Abstract <p>This study addresses the problem of environmental monitoring of air in cities and industrial areas, which consists of detecting gases and volatile organic compounds using semiconductor gas sensors. To provide selectivity in the detection of certain gases, as well as high temporal resolution of the sensors, nonlinear temperature operating conditions were used—the so-called heating dynamics. Due to high complexity of physical and chemical models describing the processes of interaction between gases and sensors, machine learning methods based on the use of physical experiment data were used to process the sensor response. To provide additional selectivity in the detection of specific gases, this study considers simultaneous use of data from multiple semiconductor sensors with various doping components with building machine learning models capable of providing joint processing. Based on the results of the computational experiments, it was shown that in many cases, simultaneous use of data from multiple sensors improves the quality of the regression solution for determining gas concentrations. Detailed conclusions were also drawn regarding the selection of optimal sensor combinations, heating dynamics, and machine learning methods, both for each specific gas and for all gases.</p>