Multi-Site and Multi-Pollutant Air Quality Data Modeling

Min Hu, Bin Liu () and Guosheng Yin
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Min Hu: School of Finance, Southwestern University of Finance and Economics, Chengdu 610074, China
Bin Liu: Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu 610074, China
Guosheng Yin: Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China

Sustainability, 2023, vol. 16, issue 1, 1-16

Abstract: This study proposes a new method for predicting air quality in major industrialized cities around the world. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO 2 , CO, PM 2.5 , and PM 10 , over time. At every monitoring timestamp t , we observe one station × feature matrix x t of the pollutant data, which represents a spatio-temporal process. Traditional methods of prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5 ) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly non-stationary. This study has explicitly overcome the limitations of these two aspects, forming its unique contributions. Specifically, we propose a de-trending graph convolutional LSTM (long short-term memory) to continuously predict the whole station × feature matrix in the next 1 to 48 h, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transforms a non-stationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city of Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

Keywords: air quality; multi-pollutant prediction; graph convolutional neural network; long short-term memory (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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