# Foreseeable Co-occurring O3 and PM2.5 Pollution in Eastern China Driven by Climate Teleconnections

## TL;DR

This study explores how climate patterns influence the co-occurrence of ozone and PM2.5 pollution in Eastern China, offering insights for pollution control.

## Contribution

The study identifies climate teleconnections that predict co-occurring O3 and PM2.5 pollution three months in advance.

## Key findings

- Three major climate modes influence COP frequency in Eastern China.
- Preseasonal SST and SI signals can predict COP three months ahead.
- Climate factors like Arctic sea ice loss and NAO drive pollution patterns.

## Abstract

The co-occurrence
of surface ozone (O3) and particulate
matter (PM2.5) pollution (COP) has been frequently observed
in China, particularly in the North China Plain (NCP) during warmer
months, posing significant threats to human health and ecosystems.
However, the impact of climate factors on COP remains inadequately
understood. This study identifies three major modes of interannual
variability in the COP frequency in Eastern China, revealing a consistent
spatial pattern, a North–south dipole, and heightened sensitivity
in coastal regions. These modes are linked to preseasonal cooling
sea surface temperatures (SSTs) in the Western Pacific Ocean, Arctic
sea ice (SI) loss near the Barents Sea, and North Atlantic tripole
SST anomalies associated with the North Atlantic Oscillation, respectively.
Both observations and model simulations confirm that Western Pacific
cooling suppresses the Western Pacific subtropical high, promoting
pollutant accumulation in the NCP; Barents Sea SI loss triggers atmospheric
wave trains, facilitating water vapor transport to southern China
and air pollutants transport to Northern China, resulting in a North–south
dipole in COP frequency; and North Atlantic Oscillation (NAO)-driven
SST anomalies generate westerly wind anomalies, driving pollutants
to coastal regions of Eastern China. A model that incorporates preseasonal
SST and SI signals is demonstrated to be capable of predicting COP
frequency three months in advance. Our results could allow the Chinese
government to improve plans for pollution control and safeguard the
health of both humans and ecosystems.