Fourier transform infrared spectroscopy (FTIR) enables the simultaneous measurement of multiple pollutants at highspeed and is thus a powerful technique that can be used for the rapid detection and online monitoring of air pollutants. Air pollution monitoring through FTIR is mainly affected by water vapor, which interferes with the measurement of pollutants that share the same spectral region with water vapor, particularly NOX and SO2. One of the approaches for increasing the accuracy of a method for measuring pollutants is removing water vapor interference from a sample spectrum by subtractingthe background water vapor spectrum. This method is greatly useful in analyzing spectra with water vapor interference. Water vapor spectra in different concentrations do not ideally follow the Beer-Lambert’s law because of several nonlinear effects, such as water molecule clusters and instrument line shape function. Thus, a numerically calculated water vapor spectrum by Beer-Lambert’s law presents substantial error. Therefore, the background water vapor spectrum is usually directly measured by the same FTIR instrument that measures the sample spectrum at the same water vapor concentration as that of a sample spectrum. The background water vapor spectrum can be measured by two methods. One is adjusting water vapor concentration in a water vapor/nitrogen mixture toresemble a sample spectrum. This method is time consuming and extremely difficult to use in the field. The other method involves premeasuring multiple water vapor reference spectra with different concentrations and fitting. After sample spectrum measurement, two reference spectra are selected from premeasured water vapor spectra, and a background water vapor spectrum is linearly fitted by these reference spectra. The fitting method can obtain a highly approximated water vapor background spectrum when the water vapor concentrations in the reference spectra are extremely close to the sample spectrum’s water vapor concentrations and the water vapor concentration of sample spectrum is in the middle of two reference spectra. Currently, the fitting method cannot be applied in the rapid automatic elimination of water vapor interference due to lack of automatic algorithm. Thus, this study proposes an automatic algorithm, which includes reference spectrum selection and background spectrum fitting, for the fitting method. In the reference spectrum selection, the sample spectrum deducts the premeasured water vapor spectra from low to high concentrations. Two reference spectra are selected by the criteria according to the number of wavenumbers of negative absorbance in the subtracted spectra. The background water vapor spectrum is fitted through the iterative least square method, which gradually deletes the wavenumbers that are interfered by pollutants, and a background water vapor spectrum, which has an absorption feature that is consistently identical to that of water vapor in the sample spectrum, is linearly fitted by the remaining wavenumbers. The water vapor interference in the sample spectrum is eliminated by subtracting its background water vapor spectrum. In this study, we automatically remove water vapor interference in an air spectrum that contains NO2. Results show that the proposed algorithm accurately remove water vapor interference. After the elimination of water vapor interference, NO2 can be detected by its absorption peak located at 1 629 cm-1. The detection limit of NO2 remarkably improves when compared with detecting by its weaker absorption peak located at 2 917 cm-1 that is not interfered by water vapor.