This is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemometrics and Intelligent Laboratory Systems, VOL 157, (2016) DOI 10.1016/j.chemolab.2016.07.004 ; A method for online decorrelation of chemical sensor signals from the e ects of envi- ronmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humid- ity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor air for two years in the residence of one of the authors and it col- lected data continuously during 537 days with a sampling rate of 1 samples per second. To estimate the e ects of variations in air humidity and temperature on the chemical sensors signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we con rmed that the most statistically signi cant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coe cient of determination R2 close to 1. To show how the humidity-temperature correc- tion model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the ltered signal of the chemical sensors.