Ultrafine-Particle Emission Factors as a Function of Vehicle Mode of Operation for LDVs Based on Near-Roadway Monitoring

Zhai, Wenjuan; Wen, Dongqi; Xiang, Sheng; Hu, Zhice; Noll, Kenneth E.

doi:10.1021/acs.est.5b03885

Cited by 9 publications

(8 citation statements)

References 32 publications

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“…Coal and biomass burning, from the residential sectors, are more important during winter in Beijing and the North China Plain (Hu et al, 2017;Sun et al, 2018). In addition, the transition in energy consumption from coal burning to natural gas and electricity in urban Beijing took place from the years 2009 to 2017, which led to a decrease in the proportion of coal to total primary energy consumption from 43 % in 2007 to less than 20 % in 2015 (Zhang et al, 2018). The effects of residential coal combustion and biomass burning were not strong during our sampling period, which is supported by the chemical component measurements (more supporting information is provided in Sect.…”

Section: Measurement Site and Instrumentationmentioning

confidence: 99%

Size-segregated particle number and mass concentrations from different emission sources in urban Beijing

et al. 2020

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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.

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Section: Measurement Site and Instrumentationmentioning

confidence: 99%

Size-segregated particle number and mass concentrations from different emission sources in urban Beijing

et al. 2020

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“…This makes the measured concentrations of UFPs from the following vehicle notably higher than those from the front vehicle when they were very close (<10m) due to the impacts of the exhaust of the front vehicle. According to previous studies, both heavy-duty diesel trucks and light-duty vehicles are the major source of on-road UFPs while the former is the major source of on-road BC. − This explains why the NRMSDs for the scenario of 0–10 m buffer compared with other scenarios within the optimal distances for the BC monitors are not as large as those for the UFP monitors. Although the corresponding NRMSDs for the scenario of a 0–10 m buffer for the UFP monitors might be smaller if the orders of the two vehicles were randomized when they were within 10 m, the situation where the following vehicle measured pollutants in the front vehicle’s exhaust probably remained.…”

Section: Discussionmentioning

confidence: 96%

Using Vehicles’ Rendezvous for In Situ Calibration of Instruments in Fleet Vehicle-Based Air Pollution Mobile Monitoring

Xiang

Austin

Gould

et al. 2020

Environ. Sci. Technol.

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This study examines the feasibility of the in situ calibration of instruments for fleet vehicle-based mobile monitoring of ultrafine particles (UFPs) and black carbon (BC) by comparing rendezvous vehicle measurements. Two vehicles with identical makes and models of UFP and BC monitors as well as GPS receivers were sampled within 140 m of each other for 2 h in total during winter in Seattle, Washington. To identify an optimal intervehicle distance for rendezvous calibration, 6 different buffers within 0−140 m for UFP monitors and 5 different buffers within 0−90 m for BC monitors were chosen, and the results of calibration were compared against a reference scenario, which consisted of mobile colocation measurements with both sets of the UFP and BC monitors deployed in one of the vehicles. Results indicate that the optimal distances for rendezvous calibration are 10−80 m for UFP monitors and 0− 30 m for BC monitors. In comparison with the mobile colocation calibration, the rendezvous calibration shows a normalized root mean squared deviation of 6−14% and a normalized mean absolute deviation of 4−8% for these monitors. Criteria for applying a rendezvous calibration approach are presented, and an extension of this approach to an instrumented fleet of mobile monitoring vehicles is discussed.

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“…Ultrafine particle (UFP, <100 nm) EFs were e.g. found to be 2 to 4 times higher during traffic congestion than during free flow (Zhai et al, 2016). Yet, additional processes not resolved in our analysis might also contribute to this observation, such as the variation of boundary layer height.…”

Section: Traffic-ultrafine Particlesmentioning

confidence: 99%

Size segregated particle number and mass emissions in urban Beijing

Cai

Chu

Yao

et al. 2020

Preprint

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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&#8201;% and 24&#8201;% of total particle number and organic aerosol (OA) mass, respectively. Similar source-types, including particles related to vehicular emissions (1.6&#8201;&#177;&#8201;1.1&#8201;&#956;g&#8201;m&#8722;3; 2.4&#8201;&#177;&#8201;1.8&#8201;&#215;&#8201;103&#8201;cm&#8722;3 and 5.5&#8201;&#177;&#8201;2.8&#8201;&#215;&#8201;103&#8201;cm&#8722;3 for two traffic-related components), cooking emissions (2.6&#8201;&#177;&#8201;1.9&#8201;&#956;g&#8201;m&#8722;3 and 5.5&#8201;&#177;&#8201;3.3&#8201;&#215;&#8201;103&#8201;cm&#8722;3) and secondary aerosols (51&#8201;&#177;&#8201;41&#8201;&#956;g&#8201;m&#8722;3 and 4.2&#8201;&#177;&#8201;3.0&#8201;&#215;&#8201;103&#8201;cm&#8722;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&#8201;nm (<q>Traffic-ultrafine</q>) and a component with a mode diameter of 100&#8201;nm (<q>Traffic-fine</q>). Consistent with similar day and night-time diesel vehicle PM2.5 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&#8201;% and 30&#8201;% for ultrafine and fine particles, respectively) and cooking (32&#8201;%) emissions dominate the particle number concentration while secondary particulate matter (over 80&#8201;%) governs PM2.5 mass during the non-heating season in Beijing.

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Ultrafine-Particle Emission Factors as a Function of Vehicle Mode of Operation for LDVs Based on Near-Roadway Monitoring

Cited by 9 publications

References 32 publications

Size-segregated particle number and mass concentrations from different emission sources in urban Beijing

Size-segregated particle number and mass concentrations from different emission sources in urban Beijing

Using Vehicles’ Rendezvous for In Situ Calibration of Instruments in Fleet Vehicle-Based Air Pollution Mobile Monitoring

Size segregated particle number and mass emissions in urban Beijing

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