Increase In Average Speed Research Articles

Analysis about Change of the Armature's Average Speed for an Experimental Rail Launcher

In this paper, we measured the armature velocity of an experimental electromagnetic launcher by B‐dot method and analyzed the change of the armature's average speed. The electromagnetic gun launching process was also simulated by Ansoft Maxwell simulation software. The launch current is used as the model's excitation which means that the current in the model is the same as the rail current. Ansoft Maxwell's 3D model can accurately simulate the launching process, in theory. We found that there was an unexpected rise during the real launching process. The phenomenon can be discovered in the following several experiments. We analyzed several possible causes such as the inductive gradient change, rail broken and inaccurate measurement of B‐dot probe. The inductive gradient is an important parameter in the launching process and can affect the armature speed. After checking the measurement problem, we also found that the wave form of the output signal from the probe where the unexpected change occurred was normal but the amplitude was very small. The measurement method was proved correct. The rise of average inductance gradient leads to the unexpected increase in average speed and the small signal. Furthermore, the physical reason for the change of inductance gradient should be the change of current distribution. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Aerodynamic design optimization of ducted openings through high-rise buildings for wind energy harvesting

On-site renewable energy generation in the built environment can be achieved by incorporating wind turbines in the integral design of buildings. Passages through buildings are considered promising to strengthen the local wind resource availability but information concerning their design and performance is scarce. Therefore, two key design parameters that can enhance the wind energy performance of ducted openings in high-rise buildings are addressed and optimized via CFD simulations: the fillet radius (r) of the opening and the duct diameter (d0). 3D steady RANS simulations are performed and validated with wind tunnel data from the literature. Fillets are shown to suppress flow separation, thereby enhancing the magnitude and uniformity of the wind speed in the duct and reducing the turbulent kinetic energy. With a reference diameter d0 = D, the best-performing configuration has a normalized fillet radius r/d0 = 0.2, which increases the average wind speed in the duct by 65% and the wind power by 354%. Modifying the duct diameter alone has limited influence. However, combining a larger duct diameter d0 = 1.5D with fillets with r/d0 = 0.4, can yield up to 78% increase in average wind speed and 650% in wind power density. Findings indicate that the dimensionless wind speed in the duct (U/U0) scales closely in proportion to the normalized fillet radius (r/d0). With these results, the present study demonstrates the aerodynamic advantage of ducted openings in buildings and identifies relevant design conditions required to improve the wind resource availability for the prospective implementation of wind turbines.

Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data.

Objective: Finishing a marathon requires to prepare for a 42.2 km run. Current literature describes which training characteristics are related to marathon performance. However, which training is most effective in terms of a performance improvement remains unclear.Methods: We conducted a retrospective analysis of training responses during a 16 weeks training period prior to an absolved marathon. The analysis was performed on unsupervised fitness app data (Runtastic) from 6,771 marathon finishers. Differences in training volume and intensity between three response and three marathon performance groups were analyzed. Training response was quantified by the improvement of the velocity of 10 km runs Δv10 between the first and last 4 weeks of the training period. Response and marathon performance groups were classified by the 33.3rd and 66.6th percentile of Δv10 and the marathon performance time, respectively.Results: Subjects allocated in the faster marathon performance group showed systematically higher training volume and higher shares of training at low intensities. Only subjects in the moderate and high response group increased their training velocity continuously along the 16 weeks of training.Conclusion: We demonstrate that a combination of maximized training volumes at low intensities, a continuous increase in average running speed up to the aimed marathon velocity and high intensity runs ≤ 5 % of the overall training volume was accompanied by an improved 10 km performance which likely benefited the marathon performance as well. The study at hand proves that unsupervised workouts recorded with fitness apps can be a valuable data source for future studies in sport science.

Evolution towards optimal driving strategies for large‐scale autonomous vehicles

With rapidly developing autonomous vehicle (AV) technologies, the optimal driving strategy should consider multi-objective optimization problems of large-scale transportation systems, including safety and efficiency. Different driving strategies have different performance, and there is an interaction between vehicles with different strategies. Since the Nash equilibrium is hard to find for an n-player game, it is difficult to get an analytical solution to this multi-objective optimization problem. Therefor a coevolutionary algorithm is proposed to explore the interactions between populations with different strategies and investigate the cooperation and competition among vehicles. Combining the multi-objective optimization algorithm and the incentive mechanism of survival of the fittest reproduction law, the system structure reaches a stable equilibrium state and the optimal group driving strategy evolves. Simulation results, with 40,000 vehicles driving in Luxembourg SUMO Traffic Scenario, demonstrate that the rational–rational strategy performs best among six typical strategies. Meanwhile, the accident rate drops by 56%, while the overall average speed increases by 30%. The results of multi-vehicle and multi-objective coevolution are enlightening in designing optimal driving strategy with AVs.

Acute and chronic behavioral effects of kelp gull micropredation on southern right whale mother-calf pairs off Península Valdés, Argentina

Kelp gullsLarus dominicanus(KG) feed on the skin and blubber of living southern right whalesEubalaena australis(SRWs) off Península Valdés (PV), Argentina. The whales respond strongly to KG micropredation by changing their immediate (acute) behavior during attacks and their overall (chronic) surfacing pattern and body posture to minimize gull exposure. The energetic and large-scale behavioral consequences of these attacks are unknown. To address this knowledge gap, we quantified the effect size of both acute (during attacks) and chronic (not during attacks) responses by comparing the respiration rates, swim speed, and nursing behavior of PV SRWs to undisturbed (control) SRW mother-calf pairs in Head of Bight, Australia, using unmanned aerial vehicle focal follows. Even when gulls were not attacking, PV SRW mothers and calves demonstrated ~50 and ~25% higher respiration rates, respectively, than whales in Australia. During attacks, PV calf respiration rates increased by an additional 10%. PV SRW mothers also frequently (>76% of respirations) exhibited irregular breathing postures, causing the whales to potentially expend extra energy by working against their natural buoyancy. Despite no significant increase in average maternal swim speed, 76 and 90% of gull attacks elicited strong behavioral reactions from mothers and calves, respectively. Overall, PV calves spent less time nursing during individual bouts compared to those in Australia but entered suckling position more frequently. Furthermore, kelp gulls seemed to show a preference for attacking previously wounded calves and at a higher rate. These chronic and acute behavioral effects may carry energetic costs, which could have long-term consequences for SRW survival and reproduction.

Quantification of Non-Exhaust Particulate Matter Traffic Emissions and the Impact of COVID-19 Lockdown at London Marylebone Road

This research quantifies current sources of non-exhaust particulate matter traffic emissions in London using simultaneous, highly time-resolved, atmospheric particulate matter mass and chemical composition measurements. The measurement campaign ran at Marylebone Road (roadside) and Honor Oak Park (background) urban monitoring sites over a 12-month period between 1 September 2019 and 31 August 2020. The measurement data were used to determine the traffic increment (roadside–background) and covered a range of meteorological conditions, seasons, and driving styles, as well as the influence of the COVID-19 “lockdown” on non-exhaust concentrations. Non-exhaust particulate matter (PM)10 concentrations were calculated using chemical tracer scaling factors for brake wear (barium), tyre wear (zinc), and resuspension (silicon) and as average vehicle fleet non-exhaust emission factors, using a CO2 “dilution approach”. The effect of lockdown, which saw a 32% reduction in traffic volume and a 15% increase in average speed on Marylebone Road, resulted in lower PM10 and PM2.5 traffic increments and brake wear concentrations but similar tyre and resuspension concentrations, confirming that factors that determine non-exhaust emissions are complex. Brake wear was found to be the highest average non-exhaust emission source. In addition, results indicate that non-exhaust emission factors were dependent upon speed and road surface wetness conditions. Further statistical analysis incorporating a wider variability in vehicle mix, speeds, and meteorological conditions, as well as advanced source apportionment of the PM measurement data, were undertaken to enhance our understanding of these important vehicle sources.

A retrospective study on the regional benefits and spillover effects of high-speed broadband networks: Evidence from German counties

This study aims to assess the economic benefits of high-speed broadband within and across neighboring counties in Germany. Utilizing a balanced panel dataset of 401 German counties with data from 2010 to 2015 as well as different panel estimation techniques, we find that an increase in average broadband speed has a significantly positive effect on regional GDP in the average German county. Furthermore, we find that broadband deployment in German counties induces not only substantial economic benefits in terms of direct effects within counties but also positive regional externalities across counties. According to our estimation results, an increase in average bandwidth speed by one unit (1 Mbit/s) induces a rise in regional GDP of 0.18%. This effect is almost doubled if we also take regional externalities into account (0.31%). Moreover, we find that regional agglomeraton effects are of particular relevance for rural counties. Our cost-benefit analysis of subsidies based on conservative estimates suggests efficiency gains, as the total economic per capita benefits (€164) of subsidy programs to encourage broadband expansion exceeded their associated per capita costs (€114).

Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks

The unstable performance of radar rainfall measurement hinders its application in hydrology. Radar rainfall uncertainties induced by atmospheric conditions as raindrops fall from the radar sampling altitude to the ground are critical to radar-based quantitative precipitation estimation. However, these have rarely been considered in previous radar rainfall correction procedures. This study first demonstrates that the correlations between the radar–gauge rainfall discrepancy (RGD) and atmospheric fields are strong. A systematic radar-rainfall adjustment method is then proposed to decrease the discrepancies originating from changes in atmospheric conditions using a long short-term memory (LSTM) network. Three RGD adjustment models were established using a mass-variation scheme, a wind-drift scheme, and a combined mass-variation and wind-drift scheme, based on long-term (2013–2017) data covering most of the United Kingdom. The evaluation results demonstrate that all of the designed models performed well from overall, single-site, and event perspectives. Overall, the combined model, which exhibited the best performance, decreased the root-mean-square error between the rainfall levels measured by radar and gauges by 23.84%, increased Pearson’s correlation coefficient from 0.23 to 0.53, and improved the critical success index of the radar rainfall estimation from 0.56 to 0.92. The results indicate that the performances of the proposed models improved with an increase in average relative humidity or wind speed, which demonstrates that they can correct rainfall under high levels of relative humidity and wind speed. This study establishes a highly capable and comprehensive adjustment framework for radar rainfall estimation uncertainty induced by atmospheric conditions, which is essential for achieving high-quality radar products.

Combined impact of road and traffic characteristic on driver behavior using smartphone sensor data

The objective of this research is to exploit high resolution driving behavior data collected via sensors of smartphones from 303 drivers in order to examine driver behavior at road segment and junction level. These sensor data are combined with traffic and road geometry characteristics and subsequently depicted spatially using Geographical Information System software. Events of harsh driver behavior (8592 harsh accelerations and 3946 harsh brakings) were mapped to delimited segments and junctions of two urban expressways in Athens, Greece. For the analysis, two multiple linear regression models and two log-linear regression models were developed. Results indicate that in road segments there is an increase in the number of harsh events if average traffic flow per lane increases in the respective areas. Furthermore, as the average occupancy increases in junctions, there is an increase in harsh accelerations, and as the average speed increases, more harsh deceleration events occur. It is evident that traffic characteristics (traffic flow & speed) have the most statistically significant impact on the frequency of harsh events compared to factors related to road geometry and driver behavior.

The delayed effect of temperature on the risk of hospitalization due to COVID-19: evidence from Mumbai, India

Abstract Objectives Meteorological factors and climatic variability have an immense influence on the transmission of infectious diseases and significantly impact human health. Present study quantifies the delayed effect of atmospheric temperature on the risk of hospitalization due to the Coronavirus disease 2019 (COVID-19) with adjusting the effects of other environmental factors in Mumbai, India. Methods The daily reported data of the number of hospitalized COVID-19 positive cases and the environmental factors at Mumbai, Maharashtra, India were collected and analyzed to quantify the main and the delayed effects. Exploratory data analysis and Distributed Linear and Non-linear lag Model (DLNM) with Generalized Additive Model (GAM) specification have applied to analyze the data. Results The study identified the Diurnal Temperature Range (DTR) delayed effect on the risk of hospitalization changed over the lag period of 0–14 days with increasing Relative Risk (RR) at the low DTR and decreasing RR at the higher DTR values. The extreme DTR suggests a high risk of hospitalization at earlier lags (i.e., 0–5 days). DTR’s cumulative effect was significant at higher 0–10 lag days (p-value <0.05). Exposure to the low and moderate DTR suggests a high risk of hospitalization with more than six days of lag. The RR for daily average humidity with 95% C.I was 0.996 (0.967, 1.027). The risk of hospitalization due to COVID-19 showed an increasing nature (p-value <0.05) with the increase in air pollution and average wind speed (WSAvg) at lag 0. Also, the risk of hospitalization changed through different lag periods of DTR. The analysis confirms the higher amount of delayed effect due to low DTR compared with moderate and high DTR. Conclusions The study suggests that both the climatic variations and air quality have significant impact on the transmission of the global pandemic COVID-19.

PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform

For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.

Behavioural assessment of sheep is sensitive to level of gastrointestinal parasite infection

Qualitative behavioural assessment (QBA) was applied to investigate the expressive behaviour of sheep with varying intestinal parasite burdens over two experiments. The expressive behaviour of sheep naturally infected with intestinal parasites was assessed from video footage collected in the paddock, and assessments were compared pre- and post-treatment with anthelmintic drench. The first experiment assessed sheep with a range of parasite burdens (n = 28), and the second compared sheep that expressed clinical symptoms of parasitism (Anaemic, n = 5) with those that did not (Non-anaemic, n = 5). Behavioural expression scores were validated against individual clinical evaluations (faecal egg counts – FEC, faecal consistency and anaemia scores), production parameters (body mass and body condition score), and quantitative locomotive measures (walking speed and return order to paddock). Twenty-two observers scored 28 video clips using QBA in experiment 1, and in the second experiment, 35 observers scored 20 video clips that depicted the 10 focal sheep pre- and post-treatment. QBA scores were analysed using Generalised Procrustes Analysis (GPA), and sheep scores on the main GPA dimensions were evaluated in relation to parasite burden using Spearman Rank correlations and repeated-measures ANOVA for experiment 1 and 2, respectively. In both experiments, observers reached significant (P < 0.001) consensus in their assessment of the behavioural expression of sheep. In experiment 1, sheep with higher FEC had lower anaemia scores (indicative of anaemia), poor body condition, lower body mass, walked slower, ranked lower in return order, and were scored by observers as more docile/at ease, and more assertive/motivated on GPA dimensions 1 and 3, respectively (P < 0.05). In experiment 2, observers scored Anaemic sheep as significantly more unsettled/apprehensive compared to Non-anaemic sheep on GPA dimension 1 (P < 0.05). In addition, observers consistently scored Non-anaemic sheep as more bright/observant following treatment on dimension 2 (P < 0.001). On dimension 3, all animals were scored by observers as less depressed/suspicious post-treatment (P < 0.05). An increase in average walking speed across all sheep was also identified post-treatment (P < 0.05). We conclude that under the conditions tested, QBA could provide information on the behavioural expression of sheep related to varying parasite burdens and the impact of treatment. The data presented herein offers proof of concept that assessments of behavioural expression could aid producers in the monitoring and management of parasitised sheep on-farm in terms of identification of individuals for treatment, and efficacy of anthelmintic treatment.

Carbon Footprint of autonomous vehicles at the urban mobility system level: A traffic simulation-based approach

This paper presents the results of a Carbon Footprint (CF) study of Autonomous Vehicles (AVs) and their environmental impact on the transportation network. By assuming that fully AVs are battery electric vehicles (BEVs) with connectivity, light detection and ranging sensors, this study measures the environmental impact at the urban mobility level. The AV complete life cycle impact was firstly evaluated. Next, by comparing the current situation with a future hypothetical scenario (100% AVs penetration), the positive environmental effect of the adoption of AVs on a real road network (city of Rome) is shown. For this scope, a traffic simulation-based approach was used to investigate the effects of AVs on the network congestion.The results show that the full AVs penetration scenario leads to an improvement in the network performances in terms of travel time and average speed. The Total Time Spent (TTS) decreases (−35% for intra-urban roads and −21% for highways), and the average network speed increases (48% for intra-urban road and 37% for highways). Moreover, the final amount of Vehicle Kilometer Traveled (VKT) shows an 8% increase on longer extra-urban routes, due to the higher capacity impact of AVs on highways, with a consequent load reduction for intra-urban shortcutting routes. In terms of life cycle impacts, AVs are characterized by the highest Greenhouse Gases (GHG) emissions related to construction, maintenance and end-of-life processes (on average 35% compared to internal combustion engine vehicles, 22% compared to hybrid electric vehicles and 5% compared to battery electric vehicles). Nevertheless, a 100% AVs penetration scenario generates a reduction of the environmental impact at the mobility system level of about 60%.

CFD evaluation of building geometry modifications to reduce pedestrian-level wind speed

High-rise buildings can significantly increase the wind speed at pedestrian level, and knowledge of building aerodynamics and pedestrian-level wind (PLW) conditions is therefore imperative in their design. This study aims at evaluating different building geometry modifications to reduce PLW speed around an isolated high-rise building. Numerical simulations with computational fluid dynamics (CFD) are performed to evaluate the effect of canopies, podiums and permeable floors. To the best knowledge of the authors, a systematic study on the impact of these modifications on PLW conditions using validated CFD simulations has not been reported before. Grid-sensitivity analyses are performed and sub-configuration validation is applied using wind-tunnel measurements from the literature. It is shown that a canopy or a podium can significantly reduce the area-averaged PLW speed (up to 29%) and maximum PLW speed (up to 36%) around the high-rise building. In general, the PLW speeds decrease with increasing canopy or podium size. The introduction of a permeable floor to the building can reduce the maximum and area-averaged mean wind speed. However, when low-floor building layers are removed, adverse effects are noted, i.e. the average PLW speed increases (up to 21%) and the lower-speed wake region behind the building is reduced in size.

The impact of the average traffic flow speed on occurrence risk of road crash

Traffic flow characteristics have a significant impact on occurrence risk of road crash. The most important characteristics of the traffic flow, the impact of which is the subject of numerous studies, are the traffc flow, density, average traffic flow speed, dispersion of traffic flow speeds, as well as the contents of vehicle in traffic flow. These characteristics are in strong correlation between each other, so changes in one parameter conditional make change of other parameters. Research shows that speed-related traffic flow parameters have a significant impact on occurrence risk of road crash. Therefore, in this study an analysis of the impact of the change in the average speed of the traffic flow on the risk of an accident occurred. The research includes a section of the single carriageway from Preljina to Ljig. After the construction of the highway in the stated section of the signle carriageway, a change in the characteristics of the traffic flow occurred, with this study examining the impact of changing the average speed of the traffic flow to the occurrence risk of road crash. The connection between the speed of traffic flow and the risk of accidents has been confirmed in this study, so with the increase in average speed the risk of accidents increases.

Assessing Fuel Distribution in Urban Areas: Off-Hours Deliveries in the City of Rio de Janeiro

Abstract This article analyzes impact that initiatives for improving freight system performance can generate in fuel distribution in urban areas, especially in mega-cities. We propose a comprehensive methodology to identify initiatives with the most potential in terms of cost and efficiency for the fuel distribution process in urban areas, and then to assess impacts of such initiatives in terms of environmental and economic sustainability. Results indicated off-hours deliveries as having a higher potential to increase sustainability in fuel distribution within city borders. Therefore, an application of the proposed methodology is presented to assess results of a live experiment of off-hours deliveries held in a fuel-distribution process in a mega-city in comparison to daytime deliveries, which is companies’ standard practice. Several positive results were observed during off-hour deliveries, such as reduction in fuel consumption, reduction in the emission of air pollutants and greenhouse gases, reduction in travel time, increase in average operational speed as well as the possibility of carrying out two trips with the same vehicle per day leading to a reduction in expenses with drivers when there is an increase in daily truck deliveries. Although the case assessed was conducted during the Olympic Games of Rio de Janeiro, the method can be applied in any other mega-city.

The effect of cycling using active-passive trainers on spasticity, cardiovascular fitness, function and quality of life in people with moderate to severe Multiple Sclerosis (MS); a feasibility study.

BackgroundExercise options for those with moderate to high levels of disability are limited. The aim of the study was to evaluate the feasibility of a progressive, four week lower limb cycling programme using active-passive trainers (APT's) on spasticity, cardiovascular fitness, function and quality of life in people with moderate to severe MS. MethodsParticipants were in-patients in the Physical Disability Rehabilitation Unit, Queen Elizabeth University Hospital, Glasgow, UK and randomised to APT + usual care or usual care only. The APT group received 30 min of APT (2 min passive warm up, 26 min active cycling, 2 min passive cool down), five days per week for 4 weeks. Outcome measures; Oxygen Uptake Efficiency Slope, Modified Ashworth Scale, Multiple Sclerosis Spasticity Scale, Functional Independence Measure, Timed 25 foot walk test and the MSQOL-54, were taken before and after the intervention period. Symmetry, distance cycled and active participation were also recorded for each cycling session. Results24 participants were recruited, 15 to the intervention and 9 to the control group. There was a 100% adherence to the intervention and a significant increase in average speed, power output and distance cycled (p < 0.001 for each) over the four weeks. There were no adverse events and both groups improved in average scores for all outcome measures. ConclusionsAPT cycling was well tolerated, while the cycling parameters improved it was difficult to separate the effects of the therapy programme and APT cycling. A longer duration, fully powered trial in a community setting is merited.

Evaluating Urban Bus Emission Characteristics Based on Localized MOVES Using Sparse GPS Data in Shanghai, China

Bus emissions have become one of the important contributing factors in urban environmental pollution due to the frequent use of heavy-duty diesel engines in the day-time. Local bus driving cycles have a significant influence on bus emissions under the different traffic conditions. This study investigated the operation mode distributions and emission characteristics for urban buses based on localized MOtor Vehicle Emission Simulator (MOVES) using sparse Global Position System (GPS) data in Shanghai, China. Sparse GPS data from forty-three buses were prepared, and then bus trajectories were reconstructed to calculate local bus driving cycles, including model description, model calibration, and trajectory reconstruction. MOVES localization was conducted for emission estimation mainly focusing on the bus emission inventory comparison between US and China. Bus emission factors were estimated based on the localized MOVES from the aspect of different driving conditions. Results show that with the increase in average traveling speed, the proportion of idling operation mode showed a decreasing trend. Four typical vehicle operation mode distributions were identified with different average speeds to show the impact of traffic conditions. Bus emission factors first rapidly decreased and then slowly declined towards some minimum values. Bus lanes exhibited emission reduction benefits under serious traffic congestion. The findings of this study have great importance for transportation operation management and policy-making to reduce bus emissions, as well as improving air quality.

Sensing schemes for STT-MRAMs structured with high TMR in low RA MTJs

In this work, we investigated the sensing challenges of spin-transfer torque MRAMs structured with perpendicular magnetic tunnel junctions with a high tunneling magnetoresistance ratio in a low resistance-area product. To overcome the problems of reading this type of memory, we have proposed a voltage sensing amplifier topology and compared its performance to that of the current sensing amplifier in terms of power, speed, and bit error rate performance. We have verified that the proposed sensing scheme offers a substantial improvement in bit-error-rate performance. To enumerate the read operations of the proposed sensing scheme with the proposed cross-coupled capacitive feedback technique on the clamped circuity have successfully been performed a 2.5X reduction in average low power and a 13X increase in average reading speed compared with the previous works due to its device structure and the proposed circuit technique.

Characterization of Emission Factors Concerning Gasoline, LPG, and Diesel Vehicles via Transient Chassis-Dynamometer Tests

Gaseous emissions from vehicles contribute substantially to air pollution and climate change. Vehicular emissions also contain secondary pollutants produced via chemical reactions that occur between the emitted gases and atmospheric air. This study aims at understanding patterns concerning emission of regulated, greenhouse, and precursor gases, which demonstrate potential for secondary aerosol (SA) formation, from vehicles incorporating different engine technologies—multi-point injection (MPI) and gasoline direct injection (GDI)—and using different fuels—gasoline, liquefied petroleum gas (LPG), and diesel. Drive cycles from the National Institute of Environmental Research (NIER) were used in this study. Results obtained from drive cycle tests demonstrate a decline in aggregate gas emissions corresponding to an increase in average vehicle speed. CO2 accounts for more than 99% of aggregate gaseous emissions. In terms of concentration, CO and NH3 form predominantly non-CO2 emissions from gasoline and LPG vehicles, whereas nitrogen oxides (NOx) and non-methane hydrocarbons (NMHC) dominate diesel-vehicle emissions. A higher percentage of SO2 is emitted from diesel vehicles compared to their gasoline- and LPG-powered counterparts. EURO-5- and EURO-6-compliant vehicles equipped with diesel particulate filters (DPFs) tend to emit higher amounts of NO2 compared to EURO-3-compliant vehicles, which are not equipped with DPFs. Vehicles incorporating GDI tend to emit less CO2 compared to those incorporating MPI, albeit at the expense of increased CO emissions. The authors believe that results reported in this paper concerning regulated and unregulated pollutant-emission monitoring can contribute towards an accurate evaluation of both primary and secondary air-pollution scenarios in Korea.