The air we breathe consists of contaminants such as particulate matter (PM), carbon dioxide (CO2), nitrogen dioxide (NO2), nitric oxide (NO) which when inhaled brings about several changes in the autonomous responses of our body. Our previous work showed that we can use the human body as a sensor by making use of autonomous responses (or biometrics) such as changes in electrical activity in the brain measured using electroencephalogram (EEG) and physiological changes such as skin temperature, Galvanic Skin Response (GSR), blood oxygen saturation (SpO2) and few others can be used to estimate pollutants, in particular, PM1 and CO2 with high degree of accuracy using machine learning. In this study, we introduce a novel approach to obtain the power spectral density (PSD) from the timeseries of EEG developed by Astrapi called the Astrapi Spectrum Analyzer (ASA). The physiological responses of a participant cycling outdoors is measured using a biometric suite and ambient CO2, NO2 and NO is measured simultaneously. We made use of physiological responses and combined it with the PSD from an EEG timeseries using Welch Method (WM) and then with the PSD using ASA to estimate the inhaled concentration of CO2, NO2 and NO. This work shows that, indeed the PSD obtained from ASA combined with other physiological responses provides much better result (RMSE=9.28 ppm in an independent test set) in estimating inhaled CO2 compared to making use of the same physiological responses and PSD from WM (RMSE=17.55 ppm in an independent test set). Small improvements are also seen in the estimation of NO2 and NO when using physiological responses and PSD from ASA, which can be further confirmed by making use of large number of data collection.