The interannual meteorological variability plays an important role in wintertime air quality in East Asia. In particular, monsoons and the El Niño Southern Oscillation (ENSO) are known as important mechanisms for determining wintertime PM_2.5 concentrations. In addition, Arctic Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation are also known to affect meteorological conditions and thus PM_2.5 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 PM_2.5 concentrations in this region. We show that wintertime PM_2.5 concentrations in Northeast Asia (33−41°N, 118−141°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 PM_2.5 concentrations. Despite the use of a single predictor, the SLR model shows good performance with r > 0.72 in reproducing targeted PM_2.5 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 PM_2.5 concentrations in Northeast Asia.

Capsule abstract

Simple linear regression is useful for predicting wintertime PM_2.5 concentrations in Northeast Asia.

中文翻译：

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使用简单线性回归的东亚冬季PM _2.5浓度的统计可预测性

年际气象变异性在东亚冬季空气质量中起着重要作用。特别是，季风和厄尔尼诺南方涛动（ENSO）是确定冬季PM _2.5浓度的重要机制。此外，北极涛动，北大西洋涛动和太平洋年代际涛动也已知会影响气象条件，从而影响东亚的PM _2.5浓度。在这里，我们使用了具有同化气象场的全球3-D化学迁移模型（GEOS-Chem），研究了该地区16种不同气候指数与冬季PM _2.5浓度之间的长期关系（1980-2014年）。我们展示了冬季的PM _2.5东北亚（33-41°N，118-141°E）的浓度与ENSO指数和西伯利亚高压系统高度相关。此外，我们开发了用于预测冬季PM _2.5浓度的简单线性回归（SLR）模型。尽管使用了单个预测变量，但SLR模型在再现目标PM _2.5浓度时仍显示出r > 0.72的良好性能。命中率和误报率分别为77％和11％，表明该模型具有较高的预测准确性。特别是，该模型显示出出色的性能，可捕获东北亚冬季PM _2.5浓度的异常变化。

胶囊摘要

简单的线性回归对于预测东北亚冬季的PM _2.5浓度很有用。