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Accurately predicting the concentration of PM 2.5 (fine particles with a diameter of 2.5 μm or less) is essential for health risk assessment and formulation of air pollution control strategies. At present, there is also a large amount of air pollution data. How to efficiently mine its hidden features to obtain the future concentration of pollutants is very important for the prevention and control of air pollution. Therefore we build a pollutant prediction model based on Lightweight Gradient Boosting Model (LightGBM) shallow machine learning and Long Short-Term Memory (LSTM) neural network. Firstly, the PM 2.5 pollutant concentration data of 34 air quality stations in Beijing and the data of 18 weather stations were matched in time and space to obtain an input data set. Subsequently, the input data set was cleaned and preprocessed, and the training set was obtained by methods such as input feature extraction, input factor normalization, and data outlier processing. The hourly PM 2.5 concentration value prediction was achieved in accordance with experiments conducted with the hourly PM 2.5 data of Beijing from January 1, 2018 to October 1, 2020. Ultimately, the optimal hourly series prediction results were obtained after model comparisons. Through the comparison of these two models, it is found that the RMSE predicted by LSTM model for each pollutant is nearly 50% lower than that of LightGBM, and is more consistent with the fitting curve between the actual observations. The exploration of the input step size of LSTM model found that the accuracy of 3-h input data was higher than that of 12-h input data. It can be used for the management and decision-making of environmental protection departments and the formulation of preventive measures for emergency pollution incidents., (© 2022. The Author(s).)

The development of industry has brought about the pollution of the atmospheric environment. Pollution is harmful to people's health. Realizing the real-time monitoring of atmospheric environmental quality parameters can improve the above-mentioned effects. China's existing environmental monitoring systems focus on the accuracy of the system hardware itself for assessment, lack of data analysis and forecasting and early warning, and cannot provide managers and ordinary people with decision-making and activity guidance. This paper develops an IPV6-based high-spatial-temporal precision air pollutant monitoring and early warning platform. The feasibility of the system is verified through networking tests, operation tests, and early warning tests. Through actual data analysis and comparison, it is concluded that the monitoring system has field feasibility, and the atmospheric environment monitoring for the target observation area has achieved the desired observation function. This system integrates GIS technology and B/S architecture to analyze changes in the regional environment to provide support for regional environmental air quality management. The forecast and early warning module constructed by combining the weight method of the influence of different input factors on the environmental quality index and minute-level observations can provide technical support for the government to improve the level of supervision., (© 2022. The Author(s).)