Densely connected convolutional networks with attention long short-term memory for estimating PM2.5 values from images

Abstract

The PM2.5 index is a vitally important air pollution indicator that reflects the atmospheric concentration of particulate matter with diameters less than 2.5 μm. Recently, researchers have sought to address the inadequacies involved with the capture of environmental data from monitoring stations by developing deep learning methods for estimating PM2.5 concentrations in real time based on image data. However, the currently available methods neglect the effect of PM2.5 concentrations in earlier periods on the current air quality. The present work addresses this issue by proposing a novel hybrid model that first applies the DenseNet model to extract the local high-order spatial features of images captured over several hours or days at a fixed location, and then feeds the extracted features as a time series into an attention-based long short-term memory model. Ultimately, a fully connected layer is applied to establish a regression model to evaluate the PM2.5 concentrations reflected in the image data. The effectiveness of the proposed method for assessing PM2.5 concentrations is demonstrated by its application to two datasets composed of images captured in Shanghai and Beijing, China.