Size-segregated particle number and mass concentrations from different emission sources in urban Beijing
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Published:2020-11-04
Issue:21
Volume:20
Page:12721-12740
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Cai Jing, Chu BiwuORCID, Yao Lei, Yan ChaoORCID, Heikkinen Liine M.ORCID, Zheng Feixue, Li Chang, Fan Xiaolong, Zhang Shaojun, Yang Daoyuan, Wang YonghongORCID, Kokkonen Tom V.ORCID, Chan Tommy, Zhou Ying, Dada LubnaORCID, Liu YongchunORCID, He Hong, Paasonen PauliORCID, Kujansuu Joni T., Petäjä TuukkaORCID, Mohr ClaudiaORCID, Kangasluoma JuhaORCID, Bianchi FedericoORCID, Sun YeleORCID, Croteau Philip L., Worsnop Douglas R., Kerminen Veli-MattiORCID, Du Wei, Kulmala MarkkuORCID, Daellenbach Kaspar R.
Abstract
Abstract. Although secondary particulate matter is reported to be the main
contributor of PM2.5 during haze in Chinese megacities,
primary particle emissions also affect particle concentrations. In
order to improve estimates of the contribution of primary sources to
the particle number and mass concentrations, we performed source
apportionment analyses using both chemical fingerprints and particle
size distributions measured at the same site in urban Beijing from
April to July 2018. Both methods resolved factors related to primary
emissions, including vehicular emissions and cooking emissions, which
together make up 76 % and 24 % of total particle number and
organic aerosol (OA) mass, respectively. Similar source types,
including particles related to vehicular emissions (1.6±1.1 µg m−3; 2.4±1.8×103 cm−3 and 5.5±2.8×103 cm−3
for two traffic-related components), cooking emissions (2.6±1.9 µg m−3 and 5.5±3.3×103 cm−3) and secondary aerosols (51±41 µg m−3 and 4.2±3.0×103 cm−3), were resolved by both methods. Converted mass
concentrations from particle size distributions components were
comparable with those from chemical fingerprints. Size distribution
source apportionment separated vehicular emissions into a component
with a mode diameter of 20 nm (“traffic-ultrafine”) and a
component with a mode diameter of 100 nm
(“traffic-fine”). Consistent with similar day- and nighttime diesel
vehicle PM2.5 emissions estimated for the Beijing area,
traffic-fine particles, hydrocarbon-like OA (HOA, traffic-related factor
resulting from source apportionment using chemical fingerprints) and
black carbon (BC) showed similar diurnal patterns, with higher
concentrations during the night and morning than during the afternoon
when the boundary layer is higher. Traffic-ultrafine particles showed
the highest concentrations during the rush-hour period, suggesting a
prominent role of local gasoline vehicle emissions. In the absence of
new particle formation, our results show that vehicular-related
emissions (14 % and 30 % for ultrafine and fine particles,
respectively) and cooking-activity-related emissions (32 %)
dominate the particle number concentration, while secondary particulate
matter (over 80 %) governs PM2.5 mass during the
non-heating season in Beijing.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference113 articles.
1. Abdullahi, K. L., Delgado-Saborit, J. M., and Harrison, R. M.: Emissions
and indoor concentrations of particulate matter and its specific chemical
components from cooking: A review, Atmos. Environ., 71, 260–294,
https://doi.org/10.1016/j.atmosenv.2013.01.061, 2013. 2. Beddows, D. C. S. and Harrison, R. M.: Receptor modelling of both particle
composition and size distribution from a background site in London, UK – a two-step approach, Atmos. Chem. Phys., 19, 4863–4876, https://doi.org/10.5194/acp-19-4863-2019, 2019. 3. Budisulistiorini, S. H., Canagaratna, M. R., Croteau, P. L., Baumann, K., Edgerton, E. S., Kollman, M. S., Ng, N. L., Verma, V., Shaw, S. L., Knipping, E. M., Worsnop, D. R., Jayne, J. T., Weber, R. J., and Surratt, J. D.: Intercomparison of an Aerosol Chemical Speciation Monitor (ACSM) with ambient fine aerosol measurements in downtown Atlanta, Georgia, Atmos. Meas. Tech., 7, 1929–1941, https://doi.org/10.5194/amt-7-1929-2014, 2014. 4. Buonanno, G., Dell'Isola, M., Stabile, L., and Viola, A.: Uncertainty Budget
of the SMPS–APS System in the Measurement of PM1, PM2.5, and PM10, Aerosol
Sci. Tech., 43, 1130–1141, https://doi.org/10.1080/02786820903204078, 2009a. 5. Buonanno, G., Morawska, L., and Stabile, L.: Particle emission factors
during cooking activities, Atmos. Environ., 43, 3235–3242,
https://doi.org/10.1016/j.atmosenv.2009.03.044, 2009b.
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