High-Resolution PM2.5 Estimation Based on the Distributed Perception Deep Neural Network Model

The accurate measurement of the PM2.5 individual exposure level is a key issue in the study of health effects. However, the lack of historical data and the minute coverage of ground monitoring points are obstacles to the study of such issues. Based on the aerosol optical depth provided by NASA, combined with ground monitoring data and meteorological data, it is an effective method to estimate the near-ground concentration of PM2.5. With the deepening of related research, the models used have developed from univariate and multivariate linear models to nonlinear models such as support vector machine, random forest model, and deep learning neural network model. Among them, the depth neural network model has better performance. However, in the existing research, the variables used are input into the same neural network together, that is, the complex relationship caused by the lag effect of features and the correlation and partial correlation between features have not been considered. The above neural network framework can not be well applied to the complex situation of atmospheric systems and the estimation accuracy of the model needs to be improved. This is the first problem that we need to be overcome. Secondly, in the missing data value processing, the existing studies mostly use single interpolation methods such as linear fitting and Kriging interpolation. However, because the time and place of data missing are complex and changeable, a single method is difficult to deal with a large area of strip and block missing data. Moreover, the linear fitting method is easy to smooth out the special data in bad weather. This is the second problem that we need to overcome. Therefore, we construct a distributed perception deep neural network model (DP-DNN) and spatiotemporal multiview interpolation module to overcome problems 1 and 2. In empirical research, based on the Beijing-Tianjin-Hebei-Shandong region in 2018, we introduce 50 features such as meteorology, NDVI, spatial-temporal feature to analyze the relationship between AOD and PM2.5, and test the performance of DP-DNN and spatiotemporal multiview interpolation module. The results show that after applying the spatiotemporal multiview interpolation module, the average proportion of missing data decreases from 52.1% to 4.84%, and the relative error of the results is 27.5%. Compared with the single interpolation method, this module has obvious advantages in accuracy and level of completion. The mean absolute error, relative error, mean square error, and root mean square error of DP-DNN in time prediction are 17.7 mu g/m(3), 46.8%, 766.2 g(2)/m(6), and 26.9 mu g/m(3), respectively, and in space prediction, they are 16.6 mu g/m(3), 41.8%, 691.5 mu g(2)/m(6), and 26.6 mu g/m(3). DP-DNN has higher accuracy and generalization ability. At the same time, the estimation method in this paper can estimate the PM2.5 of the selected longitude and latitude, which can effectively solve the problem of insufficient coverage of China's meteorological environmental quality monitoring stations.

Automated summary

Accurately measuring the individual exposure level of PM2.5 is crucial for studying health effects, but challenges arise from the lack of historical data and limited ground monitoring. Techniques utilizing NASA's aerosol optical depth along with ground monitoring and meteorological data has proven effective in estimating PM2.5 concentrations near the ground. However, existing models fail to consider complexities such as lag effects and correlations between features, leading to accuracy issues. Various machine learning models have been explored to address the challenges, with the deep neural network model showing promising performance. Efforts to enhance estimation accuracy and overcome data processing challenges are ongoing.