Prediction of the air quality index of Hefei based on an improved ARIMA model

Jia-Bao Liu; Xi-Yu Yuan; Jia-Bao Liu; Xi-Yu Yuan

doi:10.3934/math.2023953

AIMS Mathematics

2023, Volume 8, Issue 8: 18717-18733. doi: 10.3934/math.2023953

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Prediction of the air quality index of Hefei based on an improved ARIMA model

Jia-Bao Liu ^,,
Xi-Yu Yuan ^,

School of Mathematics and Physics, Anhui Jianzhu University, Hefei 23060, China

Received: 04 December 2022 Revised: 06 May 2023 Accepted: 12 May 2023 Published: 02 June 2023

With the rapid development of the economy, the air quality is facing increasingly severe pollution challenges. The air quality is related to public health and the sustainable development of the environment of China. In this paper, we first investigate the changes in the monthly air quality index data of Hefei from 2014 to 2020. Second, we analyze whether the Spring Festival factors lead to the deterioration of the air quality index according to the time sequence. Third, we construct an improved model to predict the air quality index of Hefei. There are three primary discoveries: (1) The air quality index of Hefei has obvious periodicity and a trend of descent. (2) The influencing factors of Spring Festival have no significant effect on the air quality index series. (3) The air quality index of Hefei will maintain a fluctuating and descending trend for a period of time. Finally, some recommendations for the air quality management policy in Hefei are presented based on the obtained results.
- air quality index,
- univariate regression,
- prediction model,
- X-12-ARIMA
Citation: Jia-Bao Liu, Xi-Yu Yuan. Prediction of the air quality index of Hefei based on an improved ARIMA model[J]. AIMS Mathematics, 2023, 8(8): 18717-18733. doi: 10.3934/math.2023953

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Abstract

With the rapid development of the economy, the air quality is facing increasingly severe pollution challenges. The air quality is related to public health and the sustainable development of the environment of China. In this paper, we first investigate the changes in the monthly air quality index data of Hefei from 2014 to 2020. Second, we analyze whether the Spring Festival factors lead to the deterioration of the air quality index according to the time sequence. Third, we construct an improved model to predict the air quality index of Hefei. There are three primary discoveries: (1) The air quality index of Hefei has obvious periodicity and a trend of descent. (2) The influencing factors of Spring Festival have no significant effect on the air quality index series. (3) The air quality index of Hefei will maintain a fluctuating and descending trend for a period of time. Finally, some recommendations for the air quality management policy in Hefei are presented based on the obtained results.

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