Articles | Volume 19, issue 22
https://doi.org/10.5194/acp-19-13841-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-19-13841-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2019
Research article |
| 18 Nov 2019
| 18 Nov 2019
Using wavelet transform to analyse on-road mobile measurements of air pollutants: a case study to evaluate vehicle emission control policies during the 2014 APEC summit
Yingruo Li
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Institute of Urban Meteorology, China Meteorological Administration,
Beijing, 100089, China
Ziqiang Tan
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Chunxiang Ye
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Junxia Wang
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Yanwen Wang
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Yi Zhu
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Pengfei Liang
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Xi Chen
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Yanhua Fang
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Yiqun Han
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Qi Wang
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
Di He
Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei,
China Meteorological Administration, Beijing, 100089, China
Yao Wang
Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei,
China Meteorological Administration, Beijing, 100089, China
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, China
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Cited
12 citations as recorded by crossref.
- Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China M. Wu et al. https://doi.org/10.1016/j.ecolind.2023.109862
- A hybrid neural network for urban rail transit short-term flow prediction C. Zhu et al. https://doi.org/10.1007/s11227-024-06331-2
- On-road mobile mapping of spatial variations and source contributions of ammonia in Beijing, China W. Pu et al. https://doi.org/10.1016/j.scitotenv.2022.160869
- Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods B. Luo et al. https://doi.org/10.1007/s00477-023-02411-6
- Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland J. Chen et al. https://doi.org/10.1007/s11356-024-34903-5
- ESG greenwashing behaviour in the electric vehicle supply chain: Insights from evolutionary game theory Y. Gao et al. https://doi.org/10.1016/j.ijpe.2025.109798
- Impacts of condensable particulate matter on atmospheric organic aerosols and fine particulate matter (PM2.5) in China M. Li et al. https://doi.org/10.5194/acp-22-11845-2022
- Measurement report: Diurnal variations of brown carbon during two distinct seasons in a megacity in northeast China Y. Cheng et al. https://doi.org/10.5194/acp-23-6241-2023
- Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network P. Wei et al. https://doi.org/10.1016/j.envpol.2021.118055
- Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment P. Wei et al. https://doi.org/10.1016/j.envint.2024.108992
- Grey relational analysis model with cross-sequences and its application in evaluating air quality index N. Lu et al. https://doi.org/10.1016/j.eswa.2023.120910
- Assessing traffic emissions during the summer world university games 2023: Insights for multisectoral synergetic decontamination H. Li et al. https://doi.org/10.1016/j.scitotenv.2024.176488
12 citations as recorded by crossref.
- Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China M. Wu et al. https://doi.org/10.1016/j.ecolind.2023.109862
- A hybrid neural network for urban rail transit short-term flow prediction C. Zhu et al. https://doi.org/10.1007/s11227-024-06331-2
- On-road mobile mapping of spatial variations and source contributions of ammonia in Beijing, China W. Pu et al. https://doi.org/10.1016/j.scitotenv.2022.160869
- Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods B. Luo et al. https://doi.org/10.1007/s00477-023-02411-6
- Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland J. Chen et al. https://doi.org/10.1007/s11356-024-34903-5
- ESG greenwashing behaviour in the electric vehicle supply chain: Insights from evolutionary game theory Y. Gao et al. https://doi.org/10.1016/j.ijpe.2025.109798
- Impacts of condensable particulate matter on atmospheric organic aerosols and fine particulate matter (PM2.5) in China M. Li et al. https://doi.org/10.5194/acp-22-11845-2022
- Measurement report: Diurnal variations of brown carbon during two distinct seasons in a megacity in northeast China Y. Cheng et al. https://doi.org/10.5194/acp-23-6241-2023
- Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network P. Wei et al. https://doi.org/10.1016/j.envpol.2021.118055
- Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment P. Wei et al. https://doi.org/10.1016/j.envint.2024.108992
- Grey relational analysis model with cross-sequences and its application in evaluating air quality index N. Lu et al. https://doi.org/10.1016/j.eswa.2023.120910
- Assessing traffic emissions during the summer world university games 2023: Insights for multisectoral synergetic decontamination H. Li et al. https://doi.org/10.1016/j.scitotenv.2024.176488
Saved (final revised paper)
Latest update: 09 Sep 2026
Short summary
Vehicle emissions are a major source of Beijing's pollution. Various vehicle emission control policies have been implemented at great cost, but there is a lack of appropriate methods to evaluate the effectiveness of such policies. Here we developed a new method to evaluate the effectiveness of vehicle emission control policies during APEC. Our findings are instructive for air pollution control policy making.
Vehicle emissions are a major source of Beijing's pollution. Various vehicle emission control...
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