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In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources.

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Aerosol acidity is closely related to particle properties and the explosive growth of secondary particles. Aerosol pH is difficult to measure directly but can be estimated indirectly by thermodynamic equilibrium modeling. ISORROPIA-Ⅱ is one of the most commonly used thermodynamic models and includes different modes (forward and reverse) and aerosol states (stable and metastable). Studies have shown that the calculated pH results vary with the selected mode and phase state. In addition to the selection of modes and phases, there are also other factors that influence the modeling results. In order to explore the appropriate mode and phase selection of ISORROPIA-Ⅱ as well as the factors influencing the model results under the air pollution characteristics of typical Chinese cities, the simulation results of different modes and aerosol states were analyzed by using online hourly data for Tianjin. The results showed that the pH calculations using the forward mode and metastable state were satisfactory at a higher RH. With increased temperature, the pH, aerosol water content, and concentration proportion in the aerosol phase of semi-volatile components all decreased. RH affected aerosol pH by influencing the aerosol water content and concentration of semi-volatile components. An increased cation concentration led to an increased pH and NH3 concentration but a decreased HNO3 concentration, whereas an increased anion concentration had the opposite effect. Ca2+, SO42-, NO3-, and NH4+ had a great influence on pH. Compared with SO42-, NO3- had less effect on pH. Sensitive areas exist in the influence of NH4+ on pH, and a high NH4+ concentration did not cause a continuous pH increase. This study can improve the understanding of aerosol pH simulation using ISORROPIA-Ⅱ, and provides reference for research on the pH-related secondary generation mechanism, semi-volatile component gas-particle distribution, and pollution control measures.

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Tianjin is located in the Beijing-Tianjin-Hebei region. Recently, particulate matter pollution has received wide attention; therefore, studying the chemical composition and sources of particulate matter in the atmospheric environment is of great significance. To clarify the mixed state and possible sources of particulate matter in the summer ambient air in Tianjin, this study used single particle aerosol mass spectrometer (SPAMS) to collect 209887 samples. Particle size and complete spectrometry information were collected in July 2017. A total of 369 particle classes were obtained with respect to clustering particles with similarities in mass spectrometry characteristics using ART-2a. Then, according to the similarity in the chemical composition (mass spectrometry) of the categories, 19 particulate matter categories were artificially merged: K-EC (0.20%), K-EC-Sec (0.18%), K-NO3-PO3(12.00%), K-NO3-SiO3(2.98%), K-Sec (0.16%), EC (39.60%), EC-Sec (3.46%), EC-HM-Sec (3.93%), HEC (1.49%), HEC-Sec (1.38%), OC-Amine-Sec (3.58%), OC-Sec (0.36%), OCEC-Sec (0.71%), Dust-HEC (21.35%), Dust-Sec (0.72%), Cl-EC-NO3(1.22%), Na-Cl-NO3(3.20%), HM-Sec (2.58%), and PAH-Sec (0.90%). The obtained particle classes can be attributed to different sources of aerosol particles and different transmission and reaction processes. According to comprehensive analysis, the collected particle contribution sources were found to mainly include motor vehicle emission sources, biomass combustion sources, process sources, dust sources, and secondary processes. Among them, K-EC, EC, HEC, and Dust-HEC particles were mainly from direct emissions of primary sources. K-Sec, OC-Amine-Sec, OC-Sec, OCEC-Sec, Na-Cl-NO3, and PAH-Sec particles mainly undergo different degrees of aging or mixed with secondary components.

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Based on the source apportionment by positive matrix factorization (PMF) model, we analyze the main sources and characteristics of aerosol fine particulate matter (PM2.5) during winter and summer in the Hohhot-Baotou-Ordos region, China. We found that organic (19.9%-44.6%) and crustal compositions (9.7%-46.2%) accounted for a large proportion of aerosol PM2.5 according to the results of mass closure. The results of source apportionment showed that the contribution of sources rank as:secondary inorganic aerosol (26.7%) > coal (26.1%) > motor vehicle (19.1%) > dust (18.1%) during winter, and as:secondary inorganic aerosol (26.7%) > dust (22.3%) > coal (16.6%) > vehicle exhaust (15.1%) > SOC (8.7%) during summer. Findings suggest that the contribution of sources with secondary inorganic aerosol were the largest sources both in winter and summer, and that the Hohhot-Baotou-Ordos region was also affected by coal during the winter and dust during the summer. Corresponding to the source apportionment, analysis of typical heavy pollution episodes in winter and summer showed that the pollution sources during the winter were mainly secondary inorganic aerosol and coal, whereas they were mainly secondary inorganic aerosol during the summer.

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Recently, a new method combining positive matrix factorization (PMF) and heavy metal health risk (HMHR) assessment was proposed to apportion sources of heavy metals in ambient particulate matter and the associated heavy metal cancer health risk (HMCR), which has been applied to data collected in Yangzhou, China. The annual average concentrations of six measured heavy metals were Pb (64.4 ng·m-3), followed by Cr (25.24 ng·m-3), As (6.36 ng·m-3), Ni (5.36 ng·m-3), Cd (3.34 ng·m-3), and Co (1.21 ng·m-3). The results showed that the major sources of PM2.5 were secondary sources (37.7%), followed by coal combustion (19.4%), resuspended dust (17.5%), vehicle emissions (16.9%), construction dust (5.2%), and industrial emissions (3.4%). As was primarily emitted from coal combustion, vehicle emissions, and resuspended dust. Co originated from industry emissions. Pb was mainly emitted from coal combustion. Ni and Cd were from industrial emissions. The major sources that contributed to HMCR were resuspended dust, coal combustion, vehicle emissions, industry emissions, and construction dust. The high contributions of resuspended dust and coal to HMCR were likely due to the high heavy metals concentrations in coal and the resuspended dust profile as well as high emissions of these sources.

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As an important megacity of the Beijing-Tianjin-Hebei air pollution transmission channel and the Bohai Sea Economic Zone, Tianjin is influenced by air pollution in recent years, thus research on the fine particulate matter in Tianjin is of vital value. In this study, single particle aerosol mass spectrometry (SPAMS) was used to measure data of Jinnan District in Tianjin during August 2017, to describe the chemical features of fine particles in summer ambient air and estimate the potential pollution sources of fine particles. By using the ART-2a clustering method, 12 classes of PM were acquired, such as elemental carbon particles, Fe-NO3 particles, Na-K particles, and metal particles. After monitoring the size distribution and diurnal variation of fine particles, it was concluded that the ratio of EC particles decreased as the size increased, whereas dust particles and Fe-NO3 particles showed the opposite trend; three types of EC particles varied differently in a day according to the photochemical reaction; and the ratio of Fe-NO3 particles was elevated in the daytime because of industrial production during that period. Backward trajectories of daily airflow at the measured spot were also calculated. When the monitoring site was affected by the air mass from the southwest, coal-burning particles may have contributed more; whereas, when the air mass from the southeast occurred more frequently, biomass burning and sea salt particles possibly contributed more.

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Considering the lack of numbers and updates of particulate matter (PM) source profiles, which show PM emitted from the Chinese iron and steel industry, a dilution tunnel system was used to sample PM discharged from the three main processes (sintering, puddling, and steelmaking) of an iron and steel company in Wuhan, China. Six source profiles for fine and coarse PM were established, and their characteristics were researched. The main conclusions were as follows:① For the sintering source profiles, SO42-, Al, and NH4+ were the dominant components, with mass fractions of 22.2%, 4.5%, and 3.5% in the PM2.5 profile and 36.0%, 5.2%, and 2.7% in the PM10 profile, respectively. Fe was abundant in puddling source profiles, the mass fractions of which reached 28.3% and 24.5% for PM2.5 profile and PM10 profile, respectively. As for steelmaking, the main components were Ca and Fe. ② For the element component features, S was enriched in the sintering source profiles. Metal elements, such as Pb and Cr, were more abundant in the puddling source profiles. ③ The coefficients of divergence for profiles were calculated. Profiles of different sizes for the same processes showed similarities, whereas the diversities between the sintering and the other two profiles were higher. 4 Compared with research in other regions, similarities and differences were found and analyzed.

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A hybrid model based on the positive matrix factorization (PMF) model and the health risk assessment model for assessing risks associated with sources of perfluoroalkyl substances (PFASs) in water was established and applied at Dianchi Lake to test its applicability. The new method contains 2 stages: 1) the sources of PFASs were apportioned by the PMF model and 2) the contribution of health risks from each source was calculated by the new hybrid model. Two factors were extracted by PMF, with factor 1 identified as aqueous fire-fighting foams source and factor 2 as fluoropolymer manufacturing and processing and perfluorooctanoic acid production source. The health risk of PFASs in the water assessed by the health risk assessment model was 9.54 × 10-7 a-1 on average, showing no obvious adverse effects to human health. The 2 sources' risks estimated by the new hybrid model ranged from 2.95 × 10-10 to 6.60 × 10-6 a-1 and from 1.64 × 10-7 to 1.62 × 10-6 a-1 , respectively. The new hybrid model can provide useful information on the health risks of PFAS sources, which is helpful for pollution control and environmental management. Environ Toxicol Chem 2018;37:107-115. © 2017 SETAC.

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Source and ambient samples were collected in a city in China that uses considerable biofuel, to assess influence of biofuel combustion and other sources on particulate matter (PM). Profiles and size distribution of biofuel combustion were investigated. Higher levels in source profiles, a significant increase in heavy-biomass ambient and stronger correlations of K+, Cl-, OC and EC suggest that they can be tracers of biofuel combustion. And char-EC/soot-EC (8.5 for PM2.5 and 15.8 for PM10 of source samples) can also be used to distinguish it. In source samples, water-soluble organic carbon (WSOC) were approximately 28.0%-68.8% (PM2.5) and 27.2%-43.8% (PM10) of OC. For size distribution, biofuel combustion mainly produces smaller particles. OC1, OC2, EC1 and EC2 abundances showed two peaks with one below 1 µm and one above 2 µm. An advanced three-way factory analysis model was applied to quantify source contributions to ambient PM2.5 and PM10. Higher contributions of coal combustion, vehicular emission, nitrate and biofuel combustion occurred during the heavy-biomass period, and higher contributions of sulfate and crustal dust were observed during the light-biomass period. Mass and percentage contributions of biofuel combustion were significantly higher in heavy-biomass period. The biofuel combustion attributed above 45% of K+ and Cl-, above 30% of EC and about 20% of OC. In addition, through analysis of source profiles and contributions, they were consistently evident that biofuel combustion and crustal dust contributed more to cation than to anion, while sulfate & SOC and nitrate showed stronger influence on anion than on cation.

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Long-term and synchronous monitoring of PM10 and PM2.5 was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models (PMF2, ME2-2way and ME2-3way). Consistent results were found: the primary source categories contributed 63.4% (PMF2), 64.8% (ME2-2way) and 66.8% (ME2-3way) to PM10, and contributed 60.9% (PMF2), 65.5% (ME2-2way) and 61.0% (ME2-3way) to PM2.5. Secondary sources contributed 31.8% (PMF2), 32.9% (ME2-2way) and 31.7% (ME2-3way) to PM10, and 35.0% (PMF2), 33.8% (ME2-2way) and 36.0% (ME2-3way) to PM2.5. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment (SDA) was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east (ENE) contributed the highest to both PM10 (12.7%) and PM2.5 (9.7%) in Chengdu, followed by the crustal dust from south-east (SE) for PM10 (9.8%) and secondary nitrate & secondary organic carbon from ENE for PM2.5 (9.6%). Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and to develop effective pollution control strategies.

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To characterize the sources of to PM10 and PM2.5, a long-term, speciate and simultaneous dataset was sampled in a megacity in China during the period of 2006-2014. The PM concentrations and PM2.5/PM10 were higher in the winter. Higher percentages of Al, Si, Ca and Fe were observed in the summer, and higher concentrations of OC, NO3(-) and SO4(2-) occurred in the winter. Then, the sources were quantified by an advanced three-way model (defined as an ABB three-way model), which estimates different profiles for different sizes. A higher percentage of cement and crustal dust was present in the summer; higher fractions of coal combustion and nitrate+SOC were observed in the winter. Crustal and cement contributed larger portion to coarse part of PM10, whereas vehicular and secondary source categories were enriched in PM2.5. Finally, potential source contribution function (PSCF) and source regional apportionment (SRA) methods were combined with the three-way model to estimate geographical origins. During the sampling period, the southeast region (R4) was an important region for most source categories (0.6%-11.5%); the R1 (centre region) also played a vital role (0.3-6.9%).

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Environmental contaminant source apportionment is essential for pollution management and control. This study analysed surface sediment samples for 16 priority polycyclic aromatic hydrocarbons (PAHs). PAH sources were identified by two receptor models, which included positive matrix factorization (PMF) and multilinear engine 2 (ME2). Three PAH sources in the Liaohe River sediments were identified by PMF, including traffic, coke oven and coal combustion. The ME2 model apportioned one additional source. The two models yielded excellent correlation coefficients between the measured and predicted PAH concentrations. Traffic emission was the primary PAH source associated with the Liaohe River sediments, with estimated PMF contributions of 58% in May and 63% in September. Coke oven (19%-25%) and coal combustion (13%-18%) were the other two major PAH sources. For ME2, gasoline and diesel were separated: accounted for 14% in May and 16% in September; and 53% in May and 48% in September. This study marks the first application of the ME2 model to study sediment contaminant source apportionment. The methodology can potentially be applied to other aquatic environment contaminants.

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To understand the influence of quarry mining dust on particulate matter, ambient PM2.5 and quarry mining dust source samples were collected in a city near quarry facilities during 2013-2014. Samples were subject to chemical analysis for dust-related species (Al, Si, Ca, Fe, Ti), tracer metals, carbon components and water-soluble ions. Seasonal variations of PM2.5 and its main chemical components were investigated. Distinctive seasonal variations of PM2.5 were observed, with the highest PM2.5 concentrations (112.42µgm(-3)) in fall and lowest concentrations in summer (45.64µgm(-3)). For dust-related species, mass fractions of Si and Al did not show obvious seasonal variations, whereas Ca presented higher fractions in spring and summer and lower fractions in fall and winter. A combined receptor model (PMF-CMB) was applied to quantify the quarry mining dust contribution to PM2.5. Seven sources were identified, including quarry mining dust, soil dust, cement dust, coal combustion vehicles, secondary sulfate and secondary nitrate. On a yearly average basis, the contribution of quarry mining dust to PM2.5 was 6%. The contribution of soil dust to PM2.5 was comparable with cement dust (13% and 13%, respectively). Other identified sources included vehicle, secondary sulfate, secondary nitrate and coal combustion, which contributed 23, 15, 9 and 18% of the total mass, respectively. Air mass residence time (AMRT) analysis showed that northeast and southeast regions might be the major PM2.5 source during the sampling campaign. The findings of this study can be used to understand the characteristics of quarry mining dust and control strategies for PM2.5.

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To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were investigated. Sources were determined by Multilinear Engine 2 (ME2) model, and results showed that the PM2.5 in Tianjin was mainly influenced by secondary sulphate & secondary organic carbon SOC (percent contribution of 26.2%), coal combustion (24.6%), crustal dust & cement dust (20.3%), secondary nitrate (14.9%) and traffic emissions (14.0%). The SRA method showed that northwest region R2 was the highest regional contributor to secondary sources, with percent contributions to PM2.5 being 9.7% for secondary sulphate & SOC and 6.0% for secondary nitrates; the highest coal combustion was from local region R1 (6.2%) and northwest R2 (8.0%); the maximum contributing region to crustal & cement dust was southeast region R4 (5.0%); and contributions of traffic emissions were relatively spatial homogeneous. The seasonal variation of regional source contributions was observed: in spring, the crustal and cement dust contributed a higher percentage and the R4 was an important contributor; the secondary process attributed an increase fraction in summer; the mixed coal combustion from southwest R5 enhanced in autumn.

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As the widespread application of online instruments penetrates the environmental fields, it is interesting to investigate the sources of fine particulate matter (PM2.5) based on the data monitored by online instruments. In this study, online analyzers with 1-h time resolution were employed to observe PM2.5 composition data, including carbon components, inorganic ions, heavy metals and gas pollutants, during a summer in Beijing. Chemical characteristics, temporal patterns and sources of PM2.5 are discussed. On the basis of hourly data, the mean concentration value of PM2.5 was 62.16±39.37 µg m(-3) (ranging from 6.69 to 183.67 µg m(-3)). The average concentrations of NO3(-), SO4(2-), NH4(+), OC and EC, the major chemical species, were 15.18±13.12, 14.80±14.53, 8.90±9.51, 9.32±4.16 and 3.08±1.43 µg m(-3), respectively. The concentration of PM2.5 varied during the online-sampling period, initially increasing and then subsequently decreasing. Three factor analysis models, including principal component analysis (PCA), positive matrix factorization (PMF) and Multilinear Engine 2 (ME2), were applied to apportion the PM2.5 sources. Source apportionment results obtained by the three different models were in agreement. Four sources were identified in Beijing during the sampling campaign, including secondary sources (38-39%), crustal dust (17-22%), vehicle exhaust (25-28%) and coal combustion (15-16%). Similar source profiles and contributions of PM2.5 were derived from ME2 and PMF, indicating the results of the two models are reasonable. The finding provides information that could be exploited for regular air control strategies.

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In this study, PM2.5 samples were collected at four heights (10m, 40m, 120m and 220m) at a meteorological tower in the daytime and nighttime during the heating season in Tianjin, China. The vertical variation and diurnal variability of the concentrations of PM2.5 and main chemical compositions were analyzed in clear days and heavy pollution days. Generally, mass concentrations of PM2.5 and the chemical compositions showed a decreasing trend with increasing height, while mass percentages of SO4(2-), NO3(-) and OC showed an increasing trend with increasing height. Concentrations of ion species and carbon compound in PM2.5 samples in the daytime were higher than those collected at night, which was due to intense human activities and suitable meteorological condition in the daytime. The ratios of NO3(-)/SO4(2-) and OC/EC were also considered, and we have observed that their levels on heavy pollution days were higher than those on clear days. In addition, source apportionments were identified quantitatively using the CMB-iteration model. The results indicated that contributions of secondary ion species increased with increasing height, while contributions of other pollutant sources decreased, and contributions of vehicle exhaust were relatively high on clear days.

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The transport of particulate matter (PM) and chemical species is an essential mechanism for determining the fate of PM pollutants and their effects. To determine source transport quantitatively, an ambient PM2.5 dataset from a megacity in China was analysed using a novel method called "Source Directional Apportionment" (SDA). The SDA method is developed in this work to quantify contributions of each source category from various directions. The three steps of SDA are (1) to estimate source categories and time series of source contributions to PM with a factor analysis model, (2) to identify directions by trajectory cluster analysis and (3) to quantify source directional contributions for each source category by combining the time series of source contributions to the back trajectories in each direction. For PM2.5 in Chengdu, crustal dust, vehicular exhaust, coal combustion and secondary sulphate are all important contributors to PM; secondary nitrate and cement dust are relatively less influential. Four potential source directions were identified in Chengdu during the sampling period from 2009 to 2011. The percentages of source directional contributions from Directions 1-4 (northeast, southwest to south, southwest and west) were estimated as follows: crustal dust (7.9%, 9.1%, 6.4% and 6.2%, respectively), cement dust (1.0%, 1.2%, 1.3% and 1.1%, respectively), vehicular exhaust (6.4%, 6.0%, 5.6% and 7.0%, respectively), secondary sulphate (5.1%, 5.2%, 5.6% and 8.6%, respectively) and secondary nitrate (2.0%, 2.4%, 2.5% and 2.3%, respectively). Finally, the source directional contributions to important chemical species were quantified to determine their transport from sources to receptor.

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Concentrations of particulate matter with an aerodynamic diameter less than 10 µm (PM10 ) and PM with an aerodynamic diameter less than 2.5 µm (PM2.5 ), and 16 polycyclic aromatic hydrocarbons (PAHs) were measured. The average concentrations of PM10 and PM2.5 reached 209.75 µg/m(3) and 141.87 µg/m(3) , respectively, and those of ΣPAHs were 41.46 ng/m(3) for PM10 and 36.77 ng/m(3) for PM2.5 . The mass ratio concentrations were 219.23 µg/g and 311.01 µg/g in PM10 and PM2.5 , respectively. Three sources and their contributions for PAHs were obtained. For individual input mode, diesel exhaust contributed 46.77% (PM10 ) and 41.12% (PM2.5 ) for mass concentration and 48.69% (PM10 ) and 39.47% (PM2.5 ) for mass ratio concentration; gasoline exhaust contributed 31.02% (PM10 ) and 39.47% (PM2.5 ) for mass concentration and 28.95% (PM10 ) and 36.46% (PM2.5 ) for mass ratio concentration; and coal combustion contributed 22.22% (PM10 ) and 19.41% (PM2.5 ) for mass concentration and 22.36% (PM10 ) and 15.89% (PM2.5 ) for mass ratio concentration. For combined input mode, the same source categories were obtained. Source contributions to PM10 and PM2.5 were diesel exhaust (40.70% and 36.64%, respectively, for mass concentration; 49.19% and 38.47%, respectively, for mass ratio concentration), gasoline exhaust (35.09% and 38.47%, respectively, for mass concentration; 32.50% and 33.43%, respectively, for mass ratio concentration), and coal combustion (24.21% and 24.89%, respectively, for mass concentration; 18.31% and18.17%, respectively, for mass ratio concentration). Source risk assessment showed that vehicle emission was a significant contributor. The findings can help elucidate sources of PAHs and provide evidence supporting further applications of the Unmix model and additional studies about PAHs. Environ Toxicol Chem 2015;34:480-487. © 2014 SETAC.

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PM10 and PM2.5 samples were simultaneously collected during a one-year monitoring period in Chengdu. The concentrations of 16 particle-bound polycyclic aromatic hydrocarbons (Σ16PAHs) were measured. Σ16PAHs concentrations varied from 16.85 to 160.24 ng m(-3) and 14.93 to 111.04ngm(-3) for PM10 and PM2.5, respectively. Three receptor models (principal component analysis (PCA), positive matrix factorization (PMF), and Multilinear Engine 2 (ME2)) were applied to investigate the sources and contributions of PAHs. The results obtained from the three receptor models were compared. Diesel emissions, gasoline emissions, and coal and wood combustion were the primary sources. Source apportionment results indicated that these models were able to track the ΣPAHs. For the first time, the cancer risks for each identified source were quantitatively calculated for ingestion and dermal contact routes by combining the incremental lifetime cancer risk (ILCR) values with the estimated source contributions. The results showed that gasoline emissions posed the highest cancer risk, even though it contributed less to Σ16PAHs. The results and method from this work can provide useful information for quantifying the toxicity of source categories and studying human health in the future.

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Samples of PM10 and PM2.5 were synchronously collected from a megacity in China (Chengdu) during the 2011 sampling campaign and then analyzed by an improved three-way factor analysis method based on ME2 (multilinear engine 2), to investigate the contributions and size distributions of the source categories for size segregated particulate matter (PM). Firstly, the synthetic test was performed to evaluate the accuracy of the improved three-way model. The same five source categories with slightly different source profiles were caught. The low AAE (average absolute error) values between the estimated and the synthetic source contributions (<15%) and the approachable estimated PM2.5/PM10 ratios with the simulated ratios might indicate that the results of the improved three-way factor analysis might be satisfactory. Then, for the ambient PM samples, the mean levels were 206.65 ± 69.90 µg/m(3) (PM10) and 130.47 ± 43.67 µg/m(3) (PM2.5). The average ratio of PM2.5/PM10 was 0.63. PM10 and PM2.5 in Chengdu were influenced by the same source categories and their percentage contributions were in the same order: crustal dust & coal combustion presented the highest percentage contributions, accounting for 58.20% (PM10) and 53.73% (PM2.5); followed by vehicle exhaust & secondary organic carbon (18.45% for PM10 and 21.63% for PM2.5), secondary sulfate and nitrate (17.06% for PM10 and 20.91% for PM2.5) and cement dust (6.30% for PM10 and 3.73% for PM2.5). The source profiles and contributions presented slightly different distributions for PM10 and PM2.5, which could better reflect the actual situation. The findings based on the improved three-way factor analysis method may provide clear and deep insights into the sources of synchronously size-resolved PM.

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