Coastal ozone dynamics and formation regime in Eastern China: Integrating trend decomposition and machine learning techniques

Machine-learning is a robust technique for understanding pollution characteristics of surface ozone, which are at high levels in urban China. This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China. During the period of 2017–2022, significant inter-annual fluctuations emerged, with peaks in mid-2017 attributed to volatile organic compounds (VOCs), and in late-2019 influenced by air temperature. Multifaceted periodicities (daily, weekly, holiday, and yearly) in ozone were revealed, elucidating substantial influences of daily and yearly components on ozone periodicity. A VOC-sensitive ozone formation regime was identified, characterized by lower VOCs/NO_x ratios (average = 0.88) and significant positive correlations between ozone and VOCs. This interplay manifested in elevated ozone during weekends, holidays, and pandemic lockdowns. Key variables influencing ozone across diverse timescales were uncovered, with solar radiation and temperature driving daily and yearly ozone variations, respectively. Precursor substances, particularly VOCs, significantly shaped weekly/holiday patterns and long-term trends of ozone. Specifically, acetone, ethane, hexanal, and toluene had a notable impact on the multi-year ozone trend, emphasizing the urgency of VOC regulation. Furthermore, our observations indicated that NO_x primarily drived the stochastic variations in ozone, a distinguishing characteristic of regions with heavy traffic. This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machine-learning methods in atmospheric pollution studies, with implications for targeted mitigation strategies beyond this specific region and pollutant.