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Urban industrial plant areas are highly concentrated, and air pollution is increasingly serious. The quantity of outdoor air quality monitoring sites is insufficient. Aiming at the above questions, related studies propose solutions that use relatively cheap equipment networking to collect pollution data and accurately analyze local monitoring information. In this paper, a new type of outdoor air quality monitoring system is studied and preliminarily practiced and has proven certain feasibility and applicability. The main contributions of this paper are: first, we improve the network layout by employing the Zigbee network, which is combined with factory characteristics, and collected data on carbonic oxide, nitrogen dioxide, sulfur dioxide, ozone, particulate matter, temperature, and humidity. And then, to establish the dilution coefficient and diffusion coefficient of pollution diffusion, we adopt air movement as the energy model and, by utilizing the method of pollution traceability, achieve the complete coverage pollution monitoring of the whole city by local monitoring sites. Finally, we propose an improved long short-term memory (LSTM) method to predict the pollution period of urban air quality. The experimental results show that the improved LSTM prediction model has strong applicability and high accuracy in the period prediction of pollution weather. Meanwhile, by analyzing the specific case in detail, we prove that air pollution in the city is mainly caused by the manufacturing industry. We conclude that it will make a great contribution to the atmospheric environment protection of cities by using weather quality prediction to dynamically adjust the production.
Han et al. (Tue,) studied this question.