A micro air quality monitor can realize grid monitoring and real-time monitoring of air pollutants. Its development can effectively help human beings to control air pollution and improve air quality. However, affected by many factors, the measurement accuracy of micro air quality monitors needs to be improved. In this paper, a combined calibration model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the very widely used and easily interpretable multiple linear regression model is used to find the linear relationship between various pollutant concentrations and the measurement data of the micro air quality monitor to obtain the fitted values of various pollutant concentrations. Second, we take the measurement data of the micro air quality monitor and the fitted value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of the MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance of the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4–95.4%.