Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality
A companion paper developed a vehicle emission inventory with high temporal–spatial resolution (HTSVE) with a bottom-up methodology based on local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on a specific road segment fo...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-03-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/16/3171/2016/acp-16-3171-2016.pdf |
| _version_ | 1818503538175639552 |
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| author | J. He L. Wu H. Mao H. Liu B. Jing Y. Yu P. Ren C. Feng X. Liu |
| author_facet | J. He L. Wu H. Mao H. Liu B. Jing Y. Yu P. Ren C. Feng X. Liu |
| author_sort | J. He |
| collection | DOAJ |
| description | A companion paper developed a vehicle emission inventory with high temporal–spatial resolution (HTSVE) with a bottom-up methodology based on local emission factors,
complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on a specific road segment for 2013 in urban Beijing
(Jing et al., 2016), which is used to investigate the impact of vehicle pollution on air pollution in this study.
Based on the sensitivity analysis method of
switching on/off pollutant emissions in the Chinese air quality forecasting
model CUACE, a modelling study was carried out to evaluate the contributions
of vehicle emission
to the air pollution in Beijing's main urban areas in the
periods of summer (July) and winter (December) 2013. Generally, the CUACE model
had good performance of the concentration simulation of pollutants. The model
simulation has been improved by using HTSVE. The vehicle emission
contribution (VEC) to ambient pollutant concentrations not only changes with
seasons but also changes with time. The mean VEC, affected by regional
pollutant transports significantly, is 55.4 and 48.5 % for NO<sub>2</sub> and 5.4 and 10.5 % for PM<sub>2.5</sub> in July and December 2013
respectively. Regardless of regional transports, relative vehicle emission
contribution (RVEC) to NO<sub>2</sub> is 59.2 and 57.8 % in July and December
2013, while it is 8.7 and 13.9 % for PM<sub>2.5</sub>. The RVEC to PM<sub>2.5</sub> is
lower than the PM<sub>2.5</sub> contribution rate for vehicle emission in total
emission, which may be due to dry deposition of PM<sub>2.5</sub> from vehicle emission in the near-surface layer occuring more easily than from elevated source emission. |
| first_indexed | 2024-12-10T21:25:18Z |
| format | Article |
| id | doaj.art-e3fe99ef606d4e97b8e1f08989e75ca8 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-10T21:25:18Z |
| publishDate | 2016-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-e3fe99ef606d4e97b8e1f08989e75ca82022-12-22T01:33:00ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242016-03-01163171318410.5194/acp-16-3171-2016Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air qualityJ. He0L. Wu1H. Mao2H. Liu3B. Jing4Y. Yu5P. Ren6C. Feng7X. Liu8The College of Environmental Science and Engineering, Nankai University, Tianjin, ChinaThe College of Environmental Science and Engineering, Nankai University, Tianjin, ChinaThe College of Environmental Science and Engineering, Nankai University, Tianjin, ChinaChinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, ChinaThe College of Environmental Science and Engineering, Nankai University, Tianjin, ChinaCold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, ChinaThe College of Environmental Science and Engineering, Nankai University, Tianjin, ChinaTianjin Vehicle Emission Control Center, Tianjin, ChinaTianjin Vehicle Emission Control Center, Tianjin, ChinaA companion paper developed a vehicle emission inventory with high temporal–spatial resolution (HTSVE) with a bottom-up methodology based on local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on a specific road segment for 2013 in urban Beijing (Jing et al., 2016), which is used to investigate the impact of vehicle pollution on air pollution in this study. Based on the sensitivity analysis method of switching on/off pollutant emissions in the Chinese air quality forecasting model CUACE, a modelling study was carried out to evaluate the contributions of vehicle emission to the air pollution in Beijing's main urban areas in the periods of summer (July) and winter (December) 2013. Generally, the CUACE model had good performance of the concentration simulation of pollutants. The model simulation has been improved by using HTSVE. The vehicle emission contribution (VEC) to ambient pollutant concentrations not only changes with seasons but also changes with time. The mean VEC, affected by regional pollutant transports significantly, is 55.4 and 48.5 % for NO<sub>2</sub> and 5.4 and 10.5 % for PM<sub>2.5</sub> in July and December 2013 respectively. Regardless of regional transports, relative vehicle emission contribution (RVEC) to NO<sub>2</sub> is 59.2 and 57.8 % in July and December 2013, while it is 8.7 and 13.9 % for PM<sub>2.5</sub>. The RVEC to PM<sub>2.5</sub> is lower than the PM<sub>2.5</sub> contribution rate for vehicle emission in total emission, which may be due to dry deposition of PM<sub>2.5</sub> from vehicle emission in the near-surface layer occuring more easily than from elevated source emission.https://www.atmos-chem-phys.net/16/3171/2016/acp-16-3171-2016.pdf |
| spellingShingle | J. He L. Wu H. Mao H. Liu B. Jing Y. Yu P. Ren C. Feng X. Liu Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality Atmospheric Chemistry and Physics |
| title | Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality |
| title_full | Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality |
| title_fullStr | Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality |
| title_full_unstemmed | Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality |
| title_short | Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality |
| title_sort | development of a vehicle emission inventory with high temporal spatial resolution based on nrt traffic data and its impact on air pollution in beijing part 2 impact of vehicle emission on urban air quality |
| url | https://www.atmos-chem-phys.net/16/3171/2016/acp-16-3171-2016.pdf |
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