Prediction of air pollutant emission in Xi’an based on LEAP model
In order to explore the low-carbon development direction of the transportation sector, based on the LEAP model, a transportation energy and environment model for the road traffic sector in Xi′an was established to simulate the energy demand, CO2 and pollutant emission trends and emission reduction p...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of XPU
2024-06-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
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| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1471 |
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| _version_ | 1850134578594840576 |
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| author | TAN Zhihai YUAN Yubo WANG Xuemei LEI Qiujing MIAO Jihong GU Maolin TAN Tantan |
| author_facet | TAN Zhihai YUAN Yubo WANG Xuemei LEI Qiujing MIAO Jihong GU Maolin TAN Tantan |
| author_sort | TAN Zhihai |
| collection | DOAJ |
| description | In order to explore the low-carbon development direction of the transportation sector, based on the LEAP model, a transportation energy and environment model for the road traffic sector in Xi′an was established to simulate the energy demand, CO2 and pollutant emission trends and emission reduction potential of the transportation sector under different scenarios in 2021—2050. The results show that the energy consumption and CO2 emissions under the low-carbon scenario (LC) peak around 2031, and the reduction rates in 2050 relative to the baseline scenario (BAU) are 32.62% and 30.21%, respectively. CO, NOx and PM10 all show good emission reduction effects, and the reduction rates relative to BAU are 33.88%, 36.27% and 40.33%, respectively. Among the sub-scenarios, the transportation structure adjustment scenario (TSA) contributes the most to energy conservation and emission reduction, followed by the green car scenario (GC) and the technical energy-saving scenarios (TES). In order to achieve carbon emission reduction and pollutant emission control in the transportation sector, it is necessary to adjust the traffic structure, eliminate old models and vigorously develop public transportation, and constantly improve the corresponding infrastructure to increase the market share of new energy vehicles. |
| format | Article |
| id | doaj-art-6189d5add2344de7bc23dfae013bda05 |
| institution | OA Journals |
| issn | 1674-649X |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Editorial Office of Journal of XPU |
| record_format | Article |
| series | Xi'an Gongcheng Daxue xuebao |
| spelling | doaj-art-6189d5add2344de7bc23dfae013bda052025-08-20T02:31:41ZzhoEditorial Office of Journal of XPUXi'an Gongcheng Daxue xuebao1674-649X2024-06-01383758210.13338/j.issn.1674-649x.2024.03.011Prediction of air pollutant emission in Xi’an based on LEAP modelTAN Zhihai0YUAN Yubo1WANG Xuemei2LEI Qiujing3MIAO Jihong4GU Maolin5TAN Tantan6School of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, ChinaIn order to explore the low-carbon development direction of the transportation sector, based on the LEAP model, a transportation energy and environment model for the road traffic sector in Xi′an was established to simulate the energy demand, CO2 and pollutant emission trends and emission reduction potential of the transportation sector under different scenarios in 2021—2050. The results show that the energy consumption and CO2 emissions under the low-carbon scenario (LC) peak around 2031, and the reduction rates in 2050 relative to the baseline scenario (BAU) are 32.62% and 30.21%, respectively. CO, NOx and PM10 all show good emission reduction effects, and the reduction rates relative to BAU are 33.88%, 36.27% and 40.33%, respectively. Among the sub-scenarios, the transportation structure adjustment scenario (TSA) contributes the most to energy conservation and emission reduction, followed by the green car scenario (GC) and the technical energy-saving scenarios (TES). In order to achieve carbon emission reduction and pollutant emission control in the transportation sector, it is necessary to adjust the traffic structure, eliminate old models and vigorously develop public transportation, and constantly improve the corresponding infrastructure to increase the market share of new energy vehicles.http://journal.xpu.edu.cn/en/#/digest?ArticleID=1471leap modelcarbon emissionsscenario analysisroad trafficair pollutants |
| spellingShingle | TAN Zhihai YUAN Yubo WANG Xuemei LEI Qiujing MIAO Jihong GU Maolin TAN Tantan Prediction of air pollutant emission in Xi’an based on LEAP model Xi'an Gongcheng Daxue xuebao leap model carbon emissions scenario analysis road traffic air pollutants |
| title | Prediction of air pollutant emission in Xi’an based on LEAP model |
| title_full | Prediction of air pollutant emission in Xi’an based on LEAP model |
| title_fullStr | Prediction of air pollutant emission in Xi’an based on LEAP model |
| title_full_unstemmed | Prediction of air pollutant emission in Xi’an based on LEAP model |
| title_short | Prediction of air pollutant emission in Xi’an based on LEAP model |
| title_sort | prediction of air pollutant emission in xi an based on leap model |
| topic | leap model carbon emissions scenario analysis road traffic air pollutants |
| url | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1471 |
| work_keys_str_mv | AT tanzhihai predictionofairpollutantemissioninxianbasedonleapmodel AT yuanyubo predictionofairpollutantemissioninxianbasedonleapmodel AT wangxuemei predictionofairpollutantemissioninxianbasedonleapmodel AT leiqiujing predictionofairpollutantemissioninxianbasedonleapmodel AT miaojihong predictionofairpollutantemissioninxianbasedonleapmodel AT gumaolin predictionofairpollutantemissioninxianbasedonleapmodel AT tantantan predictionofairpollutantemissioninxianbasedonleapmodel |