Monitoring of PM 2.5 Concentrations by Learning from Multi-Weather Sensors.

This paper aims to monitor the ambient level of particulate matter less than 2.5 μ m (PM 2.5 ) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM 2.5 by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM 2.5 by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 μ g/m 3 with a correlation coefficient of 0.6281, by referring to the ground truth of PM 2.5 time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 μ g/m 3 with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM 2.5 estimates is 15.6391 μ g/m 3 with the correlation coefficient of 0.8701. [ABSTRACT FROM AUTHOR]