Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation
Abstract To characterize the spatial and temporal distribution of NOx exhausted by urban buses, we measured real-world on-road NOx emissions from these vehicles in the city of Kunming, China, using an onboard monitoring platform. To fill the data gaps and produce a complete data set, we combined Bay...
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| Format: | Article |
| Language: | English |
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Springer
2021-02-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.200059 |
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| author | Qikai Peng Jiaqiang Li Yanyan Wang Longqing Zhao Jianwei Tan Chao He |
| author_facet | Qikai Peng Jiaqiang Li Yanyan Wang Longqing Zhao Jianwei Tan Chao He |
| author_sort | Qikai Peng |
| collection | DOAJ |
| description | Abstract To characterize the spatial and temporal distribution of NOx exhausted by urban buses, we measured real-world on-road NOx emissions from these vehicles in the city of Kunming, China, using an onboard monitoring platform. To fill the data gaps and produce a complete data set, we combined Bayesian network modeling and probabilistic inference. The complete data set was then used to generate an NOx emission heat map, and spatial autocorrelation was applied to evaluate the distribution characteristics. The results show that our method for filling in the missing data provides highly accurate values, with spatial autocorrelation indices of 0.648, 0.836, 0.935, and 0.798 for the morning, midday, afternoon, and evening, respectively. The NOx emissions showed spatial correlation during all four periods, whereas the pollutive emissions showed spatial aggregation. According to the heat map, the NOx concentrations peaked during the midday and the afternoon. Furthermore, regardless of the period, the largest emissions accumulated in Road Sections 1–3 and 6–9, and the highest as well as the fastest-growing emission intensity occurred in Road Sections 5–9. |
| format | Article |
| id | doaj-art-20ad7da7be3d4836a09eb599d1542c0e |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2021-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-20ad7da7be3d4836a09eb599d1542c0e2025-02-09T12:19:43ZengSpringerAerosol and Air Quality Research1680-85842071-14092021-02-0121611610.4209/aaqr.200059Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial AutocorrelationQikai Peng0Jiaqiang Li1Yanyan Wang2Longqing Zhao3Jianwei Tan4Chao He5School of Mechanical and Transportation Engineering, Southwest Forestry UniversitySchool of Mechanical and Transportation Engineering, Southwest Forestry UniversitySchool of Mechanical and Transportation Engineering, Southwest Forestry UniversitySchool of Mechanical and Transportation Engineering, Southwest Forestry UniversitySchool of Mechanical Engineering, Beijing Institute of TechnologySchool of Mechanical and Transportation Engineering, Southwest Forestry UniversityAbstract To characterize the spatial and temporal distribution of NOx exhausted by urban buses, we measured real-world on-road NOx emissions from these vehicles in the city of Kunming, China, using an onboard monitoring platform. To fill the data gaps and produce a complete data set, we combined Bayesian network modeling and probabilistic inference. The complete data set was then used to generate an NOx emission heat map, and spatial autocorrelation was applied to evaluate the distribution characteristics. The results show that our method for filling in the missing data provides highly accurate values, with spatial autocorrelation indices of 0.648, 0.836, 0.935, and 0.798 for the morning, midday, afternoon, and evening, respectively. The NOx emissions showed spatial correlation during all four periods, whereas the pollutive emissions showed spatial aggregation. According to the heat map, the NOx concentrations peaked during the midday and the afternoon. Furthermore, regardless of the period, the largest emissions accumulated in Road Sections 1–3 and 6–9, and the highest as well as the fastest-growing emission intensity occurred in Road Sections 5–9.https://doi.org/10.4209/aaqr.200059BusesSpatial autocorrelationNOx emissionsTemporal and spatial distribution characteristics |
| spellingShingle | Qikai Peng Jiaqiang Li Yanyan Wang Longqing Zhao Jianwei Tan Chao He Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation Aerosol and Air Quality Research Buses Spatial autocorrelation NOx emissions Temporal and spatial distribution characteristics |
| title | Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation |
| title_full | Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation |
| title_fullStr | Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation |
| title_full_unstemmed | Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation |
| title_short | Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation |
| title_sort | temporal and spatial distribution characteristics of nox emissions of city buses on real road based on spatial autocorrelation |
| topic | Buses Spatial autocorrelation NOx emissions Temporal and spatial distribution characteristics |
| url | https://doi.org/10.4209/aaqr.200059 |
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