Behavioral visualization of nicotine in tobacco smoke in simulated space

Abstract

Smoke separation is accelerating towards the Tokyo Olympics and Paralympics. However, there is no method for monitoring harmful chemical substances in tobacco smoke in real-time, so there are scientific lacks against non-smokers health whether the smoke separated space have no impact or not to their health. The main chemical substance in tobacco smoke is nicotine. We decided to build up a model first for nicotine. In this study, we use a newly developing gas analyzer which measures nicotine in real-time. From that nicotine measurement result, analyzes the nicotine behavior with FEM in a simulated space. Then make it learn the parameters of the nicotine behavior model, and using a different modeling and learning algorithm than general multi-agent reinforcement learning, we visualize the nicotine behavior in tobacco smoke through the new achieving model. First, we confirmed the availability of nicotine analysis with the gas analyzer.