scholarly journals New insights from comprehensive on-road measurements of NOx, NO2 and NH3 from vehicle emission remote sensing in London, UK

2013 ◽  
Vol 81 ◽  
pp. 339-347 ◽  
Author(s):  
David C. Carslaw ◽  
Glyn Rhys-Tyler
Keyword(s):  
Remote Sensing ◽  
Vehicle Emission

scholarly journals Distance-based emission factors from vehicle emission remote sensing measurements

2020 ◽  
Vol 739 ◽  
pp. 139688 ◽  
Author(s):  
Jack Davison ◽  
Yoann Bernard ◽  
Jens Borken-Kleefeld ◽  
Naomi J. Farren ◽  
Stefan Hausberger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Emission Factors ◽  
Vehicle Emission

Have vehicle emissions of primary NO2 peaked?

2016 ◽  
Vol 189 ◽  
pp. 439-454 ◽  
Author(s):  
David C. Carslaw ◽  
Tim P. Murrells ◽  
Jon Andersson ◽  
Matthew Keenan
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Vehicle Emissions ◽  
Remote Sensing Data ◽  
Vehicle Emission ◽  
Oxides Of Nitrogen ◽  
Vehicle Exhaust ◽  
Road Vehicles ◽  
Limit Values ◽  
Sensing Data

Reducing ambient concentrations of nitrogen dioxide (NO2) remains a key challenge across many European urban areas, particularly close to roads. This challenge mostly relates to the lack of reduction in emissions of oxides of nitrogen (NOx) from diesel road vehicles relative to the reductions expected through increasingly stringent vehicle emissions legislation. However, a key component of near-road concentrations of NO2 derives from directly emitted (primary) NO2 from diesel vehicles. It is well-established that the proportion of NO2 (i.e. the NO2/NOx ratio) in vehicle exhaust has increased over the past decade as a result of vehicle after-treatment technologies that oxidise carbon monoxide and hydrocarbons and generate NO2 to aid the emissions control of diesel particulate. In this work we bring together an analysis of ambient NOx and NO2 measurements with comprehensive vehicle emission remote sensing data obtained in London to better understand recent trends in the NO2/NOx ratio from road vehicles. We show that there is evidence that NO2 concentrations have decreased since around 2010 despite less evidence of a reduction in total NOx. The decrease is shown to be driven by relatively large reductions in the amount of NO2 directly emitted by vehicles; from around 25 vol% in 2010 to 15 vol% in 2014 in inner London, for example. The analysis of NOx and NO2 vehicle emission remote sensing data shows that these reductions have been mostly driven by reduced NO2/NOx emission ratios from heavy duty vehicles and buses rather than light duty vehicles. However, there is also evidence from the analysis of Euro 4 and 5 diesel passenger cars that as vehicles age the NO2/NOx ratio decreases. For example the NO2/NOx ratio decreased from 29.5 ± 2.0% in Euro 5 diesel cars up to one year old to 22.7 ± 2.5% for four-year old vehicles. At some roadside locations the reductions in primary NO2 have had a large effect on reducing both the annual mean and number of hourly exceedances of the European Limit Values of NO2.

A networked remote sensing system for on-road vehicle emission monitoring

2017 ◽  
Vol 60 (4) ◽  
Author(s):  
Yu Kang ◽  
Yan Ding ◽  
Zerui Li ◽  
Yang Cao ◽  
Yunbo Zhao
Keyword(s):  
Remote Sensing ◽  
Vehicle Emission ◽  
Road Vehicle ◽  
Sensing System ◽  
Emission Monitoring

A Novel Location Strategy for Minimizing Monitors in Vehicle Emission Remote Sensing System

2018 ◽  
Vol 48 (4) ◽  
pp. 500-510 ◽  
Author(s):  
Yu Kang ◽  
Zerui Li ◽  
Yunbo Zhao ◽  
Jiahu Qin ◽  
Weiguo Song
Keyword(s):  
Remote Sensing ◽  
Vehicle Emission ◽  
Location Strategy ◽  
Sensing System

scholarly journals Potential of big data approach for remote sensing of vehicle exhaust emissions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lijun Hao ◽  
Hang Yin ◽  
Junfang Wang ◽  
Xiaohu Wang ◽  
Yunshan Ge
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Vehicle Emissions ◽  
Exhaust Emissions ◽  
Specific Power ◽  
Scientific Management ◽  
Vehicle Emission ◽  
Vehicle Exhaust ◽  
Threshold Limit ◽  
Driving Conditions

AbstractAt present, remote sensing (RS) is applied in detecting vehicle exhaust emissions, and usually the RS emission results in a definite vehicle specific power (VSP) range are used to evaluate vehicle emissions and identify high-emitting vehicles. When the VSP exceeds this range, the corresponding vehicle emission RS data will not be used to assess vehicle emissions. This method is equivalent to setting only one VSP Bin qualified for vehicle emission evaluation, and generally only one threshold limit is given for each emission pollutant without considering the fluctuation characteristics of vehicle emissions with VSP. Therefore, it is easy to cause misjudgment in identifying high-emitting vehicles and is not conducive to scientific management of vehicle emissions. In addition, the vehicle emissions outside the selected VSP Bin are more serious and should be included in the scope of supervision. This research proposed the methods of vehicle classifications and VSP Binning in order to categorize the driving conditions of each kind of vehicles, and a big data approach was proposed to analyze the vehicle emission RS data in each VSP Bin for vehicle emission evaluation.

scholarly journals Vehicle Emission Detection in Data-Driven Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zheng He ◽  
Gang Ye ◽  
Hui Jiang ◽  
Youming Fu
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Vehicle Emission ◽  
Fuel Type ◽  
Essential Information ◽  
Sensing Technology ◽  
Main Component ◽  
Emission Detection ◽  
Remote Sensing Methods

Environmental protection is a fundamental policy in many countries, where the vehicle emission pollution turns to be outstanding as a main component of pollutions in environmental monitoring. Remote sensing technology has been widely used on vehicle emission detection recently and this is mainly due to the fast speed, reality, and large scale of the detection data retrieved from remote sensing methods. In the remote sensing process, the information about the fuel type and registration time of new cars and nonlocal registered vehicles usually cannot be accessed, leading to the failure in assessing vehicle pollution situations directly by analyzing emission pollutants. To handle this problem, this paper adopts data mining methods to analyze the remote sensing data to predict fuel type and registration time. This paper takes full use of decision tree, random forest, AdaBoost, XgBoost, and their fusion models to successfully make precise prediction for these two essential information and further employ them to an essential application: vehicle emission evaluation.

Sign in / Sign up

Export Citation Format

Share Document