Impacts of different vehicle emissions on ozone levels in Beijing: Insights into source contributions and formation processes

Beijing, with the highest number of motor vehicles in China, significantly contributes to O<sub>3</sub> pollution through substantial NO<sub>x</sub> and VOC emissions in the on-road transportation sector. Understanding the unique impact of emissions from different vehicle types on O<sub>3</sub> levels is crucial for developing targeted strategies for O<sub>3</sub> pollution. This study applied the Community Multiscale Air Quality Modeling System (CMAQ) to comprehensively investigate the impacts of emissions from different vehicle types on O<sub>3</sub> levels in various regions of Beijing and to provide valuable insights into source contributions and formation processes. The results revealed that various vehicle types exhibited different spatial-temporal emission patterns, with medium-heavy duty trucks (HDT) and mini-light passenger vehicles (LDPV) identified as the primary contributors to NO<sub>x</sub> (36.1 %) and VOC (57.6 %) emissions. Using the Integrated Source Apportionment Method (ISAM) coupled in CMAQ, we found the total vehicle emissions contributed to over 20 % of daily maximum 8-h average O<sub>3</sub> (MDA8 O<sub>3</sub>) concentration, ranked as the second largest contributor after regional transport. Contributions to O<sub>3</sub> formation from LDPV and medium-large passenger vehicles (MDPV) were 2.6-4.0 and 4.2-6.8 ppb and mainly concentrated in urban areas, while the contributions from mini-light duty trucks (LDT) and HDT were 3.5-4.8 and 3.7-6.2 ppb and mainly concentrated in suburban areas. Through scenario analysis that removed emissions from specific types of vehicles, we found removing LDPV emissions led to decreases in daytime O<sub>3</sub> concentration by 0.3-3.8 ppb. In contrast, removing MDPV emissions led to notable O<sub>3</sub> increases by 4.0-11.8 ppb at rush hours. Removing LDT and HDT emissions resulted in 0.6-8.0 ppb increases in nocturnal O<sub>3</sub> concentrations while 0.8-2.0 ppb decreases during the afternoon. This research highlights the necessity of tailoring control strategies for different vehicle types to effectively reduce O<sub>3</sub> levels in Beijing and provides useful information for decision-makers to formulate effective measures of vehicle management in the future.

Authors

Institutions

Publication Details

Journal
Environment International
Published
2024-09-01
DOI
https://doi.org/10.1016/j.envint.2024.109002
Citations
13
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
4.89
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impacts of different vehicle emissions on ozone levels in Beijing: Insights into source contributions and formation processes

Ying Cheng, Tao Xue, Jun Liu, Tong Fei Zhu et al.
13 citations
Environment International
Atmospheric chemistry and aerosols
4.89
article

Impacts of different vehicle emissions on ozone levels in Beijing: Insights into source contributions and formation processes

Ying Cheng, Tao Xue, Jun Liu, Tong Fei Zhu, Fangzhou Li, Jingyuan Cao, Siqi Ai, Qiang Zhang
article en
13 citations

Abstract

Beijing, with the highest number of motor vehicles in China, significantly contributes to O<sub>3</sub> pollution through substantial NO<sub>x</sub> and VOC emissions in the on-road transportation sector. Understanding the unique impact of emissions from different vehicle types on O<sub>3</sub> levels is crucial for developing targeted strategies for O<sub>3</sub> pollution. This study applied the Community Multiscale Air Quality Modeling System (CMAQ) to comprehensively investigate the impacts of emissions from different vehicle types on O<sub>3</sub> levels in various regions of Beijing and to provide valuable insights into source contributions and formation processes. The results revealed that various vehicle types exhibited different spatial-temporal emission patterns, with medium-heavy duty trucks (HDT) and mini-light passenger vehicles (LDPV) identified as the primary contributors to NO<sub>x</sub> (36.1 %) and VOC (57.6 %) emissions. Using the Integrated Source Apportionment Method (ISAM) coupled in CMAQ, we found the total vehicle emissions contributed to over 20 % of daily maximum 8-h average O<sub>3</sub> (MDA8 O<sub>3</sub>) concentration, ranked as the second largest contributor after regional transport. Contributions to O<sub>3</sub> formation from LDPV and medium-large passenger vehicles (MDPV) were 2.6-4.0 and 4.2-6.8 ppb and mainly concentrated in urban areas, while the contributions from mini-light duty trucks (LDT) and HDT were 3.5-4.8 and 3.7-6.2 ppb and mainly concentrated in suburban areas. Through scenario analysis that removed emissions from specific types of vehicles, we found removing LDPV emissions led to decreases in daytime O<sub>3</sub> concentration by 0.3-3.8 ppb. In contrast, removing MDPV emissions led to notable O<sub>3</sub> increases by 4.0-11.8 ppb at rush hours. Removing LDT and HDT emissions resulted in 0.6-8.0 ppb increases in nocturnal O<sub>3</sub> concentrations while 0.8-2.0 ppb decreases during the afternoon. This research highlights the necessity of tailoring control strategies for different vehicle types to effectively reduce O<sub>3</sub> levels in Beijing and provides useful information for decision-makers to formulate effective measures of vehicle management in the future.

Environment InternationalVol. 191
Peking University (CN), National Health and Family Planning Commission (CN), China Railway Economic and Planning Research Institute (CN), Ministry of Health (OM), University of Science and Technology Beijing (CN), Tsinghua University (CN)
Climate action
Openalex Percentile: Top 4%
Atmospheric chemistry and aerosols
4.89
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.