Yong Li , Huanqin Wang , Mengqi Fu , Jing Wang , Yanyan Yang , Huaqiao Gui
{"title":"基于实时数据的篡改重型车辆氮氧化物超标排放及其对空气污染的影响分析","authors":"Yong Li , Huanqin Wang , Mengqi Fu , Jing Wang , Yanyan Yang , Huaqiao Gui","doi":"10.1016/j.apr.2024.102240","DOIUrl":null,"url":null,"abstract":"<div><p>Vehicle tampering leads to substantial excessive emissions, but few methods could identify the tampered ones from vehicles on road accurately in one day or less. A fast response model based on real time data from terminal box (T-BOX) was built in this study for heavy-duty vehicle tampering identification, which could identify the tampered vehicles from vehicles with excessive emission caused by bad driving conditions, low ambient temperature or on-board diagnostic (OBD) faults. By analyzing the existing means of tampering in the last decade, the vehicle tampering identification model was established according to the data characteristics of tampered vehicles. Two main modules based on emission and emission factors were built and three corrections were added in the model to avoid disturbances led to misjudge. In our research, 66 heavy-duty vehicles from the big data platform were used to screen for vehicle tampering. It was found that 15 vehicles existed excessive emissions, and 2 vehicles were tampered. Tampered vehicles only account for 3% of the sample, but emitted 1.4 times nitrogen oxides (NOx) of total emission of other vehicles. The model solved the problem that the traditional model could not identify the vehicle tampering accurately. It could be used in emission accounting and management of tampered vehicles for government.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102240"},"PeriodicalIF":3.9000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of excessive NOx emission from tampered heavy-duty vehicles based on real-time data and its impact on air pollution\",\"authors\":\"Yong Li , Huanqin Wang , Mengqi Fu , Jing Wang , Yanyan Yang , Huaqiao Gui\",\"doi\":\"10.1016/j.apr.2024.102240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Vehicle tampering leads to substantial excessive emissions, but few methods could identify the tampered ones from vehicles on road accurately in one day or less. A fast response model based on real time data from terminal box (T-BOX) was built in this study for heavy-duty vehicle tampering identification, which could identify the tampered vehicles from vehicles with excessive emission caused by bad driving conditions, low ambient temperature or on-board diagnostic (OBD) faults. By analyzing the existing means of tampering in the last decade, the vehicle tampering identification model was established according to the data characteristics of tampered vehicles. Two main modules based on emission and emission factors were built and three corrections were added in the model to avoid disturbances led to misjudge. In our research, 66 heavy-duty vehicles from the big data platform were used to screen for vehicle tampering. It was found that 15 vehicles existed excessive emissions, and 2 vehicles were tampered. Tampered vehicles only account for 3% of the sample, but emitted 1.4 times nitrogen oxides (NOx) of total emission of other vehicles. The model solved the problem that the traditional model could not identify the vehicle tampering accurately. It could be used in emission accounting and management of tampered vehicles for government.</p></div>\",\"PeriodicalId\":8604,\"journal\":{\"name\":\"Atmospheric Pollution Research\",\"volume\":\"15 10\",\"pages\":\"Article 102240\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric Pollution Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1309104224002058\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Pollution Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1309104224002058","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Analysis of excessive NOx emission from tampered heavy-duty vehicles based on real-time data and its impact on air pollution
Vehicle tampering leads to substantial excessive emissions, but few methods could identify the tampered ones from vehicles on road accurately in one day or less. A fast response model based on real time data from terminal box (T-BOX) was built in this study for heavy-duty vehicle tampering identification, which could identify the tampered vehicles from vehicles with excessive emission caused by bad driving conditions, low ambient temperature or on-board diagnostic (OBD) faults. By analyzing the existing means of tampering in the last decade, the vehicle tampering identification model was established according to the data characteristics of tampered vehicles. Two main modules based on emission and emission factors were built and three corrections were added in the model to avoid disturbances led to misjudge. In our research, 66 heavy-duty vehicles from the big data platform were used to screen for vehicle tampering. It was found that 15 vehicles existed excessive emissions, and 2 vehicles were tampered. Tampered vehicles only account for 3% of the sample, but emitted 1.4 times nitrogen oxides (NOx) of total emission of other vehicles. The model solved the problem that the traditional model could not identify the vehicle tampering accurately. It could be used in emission accounting and management of tampered vehicles for government.
期刊介绍:
Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.