The air quality in a car’s cabin can be five times worse than that of residential and non-residential buildings, resulting in a variety of health issues, such as headache, sore throat, and nausea, ...which are all symptoms of in-vehicle air pollution. The monitoring of carbon dioxide, which is one of the principal pollutants in car cabins, is required before regulating it. The current research is focused on the recording of carbon dioxide levels for different levels of human load, air speed, and temperature. A statistical analysis and plots were obtained for recorded responses to determine air quality parameters for a minimum value of carbon dioxide level in a five-seat car cabin. Three algorithms (generalized reduced gradient (GRG), response surface methodology (RSM), and Genetic Algorithm (GA)) were successfully used in a 5-seat car to optimize the in-vehicle carbon dioxide level. The GRG method provided the minimum carbon dioxide level of 471.531 ppm for a one-human load at an air speed of 2 m/s and a temperature of 24 °C. For varying human loads of 2,3,4, and 5, the GRG and GA methods provided carbon dioxide levels of 508.785 ppm, 580.722 ppm, 659.839 ppm and 769.016 ppm, respectively. Comparing all three techniques, the RSM provided carbon dioxide levels of 471.876 ppm, 508.865 ppm, 580.79 ppm, 659.905 ppm, and 769.362 ppm for human loads of 1, 2, 3, 4, and 5, respectively. This study will provide a platform for the researchers working on indoor air quality characteristics to apply soft computing techniques effectively for the evaluation of comfortable healthy environments and for the benefit of the passengers in car cabins.
