Prediction of the Tropospheric NO 2 Column Concentration and Distribution Using the Time Sequence-Based versus Influencing Factor-Based Random Forest Regression Model

Tunyang Geng, Tianzhen Ju (), Bingnan Li, Bin An and Haohai Su
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Tunyang Geng: College of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China
Tianzhen Ju: College of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China
Bingnan Li: Faculty of Atmospheric Remote Sensing, Shaanxi Normal University, Xi’an 710062, China
Bin An: Meteorology of Zhangjiachuan Hui Autonomous County, Tianshui 741000, China
Haohai Su: College of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China

Sustainability, 2023, vol. 15, issue 3, 1-15

Abstract: The prediction of air pollutants has always been an issue of great concern to the whole of society. In recent years, the prediction and simulation of air pollutants via machine learning have been widely used. In this study, we collected meteorological data and tropospheric NO 2 column concentration data in Beijing, China, between 2012 and 2020, and compared the two methods of time sequence-based and influencing factor-based random forest regression in predicting the tropospheric NO 2 column concentration. The results showed that prediction of the tropospheric NO 2 column concentration using random forest regression was affected by the changes of human activities, especially emergency events and policy variations. The advantage of time sequence analysis lies in its ability to calculate the distribution of air pollutants with a long-time scale of prediction, but it may produce large errors in numerical value. The advantage of influencing factor prediction lies in its high precision and that it can identify the specific impact of each influencing factor on the NO 2 column concentration, but it needs more data and work quantities before it can make a prediction about the future.

Keywords: nitrogen dioxide; air monitoring; meteorological factors; random forest regression model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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