{"title":"利用综合车辆、道路、旅行和环境数据估算异质车辆二氧化碳排放轨迹","authors":"Hui Ding , Hui Gao , Yonghong Liu","doi":"10.1016/j.aeaoa.2025.100359","DOIUrl":null,"url":null,"abstract":"<div><div>Individual vehicle travel carbon dioxide (CO<sub>2</sub>) emission (CE) trajectories were crucial for targeting high-emitters for precise control and guiding low-carbon travel. Variations in CE arise from vehicle performance, traffic conditions, and trip purposes. Using real Automatic Vehicle Identification (AVI) data and integrating multi-source vehicle, road, trip, and environmental data, this study proposed an \"Identification-Calculation-Evaluation\" framework to quantify and analyze city-scale full-individual vehicle CE trajectories. A case in Xuancheng, China, was conducted and revealed spatiotemporal CE heterogeneity. The results showed that approximately 50 % of CE was contributed by the top 5 % of high-emission vehicles, exhibiting a significant “Pareto Principle”. Among the top 5 % of high-emission vehicles, LPC-gasoline (57 % of vehicles, 40 % of CE), HDT-diesel (32 %, 42 %), and Taxi-gasoline (5 %, 12 %) were the main contributors. Their daily CE trajectory ranges were [0, 6] kg, [0, 15] kg, and [0, 8] kg, respectively. Taxi-gasoline and HDT-diesel exhibit more individual variation. Peak-time CE trajectories on these Top 5 % vehicles were 2–6 times higher than off-peak. For LPC-gasoline and Taxi-gasoline, over 60 % of CE occurred during congestion links. Peak times of CE trajectories occurred around 7:00 and 17:00 on a day, with spatial hotspots predominantly concentrated in urban core areas. Notably, Taxi-gasoline vehicles exhibited more clustered hotspots. HDT-diesel CE trajectories peaked earlier (6:00–7:00), with hotspots distributed along major urban corridors, and CE was 1–3 times higher than in ordinary areas. This study provided precise support for low-carbon traffic governance, and the framework could be extended to other cities to inform carbon reduction strategies.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"27 ","pages":"Article 100359"},"PeriodicalIF":3.4000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of heterogeneous vehicle carbon dioxide emission trajectories using integrated vehicle, road, travel, and environmental data\",\"authors\":\"Hui Ding , Hui Gao , Yonghong Liu\",\"doi\":\"10.1016/j.aeaoa.2025.100359\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Individual vehicle travel carbon dioxide (CO<sub>2</sub>) emission (CE) trajectories were crucial for targeting high-emitters for precise control and guiding low-carbon travel. Variations in CE arise from vehicle performance, traffic conditions, and trip purposes. Using real Automatic Vehicle Identification (AVI) data and integrating multi-source vehicle, road, trip, and environmental data, this study proposed an \\\"Identification-Calculation-Evaluation\\\" framework to quantify and analyze city-scale full-individual vehicle CE trajectories. A case in Xuancheng, China, was conducted and revealed spatiotemporal CE heterogeneity. The results showed that approximately 50 % of CE was contributed by the top 5 % of high-emission vehicles, exhibiting a significant “Pareto Principle”. Among the top 5 % of high-emission vehicles, LPC-gasoline (57 % of vehicles, 40 % of CE), HDT-diesel (32 %, 42 %), and Taxi-gasoline (5 %, 12 %) were the main contributors. Their daily CE trajectory ranges were [0, 6] kg, [0, 15] kg, and [0, 8] kg, respectively. Taxi-gasoline and HDT-diesel exhibit more individual variation. Peak-time CE trajectories on these Top 5 % vehicles were 2–6 times higher than off-peak. For LPC-gasoline and Taxi-gasoline, over 60 % of CE occurred during congestion links. Peak times of CE trajectories occurred around 7:00 and 17:00 on a day, with spatial hotspots predominantly concentrated in urban core areas. Notably, Taxi-gasoline vehicles exhibited more clustered hotspots. HDT-diesel CE trajectories peaked earlier (6:00–7:00), with hotspots distributed along major urban corridors, and CE was 1–3 times higher than in ordinary areas. This study provided precise support for low-carbon traffic governance, and the framework could be extended to other cities to inform carbon reduction strategies.</div></div>\",\"PeriodicalId\":37150,\"journal\":{\"name\":\"Atmospheric Environment: X\",\"volume\":\"27 \",\"pages\":\"Article 100359\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric Environment: X\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590162125000498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Environment: X","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590162125000498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Estimation of heterogeneous vehicle carbon dioxide emission trajectories using integrated vehicle, road, travel, and environmental data
Individual vehicle travel carbon dioxide (CO2) emission (CE) trajectories were crucial for targeting high-emitters for precise control and guiding low-carbon travel. Variations in CE arise from vehicle performance, traffic conditions, and trip purposes. Using real Automatic Vehicle Identification (AVI) data and integrating multi-source vehicle, road, trip, and environmental data, this study proposed an "Identification-Calculation-Evaluation" framework to quantify and analyze city-scale full-individual vehicle CE trajectories. A case in Xuancheng, China, was conducted and revealed spatiotemporal CE heterogeneity. The results showed that approximately 50 % of CE was contributed by the top 5 % of high-emission vehicles, exhibiting a significant “Pareto Principle”. Among the top 5 % of high-emission vehicles, LPC-gasoline (57 % of vehicles, 40 % of CE), HDT-diesel (32 %, 42 %), and Taxi-gasoline (5 %, 12 %) were the main contributors. Their daily CE trajectory ranges were [0, 6] kg, [0, 15] kg, and [0, 8] kg, respectively. Taxi-gasoline and HDT-diesel exhibit more individual variation. Peak-time CE trajectories on these Top 5 % vehicles were 2–6 times higher than off-peak. For LPC-gasoline and Taxi-gasoline, over 60 % of CE occurred during congestion links. Peak times of CE trajectories occurred around 7:00 and 17:00 on a day, with spatial hotspots predominantly concentrated in urban core areas. Notably, Taxi-gasoline vehicles exhibited more clustered hotspots. HDT-diesel CE trajectories peaked earlier (6:00–7:00), with hotspots distributed along major urban corridors, and CE was 1–3 times higher than in ordinary areas. This study provided precise support for low-carbon traffic governance, and the framework could be extended to other cities to inform carbon reduction strategies.