Supplementary data
Description
As the problem of atmospheric particulate matter pollution becomes more and more serious and has a great impact on both the atmospheric environment and human health, more and more scholars begin to study the problem of particulate matter pollution. Optical particle counters rely on light scattering to measure particle mass concentrations, and their measurement process requires the extraction of particle voltage pulse signal amplitude distributions to invert the mass concentration. The pulse signal amplitude distribution extraction method depends on the division method of the optical particle counter counting channel. To achieve more accurate mass concentration measurement results, this paper proposes a new way of pulse signal amplitude distribution extraction based on information entropy theory and verifies the effectiveness of the new method by comparing experimental measurements with standard instruments. The experimental results indicated that the information entropy obtained by adaptive division was higher than that of the traditional method, and the stability and stability speed of mass concentration inversion accuracy were better than that of the traditional method. The increase in stabilization speed means the ability to speed up mass concentration inversion and reduce measurement time without generating measurement errors. The adaptive division was applied to particles of different particle sizes, and the measurement results were in good agreement with the standard instrument. In the future, this adaptive division method can be applied to the division of counting channels in optical particle counters to obtain greater information entropy to ensure stability and steady speed of mass concentration measurements.