Abstract
Accurate identification and classification of atmospheric particulates can provide the basis for their source apportionment. Most current research studies mainly focus on the classification of atmospheric particles based on the energy spectrum of particles, which has the problems of low accuracy and being time-consuming. It is necessary to study the classification method of atmospheric particles with higher accuracy. In this paper, a convolutional neural network (CNN) model with attention mechanism is proposed to identify and classify the scanning electron microscopy (SEM) images of atmospheric particles. First, this work established a database, Qingdao 2016–2018, for atmospheric particles classification research. This database consists of 3469 SEM images of single particulates. Secondly, by analyzing the morphological characteristics of single particle SEM images, it can be divided into four categories: fibrous particles, flocculent particles, spherical particles, and mineral particles. Thirdly, by introducing attention mechanism into convolutional neural network, an Attention-CNN model for the identification and classification of the four types of atmospheric particles based on the SEM images is established. Finally, the Attention-CNN model is trained and tested based on the SEM images database, and the results of identification and classification for four types of particles are obtained. Under the same SEM images database, the classification results from Attention-CNN are compared with those of CNN and SVM. It is found that Attention-CNN has higher classification accuracy and reduces significantly the misclassification number of particles, which shows the focusing effect of attention mechanism.
Highlights
Atmospheric particulates refer to the micro-solid or liquid matters suspending in the atmosphere
In order to verify the performance of Attention-convolutional neural network (CNN) model, the CNN and SVM which are commonly used in image classification are applied to the identification and classification of scanning electron microscopy (SEM) images of atmospheric particles. e structure of CNN is the same as that of the Attention-CNN model, but there is no attention layer. e three models were trained and tested using the same SEM images database
Compared with CNN, Attention-CNN significantly reduces the number of particles misclassified after adding attention mechanism to CNN, which indicates the focusing effect of the attention mechanism
Summary
Atmospheric particulates refer to the micro-solid or liquid matters suspending in the atmosphere. Atmospheric particulates have different morphological characteristics and chemical composition. Morphological characteristics of particles were analyzed by scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectrometry, and the distribution of the chemical composition of particulate matter was obtained. Pipal et al [10] investigated the shapes, morphology, and elemental composition of aerosols in PM10 and PM2.5 in Agra located in north central India using SEMEDS and concluded that SEM-EDS was a convenient method to identify the sources of particulate air pollution emissions. Li and Shao [12] applied TEM and SEM to study morphologies, sizes, and compositions of aerosol particles during the fog and nonfog episode in Beijing. The CNN with attention mechanism applied to the identification and classification of SEM images of atmospheric particles is studied.
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