Climate modulation of external forcing factors on air quality change in Eastern China: Implications for PM2.5 seasonal prediction

Jinghui Ma; Yuanhao Qu; Zhongqi Yu; Shiquan Wan

doi:10.1016/j.scitotenv.2023.166989

Climate modulation of external forcing factors on air quality change in Eastern China: Implications for PM_2.5 seasonal prediction

Sci Total Environ. 2023 Dec 20:905:166989. doi: 10.1016/j.scitotenv.2023.166989. Epub 2023 Sep 24.

Authors

Jinghui Ma¹, Yuanhao Qu², Zhongqi Yu³, Shiquan Wan⁴

Affiliations

¹ Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai 200030, China; Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, China.
² Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai 200030, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, China. Electronic address: qyh611@126.com.
³ Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai 200030, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, China.
⁴ Yangzhou Meteorological Office, Yangzhou, China.

PMID: 37751842
DOI: 10.1016/j.scitotenv.2023.166989

Abstract

Meteorological conditions significantly influence the frequency and duration of air pollution events, making the prediction of seasonal variations of PM_2.5 concentration crucial for air quality control. This study analyzed the spatiotemporal variations of PM_2.5 concentration anomalies over the past 39 years (1980-2018) in winter (November to January) over eastern China based on the empirical orthogonal function (EOF) method. Regression analysis is conducted on external forcing factors such as sea ice, sea temperature, and snow cover in the pre-autumn (September to October) using the time series of the first three modes. Nine key factors were selected, which further led to establishing a model for predicting winter PM_2.5 concentration in eastern China using the long short-term memory deep learning algorithm (LSTM). Independent verification revealed that the predicted and observed PM_2.5 concentration distributions were consistent, with the absolute value of deviation within 15 μg·m^-3 between 2016 and 2018. The correlation coefficients between the predicted and observed values were between 0.42 and 0.93 over eight key cities in the past 10 years (2009-2018). The contribution rates of the nine factors to PM_2.5 concentration were calculated to explore their impact on PM_2.5 concentration during winter. The Arctic sea ice (ASI) was found to be the key contributor to the winter PM_2.5 concentration in eastern China. The predictors can be monitored in real time; hence, the model provides a real-time predictive tool, improving the prospects of predicting seasonal PM_2.5 pollution, especially in vulnerable regions such as eastern China.

Keywords: External forcing factor; LSTM; PM(2.5) concentration; Seasonal prediction.