posted on 2024-03-15, 04:30authored byZelin Mai, Huizhong Shen, Aoxing Zhang, Haitong Zhe Sun, Lianming Zheng, Jianfeng Guo, Chanfang Liu, Yilin Chen, Chen Wang, Jianhuai Ye, Lei Zhu, Tzung-May Fu, Xin Yang, Shu Tao
Ozone pollution is profoundly modulated
by meteorological
features
such as temperature, air pressure, wind, and humidity. While many
studies have developed empirical models to elucidate the effects of
meteorology on ozone variability, they predominantly focus on local
weather conditions, overlooking the influences from high-altitude
and broader regional meteorological patterns. Here, we employ convolutional
neural networks (CNNs), a technique typically applied to image recognition,
to investigate the influence of three-dimensional spatial variations
in meteorological fields on the daily, seasonal, and interannual dynamics
of ozone in Shenzhen, a major coastal urban center in China. Our optimized
CNNs model, covering a 13° × 13° spatial domain, effectively
explains over 70% of daily ozone variability, outperforming alternative
empirical approaches by 7 to 62%. Model interpretations reveal the
crucial roles of 2-m temperature and humidity as primary drivers,
contributing 16% and 15% to daily ozone fluctuations, respectively.
Regional wind fields account for up to 40% of ozone changes during
the episodes. CNNs successfully replicate observed ozone temporal
patterns, attributing −5–6 μg·m–3 of interannual ozone variability to weather anomalies. Our interpretable
CNNs framework enables quantitative attribution of historical ozone
fluctuations to nonlinear meteorological effects across spatiotemporal
scales, offering vital process-based insights for managing megacity
air quality amidst changing climate regimes.