Research on prediction of environmental aerosol and PM2.5 based on artificial neural network

With the increasing severity of air pollution, PM2.5 in aerosols, as the most important air pollutant, has adversely affected people's normal production, life and work, and has caused harm to people's health. Scientific and effective prediction of PM2.5 can enable people to take precautions in advance to avoid or reduce harm to the human body. Therefore, the prediction of PM2.5 concentration has become a topic of great practical significance. This paper selects the air quality data released in real time, obtains the historical monitoring data of air environmental pollutants, and normalizes the data, then divides the sample data, and divides it into training data set and test data set in appropriate proportion. Design the optimal network structure based on BP neural network. An improved neural network is proposed, and the neural network is optimized using genetic algorithms. The preprocessed data are input into the network for training and testing. The fitting and prediction results were statistically and comparatively analyzed. The data results show that the neural network optimized by genetic algorithm has better performance in PM2.5 mass concentration prediction, which improves the accuracy of prediction results and reduces the error rate.