Use of remote sensing measurements to evaluate vehicle emission monitoring programs: results from Phoenix, Arizona

2003 ◽  
Vol 6 (2) ◽  
pp. 153-166 ◽  
Author(s):  
Tom Wenzel
Keyword(s):  
Remote Sensing ◽  
Vehicle Emission ◽  
Emission Monitoring ◽  
Monitoring Programs

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

scholarly journals A multi-pronged approach to strengthen diesel vehicle emission monitoring

2022 ◽  
Author(s):  
Lijun Hao ◽  
Hang Yin ◽  
Junfang Wang ◽  
Lanju Li ◽  
Wenhui Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Exhaust Emissions ◽  
Vehicle Emission ◽  
Diesel Vehicles ◽  
Emission Monitoring ◽  
Periodic Inspections ◽  
Diesel Vehicle ◽  
Diesel Vehicle Emission

China is constructing an vehicle emission monitoring system, aimed at combining remote OBD, periodic inspections, remote sensing and roadside checks. In this study, the exhaust emissions from diesel vehicles were investigated and analysed.

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.

IoT Based Vehicle Emission Monitoring and Alerting System

2019 ◽  
Author(s):  
P. Arun Mozhi Devan ◽  
Fawnizu Azmadi Hussin ◽  
Rosdiazli Ibrahim ◽  
Kishore Bingi ◽  
M. Nagarajapandian
Keyword(s):  
Vehicle Emission ◽  
Emission Monitoring ◽  
Alerting System

scholarly journals Biodiversity Monitoring in Australia.

2014 ◽  
Vol 20 (4) ◽  
pp. 403
Author(s):  
Mark J Garkaklis
Keyword(s):  
Remote Sensing ◽  
Conservation Biology ◽  
The State ◽  
Senior Management ◽  
Ecological Community ◽  
Biodiversity Monitoring ◽  
Natural Heritage ◽  
Management Intervention ◽  
Monitoring Programs ◽  
Common Framework

EFFECTIVE biodiversity monitoring, that allows an evaluation of how well we manage Australia’s natural heritage, remains a frustration to many who have worked in conservation biology over the decades. Too many times colleagues have audibly groaned when presented with yet another new tool or pet interest, with an appropriate price tag, that has been paraded to senior management as a panacea to biodiversity monitoring. The hotchpotch of vertebrate, one-off botanical, one-off remote sensing, wetland, riparian ecosystem, Threatened and Priority Ecological Community, and species-focused monitoring programs represents the collective failure to provide consistent measure of the state of the Australian environment within a common framework. We could audit the effectiveness of many of these monitoring programs; if we could find the data. If we can find the data, too often it is difficult to understand what the objective of the management intervention was. Effective biodiversity monitoring programs are in the minority and this must not continue.

Radial Plume Mapping: A US EPA Test Method for Area and Fugitive Source Emission Monitoring Using Optical Remote Sensing

2008 ◽  
pp. 21-36 ◽  
Author(s):  
Ram A. Hashmonay ◽  
Ravi M. Varma ◽  
Mark T. Modrak ◽  
Robert H. Kagann ◽  
Robin R. Segall ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Test Method ◽  
Optical Remote Sensing ◽  
Emission Monitoring ◽  
Us Epa ◽  
Source Emission

scholarly journals Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thais Lourençoni ◽  
Carlos Antonio da Silva Junior ◽  
Mendelson Lima ◽  
Paulo Eduardo Teodoro ◽  
Tatiane Deoti Pelissari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Kernel Density ◽  
Cloud Platform ◽  
Mato Grosso ◽  
Monitoring Programs ◽  
Data Variability ◽  
Southern Amazonia ◽  
Remote Sensing Techniques ◽  
Crop Enhancement

AbstractThe guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo.

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