Characterization of atmospheric aerosols and source apportionment analyses in urban Harbin, northeast China

Journal Pre-proofs Characterization of atmospheric aerosols and source apportionment analyses in urban Harbin, northeast China Qi-Xiang Chen, Chun-Lin Huang, Ting Xiao, Yuan Yuan, Qian-Jun Mao, HePing Tan PII: DOI: Reference:

S1350-4495(19)30528-6 https://doi.org/10.1016/j.infrared.2019.103109 INFPHY 103109

To appear in:

Infrared Physics & Technology

Received Date: Revised Date: Accepted Date:

15 July 2019 22 August 2019 1 November 2019

Please cite this article as: Q-X. Chen, C-L. Huang, T. Xiao, Y. Yuan, Q-J. Mao, H-P. Tan, Characterization of atmospheric aerosols and source apportionment analyses in urban Harbin, northeast China, Infrared Physics & Technology (2019), doi: https://doi.org/10.1016/j.infrared.2019.103109

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier B.V.

Characterization of atmospheric aerosols and source apportionment analyses in urban Harbin, northeast China

Qi-Xiang Chen1, Chun-Lin Huang2, Ting Xiao3, Yuan Yuan1,*, Qian-Jun Mao2,*, He-Ping Tan1

1. Key Laboratory of Aerospace Thermophysics, Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150001, China. 2. School of Urban Construction, Wuhan University of Science and Technology, 2 Huangjiaxi Street, Wuhan, 063009, PR China. 3. School of Mechanical Engineering and Automation, Harbin Institute of Technology, 6 Pingshan Road, Shenzhen, 518055, PR China.

Corresponding: *[email protected], * [email protected]

Abstract: Detailed knowledge of aerosol chemical characteristics and emission sources play a key role in climate and human health. In this study, we collected the atmospheric aerosol particles in urban Harbin, the northernmost provincial capital in northeast China, using an ambient sampler during the period from April to June 2017. Morphological and chemical composition analyses of collected particles were performed using a Fourier transform infrared spectrometer (FTIR), an X-ray fluorescence spectrometer (XRF), and a scanning electron microscope equipped with an energy dispersion spectrum (SEM-EDS). Emission sources of these aerosol particles were determined via an enrichment factor analysis and then their variation were further discussed. During the studying period, the atmospheric particles were mainly composed of organics, inorganic ions and oxides with 20 elements (i.e., C, O, Na, Mg, Al, Si, P, and S) observed. Among the detected elements, Cr, Ni, Cu, Br, and Zn were found to be enriched, indicating considerable

anthropogenic influence during high pollution days. In early April, most of the aerosol particles were emitted from coal-fired boilers and coal-fired power plants; in early May, they mainly originated from soil dust and dust, traffic, and industry emissions; and in late May and June, traffic and industry became the major aerosol sources over Harbin.

Keywords: Aerosols; particulate matter; source apportioning; emission variation ； Northeast China.

1. Introduction Atmospheric aerosols are closely related to the regional environment and climate [1-3]. Epidemiology and toxicology studies have shown that overexposure to high concentrations of air pollutants will increase the incidence of respiratory, cardiovascular, and cerebrovascular diseases [4, 5]. Previous research has shown that although atmospheric particles have distinct physical and chemical properties [6-8] and originate from various natural and anthropogenic sources [9], their properties and origin are connected [10, 11], such that studying one sheds light on the other. The origin of particles can be estimated by analyses of their chemical and morphological properties [12, 13]. This is important for controlling and reducing particle emissions and pollutant concentrations [14, 15]. Much research on air pollution in China has focused on the relationships between particles’ chemical and morphological properties and their sources, and the majority of the studies have centered on the regions of Beijing-Tianjin-Hebei and the Yangtze River Delta [16-19]. For example, Zhang et al. [16] studied source categories and source areas of PM1 and PM2.5 in Beijing in Autumn using positive matrix factorization (FMF) backward trajectories and a potential source contribution function (PSCF) model. They found that secondary aerosols and coal combustion, vehicle, industry, biomass burning, and dust were the important sources of PM. Wang et al. [17] apportioned the sources of aerosol light extinction in Hangzhou through a coupled model of chemical mass balance (CMB) and modified Interagency Monitoring of Protected Visual Environment (IMPROVE). They found that Vehicle exhaust, secondary nitrate and secondary

sulfate were identified as the most significant sources for aerosol. In recent years, much of this research has focused on southwest China. Cheng et al. [18] measured the concentrations of SO2, NO2, CO, O3, and PM10 over a one-year period from January to December 2014 in three areas of Panzhihua, and they analyzed the enrichment factor to distinguish the origins of trace elements. Similarly, Zhang et al. [19] investigated the composition and possible sources of PM2.5 in Chengdu during the summer of 2016 using Single Particle Aerosol Mass Spectrometer (SPAMS). As a historic industrial base, northeast China has long been faced with serious pollution problems [20-22]. Although its degree of pollution is similar to the Beijing-Tianjin-Hebei and the Yangtze River Delta regions, the emission sources may differ due to its geographical location, economic structure, and climate differences [19]. However, only a few studies have focused on the chemical and morphological characteristics of atmospheric particles in this region [23, 24]. Thus, it is important to conduct an in-depth study of the relationship between the composition and morphology of particles and their sources for northeast China. Harbin, the northernmost provincial capital city in China, has a large number of residents and motor vehicles in the urban area. The industries in this district are flourishing, adding to the air pollution problem. Spring and winter, in particular, are characterized by various pollution sources (e.g., coal combustion, dust, automobile exhaust, and industrial discharge), which influence each other, thereby complicating the identification of pollutant sources. Though a few studies have tried to apportion air pollutants in Harbin [25-28], there is still a lack of detailed analysis of aerosol emissions, especially in late winter and spring. This study presents a three-month observation of suspended particles by using several instrumental measurements at an urban site in Harbin from April to June 2017. The characteristics of the samples and their sources and source variations are then discussed. The results presented in this article will hopefully promote our current understanding of the origins and variations of regional pollutants, and provide policy-makers supporting data to formulate a reasonable policy for pollution control. 2. Materials and methods 2.1 Site description

Harbin (126.63°E, 45.75°N), the capital of Heilongjiang Province, has a long winter (from October to April), and is prone to drought and windy weather in spring. The monthly temperature variation was generally about 8–10 ºC during our study period. The energy consumption of industrial enterprises in Harbin is dominated by coal, and the total amount increases annually. In 2016, industries consumed energy worth nearly 13.5 billion kg of standard coal. By the end of 2016, the resident population of Harbin exceeded 10 million, and the number of motor vehicles in urban areas reached 1.5 million [29]. 2.2 Sampling The most widely used techniques for collecting and capturing atmospheric particles are filtration and impact [30]. As the air passes through the sampling apparatus, particles collide with the surface of the instrument and get deposited on it through inertial collision, interception, and gravitational sedimentation. In this study, atmospheric particles were collected from April to June 2017 using an 8-stage non-viable cascade impactor with an airflow capacity of 16 Lmin-1 (TE-10-800, Tisch Environmental, America), and the interval between two sampling periods was two to four days depending on the air quality. The metal plates and cutters of the collector were cleaned with an ultrasonic cleaner before sampling, and the samples were sealed in the centrifuge tube. The impactor was placed on the roof of the powerhouse of the Harbin Institute of Technology (approximately 20 m above ground) and was close to the main streets, residential areas, and commercial areas, with no major industrial pollution sources nearby. 2.3 Method First, a preliminary analysis of aerosol source apportionment is obtained based on the results of Fourier transform infrared (FTIR) spectrometer of aerosols particulate collected in the sampling period. SEM-EDS is used to observed the morphology and chemical composition of individual particles, with an objective of further describing the source of aerosols. In addition, cases studies are carried out to analyze aerosol source apportionment in detail. The experimental equipment/procedures using in the study are as follow: Fourier transform infrared spectrometer measurement

The FTIR spectrometer is capable of qualitative analysis of organic and inorganic compounds [31]. The instrument used in our study was a Spectrum 100 spectrometer manufactured by PerkinElmer. The spectra were recorded in the range of 4000-400 cm-1. Samples were mixed with dried potassium bromide (KBr) at a ratio of 1:100, and they were ground in an agate mortar until they were fully mixed. After the conventional pressing plate treatment, the prepared samples were placed in the FTIR spectrometer for analysis. To avoid possible errors, each sample was measured twice. Infrared spectrograms were obtained directly from the software attached to the instrument. The final spectrograms were normalized to facilitate observation and analysis, and were smoothed to eliminate the apparent noise in the near infrared and far infrared regions. The spectra were normalized such that the transmittance of the largest absorption peak in the spectrum was changed to 10 % and the baseline was changed to 100 %, and thus, all the data fell in the range of 10 %– 100 %. We collected a total of 25 sample sets from April 1 to June 20. The sample numbers and sampling times are shown in Table 1. Table 1. Check list of sample numbers (No) and sampling date (SD). No

SD

No

SD

No

SD

No

SD

No.

SD

1

April 1

6

April 17

11

May 4 (Coarse)

16

May 16

21

May 31

2

April 3

7

April 21

12

May 7 (Fine)

17

May 19

22

June 7

3

April 5

8

April 25

13

May 7 (Coarse)

18

May 22

23

June 11

4

April 7

9

May 1

14

May 10

19

May 25

24

June 15

5

April 15

10

May 4 (Fine)

15

May 13

20

May 28

25

June 20

Energy dispersion spectrum scanning electron microscope measurements To determine the morphology and chemical composition of individual particles [24, 32], we used a Hitachi SU8010 scanning electron microscope coupled with an energy dispersed spectrometer (SEM-EDS). Original samples collected in centrifuge tubes were mixed with moderate amounts of absolute ethanol using the ultrasonic vibration method at 60 ºC for 8 min. The mixtures were then dropped onto separate monocrystalline silicon plates, which were fixed on

a sample stand. To enhance the electrical conductivity of particles, the entire sample stand was sprayed with a thin layer of gold after the ethanol totally volatilized. Then, the sample stand was placed into the electron microscope sample room for further analysis. X-ray fluorescence spectrometry measurements To obtain the elemental composition of the whole sample [33, 34], we used X-ray fluorescence spectrometry (XRF) (PANalytical PW4400 model). In this study, we used enrichment factor (EF) analysis with the XRF data to estimate the source of aerosol particles. The basic principle of the EF method is that the inorganic elements in the particles are compared with the corresponding elements in the crustal material so that the degree of enrichment of the elements can be investigated. Then, these data are used to identify the possible natural or anthropogenic sources [35, 36]. EF is defined by the following equation: EF  ( X / R) air / ( X / R)crust , where X represents a particular element in the particles, and R is a reference element. In this study, Fe was selected as the reference element. Back trajectory analysis The HYSPLIT model calculates trajectories of air parcels [37, 38]. It aids in simulation of transport, dispersal, deposition, and transformation of air parcels. The model has been continually upgraded for over three decades and is based on the Lagrangian and Eulerian approaches. Detailed information

can

be

found

at

the

web

site

of

the

Air

Resources

Laboratory

(http://ready.arl.noaa.gov/HYSPLIT.php). Our results were calculated using the web version.

3. Results 3.1 Characteristic of regional pollution over urban Harbin Daily mean concentration data for PM2.5 and PM10 from 2014 to 2017 were obtained from the Ministry of Environmental Protection (http://106.37.208.233:20035/). Monthly mean particulate matter concentrations in Harbin from 2014 to 2017 are shown in Figure 1. In general, particulate matter concentrations were low in summer and high in winter, and showed a concave parabolic shape. Atmospheric activities and intense rainfall likely dissipated pollutants in summer, resulting in a relatively low particulate matter concentration [19], while in winter, large-scale coal heating emissions and stationary weather conditions likely lead to a dramatic increase in PM2.5 concentrations [20]. The concave parabolic shape of particulate matter concentration is consistent with the results of related studies [39, 40]. The concentrations of PM2.5 and PM10 during the study period were calculated as the average of four measurements at 0800, 1100, 1400, and 1700 UTC using a US TSI 8532 portable dust meter. Daytime mean mass concentrations of PM2.5 and PM10 were 59.39±46.9 μg/m3 and 88.31±53.32 μg/m3, respectively, and both exceeded the new national ambient air quality standard (NAAQS: 35 μg/m3 for PM2.5, 70 μg/m3 for PM10) and the applicable air quality guidelines (10 μg/m3 for PM2.5, 20 μg/m3 for PM10) recommended by the World Health Organization (WHO). The high concentrations of particulate matter at the study site may result from long-range transport of pollutants and dust, the regional petroleum industry, and local traffic. In addition, although the concentrations of PM2.5 and PM10 in Harbin exceeded the national standards, a decreasing trend was observed, indicating that Harbin's air quality has been gradually improving.

Figure 1. Monthly mean concentrations of PM2.5 and PM10 over urban Harbin during the period from 2014 to 2017, collected from (a) the Ministry of Environmental Protection and (b) US TSI 8532 portable 104 dust meter.

3.2 Aerosol composition analysis by FTIR The detailed observations by FTIR are presented in Table 2. The atmospheric samples were mainly composed of organics, inorganics, and some oxides, dominated by C, O, Si, and Fe. Atmospheric particles collected during our sampling period generally contained organic matter. The main groups found in the organic substances were OH, CH3, CH2, C=O, and Si-C. OH was found in all the samples. CH2, C=O, and Si-C were present in most of the samples, while CH3 appeared less often. NO3-, NH4+, SO42-, CO32-, and SiO44- were the main ions found in inorganic salts, and they were probably generated by the atmospheric chemical reactions of CO2, NOx, SO2, and NH3.NOx, and SO2 emitted from automobile exhaust and coal-fired boilers. Ammonia (NH3) mainly originates from chemical fertilizer production, animal waste, coke production, and freezing facilities [31]. The most common oxides in our samples were SiO2, Fe2O3, and Fe3O4. With the exception of sample 1, the highest spectrometer peak of 1035 cm-1 appeared in the remaining samples for SiO2, indicating the collected samples were mainly composed of dust particles. Most samples had a peak induced by SiO44- at 525 cm-1, indicating the presence of silicate.

Table 2. Summary of infrared spectrum results obtained by conducting FTIR analysis on the samples collected from urban Harbin from April to June 2017 Wave number (cm-1)

Species name

3410–3460

OH

1–25

3172

NH4+

1, 5

2956

CH3

1,2, 6–11

2924

CH2

1–14, 18–25

2854

CH2

1–14, 18–25

2380

CO2

3, 4, 5, 19, 23

1719

NH4+

1, 5

1630

C=C

1–4, 6–14, 16, 17, 19–24

1600

NH3+

5, 25

1410–1430

CO32-

3, 4, 7– 25

1385

NO3-

1, 2, 5–18, 20, 22–25

1350

NO2

25

1100

SO42-

1–4, 7, 14, 16, 18–25

1035

SiO2

1–4, 6–13, 15–18, 20–22, 24

878

Si-C

7–13, 15, 18, 20–25

795

SiO2

1, 2, 5, 6, 8–13,15–17

720

CaCO3

4–7

692

SiO2

3, 4, 6, 8–13, 15, 17, 19, 23, 24

646

SO42-

3, 4, 6, 8–13, 15, 17, 19, 20, 23, 24, 25

617

Al2O3

1

580

CrO3

25

561

Fe3O4

1, 7, 14, 16, 18–24

525

SiO44-

2, 6, 8–13, 15, 17, 23, 25

468

Fe2O3

1, 2, 6–25

Sample No.

We conducted a more thorough analysis of samples on specific days in April. Figure 2 shows the infrared spectrograph results of select samples on April 1, 17, and 28. The intensity of the characteristic peak at 2380 cm-1 indicated that CO2 constituted the highest proportion on April 1, and then, it decreased gradually. Coal-fired heating continued until April 10 in Harbin, and thus, the samples collected on April 1 likely had a high concentration of coal emissions, producing

considerable amounts of CO2, SO2, and fly ash. The CO2 peak gradually reduced to a normal level on April 17 and 28, likely due to reduced coal-fired heating and increased windy weather in the warmer season. The intensity of the SO2 peak at 1100 cm-1 was very high on April 1, while that of the SiO2 peak at 1035 cm-1 was negligible. The opposite was observed on April 17 and 28. These results, combined with the observed changes in CO2, indicated that SO2 from coal-fired emissions dropped sharply and air quality improved. A distinct peak at 1390 cm-1, which is a characteristic peak of NO3-, appeared in all the selected samples. This suggests that a certain portion of the particles in the atmosphere always originated from automobile exhaust.

1.2

Transmittance

1.0

April 1 April 17 April 28

0.8

0.6

0.4

0.2

0.0 4000

3500

3000

2500

2000

1500

1000

500

wave number (cm-1)

Figure 2. Infrared spectrograph results of particles collected on April 1, 17, and 28 in Harbin

3.3 Morphology of single particles by SEM measurements We divided the collected particles into four categories: (1) spherical particles, (2) plate particles, (3) porous particles, and (4) agglomerated particles based on the surface morphology, texture, and edge shape. A particle's origin can be recognized based on its microscopic and chemical characteristics. For example, particles from natural sources usually have regular morphology and are rich in Si and O (e.g., suspended dust from rocky substrate), while high temperature industrial processes produce particles with spherical morphology [41]. Figure 3 shows the morphology and energy spectra of samples of particles from our study based on SEM-EDS measurements.

40um 50um

0

1

2

3

4

5

6

7

8

keV

0

1

2

3

4

(a)

5

6

7

1

2

3

4

5 (c)

keV

(b)

40um

50um

0

8

6

7

8

keV

0

1

2

3

4

5

6

7

8

keV

(d)

Figure 3. Morphology and energy spectra of typical particles collected over urban Harbin based on SEM-EDS: (a) spherical particle, (b) granular particle, (c) porous particle, and (d) agglomerated particle.

Figure 3a shows the morphology and chemical components of a smooth-surfaced spherical particle collected during sampling period. Based on the surface elements analyzed by EDS, Si, O, Fe, and Al were the main elements, and the C content was low. In addition, K, Ca, Na, Mg, and Ti were detected on the surface. These observations suggested that this kind of particle originated from fly ash from a combustion process (coal-fired power plants, boilers, metallurgical plants, traffic, etc.) [42]. The proportion of these particles collected in early April was relatively high, and then, it decreased with time. Since the use of coal-fired energy for heating continues until early April in Harbin, it is likely that the fly ash particles were mainly emitted from coal-fired boilers. Figure 3b shows the morphology and chemical components of a particle on the plate. The outline of these particles is clear and irregular, and the surface is relatively rough. The main elemental composition of these particles comprised O, Si, and C, with some Al, Fe, K, Ca, Ti, Na,

and Mg on the surface. This kind of particle may originate from ore particles (such as silicate feldspar, and clay minerals), ground dust and rock weathering dust, or mineral wear and re-suspension processes [42]. Figure 3c shows the micro morphology and surface elements of a porous particle from our sampling. Such particles have many pits and holes on the surface. The particle in Figure 3c mainly contained the elements O, Si, and C, and the remaining elements were similar to the particle on the plate. This similarity suggests that the porous particle may have also originated from mineral matter. The pores in the particles suggest that they could adsorb smaller-sized particles and some gases; this increases the complexity of the particle composition analysis and atmospheric modelling. The carbon content in the porous particle was obviously higher than that in the particle on the plate (Figure 3b), indicating the probable presence of organic matter in the porous particle. Figure 3d shows the microstructure of an agglomerated particle. The agglomerated particle is composed of many small particles and these small particles range in size from nanometers to micrometers. EDS observations indicated that C and O were the main elements in the agglomerated particle, and it also contained S, K, Si, Al, and Fe, indicating it was a type of soot particle. In general, these agglomerated particles usually originate from the incomplete combustion of fossil and biomass fuels [32, 41]. 3.4 Source apportionment by XRF and enrichment factor analysis Table 3 shows the results of the elemental composition analysis using XRF. XRF measurements were used to further analyze seven groups of samples collected during the sampling period (from April to June 2017). As Table 3 shows, C, O, and Si showed the highest proportions in the samples. Specifically, C accounted for more than half the proportion of all samples, and the O content accounted for about 20 %. The content of Si was approximately between 5 % and 10 %. Al, Fe, Ca, and Cl showed low proportions, while trace elements (less than 1 %) included Na, Mg, Al, P, S, K, Ti, Mn, and Cr.

Table 3. Results of the XRF analysis of sampled particles

Element (%)

Sampling date (MM/DD) April 1

April 28

May 1

May 4

May 7

May 19

June 20

Na

0.68

0.81

0.70

0

0.74

0.63

0.85

Mg

0.24

0.38

0.21

0.34

0.92

0.33

0.47

Al

2.12

3.5

1.72

2.95

4.47

2.50

4.12

Si

4.25

8.11

3.94

7.58

5.76

10.49

9.91

P

0.05

0.13

0.05

0.09

0.07

0.06

0.17

S

0.27

1.27

0.14

0.28

0.30

0.41

1.70

Cl

1.13

1.12

1.08

1.07

0.84

1.17

1.03

K

0.52

0.96

0.35

0.83

1.21

0.75

1.11

Ca

1.45

2.94

0.75

1.51

2.30

1.48

4.09

Ti

0.09

0.19

0.06

0.14

0.21

0.11

0.26

Cr

0.38

0.08

0.05

0.06

0.07

0.39

0

Mn

0.04

0.04

0.02

0.03

0.04

0.04

0.06

Fe

1.89

2.22

0.45

0.85

1.57

1.83

4.39

Ni

0.13

0.03

0.02

0.02

0.02

0.12

0.01

Cu

0.01

0.02

0.01

0.01

0.01

0.02

0.02

Zn

0.01

0.03

0

1.26

0.01

0.01

0.06

Br

0.01

0.03

0

0.01

0

0

0

Zr

0.01

0.02

0

0.01

0.01

0.01

0.02

Figure 4 shows the EF analysis for 13 elements. It is generally assumed that if EF is close to 1, the particles originate mainly from natural sources; if EF is >10, the particles originate mainly from anthropogenic emissions. Additionally, the higher the EF value, the more likely it is to have originated from human activities; if EF is <10 and >1, then both natural and anthropogenic sources are possible [37,38]. Most of the EF values of Na, Mg, Al, K, Ti, and Mn were less than or close to 1, indicating that these elements originated mainly from natural sources with fewer anthropogenic emissions. The EF value of Ca was between 1 and 10 during the sampling period. Therefore, the Ca originated from both natural and human sources, and the latter may be related to construction activities near the sampling point. In addition, Cr, Ni, and Cu had high EF levels on all sampling

dates, and in some cases, EF exceeded 100. Such high EF levels suggest that these elements were highly influenced by human factors such as coal combustion, industrial pollutant discharge, and automobile exhaust.

Fig. 4. Results of enrichment factor analysis based on the XRF data

4. Discussion on emission source variation This work focuses on the identification of the species and main sources of atmospheric particles based on multi-analysis of FTIR, SEM-EDS and XRF instrumental measurements. The species of collected samples are determined by qualitative analysis of organic and inorganic compounds (FTIR), morphology and chemical composition (SEM-EDS), and the variation of pollution sources during the sample period is further assessed via the combined analysis of XRF and EF analysis. Early April: SEM-EDS analysis showed that the proportion of coal fly ash particles collected in early April was high at first, followed by a decrease. Peak CO2 and SO2 concentrations of FTIR instrumental measurements gradually decreased from early April (when heating buildings decreases significantly), indicating that CO2 and SO2 emissions originating from coal-fired had reduced. Given this knowledge about emission changes over urban Harbin, we can infer that coal-fired emissions were the leading source of atmospheric pollution in the period and caused high particulate matter concentrations, and the degree of pollution are decreased with the end of heating buildings. The inference is well agreeing with EF analysis, the main enriched

elements in early April were Cr and Ni, which mainly originate from coal combustion used for heating, and by the end of April, the enrichment of these two elements was significantly reduced to just under 25 (Fig. 4). Additionally, the weekly concentration of PM2.5 measured in April was 125.29 μgm-3, 56.86 μgm-3, 52 μgm-3, and 28 μgm-3, and the weekly concentration of PM10 was 205.43 μgm-3, 125.71 μgm-3, 91.43 μgm-3, and 57.14 μgm-3. Changes in particulate matter concentrations confirm our hypothesis that air quality is improving gradually. Early May: aerosol particles mainly originated from soil dust and dust, coal, and industry in early May. The proportion of quartz particles derived from ground dust and rock weathering dust maintain high level in SEM-EDS measurements. Similarly, the results of the FTIR analysis for early May also showed a high characteristic SiO2 peak intensity. The EF analysis for the same period showed that typical crustal elements, such as Na, Mg, Al, K, Ti, and Mn, were enriched, and these were likely from coal burning and industry. We conducted a 72-hour backward trajectory tracking analysis for Harbin on May 1 and 4 to determine the possible origins of the pollutants (Figure 5). During this period, Harbin was mainly affected by airflow from Russia's Siberian region and China's Bohai Rim region. The vast tracts of bare land in the Russian Siberian region can be a source of soil dust and dust, and the Bohai Rim region has significant industrial pollution [43, 44]; airflow in these areas travels to Harbin, greatly increasing the enrichment of Zn and Br over Harbin.

(a)

(b)

Figure 5 The 72-hour backward trajectory tracking analysis results for Harbin using the HYSPLIT model

(web-version) on (a) May 1, and (b) May 4, 2017. The red, blue, and green lines represent the trajectories of different high airflow sources in the study area

Late May and June: Traffic and industry were the main source of pollution in Harbin, contributing to air pollution in late May. As shown in the EF analysis, the enrichment of Cr and Ni in the particulate matter collected on May 19 was very high, and the EF was approximately 100. Nickel is an indicator of motor vehicle exhaust, and Cr may originate from fuel combustion or industrial (metallurgy) processes [45]. The same conclusion was also obtained from the FTIR analysis. The NO3- content in the sampled particles in the period was higher than that in early May. As discussed earlier, NO3- is generated by the chemical reaction of NOX in the exhaust gas of motor vehicles, indicating that during this period, traffic significantly contributed to air pollution. In June, the intensity peaks of the infrared spectra of NO3-, NH4+, and SO42- were all low and the EF values of detected elements was less than 10, indicating a low presence of anthropogenic aerosols. Combining with Figure 1, the concentration of PM2.5 and PM10 in June were low and their difference was quiet small, inferring that the contribution from soil dust and dust particles was quite limited and the amount of dominant fine mode particles was low. This was because, from Figure 6, frequent rainfalls during late May and June cleaned the atmosphere by wet deposition processes, and due to the significant reduction of heating purpose emissions, the concentration of aerosol particles kept in low level and the major aerosol source turned to traffic and industrial emissions. 25

10

May June

Precipitation Wind Speed

20

8

15

6

10

4

5

2

0

0

2

4

6

8

10

12

14

Precipitation

Wind Speed

April

0

Week Figure 6 Variation of wind speed and precipitation over Harbin from April to June, 2017.

Through the above analysis, it was obvious that In early April, most of the aerosol particles

originated from coal-fired boilers and coal-fired power plants; in early May, atmospheric particles originated from soil dust and dust, coal, and industry; while in late May and June, they originated mainly from traffic and industrial emissions, and air quality significantly improved due to the rainfall removing the aerosol particles. 5. Conclusion In this study, we studied the chemical properties of atmospheric particles collected in urban Harbin from April to June 2017 using the SEM-EDS, FTIR, and XRF instrumental measurements. Variations in emission sources were then analyzed based on the combined use of morphology and chemical components of the sample particles. Here are the main conclusions: 1. The average concentrations of PM2.5 and PM10 during the study period were 59.39±46.9 μg/m3 and 88.31±53.32 μg/m3, respectively, indicating severe air pollution during the study period. 2 According to the analysis of FTIR and XRF instrumental measurements, collected samples were mainly composed of organics, inorganic ions and oxides, and 20 elements (i.e., C, O, Na, Mg, Al, Si, P, and S), which come from coal combustion, industrial pollutant discharge, and automobile exhaust. 3. Four types of particle morphology were classified from SEM observations. Combined with the EDS analysis, we found that sample particles mainly originated from dust, coal fly ash, and the incomplete combustion of fossil and biomass fuels. 4. Affected by human activities and weather, the morphology and composition of collected particle samples changed over time. In early April, the dominant pollution sources are coal-fired boilers and coal-fired power plants; In early May, aerosol particles mainly originated from soil dust and dust, coal, and industry; while in late May and June, traffic as well as industry emission became the main aerosol sources and air quality were significantly improved due to frequent rainfalls and reduction of anthropogenic emissions. We acknowledge that there are limitations in our present work. The use of a single location for sampling atmospheric particles and the relatively low number of observations in source apportioning may cause result in deviations to a certain extent. Future work will address the above

limitations. Despite these, however, we believe our findings provide a clearer understanding of the current air pollution situation in Harbin, and they will help elucidate the sources of regional particulate matter pollution and particle formation mechanisms.

Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant Nos. 51776051 and 51876147). A very special acknowledgement is made to the editors and referees who made important comments to improve this paper. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model, as well as the READY website. The authors are also grateful to the Chinese Ministry of Environmental Protection for providing the PM2.5 and PM10 data. We would like to thank Editage [www.editage.cn] for editing this manuscript for English language.

References [1] F. Xu, G. van Harten, D.J. Diner, O.V. Kalashnikova, F.C. Seidel, C.J. Bruegge, O. Dubovik, Coupled retrieval of aerosol properties and land surface reflection using the Airborne Multiangle SpectroPolarimetric Imager, J Geophys Res-Atmos, 122 (2017) 7004-7026. [2] Q.X. Chen, Y. Yuan, Y. Shuai, H.P. Tan, Graphical aerosol classification method using aerosol relative optical depth, Atmos Environ, 135 (2016) 84-91. [3] Q.J. Mao, Recent developments in geometrical configurations of thermal energy storage for concentrating solar power plant, Renewable Sustainable Energy Rev., 59 (2016) 320-327. [4] V. Jain, S. Dey, S. Chowdhury, Ambient PM2.5 exposure and premature mortality burden in

the holy city Varanasi, India, Environ Pollut, 226 (2017) 182-189. [5] J.D. Wang, J. Xing, R. Mathur, J.E. Pleim, S.X. Wang, C. Hogrefe, C.M. Gan, D.C. Wong, J.M. Hao, Historical Trends in PM2.5-Related Premature Mortality during 1990-2010 across the Northern Hemisphere, Environ Health Persp, 125 (2017) 400-408. [6] Y. Yuan, H.L. Yi, Y. Shuai, B. Liu, H.P. Tan, Inverse problem for aerosol particle size distribution using SPSO associated with multi-lognormal distribution model, Atmos Environ, 45 (2011) 4892-4897. [7] Q.J. Mao, Y. Yuan, Y. Shuai, Effects of atmospheric aerosol on the direct normal irradiance on the earth's surface, Int J Hydrogen Energ, 39 (2014) 6364-6370. [8] Q.X. Chen, W.X. Shen, Y. Y, H.P. Tan, Verification of aerosol classification methods through satellite and ground-based measurements over Harbin, Northeast China, Atmos. Res., 216 (2019) 167-175. [9] Y. Yuan, Y. Shuai, X.W. Li, B. Liu, H.P. Tan, Using a new aerosol relative optical thickness concept to identify aerosol particle species, Atmos Res, 150 (2014) 1-11. [10] A. Roth, J. Schneider, T. Klimach, S. Mertes, D. van Pinxteren, H. Herrmann, S. Borrmann, Aerosol properties, source identification, and cloud processing in orographic clouds measured by single particle mass spectrometry on a central European mountain site during HCCT-2010, Atmos Chem Phys, 16 (2016) 505-524. [11] P. Winiger, A. Andersson, K.E. Yttri, P. Tunved, O. Gustafsson, Isotope-Based Source Apportionment of EC Aerosol Particles during Winter High-Pollution Events at the Zeppelin Observatory, Svalbard, Environ Sci Technol, 49 (2015) 11959-11966. [12] Y. Chen, J.C. Wenger, F.M. Yang, J.J. Cao, R.J. Huang, G.M. Shi, S.M. Zhang, M. Tian,

H.B. Wang, Source characterization of urban particles from meat smoking activities in Chongqing, China using single particle aerosol mass spectrometry, Environ Pollut, 228 (2017) 92-101. [13] Q.X. Chen, W. Shen, Y. Yuan*, M. Xie, H. Tan, Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China., Atmosphere, 10 (2019) 158-174. [14] N. Singh, V. Murari, M. Kumar, S.C. Barman, T. Banerjee, Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model, Environ Pollut, 223 (2017) 121-136. [15] F.Q. Wang, Z.M. Cheng, J.Y. Tan, Y. Yuan, Y. Shuai, L.H. Liu, Progress in concentrated solar power technology with parabolic trough collector system: A comprehensive review, Renewable Sustainable Energy Rev., 79 (2017) 1314-1328. [16] Y.Y. Zhang, J.L. Lang, S.Y. Cheng, S.Y. Li, Y. Zhou, D.S. Chen, H.Y. Zhang, H.Y. Wang Chemical composition and sources of PM 1 and PM 2.5 in Beijing in autumn, Sci Total Environ, 630 (2018) 72-82. [17] J. Wang, Y.F. Zhang, Y.C. Feng, X.J. Zheng, L. Jiao, S.M. Hong, J.D. Shen, T. Zhu, J. Ding, Q. Zhang, Characterization and source apportionment of aerosol light extinction with a coupled model of CMB-IMPROVE in Hangzhou, Yangtze River Delta of China, Atmos Res, 178-179 (2016) 570-579. [18] X. Cheng, Y. Huang, C. Liu, S. J. Ni, R. Wang, Z. J. Long, Assessment of Air Pollution around the Panzhihua V-Ti Magnetite Mine Region, Southwest China, Aerosol Air Qual. Res, 17 (2017) 1204-1213.

[19] J.K. Zhang, B. Luo, J.Q. Zhang, O. Feng, H.Y. Song, P.C. Liu, C. Pan, K. Schäfer, S.G. Wang, X.J. Huang, Analysis of the characteristics of single atmospheric particles in Chengdu using single particle mass spectrometry, Atmos Environ, 157 (2017) 91-100. [20] H. Guo, T.H. Cheng, X.F. Gu, Y. Wang, H. Chen, F.W. Bao, S.Y. Shi, B.R. Xu, W.N. Wang, X. Zuo, X.C. Zhang, C. Meng, Assessment of PM2.5 concentrations and exposure throughout China using ground observations, Sci Total Environ, 601 (2017) 1024-1030. [21] Q.X. Chen, Y. Yuan, X. Huang, Y.Q. Jiang, H.P. Tan, Estimation of surface-level PM2.5 concentration using aerosol optical thickness through aerosol type analysis method, Atmos Environ, 159 (2017) 26-33. [22] K.H. Li, Study on OC and EC Characteristics of Atmospheric Particulate Matter in Harbin, Adv. Mater. Res, 777 (2013) 412-415. [23] L.K. Huang, C.S. Yuan, G.Z. Wang, K. Wang, Chemical characteristics and source apportionment of PM10 during a brown haze episode in Harbin,China, Particuology, 9 (2011) 32-38. [24] L.k. Huang, G.Z. Wang, Chemical characteristics and source apportionment of atmospheric particles during heating period in Harbin, China, J Environ Sci, 26 (2014) 2475-2483. [25] L.K. Huang, G.Z. Wang, D.Y. Mu, Z. Xia, W. Wang, Seasonal-Varied Chemical Characteristics and Pollution Sources of Pm2.5 in Harbin, China, Fresen Environ Bull, 25 (2016) 1183-1198. [26] L.K. Huang, K. Wang, C.S. Yuan, G.Z. Wang, Study on the Seasonal Variation and Source Apportionment of PM10 in Harbin, China, Aerosol Air Qual Res, 10 (2010) 86-93. [27] Q.X. Chen, Y. Yuan, H. Xing, Z.H. He, H.P. Tan, Assessment of column aerosol optical

properties using ground-based sun-photometer at urban Harbin, Northeast China, J. Environ. Sci, 74 (2018) 53-60. [28] Q.J. Mao, C.C. Huang, H.X. Zhang, Q.X. Chen, Y. Yuan, Aerosol optical properties and radiative effect under different weather conditions in Harbin, China, Infrared Phys. Technol, 89 (2018) 304-314. [29] Harbin-Statistics-Bureau, Harbin Statistical Yearbook 2016, in, China Statistics Press, Beijing, 2017. [30] J. Yang, S.X. Ma, B. Gao, X.Y. Li, Y.J. Zhang, J. Cai, M. Li, L.A. Yao, B. Huang, M. Zheng, Single particle mass spectral signatures from vehicle exhaust particles and the source apportionment of on-line PM2.5 by single particle aerosol mass spectrometry, Sci Total Environ, 593 (2017) 310-318. [31] S. Takahama, A.M. Dillner, A.T. Weakley, M. Reggente, C. Bürki, M. Lbadaoui-Darvas, B. Debus, A. Kuzmiakova, A.S. Wexler, Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks, Atmos. Meas. Tech, 12 (2019) 525-567. [32] C. Samara, N. Kantiranis, P. Kollias, S. Planou, A. Kouras, A. Besis, E. Manoli, D. Voutsa, Spatial and seasonal variations of the chemical, mineralogical and morphological features of quasi-ultrafine particles (PM0.49) at urban sites, Sci Total Environ, 553 (2016) 392-403. [33] M. Furger, M.C. Minguillon, V. Yadav, J.G. Slowik, C. Huglin, R. Frohlich, K. Petterson, U. Baltensperger, A.S.H. Prevot, Elemental composition of ambient aerosols measured with high temporal resolution using an online XRF spectrometer, Atmos Meas Tech, 10 (2017). [34] S. Yatkin, C.A. Belis, M. Gerboles, G. Calzolai, F. Lucarelli, F. Cavalli, K. Trzepla, An

interlaboratory comparison study on the measurement of elements in PM10, Atmos Environ, 125 (2016) 61-68. [35] A.M. Lai, M.M. Shafer, J.E. Dibb, C.M. Polashenski, J.J. Schauer, Elements and inorganic ions as source tracers in recent Greenland snow, Atmos Environ, 164 (2017) 205-215. [36] L.R. Crilley, F. Lucarelli, W.J. Bloss, R.M. Harrison, D.C. Beddows, G. Calzolai, S. Nava, G. Valli, V. Bernardoni, R. Vecchi, Source apportionment of fine and coarse particles at a roadside and urban background site in London during the 2012 summer ClearfLo campaign, Environ Pollut, 220 (2017) 766-778. [37] G. Rolph, A. Stein, B. Stunder, Real-time Environmental Applications and Display sYstem: READY, Environ Modell Softw, 95 (2017) 210-228. [38] A.F. Stein, R.R. Draxler, G.D. Rolph, B.J.B. Stunder, M.D. Cohen, F. Ngan, Noaa's Hysplit Atmospheric Transport and Dispersion Modeling System, B Am Meteorol Soc, 96 (2015) 2059-2077. [39] G.L. Shi, Y.Z. Tian, S. Ye, X. Peng, J. Xu, W. Wang, B. Han, Y.C. Feng, Source apportionment of synchronously size segregated fine and coarse particulate matter, using an improved three-way factor analysis model, Sci Total Environ, 505 (2015) 1182-1190. [40] Y. Li, W. Hutchins, X.G. Wu, C.R. Liang, C.F. Zhang, X.C. Yuan, D. Khokhani, X. Chen, Y.Z. Che, Q. Wang, C.H. Yang, Derivative of plant phenolic compound inhibits the type III secretion system of Dickeya dadantii via HrpX/HrpY two-component signal transduction and Rsm systems, Mol Plant Pathol, 16 (2015) 150-163. [41] L.T. González, F.E. Longoria Rodríguez, M. Sánchez-Domínguez, A. Cavazos, C. Leyva-Porras, L.G. Silva-Vidaurri, K.A. Askar, B.I. Kharissov, J.F. Villarreal Chiu, J.M. Alfaro

Barbosa, Determination of trace metals in TSP and PM2.5 materials collected in the Metropolitan Area of Monterrey, Mexico: A characterization study by XPS, ICP-AES and SEM-EDS, Atmos Res, 196 (2017) 8-22. [42] A. Campos-Ramos, A. Aragon-Pina, I. Galindo-Estrada, X. Querol, A. Alastuey, Characterization of atmospheric aerosols by SEM in a rural area in the western part of Mexico and its relation with different pollution sources, Atmos Environ, 43 (2009) 6159-6167. [43] Y.P. Pan, S.L. Tian, X.R. Li, Y. Sun, Y. Li, G.R. Wentworth, Y.S. Wang, Trace elements in particulate matter from metropolitan regions of Northern China: Sources, concentrations and size distributions, Sci Total Environ, 537 (2015) 9-22. [44] B. Han, S.F. Kong, Z.P. Bai, G. Du, T. Bi, X. Li, G.L. Shi, Y. Hu, Characterization of Elemental Species in PM2.5 Samples Collected in Four Cities of Northeast China, Water Air Soil Poll, 209 (2010) 15-28. [45] S. Police, S.K. Sahu, G.G. Pandit, Chemical characterization of atmospheric particulate matter and their source apportionment at an emerging industrial coastal city, Visakhapatnam, India, Atmos Pollut Res, 7 (2016) 725-733.

Highlights (1) We studied the chemical and morphological characteristics of atmospheric particles. (2) Particles were sampled in Harbin, Northeast China, from April to June 2017. (3) We used spectrometers, electron microscopy, and factorial analysis to study particles. (4) Emission sources were mainly dust, coal/oil combustion, traffic, and industry.