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.     

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 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.     

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 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 of visual impairment during the California regional PM10/PM2.5 air quality study

Gases and particulate matter predictions from the UCD/CIT air quality model were used in a visibility model to predict source contributions to visual impairment in the San Joaquin Valley (SJV), the southern portion of California's Central Valley, during December 2000 and January 2001. Within the SJV, daytime (0800–1700 PST) light extinction was dominated by scattering associated with airborne particles. Measured daytime particle scattering coefficients were compared to predicted values at approximately 40 locations across the SJV after correction for the increased temperature and decreased relative humidity produced by “smart heaters” placed upstream of nephelometers. Mean fractional bias and mean fractional error were ?0.22 and 0.65, respectively, indicating reasonable agreement between model predictions and measurements. Particulate water, nitrate, organic matter, and ammonium were the major particulate species contributing to light scattering in the SJV. Daytime light extinction in the SJV averaged between December 25, 2000 and January 7, 2001 was mainly associated with animal ammonia sources (28%), diesel engines (18%), catalyst gasoline engines (9%), other anthropogenic sources (9%), and wood smoke (7%) with initial and boundary conditions accounting for 13%. The source apportionment results from this study apply to wintertime conditions when airborne particulate matter concentrations are typically at their annual maximum. Further study would be required to quantify source contributions to light extinction in other seasons.     

Source apportionment of visibility degradation problems in Brisbane (Australia) using the multiple linear regression techniques

Different aspects of visibility degradation problems in Brisbane were investigated through concurrent visibility monitoring and aerosol sampling programs carried out in 1995. The relationship between the light extinction coefficients and aerosol mass/composition was derived by using multiple linear regression techniques. The visibility properties at different sites in Brisbane were found to be correlated with each other on a daily basis, but not correlated with each other hour by hour. The cause of scattering of light by moisture (b_sw) was due to sulphate particles which shift to a larger size under high-humidity conditions. The scattering of light by particulate matter (b_sp) was found to be highly correlated with the mass of fine aerosols, in particular the mass of fine soot, sulphate and non-soil K. For the period studied, on average, the total light extinction coefficient (b_ext) at five sites in Brisbane was 0.65×10⁻⁴ m⁻¹, considerably smaller than those values found in other Australian and overseas cities. On average, the major component of b_ext is b_sp (49% of b_ext), followed by b_ap (the absorption of light, mainly by fine soot particles, 28%), b_sg (Rayleigh scattering, 20%) and b_sw (3%). The absorption of light by NO₂ (b_ag) is expected to contribute less than 5% of b_ext. On average, the percentage contribution of the visibility degrading species to b_ext (excluding b_ag) were: soot (53%), sulphate (21%), Rayleigh scattering (20%), non-soil K (2%) and humidity (3%). In terms of visibility degrading sources, motor vehicles (including soot and the secondary products) are expected to contribute more than half of the b_ext (excluding b_ag) in Brisbane on average, followed by secondary sulphates (17%) and biomass burning (10%).     

Source apportionment of wintertime secondary organic aerosol during the California regional PM10/PM2.5 air quality study

The UCD/CIT air quality model with the Caltech Atmospheric Chemistry Mechanism (CACM) was used to predict source contributions to secondary organic aerosol (SOA) formation in the San Joaquin Valley (SJV) from December 15, 2000 to January 7, 2001. The predicted 24-day average SOA concentration had a maximum value of 4.26 μg m^?3 50 km southwest of Fresno. Predicted SOA concentrations at Fresno, Angiola, and Bakersfield were 2.46 μg m^?3, 1.68 μg m^?3, and 2.28 μg m^?3, respectively, accounting for 6%, 37%, and 4% of the total predicted organic aerosol. The average SOA concentration across the entire SJV was 1.35 μg m^?3, which accounts for approximately 20% of the total predicted organic aerosol. Averaged over the entire SJV, the major SOA sources were solvent use (28% of SOA), catalyst gasoline engines (25% of SOA), wood smoke (16% of SOA), non-catalyst gasoline engines (13% of SOA), and other anthropogenic sources (11% of SOA). Diesel engines were predicted to only account for approximately 2% of the total SOA formation in the SJV because they emit a small amount of volatile organic compounds relative to other sources. In terms of SOA precursors within the SJV, long-chain alkanes were predicted to be the largest SOA contributor, followed by aromatic compounds. The current study identifies the major known contributors to the SOA burden during a winter pollution episode in the SJV, with further enhancements possible as additional formation pathways are discovered.     

Source apportionment of PM2.5 at two Seattle chemical speciation sites

ABSTRACT

Positive Matrix Factorization analysis of PM_2.5 chemical speciation data collected from 2015–2017 at Washington State Department of Ecology’s urban NCore (Beacon Hill) and near-road (10th and Weller) sites found similar PM_2.5 sources at both sites. Identified factors were associated with gasoline exhaust, diesel exhaust, aged and fresh sea salt, crustal, nitrate-rich, sulfur-rich, unidentified urban, zinc-rich, residual fuel oil, and wood smoke. Factors associated with vehicle emissions were the highest contributing sources at both sites. Gasoline exhaust emissions comprised 26% and 21% of identified sources at Beacon Hill and 10th and Weller, respectively. Diesel exhaust emissions comprised 29% of identified sources at 10th and Weller but only 3% of identified sources at Beacon Hill. Correlation of the diesel exhaust factor with measured concentrations of black carbon and nitrogen oxides at 10th and Weller suggests a method to predict PM_2.5 from diesel exhaust without a full chemical speciation analysis. While most PM_2.5 sources exhibit minimal change over time, primary PM_2.5 from gasoline emissions is increasing on average 0.18 µg m^?3 per year in Seattle.     

南昌市夏季PM_(2.5)中多环芳烃来源解析

在南昌市设立了5个不同功能区采样点,分别为居民区、工业区、商业区、交通干线区以及郊区,于2008年夏季进行PM2.5采样,对样品进行测定和分析后,通过因子分析法判断PM2.5中多环芳烃(PAHs)的主要污染源,再利用多元线性回归法确定各主要污染源对PAHs的贡献率。结果表明,南昌市夏季PM2.5中PAHs的主要污染源为车辆排放源、高温加热源、燃煤污染源,它们对PAHs的贡献率分别为37.9%、28.2%和22.0%;要控制南昌市夏季PM2.5中的PAHs,主要是要对机动车尾气排放量进行控制,并加强机动车尾气治理工作。     

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 PM10 in six cities of northern China

Ambient PM₁₀ was sampled in six northern China cities (Urumqi, Yinchuan, Taiyuan, Anyang, Tianjin and Jinan) from December 1999 to July 2002, and analyzed for 16 chemical elements, two water-soluble ions, total carbon, and organic carbon. In addition, chemical source profiles consisting of the same particulate components were obtained from a number of naturally occurring geological sources (soil dust from exposed lands) and sources of atmospheric particulates resulting from human activities (resuspended dust, cement, coal combustion fly ash, vehicle exhaust, and secondary particles). Ambient and source data were used in a chemical mass balance (CMB) receptor model to determine the major source of PM₁₀ in these six cities. Results of CMB modeling showed that the major source of ambient PM₁₀ in all the cities was resuspended dust. Significant contributions from coal fly ash were also found in all six cities.     

PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain

Levels of total suspended particles, PM10, PM2.5 and PM1 were continuously monitored at an urban kerbside in the Metropolitan area of Barcelona from June 1999 to June 2000. The results show that hourly levels of PM2.5 and PM1 are consistent with the daily cycle of gaseous pollutants emitted by traffic, whereas TSP and PM10 do not follow the same trend, at least in the diurnal period. The PM2.5/PM10 ratio is dependent on the traffic emissions, whereas additional contribution sources for the >10 μm fraction must be taken into account in the diurnal period. Different PM10 and PM2.5 source apportionment techniques were compared. A methodology based on the chemical determination of 83% of both PM10 and PM2.5 masses allowed us to quantify the marine (4% in PM10 and <1% in PM2.5), crustal (26% in PM10 and 8% in PM2.5) and anthropogenic (54% in PM10 and 73% in PM2.5) loads. Peaks of crustal contribution to PM10 (up to 44% of the PM10 mass) were recorded under Saharan air mass intrusions. A different seasonal trend was observed for levels of sulphate and nitrate, probably as a consequence of the different thermodynamic behaviour of these PM species and the higher summer oxidation rate of SO₂.     

Wintertime PM2.5 and PM10 Source Apportionment at Sacramento,California

ABSTRACT

The chemical mass balance (CMB) model was applied to winter (November through January) 1991–1996 PM2.5 and PM10 data from the Sacramento 13th and T Streets site in order to identify the contributions from major source categories to peak 24-hr ambient PM_2.5 and PM₁₀ levels. The average monthly PM₁₀ monitoring data for the nine-year period in Sacramento County indicate that elevated concentrations are typical in the winter months. Concentrations on days of highest PM₁₀ are dominated by the PM2.5 fraction. One factor contributing to increased PM_2.5 concentrations in the winter is meteorology (cool temperatures, low wind speeds, low inversion layers, and more humid conditions) that favors the formation of secondary nitrate and sulfate aerosols. Residential wood burning also elevates fine particulate concentrations in the Sacramento area.

The results of the CMB analysis highlight three key points. First, the source apportionment results indicate that primary motor vehicle exhaust and wood smoke are significant sources of both PM_2.5 and PM₁₀ in winter. Second, nitrates, secondarily formed as a result of motor-vehicle and other sources of nitrogen oxide (NO_x), are another principal cause of the high PM_2.5 and PM₁₀ levels during the winter months. Third, fugitive dust, whether it is resuspended soil and dust or agricultural tillage, is not the major contributor to peak winter PM_2.5 and PM10 levels in the Sacramento area.     

Identification of dicarboxylic acids and aldehydes of PM10 and PM2.5 aerosols in Nanjing,China

In this study aerosol samples of PM10 and PM2.5 collected from 18 February 2001 to 1 May 2001 in Nanjing, China were analyzed for their water-soluble organic compounds. A series of homologous dicarboxylic acids (C_2–10) and two kinds of aldehydes (methylglyoxal and 2-oxo-malonaldehyde) were detected by GC and GC/MS. Among the identified compounds, the concentration of oxalic acid was the highest at all the five sites, which ranged from 178 to 1423 ng/m³. The second highest concentration of dicarboxylic acids were malonic and succinic acids, which ranged from 26.9 to 243 ng/m³. Higher level of azelaic acid was also observed, of which the maximum was 301 ng/m³. As the highest fraction of dicarboxylic acids, oxalic acid comprised from 28% to 86% of total dicarboxylic acids in PM10 and from 41% to 65% of total dicarboxylic acids in PM2.5. The dicarboxylic acids (C₂, C₃, C₄) together accounted for 38–95% of total dicarboxylic acids in PM10 and 59–87% of dicarboxylic acids in PM2.5. In this study, the total dicarboxylic acids accounted for 2.8–7.9% of total organic carbon (TOC) of water-soluble matters for PM10 and 3.4–11.8% of TOC for PM2.5. All dicarboxylic acids detected in this study together accounted for about 1% of particle mass. The concentration of azelaic acid was higher at one site than others, which may be resulted from higher level of volatile fat used for cooking. The amounts of dicarboxyic acids (C_2,3,4,9) and 2-oxo-malonaldehyde of PM2.5 were higher in winter and lower in spring. Compared with other major metropolitans in the world, the level of oxalic acid concentration of Nanjing is much higher, which may be contributed to higher level of particle loadings, especially for fine particles.     

Characterization and contribution to PM2.5 of semi-volatile aerosols in Paris (France)

Collocated PM_2.5 measurements using a conventional R&P TEOM (model 1400a) and a TEOM-FDMS were performed at a Paris urban background site during winter/summer field experiments. Results showed that conventional TEOM underestimates PM_2.5 mass concentrations by about 50% in winter and 35% in summer. They also confirmed that this negative sampling artifact, due to the volatilization of semi-volatile material (SVM) inside the instrument, cannot be accurately accommodated by a single correction factor because of SVM routine fluctuations. A basic filter-based investigation of the SVM chemical composition also indicated that SVM, measured by the TEOM–FDMS, is mainly formed by ammonium nitrate in winter while significant contributions of semi-volatile organic matter were observed in summer. The latter species was found to possibly account for more than 50% of secondary organic aerosol formed during summer afternoons. These findings call for more investigation of the SVM chemical composition, particularly during the summer season, in Paris and in Europe.     

One-year study of the elemental composition and source apportionment of PM10 aerosols in Florence, Italy

An extensive investigation was carried out for the characterisation of the air particulate composition in Florence. The aim was to determine the aerosol elemental concentrations, as well as to identify pollution sources. For our investigation, the external Particle-Induced X-Ray Emission-Particle-Induced gamma-Ray Emission beam facility of the Istituto Nazionale di Fisica Nucleare, Van de Graaff accelerator at the Physics Department of the Florence University was used. We report the results of the analysis of a long temporal series (approximately 1 yr) of PM10 particulate samples, collected on Millipore filters on a daily basis in three different sites (characterised by different urban settings). Daily concentrations of more than 20 elements were detected. The long sampling period (approximately 1 yr) allowed a comparison with the air quality recommended values and the identification of seasonal variations. Four main sources (traffic, oil-combustion, soil-dust, and wind transported sea-salt) were extracted with the help of Principal Component Analysis (PCA). An absolute PCA showed traffic to be the major source both in the high traffic site and in the urban background site.     

Source apportionment of ambient PM2.5 at five spanish centres of the european community respiratory health survey (ECRHS II)

Fine particulate matter (PM_2.5) was sampled at 5 Spanish locations during the European Community Respiratory Health Survey II (ECRHS II). In an attempt to identify and quantify PM_2.5 sources, source contribution analysis by principal component analysis (PCA) was performed on five datasets containing elemental composition of PM_2.5 analysed by ED-XRF. A total of 4–5 factors were identified at each site, three of them being common to all sites (interpreted as traffic, mineral and secondary aerosols) whereas industrial sources were site-specific. Sea-salt was identified as independent source at all coastal locations except for Barcelona (where it was clustered with secondary aerosols). Despite their typically dominant coarse grain-size distribution, mineral and marine aerosols were clearly observed in PM_2.5. Multi-linear regression analysis (MLRA) was applied to the data, showing that traffic was the main source of PM_2.5 at the five sites (39–53% of PM_2.5, 5.1–12.0 μg m⁻³), while regional-scale secondary aerosols accounted for 14–34% of PM_2.5 (2.6–4.5 μg m⁻³), mineral matter for 13–31% (2.4–4.6 μg m⁻³) and sea-salt made up 3–7% of the PM_2.5 mass (0.4–1.3 μg m⁻³). Consequently, despite regional and climatic variability throughout Spain, the same four main PM_2.5 emission sources were identified at all the study sites and the differences between the relative contributions of each of these sources varied at most 20%. This would corroborate PM_2.5 as a useful parameter for health studies and environmental policy-making, owing to the fact that it is not as subject to the influence of micro-sitting as other parameters such as PM₁₀. African dust inputs were observed in the mineral source, adding on average 4–11 μg m⁻³ to the PM_2.5 daily mean during dust outbreaks. On average, levels of Al, Si, Ti and Fe during African episodes were higher by a factor of 2–8 with respect to non-African days, whereas levels of local pollutants (absorption coefficient, S, Pb, Cl) showed smaller variations (factor of 0.5–2).     

Source apportionment of PM2.5 for supporting control strategies in the Monterrey Metropolitan Area,Mexico

The Monterrey Metropolitan Area (MMA) in Northeast Mexico has shown high PM_2.5 concentrations since 2003. The data shows that the annual average concentration exceeds from 2 to 3 times the Mexican PM_2.5 annual air quality standard of 12 µg/m³. In a previous work we studied the chemical characterization of PM_2.5 in two sites of the MMA during the winter season. Among the most important components we found ammonium sulfate and nitrate, elemental and organic carbon, and crustal matter. In this work we present the results of a second chemical characterization study performed during the summer time and the application of the chemical mass balance (CMB) model to determine the source apportionment of air pollutants in the region. The chemical analysis results show that the chemical composition of PM_2.5 is similar in both sites and periods of the year. The results of the chemical analysis and the CMB model show that industrial, traffic, and combustion activities in the area are the major sources of primary PM_2.5 and precursor gases of secondary inorganic and organic aerosol (SO₂, NOx, NH₃, and volatile organic compounds [VOCs]). We also found that black carbon and organic carbon are important components of PM_2.5 in the MMA. These results are consistent with the MMA emission inventory that reports as major sources of particles and SO₂ a refinery and fuel combustion, as well as nitrogen oxides and ammonium from transportation and industrial activities in the MMA and ammonium form agricultural activities in the state. The results of this work are important to identify and support effective actions to reduce direct emissions of PM_2.5 and its precursor gases to improve air quality in the MMA. Implications: The Monterrey Metropolitan Area (MMA) has been classified as the most air-polluted area in Mexico by the World Health Organization (WHO). Effective actions need to be taken to control primary sources of PM_2.5 and its precursors, reducing health risks on the population exposed and their associated costs. The results of this study identify the main sources and their estimated contribution to PM_2.5 mass concentration, providing valuable information to the local environmental authorities to take decisions on PM_2.5 control strategies in the MMA.