PM_2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Fine aerosols with a diameter of less than 2.5 microns (PM_2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM_2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM_2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM_2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM_2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM_2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan's government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM_2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.