network began in 1998 and continues today as new technologies are developed. Measurement of fine particulate matter (PM2.5) mass is performed using a dried, 30 degrees C tapered element oscillating microbalance (TEOM). TEOM measurements are complemented by observations of light scattering by nephelometry. Measurements of major constituents include: (1) SO4(2-) via reduction to SO2; (2) NH4+ and NO3- via respective catalytic oxidation and reduction to NO, (3) black carbon (BC) by optical absorption, (4) total carbon by combustion to CO2, and (5) organic carbon by difference between the latter two measurements. Several illustrative examples of continuous data from the SEARCH network are presented. A distinctive composite annual average diurnal pattern is observed for PM2.5 mass, nitrate, and BC, likely indicating the influence of traffic-related emissions, growth, and break up of the boundary layer and formation of ammonium nitrate. Examination of PM2.5 components indicates the need to better understand the continuous composition of the unmeasured "other" category, because it contributes a significant fraction to total mass during periods of high PM2.5 loading. Selected episodes are presented to illustrate applications of SEARCH data. An SO2 conversion rate of 0.2%/hr is derived from an observation of a plume from a coal-fired power plant during early spring, and the importance of local, rural sources of NH3 to the formation of ammonium nitrate in particulate matter (PM) is demonstrated.     

Development and application of a three-dimensional aerosol chemical transport model,PMCAMx

A three-dimensional chemical transport model (PMCAMx) is used to simulate PM mass and composition in the eastern United States for a July 2001 pollution episode. The performance of the model in this region is evaluated, taking advantage of the highly time and size-resolved PM and gas-phase data collected during the Pittsburgh Air Quality Study (PAQS). PMCAMx uses the framework of CAMx and detailed aerosol modules to simulate inorganic aerosol growth, aqueous-phase chemistry, secondary organic aerosol formation, nucleation, and coagulation. The model predictions are compared to hourly measurements of PM_2.5 mass and composition at Pittsburgh, as well as to measurements from the AIRS and IMPROVE networks. The performance of the model for the major PM_2.5 components (sulfate, ammonium, and organic carbon) is encouraging (fractional errors are in general smaller than 50%). Additional improvements are possible if the rainfall measurements are used instead of the meteorological model predictions. The modest errors in ammonium predictions and the lack of bias for the total (gas and particulate) ammonium suggest that the improved ammonia inventory used is reasonable. The significant errors in aerosol nitrate predictions are mainly due to difficulties in simulating the nighttime formation of nitric acid. The concentrations of elemental carbon (EC) in the urban areas are significantly overpredicted. This is a problem related to both the emission inventory but also the different EC measurement methods that have been used in the two measurement networks (AIRS and IMPROVE) and the actual development of the inventory. While the ability of the model to reproduce OC levels is encouraging, additional work is necessary to confirm that that this is due to the right reasons and not offsetting errors in the primary emissions and the secondary formation. The model performance against the semi-continuous measurements in Pittsburgh appears to be quite similar to its performance against daily average measurements in a wide range of stations across the Eastern US. This suggests that the skill of the model to reproduce the diurnal variability of PM_2.5 and its major components is as good as its ability to reproduce the daily average values and also the significant value of high temporal resolution measurements for model evaluation.     

Chemical speciation of PM2.5 collected during prescribed fires of the Coconino National Forest near Flagstaff, Arizona

The use of prescribed fire is expected to increase in an effort to reduce the risk of catastrophic fire, particularly at urban/forest interfaces. Fire is a well-known source of particulate matter (PM) with particle sizes < or =2.5 microm (PM2.5), small diameter PM known to affect climate, visibility, and human health. In this work, PM2.5 was collected during seven first-entry burns (flaming and smoldering stages) and one maintenance burn of the Coconino National Forest. Samples were analyzed for organic and elemental carbon, cations (sodium, potassium [K+], and ammonium [NH4+]), anions (nitrate [NO3-] and sulfate), and 48 elements (with atomic weights between sodium and lead). The PM2.5 contained high organic carbon levels (typically >90% by mass), commonly observed ions (K+, NH4+, and NO3-) and elements (K+, chlorine, sulfur, and silicon), as well as titanium and chromium. Flaming produced higher K+ and NH4+ levels than smoldering, and the elemental signature was more complex (20 versus 7 elements). Average organic carbon x 1.4 mass fractions (+/-standard deviation) were lower during flaming (92+/-14%) than during smoldering (124+/-24%). The maintenance (grassland) burn produced lower particle concentrations, lower NH4+ and NO3- levels, and higher K and chlorine levels than did the first-entry fires.     

Analysis of aerosol particles and coarse particulate matter concentrations in Chillán, Chile, 2001-2003

Daily particle samples were collected in Chillán, Chile, at six urban locations from September 1, 2001, through September 30, 2003. Aerosol samples were collected using monitors equipped with a Sierra Andersen 246-b cyclone inlet on Teflon filters. Average concentrations of coarse particulate matter (PM10) for the 2001-2003 period ranged from 43.4 microg/m3 to 81.8 microg/m3 across the six sites. Annual PM10 concentration levels exceeded the European Union concentration limits. Mean PM10 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March). Average contributions to PM10 from organic matter, soil dust, nitrate (NO3-), elemental carbon, ammonium (NH4+), and sulfate (SO4(2-)) were 31%, 27%, 11%, 8%, 7%, and 5%, respectively. The chemical analyses indicated that carbonaceous substances were the most abundant components of PM10 in cold months, whereas crustal material was the most abundant component of PM10 during warm months. Higher concentration levels were observed in the downtown area suggesting a clear anthropogenic origin, whereas in the rural sites the source was mainly natural, such as resuspended soil dust associated with traffic on unpaved roads and agricultural activities.