A virtual impactor-based portable optical sensor with probabilistic neural network: for particle property identification

With the increasing problem of atmospheric particulate matter pollution, an increasing number of scholars have conducted studies related to particulate matter. Most of the research on particulate matter has focused on information measurement such as particle size, where light scattering method is widely used in the field of particulate matter measurement. Numerous studies have shown that the particle properties have a great influence on the measurement accuracy of light scattering method. However, there are relatively few studies related to the identification of particle properties. Therefore, according to the difference of particle signals with different properties, this paper proposes a new method of particle property identification based on probabilistic neural network. Also, a new portable light scattering sensor integrated with virtual impactor is designed for particle signal acquisition. In the particle signal feature extraction session, we innovatively select Image entropy, kurtosis and sample entropy of discrete wavelet approximation signal as eigenvalues of the particle signal. The experimental results show that the virtual impactor has a good separation with a cutting particle size of 2.67 μm. The designed new portable light scattering sensor has good performance which can realize the task of particle light scattering signal acquisition. Meanwhile, the proposed new method of particle property identification has a 99% correct classification rate. In future work, we will further optimize the design of portable sensors and thus reduce the specifications of the measurement system.