Real-Time Monitoring and Early Warning Algorithm of Ozone + Nitrogen Oxides in High-Density Residential Space Based on Big Data

Abstract

In order to monitor ozone and nitrous oxide pollutants in residential space, artificial intelligence algorithm is integrated on the basis of traditional monitoring mode. Through the comparison of system detection accuracy and early warning sensitivity of different detection methods, it can be seen that the application of artificial intelligence detection system can improve the monitoring performance of ozone and realize the timeliness of large-scale monitoring coverage and data update. The accuracy and sensitivity of system detection under the two detection methods are analyzed, and the main harm caused by ozone pollution is analyzed. The significance of monitoring ozone pollution in ambient air is emphasized. The air index of high-density residential space using intelligent big data detection method is obviously superior to previous detection methods in terms of timeliness, accuracy, and early warning sensitivity of the system. It also provides an important data reference for the real-time monitoring and early warning of ozone + nitrogen oxides in high-density residential space, and also makes an important contribution to reducing the impact on residents’ health and living environment.