Machine Learning Model for Reduction of Airborne Infections and Cognitive Load in a Car Cabin
The importance of medical care has grown massively, making it one of life's most essential components. People often use their vehicles in recirculation mode to provide optimum cooling in many cities with high air humidity and temperatures. On the other side, the recirculation mode of the cabin's air prevents O2 from entering and causes a rise in CO2. Increased health concerns, a decline in focus, and poor performance are all related to the increased CO2 concentration brought on by human exhale and metabolism. The paper describes an experimental investigation on how carbon dioxide builds up in a car's interior as a result of metabolism and breathing by passengers; specific levels of this gas may be dangerous for everyone inside, especially drivers. It is critical to maintain cabin concentration levels within the authorized limits since inhaling this gas may impair a driver's ability to make intelligent decisions. Opening the cabin windows may be a practical, easy, and affordable way to do this. Opening the cabin windows, though might always make it less comfortable inside. As a response, given model passengers can temporarily open the windows may significantly affect how much CO2 is present within the cabin. Using Our MVPR machine learning model and we demonstrate a GUI Framework, that can predict the forecast of CO2 concentrations in a cabin at a particular time, temperature, and relative humidity to avoid negative health impacts caused by CO2 gas.
Klompas M, Baker MA, Rhee C. Airborne Transmission of SARS-CoV-2: Theoretical Considerations and Available Evidence. JAMA [Internet]. 2020;324(5):441–2. Available from: https://doi.org/10.1001/jama.2020.12458
Angelova RA, Markov DG, Simova I, Velichkova R, Stankov P. Accumulation of metabolic carbon dioxide (CO2) in a vehicle cabin. IOP Conf Ser Mater Sci Eng. 2019;664(1).
Kajtár L, Herczeg L. Influence of carbon-dioxide concentration on human well-being and intensity of mental work. Időjárás. 2012 May 1;116:145–69.
FROST J. Curve Fitting Using Linear and Nonlinear Regression.[Online] Statistics by Jim [Internet]. 2018. Available from: https://statisticsbyjim.com/regression/curve-fitting-linear-nonlinear-regression/
Montgomery DC. Design and Analysis of Experiments Eighth Edition. Arizona State University. Vol. 2009, Copyright. 2013. 2001 p.
Costa V. Correlation and regression. In: Fundamentals of Statistical Hydrology [Internet]. The BMJ; 2016. p. 391–440. Available from: https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one
Abdul-Wahab SA, Chin Fah En S, Elkamel A, Ahmadi L, Yetilmezsoy K. A review of standards and guidelines set by international bodies for the parameters of indoor air quality. Atmos Pollut Res [Internet]. 2015;6(5):751–67. Available from: https://www.sciencedirect.com/science/article/pii/S1309104215301689
Jacobson TA, Kler JS, Hernke MT, Braun RK, Meyer KC, Funk WE. Direct human health risks of increased atmospheric carbon dioxide. Nat Sustain [Internet]. 2019;2(8):691–701. Available from: https://doi.org/10.1038/s41893-019-0323-1
Copyright (c) 2022 Journal of Environmental Treatment Techniques
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.