Despite much work in recent years, vehicle emissions remain a significant contributor in many areas where air quality standards are under threat. Policy-makers are actively exploring options for next generation vehicle emission control and local fleet management policies, and new monitoring technologies to aid these activities. Therefore, we report here on findings from two separate but complementary blind evaluation studies of one new-to-market real-world monitoring option, HEAT LLC's Emission Detection And Reporting system or EDAR, an above-road open path instrument that uses Differential Absorption LIDAR to provide a highly sensitive and selective measure of passing vehicle emissions. The first study, by Colorado Department of Public Health and Environment and Eastern Research Group, was a simulated exhaust gas test exercise used to investigate the instrumental accuracy of the EDAR. Here, CO, NO, CH and CH measurements were found to exhibit high linearity, low bias, and low drift over a wide range of concentrations and vehicle speeds. Instrument accuracy was high (R 0.996 for CO, 0.998 for NO; 0.983 for CH; and 0.976 for CH) and detection limits were 50 to 100ppm for CO, 10 to 30ppm for NO, 15 to 35ppmC for CH, and, depending on vehicle speed, 100 to 400ppmC for CH. The second study, by the Universities of Birmingham and Leeds and King's College London, used the comparison of EDAR, on-board Portable Emissions Measurement System (PEMS) and car chaser (SNIFFER) system measurements collected under real-world conditions to investigate in situ EDAR performance. Given the analytical challenges associated with aligning these very different measurements, the observed agreements (e.g. EDAR versus PEMS R 0.92 for CO/CO; 0.97 for NO/CO; ca. 0.82 for NO/CO; and, 0.94 for PM/CO) were all highly encouraging and indicate that EDAR also provides a representative measure of vehicle emissions under real-world conditions.
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http://dx.doi.org/10.1016/j.scitotenv.2017.07.137 | DOI Listing |
Sci Rep
December 2024
Clean Energy Technologies Research Institute (CETRI), Process Systems Engineering, Faculty of Engineering & Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada.
Real-world emissions of particulate matter (PM) and smoke opacity were studied for an older-model diesel pickup truck during four types of driving tests, namely fixed-point test, snap-acceleration test, road test, and hill road test (uphill/downhill). A portable emissions measurement system (PEMS) and an opacimeter were used to measure real-time concentrations of PM and smoke opacity, respectively, and simultaneously. Correlation analysis showed a significant positive association between PM and opacity, suggesting the feasibility of using an opacimeter to estimate PM mass emissions from diesel vehicles.
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December 2024
Chongqing Jianzhu College, Chongqing, 400072, China.
Prefabricated construction involves manufacturing components in a factory and then transporting them to a construction site for assembly, yielding resource savings and improved efficiency. However, the large size and weight of prefabricated components, along with strict delivery requirements, introduce logistical challenges, such as increased carbon emissions during transport and site congestion. This study addresses the dual-objective vehicle scheduling problem for prefabricated components.
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December 2024
Department of Public Health and Informatics, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh.
Background: The increasing number of motor vehicles in Dhaka city is contributing to a rise in air pollution. Prolonged exposure to vehicle emissions has led to various health issues for everyone, but traffic policies might be particularly affected. This study aims to evaluate their knowledge, attitudes, and practices regarding air pollution, with the goal of raising awareness and promoting healthier practices to mitigate the adverse effects of pollution.
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December 2024
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration.
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December 2024
School of Civil Engineering, Henan University of Technology, Zhengzhou, 450001, China.
The transit signal priority, as an effective method to address public transport operation issues, has been widely applied. With the continuous advancement of connected technology, research on developing transit signal priority strategies using vehicle-to-everything technology is gaining increasing attention. However, current traffic signal priority studies primarily focus on optimizing bus speeds on dedicated bus lanes, neglecting the adverse impacts of private vehicle queuing on priority strategies, as well as the carbon emissions resulting from speed fluctuations.
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