Major Sources Of PM2 Research Articles

Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment

Abstract. The health implications of PM2.5 in the tropical region of Southeast Asia (SEA) are significant as PM2.5 can pose serious health concerns. PM2.5 concentration and sources here are strongly influenced by changes in the monsoon regime from the south-west quadrant to the north-east quadrant in the region. In this work, PM2.5 samples were collected at a semi-urban area using a high-volume air sampler at different seasons on 24 h basis. Analysis of trace elements and water-soluble ions was performed using inductively coupled plasma mass spectroscopy (ICP-MS) and ion chromatography (IC), respectively. Apportionment analysis of PM2.5 was carried out using the United States Environmental Protection Agency (US EPA) positive matrix factorization (PMF) 5.0 and a mass closure model. We quantitatively characterized the health risks posed to human populations through the inhalation of selected heavy metals in PM2.5. 48 % of the samples collected exceeded the World Health Organization (WHO) 24 h PM2.5 guideline but only 19 % of the samples exceeded 24 h US EPA National Ambient Air Quality Standard (NAAQS). The PM2.5 concentration was slightly higher during the north-east monsoon compared to south-west monsoon. The main trace metals identified were As, Pb, Cd, Ni, Mn, V, and Cr while the main ions were SO42−, NO3−, NH4+, and Na. The mass closure model identified four major sources of PM2.5 that account for 55 % of total mass balance. The four sources are mineral matter (MIN) (35 %), secondary inorganic aerosol (SIA) (11 %), sea salt (SS) (7 %), and trace elements (TE) (2 %). PMF 5.0 elucidated five potential sources: motor vehicle emissions coupled with biomass burning (31 %) were the most dominant, followed by marine/sulfate aerosol (20 %), coal burning (19 %), nitrate aerosol (17 %), and mineral/road dust (13 %). The hazard quotient (HQ) for four selected metals (Pb, As, Cd, and Ni) in PM2.5 mass was highest in PM2.5 mass from the coal burning source and least in PM2.5 mass originating from the mineral/road dust source. The main carcinogenic heavy metal of concern to health at the current location was As; the other heavy metals (Ni, Pb, and Cd) did not pose a significant cancer risk in PM2.5 mass concentration. Overall, the associated lifetime cancer risk posed by the exposure of hazardous metals in PM2.5 is 3–4 per 1 000 000 people at this location.

Chemical Characterization and Source Apportionment of PM2.5 in Rabigh, Saudi Arabia

The present study describes the measurement, chemical characterization and delineation of sources of fine particulate matter (PM2.5) in Rabigh, Saudi Arabia. The 24-h PM2.5 was collected from May 6th–June 17th, 2013. The sources of various air pollutants and their characterization was carried by computations of Enrichment Factor (EF), Positive Matrix Factorization (PMF) and Backward-in-time Trajectories. The 24-h PM2.5 showed significant temporal variability with average (37 ± 16.2 μg m–3) exceeding the WHO guideline (20 μg m–3) by 2 fold. SO42–, NO3–, NH4+ and Cl– ions dominated the ionic components. Two broad categories of aerosol Trace Elements (TEs) sources were defined as anthropogenic (Ni, V, Zn, Pb, S, Lu and Br) and soil/crustal derived (Si, Rb, Ti, Fe, Mn, Mg, K, Sr, Cr, Ca, Cu, Na and Al) elements from computations of EF. Anthropogenic elements originated primarily from fossil-fuel combustion, automobile and industrial emissions. A factor analysis model (PMF) indicated the major sources of PM2.5 as Soil (Si, Al, Ti, Fe, Mg, K and Ca); Industrial Dust (Ca, Fe, Al, and Si); Fossil-Fuel combustion (V, Ni, Pb, Lu, Cu, Zn, NH4+, SO42– and BC); Vehicular Emissions (NO3–, C2O42–, V and BC) and Sea Sprays (Cl– and Na). Backward-in-time trajectories showed a significant contribution by long distance transport of fine aerosols to the overall daily PM2.5 levels. Results are consistent with previous studies and highlight the need for more comprehensive research into particulate air pollution in Rabigh and the neighboring areas. This is essential for the formulation of sustainable guidelines on air pollutant emissions in Saudi Arabia and the whole Middle East.

Chemical composition and source apportionment of ambient PM2.5 during the non-heating period in Taian, China

Ambient PM2.5 samples were collected in the urban area of Taian in China in August–September and November, 2014. The chemical compositions and emission sources of PM2.5 were analyzed. The results indicated that the mean concentration of PM2.5 reached 70.8μg/m3 during the non-heating period, and water soluble inorganic ions (WSIIs), carbonaceous materials, including elemental carbon (EC) and organic carbon (OC); and elements contributed 43.80%, 10.34% and 17.36%, respectively, to PM2.5. The mean concentrations of WSIIs at three sampling sites decreased in the same order: SO42−>NH4+>NO3−>Cl− during the non-heating period. NO3− and NH4+, SO42− and NH4+, showed extremely significant positive-correlations (r=0.79, 0.54; P<0.01). The variability of OC was larger than the variability of EC during the non-heating period. The high concentration of secondary organic carbon (SOC) could reduce correlation-level between the OC and EC. Moreover, the percentages and concentrations of the total detected elements (TDE) increased significantly, ranging from August–September to November (P<0.01). Major sources of PM2.5 identified from positive matrix factorization (PMF) model and enrichment factors (EFs) included secondary aerosol, coal combustion, metal manufacturing, soil dust/resuspended dust/construction dust and vehicle exhaust/biomass burning, which contributed 27.47%, 17.94%, 19.06%, 9.41% and 16.65%, respectively, to PM2.5. The backward trajectory analysis identified three transport pathways that originated from Mongolia (12% of the total trajectories), Inner Mongolia (2%), and southeast of Shandong Province (86%), and the potential source contribution function (PSCF) model identified southeast of Shandong Province was mainly a potential source-area that affected air quality in Taian.

Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements

Abstract. Air quality directly affects the health and living of human beings, and it receives wide concern of public and attaches great important of governments at all levels. The estimation of the concentration distribution of PM2.5 and the analysis of its impacting factors is significant for understanding the spatial distribution regularity and further for decision supporting of governments. In this study, multiple sources of remote sensing and GIS data are utilized to estimate the spatial distribution of PM2.5 concentration in Shijiazhuang, China, by utilizing multivariate linear regression modelling, and integrating year average values of PM2.5 collected from local environment observing stations. Two major sources of PM2.5 are collected, including dust surfaces and industrial polluting sources. The area attribute of dust surfaces and point attribute of industrial polluting enterprises are extracted from high resolution remote sensing images and GIS data in 2013. 30m land cover products, annual average PM2.5 concentration values from the 8 environment monitoring stations, annual mean MODIS AOD data, traffic and DEM data are utilized in the study for regression modeling analysis. The multivariate regression analysis model is applied to estimate the spatial distribution of PM2.5 concentration. There is an upward trend of the spatial distribution of PM2.5 concentration gradually from west to east, of which the highest concentration appears in the municipal district and its surrounding areas. The spatial distribution pattern relatively fit the reality.

Characterization of particulate matter 2.5 in an urban tertiary care hospital in the Philippines

This study characterized particulate matter 2.5 microns in the naturally ventilated Pediatric and Medicine wards and mechanically ventilated Central and Neonatal Intensive Care Units (CENICU and NICU) of an urban tertiary care hospital in the Philippines in terms of concentration, elemental composition, indoor/outdoor (I/O) ratios and enrichment factors. The samples were collected from October to December 2013 and from March to April 2014. Results showed that the average of PM2.5 in the Pediatric (32.8 μgm-3 in October to December 2013 and 28.4 μgm-3 in March to April 2014) and Medicine Wards (30.0 μgm-3 in October to December 2013) exceeded the WHO guideline value of 25 μgm-3. The I/O ratios suggest that outdoor air is the major source of PM2.5 in all sites. Comparison of pollutant levels showed differences between the PM2.5 concentrations in Pediatrics and NICU (p < 0.0001 in October to December 2013 and p = 0.003 in March to April 2014) and in Medicine and CENICU (p = 0.002 in October to December 2013 and p = 0.037 in March to April 2014). Moreover, outdoor PM2.5 concentrations were higher than the measurements in NICU and CENICU (p < 0.0001). X-ray fluorescence analysis measured 15 elements, including manganese, iron, vanadium, lead and mercury, which are highly hazardous to health. Enrichment factor analysis showed that mercury, sulfur and bromine were highly enriched indicating significant contamination from anthropogenic sources. The researchers recommend the determination of these elements in soil within the area and other compartments and undertake a source apportionment study to ascertain the sources of these elements.

Environment Analysis on Controlling PM2.5 in Beijing

PM2.5 pollution in Beijing not only causes the greatest health problems, and reduces the visibility (haze), but also impacts on the China governments credibility and international image. The paper showed that the average mass concentration of PM2.5 in Beijing was 103μg/m 3 in the first half year of 2013; the annual mean mass concentration of PM2.5 has risen in recent years; the transfer of regional pollution, motor vehicles, coal combustion and dust are the major sources of PM2.5 during 2012 in Beijing; and finally the government should take effective measures to control PM2.5. The paper may provide some theoretical guidance for the government implementing environmental protection policy and pollution control measures.

The Emission Control Technology of PM&lt;sub&gt;2.5&lt;/sub&gt; from Coal-Fired Plant

A major source of PM2.5is the combustion of coal-fired power plant, so its very necessary to monitor and control the emission of PM2.5form power plant. This paper summarizes the main emission control technologies of PM2.5for coal-fired plant. As the traditional electrostatic precipitators has low removal efficiency of submicron particles, so we improved the supply conditions of the precipitator, and pre-charged to improve the particle properties. Through this article, we know that using electric coagulation technology to control the particles form plant is practicable, and it has a good prospect for development.

Characterization of Water-Soluble Anions of PM&lt;sub&gt;2.5&lt;/sub&gt; Aerosols in Wuhan Economic and Technological Development Zone, China

The pollution characterizations of water-soluble anions of PM2.5(fine particle matter with aerodynamical diameter &lt;2.5 m="" in="" wuhan="" economic="" and="" technological="" development="" zone="" china="" were="" studied="" during="" april="" 2013="" the="" concentration="" of="" pm="" sub=""&gt;2.5 and four kinds of water-soluble anions that consisted of fluoride (F-), chloride (Cl-), sulfate (SO42-), and nitrate (NO3-) were determined. The results indicated that the urban air quality in Wuhan was polluted by fine particle matter to some extent. During the monitoring period, there were 50% of the daily mean concentration of PM2.5exceeded the China second-class ambient air quality standards (75μgm-3). The concentrations of water-soluble anions in PM2.5followed the order of SO42-&gt; NO3-&gt; F-&gt; Cl-. SO42-and NO3-were the dominant anions in Wuhan Economic and Technological Development Zone, and contributed more than 70% to the total measured water-soluble anions. The mean ratio of NO3-and SO42-in PM2.5was calculated to be 0.49, which indicated that coal combustion was the major source of PM2.5in Wuhan Economic and Technological Development Zone, China.

Source apportionment of PM2.5 at the coastal area in Korea

In this study, we analyzed the chemical composition of fine particulate matter 2.5μm or less (PM2.5) collected at Incheon, the coastal area in Seoul, Korea every third day from June 2009 to May 2010. Based on the analyzed chemical species in the PM2.5 samples, the sources of PM2.5 were identified using a positive matrix factorization (PMF). Nine sources of PM2.5 were determined from PMF analysis. The major sources of PM2.5 were secondary nitrate (25.4%), secondary sulfate (19.0%), motor vehicle 1 (14.8%) with a lesser contribution from industry (8.5%), motor vehicle 2 (8.2%), biomass burning (6.1%), soil (6.1%), combustion and copper production emissions (6.1%), and sea salt (5.9%). From a paired t-test, it was found that yellow sand samples were characterized as having higher contribution from soil sources (p<0.05). Furthermore, the likely source areas of PM2.5 emissions were determined using the conditional probability function (CPF) and the potential source contribution function (PSCF). CPF analysis identified the likely local sources of PM2.5 as motor vehicles and sea salt. PSCF analysis indicated that the likely source areas for secondary particles (sulfate and nitrate) were the major industrial areas in China. Finally, using the source contribution of PM2.5 and associated organic composition data, principal component analysis (PCA) was conducted to evaluate the accuracy of the PM2.5 source apportionments by PMF. The PCA analysis confirmed eight of the nine PM2.5 sources. Our result implies that the chemical composition analysis of PM2.5 data and various modeling techniques can effectively identify the potential contributing sources.

PM2.5 in an industrial district of Zhengzhou, China: Chemical composition and source apportionment

Zhengzhou is a developing city in China, that is heavily polluted by high levels of particulate matter. In this study, fine particulate matter (PM2.5) was collected and analyzed for their chemical composition (soluble ions, elements, elemental carbon (EC) and organic carbon (OC)) in an industrial district of Zhengzhou in 2010. The average concentrations of PM2.5 were 181, 122, 186 and 211μg/m3 for spring, summer, autumn and winter, respectively, with an annual average of 175μg/m3, far exceeding the PM2.5 regulation of USA National Air Quality Standards (15μg/m3). The dominant components of PM2.5 in Zhengzhou were secondary ions (sulphate and nitrate) and carbon fractions. Soluble ions, total carbon and elements contributed 41%, 13% and 3% of PM2.5 mass, respectively. Soil dust, secondary aerosol and coal combustion, each contributing about 26%, 24% and 23% of total PM2.5 mass, were the major sources of PM2.5, according to the result of positive matrix factorization analysis. A mixed source of biomass burning, oil combustion and incineration contributed 13% of PM2.5. Fine particulate matter arising from vehicles and industry contributed about 10% and 4% of PM2.5, respectively.

Source Apportionment of PM2.5 in the Metropolitan Area of Costa Rica Using Receptor Models

In this work, receptor models were used to identify the PM2.5 sources and its contribution to the air quality in residential, comercial and industrial sampling sites in the Metropolitan Area of Costa Rica. Principal component analysis with absolute principal component scores (PCA-APCS), UNIMX and positive matrix factorization (PMF) was applied to analyze the data collected during 1 year of sampling campaign (2010-2011). The PM2.5 samples were characterized through its composition looking for trace elements, inorganic ions and organic and elemental carbon. These three models identified some common sources of PM2.5: marine aerosol, crustal material, traffic, secondary aerosols (secondary sulfate and secondary nitrate resolved by PMF), a mixed source of heavy fuels combustion and biomass burning, and industrial emissions. The three models predicted that the major sources of PM2.5 in the Metropolitan Area of Costa Rica were related to anthropogenic sources (73%, 65% and 69%, respectively, for PCA-APCS, Unmix and PMF) although natural sources also contributed to PM2.5 (21%, 24% and 26%). On average, PCA and PMF methods resolved 94% and 95% of the PM2.5 mass concentrations, respectively. The results were comparable to the estimate using UNMIX.

Fine mode aerosol chemistry over a tropical urban atmosphere: characterization of ionic and carbonaceous species

An extensive aerosol sampling program was conducted during January-December 2006 over Kolkata (22o33′ N and 88o20′ E), a mega-city in eastern India in order to understand the sources, distributions and properties of atmospheric fine mode aerosol (PM2.5). The primary focus of this study is to determine the relative contribution of natural and anthropogenic as well as local and transported components to the total fine mode aerosol loading and their seasonal distributions over the metropolis. The average concentrations of fine mode aerosol was found to be 71.2 ± 25.2 μgm-3 varying between 34.5 μgm-3 in monsoon and 112.6 μgm-3 in winter. The formation pathways of major secondary aerosol components like nitrate and sulphate in different seasons are discussed. A long range transport of dust aerosol from arid and semi-arid regions of western India and beyond was observed during pre-monsoon which significantly enriched the total aerosol concentration. Vehicular emissions, biomass burning and transported dust particles were the major sources of PM2.5 from local and continental regions whereas sea-salt aerosol was the major source of PM2.5 from marine source regions.

Characterization of PM2.5- and PM10-Bound Polycyclic Aromatic Hydrocarbons in Urban and Rural Areas in Beijing during the Winter

Polycyclic aromatic hydrocarbons (PAHs) were here investigated in airborne particulate matter (PM2.5 and PM10) samples collected in urban (including residential, commercial, industrial, and traffic zones) and rural areas in and around Beijing from December 2005 to January 2006. Sixteen polycyclic aromatic hydrocarbons adsorbed onto fine- (PM2.5) and coarse- (PM10) grained samples were analyzed by gas chromatography-mass spectrometry (GC-MS). The mean total PAH concentrations were 30.1 and 80.1 ng/m3 in PM2.5, and 44.1 and 99.8 ng/m3 in PM10 in urban and rural areas, respectively. PAHs were mostly adsorbed on small inhalable particles (&gt;50%) with four to six rings, which dominated both fractions. Strong correlations between PM-associated extractable organic matter (EOM) and particulate PAHs were found, especially in PM2.5, indicating that PM-PAHs concentrations were strongly influenced by PM-associated EOM. Of the different functional areas studied, the total PAH concentrations in both fractions were highest in industrial and rural areas and lowest in traffic zones. This may have been the result of the different emission sources and more efficient energy structures that occur during the winter. Distribution profiles and diagnostic ratios revealed that coal combustion was the major source of PM2.5- and PM10- associated PAHs. Additional sources of PAHs may include vehicle emissions, natural gas combustion and wood burning. BaP-based toxic equivalence factor (BaPeq), BaP-equivalent carcinogenic power (BaPE) and a sum of five carcinogenic PAHs showed that the industrial and rural areas were the most vulnerable to PAHs.

Source Apportionment of PM2.5 in a Subarctic Airshed - Fairbanks, Alaska

ABSTRACTFairbanks, Alaska has some of the highest measured ambient PM2.5 concentrations in the United States, with wintertime levels often exceeding the 24-hour PM2.5 National Ambient Air Quality Standard (NAAQS) of 35 µg/m3. In an effort to understand the sources of PM2.5 in the Fairbanks airshed, source apportionment using Chemical Mass Balance (CMB) modeling was conducted at four locations in Fairbanks over a three-winter period (2008/2009, 2009/2010, and 2010/2011). At each of the four sites, PM2.5 concentrations averaged between 22.5 ± 12.0 µg/m3 and 26.5 ± 18.9 µg/m3, with frequent exceedances of the 24-hour NAAQS on the scheduled sample days. The results of the CMB modeling revealed that wood smoke (likely residential wood combustion) was the major source of PM2.5 throughout the winter months in Fairbanks, contributing between 60% and nearly 80% of the measured PM2.5 at the four sites. The other sources of PM2.5 identified by the CMB model were secondary sulfate (8–20%), ammonium nitrate (3–11%), diesel exhaust (not detected-10%), and automobiles (not detected-7%). Approximately 1% of the PM2.55 was unexplained by the CMB model. Additional research is needed to confirm the woodsmoke results of the CMB model, as well as determine which sources (fuel oil residential heating, coal combustion, etc.) contribute to the measured secondary sulfate.

Atmospheric Emission Inventory for Natural and Anthropogenic Sources and Spatial Emission Mapping for the Greater Athens Area

A spatially, temporally and chemically resolved emission inventory for particulate matter and gaseous species from anthropogenic and natural sources was created for the Greater Athens Area (GAA; base year, 2007). Anthropogenic sources considered in this study include combustion (industrial, non-industrial, commercial and residential), industrial production, transportation, agriculture, waste treatment and solvent use. The annual gaseous pollutants (ΝΟx, SOx, non-methane volatile organic compounds (NMVOCs), CO and ΝΗ3) and particulate matter (PM2.5 and PM2.5–10) emissions were derived from the UNECE/EMEP database for most source sectors (SNAP 1–9; 50 × 50 km2) and their spatial resolution was increased using surrogate spatial datasets (land cover, population density, location and emissions of large point sources, emission weighting factors for the GAA; 1 × 1 km2). The emissions were then temporally disaggregated in order to provide hourly emissions for atmospheric pollution modelling using monthly, daily and hourly disintegration coefficients, and additionally the chemical speciation of size-segregated particles and NMVOCs emissions was performed. Emissions from agriculture (SNAP 10) and natural emissions of particulate matter from the soil (by wind erosion) and the sea surface and of biogenic gaseous pollutants from vegetation were also estimated. During 2007 the anthropogenic emissions of CO, SOx, NOx, NMVOCs, NH3, PM2.5 and PM2.5–10 from the GAA were 151,150, 57,086, 68,008, 38,270, 2,219, 9,026 and 3,896 Mg, respectively. It was found that road transport was the major source for CO (73.3%), NMVOCs (31.6%) and NOx (35.3%) emissions in the area. Another important source for NOx emissions was other mobile sources and machinery (23.1%). Combustion for energy production and transformation industries was the major source for SOx (38.5%), industrial combustion for anthropogenic PM2.5–10 emissions (59.5%), whereas non-industrial combustion was the major source of PM2.5 emissions (49.6%). Agriculture was the primary NH3 source in the area (72.1%). Natural vegetation was found to be an important source of VOCs in the area which accounted for approximately the 5% of total VOCs emitted from GAA on a typical winter day. The contribution of sea-salt particles to the emissions of PM2.5 was rather small, whereas the emissions of resuspended dust particles exceeded by far the emissions of PM2.5 and PM2.5–10 from all anthropogenic sources.

Identification of major sources of PM2.5 in St. Louis Missouri USA

The objective of this study is to examine the use of the conditional probability function (CPF) and nonparametric regression (NPR) to identify the relationship between wind direction and concentration of PM2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm). Twenty four-hour integrated PM2.5 mass and species concentrations were measured at the St. Louis-Midwest Supersite in East St. Louis, Illinois, USA in the periods of 22–28 June 2001, 7–13 November 2001, and 19–25 March 2002. Wind directions were measured on site. The concentrations of 15 elements and ions, i.e. Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Se, Zn, OC, EC, SO4, and NO3 were calculated using the CPF and NPR. The comparison between the results obtained from the CPF and NPR demonstrated that they both agreed well with the locations of the known local point sources. The CPF was simpler and easier to calculate than NPR. In contrast, NPR provided PM2.5 concentrations but with some uncertainties. This study indicates that both methods can be utilized to promote the source apportionment study of ambient PM2.5.

Analysis of acidic components, heavy metals and PAHS of particulate in the Changwon–Masan area of Korea

This study is an analysis of the concentrations and components of heavy metals in PM2.5 and the total suspended particulate (TSP) collected at a mechanical industrial complex (IC) site in Changwon and at a residential site in Masan, Korea. Particulate was collected during two sampling periods, from the late summer to the early fall and from the middle to late fall, at the IC site and one sampling period, from the middle fall to the early winter, at the residential site. PM2.5 and TSP samples were taken by an annular denuder system and a hi-volume air sampler, respectively. The authors also identified the concentrations and components of heavy metals extracted from the PM2.5 and TSP filters, the acidic components extracted from the PM2.5 filters, and the polycyclic aromatic hydrocarbons (PAHs) extracted from polyurethane foam (PUF) plug. The average concentrations of the PM2.5 collected at the IC and residential sites were very similar. Major sources of PM2.5 at the study sites, however, were air emissions from vehicles and industry as well as emissions from residential heating and soil origins, respectively. The higher concentrations of the TSP at the IC site, as compared to those at the residential site, were due to either increased suspended dust from vehicle emissions or re-suspended road dust because of increased vehicle speeds near the IC site. Heavy metal concentrations in the TSPs were higher than those in the PM2.5. The heavy metal concentrations in the PM2.5 and TSP at the IC site with heavy traffic were substantially greater than those at the residential site. The concentrations of TSP and heavy metals and PAHs in PM during the period of the middle to late fall was much higher than those during the period of the late summer to early fall at the IC site. This is because of the difference in meteorological characteristics and energy uses between two periods. The residential site also showed higher concentrations of acidic anions while the IC site showed higher concentrations of acidic cation. Secondary aerosols or particulates, such as ammonium nitrate or ammonium nitrite, might have been important constituents of the PM2.5 at the residential site. The PAHs in the TSP collected at the IC site was greatly affected by traffic and industry emissions consisting mostly of high molecular weight PAHs with two to four rings. PAHs in the TSP at the site, however, were affected by residential heating and air emissions from small chemical plants having higher concentrations of low molecular weight PAHs with five to six rings.

Secondary particle formation and evidence of heterogeneous chemistry during a wood smoke episode in Texas

To evaluate the impact of regional wildfires in east Texas on fine particulate matter concentration and composition, source apportionment calculations were performed on a set of samples collected at three sites in Houston during a wood smoke episode. Separately, samples collected at the same sites on days not influenced by wood smoke, were analyzed for comparison. The analysis of the data collected on non–wood smoke episode days indicated that the major contributors to PM2.5 mass were secondary sulfate, diesel and gasoline powered vehicles, other organics (difference between the measured organic mass by carbon (OMC) and the sum of the primary OC source contributions), wood combustion and meat cooking. Secondary sulfate (not apportioned to a primary source) represented almost 100% of the sulfate measured whereas primary sources were found to account for the majority of the measured organic carbon at three sites (64–69%). On the wood smoke episode days, major sources of PM2.5 were found to be the same as on the days without wood smoke, except for the contribution of the meat cooking source, which became insignificant on the wood smoke episode days. The contribution of the wood combustion source increased by an average of 200% at all sites on wood smoke episode days, whereas the contributions of other primary sources did not increase significantly during the wood smoke episode. PM2.5 mass almost doubled during the wood smoke episode, largely because of the contributions of the secondary sources. The mass concentrations of secondary sulfate and organics not attributed to primary sources increased 68% and 228% at each site, respectively, during the wood smoke episode. The increase in the contributions of secondary sulfate aerosols during the wood smoke episode was examined using a 3‐D photochemical grid model. The simulations, together with ambient data, indicated that the increases in sulfate concentrations observed during this wood smoke episode were consistent with heterogeneous/surface reactions on wood smoke particles.

Seasonal trends in PM2.5 source contributions in Beijing, China

The 24-h PM2.5 samples (particles with an aerodynamic diameter of 2.5 μm or less) were taken at 6-day intervals at five urban and rural sites simultaneously in Beijing, China for 1 month in each quarter of calendar year 2000. Samples at each site were combined into a monthly composite for the organic tracer analysis by GC/MS (gas chromatography/mass spectrometry). Compared to the data obtained from other metropolitan cities in the US, the PM2.5 mass and fine organic carbon (OC) concentrations in Beijing were much higher with an annual average of 101 and 20.9 μg m^(−3), respectively. Over one hundred organic compounds including unique tracers for important sources were quantified in PM2.5 in Beijing. Source apportionment of fine OC was conducted using chemical mass balance receptor model (CMB) in combination with particle-phase organic compounds as fitting tracers. Carbonaceous aerosols and major ions (sulfate, nitrate and ammonium) constituted 69% of PM2.5 mass on average. The major sources of PM2.5 mass in Beijing averaged over five sites on an annual basis were determined as dust (20%), secondary sulfate (17%), secondary nitrate (10%), coal combustion (7%), diesel and gasoline exhaust (7%), secondary ammonium (6%), biomass aerosol (6%), cigarette smoke (1%), and vegetative detritus (1%). The lowest PM2.5 mass concentration was found in January (60.9 μg m^(−3)), but the contribution of carbonaceous aerosol to PM2.5 mass was maximal during this season, accounting for 57% of the mass. During cold heating season, the contributions from coal combustion and biomass aerosol to PM2.5 mass increased, accounting for 20.9% of fine particle mass in October and 24.5% in January. The contribution of the biomass aerosols peaked in the fall. In April 2000, the impact of dust storms was so significant that dust alone constituted 36% of PM2.5 mass. On average, the model resolved 88% of the sources of the PM2.5 mass concentrations in Beijing.

Source apportionment of PM2.5 at an urban IMPROVE site in Seattle, Washington.

The multivariate receptor models Positive Matrix Factorization (PMF) and Unmix were used along with the EPA's Chemical Mass Balance model to deduce the sources of PM2.5 at a centrally located urban site in Seattle, WA. A total of 289 filter samples were obtained with an IMPROVE sampler from 1996 through 1999 and were analyzed for 31 particulate elements including temperature-resolved fractions of the particulate organic and elemental carbon. All three receptor models predicted that the major sources of PM2.5 were vegetative burning (including wood stoves), mobile sources, and secondary particle formation with lesser contributions from resuspended soil and sea spray. The PMF and Unmix models were able to resolve a fuel oil combustion source as well as distinguish between diesel emissions and other mobile sources. In addition, the average source contribution estimates via PMF and Unmix agreed well with an existing emissions inventory. Using the temperature-resolved organic and elemental carbon fractions provided in the IMPROVE protocol, rather than the total organic and elemental carbon, allowed the Unmix model to separate diesel from other mobile sources. The PMF model was able to do this without the additional carbon species, relying on selected trace elements to distinguish the various combustion sources.