Comparison of the MOVES2010a, MOBILE6.2, and EMFAC2007 mobile source emission models with on-road traffic tunnel and remote sensing measurements

2012 ◽  
Vol 62 (10) ◽  
pp. 1134-1149 ◽  
Author(s):  
Eric M. Fujita ◽  
David E. Campbell ◽  
Barbara Zielinska ◽  
Judith C. Chow ◽  
Christian E. Lindhjem ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Road Traffic ◽  
Mobile Source ◽  
Traffic Tunnel ◽  
Emission Models ◽  
Source Emission

scholarly journals Designing On-Road Vehicle Test Programs for the Development of Effective Vehicle Emission Models

2005 ◽  
Vol 1941 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Theodore Younglove ◽  
George Scora ◽  
Matthew Barth
Keyword(s):  
Estimation Error ◽  
Vehicle Emission ◽  
Emission Measurement ◽  
Mobile Source ◽  
Emission Data ◽  
Data Set ◽  
Mobile Vehicle ◽  
Emission Models ◽  
Road Emission ◽  
Source Emission

Mobile source emission models for years have depended on laboratory-based dynamometer data. Recently, however, portable emission measurement systems (PEMS) have become commercially available and in widespread use, and make on-road real-world measurements possible. As a result, the newest mobile source emission models (e.g., U.S. Environmental Protection Agency's mobile vehicle emission simulator) are becoming increasingly dependent on PEMS data. Although on-road measurements are made under more realistic conditions than laboratory-based dynamometer test cycles, they introduce influencing variables that must be carefully measured for properly developed emission models. Further, test programs that simply measure in-use driving patterns of randomly selected vehicles will result in models that can effectively predict current-year emission inventories for typical driving conditions. However, when predicting more aggressive transportation operations than current typical operations (e.g., higher speeds, accelerations), the model predictions will be less certain. In this paper, various issues associated with on-road emission measurements and modeling are presented. Further, an example on-road emission data set and the reduction in estimation error through the addition of a short aggressive driving test to the in-use data are examined. On the basis of these results, recommendations are made on how to improve the on-road test programs for developing more robust emission models.

scholarly journals Auroral ionospheric E region parameters obtained from satellite-based far ultraviolet and ground-based ionosonde observations: Effects of proton precipitation

2019 ◽  
Author(s):  
Harold K. Knight
Keyword(s):  
Remote Sensing ◽  
Optical Emission ◽  
Electron Precipitation ◽  
E Region ◽  
Emission Models ◽  
Proton Precipitation ◽  
Far Ultraviolet ◽  
Proton Aurora ◽  
Remote Sensing Methods

Abstract. Coincident auroral far ultraviolet (FUV) and ground-based ionosonde observations are compared for the purpose of determining whether auroral FUV remote sensing algorithms that assume pure electron precipitation are biased in the presence of proton precipitation. Auroral particle transport and optical emission models, such as the Boltzmann 3-Constituent (B3C) model, predict that maximum E region electron density (NmE) values derived from auroral Lyman-Birge-Hopfield (LBH) emission assuming electron precipitation will be biased high by up to ~ 20 % for pure proton aurora, while comparisons between LBH radiances and radiances derived from in situ particle flux observations (i.e., Knight et al., 2008, 2012) indicate that the bias associated with proton aurora should be much larger. Surprisingly, in the comparisons with ionosonde observations described here, no bias associated with proton aurora is found in FUV-derived auroral NmE, which means that auroral FUV remote sensing methods for NmE are more accurate in the presence of proton precipitation than was suggested in the aforementioned earlier works. Possible explanations for the discrepancy with the earlier results are discussed.

scholarly journals Dynamic Congestion and Tolls with Mobile Source Emission

2013 ◽  
Vol 80 ◽  
pp. 818-836 ◽  
Author(s):  
Terry L. Friesz ◽  
Ke Han ◽  
Hongcheng Liu ◽  
Tao Yao
Keyword(s):  
Mobile Source ◽  
Source Emission

scholarly journals Enhancement of Remote Sensing for Mobile Source Nitric Oxide

1996 ◽  
Vol 46 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Yi Zhang ◽  
Gary A. Bishop ◽  
Stuart P. Beaton ◽  
Paul L. Guenther ◽  
lain F. McVey
Keyword(s):  
Nitric Oxide ◽  
Remote Sensing ◽  
Mobile Source

scholarly journals A Critical Review of Unmanned Aerial Vehicles (UAVs) Use in Architecture and Urbanism: Scientometric and Bibliometric Analysis

2021 ◽  
Vol 11 (21) ◽  
pp. 9966
Author(s):  
Marta Videras Rodríguez ◽  
Sergio Gómez Melgar ◽  
Antonio Sánchez Cordero ◽  
José Manuel Andújar Márquez
Keyword(s):  
Remote Sensing ◽  
Literature Review ◽  
Unmanned Aerial Vehicles ◽  
Bibliometric Analysis ◽  
Critical Review ◽  
Structural Damage ◽  
Road Traffic ◽  
Large Body ◽  
Relevant Information ◽  
Aerial Vehicles

In recent years the use of UAVs (Unmanned aerial vehicles) have proliferated in the civil sector for purposes such as search and rescue, remote sensing or real-time monitoring of road traffic, among others. In the architecture, engineering and construction fields (AEC) UAVs have demonstrated to be an ideal technology due to their optimal performance in terms of time, precision, safety and cost. Given the rapid growth of interest in this technology, this research presents a critical review of the literature on the use of UAVs in architecture and urbanism to define the most widely used techniques and delimit the fields of application based on the experimentation published by the scientific community. A scientific mapping was carried out in two stages using the VOSviewerTM software: a scientometric and a bibliometric analysis. This technique allowed us to analyse a large body of literature and bibliographic data to obtain trends, patterns and directions of this domain of knowledge. Then, a literature review was presented, highlighting the relevant information identified in the previous analysis. The fields of application of UAVs were delimited and the most commonly used payload types and the most appropriate post-processing techniques were specified, depending on the aerial mission objective. The fields of application identified included different techniques related to the generation of 3D models, land mapping, construction site monitoring, building surveying to detect structural damage and energy losses and urban remote sensing. The literature review showed that UAVs provide a useful multi-tasking tool at any stage of an architectural project. These techniques can be applied to buildings or public spaces from the design and construction processes when the project is initiated to the later stages of maintenance and inspection of the building during its life cycle.

Modal Emissions Modeling: A Physical Approach

1996 ◽  
Vol 1520 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Matthew Barth ◽  
Feng An ◽  
Joseph Norbeck ◽  
Marc Ross
Keyword(s):  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Control Measures ◽  
Mobile Source ◽  
Alternative Fuel Vehicles ◽  
Modeling Approach ◽  
Emissions Modeling ◽  
Vehicle Operation ◽  
Wide Range ◽  
Emission Models

Mobile source emission models currently used by state and federal agencies (e.g., Environmental Protection Agency's MOBILE and California Air Resources Board's EMFAC) are often inadequate for analyzing the emissions impact of various transportation control measures, intelligent transportation systems, alternative fuel vehicles, and more sophisticated inspection/maintenance programs contained in most state air quality management plans. These emission models are based on the assumption that vehicle running exhaust emissions can be represented as integrated values for a specific driving cycle, and then later adjusted by speed correction factors. What is needed in addition to these “regional-type” mobile source models is an emissions model that considers at a more fundamental level the modal operation of a vehicle (i.e., emissions that directly relate to vehicle operating modes such as idle, steady-state cruise, various levels of acceleration/deceleration, and so forth). A new modal-emissions modeling approach that is deterministic and based on analytical functions that describe the physical phenomena associated with vehicle operation and emissions productions is presented. This model relies on highly time-resolved emissions and vehicle operation data that must be collected from a wide range of vehicles of varying emission control technologies. Current emission modeling techniques are discussed and the modeling approach and implementation plan for a new, three-year NCHRP Project entitled “Development of a Modal Emissions Model” are described.

NEMO project: developing a N-RSD (Noise Remote Sensing Device) to identify high noise emitters in the road traffic flow

2021 ◽  
Vol 263 (5) ◽  
pp. 1773-1783
Author(s):  
Maximilian Ertsey-Bayer ◽  
Nikolas Kirchhoff ◽  
Sonia Alves ◽  
Bert Peeters ◽  
Viggo Henriksen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Traffic Flow ◽  
Engine Speed ◽  
Road Traffic ◽  
Spectral Characteristics ◽  
Air Pollutant ◽  
Classification Model ◽  
Pollutant Emissions ◽  
Noise Levels ◽  
Driving Conditions

NEMO (Noise and Emissions MOnitoring and Radical mitigation) is a research project aiming at developing an autonomous system to detect noise and air pollutant emissions from individual vehicles within the traffic flow. The objective is to identify high emitters within the normal traffic. For noise, a high emitter is a vehicle that is either in a poor or modified condition (e.g., with an illegal or malfunctioning exhaust) or that is driven in a noisy way (fast acceleration, high engine speed in low gear, etc.). A vehicle that has been type approved, is well maintained, and is driven under normal conditions is never a high-emitter vehicle, even if it is subjectively perceived as annoying. A Noise Remote Sensing Device (N-RSD) is being developed. This device will capture, for each individual vehicle, the driving conditions (vehicle speed, acceleration, engine speed and load) and the single-event noise levels and spectral characteristics. The noise levels will be normalized to comparable driving conditions and fed into a classification model. The classification model will then be able to identify the high emitters vehicles. When finished, the NEMO system will allow cities and road authorities to reduce annoyance and health impacts from noisy and polluting vehicles, for instance by raising awareness among drivers or by restricting access to low emission zones.

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