scholarly journals Real-World Contribution of Electrification and Replacement Scenarios to the Fleet Emissions in West Midland Boroughs, UK

2021 ◽  
Author(s):  
Louisa Osei ◽  
Omid Ghaffarpasand ◽  
Francis Pope
Keyword(s):  
Remote Sensing ◽  
Real World ◽  
Mitigation Strategies ◽  
Large Dataset ◽  
Rural And Urban ◽  
Fleet Composition ◽  
Emission Reduction Potential ◽  
West Midlands ◽  
Vehicle Fleet ◽  
Fleet Replacement

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO6 vehicles on the exhaust emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterise the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0-37.9%, 44.3-48.3%, 46.9-50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO6 replacement fleet scenario would lead to reductions ranging from 10.0-10.4%, 4.0-4.2%, and 6.0-6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways than on rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies.

scholarly journals Real-World Contribution of Electrification and Replacement Scenarios to the Fleet Emissions in West Midland Boroughs, UK

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 332
Author(s):  
Louisa K. Osei ◽  
Omid Ghaffarpasand ◽  
Francis D. Pope
Keyword(s):  
Remote Sensing ◽  
Real World ◽  
Mitigation Strategies ◽  
Large Dataset ◽  
Rural And Urban ◽  
Fleet Composition ◽  
Emission Reduction Potential ◽  
West Midlands ◽  
Vehicle Fleet ◽  
Fleet Replacement

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO 6 vehicles on the exhaustive emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions, and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterize the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0 to 37.9%, 44.3 to 48.3%, and 46.9 to 50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO 6 replacement fleet scenario would lead to reductions ranging from 10.0 to 10.4%, 4.0 to 4.2%, and 6.0 to 6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways compared to rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies.

scholarly journals P130 Efficacy and safety of ustekinumab in Crohn’s disease: a real-world study from the west midlands

2021 ◽  
Author(s):  
Ella Mozdiak ◽  
Muhammad Junaid Aleem ◽  
Noor Alhamamy ◽  
Harkaran Kalkat ◽  
Saskia Port ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Real World ◽  
Efficacy And Safety ◽  
The West ◽  
West Midlands

scholarly journals Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters

2015 ◽  
Vol 8 (3) ◽  
pp. 2881-2912 ◽  
Author(s):  
J. M. Wang ◽  
C.-H. Jeong ◽  
N. Zimmerman ◽  
R. M. Healy ◽  
D. K. Wang ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Black Carbon ◽  
Time Resolution ◽  
Real World ◽  
Vehicle Emissions ◽  
Particle Number ◽  
Emission Factors ◽  
Air Pollutant ◽  
Automated Identification ◽  
Vehicle Fleet

Abstract. An automated identification and integration method has been developed to investigate in-use vehicle emissions under real-world conditions. This technique was applied to high time resolution air pollutant measurements of in-use vehicle emissions performed under real-world conditions at a near-road monitoring station in Toronto, Canada during four seasons, through month-long campaigns in 2013–2014. Based on carbon dioxide measurements, over 100 000 vehicle-related plumes were automatically identified and fuel-based emission factors for nitrogen oxides; carbon monoxide; particle number, black carbon; benzene, toluene, ethylbenzene, and xylenes (BTEX); and methanol were determined for each plume. Thus the automated identification enabled the measurement of an unprecedented number of plumes and pollutants over an extended duration. Emission factors for volatile organic compounds were also measured roadside for the first time using a proton transfer reaction time-of-flight mass spectrometer; this instrument provided the time resolution required for the plume capture technique. Mean emission factors were characteristic of the light-duty gasoline dominated vehicle fleet present at the measurement site, with mean black carbon and particle number emission factors of 35 mg kg−1 and 7.7 × 1014 kg−1, respectively. The use of the plume-by-plume analysis enabled isolation of vehicle emissions, and the elucidation of co-emitted pollutants from similar vehicle types, variability of emissions across the fleet, and the relative contribution from heavy emitters. It was found that a small proportion of the fleet (< 25%) contributed significantly to total fleet emissions; 95, 93, 76, and 75% for black carbon, carbon monoxide, BTEX, and particle number, respectively. Emission factors of a single pollutant may help classify a vehicle as a high emitter. However, regulatory strategies to more efficiently target multi-pollutants mixtures may be better developed by considering the co-emitted pollutants as well.

scholarly journals An a posteriori measure of network modularity

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 130 ◽  
Author(s):  
Timothée Poisot
Keyword(s):  
Real World ◽  
Ecological Networks ◽  
A Posteriori ◽  
Large Dataset ◽  
Network Modularity

Measuring modularity is important to understand the structure of networks, and has an important number of real-world implications. However, several measures exists to assess the modularity, and give both different modularity values and different modules composition. In this article, I propose an a posteriori measure of modularity, which represents the ratio of interactions between members of the same modules vs. members of different modules. I apply this measure to a large dataset of 290 ecological networks, to show that it gives new insights about their modularity.

Generating Pseudo-Data to Enhance the Performance of Classification-Based Engineering Design: A Preliminary Investigation

2020 ◽  
Author(s):  
Xianping Du ◽  
Onur Bilgen ◽  
Hongyi Xu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Real World ◽  
Surrogate Model ◽  
Preliminary Investigation ◽  
Learning Model ◽  
Classification Model ◽  
Design Decision ◽  
Large Dataset ◽  
Machine Learning Model

Abstract Machine learning for classification has been used widely in engineering design, for example, feasible domain recognition and hidden pattern discovery. Training an accurate machine learning model requires a large dataset; however, high computational or experimental costs are major issues in obtaining a large dataset for real-world problems. One possible solution is to generate a large pseudo dataset with surrogate models, which is established with a smaller set of real training data. However, it is not well understood whether the pseudo dataset can benefit the classification model by providing more information or deteriorates the machine learning performance due to the prediction errors and uncertainties introduced by the surrogate model. This paper presents a preliminary investigation towards this research question. A classification-and-regressiontree model is employed to recognize the design subspaces to support design decision-making. It is implemented on the geometric design of a vehicle energy-absorbing structure based on finite element simulations. Based on a small set of real-world data obtained by simulations, a surrogate model based on Gaussian process regression is employed to generate pseudo datasets for training. The results showed that the tree-based method could help recognize feasible design domains efficiently. Furthermore, the additional information provided by the surrogate model enhances the accuracy of classification. One important conclusion is that the accuracy of the surrogate model determines the quality of the pseudo dataset and hence, the improvements in the machine learning model.

First-line and beyond: West Midlands real-world data for EGFR mutant (EGFRm) NSCLC

Lung Cancer ◽  
2020 ◽  
Vol 139 ◽  
pp. S60-S61
Author(s):  
R. Powell ◽  
R. Kussaibati ◽  
A. Khan ◽  
A. Sivapalasuntharam ◽  
P. Wilson ◽  
...  
Keyword(s):  
Real World ◽  
First Line ◽  
Real World Data ◽  
World Data ◽  
West Midlands ◽  
Egfr Mutant

scholarly journals Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

2014 ◽  
Vol 11 (23) ◽  
pp. 6827-6840 ◽  
Author(s):  
M. Réjou-Méchain ◽  
H. C. Muller-Landau ◽  
M. Detto ◽  
S. C. Thomas ◽  
T. Le Toan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Mitigation Strategies ◽  
Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

scholarly journals Data-Driven Analysis of Departure Procedures for Aviation Noise Mitigation

2021 ◽  
Vol 13 (1) ◽  
pp. 2
Author(s):  
Jirat Bhanpato ◽  
Tejas G. Puranik ◽  
Dimitri N. Mavris
Keyword(s):  
Real World ◽  
Clustering Algorithms ◽  
Mitigation Strategies ◽  
High Fidelity ◽  
Noise Mitigation ◽  
Noise Abatement ◽  
Effective Measure ◽  
Community Noise ◽  
Aviation Noise ◽  
The Difference

The mitigation of aviation environmental effects is one of the key requirements for sustainable aviation growth. Among various mitigation strategies, Noise Abatement Departure Procedures (NADPs) are a popular and effective measure undertaken by several operators. However, a large variation in departure procedures is observed in real operations. This study demonstrates the use of OpenSky ADS-B departure data for comparison and quantification of the differences in trajectories and the resulting community noise impact between real-world operations and NADPs. Trajectory comparison is accomplished in order to gain insights into the similarity between NADPs and real-world procedures. Clustering algorithms are employed to identify representative departure procedures, enabling efficient high-fidelity noise modeling. Finally, noise results are compared in order to quantify the difference in environmental impacts arising from variability in real-world trajectories. The methodology developed enables more efficient and accurate environmental analyses, thereby laying the foundation for future impact assessment and mitigation efforts.

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