Safe deep reinforcement learning in diesel engine emission control.

A deep reinforcement learning application is investigated to control the emissions of a compression ignition diesel engine. The main purpose of this study is to reduce the engine-out nitrogen oxide emissions and to minimize fuel consumption while tracking a reference engine load. First, a physics-based engine simulation model is developed in GT-Power and calibrated using experimental data.

Cold Climate Impact on Air-Pollution-Related Health Outcomes: A Scoping Review.

In cold temperatures, vehicles idle more, have high cold-start emissions including greenhouse gases, and have less effective exhaust filtration systems, which can cause up to ten-fold more harmful vehicular emissions. Only a few vehicle technologies have been tested for emissions below -7 °C (20 °F). Four-hundred-million people living in cities with sub-zero temperatures may be impacted.