Characterization of Traffic Emissions Exposure Metrics in the Dorm Room Inhalation to Vehicle Emissions (DRIVE) Study

This study examines the ability of nanozeolite-geopolyme ras adsorbent material for motor vehicle emissions. There were three samples that have been synthesized by varying metakaolin and rice husk ash masses. Nanozeolite-geopolymer samples were synthesized through geopolymerization method at 70°C by mixing metakaolin with rice husk ash and activated with NaOH solution. Samples were recurring at 200°C to improve the crystalline level. Based on the XRD characterization of the three samples, two of them contain zeolite X, namely ZG_A (100% metakaolin, ASP 0%) and ZG_B (metakaolin: ASP, 2: 1). However, zeolite X phase most conceived by ZG_A. Therefore, the ZG_A sample was then used as adsorbent to absorb motor vehicle exhaust. The tests were performed four times with time variation of 10, 20, 30, and 40 minutes. The XRD results showed that the material was successfully absorbed lead oxide, manganese oxide and phosphorus which are hazardous compounds when inhale by human being.

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Development and characterization of an exposure generation system to investigate the health effects of particles from fresh and aged traffic emissions

Air Quality Atmosphere & Health ◽

10.1007/s11869-012-0178-3 ◽

2012 ◽

Vol 6 (2) ◽

pp. 419-429 ◽

Cited By ~ 10

Author(s):

Vasileios Papapostolou ◽

Joy E. Lawrence ◽

Stephen T. Ferguson ◽

Jack M. Wolfson ◽

Edgar A. Diaz ◽

...

Keyword(s):

Health Effects ◽

Traffic Emissions ◽

Generation System

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Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

International Journal of Automation and Computing ◽

10.1007/s11633-020-1271-y ◽

2021 ◽

Vol 18 (2) ◽

pp. 219-232

Author(s):

Zhen-Yi Zhao ◽

Yang Cao ◽

Yu Kang ◽

Zhen-Yi Xu

Keyword(s):

Urban Areas ◽

Vehicle Emissions ◽

Motor Vehicle ◽

Road Networks ◽

Urban Traffic ◽

Traffic Emissions ◽

Spatiotemporal Evolution ◽

The Future ◽

Precise Prediction ◽

Gps Trajectories

AbstractWith the rapid increase of the amount of vehicles in urban areas, the pollution of vehicle emissions is becoming more and more serious. Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making. Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning. Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions, however, they are not effective and efficient enough in the combination and utilization of different inputs. To address this issue, we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator (MOVES) model. Specifically, we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms. These matrices can reflect the attributes related to the traffic status of road networks such as volume, speed and acceleration. Then, our multi-channel spatiotemporal network is used to efficiently combine three key attributes (volume, speed and acceleration) through the feature sharing mechanism and generate a precise prediction of them in the future period. Finally, we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states, road networks and the statistical information of urban vehicles. We evaluate our model on the Xi’an taxi GPS trajectories dataset. Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.

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