[Construction and Evaluation on Size Resolved Source Apportionment Methods Based on Particle Size Distribution of Chemical Species].

The analysis of the sources of atmospheric particulate pollution can provide scientific support for the prevention and control of air pollution. Most particulate matter (PM) source analysis studies are based on the chemical composition of PM. In addition, particle size characteristics are also one of the important properties of PM. The accuracy of analytical results can be improved by analyzing the particle size characteristics of chemical components. In this study we aim to to solve the problem of insufficient utilization of component particle size information by using a the three-dimensional multi-particle size factor analysis model (ABB), where the particle size distribution of marked components is regarded as the constraint limit, and a multi-particle size source analytical model (SDABB) based on the characteristics of the components particle size distribution is constructed. The sensitivity of the SDABB model to the collinearity of the source spectrum and the similarity of the particle size distribution of the source contributions are investigated by evaluating the model through the simulation of the data set. The results showed that the ABB model was sensitive to the collinearity of the source spectrum and to the similarity of the particle size distribution of the source contributions. When particle size distribution rules were incorporated into the SDABB model, the effects of the two scenarios were significantly improved, that is, the SDABB model was able to better analyze collinear source spectrum and was insensitive to the similarity of the contribution particle size distribution.

[Spatial Temporal Characteristics and Cluster Analysis of Chemical Components for Ambient PM2.5 in Wuhan].

The characteristics of chemical components of particulate matter are good indicators for analyzing sources and causes of pollution. The spatial and temporal distribution characteristics of particulate matter can reflect regional pollution problems in urban development, providing a basic dataset to support effective control of particulate matter sources. We collected PM2.5 and analyzed its concentration and chemical components at eight sites during different seasons. The results indicated that the average concentration of PM2.5 in Wuhan reached 70.7 µg·m-3. The concentration of PM2.5 in winter (103.1 µg·m-3) was significantly higher than that of other seasons, and the lowest concentration was in autumn (52.4 µg·m-3). The concentrations of PM2.5 in Donghu Gaoxin, Zhuankou New Area, and Qingshan Ganghua Station were significantly higher than those at the other sites. The main chemical components in PM2.5 were OC and SO42-, accounting for 15.4% and 14.2%, respectively. The OC concentration was the highest in winter, whereas SO42-concentration was the highest in summer. The average annual OC/EC ratio was up to 2.80, lower in winter and spring, and higher in summer and autumn. Material reconstruction showed that secondary particles and organic matter (OM) were major substances, accounting for 32.34% and 20.44% of PM2.5 mass, respectively. Coal combustion and vehicle exhaust might be the main contributors to ambient PM2.5. The highest fractions for OM were at the Wujiashan and Donghu Gaoxin sites, whereas the fraction of secondary particles was higher at each site, suggesting that secondary pollution had obvious regional characteristics in Wuhan. Cluster analysis based on the characteristics of chemical components showed that the eight sites were divided into three clusters:1 Hanyang Yuehu, Haze, Donghu Liyuan, and Huangpi sites, where the main characteristics were that the concentrations of components at each point were low; ② Zhuankou New Area and Qingshan Ganghua, which were characterized by higher nitrogen components; and ③ Donghu Gaoxin and Wujiashan, where not only industrial sources were heavily polluted in Wuhan, but also motor vehicles and dust pollution greatly contributed.

[Pollution Characteristics and Source Apportionment of Ambient PM2.5 During Four Seasons in Yantai City].

PM2.5 samples were collected at three sites in Yantai City during all four seasons of 2016-2017, and the mass concentration and chemical composition characteristics were analyzed. The CMB model was used to calculate source apportionment of ambient PM2.5, and the backward trajectory cluster and PSCF were used to analyze the transport flow and potential source regions. The results showed that the average mass concentrations of PM2.5 in winter, spring, summer, and autumn in Yantai were (89.45±56.80), (76.78±28.44), (32.65±17.92) and (57.32±24.60) µg·m-3, respectively. The PM2.5 concentration showed a significant seasonal variation (P<0.01). The contribution of PM2.5 sources was as follows:secondary nitrate (20.3%) > crustal dust (15.7%) > vehicle exhaust (14.9%) > coal combustion (13.8%) > secondary sulphate (12.8%) > SOC (6.1%) > cement dust (5.5%) > sea salt source (2.9%). It can be seen that the predominant sources were secondary sources, crustal dust, vehicle exhaust, and coal combustion. The sources of nitrate in spring and of crustal dust were important contributors. The contribution of sulfate in summer was prominent, and the proportion of coal combustion was high in autumn and winter. Yantai City's airflow transport and potential source regions also showed significant seasonal changes:winters were mainly affected by short-distance transport in Yantai City; summers were mainly affected by the coastal of eastern Yantai City and local areas; springs and autumns were mainly affected by regional transmission in the northeast and in the eastern coastal areas of Shandong Province and by local sources in Yantai City.

[Analysis of Chemical Composition of the Fine Particulate Matter in Summer in Tianjin City via a Single Particle Aerosol Mass Spectrometer (SPAMS)].

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.

A new method to quantify the health risks from sources of perfluoroalkyl substances, combined with positive matrix factorization and risk assessment models.

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.

[Chemical Source Profiles of PM Emitted from the Main Processes of the Iron and Steel Industry in China].

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.

Source profiles and contributions of biofuel combustion for PM2.5, PM10 and their compositions, in a city influenced by biofuel stoves.

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.

Size distribution, directional source contributions and pollution status of PM from Chengdu, China during a long-term sampling campaign.

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.

Seasonal and regional variations of source contributions for PM10 and PM2.5 in urban environment.

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%).

Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method.

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.

A new receptor model-incremental lifetime cancer risk method to quantify the carcinogenic risks associated with sources of particle-bound polycyclic aromatic hydrocarbons from Chengdu in China.

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.

Source apportionment of synchronously size segregated fine and coarse particulate matter, using an improved three-way factor analysis model.

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.

Physically constrained source apportionment (PCSA) for polycyclic aromatic hydrocarbon using the Multilinear Engine 2-species ratios (ME2-SR) method.

An improved physically constrained source apportionment (PCSA) technology using the Multilinear Engine 2-species ratios (ME2-SR) method was proposed and applied to quantify the sources of PM10- and PM2.5-associated polycyclic aromatic hydrocarbons (PAHs) from Chengdu in winter time. Sixteen priority PAH compounds were detected with mean ΣPAH concentrations (sum of 16 PAHs) ranging from 70.65 ng/m(3) to 209.58 ng/m(3) and from 59.17 ng/m(3) to 170.64 ng/m(3) for the PM10 and PM2.5 samples, respectively. The ME2-SR and positive matrix factorization (PMF) models were employed to estimate the source contributions of PAHs, and these estimates agreed with the experimental results. For the PMF model, the highest contributor to the ΣPAHs was vehicular emission (81.69% for PM10, 82.06% for PM2.5), followed by coal combustion (12.68%, 12.11%), wood combustion (5.65%, 4.45%) and oil combustion (0.72%, 0.88%). For the ME2-SR method, the highest contributions were from diesel (43.19% for PM10, 47.17% for PM2.5) and gasoline exhaust (34.94%, 32.44%), followed by wood combustion (8.79%, 6.37%), coal combustion (12.46%, 12.37%) and oil combustion (0.80%, 1.22%). However, the PAH ratios calculated for the factors extracted by ME2-SR were closer to the values from actual source profiles, implying that the results obtained from ME2-SR might be physically constrained and satisfactory.

Using an improved Source Directional Apportionment method to quantify the PM(2.5) source contributions from various directions in a megacity in China.

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.

Further insights into the composition, source, and toxicity of PAHs in size-resolved particulate matter in a megacity in China.

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.

Source contributions and spatiotemporal characteristics of PAHs in sediments: Using three-way source apportionment approach.

Polycyclic aromatic hydrocarbon (PAHs) were measured in sediments from 29 sites throughout Taihu Lake in China during 2 seasons to investigate spatiotemporal characteristics and source contributions using a 3-way source apportionment approach to positive matrix factorization (PMF3). Seasonal and spatial variations of levels and toxicity suggested higher individual carcinogenic PAH concentrations and toxic equivalent quantity (TEQ) in the flooding season. Three-way PAHs dataset (PAH species, spatial variability, and seasonal variability) was analyzed by PMF3, and its results were compared with a widely used 2-way model (PMF2). Consistent results were observed: vehicular emission was the most important contributor (67.08% by PMF2 and 61.83% by PMF3 for the flooding season; 54.21% by PMF2 and 52.94% by PMF3 for dry season), followed by coal combustion and wood combustion in both seasons. The PMF-cluster analysis was employed to investigate spatial variability of source contributions. Findings can introduce the 3-way approach to apportion sources of PAHs and other persistent organic pollutants (POPs) in sediments, offering the advantage of accounting for information on 3-way datasets.

Chemical characteristic and toxicity assessment of particle associated PAHs for the short-term anthropogenic activity event: During the Chinese New Year's Festival in 2013.

PM10 and PM2.5 samples were simultaneously collected during a period which covered the Chinese New Year's (CNY) Festival. The concentrations of particulate matter (PM) and 16 polycyclic aromatic hydrocarbons (PAHs) were measured. The possible source contributions and toxicity risks were estimated for Festival and non-Festival periods. According to the diagnostic ratios and Multilinear Engine 2 (ME2), three sources were identified and their contributions were calculated: vehicle emission (48.97% for PM10, 53.56% for PM2.5), biomass & coal combustion (36.83% for PM10, 28.76% for PM2.5), and cook emission (22.29% for PM10, 27.23% for PM2.5). An interesting result was found: although the PAHs are not directly from the fireworks display, they were still indirectly influenced by biomass combustion which is affiliated with the fireworks display. Additionally, toxicity risks of different sources were estimated by Multilinear Engine 2-BaP equivalent (ME2-BaPE): vehicle emission (54.01% for PM10, 55.42% for PM2.5), cook emission (25.59% for PM10, 29.05% for PM2.5), and biomass & coal combustion source (20.90% for PM10, 14.28% for PM2.5). It is worth to be noticed that the toxicity contribution of cook emission was considerable in Festival period. The findings can provide useful information to protect the urban human health, as well as develop the effective air control strategies in special short-term anthropogenic activity event.

Long-term variation of the levels, compositions and sources of size-resolved particulate matter in a megacity in China.

To investigate the long-term trends and variations of the levels, compositions, size distribution and sources of particulate matter (PM), long-term monitoring campaigns of PM10 and PM2.5 were performed in a megacity in China (Chengdu) during the period from 2009 to 2011. The average concentration of PM10 was 172.01±89.80 µg/m(3) and that of PM2.5 was 103.15±59.83 µg/m(3), with an average PM2.5/PM10 of 0.60. Enrichments of the important species indicated that the fractions of crustal elements were higher in PM10 than those in PM2.5, while the abundance of organic carbon (OC) and secondary ions was enriched in the fine PM. Quantitative source apportionments of both PM10 and PM2.5 were performed by PMF. PM10 and PM2.5 in Chengdu were influenced by similar source categories, and their percentage contributions were in the same order: crustal dust was the highest contributor, followed by vehicular exhaust, secondary sulfate, secondary nitrate and cement dust. Crustal dust and cement dust contributed a higher percentage to PM10 than to PM2.5, while vehicular exhaust and secondary particles provided higher percentage contributions to PM2.5. In addition, PMF-HCA was performed to investigate the characteristics of the sources of the clustered samples, identifying three periods: crustal dust dominant-period, secondary sulfate dominant-period and comprehensive source influenced-period. Planting, reduction of precursors, and banning high-emission vehicles should be implemented to control crustal dust, secondary particles and vehicular exhaust in Chengdu. Furthermore, the size-resolved and the period-resolved control would be more effective.

Source apportionment of perfluorinated compounds (PFCs) in sediments: using three multivariate factor analysis receptor models.

Understanding the levels, distribution and sources of perfluorinated compounds (PFCs) in sediments is of great significance for the management of aquatic environments. In this work, 26 sediment samples were collected from Dianchi Lake in China and ten PFCs compounds were measured. The concentrations of the total PFCs (∑PFCs) in the sediments ranged from 0.21 to 2.45 ng g(-1)dw (dry weight), with an average value of 0.95 ng g(-1)dw. PFOS was the most abundant compound among the ten PFCs with the average concentration of 0.33 ng g(-1)dw, followed by PFOA at 0.21 ng g(-1)dw. A two-dimensional HCA (hierarchical cluster analysis) heat map was depicted to analyze the spatial variation of individual PFCs compound and the possible origins in the sediments. Two groups were clustered by HCA, showing the possible source categories (PFOS-cluster and PFOA-cluster). Additionally, PCA-MLR, PMF and Unmix models were employed to quantitatively calculate the contribution of extracted sources. Three models concluded consistent results that PFOS-factor and PFOA-factor were the two main source categories for PFCs in the sediments. The contribution percentages were 43% (PCA-MLR), 48% (PMF) and 46% (Unmix) from the former source, and were 54% (PCA-MLR), 43% (PMF) and 44% (Unmix) from the latter source, respectively. The findings and the approaches used in this work can provide useful information for further study of source apportionment for PFCs in sediments and other environmental compartments.

Vertical characteristics of levels and potential sources of water-soluble ions in PM10 in a Chinese megacity.

To investigate the vertical characteristics of ions in PM10 as well as the contributions and possible locations of their sources, eight water-soluble ions were measured at four heights simultaneously along a meteorological tower in Tianjin, China. The total ion concentrations showed a general decreasing trend with increasing height, ranging from 64.94µgm(-3) at 10m to 44.56µgm(-3) at 220m. NH4(+), SO4(2-) and NO3(-) showed higher height-to-height correlations. In addition, relationships between ions are discussed using Pearson correlation coefficients and hierarchical clustering analysis (HCA), which implied that, for each height, the correlations among NH4(+), SO4(2-) and NO3(-) were higher. Finally, sources were identified qualitatively by the ratio of certain ions and quantitatively by principal component analysis/multiple linear regression (PCA/MLR) and positive matrix factorisation (PMF). Secondary sources played a dominant role for PM10 and water-soluble ions at four heights and became more important at greater heights (the percentage contributions were 43.04-66.41% for four heights by PCA/MLR and 46.93-67.62% by PMF). Then, the redistributed concentration field (RCF) combined with PCA/MLR and PMF was applied, which indicated the high potential source regions. The vertical characteristics of the levels, relationships, source contributions and locations would support the effective management of the water-soluble ions in particulate matter.