Statistical predictability of wintertime PM2.5 concentrations over East Asia using simple linear regression

The interannual meteorological variability plays an important role in wintertime air quality in East Asia. In particular, monsoons and the El Nino Southern Oscillation (ENSO) are known as important mechanisms for determining wintertime PM2.5 concentrations. In addition, Arctic Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation are also known to affect meteorological conditions and thus PM2.5 concentrations in East Asia. Here, we used a global 3-D chemical transport model (GEOS-Chem) with assimilated meteorological fields to investigate the long-term (1980-2014) relationship between 16 different climate indices and wintertime PM2.5 concentrations in this region. We show that wintertime PM2.5 concentrations in Northeast Asia (33-41 degrees N,118-141 degrees E) are highly correlated with ENSO indices and the Siberian high-pressure system. Furthermore, we develop a simple linear regression (SLR) model for the prediction of wintertime PM2.5 concentrations. Despite the use of a single predictor, the SLR model shows good performance with r > 0.72 in reproducing targeted PM2.5 concentrations. The hit and false alarm rates are 77% and 11%, respectively, indicating the high predictive accuracy of the model. In particular, the model shows excellent performance for capturing the abnormal variability of wintertime PM2.5 concentrations in Northeast Asia. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study reveals the impact of monsoons, ENSO, and other climate mechanisms on wintertime air quality in East Asia. Using a global 3-D chemical transport model, researchers found a strong correlation between PM2.5 concentrations and ENSO indices in Northeast Asia. A simple linear regression model was developed for predicting PM2.5 concentrations with high accuracy, especially in capturing abnormal variability in this region.