Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modelling

Aerosol samples for PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters less than 2.5 and 10 μm, respectively) were collected from 1993 to 1995 at five sites in Brisbane, a subtropical coastal city in Australia. This paper investigates the contributions of emission sources to PM_2.5 and PM₁₀ aerosol mass in Brisbane. Source apportionment results derived from the chemical mass balance (CMB), target transformation factor analysis (TTFA) and multiple linear regression (MLR) methods agree well with each other. The contributions from emission sources exhibit large variations in particle size with temporal and spatial differences. On average, the major contributors of PM₁₀ aerosol mass in Brisbane include: soil/road side dusts (25% by mass), motor vehicle exhausts (13%, not including the secondary products), sea salt (12%), Ca-rich and Ti-rich compounds (11%, from cement works and mineral processing industries), biomass burning (7%), and elemental carbon and secondary products contribute to around 15% of the aerosol mass on average. The major sources of PM_2.5 aerosols at the Griffith University (GU) site (a suburban site surrounded by forest area) are: elemental carbon (24% by mass), secondary organics (21%), biomass burning (15%) and secondary sulphate (14%). Most of the secondary products are related to motor vehicle exhausts, so, although motor vehicle exhausts contribute directly to only 6% of the PM_2.5 aerosol mass, their total contribution (including their secondary products) could be substantial. This pattern of source contribution is similar to the results for Rozelle (Sydney) among the major Australian studies, and is less in contributions from industrial and motor vehicular exhausts than the other cities. An attempt was made to estimate the contribution of rural dust and road side dust. The results show that road side dusts could contribute more than half of the crustal matter. More than 80% of the contribution of vehicle exhausts arises from diesel-fuelled trucks/buses. Biomass burning, large contributions of crustal matter, and/or local contributing sources under calm weather conditions, are often the cause of the high PM₁₀ episodes at the GU site in Brisbane.     

Source apportionment of ultrafine and fine particle concentrations in Brisbane, Australia

Purpose

To investigate the significance of sources around measurement sites, assist the development of control strategies for the important sources and mitigate the adverse effects of air pollution due to particle size.

Methods

In this study, sampling was conducted at two sites located in urban/industrial and residential areas situated at roadsides along the Brisbane Urban Corridor. Ultrafine and fine particle measurements obtained at the two sites in June?CJuly 2002 were analysed by positive matrix factorization.

Results

Six sources were present, including local traffic, two traffic sources, biomass burning and two currently unidentified sources. Secondary particles had a significant impact at site 1, while nitrates, peak traffic hours and main roads located close to the source also affected the results for both sites.

Conclusions

This significant traffic corridor exemplifies the type of sources present in heavily trafficked locations and future attempts to control pollution in this type of environment could focus on the sources that were identified.     

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.     

Combined source apportionment,using positive matrix factorization–chemical mass balance and principal component analysis/multiple linear regression–chemical mass balance models

The methods of positive matrix factorization–chemical mass balance and principal component analysis/multiple linear regression–chemical mass balance were studied in this paper, for combined source apportionment. Due to the high similarity among the source profiles, several problems would raised when only one receptor model was applied. For example, the collinearity problem would result in the negative contributions when applying CMB model; certain sources would not to be separated out when applying PCA or PMF model. In this study, PCA/MLR–CMB model and PMF–CMB were attempted to resolve the problem, where the combined models were applied to study the synthetic and ambient datasets. In synthetic dataset, there were seven sources (six actual sources from real world, and one unknown source). The results obtained by the combined models show that the combined source apportionment technique is feasible. In addition, an ambient dataset from a northern city in China was analyzed by PCA/MLR–CMB model and PMF–CMB model, and these two models got the similar results. The results show that coal combustion contributed the largest fraction to the total mass.     

Source apportionment of fine particles in Tennessee using a source-oriented model

Source apportionment of fine particles (PM2.5, particulate matter < 2 microm in aerodynamic diameter) is important to identify the source categories that are responsible for the concentrations observed at a particular receptor. Although receptor models have been used to do source apportionment, they do not fully take into account the chemical reactions (including photochemical reactions) involved in the formation of secondary fine particles. Secondary fine particles are formed from photochemical and other reactions involving precursor gases, such as sulfur dioxide, oxides of nitrogen, ammonia, and volatile organic compounds. This paper presents the results of modeling work aimed at developing a source apportionment of primary and secondary PM2.5. On-road mobile source and point source inventories for the state of Tennessee were estimated and compiled. The national emissions inventory for the year 1999 was used for the other states. U.S. Environmental Protection Agency Models3/Community Multi-Scale Air Quality modeling system was used for the photochemical/secondary particulate matter modeling. The modeling domain consisted of a nested 36-12-4-km domain. The 4-km domain covered the entire state of Tennessee. The episode chosen for the modeling runs was August 29 to September 9, 1999. This paper presents the approach used and the results from the modeling and attempts to quantify the contribution of major source categories, such as the on-road mobile sources (including the fugitive dust component) and coal-fired power plants, to observed PM2.5 concentrations in Tennessee. The results of this work will be helpful in policy issues targeted at designing control strategies to meet the PM2.5 National Ambient Air Quality Standards in Tennessee.     

Source apportionment of VOCs in the Los Angeles area using positive matrix factorization

Eight 3-h speciated hydrocarbon measurements were collected daily by the South Coast Air Quality Management District (SCAQMD) as part of the Photochemical Assessment Monitoring Stations (PAMS) program during the summers of 2001–03 at two sites in the Los Angeles air basin, Azusa and Hawthorne. Over 30 hydrocarbons from over 500 samples at Azusa and 600 samples at Hawthorne were subsequently analyzed using the multivariate receptor model positive matrix factorization (PMF). At Azusa and Hawthorne, five and six factors were identified, respectively, with a good comparison between predicted and measured mass. At Azusa, evaporative emissions (a median of 31% of the total mass), motor vehicle exhaust (22%), liquid/unburned gasoline (27%), coatings (17%), and biogenic emissions (3%) factors were identified. Factors identified at Hawthorne were evaporative emissions (a median of 34% of the total mass), motor vehicle exhaust (24%), industrial process losses (15%), natural gas (13%), liquid/unburned gasoline (13%), and biogenic emissions (1%). Together, the median contribution from mobile source-related factors (exhaust, evaporative emissions, and liquid/unburned gasoline) was 80% and 71% at Azusa and Hawthorne, respectively, similar to previous source apportionment results using the chemical mass balance (CMB) model. There is a difference in the distribution among mobile source factors compared to the CMB work, with an increase in the contribution from evaporative emissions, though the cause (changes in emissions or differences between models) is unknown.     

Source apportionment of primary and secondary organic aerosols using positive matrix factorization (PMF) of molecular markers

Monthly average ambient concentrations of more than eighty particle-phase organic compounds, as well as total organic carbon (OC) and elemental carbon (EC), were measured from March 2004 through February 2005 in five cities in the Midwestern United States. A multi-variant source apportionment receptor model, positive matrix factorization (PMF), was applied to explore the average source contributions to the five sampling sites using molecular markers for primary and secondary organic aerosols (POA, SOA). Using the molecular makers in the model, POA and SOA were estimated for each month at each site. Three POA factors were derived, which were dominated by primary molecular markers such as EC, hopanes, steranes, and polycyclic aromatic hydrocarbons (PAHs), and which represented the following POA sources: urban primary sources, mobile sources, and other combustion sources. The three POA sources accounted for 57% of total average ambient OC. Three factors, characterized by the presence of reaction products of isoprene, α-pinene and β-caryophyllene, and displaying distinct seasonal trends, were consistent with the characteristics of SOA. The SOA factors made up 43% of the total average measured OC. The PMF-derived results are in good agreement with estimated SOA concentrations obtained from SOA to tracer yield estimates obtained from smog chamber experiments. A linear regression comparing the smog chamber yield estimates and the PMF SOA contributions had a regression slope of 1.01 ± 0.07 and an intercept of 0.19 ± 0.10 μg OC m^?3 (adjusted R² of 0.763, n = 58).     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

南昌市大气PM2.5中多环芳烃的来源解析

在南昌市布设5个采样点,分别代表工业区、居住区、交通干线区、商业区以及郊区,于2007年7～8月进行大气PM2.5的采样.根据5个采样点测得的数据,通过因子分析法判断南昌市大气PM2.5中多环芳烃的主要来源,再利用多元线性回归法确定各主要来源对多环芳烃的贡献率.结果表明,南昌市多环芳烃的主要来源为车辆排放源、高温加热源、燃煤污染源,对多环芳烃的贡献率分别为37.9%、28.2%、22.0%.     

Source apportionment of particulate matter at urban mixed site in Indonesia using PMF

A receptor model of positive matrix factorization (PMF) was used to identify the emission sources of fine and coarse particulates in Bandung, a city located at about 150 km south-east of Jakarta. Total of 367 samples were collected at urban mixed site, Tegalega area, in Bandung City during wet and dry season in the period of 2001–2007. The samples of fine and coarse particulate matter were collected simultaneously using dichotomous samplers and mini-volume samplers. The Samples from dichotomous Samplers were analyzed for black carbon and elements while samples from mini-volume samplers were analyzed for ions. The species analyzed in this study were Na, Mg, Al, Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Pb, Cl^?, NO₃^?, SO₄^2?, and NH₄⁺. The data were then analyzed using PMF to determine the source factors. Different numbers of source factors were found during dry and wet season. During dry season, the main source factors for fine particles were secondary aerosol (NH₄)₂SO₄, electroplating industry, vehicle emission, and biomass burning, while for coarse particles, the dominant source factors were electroplating industry, followed by aged sea salt, volcanic dust, soil dust, and lime dust. During the wet season, the main source factors for fine particulate matter were vehicle emission and secondary aerosol. Other sources detected were biomass burning, lime dust, soil and volcanic dust. While for coarse particulate matter, the main source factors were sulphate-rich industry, followed by lime dust, soil dust, industrial emission and construction dust.     

Analysis of regional visibility in the southwest using principal component and back trajectory techniques

Approximately 3 years of visibility data from a 13-station teleradiometer network in the southwest desert is the basis for the analysis presented. Principal component analysis is employed to identify regions of similarly varying visibility for the enitre data set and by seasons. ‘North’, ‘Center’ and ‘South’ regions are identified in each of the four seasons. These regions change their size, shape and location somewhat through the seasons and thus are referred to as groups with each group containing four seasonal regions. Distinctive mean visibility levels and variations characterize the three groups. Back trajectoryanalysis techniques are developed to infer the nature and extent of influence of upwind areas on the three visibility groups. Two years of four daily back trajectories indicate primary detrimental influence from the southwest for the ‘North’ group and from the southeast for the ‘South’. Areas influencing the ‘Center’-group visibility are a combination of those affecting the other two groups. A method to calculate transport extinction budgets is demonstrated for the three visibility groups.     

Source apportionment of atmospheric polycyclic aromatic hydrocarbons (PAHs) in Palm Beach County,Florida

Due to concerns about adverse health effects associated with inhalation of atmospheric polycyclic aromatic hydrocarbons (PAHs), 30 ambient air samples were obtained at an air quality monitoring station in Palm Beach County, Florida, from March 2013 to March 2014. The ambient PAH concentration measurements and fractional emission rates of known sources were incorporated into a chemical mass balance model, CMB8.2, developed by EPA, to apportion contributions of three major PAH sources including preharvest sugarcane burning, mobile vehicles, and wildland fires. Strong association between the number of benzene rings and source contribution was found, and mobile vehicles were identified to be the prevailing source (contribution ≥56%) for the observed PAHs concentration with lower molecular weights (four or fewer benzene rings) throughout the year. Preharvest sugarcane burning was the primary contributing source for PAHs with relatively higher molecular weights (five or more benzene rings) during the sugarcane burning season (from October to May of the next year). Source contribution of wildland fires varied among PAH compounds but was consistently lower than for sugarcane burning during the sugarcane harvest season. Determining the major sources responsible for ground-level PAHs serves as a tool to improving management strategies for PAH emitting sources and a step toward better protection of the health of residents in terms of exposure to PAHs. The results obtain insight into temporal dominance of PAH polluting sources for those residential areas located near sugarcane burning facilities and have implications beyond Palm Beach County, in areas with high concerns of PAHs and their linked sources.

Implications: Source apportionment of atmospheric polycyclic hydrocarbons (PAHs) in Palm Beach County, Florida, meant to estimate contributions of major sources in PAH concentrations measured at Belle Glade City of Palm Beach County. Number of benzene rings was found to be the key parameter in determining the source with the prevailing contribution. Mobile vehicle sources showed a higher contribution for species with four or fewer benzene rings, whereas sugarcane burning contributed more for species with five or more benzene rings. Results from this study encourage more control for sugarcane burns and help to better manage authorization of the sugarcane burning incidents and more restrictive transportation plans to limit PAH emissions from mobile vehicles.     

Source apportionment of ambient particles in steubenville,oh using specific rotation factor analysis

A statistical approach, Specific Rotation Factor Analysis (SRFA), has been developed to identify and apportion sources of ambient air pollutants. To increase the statistical weight of the source tracer elements, this technique is based on the analysis of the covariance matrix. The obtained eigenvectors are obliquely rotated in order to maximize the loadings of the tracer quantities of the corresponding source (specific rotations). Source contributions and profiles are estimated by regressing elemental and mass concentrations on the factor loadings.The ability of the SRFA technique to resolve major aerosol sources and determine their profiles and contributions at the receptor site is demonstrated by applying it to a subset of fine particle elemental and mass concentration data from Steubenville, OH.     

Source apportionment of PM10 and PM2.5 in five Chilean cities using factor analysis

Chile is a fast-growing country with important industrial activities near urban areas. In this study, the mass and elemental concentrations of PM10 and PM2.5 were measured in five major Chilean urban areas. Samples of particles with diameter less than 10 microm (PM10) and 2.5 microm (PM2.5) were collected in 1998 in Iquique (northern Chile), Valparaiso, Vi?a del Mar, Rancagua (central Chile), and Temuco (southern Chile). Both PM10 and PM2.5 annual mean concentrations (PM10: 56.9-77.6 microg/m3; PM2.5: 22.4-42.6 microg/m3) were significantly higher than the corresponding European Union (EU) and U.S. Environmental Protection Agency (EPA) air quality standards. Moreover, the 24-hr PM10 and PM2.5 U.S. standards were exceeded infrequently for some of the cities (Rancagua and Valparaiso). Elements ranging from Mg to Pb were detected in the aerosol samples using X-ray fluorescence (XRF). For each of the five cities, factor analysis (FA) was applied to identify and quantify the sources of PM10 and PM2.5. The agreement between calculated and measured mass and elemental concentrations was excellent in most of the cities. Both natural and anthropogenic sources were resolved for all five cities. Soil and sea were the most important contributors to coarse particles (PM10-PM2.5), whereas their contributions to PM2.5 were negligible. Emissions from Cu smelters and oil refineries (and/or diesel combustion) were identified as important sources of PM2.5, particularly in the industrial cities of Rancagua, Valparaiso, and Vi?a del Mar. Finally, motor vehicles and wood burning were significant sources of both PM2.5 and PM10 in most of the cities (wood burning was not identified in Iquique).     

Source apportionment of fine particulate matter in Phoenix, AZ, using positive matrix factorization

Speciated particulate matter (PM)2.5 data collected as part of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program in Phoenix, AZ, from April 2001 through October 2003 were analyzed using the multivariate receptor model, positive matrix factorization (PMF). Over 250 samples and 24 species were used, including the organic carbon and elemental carbon analytical temperature fractions from the thermal optical reflectance method. A two-step approach was used. First, the species excluding the carbon fractions were used, and initially eight factors were identified; non-soil potassium was calculated and included to better refine the burning factor. Next, the mass associated with the burning factor was removed, and the data set rerun with the carbon fractions. Results were very similar (i.e., within a few percent), but this step enabled a separation of the mobile factor into gasoline and diesel vehicle emissions. The identified factors were burning (on average 2% of the mass), secondary transport (7%), regional power generation (13%), dust (25%), nitrate (9%), industrial As/Pb/Se (2%), Cu/Ni/V (7%), diesel (9%), and general mobile (26%). The overall contribution from mobile sources also increased, as some mass (OC and nitrate) from the nitrate and regional power generation factors were apportioned with the mobile factors. This approach allowed better apportionment of carbon as well as total mass. Additionally, the use of multiple supporting analyses, including air mass trajectories, activity trends, and emission inventory information, helped increase confidence in factor identification.     

Source apportionment of particulate matter in a large city of southeastern Po Valley (Bologna, Italy)

This study reports the results of an experimental research project carried out in Bologna, a midsize town in central Po valley, with the aim at characterizing local aerosol chemistry and tracking the main source emissions of airborne particulate matter. Chemical speciation based upon ions, trace elements, and carbonaceous matter is discussed on the basis of seasonal variation and enrichment factors. For the first time, source apportionment was achieved at this location using two widely used receptor models (principal component analysis/multi-linear regression analysis (PCA/MLRA) and positive matrix factorization (PMF)). Four main aerosol sources were identified by PCA/MLRA and interpreted as: resuspended particulate and a pseudo-marine factor (winter street management), both related to the coarse fraction, plus mixed combustions and secondary aerosol largely associated to traffic and long-lived species typical of the fine fraction. The PMF model resolved six main aerosol sources, interpreted as: mineral dust, road dust, traffic, secondary aerosol, biomass burning and again a pseudo-marine factor. Source apportionment results from both models are in good agreement providing a 30 and a 33 % by weight respectively for PCA-MLRA and PMF for the coarse fraction and 70 % (PCA-MLRA) and 67 % (PMF) for the fine fraction. The episodic influence of Saharan dust transport on PM₁₀ exceedances in Bologna was identified and discussed in term of meteorological framework, composition, and quantitative contribution.     

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.     

Source apportionment of fine particulate matter (PM2.5) at a rural Ohio River Valley site

Twenty four-hour averaged concentrations of fine particulate matter were collected at Athens, OH between March 2004 and November 2005 in an effort to characterize the nature of PM_2.5 and apportion its sources. PM_2.5 samples were chemically analyzed and positive matrix factorization was applied to this speciation data to identify the probable sources. PMF arrived at a 7-factor model to most accurately apportion sources of the PM_2.5 observed at Athens. Conditional probability function (CPF) and potential source contribution function (PSCF) were applied to the identified sources to investigate the geographical location of these sources. Secondary sulfate source dominated the contributions with a total contribution of 62.6% with the primary and secondary organic source following second with 19.9%. Secondary nitrate contributed a total of 6.5% with the steel production source and Pb- and Zn-source coming in at 3.1% and 2.9%, respectively. Crustal and mobile sources were small contributors (2.5% each) of PM_2.5 to the Athens region. The secondary sulfate, secondary organic and nitrate portrayed a clear seasonal nature with the sulfate and secondary organic peaking in the warm months and the nitrate reaching a high in the cold months. The high percentage of secondary sulfate observed at a rural site like Athens suggests the involvement of regional transport mechanisms.     

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.