Next Article in Journal
Emissions from a Modern Euro 6d Diesel Plug-In Hybrid
Previous Article in Journal
From Gustiness to Dustiness—The Impact of Wind Gusts on Particulate Matter Emissions in Field Experiments in La Pampa, Argentina
Previous Article in Special Issue
Influence of Meteorological Factors and Chemical Processes on the Explosive Growth of PM2.5 in Shanghai, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 13
Issue 8
10.3390/atmos13081174
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Source Apportionment of Ambient Aerosols during a Winter Pollution Episode in Yinchuan by Using Single-Particle Mass Spectrometry

by
Kangning Li
1,*,
Liukun Li
1,
Bin Huang
2 and
Zengyu Han
3
1
Ningxia Key Laboratory of Intelligent Sensing for the Desert Information, School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China
2
Enviromental Monitoring Site of Ningxia Ningdong Energy and Chemical Industry Base, Yinchuan 754100, China
3
Ningxia Environmental Monitoring Center, Yinchuan 750000, China
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(8), 1174; https://doi.org/10.3390/atmos13081174
Submission received: 2 July 2022 / Revised: 17 July 2022 / Accepted: 18 July 2022 / Published: 25 July 2022
(This article belongs to the Special Issue Physical Models and Statistical Methods in Atmospheric Environment)
Browse Figures
Review Reports Versions Notes

Abstract

:
For a winter pollution episode in Yinchuan, a city in Northwestern China, ambient aerosols were characterized using a real-time single-particle aerosol mass spectrometer (SPAMS). More than 160,000 individual particles analyzed with the SPAMS were classified into eight major categories on the basis of their mass spectral patterns: traffic emissions, biomass burning, dust, coal burning, industrial emissions, secondary inorganic, cooking, and others, all of which contribute to fine particles. The results revealed that coal burning (29.6%) and traffic emissions (23.4%) were the main sources during the monitoring period. Industrial emissions and secondary inorganic aerosols accounted for 16.6% and 14.0%, respectively. The SPAMS data indicated that the number concentration of the eight types of particles was markedly different in the different pollution cases, and higher number concentrations were discovered more often during pollution episodes. The three pollution cases were mainly caused by the accumulation of fine particles, mainly from traffic emissions, industrial emissions, and increased secondary inorganic conversion.

1. Introduction

Aerosols are uniformly dispersed in a relatively stable suspension system formed by gases. They strongly influence the global climate, regional environmental pollution, and human health by scattering and absorbing sunlight, ground radiation, and cloud condensation nuclei, which are an essential component of the earth’s atmosphere [1,2,3]. Aerosols are fine particles (aerodynamic particle size < 2.5 μm (PM2.5)) with a large specific surface area, long atmospheric residence time, and high scattering efficiency; they therefore exert extremely severe effects on the atmospheric environment and human health. Aerosol pollution has consequently attracted extensive attention [4,5,6].
Many methods exist for studying aerosol particles. Early studies used the filter sampling method. At present, the aerosol time-of-flight mass spectrometer (ATOFMS) and single-particle aerosol mass spectrometer (SPAMS) are widely used to analyze aerosols’ chemical composition and source apportionment [7,8,9,10,11,12,13]. SPAMSs have the advantage of high time resolution and an ability to continuously analyze the size and chemical composition of individual particles in the atmosphere in real time [14,15,16]. The University of California has one of the most representative research teams worldwide, studying atmospheric particles by using single-particle technology [17]. They developed an ATOFMS to study the type and mixing states of atmospheric particles in northern Mexico [17]. The SPAMS produced by Hexin Analytical Instruments, Guangzhou, is widely used in China [14,18,19,20]; it can detect the chemical and physical properties of single particles. Numerous studies have been conducted in Chinese cities—including Beijing [16], Shanghai [21,22], Guangzhou [23,24], and Chongqing [25]—to determine the size and chemical composition of local atmospheric particles and comprehensively understand the source and mixing state of individual particles [26]. For example, Ma et al. used a SPAMS to assess the chemical composition and source appointment of aerosols in Beijing on clear, hazy, and dusty days; their results revealed that carbonaceous and K-rich particles were dominant under different conditions and that the contribution of industrial metal particles was higher during hazy periods [16]. Mu et al. used a SPAMS to analyze the mixing state of fine particles during pollution periods in Shanghai and reported that EC (Elemental Carbon) particles tended to mix with secondary ions in the atmosphere [22]. Bi et al. determined the chemical composition of fog droplet residue particles by using a SPAMS at the ground level in an urban area. The results revealed that the effects of organic particles and ammonium on fog formation were weak in Guangzhou [24]. A SPAMS-based analysis of the mixing state of particles from meat smoking activities in Chongqing revealed that smoked meat contained numerous biomass-burning and ECOC (EC mixed with organic carbon) particles [25].
In Yinchuan, a city located in northwest China, ambient air quality is automatically monitored under the orders of the local government; the annual average PM2.5 concentration has been generally declining, but the regional PM2.5 pollution is exacerbated in winter, which cannot be ignored [27]. In recent years, Yinchuan has become a complex pollution area, with this pollution comprising PM2.5 and gaseous pollutants. Because of local pollutant emissions and long-range transport of pollution from other heavily polluted areas, the physicochemical properties of ambient aerosols in Yinchuan vary greatly.
To understand the characteristics, composition, and source appointment of fine particles in Yinchuan, we used a SPAMS to conduct real-time online source analysis of the main components of PM2.5 and to analyze the influence of various pollution sources on PM2.5. By using meteorological data and source analysis results, we analyzed the temporal distribution of various pollution sources with the aim of providing basic data for the source analysis of PM2.5 and a technical reference for effective formulation of strategies for controlling the atmospheric environment in Yinchuan.

2. Methods

In situ measurements of ambient aerosols were conducted from 18 to 29 December 2020, by using a SPAMS (Hexin Analytical Instrument, Guangzhou, China). Descriptions of the SPAMS and its operational principles can be found in our previous articles [28,29,30]. In brief, the air first passed through a 0.1 mm orifice at 0.08 L/min and was sucked into a series of aerodynamic lenses in the SPAMS, where the aerosol particles were focused and accelerated. Next, the particles entered a vacuum dual-beam diameter measuring system. The number of particles was determined and their size was measured using the different flight times required by two consecutive but separate 532 nm laser beams. When the particles entered the ionization zone, positive and negative ions were generated through laser ionization. Finally, the chemical composition of the particles was detected using a bipolar time-of-flight mass analyzer. Polystyrene latex spheres (PLSs) (0.2–2.0 μm) and standard metal solutions were used to calibrate the particle size and mass before sampling [7,31]. The aerodynamic diameter measurement was calibrated with a curve generated by monodisperse PSL with known aerodynamic diameters. The particle sizes measured by SPAMS are in the range of 200 to 2000 nm. In this study, the ionization energy was set at 0.7 mJ/Pulse.
Yinchuan is located in the temperate continental climate, with sparse rain and snow, intense evaporation, dry climate, and large temperature difference between day and night. It is one of the regions with the largest solar radiation and sunshine hours in China. Yinchuan is divided into mountainous areas and plains. It is higher in the west and south, and lower in the north and east. Yinchuan has the Helan Mountain in the west, north to east.
The sampling site (Figure 1) was located in the Linhe Industrial Park of Ningdong Base (106°32′12″ E, 38°13′4″ N); it is surrounded by Hengshan Road and Chaoyang Road, which have a lot of traffic flow in the morning and evening rush hours. To the north is the Ningxia Baofeng Energy Zone, to the southwest is the Ningdong Aluminum Branch of Qingtongxia Aluminum, and to the southeast is Ningxia Jingneng Ningdong Power Generation.

3. Results and Discussion

3.1. Air Quality and Meteorological Conditions

Meteorological and air quality information were obtained simultaneously at an air quality monitoring site close to our sampling site. The temporal profiles of SO2, CO, O3, NO2, wind direction, and wind speed as well as PM2.5 and particulate matter of size < 10 μm (PM10) mass concentrations are illustrated in Figure 2. The PM2.5 mass concentration was discovered to peak three times—on 21, 22, and 27 December—and its maximum was 148 µg/m3 (at 0:00 local time (LT) on 22 December); the average mass concentration of PM2.5 was 53 μg/m3 during the period. The PM10 concentration exhibited a similar trend to that for PM2.5. It varied from 14 to 256 µg/m3, with an average of 88.9 µg/m3. Only one major peak was found for SO2: that at 350 µg/m3 at 21:00 LT on 23 December. The CO concentration exhibited a similar trend to the SO2 concentration. The opposite trends in O3 and NO2 are illustrated in Figure 2. During the monitoring period, the average wind speed was 2.3 m/s, the average temperature was −5.7 °C, the relative humidity (RH) was 19% to 68%, and no precipitation occurred. Therefore, the monitoring period was predominantly characterized by stagnant weather conditions, highly elevated PM2.5 levels, and low RH.

3.2. Overall Source Apportionment

In this study, 167,000 individual particle mass spectra were analyzed, accounting for approximately 58% of all the particles that were sized in the SPAMS. The mass spectra were classified into several types on the basis of their similarities by using a clustering algorithm called adaptive resonance theory (ART-2a) [10]. Similar to in previous studies [29,30,32,33], the vigilance factor, learning rate, and iterations for ART-2a were set to 0.85, 0.05, and 20, respectively. Finally, fine particles were manually categorized into eight types on the basis of their chemical nature: dust, biomass burning, traffic emissions, coal burning, industrial emissions, secondary inorganic emissions, cooking, and other. The average mass spectral profiles for eight pollution source types are shown in Figure 3.
The number fractions for each group are illustrated in Figure 4. During the sampling period, the atmosphere was mainly affected by coal burning (29.6%) and traffic emissions (23.4%). Industrial emissions and secondary inorganic particles accounted for 16.6% and 14.0% of aerosols, respectively.
According to national ambient air quality standards (NAAQS) for particulate matter (PM) in 2012 (GB3095-2012), air quality is categorized on the basis of PM2.5 values as follows: 0–35 μg/m3, excellent; 35–75 μg/m3, good; 75–115 μg/m3, slight pollution; 115–150 μg/m3, moderate pollution; and 150–250 μg/m3, severe pollution. In our sample, the air quality was divided into four grades: excellent, good, slight pollution, and moderate pollution. Figure 5 presents the number fractions of the eight particle types under various pollution cases. As the average PM2.5 mass concentration increased from 28 to 95 μg/m3, the air quality changed from excellent to slight pollution, the number fraction of traffic emissions increased from 21.1% to 25.3%, and the number fraction of secondary inorganic sources increased from 12.2% to 16.3%. As the average PM2.5 mass concentration increased from 95 to 125 μg/m3, the air quality changed from slight pollution to moderate pollution, the number fraction of coal burning increased from 26.8% to 27.3%, and the number fraction of dust increased from 4.2% to 5.4%. These findings indicated that the increase in PM2.5 during the sampling period was influenced mainly by the increase in coal burning, traffic emissions, secondary inorganic aerosols, and dust.

3.3. Temporal Variation in Ambient Aerosols

The number concentrations of ambient aerosols detected using the SPAMS changed rapidly and in a complex pattern (Figure 6). The high-resolution temporal variation in the eight types of number concentration indicated several peaks, which may have been due to the typical fresh plume or municipal air pollution. As illustrated in Figure 7, strong correlation (R2 = 0.51109) between the number concentration of ambient particles collected using a SPAMS and PM2.5 mass concentration indicated that the concentration of ambient particles determined using the SPAMS can reflect the atmospheric pollution status of fine particles. To illustrate this point, we defined the periods from 11:00 to 24:00 on 21 December, 09:00 to 17:00 on 22 December, and 10:00 on 27 December to 04:00 on 28 December, as Cases 1, 2, and 3, respectively. From 0:00 on 24 December to 10:00 on 25 December, the PM2.5 mass concentration remained at a low level (<35 μg/m3), so we defined this period as the “Clean Day”. Figure 8 presents the temporal variation in the number fraction of ambient particles and in the PM2.5 mass concentration with 1 h resolution. To better illustrate the phenomenon, the three cases (Cases 1, 2, and 3) are colored yellow, whereas the “Clean Day” is colored blue. In this study, we will focus on three pollution cases. The contributions of the eight particle types in the three cases are summarized in Figure 9.
For Cases 1, 2, and 3, both the particle number concentration detected by SPAMS and PM2.5 mass concentration increased sharply with respect to time (PM2.5 = 87.75, 92.38, and 88.32 µg/m3, respectively; Figure 7). The PM2.5 mass concentrations for the three cases, represented by the height of the bar chart, were higher than 75 µg/m3 (Figure 9), indicating slight pollution. The number fraction of traffic emissions on the Clean Day and in Cases 1, 2, and 3 was 21.14%, 24.24%, 26.11%, and 25.95%, respectively. The number fraction of industry emissions on the Clean Day and in Cases 1, 2, and 3 was 13.76%, 19.97%, 16.8%, and 17.93%, respectively. The number fraction of secondary inorganic pollution on the Clean Day and in Cases 1, 2, and 3 was 10.47%, 16.72%, 15.85%, and 17.8%, respectively. All three of these parameters were higher for the pollution cases than on the Clean Day, indicating that slight pollution was caused by traffic emissions, industry emissions, and increased secondary inorganic conversion.
Compared with other cities, higher PM2.5 number concentrations often have been identified during haze episodes in Shanghai, and the PM2.5 mainly consisted of traffic emission particles, biomass burning particles and heavy-duty diesel engine emission particles [30]. In Beijing, carbonaceous and K-rich particles were dominant under different conditions and the contribution of industrial metal particles was higher during hazy periods [16]. In our work, higher number concentrations were also discovered more often during pollution episodes in Yinchuan. The pollution cases were mainly caused by the accumulation of fine particles, mainly from traffic emissions, industrial emissions, and increased secondary inorganic conversion.
Taken together, these data suggest that traffic emissions, industry emissions, and increased secondary inorganic conversion contributed to a major fraction of ambient aerosols measured at the sampling site.

4. Conclusions

In this study, we characterized ambient particles during both clean and polluted periods in winter (December 2020) in Yinchuan, China; this was achieved by analyzing the particles’ source apportionment and mixing state by using a SPAMS, which can detect the physical and chemical properties of ambient aerosols at the resolution of a single particle. The mixing state of the particles was drastically different in the different pollution cases, and higher number concentrations were discovered to occur too often during pollution episodes. Overall, source apportionment analysis revealed that the high PM2.5 concentrations during the sampling period were influenced mainly by considerable coal burning, traffic emissions, secondary inorganic aerosols, and dust. The real-time variation in ambient aerosols measured using a SPAMS indicated that at our sample site, slight pollution was caused by traffic emissions, industry emissions, and increased secondary inorganic conversion.

Author Contributions

Conceptualization, K.L. and L.L.; methodology, K.L.; software, L.L.; validation, B.H.; formal analysis, B.H.; investigation, Z.H.; resources, B.H.; data curation, K.L.; writing—original draft preparation, K.L.; writing—review and editing, K.L.; visualization, L.L.; supervision, K.L.; project administration, B.H.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.

Funding

The National Natural Science Foundation of China, grant number 42167016. The Natural Science Foundation of Ningxia Province in China, grant number 2022AAC03123. The Key Research and Development Program of Ningxia Province in China, grant number 2020BEB04003, and the 5th batch of the Ningxia Youth Science and Technology Talents Project, grant number NXTJGC147.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this work are available on request from the corresponding author.

Acknowledgments

We greatly appreciate the support for the study from Ningxia University, Enviromental Monitoring Site of Ningxia Ningdong Energy and Chemical Industry Base, and Ningxia Environmental Monitoring Center.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Pöschl, U. Atmospheric aerosols: Composition, transformation, climate and health effects. Angew. Chem. Int. Ed. 2005, 44, 7520–7540. [Google Scholar] [CrossRef] [PubMed]
  2. Gard, E.E.; Kleeman, M.J.; Gross, D.S.; Hughes, L.S.; Allen, J.O.; Morrical, B.D.; Fergenson, D.P.; Dienes, T.; Gälli, M.E.; Johnson, R.J.; et al. Direct observation of heterogeneous chemistry in the atmosphere. Science 1998, 279, 1184–1187. [Google Scholar] [CrossRef]
  3. Change, I.C. The Scientific Basis; Cambridge University Press: Cambridge, UK, 2001. [Google Scholar]
  4. Yang, F.; Tan, J.; Zhao, Q.; Du, Z.; He, K.; Ma, Y.; Duan, F.; Chen, G. Characteristics of PM 2.5 speciation in representative megacities and across China. Atmos. Chem. Phys. 2011, 11, 5207–5219. [Google Scholar] [CrossRef] [Green Version]
  5. Che, H.; Zhang, X.; Li, Y.; Zhou, Z.; Qu, J.J.; Hao, X. Haze trends over the capital cities of 31 provinces in China, 1981–2005. Theor. Appl. Climatol. 2009, 97, 235–242. [Google Scholar] [CrossRef]
  6. Wang, Y.; Zhang, J.; Wang, L.; Hu, B.; Tang, G.; Liu, Z.; Sun, Y.; Ji, D. Researching significance, status and expectation of haze in Beijing-Tianjin-Hebei region. Adv. Earth Sci. 2014, 29, 388–396. [Google Scholar]
  7. Li, L.; Huang, Z.; Dong, J.; Li, M.; Gao, W.; Nian, H.; Fu, Z.; Zhang, G.; Bi, X.; Cheng, P. Real time bipolar time-of-flight mass spectrometer for analyzing single aerosol particles. Int. J. Mass Spectrom. 2011, 303, 118–124. [Google Scholar] [CrossRef]
  8. Fu, H.; Yan, C.; Zheng, M.; Cai, J.; Li, X.; Zhang, Y.; Zhen, Z.; Fu, Z.; Li, M.; Li, L. Application of on-line single particle aerosol mass spectrometry (SPAMS) for studying major components in fine particulate matter. Huan Jing Ke Xue 2014, 35, 4070–4077. [Google Scholar]
  9. Bhave, P.V.; Allen, J.O.; Morrical, B.D.; Fergenson, D.P.; Cass, G.R.; Prather, K.A. A field-based approach for determining ATOFMS instrument sensitivities to ammonium and nitrate. Environ. Sci. Technol. 2002, 36, 4868–4879. [Google Scholar] [CrossRef] [Green Version]
  10. Song, X.-H.; Hopke, P.K.; Fergenson, D.P.; Prather, K.A. Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A. Anal. Chem. 1999, 71, 860–865. [Google Scholar] [CrossRef]
  11. Dall’Osto, M.; Harrison, R.M. Chemical characterisation of single airborne particles in Athens (Greece) by ATOFMS. Atmos. Environ. 2006, 40, 7614–7631. [Google Scholar] [CrossRef]
  12. Dall’Osto, M.; Beddows, D.C.; Gietl, J.K.; Olatunbosun, O.A.; Yang, X.; Harrison, R.M. Characteristics of tyre dust in polluted air: Studies by single particle mass spectrometry (ATOFMS). Atmos. Environ. 2014, 94, 224–230. [Google Scholar] [CrossRef] [Green Version]
  13. Huang, Y.; Li, L.; Li, J.; Wang, X.; Chen, H.; Chen, J.; Yang, X.; Gross, D.; Wang, H.; Qiao, L. A case study of the highly time-resolved evolution of aerosol chemical and optical properties in urban Shanghai, China. Atmos. Chem. Phys. 2013, 13, 3931–3944. [Google Scholar] [CrossRef] [Green Version]
  14. Zhou, J.; Ren, Y.; Hong, G.; Lu, N.; Li, Z.; Li, L.; Li, H.; Jin, W. Characteristics and formation mechanism of a multi-day haze in the winter of Shijiazhuang using a single particle aerosol mass spectrometer (SPAMS). Huan Jing Ke Xue 2015, 36, 3972–3980. [Google Scholar] [PubMed]
  15. Lu, J.; Ma, L.; Cheng, C.; Pei, C.; Chan, C.K.; Bi, X.; Qin, Y.; Tan, H.; Zhou, J.; Chen, M. Real time analysis of lead-containing atmospheric particles in Guangzhou during wintertime using single particle aerosol mass spectrometry. Ecotoxicol. Environ. Saf. 2019, 168, 53–63. [Google Scholar] [CrossRef] [PubMed]
  16. Ma, L.; Li, M.; Zhang, H.; Li, L.; Huang, Z.; Gao, W.; Chen, D.; Fu, Z.; Nian, H.; Zou, L. Comparative analysis of chemical composition and sources of aerosol particles in urban Beijing during clear, hazy, and dusty days using single particle aerosol mass spectrometry. J. Clean. Prod. 2016, 112, 1319–1329. [Google Scholar] [CrossRef]
  17. Moffet, R.; De Foy, B.; Molina, L.A.; Molina, M.; Prather, K. Measurement of ambient aerosols in northern Mexico City by single particle mass spectrometry. Atmos. Chem. Phys. 2008, 8, 4499–4516. [Google Scholar] [CrossRef] [Green Version]
  18. Wen, J.; Shi, X.-R.; Tian, Y.-Z.; Xu, J.; Shi, G.-L.; Feng, Y.-C. Analysis of chemical composition of the fine particulate matter in summer in Tianjin city via a single particle aerosol mass spectrometer (SPAMS). Huan Jing Ke Xue 2018, 39, 3492–3501. [Google Scholar]
  19. Peng, X.; Liu, X.; Shi, X.; Shi, G.; Li, M.; Liu, J.; Huangfu, Y.; Xu, H.; Ma, R.; Wang, W. Source apportionment using receptor model based on aerosol mass spectra and 1 h resolution chemical dataset in Tianjin, China. Atmos. Environ. 2019, 198, 387–397. [Google Scholar] [CrossRef]
  20. Wang, H.; Wen, J.; Xu, J.; Tian, Y.; Shi, G.; Feng, Y.; Li, M. Characteristics of single urban raised dust and soil dust in Tianjin city. Res. Environ. Sci. 2018, 31, 844–852. [Google Scholar]
  21. Yang, F.; Chen, H.; Du, J.; Yang, X.; Gao, S.; Chen, J.; Geng, F. Evolution of the mixing state of fine aerosols during haze events in Shanghai. Atmos. Res. 2012, 104, 193–201. [Google Scholar] [CrossRef]
  22. Mu, Y.-Y.; Lou, S.-R.; Chen, C.-H.; Zhou, M.; Wang, H.-L.; Zhou, Z.; Qiao, L.-P.; Huang, C.; Li, M.; Li, L. Aging and mixing state of particulate matter during aerosol pollution episode in autumn Shanghai using a single particle aerosol mass spectrometer (SPAMS). Huan Jing Ke Xue 2013, 34, 2071–2080. [Google Scholar] [PubMed]
  23. Zhang, G.; Han, B.; Bi, X.; Dai, S.; Huang, W.; Chen, D.; Wang, X.; Sheng, G.; Fu, J.; Zhou, Z. Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry. Atmos. Res. 2015, 153, 286–295. [Google Scholar] [CrossRef]
  24. Bi, X.; Lin, Q.; Peng, L.; Zhang, G.; Wang, X.; Brechtel, F.J.; Chen, D.; Li, M.; Peng, P.A.; Sheng, G. In situ detection of the chemistry of individual fog droplet residues in the Pearl River Delta region, China. J. Geophys. Res. Atmos. 2016, 121, 9105–9116. [Google Scholar] [CrossRef]
  25. Chen, Y.; Wenger, J.C.; Yang, F.; Cao, J.; Huang, R.; Shi, G.; Zhang, S.; Tian, M.; Wang, H. Source characterization of urban particles from meat smoking activities in Chongqing, China using single particle aerosol mass spectrometry. Environ. Pollut. 2017, 228, 92–101. [Google Scholar] [CrossRef]
  26. Murphy, D.M. The design of single particle laser mass spectrometers. Mass Spectrom. Rev. 2007, 26, 150–165. [Google Scholar] [CrossRef]
  27. Zou, J.H.; Xu, F.X.; Zou, B.; Li, S.X.; Yang, Z.L. Spatial-temporal characteristics of haze in the key tourism cities of China. Trop. Geogr. 2018, 38, 143–150. [Google Scholar]
  28. Zhai, J.; Wang, X.; Li, J.; Xu, T.; Chen, H.; Yang, X.; Chen, J. Thermal desorption single particle mass spectrometry of ambient aerosol in Shanghai. Atmos. Environ. 2015, 123, 407–414. [Google Scholar] [CrossRef]
  29. Li, K.; Ye, X.; Pang, H.; Lu, X.; Chen, H.; Wang, X.; Yang, X.; Chen, J.; Chen, Y. Temporal variations in the hygroscopicity and mixing state of black carbon aerosols in a polluted megacity area. Atmos. Chem. Phys. 2018, 18, 15201–15218. [Google Scholar] [CrossRef] [Green Version]
  30. Li, K.; Wang, X.; Lu, X.; Chen, H.; Yang, X. Effect of Pollution Level on Size Distributions and Mixing State of Ambient Black Carbon Particles in an Urban Area during Wintertime. Aerosol Air Qual. Res. 2021, 21, 200655. [Google Scholar] [CrossRef]
  31. Li, L.; Li, M.; Huang, Z.; Gao, W.; Nian, H.; Fu, Z.; Gao, J.; Chai, F.; Zhou, Z. Ambient particle characterization by single particle aerosol mass spectrometry in an urban area of Beijing. Atmos. Environ. 2014, 94, 323–331. [Google Scholar] [CrossRef]
  32. Li, K.; Wang, X.; Lu, X.; Chen, H.; Yang, X. Effects of Volatile Components on Mixing State and Size Distribution of Individual Black Carbon Aerosols. Aerosol Air Qual. Res. 2022, 22, 210400. [Google Scholar] [CrossRef]
  33. Spencer, M.T.; Shields, L.G.; Prather, K.A. Simultaneous measurement of the effective density and chemical composition of ambient aerosol particles. Environ. Sci. Technol. 2007, 41, 1303–1309. [Google Scholar] [CrossRef] [PubMed]
Atmosphere 13 01174 g001 550
Figure 1. The location of sampling site.
Figure 1. The location of sampling site.
Atmosphere 13 01174 g001
Atmosphere 13 01174 g002 550
Figure 2. Temporal profiles of gaseous pollutants (O3, SO2, CO, and NO2), wind direction, wind speed, and PM2.5 and PM10 mass concentrations.
Figure 2. Temporal profiles of gaseous pollutants (O3, SO2, CO, and NO2), wind direction, wind speed, and PM2.5 and PM10 mass concentrations.
Atmosphere 13 01174 g002
Atmosphere 13 01174 g003 550
Figure 3. The average mass spectral profiles for eight pollution source types.
Figure 3. The average mass spectral profiles for eight pollution source types.
Atmosphere 13 01174 g003
Atmosphere 13 01174 g004 550
Figure 4. Number fractions for each group during sampling period.
Figure 4. Number fractions for each group during sampling period.
Atmosphere 13 01174 g004
Atmosphere 13 01174 g005 550
Figure 5. Number fraction of eight particle types in various pollution cases.
Figure 5. Number fraction of eight particle types in various pollution cases.
Atmosphere 13 01174 g005
Atmosphere 13 01174 g006 550
Figure 6. Temporal variation in the PM2.5 mass concentration and eight types of number concentration.
Figure 6. Temporal variation in the PM2.5 mass concentration and eight types of number concentration.
Atmosphere 13 01174 g006
Atmosphere 13 01174 g007 550
Figure 7. Correlation between the number concentration of ambient particles collected using a SPAMS and PM2.5 mass concentration.
Figure 7. Correlation between the number concentration of ambient particles collected using a SPAMS and PM2.5 mass concentration.
Atmosphere 13 01174 g007
Atmosphere 13 01174 g008 550
Figure 8. Temporal variation in the PM2.5 mass concentration and number fraction of eight pollution source types.
Figure 8. Temporal variation in the PM2.5 mass concentration and number fraction of eight pollution source types.
Atmosphere 13 01174 g008
Atmosphere 13 01174 g009 550
Figure 9. Contribution of eight particle types in the three pollution cases.
Figure 9. Contribution of eight particle types in the three pollution cases.
Atmosphere 13 01174 g009
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Li, K.; Li, L.; Huang, B.; Han, Z. Source Apportionment of Ambient Aerosols during a Winter Pollution Episode in Yinchuan by Using Single-Particle Mass Spectrometry. Atmosphere 2022, 13, 1174. https://doi.org/10.3390/atmos13081174

AMA Style

Li K, Li L, Huang B, Han Z. Source Apportionment of Ambient Aerosols during a Winter Pollution Episode in Yinchuan by Using Single-Particle Mass Spectrometry. Atmosphere. 2022; 13(8):1174. https://doi.org/10.3390/atmos13081174

Chicago/Turabian Style

Li, Kangning, Liukun Li, Bin Huang, and Zengyu Han. 2022. "Source Apportionment of Ambient Aerosols during a Winter Pollution Episode in Yinchuan by Using Single-Particle Mass Spectrometry" Atmosphere 13, no. 8: 1174. https://doi.org/10.3390/atmos13081174

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop