Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference

Abstract

Urban forests play a crucial role in both emitting and absorbing atmospheric pollutants. Understanding the ecological processes of biogenic volatile organic compounds (BVOCs) and secondary organic aerosols (SOAs) and their interactions in urban forests can help to assess how they influence air quality. Additionally, exploring the adaptation and feedback mechanisms between urban forests and their surrounding environments can identify new pollutants and potential risks in urban forests. However, the relationship between BVOC emissions and SOA formation is complex due to the influence of meteorological conditions, photochemical reactions, and other factors. This complexity makes it challenging to accurately describe this relationship. In this study, we used time-of-flight mass spectrometry and aerosol particle size spectrometry to monitor concentrations of BVOCs and particulate matter with a diameter less than 1 µm (PM1; representing SOAs) at a frequency of 10–12 times per min in an urban forest near Shanghai. We then analyzed the temporal changes in concentrations of BVOCs, SOAs, and other chemical pollutants in different periods of the day by using subsequence clustering and causal inference methods. The results showed that after using this method for diurnal segmentation, PM1 prediction accuracy was improved by 26.77%–47.51%, and the interaction rules of BVOCs and SOAs had sequential interaction characteristics. During the day, BVOCs are an important source of SOAs and have a negative feedback relationship with O3. From night to early morning, BVOCs have a positive, balanced relationship with O3, SOAs are affected by wind speed or deposition, BVOCs have no obvious relationship with O3, and SOAs are affected by temperature or humidity. This study is the first to apply Toeplitz inverse covariance-based clustering and causal inference methods for the high-frequency monitoring of BVOCs and SOAs, revealing the temporal effects and characteristics of BVOCs and SOAs and providing a scientific basis and new methods for understanding the dynamic effects of urban forest communities on the environment.