scholarly journals Estimating mortality impacts from vehicle emission reduction efforts: The Tune In and Tune Up program in the San Joaquin Valley

2020 ◽  
Vol 78 ◽  
pp. 102190 ◽  
Author(s):  
Rachel Connolly ◽  
Gregory Pierce ◽  
Julien Gattaciecca ◽  
Yifang Zhu
Keyword(s):  
Emission Reduction ◽  
San Joaquin Valley ◽  
Vehicle Emission

scholarly journals Evaluation of low emission zone policy on vehicle emission reduction in Beijing, China

2018 ◽  
Vol 121 ◽  
pp. 052070 ◽  
Author(s):  
Yi Zhang ◽  
Michel Andre ◽  
Yao Liu ◽  
Lin Wu ◽  
Boyu Jing ◽  
...  
Keyword(s):  
Emission Reduction ◽  
Vehicle Emission ◽  
Low Emission ◽  
Beijing China

Research on Urban Intersection Vehicle Emission Reduction Based on Microcosmic Simulation

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Y. Wang ◽  
D. Guo ◽  
S. Li ◽  
C. Zheng ◽  
E. Wenjuan ◽  
...  
Keyword(s):  
Emission Reduction ◽  
Vehicle Emission

The Application of Fuzzy Control in Vehicle Emission Reduction Technology

2015 ◽  
Vol 719-720 ◽  
pp. 306-310 ◽  
Author(s):  
Wei Jiang Zhang
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Emission Reduction ◽  
Calculation Model ◽  
Bench Test ◽  
Vehicle Emission ◽  
Vehicle Exhaust ◽  
Theoretical Derivation ◽  
Nox Sensor ◽  
Scr System

In this paper, fuzzy control was applied in the technology of reducing NOx of vehicle exhaust. The calculation model of control system was obtained through theoretical derivation. The input and the output of the control system were obtained through bench test, and then established the control system table. Fulfilled the hardware installation and debugging of SCR system, DSPACE system, NOx sensor and engine system, and then the control system look-up table was be written in DSPACE /MATLAB system. It is proved that the application of fuzzy control in vehicle emission reduction is feasible through the processing and analysis of test data.

scholarly journals Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission

2021 ◽  
Vol 13 (20) ◽  
pp. 11254
Author(s):  
Bálint Kővári ◽  
Lászlo Szőke ◽  
Tamás Bécsi ◽  
Szilárd Aradi ◽  
Péter Gáspár
Keyword(s):  
Reinforcement Learning ◽  
Queue Length ◽  
Emission Reduction ◽  
Control Method ◽  
Traffic Signal ◽  
Vehicle Emission ◽  
Signal Control ◽  
Traffic Signal Control ◽  
The Core ◽  
Reward Shaping

The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is a novel rewarding concept for deep reinforcement learning (DRL) which does not utilize any reward shaping, hence exposes new insights into the traffic signal control (TSC) problem. Despite the omission of the standard measures in the rewarding scheme, the proposed approach can outperform a modern actuated control method in classic performance measures such as waiting time and queue length. Moreover, the sustainability of the realized controls is also placed under investigation to evaluate their environmental impacts. Our results show that the proposed solution goes beyond the actuated control not just in the classic measures but in emission-related measures too.

PM2.5 pollution in China’s Guanzhong Basin and the USA’s San Joaquin Valley mega-regions

2021 ◽  
Author(s):  
John G. Watson ◽  
Junji Cao ◽  
Xiaoliang Wang ◽  
Judith C. Chow
Keyword(s):  
Emission Reduction ◽  
San Joaquin Valley ◽  
Data Analyses ◽  
Reduction Strategies ◽  
Emission Reduction Strategies ◽  
Guanzhong Basin

We update a model of PM2.5 pollution in the San Joaquin Valley in California, USA, and identify parallels to the Guanzhong Basin, China. We suggest future experiments, data analyses, and modeling to better understand the regional situations to develop emission reduction strategies.

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