A brief introduction and progress summary of the PM2.5 source profile compilation project in China

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Abstract

This paper presents the background, the purpose, an outline of the studies, and the research progress to date for the “PM2.5 source profiles compilation project,” which has been supported as a key program of basic work of the Ministry of Science and Technology, China. Coal combustion (residential and industrial coal), industrial emissions, biomass burning, motor vehicle exhaust (liquefied petroleum gas, gasoline, and diesel powered vehicles), as well as open sources (dust and cooking) were investigated for the project, because they are the major PM2.5-emission sources. Here, we (1) summarize the sampling processes and analytical methods for the main chemical constituents (elements, carbonaceous species, water-soluble inorganic ions, and organic compounds), (2) review research progress with reference to the major source characterization efforts and construction of a profile database, and (3) discuss ongoing and future research plans.

Keywords

Source profiles PM2.5 Research progress 

1 Introduction

Fine particle (particulate matter, PM, with diameters less than 0.25 μm) pollution has aroused widespread concern of the public in recent years. New national ambient air quality standards (GB3095-2012) were promulgated in 2012 by the Ministry of Environmental Protection of the People’s Republic of China and officially implemented in 2016. With that implementation, all levels of the government have been confronted with new problems in PM2.5 pollution control. To reduce PM2.5 concentrations, it is essential to understand the relative contributions to PM2.5 from various sources. Effective management and control strategies can only be developed for the major emission sources when this information is obtained. Receptor models, especially the Chemical Mass Balance Model (CMB), have been widely used on PM2.5 source apportionment to strength the understanding of emission emissions. Accurate source apportionments rely on comprehensive environmental observational data and the PM2.5 source profiles from presumptive primary sources.

Here, source profiles refer to the relative abundances of chemical components in pollutants such as particulate matter (PM) and volatile organic compounds (VOCs) released from targeted emission sources. Emission sources can be classified as either natural or anthropogenic based on their characteristics; that is, natural source emissions refer to pollutants from various natural activities or processes, including dust storms, vegetative emissions, volcanic eruptions, lightning, etc., while anthropogenic source emissions include those from human activities such as the operation of motor vehicles, coal combustion, biomass burning, etc. Developed countries have done considerable research on pollution source emissions. For instance, the United States Environmental Protection Agency standardized the sampling and analytical methods for emissions and obtained a series of source profiles. Those results were combined with historical information on emission sources to compile Air-Emission Factors (AP-42) for the purpose of sorting out and summarizing various sources emission characteristics (US Environmental Protection Agency 1996). Since then, the AP-42 has been refined and updated annually, and it now covers an increasing number of emission sources: this approach has provided a solid basis for understanding the PM emission characteristics in the USA.

A representative database of PM2.5 source profiles is needed to apportion the contributions of pollutant sources for any region. Compared with USA, PM2.5 source profile research in China has been very limited. This has led to the unfortunate case that source profiles from foreign studies have been used in the calculations of receptor models (such as CMB model), and this has caused a great deal uncertainty in the results. Obviously, it is less than ideal to use these types of results for effective environmental management. Second, PM2.5 source profiles provide fundamental data and information for numerical simulations, which can be used to characterize the PM2.5 distributions and forecast trends in PM2.5 concentrations. This type of information also makes it possible to use a scientific approach for evaluating the influences of PM2.5 emission sources on visibility, human health, and regional climate change, etc. Furthermore, a database of PM2.5 source profiles is vital for source management assessments, because the database can be used to evaluate the efficacy of any controls put in place. Hence, it is important and urgent to obtain accurate source profiles for PM2.5 in China.

The PM2.5 source profiles compilation project in China, set up in 2013 by Ministry of Science and Technology of China, had the following objectives: (1) compile a database of historical source profiles; (2) establish a database of PM2.5 source profiles from primary anthropogenic sources in China; and (3) apply the database to study the source apportionment of urban particle pollution in China.

2 Outline of the project

This project has evaluated on emissions data from a variety of sources including peer-reviewed literature, other relevant data sources, and emission tests that were conducted previously. Major anthropogenic emission sources including coal burning (residential and industrial), industrial sectors, biomass burning, motor vehicle emissions (liquefied petroleum gas, gasoline, and diesel vehicles), and open sources (fugitive dust and cooking) have been investigated and are being compiled in our source profile database. The technical and research plans are plotted together in Fig. 1.
Fig. 1

Main research contents and methods in the “PM2.5 source profiles compilation project in China”

A variety of modern emission characterization methods, including dilution sampling, combustion chambers, in-site field sampling, tunnel sampling, resuspension, etc., have been used to collect fresh source samples. Collected source samples were transferred to various analytical facilities for determinations of mass, elements, water-soluble ions, carbon fractions, organic matter, isotopes (C and O), etc. The chemical concentrations are being assembled into a database, and the results are being normalized by the collected PM2.5 mass to construct chemical-specific source profiles. The ultimate plan is to establish an open-access PM2.5 source profile database parallel to the US EPA’s SPECIATE database. The main components and methodology of the project are shown in Fig. 1.

2.1 Establishing a China-specific PM2.5 source profile historical database

Particulate matter (e.g., PM2.5, PM10, and TSP) can originate from both natural and anthropogenic sources. To identify major emission sources of particulate matter, literature reviews and practical research have both been conducted. Considering that PM2.5 source profiles from China are quite limited, sources profiles of PM10 and TSP also are being considered for this project. Existing source profiles were reviewed and classified according to emission sources, sampling time, etc.; these will be used to establish a historical database and to better characterize the emission sources.

2.2 Sampling processes

This project focuses on the major PM2.5-emission sources in urban China; these include coal burning (residential and industrial), biomass burning, motor vehicle emissions (liquefied petroleum gas, gasoline, and diesel powered vehicles), open sources (fugitive dust, cooking), etc. Source profiles for PM2.5 from each of those sources will be documented in the database, and the finished product will be released to the public. The structure of the database allows for storage and retrieval of all the relevant information underlying each profile, including source types, location of sampling site, emission sources, as well as other relevant sampling information.

PM2.5 samples from fresh biomass burning emissions, including smoke from rice straw, wheat straw, corn stalk, and wood branches were collected using different sampling techniques as follows: (1) simulations of residential biomass burning in the laboratory with a combustion chamber at the Institute of Earth Environment, Chinese Academy (Fig. 2). A detailed description of the combustion chamber may be found in Tian et al. (2015). The smoke emitted from these laboratory burns was sampled with the use of dilution sampling system for stationary combustion sources. The biomass samples used for the test burns were collected from the major production regions in China; (2) on-site measurements of residential biomass burning. The smoke from residences was collected using portable dilution sampling systems; (3) both on-site samplings in the harvest season and laboratory simulations using combustion chambers of the open burning of crop residue (Tian et al. 2015); and (4) on-site measurements and laboratory simulations of forest fires (Sun et al. 2017).
Fig. 2

Combustion chamber for simulation of residential biomass burning and coal combustion

Coal samples, including essentially all types of coal burned for residential uses, were collected from the major coal-producing areas in China. Coal samples collected for this project include anthracite coal, bituminous coal, lignite coal, meagre coal, gas coal, coking coal, and honeycomb coal briquettes, etc. Burning experiments were carried out in a laboratory combustion simulator. PM2.5 samples were collected using a portable dilution sampling system. In addition to laboratory simulations, on-site sampling of residential coal combustion also was conducted.

PM2.5 from cement production, mainly from pre-calcining kilns and shaft kilns with production > 10 million tons cement per year, were sampled. On-site PM2.5 sampling was conducted at a selected representative cement kilns. For steel production, on-site sampling was carried out at selected representative steel factories. For those studies, different sizes of sintering machines and pelletizing shaft furnaces, different types of coke ovens (e.g., top loading and tamping process), and iron blast furnace with varying typical capacity were considered and included in the project. Different flue gas control methods also were examined to study their impact on PM2.5 emission characteristics; these controls include electrostatic precipitators, bag filters, and electric-bag-integrated dust removal. Characterization of motor vehicular emissions, including different fuel types (gasoline, diesel) and operational conditions, was conducted using a chassis dynamometer, tunnel studies, and chase vehicle studies. The collected PM2.5 samples from all these studies and those described below were chemically analyzed to construct the PM2.5 source profiles.

Fugitive dust was acquired from (1) urban and rural paved roads, construction sites; (2) residential and agricultural unpaved roads; (3) different type of soils, for example, loess plateau soils, southern red soil, northeastern black soils; (4) building and roadway construction soils; and (5) other soil types (e.g., deposits from dry lake beds and deserts). Soil samples were dried, sieved, resuspended, and sampled through PM2.5 inlets onto filters. Road dust and construction dust samples were collected in source-dominated environments, such as main roads and areas next to construction sites, using portable samplers.

Samples from cooking, waste incineration, picnic, and other miscellaneous sources were collected. Different cooking styles (e.g., residential vs. commercial cooking) and cooking methods (northern vs. southern China) were taken into account in the design of the study. For waste incineration, different types of trash were considered, including fuelwood trash, industrial waste, e-waste, garbage, etc. For picnic emissions, different field combustion sources were considered, for example, the use of wood vs. oil. The smoke from these sources was collected using dilution sampling system directly pointing at the emission sources.

2.3 Chemical analysis of source samples

The elemental composition of PM2.5 source samples collected on Teflon® filters was determined by Energy Dispersive X-Ray Fluorescence (ED-XRF) spectrometry (Epsilon 5 ED-XRF analyzer, PANalytical, The Netherlands). These analyses produced data for 40 elements: Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Y, Zr, Mo, Pd, Ag, Cd, In, Sn, Sb, Ba, La, Au, Hg, Tl, Pb, and U. Details of the method have been described in Cao et al. (2012).

The carbonaceous fractions of PM2.5 source samples collected on quartz filters were analyzed with a Desert Research Institute (DRI) Model 2001 Thermal/Optical Carbon Analyzer (Atmoslytic, Inc., Calabasas, CA, USA). The analytes include OC, EC, OC1, OC2, OC3, OC4, OP, EC1, EC2, and EC3. Details of the methods and QA/QC procedures have been described in Cao et al. (2003).

Five cations (Na+, K+, Mg2+, Ca2+, and NH4+) and five anions (SO42−, NO3, NO2, Cl, and F) in aqueous extracts of the filters were determined by ion chromatography (IC) with a Dionex-600 ion chromatograph (Dionex Inc., Sunnyvale, CA, USA). Details of the methods have been described in Shen et al. (2008).

The organic components of the PM2.5 source samples were analyzed by gas chromatograph/mass spectrometry (GC/MS) and high-performance liquid chromatography (HPLC); the compounds determined include 26 polycyclic aromatic hydrocarbons (PAHs), 24 alkanes, aliphatic acids, dicarboxylic acid, aliphatic alcohols, steroids, six saccharides, etc. Details of the analytical methods can be found in Xu et al. (2013).

The 13C isotopic abundances of coal and biomass burning source samples were measured with a Finnigan MAT 252 mass spectrometer (Thermo Electron Corporation, Burlington, Ontario, Canada) fitted with an online Finnigan automatic CO32− reaction system (“Kiel device”). Methods for the PM2.5 carbon isotope analyses may be found in Cao et al. (2011).

2.4 Quality control and data assessment

2.4.1 Quality assurance and quality control

The quality assurance and quality control procedures for this project focused on two areas: sampling and analysis. The PM2.5 samples were collected following the strict experimental quality control requirements. Dilution rates were adjusted to match the different PM2.5 concentrations at the various studies to satisfy the requirements for chemical component analysis, to avoid contamination during sampling, and to ensure that there were no problems of over- or under-loading the filters. For the source sample collections, we assumed that the main contaminants to be avoided were atmospheric pollutants, and measures need to be taken to avoid the contamination from these contaminants, such as cleaning the dilution system regularly as that removes any residual PM.

For the sample analysis, all instruments were operated in accordance with the standard quality control requirements. For example, the carbon analyzer was calibrated with methane gas on a daily basis and a standard sample was used for system debugging. Meanwhile, blank samples were used for the baseline debugging; sugar was used for system testing every half a month, and so on. Reference samples were collected and repeated tests were carried out for quality control, and field blanks also were analyzed to ensure the reliability of the data.

To guarantee the reliability of data for all the analytical methods, a few samples (about 20 for each kind of analysis) were selected to do comparative tests in various established laboratories, including the Hong Kong Polytechnic University, the Desert Research Institute, and the Lamont Experiment Center at Columbia University, etc.

2.4.2 Data assimilation and uncertainty evaluation

Since the amount of sample for the collections was quite variable, the measured chemical components were standardized to the PM2.5 mass concentration. This makes it possible to directly compare the different source samples and the chemical analytes among samples. To build a source profile database useable for source apportionments, the uncertainties of the profile data from the various sources were evaluated. Finally, a chemical mass balance model was used for source assessments.

2.5 Development of the database

The chemical composition (mass concentration, element, ions, OC and EC, organic matter, and isotopic compositions) of PM2.5 samples from domestic coal source, typical industrial source, motor vehicles source, biomass burning sources, dust source, and the open sources documented in the previous studies was classified and archived using Microsoft Excel® and text formats, respectively. Based on experience with the object-oriented Geodatabase, space database model from ArcGIS, spatial data engine ArcSDE, and large relational database Oracle, a plan was developed to solve technical problems such as the high efficient data storage of large capacity data and quick response, etc. in the comprehensive database that was established. The database functions were designed to meet the needs of database security, real-time updates, dynamic maintenance, scalability, and so on. Detailed source profiles can be download freely from the webpage: http://www.sourceprofile.org.cn.

3 Research progress

3.1 PM2.5 source profiles from biomass and residential coal burning

A custom-made combustion chamber and dilution sampler were used to conduct laboratory measurements that simulated the burning of 48 types of typical biomass and 12 types of residential coal burned China. Carbon fractions, water-soluble ions, element species, and particulate PAHs (PM2.5-bound PAHs) were analyzed to investigate PM2.5-emission characteristics. In all, 30 PM2.5 source profiles from biomass burning and 12 PM2.5 source profiles from residential coal combustion were obtained for the project (Ni et al. 2017; Tian et al. 2017; Sun et al. 2017).

Wheat straw, rice straw, and corn stalks, the three major agricultural crop residues in China, were collected from major producing areas and burned to determine PM2.5 open biomass burning source profiles. The profiles for the same type of crop residues collected from different provinces were similar. All the source profiles for the tested crop residues showed the highest abundances of OC, followed by high abundances of EC, Cl, K+, and K. The carbon fractions were the major constituents of PM2.5, with average abundances of over 40%. EC1 emissions accounted for about 90% of EC from crop residues open burning (Ni et al. 2015; 2017). Compared with wood combustion, herbaceous plants had higher K+/EC ratios. Large quantities of OP (pyrolyzed OC) were emitted from biomass burned for domestic heating and cooking, and this carbon fraction may be useful as an indicator of smoldering burning associated with those sources (Sun et al. 2017).

PM2.5 source profiles from three types of common residential coals (bituminous coal chunks, anthracite coal chunks, and anthracite coal briquettes) showed that OC, EC, SO42−, and S were the dominant species, with average abundances of 14.9–33.0, 3.45–35.4, 4.9–29.7, and 2.6–12.5%, respectively. Significantly higher TC/PM2.5 and EC/OC ratios were found in bituminous coal compared with anthracite coal, which implies that more mineral matter besides the carbonaceous materials were in PM2.5 from bituminous coal combustion. This finding may be helpful for identifying the origins of carbonaceous aerosol. For the carbon fractions, emissions from bituminous coal combustion were dominated by EC2 (45% of total carbon), while OC2 and OC3 were major components produced from anthracite coal combustion, ranging from 8 to 17% of TC.

Due to their carcinogenic, mutagenic, and teratogenic properties, 16 PAHs that have been classified as priority pollutants by the US EPA were measured for the project. The 16 PM2.5-bound PAHs, including naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, dibenz(a,h)anthracene, indeno(1,2,3-cd)pyrene, and benzo(g,h,i)perylene, were grouped based on the number of aromatic rings in their structures (2-, 3-, 4-, 5-, and 6 rings). The results were calculated as percentages to obtain PM2.5-bound PAHs source profiles from crop residues and coal combustion. The PM2.5-bound PAHs emitted from crop residues (fluoranthene, pyrene, benz(a)anthracene, chrysene and benzo(a)pyrene), bituminous coal chunk (benzo(b)fluoranthene and benzo(a)pyrene), and anthracite coal briquette (chrysene) were dominated by the high molecular weight (4–6 rings) compounds, while 3 ring compounds (phenanthrene and anthracene) were the major PAHs in PM2.5 samples collected from anthracite coal chunk combustion.

3.2 PM2.5 source profiles for power plant and industrial emissions

For this part of the project, we conducted a series of field studies targeting stationary combustion sources (industrial boiler, power plant, etc.) and industrial process (ironmaking, steelmaking, coking, aluminum smelting, cement kiln, brick kiln, etc.). Source profiles of PM2.5 from power plant and industrial emissions showed that carbon fractions, water-soluble ions and element species accounted for 58–98% of PM2.5, and other major constituents were SO42−, NO3, NH4+, OC, EC, and crustal elements (Al, Si, Ca, and Fe). Organic components were also analyzed, and the species that accounted for more than 10% of the PM2.5 represent obvious emission signals for specific sources; these include OC from power plants, Fe from iron and steel processes, and Si and Ca from aluminum smelting and cement production. High contributions of crustal elements (Na, Mg, Al, Si, K, Ca, Fe, and Ti) in PM2.5 were also found in all measured source profiles from power plants and industrial emission, and various elements were found to be possible markers for specific sources. For example, there were obvious enrichments of Na and V emissions from aluminum calcining; Si and Ca from cement kilns; Al from aluminum smelting; Si, Ti, and Fe from steelmaking; and Zn and As from coal combustion, respectively. Some large differences in elemental ratios also were found for among power plant and industrial emissions. Brick kilns had large Cu/Sb ratios (438.5), while coal combustion had a relatively small V/Ni ratio (0.2–0.3), and these could be used to distinguish among sources (Liu et al. 2017).

3.3 Motor vehicle exhausts source profiles

PM2.5 source samples were collected in two tunnels, and they showed similar trends in terms of chemical composition. In both sets of tunnel samples, the OC concentrations were higher than those of EC. Of the water-soluble ions, SO42−concentrations were the highest and the concentrations of NO3 and NH4+ were second only to SO42−. The concentrations of other ions were relatively low. High proportions of Si, Al, Fe, Ca, Mg, and K were found in the tunnel samples; all of these elements were above 1%. The proportions of Na, Ti, and Zn were lower, between 0.1 and 1%. The proportions of the rest of the inorganic elements analyzed relative to the PM2.5 mass loadings were < 0.1%. The total organic constituents accounted for a small percentage of the PM2.5 mass. The proportions of benzo(g, h, i)perylene, benzopyren, benzo(b)fluoranthene, coronene, fluoranthene and chrysene were relatively high compared with other organic constituents. Acenaphthene, acenaphthylene, and anthracene were not found in these analyses.

On-board PM2.5 vehicle emission tests for light-duty diesel trucks (National III and National IV), light-duty vehicles (National III, National IV, and National V), heavy-duty vehicles, and natural gas powered vehicles were also conducted. The results indicated that OC and EC were major components of the emissions as were Cl, SO42−, NO3, NH4+, and Ca2+. Comparisons showed that the quantities of the total carbon emitted by National III and National IV were almost equal, while, in contrast, there were large differences in the quantities of OC and EC. With regard to ions, the concentrations of SO42−, NO3, NH4+, and Ca2+ from the exhausts of National III vehicles were higher than those from the exhaust of National IV vehicles; interestingly, the relative concentrations of Cl, K+, and Na+ in these two types of vehicles were reversed. In terms of inorganic constituents, Al, Ca, Fe, K, Mg, Na, S, and Zn accounted for major percentages of the PM2.5 mass from the motor vehicle emissions. The concentrations of Al, Ca, Cu, Mn, Ni, and Zn were higher in the exhaust samples from National III vehicles than those from National IV, whereas the rest elements showed the opposite trend. Among the organic compounds analyzed, naphthalene, fluorene, phenanthrene, fluoranthene, and pyrene were found to be at a high levels. Dibenzo (a, h)anthracene and coronene concentrations were below detection, and the concentrations of the rest of the organic compounds were at low levels. The concentrations of fluoranthene, benzo anthracene, and chrysene were found to be higher in National III than National IV vehicles, while the concentrations of the rest organic components showed the opposite behavior.

National IV diesel engines were selected for this research. Exhaust samples were collected under different working conditions of the engine to analyze its source profile under different conditions. Currently, analyses of PM2.5 samples are underway.

3.4 Open sources

Road dust and construction dust samples were collected in 24 cities located in northeast, north, northwest, central, southwest, south, and northeast areas of China. A total of 215 samples were collected overall. Chemical analysis of the samples has been completed (Shen et al. 2016). The mass concentrations of 40 elements accounted for 21.3% of the average mass concentration of PM2.5 collected from road dust and 26% from construction dust, respectively. Overall, Ca, Si, Fe, and Al were the four most abundant elements in all samples. Ca was the most abundant element in both road dust and construction dust, accounting for 7.5% of the total quantity of road dust and 10.2% of that of construction dust. The proportion of Fe was the highest both in road and construction dust samples collected in northwestern China, which shows that PM2.5 in Northwest China is greatly influenced by crustal material that most likely is produced by sand or dust storms. The quantities of heavy metals, such as Pb and Zn, were significantly higher in road dust than that in construction dust. In northwestern China, the concentration of Cl was nearly ten times higher than that in other areas.

The water-soluble inorganic ions accounted for ~ 10% of PM2.5 mass concentration from fugitive dust sources, about 50% from road dust, and 60% from construction dust source. Compared with urban PM2.5, fugitive PM2.5 samples were characterized by a high ratio of Ca2+/Ca and low ratios of K+/K and NO3/SO42−; the percentage of the carbonaceous fraction was low, less than 1%. The spatial distribution of PAHs decreased in the following order: North China > Northwest China > Northeast China > Southwest China > Southern China > East China, while the concentrations in Shanghai, Nanjing, and Guangzhou were lower than those in the other cities sampled (Shen et al. 2016). Among the various cities, the concentrations of PAHs were the highest in the road dust and construction dust samples collected in Baoding. The concentrations of PAHs in Taiyuan, Shijiazhuang and Xi’an were 20–40 μg g−1 while at the rest of the cities, they were less than 20 μg g−1. The distribution of total n-alkanes decreased in the following order: North China > Northwest > Southern China > Southwest > Northeast/East China. The total n-alkanes concentrations in road dust collected in Taiyuan were the highest of the sites sampled. Total n-alkane concentrations in construction dust collected in Baoding and Taiyuan were higher than in other cities.

PM2.5 source profiles for a fast food restaurant, western restaurant, fried chicken steak food court, and stir-fried rice and noodles food court were obtained. The contributions of carbonaceous species from cooking fumes exceeded 50% of PM2.5 mass. The elements measured by XRF accounted for 1–2% of mass of PM2.5 mass. The contribution of organic carbon (OC) in the PM2.5 from fast food restaurants was as high as 77%. Organic carbon was also the major component in PM2.5 from residential kitchens. Elemental carbon, nitrate, and sulfate contributed to the total PM2.5 to some extent. Fatty acids were the major constituents of the organic compounds, accounting for 68–76% of the organic matter in Chinese restaurants and Western restaurants. Dicarboxylic acid contributed somewhat to the PM2.5, and its proportion in Chinese restaurant samples was slightly higher than in other types of restaurants. The concentration of particulate PAHs released by Chinese restaurants was much higher compared with other restaurants. Nonyl aldehydes C-9 were the most abundant carbonyl compounds with high molecular weights in PM2.5, and this is in line with other findings reported in the literature. For example, Rogge et al. (1991) reported that a large number of semi-volatile aldehydes, especially nonyl aldehydes, can be released in meat cooking operations or from barbecues.

4 Recommendations for future work

This project focused on the analysis of major PM2.5 sources in China and the preliminary development of a source profile database, including comprehensive data for coal burning, biomass burning, industrial emissions, and fugitive dust, and cooking emissions. Future work will more fully summarize the characteristics of selected PM2.5 sources, compare the results with those from other related studies, strengthen the source evaluations with the source apportionment model, and provide technical support for source apportionment and effective management of PM2.5. As the emission sources of PM2.5 can change rapidly, it will be highly desirable to acquire and compile more source profiles for source appointment and related studies as time progresses.

Notes

Acknowledgements

This research is supported by the Ministry of Science and Technology of China (2013FY112700) and the Key Lab of Aerosol Chemistry and Physics of the Chinese Academy of Sciences.

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Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2018

Authors and Affiliations

  1. 1.Key Lab of Aerosol Chemistry and Physics, SKLLQG, Institute of Earth EnvironmentChinese Academy of SciencesXi’anChina

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