Source apportionment of Phoenix PM2.5 aerosol with the Unmix receptor model

The multivariate receptor model Unmix has been used to analyze a 3-yr PM2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories:gasoline engines (33 +/- 4%), diesel engines (16 +/- 2%), secondary SO4(2-) (19 +/- 2%), crustal/soil (22 +/- 2%), and vegetative burning (10 +/- 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.     

Ft. McHenry tunnel study: Source profiles and mercury emissions from diesel and gasoline powered vehicles

During the fall of 1998, the US Environmental Protection Agency and the Florida Department of Environmental Protection sponsored a 7-day study at the Ft. McHenry tunnel in Baltimore, MD with the objective of obtaining PM_2.5 vehicle source profiles for use in atmospheric mercury source apportionment studies. PM_2.5 emission profiles from gasoline and diesel powered vehicles were developed from analysis of trace elements, polycyclic aromatic hydrocarbons (PAH), and condensed aliphatic hydrocarbons. PM_2.5 samples were collected using commercially available sampling systems and were extracted and analyzed using conventional well-established methods. Both inorganic and organic profiles were sufficiently unique to mathematically discriminate the contributions from each source type using a chemical mass balance source apportionment approach. However, only the organic source profiles provided unique PAH tracers (e.g., fluoranthene, pyrene, and chrysene) for diesel combustion that could be used to identify source contributions generated using multivariate statistical receptor modeling approaches. In addition, the study found significant emission of gaseous elemental mercury (Hg⁰), divalent reactive gaseous mercury (RGM), and particulate mercury (Hg(p)) from gasoline but not from diesel powered motor vehicles. Fuel analysis supported the tunnel measurement results showing that total mercury content in all grades of gasoline (284±108 ng L⁻¹) was substantially higher than total mercury content in diesel fuel (62±37 ng L⁻¹) collected contemporaneously at local Baltimore retailers.     

Comparison and Evaluation of Chemically Speciated Mobile Source PM2.5 Particulate Matter Profiles

ABSTRACT

Mobile sources are significant contributors to ambient PM_{2 5}, accounting for 50% or more of the total observed levels in some locations. One of the important methods for resolving the mobile source contribution is through chemical mass balance (CMB) receptor modeling. CMB requires chemically speciated source profiles with known uncertainty to ensure accurate source contribution estimates. Mobile source PM profiles are available from various sources and are generally in the form of weight fraction by chemical species. The weight fraction format is commonly used, since it is required for input into the CMB receptor model. This paper examines the similarities and differences in mobile source PM_2.5 profiles that contain data for elements, ions, elemental carbon (EC) and organic carbon (OC), and in some cases speciated organics (e.g., polycyclic aromatic hydrocarbons [PAHs]), drawn from four different sources.

Notable characteristics of the mass fraction data include variability (relative contributions of elements and ions) among supposedly similar sources and a wide range of average EC:OC ratios (0.60 ± 0.53 to 1.42 ± 2.99) for light-duty gasoline vehicles (LDGVs), indicating significant EC emissions from LDGVs in some cases. For diesel vehicles, average EC:OC ratios range from 1.09 ± 2.66 to 3.54 ± 3.07. That different populations of the same class of emitters can show considerable variability suggests caution should be exercised when selecting and using profiles in source apportionment studies.     

Determination of sources of fine particles in different ambient atmospheres in South Korea using a chemical mass balance model

Abstract

To determine the sources of particulate matter less than 2.5?μm (PM_2.5 in different ambient atmospheres (urban, roadside, industrial, and rural sites), the chemical components of PM_2.5 such as ions (Cl^-, NO₃^-, SO₄^2-, NH₄⁺, Na⁺, K⁺, Ca²⁺, and Mg²⁺), carbonaceous species, and elements (Al, As, Ba, Cd, Cu, Fe, Mn, Ni, Pb, Se, V, and Zn) were measured. The average mass concentrations of PM_2.5 at the urban, roadside, industrial, and rural sites were 31.5?±?14.8, 31.6?±?22.3, 31.4?±?16.0, and 25.8?±?12.4?μg/m³, respectively. Except for secondary ammonium sulfate and ammonium nitrate, the model results showed that the traffic source (i.e., the sum of gasoline and diesel vehicle sources) was the most dominant source of PM_2.5 (17.1%) followed by biomass burning (13.8%) at the urban site. The major primary sources of PM_2.5 were consistent with the site characteristics (diesel vehicle source at the roadside site, coal-fired plants at the industrial site, and biomass burning at the rural site). Seasonal data from the urban site suggested that ammonium sulfate and ammonium nitrate were the most dominant sources of PM_2.5 during all seasons. Further, the contribution of road dust source to PM_2.5 increased during spring and fall seasons. We conclude that the determination of the major PM_2.5 sources is useful for establishing efficient control strategies for PM_2.5 in different regions and seasons.     

A Fuel-Based Assessment of Off-Road Diesel Engine Emissions

ABSTRACT

The use of diesel engines in off-road applications is a significant source of nitrogen oxides (NO_x) and particulate matter (PM₁₀). Such off-road applications include railroad locomotives, marine vessels, and equipment used for agriculture, construction, logging, and mining. Emissions from these sources are only beginning to be controlled. Due to the large number of these engines and their wide range of applications, total activity and emissions from these sources are uncertain. A method for estimating the emissions from off-road diesel engines based on the quantity of diesel fuel consumed is presented. Emission factors are normalized by fuel consumption, and total activity is estimated by the total fuel consumed.

Total exhaust emissions from off-road diesel equipment (excluding locomotives and marine vessels) in the United States during 1996 have been estimated to be 1.2 × 10⁹ kg NO_x and 1.2 x 10⁸ kg PM₁₀. Emissions estimates published by the U.S. Environmental Protection Agency are 2.3 times higher for both NO_x and exhaust PM₁₀ emissions than estimates based directly on fuel consumption. These emissions estimates disagree mainly due to differences in activity estimates, rather than to differences in the emission factors. All current emission inventories for off-road engines are uncertain because of the limited in-use emissions testing that has been performed on these engines. Regional- and state-level breakdowns in diesel fuel consumption by off-road mobile sources are also presented. Taken together with on-road measurements of diesel engine emissions, results of this study suggest that in 1996, off-road diesel equipment (including     

Chemical characterization and source apportionment of fine and coarse particulate matter in Lahore,Pakistan

Lahore, Pakistan is an emerging megacity that is heavily polluted with high levels of particle air pollution. In this study, respirable particulate matter (PM_2.5 and PM₁₀) were collected every sixth day in Lahore from 12 January 2007 to 19 January 2008. Ambient aerosol was characterized using well-established chemical methods for mass, organic carbon (OC), elemental carbon (EC), ionic species (sulfate, nitrate, chloride, ammonium, sodium, calcium, and potassium), and organic species. The annual average concentration (±one standard deviation) of PM_2.5 was 194 ± 94 μg m^?3 and PM₁₀ was 336 ± 135 μg m^?3. Coarse aerosol (PM_10?2.5) was dominated by crustal sources like dust (74 ± 16%, annual average ± one standard deviation), whereas fine particles were dominated by carbonaceous aerosol (organic matter and elemental carbon, 61 ± 17%). Organic tracer species were used to identify sources of PM_2.5 OC and chemical mass balance (CMB) modeling was used to estimate relative source contributions. On an annual basis, non-catalyzed motor vehicles accounted for more than half of primary OC (53 ± 19%). Lesser sources included biomass burning (10 ± 5%) and the combined source of diesel engines and residual fuel oil combustion (6 ± 2%). Secondary organic aerosol (SOA) was an important contributor to ambient OC, particularly during the winter when secondary processing of aerosol species during fog episodes was expected. Coal combustion alone contributed a small percentage of organic aerosol (1.9 ± 0.3%), but showed strong linear correlation with unidentified sources of OC that contributed more significantly (27 ± 16%). Brick kilns, where coal and other low quality fuels are burned together, are suggested as the most probable origins of unapportioned OC. The chemical profiling of emissions from brick kilns and other sources unique to Lahore would contribute to a better understanding of OC sources in this megacity.     

PM2.5 source apportionment: reconciling receptor models for U.S. nonurban and urban long-term networks

Chemical mass balance (CMB) and trajectory receptor models were applied to speciated particulate matter with aerodynamic diameter < or =2.5 microm (PM2.5) measurements from Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network across the state of Minnesota as part of the Minnesota PM2.5 Source Apportionment Study (MPSAS). CMB equations were solved by the Unmix, positive matrix factorization (PMF), and effective variance (EV) methods, giving collective source contribution and uncertainty estimates. Geological source profiles developed from local dust materials were either incorporated into the EV-CMB model or used to verify factors derived from Unmix and PMF. Common sources include soil dust, calcium (Ca)-rich dust, diesel and gasoline vehicle exhausts, biomass burning, secondary sulfate, and secondary nitrate. Secondary sulfate and nitrate aerosols dominate PM2.5 mass (50-69%). Mobile sources outweigh area sources at urban sites, and vice versa at rural sites due to traffic emissions. Gasoline and diesel contributions can be separated using data from the STN, despite significant uncertainties. Major differences between MPSAS and earlier studies on similar environments appear to be the type and magnitude of stationary sources, but these sources are generally minor (<7%) in this and other studies. Ensemble back-trajectory analysis shows that the lower Midwestern states are the predominant source region for secondary ammoniated sulfate in Minnesota. It also suggests substantial contributions of biomass burning and soil dust from out-of-state on occasions, although a quantitative separation of local and regional contributions was not achieved in the current study. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for a summary of input data, Unmix and PMF factor profiles, and additional maps.     

Emissions of PCDD/Fs, PCBs, and PAHs from legacy on-road heavy-duty diesel engines

Exhaust emissions of seventeen 2,3,7,8-substituted polychlorinated dibenzo-p-dioxin/furan (PCDD/F) congeners, tetra-octa PCDD/F homologues, 12 WHO 2005 polychlorinated biphenyl (PCB) congeners, mono-nona chlorinated biphenyl homologues, and 19 polycyclic aromatic hydrocarbons (PAHs) from three legacy diesel engines were investigated. The three engines tested were a 1985 model year GM 6.2 J-series engine, a 1987 model year Detroit Diesel Corporation 6V92 engine, and a 1993 model year Cummins L10 engine. Results were compared to United States’ mobile source inventory for on-road diesel engines, as well as historic and modern diesel engine emission values. The test fuel contained chlorine at 9.8 ppm which is 1.5 orders of magnitude above what is found in current diesel fuel and 3900 ppm sulfur to simulate fuels that would have been available when these engines were produced. Results indicate PCDD/F emissions of 13.1, 7.1, and 13.6 pg International Toxic Equivalency (I-TEQ) L⁻¹ fuel consumed for the three engines respectively, where non-detects are equal to zero. This compares with a United States’ mobile source on-road diesel engine inventory value of 946 pg I-TEQ L⁻¹ fuel consumed and 1.28 pg I-TEQ L⁻¹ fuel consumed for modern engines equipped with a catalyzed diesel particle filter and urea selective catalytic reduction. PCB emissions are 2 orders of magnitude greater than modern diesel engines. PAH results are representative of engines from this era based on historical values and are 3-4 orders of magnitude greater than modern diesel engines.     

Loading Effect Correction for Real-Time Aethalometer Measurements of Fresh Diesel Soot

Abstract

In this study, a correction was developed for the aethalometer to measure real-time black carbon (BC) concentrations in an environment dominated by fresh diesel soot. The relationship between the actual mass-specific absorption coefficient for BC and the BC-dependent attenuation coefficients was determined from experiments conducted in a diesel exposure chamber that provided constant concentrations of fine particulate matter (PM; PM_2.5; PM <2.5 µm in aerodynamic diameter) from diesel exhaust. The aethalometer reported BC concentrations decreasing with time from 48.1 to 31.5 µg m^?3when exposed to constant PM_2.5concentrations of 55 ± 1 µg m^?3and b_scat= 95 ± 3 Mm^?1from diesel exhaust. This apparent decrease in reported light-absorbing PM concentration was used to derive a correction K(ATN) for loading of strong light-absorbing particles onto or into the aethalometer filter tape, which was a function of attenuation of light at 880 nm by the embedded particles.     

Emissions of PCDD/Fs,PCBs, and PAHs from legacy on-road heavy-duty diesel engines

Exhaust emissions of seventeen 2,3,7,8-substituted polychlorinated dibenzo-p-dioxin/furan (PCDD/F) congeners, tetra–octa PCDD/F homologues, 12 WHO 2005 polychlorinated biphenyl (PCB) congeners, mono–nona chlorinated biphenyl homologues, and 19 polycyclic aromatic hydrocarbons (PAHs) from three legacy diesel engines were investigated. The three engines tested were a 1985 model year GM 6.2 J-series engine, a 1987 model year Detroit Diesel Corporation 6V92 engine, and a 1993 model year Cummins L10 engine. Results were compared to United States’ mobile source inventory for on-road diesel engines, as well as historic and modern diesel engine emission values. The test fuel contained chlorine at 9.8 ppm which is 1.5 orders of magnitude above what is found in current diesel fuel and 3900 ppm sulfur to simulate fuels that would have been available when these engines were produced. Results indicate PCDD/F emissions of 13.1, 7.1, and 13.6 pg International Toxic Equivalency (I-TEQ) L⁻¹ fuel consumed for the three engines respectively, where non-detects are equal to zero. This compares with a United States’ mobile source on-road diesel engine inventory value of 946 pg I-TEQ L⁻¹ fuel consumed and 1.28 pg I-TEQ L⁻¹ fuel consumed for modern engines equipped with a catalyzed diesel particle filter and urea selective catalytic reduction. PCB emissions are 2 orders of magnitude greater than modern diesel engines. PAH results are representative of engines from this era based on historical values and are 3–4 orders of magnitude greater than modern diesel engines.     

Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution

Understanding the spatial–temporal variations of source apportionment of PM_2.5 is critical to the effective control of particulate pollution. In this study, two one-year studies of PM_2.5 composition were conducted at three contrasting sites in Hong Kong from November 2000 to October 2001, and from November 2004 to October 2005, respectively. A receptor model, principal component analysis (PCA) with absolute principal component scores (APCS) technique, was applied to the PM_2.5 data for the identification and quantification of pollution sources at the rural, urban and roadside sites. The receptor modeling results identified that the major sources of PM_2.5 in Hong Kong were vehicular emissions/road erosion, secondary sulfate, residual oil combustion, soil suspension and sea salt regardless of sampling sites and sampling periods. The secondary sulfate aerosols made the most significant contribution to the PM_2.5 composition at the rural (HT) (44 ± 3%, mean ± 1σ standard error) and urban (TW) (28 ± 2%) sites, followed by vehicular emission (20 ± 3% for HT and 23 ± 4% for TW) and residual oil combustion (17 ± 2% for HT and 19 ± 1% for TW). However, at the roadside site (MK), vehicular emissions especially diesel vehicle emissions were the major source of PM_2.5 composition (33 ± 1% for diesel vehicle plus 18 ± 2% for other vehicles), followed by secondary sulfate aerosols (24 ± 1%). We found that the contribution of residual oil combustion at both urban and rural sites was much higher than that at the roadside site (2 ± 0.4%), perhaps due to the marine vessel activities of the container terminal near the urban site and close distance of pathway for the marine vessels to the rural site. The large contribution of secondary sulfate aerosols at all the three sites reflected the wide influence of regional pollution. With regard to the temporal trend, the contributions of vehicular emission and secondary sulfate to PM_2.5 showed higher autumn and winter values and lower summer levels at all the sites, particularly for the background site, suggesting that the seasonal variation of source apportionment in Hong Kong was mainly affected by the synoptic meteorological conditions and the long-range transport. Analysis of annual patterns indicated that the contribution of vehicular emission at the roadside was significantly reduced from 2000/01 to 2004/05 (p < 0.05, two-tail), especially the diesel vehicular emission (p < 0.001, two-tail). This is likely attributed to the implementation of the vehicular emission control programs with the tightening of diesel fuel contents and vehicular emission standards over these years by the Hong Kong government. In contrast, the contribution of secondary sulfate was remarkably increased from 2001 to 2005 (p < 0.001, two-tail), indicating a significant growth in regional sulfate pollution over the years.     

Source Identification of Atlanta Aerosol by Positive Matrix Factorization

Abstract

Data characterizing daily integrated particulate matter (PM) samples collected at the Jefferson Street monitoring site in Atlanta, GA, were analyzed through the application of a bilinear positive matrix factorization (PMF) model. A total of 662 samples and 26 variables were used for fine particle (particles ≤2.5 µm in aerodynamic diameter) samples (PM_2.5 ), and 685 samples and 15 variables were used for coarse particle (particles between 2.5 and 10 µm in aerodynamic diameter) samples (PM_10–2.5 ). Measured PM mass concentrations and compositional data were used as independent variables. To obtain the quantitative contributions for each source, the factors were normalized using PMF-apportioned mass concentrations. For fine particle data, eight sources were identified: SO₄ ^2?-rich secondary aerosol (56%), motor vehicle (22%), wood smoke (11%), NO₃ ^?-rich secondary aerosol (7%), mixed source of cement kiln and organic carbon (OC) (2%), airborne soil (1%), metal recycling facility (0.5%), and mixed source of bus station and metal processing (0.3%). The SO₄ ^2?-rich and NO₃ ^?-rich secondary aerosols were associated with NH₄ ⁺. The SO₄ ^2?-rich secondary aerosols also included OC. For the coarse particle data, five sources contributed to the observed mass: airborne soil (60%), NO₃ ^?-rich secondary aerosol (16%), SO₄ ^2?-rich secondary aerosol (12%), cement kiln (11%), and metal recycling facility (1%). Conditional probability functions were computed using surface wind data and identified mass contributions from each source. The results of this analysis agreed well with the locations of known local point sources.     

Determination of Source Contributions to Ambient PM2.5 in Kaohsiung,Taiwan, Using a Receptor Model

ABSTRACT

Ambient particulates of PM_2.5 were sampled at three sites in Kaohsiung, Taiwan, during February and March 1999. In addition, resuspended PM_2.5 collected from traffic tunnels, paved roads, fly ash of a municipal solid waste (MSW) incinerator, and seawater was obtained. All the samples were analyzed for twenty constituents, including water-soluble ions, organic carbon (OC), elemental carbon (EC), and metallic elements. In conjunction with local source profiles and the source profiles in the model library SPECIATE EPA, the receptor model based on chemical mass balance (CMB) was then applied to determine the source contributions to ambient PM_2.5.

The mean concentration of ambient PM_2.5 was 42.6953.68 μj.g/m³ for the sampling period. The abundant species in ambient PM_2.5 in the mass fraction for three sites were OC (12.7-14.2%), SO₄ ^2- (12.8-15.1%), NO₃ ^- (8.110.3%), NH₄+ (6.7-7.5%), and EC (5.3-8.5%). Results of CMB modeling show that major pollution sources for ambient PM_2.5 are traffic exhaust (18-54%), secondary aerosols (30-41% from SO₄ ^2- and NO₃ ^-), and outdoor burning of agriculture wastes (13-17%).     

Hydrocarbon emission fingerprints from contemporary vehicle/engine technologies with conventional and new fuels

The present paper presents results from the analysis of 29 individual C₂–C₉ hydrocarbons (HCs) specified in the European Commission Ozone Directive. The 29 HCs are measured in exhaust from common, contemporary vehicle/engine/fuel technologies for which very little or no data is available in the literature. The obtained HC emission fingerprints are compared with fingerprints deriving from technologies that are being phased out in Europe. Based on the total of 138 emission tests, thirteen type-specific fingerprints are extracted (Mean ± SD percentage contributions from individual HCs to the total mass of the 29 HCs), essential for receptor modelling source apportionment. The different types represent exhaust from Euro3 and Euro4 light-duty (LD) diesel and petrol-vehicles, Euro3 heavy-duty (HD) diesel exhaust, and exhaust from 2-stroke preEuro, Euro1 and Euro2 mopeds. The fuels comprise liquefied petroleum gas, petrol/ethanol blends (0–85% ethanol), and mineral diesel in various blends (0–100%) with fatty acid methyl esters, rapeseed methyl esters palm oil methyl esters, soybean oil methyl or sunflower oil methyl esters. Type-specific tracer compounds (markers) are identified for the various vehicle/engine/fuel technologies.An important finding is an insignificant effect on the HC fingerprints of varying the test driving cycle, indicating that combining HC fingerprints from different emission studies for receptor modelling purposes would be a robust approach.The obtained results are discussed in the context of atmospheric ozone formation and health implications from emissions (mg km^?1 for LD and mopeds and mg kW h^?1 for HD, all normalised to fuel consumption: mg dm^?3 fuel) of the harmful HCs, benzene and 1,3-butadiene.Another important finding is a strong linear correlation of the regulated “total” hydrocarbon emissions (tot-HC) with the ozone formation potential of the 29 HCs (ΣPO₃ = (1.66 ± 0.04) × tot-RH; r² = 0.93). Tot-HC is routinely monitored in emission control laboratories, whereas C₂–C₉ are not. The revealed strong correlations broadens the usability of data from vehicle emission control laboratories and facilitates the comparison of the ozone formation potential of HCs in exhaust from of old and new vehicle/engine/fuel technologies.     

The stable carbon isotope composition of PM2.5 and PM10 in Mexico City Metropolitan Area air

The sources and distribution of carbon in ambient suspended particles (PM_2.5 and PM₁₀) of Mexico City Metropolitan Area (MCMA) air were traced using stable carbon isotopes (¹³C/¹²C). Tested potential sources included rural and agricultural soils, gasoline and diesel, liquefied-petroleum gas, volcanic ash, and street dust. The complete combustion of LP gas, diesel and gasoline yielded the lightest δ¹³C values (?27 to ?29‰ vs. PDB), while street dust (PM₁₀) represented the isotopically heaviest endmember (?17‰). The δ¹³C values of rural soils from four geographically separated sites were similar (?20.7 ± 1.5‰). δ¹³C values of particles and soot from diesel and gasoline vehicle emissions and agricultural soils varied between ?23 and ?26‰. Ambient PM samples collected in November of 2000, and March and December of 2001 at three representative receptor sites of industrial, commercial and residential activities had a δ¹³C value centered around ?25.1‰ in both fractions, resulting from common carbon sources. The predominant carbon sources to MCMA atmospheric particles were hydrocarbon combustion (diesel and/or gasoline) and particles of geological origin. The significantly depleted δ¹³C values from the industrial site reflect the input of diesel combustion by mobile and point source emissions. Based on stable carbon isotope mass balance, the carbon contribution of geological sources at the commercial and residential sites was approximately 73% for the PM₁₀ fraction and 54% for PM_2.5. Although not measured in this study, biomass-burning emissions from nearby forests are an important carbon source characterized by isotopically lighter values (?29‰), and can become a significant contributor (67%) of particulate carbon to MCMA air under the prevalence of southwesterly winds. Alternative sources of these ¹³C-depleted particles, such as cooking fires and municipal waste incineration, need to be assessed. Results show that stable carbon isotope measurements are useful for distinguishing between some carbon sources in suspended particles to MCMA air, and that wind direction has an impact on the distribution of carbon sources in this basin.     

Investigation of the Ultraviolet Photolysis Method for the Determination of Organic Nitrogen in Aerosol Samples

Abstract

The research objective was to adapt the ultraviolet (UV)photolysis method to determine dissolved organic nitrogen (DON) in aqueous extracts of aerosol samples. DON was assumed to be the difference in total concentration of inorganic nitrogen forms before and after sample irradiation. Using a 2² factorial design the authors found that the optimal conversion of urea, amino acids (alanine, aspartic acid, glycine, and serine), and methylamine for a reactor temperature of 44 °C occurred at pH 2.0 with a 24-hr irradiance period at concentrations < µM of organic nitrogen. Different decomposition mechanisms were evident: the photolysis of amino acids and methylamine released mainly ammonium (NH₄ ⁺), but urea released a near equimolar ratio of NH₄ ⁺ and nitrate (NO₃ ^?). The method was applied to measure DON in the extracts of aerosol samples from Tampa, FL, over a 32-day sampling period. Average dissolved inorganic (DIN) and DON concentrations in the particulate matter fraction PM₁₀ were 78.1 ± 29.2 nmol-Nm^?3and 8.3 ± 4.9 nmol-Nm^?3, respectively. The ratio between DON and total dissolved nitrogen ([TDN] = DIN + DON) was 10.1 ± 5.7%, and the majority of the DON (79.1 ± 18.2%) was found in the fine particulate matter (PM_2.5) fraction. The average concentrations of DIN and DON in the PM_2.5 fraction were 54.4 ± 25.6 nmol-Nm^?3 and 6.5 ± 4.4 nmol-Nm^?3, respectively.     

Measurement of black carbon aerosols near two Chinese megacities and the implications for improving emission inventories

Knowledge of the distribution and sources of black carbon (BC) is essential to understanding its impact on radiative forcing and the establishment of a control strategy. In this study, we analyze atmospheric BC and its relationships with fine particles (PM_2.5) and trace gases (CO, NO_y and SO₂) measured in the summer of 2005 in two areas frequently influenced by plumes from Beijing and Shanghai, the two largest cities in China. The results revealed different BC source characteristics for the two megacities. The average concentration of BC was 2.37 (±1.79) and 5.47 (±4.00) μg m^?3, accounting for 3.1% and 7.8% of the PM_2.5 mass, in Beijing and Shanghai, respectively. The good correlation between BC, CO and NO_y (R² = 0.54–0.77) and the poor correlation between BC and SO₂ suggest that diesel vehicles and marine vessels are the dominant sources of BC in the two urban areas during summer. The BC/CO mass ratio in the air mass from Shanghai was found to be much higher than that in the air mass from Beijing (0.0101 versus 0.0037 ΔgBC/ΔgCO), which is attributable to a larger contribution from diesel burning (diesel-powered vehicles and marine vessels) in Shanghai. Based on the measured ratios of BC/CO and annual emissions of CO, we estimate that the annual emissions of BC in Beijing and Shanghai are 9.51 Gg and 18.72 Gg, respectively. The improved emission rates of BC will help reduce the uncertainty in the assessment of the impact of megacities on regional climate.     

Source apportionment of airborne particulate matter in Southeast Texas using a source-oriented 3D air quality model

A nested version of the source-oriented externally mixed UCD/CIT model was developed to study the source contributions to airborne particulate matter (PM) during a two-week long air quality episode during the Texas 2000 Air Quality Study (TexAQS 2000). Contributions to primary PM and secondary ammonium sulfate in the Houston–Galveston Bay (HGB) and Beaumont–Port Arthur (BPA) areas were determined.The predicted 24-h elemental carbon (EC), organic compounds (OC), sulfate, ammonium ion and primary PM_2.5 mass are in good agreement with filter-based observations. Predicted concentrations of hourly sulfate, ammonium ion, and primary OC from diesel and gasoline engines and biomass burning organic aerosol (BBOA) at La Porte, Texas agree well with measurements from an Aerodyne Aerosol Mass Spectrometer (AMS).The UCD/CIT model predicts that EC is mainly from diesel engines and majority of the primary OC is from internal combustion engines and industrial sources. Open burning contributes large fractions of EC, OC and primary PM_2.5 mass. Road dust, internal combustion engines and industries are the major sources of primary PM_2.5. Wildfire dominates the contributions to all primary PM components in areas near the fires. The predicted source contributions to primary PM are in general agreement with results from a chemical mass balance (CMB) model. Discrepancy between the two models suggests that further investigations on the industrial PM emissions are necessary.Secondary ammonium sulfate accounts for the majority of the secondary inorganic PM. Over 80% of the secondary sulfate in the 4 km domain is produced in upwind areas. Coal combustion is the largest source of sulfate. Ammonium ion is mainly from agriculture sources and contributions from gasoline vehicles are significant in urban areas.     

Identification of Sources Contributing to Mid-Atlantic Regional Aerosol

Abstract

Source types or source regions contributing to the concentration of atmospheric fine particles measured at Brigantine National Wildlife Refuge, NJ, were identified using a factor analysis model called Positive Matrix Factorization (PMF). Cluster analysis of backward air trajectories on days of high- and low-factor concentrations was used to link factors to potential source regions. Brigantine is a Class I visibility area with few local sources in the center of the eastern urban corridor and is therefore a good location to study Mid-Atlantic regional aerosol. Sulfate (expressed as ammonium sulfate) was the most abundant species, accounting for 49% of annual average fine mass. Organic compounds (22%; expressed as 1.4 × organic carbon) and ammonium nitrate (10%) were the next abundant species. Some evidence herein suggests that secondary organic aerosol formation is an important contributor to summertime regional aerosol.

Nine factors were identified that contributed to PM_2.5 mass concentrations: coal combustion factors (66%, summer and winter), sea salt factors (9%, fresh and aged), motor vehicle/mixed combustion (8%), diesel/Zn-Pb (6%), incinerator/industrial (5%), oil combustion (4%), and soil (2%). The aged sea salt concentrations were highest in springtime, when the land breeze-sea breeze cycle is strongest. Comparison of backward air trajectories of high- and low-concentration days suggests that Brigantine is surrounded by sources of oil combustion, motor vehicle/mixed combustion, and waste incinerator/industrial emissions that together account for 17% of PM_2.5 mass. The diesel/Zn-Pb factor was associated with sources north and west of Brigantine. Coal combustion factors were associated with coal-fired power plants west and southwest of the site. Particulate carbon was associated not only with oil combustion, motor vehicle/mixed combustion, waste incinerator/industrial, and diesel/Pb-Zn, but also with the coal combustion factors, perhaps through common transport.     

Sources of submicron aerosol during fog-dominated wintertime at Kanpur

The main objective of this atmospheric study was to determine the major sources of PM₁ (particles having aerodynamic diameter <1.0 μm) within and near the city of Kanpur, in the Indo-Gangetic Plain. Day and night, 10 h long each, filter-based aerosol samples were collected for 4 months (November 2009 to February 2010) throughout the winter season. These samples were subjected to gravimetric and quantitative chemical analyses for determining water-soluble ions (NH₄ ⁺, F^?, Cl^?, NO₃ ^?, and SO₄ ^2?) using an ion chromatograph and trace elements using an inductively coupled plasma–optical emission spectrometer. The mean PM₁ mass concentrations were recorded as 114?±?71 μg/m³ (day) and 143?±?86 μg/m³ (night), respectively. A significantly higher diurnal contribution of ions (NH₄ ⁺, F^?, Cl^?, NO₃ ^?, and SO₄ ^2?) in PM₁ mass was observed during the fog-affected days and nights throughout the winter season, for which the average values were recorded as 38.09?±?13.39 % (day) and 34.98?±?12.59 % (night), respectively, of the total PM₁ mass. This chemical dataset was then used in a source-receptor model, UNMIX, and the model results are described in detail. UNMIX provided a maximum number of five source factors, including crustal material, composite vehicle, secondary aerosol, coal combustion, and iron/steel production and metallurgical industries, as the dominant air pollution sources for this study.