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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 [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: In this study, PM10 samples were collected at a meteorological tower in Tianjin, China. Four height levels (10 m, 40 m, 120 m and 220 m) were selected as the sampling sites. During the measurement campaign, the highest PM10 and species concentrations were obtained at 10 m, while lower concentrations were obtained at higher sampling sites. According to the vertical variability analysis of species concentrations and fractions (%), significant differences between different heights were found for certain species, such as Al, Si, Ca, OC and EC, while such differences for and were insignificant. In addition, the source contributions at each sampling site were calculated by a chemical mass balance (CMB) model. In all sampling sites, secondary sulfate accounted for the largest contributions (24.11–30.96%). The other estimated contributions were secondary nitrate (16.19–20.95%), crustal dust (10.74–11.37%), coal combustion (12.47–14.39%), vehicle exhaust (13.92–14.78%) and cement dust (4.60–9.89%). Finally, the conditional probability function (CPF) plots and potential source contribution function (PSCF) maps show that the ambient samples might be from local potential sources at lower sampling heights as well as regional potential sources at higher sampling heights during this measurement campaign. [Copyright &y& Elsevier]

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.

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 PM 2.5 and analyzed its concentration and chemical components at eight sites during different seasons. The results indicated that the average concentration of PM 2.5 in Wuhan reached 70.7 μg·m -3 . The concentration of PM 2.5 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 PM 2.5 in Donghu Gaoxin, Zhuankou New Area, and Qingshan Ganghua Station were significantly higher than those at the other sites. The main chemical components in PM 2.5 were OC and SO 4 2- , accounting for 15.4% and 14.2%, respectively. The OC concentration was the highest in winter, whereas SO 4 2- 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 PM 2.5 mass, respectively. Coal combustion and vehicle exhaust might be the main contributors to ambient PM 2.5 . 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.

PM 2.5 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 PM 2.5 , 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 PM 2.5 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 PM 2.5 concentration showed a significant seasonal variation ( P <0.01). The contribution of PM 2.5 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.

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, SO 4 2- , Al, and NH 4 + were the dominant components, with mass fractions of 22.2%, 4.5%, and 3.5% in the PM 2.5 profile and 36.0%, 5.2%, and 2.7% in the PM 10 profile, respectively. Fe was abundant in puddling source profiles, the mass fractions of which reached 28.3% and 24.5% for PM 2.5 profile and PM 10 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.

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-NO 3 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-NO 3 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-NO 3 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.

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 PM 2.5 and 15.8 for PM 10 of source samples) can also be used to distinguish it. In source samples, water-soluble organic carbon (WSOC) were approximately 28.0%-68.8% (PM 2.5 ) and 27.2%-43.8% (PM 10 ) 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 PM 2.5 and PM 10 . 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., (Copyright © 2017 Elsevier Ltd. All rights reserved.)