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http://dx.doi.org/10.17661/jkiiect.2021.14.4.328

Analysis and Prediction of (Ultra) Air Pollution based on Meteorological Data and Atmospheric Environment Data  

Park, Hong-Jin (Dep. of Computer Engineering, Sangji University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.4, 2021 , pp. 328-337 More about this Journal
Abstract
Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.
Keywords
Air pollution; Ultra-air pollution; Meteorological data; Atmospheric environment data; Linear regression; SVM; Ensemble models;
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