Size segregated particle number and mass emissions in urban Beijing

Abstract

<p><strong>Abstract.</strong> Although secondary particulate matter is reported to be the main contributor of PM<sub>2.5</sub> 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<sup>−3</sup>; 2.4 ± 1.8 × 10<sup>3</sup> cm<sup>−3</sup> and 5.5 ± 2.8 × 10<sup>3</sup> cm<sup>−3</sup> for two traffic-related components), cooking emissions (2.6 ± 1.9 μg m<sup>−3</sup> and 5.5 ± 3.3 × 10<sup>3</sup> cm<sup>−3</sup>) and secondary aerosols (51 ± 41 μg m<sup>−3</sup> and 4.2 ± 3.0 × 10<sup>3</sup> cm<sup>−3</sup>) 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 (<q>Traffic-ultrafine</q>) and a component with a mode diameter of 100 nm (<q>Traffic-fine</q>). Consistent with similar day and night-time diesel vehicle PM<sub>2.5</sub> emissions estimated for the Beijing area, Traffic-fine, 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 (14 % and 30 % for ultrafine and fine particles, respectively) and cooking (32 %) emissions dominate the particle number concentration while secondary particulate matter (over 80 %) governs PM<sub>2.5</sub> mass during the non-heating season in Beijing.</p>

Size-resolved particle number emissions in Beijing determined from measured particle size distributions

“…4b), suggesting that traffic contributes to particle emissions into this size range. The importance of traffic emissions is also supported by the fact that the diurnal cycle of emissions is roughly similar to the diurnal cycle of modelled PM2.5 emissions from gasoline vehicles in Cai et al (2020a), which have maxima around 7:00-8:00 and 17:00. In addition, clustering of atmospheric vapors and the following growth to 3-6 nm sizes can contribute to the emissions calculated to this size range, as atmospheric clustering seems to occur also on nonevent days.…”

“…Although traffic likely contributes to emissions into this size range, high emissions in the evening can indicate the contribution of some other source, such as cooking activities. The contribution of cooking emissions at this time is supported by studies applying PMF analysis to chemical composition and particle size distribution data from Beijing, which have found cooking-related factors peaking around 19:00-20:00 (Cai et al, 2020a;Hu et al, 2017;Liu et al, 2017). In a study by Cai et al (2020a), the cooking-related particle number size distribution factor had a GMD of ~50 nm.…”

“…The contribution of cooking emissions at this time is supported by studies applying PMF analysis to chemical composition and particle size distribution data from Beijing, which have found cooking-related factors peaking around 19:00-20:00 (Cai et al, 2020a;Hu et al, 2017;Liu et al, 2017). In a study by Cai et al (2020a), the cooking-related particle number size distribution factor had a GMD of ~50 nm. In studies focusing on cooking emissions, Chinese cooking has been found to typically produce particles with the mode diameter ranging from 20 to 100 nm (Zhao and Zhao, 2018).…”

“…According to the modeling results, PM2.5 emissions originating from gasoline vehicles in urban Beijing start to increase before 06:00 in the morning, reach the first maximum around 7:00-8:00 and the second maximum around 17:00-18:00, after which they decrease to lower night-time values. However, the modelled PM2.5 emissions from diesel vehicles are highest at night (Cai et al, 2020a). Figure 4a shows that the particle emissions to the smallest studied size bin (~2-3 nm) (which also include the growth of the particles from smaller sizes) increase in the morning, reach a maximum just before noon, and show a second, much weaker peak around 16:00.…”

“…4b, the estimated NOx emissions have a maximum around 09:00, linked to morning traffic, while they do not have a clear afternoon or evening maximum, likely due to fast photochemical loss of NOx (Lu et al, 2019). Cai et al (2020a) used EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet; Yang et al, 2019) model to estimate the diurnal cycle of PM2.5 emissions at our measurement site. According to the modeling results, PM2.5 emissions originating from gasoline vehicles in urban Beijing start to increase before 06:00 in the morning, reach the first maximum around 7:00-8:00 and the second maximum around 17:00-18:00, after which they decrease to lower night-time values.…”