Search Results (4)

As global initiatives to reduce greenhouse gas emissions and combat climate change expand, electric vehicles (EVs) powered by fuel cells and lithium-ion batteries are gaining global recognition as solutions for sustainable transportation due to their high energy conversion efficiency. Considering the driving range limitations of battery electric vehicles (BEVs) and the low efficiency of internal combustion engines (ICEs), fuel cell hybrid vehicles offer a compelling alternative for long-distance, low-emission driving with less refuelling time. To facilitate their wider scale adoption, it is essential to understand their energy performance through models that consider external weather effects, driving styles, road gradients, and their simultaneous interaction. This paper presents a microlevel, multicriteria assessment framework to investigate the performance of BEVs, fuel cell electric vehicles (FCEVs), and hybrid electric vehicles (HEVs), with a focus on energy consumption, drive systems, and emissions. Simulation models were developed using MATLAB 2021a Simulink environment, thus enabling the integration of standardized driving cycles with real-world wind and terrain variations. The results are presented for various trip scenarios, employing quantitative and qualitative analysis methods to identify the most efficient vehicle configuration, also validated through the simulation of three commercial EVs. Predictive modelling approaches are utilized to estimate a vehicle’s performance under unexplored conditions. Results indicate that trip conditions have a significant impact on the performance of all three vehicles, with HEVs emerging as the most efficient and balanced option, followed by FCEVs, making them strong candidates compared with BEVs for broader adoption in the transition toward sustainable transportation. Full article

Measurements of near-surface methane (CH₄) mixing ratio and its stable isotope ¹³C were carried out from January 2018 to December 2020 at the A.M. Obukhov Institute of Atmospheric Physics (IAP) research site in the center of Moscow city. The data show moderate interannual variations in monthly mean CH₄ with maximum values being observed predominantly in winter (2.05–2.10 ppmv on average). The most δ¹³C depleted CH₄ (up to −56‰) is observed in summer and autumn following seasonal decrease in traffic load in the city. The highest CH₄ concentrations (>2.2 ppmv) were likely to be caused by air transport from the E–SE sector where potentially large microbial CH₄ sources are located (landfills and water treatment plants, Moscow River). Keeling plots of these episodes in different seasons of 2018–2020 showed δ¹³C isotopic signatures of about −58–−59‰ for the spring–autumn period and −67‰ for winter. A good correlation was observed between CH₄ and other pollutants: CO₂, CO, and benzene in daytime (10:00–19:00) hours (R > 0.7). Contribution of urban methane emissions due to vehicle exhausts (∆[CH₄]_auto) and microbial activity (∆[CH₄]_micro+) along with regional baseline mixing ratios of CH₄ ([CH₄]_base) and CO ([CO]_base) were estimated from the linear orthogonal regression analyses of the measured daytime mixing ratios. A significant role of microbial methane in the formation of CH₄ maximums in Moscow was revealed. Contributions of the upwind continental CH₄ and CO sources to the measured species levels were estimated through comparison with the Mace Head site data representative for the Northern Hemisphere baseline air. The study provides, for the first time, important insights into the long- and short-term variations of CH₄ levels in Moscow in connection to the local (urban) emissions and long-range transport from upwind continental sources. The results will contribute to elaboration of a default emission inventory in air quality modeling and help to identify the areas for targeted mitigation efforts. Full article

In recent years, industries such as oil and gas production, waste management, and renewable natural gas/biogas have made a concerted effort to limit and offset anthropogenic sources of methane emissions. However, the state of emissions, what is emitting and at what rate, is highly variable and depends strongly on the micro-scale emissions that have large impacts on the macro-scale aggregates. Bottom-up emissions estimates are better verified using additional independent facility-level measurements, which has led to industry-wide efforts such as the Oil and Gas Methane Partnership (OGMP) push for more accurate measurements. Robust measurement techniques are needed to accurately quantify and mitigate these greenhouse gas emissions. Deployed on both fixed-wing and multi-rotor unmanned aerial vehicles (UAVs), a miniature tunable diode laser absorption spectroscopy (TDLAS) sensor has accurately quantified methane emissions from oil and gas assets all over the world since 2017. To compare bottom-up and top-down measurements, it is essential that both values are accompanied with a defensible estimate of measurement uncertainty. In this study, uncertainty has been determined through controlled release experiments as well as statistically using real field data. Two independent deployment methods for quantifying methane emissions utilizing the in situ TDLAS sensor are introduced: fixed-wing and multi-rotor. The fixed-wing, long-endurance UAV method accurately measured emissions with an absolute percentage difference between emitted and mass flux measurement of less than 16% and an average error of 6%, confirming its suitability for offshore applications. For the quadcopter rotary drone surveys, two flight patterns were performed: perimeter polygons and downwind flux planes. Flying perimeter polygons resulted in an absolute error less than 36% difference and average error of 16.2%, and downwind flux planes less than 32% absolute difference and average difference of 24.8% when flying downwind flux planes. This work demonstrates the applicability of ultra-sensitive miniature spectrometers for industrial methane emission quantification at facility level with many potential applications. Full article

Due to the advanced spatial data collection technologies, the locations of vehicles on roads are now being collected nationwide, so there is a demand for applying a micro-level emission calculation methods to estimate regional and national emissions. However, it is difficult to apply this method due to the low data collection rate and the complicated calculation procedure. To solve these problems, this study proposes a vehicle trajectory extraction method for estimating micro-level vehicle emissions using massive GPS data. We extracted vehicle trajectories from the GPS data to estimate the emission factors for each link at a specific time period. Vehicle trajectory data was divided into several groups through a k-means clustering method, in which the ratios of each operating mode were used as variables for clustering similar vehicle trajectories. The results showed that the proposed method has an acceptable accuracy in estimating emissions. Furthermore, it was also confirmed that the estimated emission factors appropriately reflected the driving characteristics of links. If the proposed method were utilized to update the link-based micro-level emission factors using continuously accumulated trajectory data for the road network, it would be possible to efficiently calculate the regional- or national-level emissions only using traffic volume. Full article