Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference
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 quali...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/14/8/1617 |
| _version_ | 1797584730930020352 |
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| author | Yuchong Long Wenwen Zhang Ningxiao Sun Penghua Zhu Jingli Yan Shan Yin |
| author_facet | Yuchong Long Wenwen Zhang Ningxiao Sun Penghua Zhu Jingli Yan Shan Yin |
| author_sort | Yuchong Long |
| collection | DOAJ |
| description | 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 (PM<sub>1</sub>; 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, PM<sub>1</sub> 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 O<sub>3</sub>. From night to early morning, BVOCs have a positive, balanced relationship with O<sub>3</sub>, SOAs are affected by wind speed or deposition, BVOCs have no obvious relationship with O<sub>3</sub>, 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. |
| first_indexed | 2024-03-10T23:56:45Z |
| format | Article |
| id | doaj.art-e9ffaa58a7aa4252bb0e6c95f0e8f3ce |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T23:56:45Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-e9ffaa58a7aa4252bb0e6c95f0e8f3ce2023-11-19T01:09:27ZengMDPI AGForests1999-49072023-08-01148161710.3390/f14081617Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal InferenceYuchong Long0Wenwen Zhang1Ningxiao Sun2Penghua Zhu3Jingli Yan4Shan Yin5School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, ChinaShanghai Urban Ecosystem National Observation and Research Station, National Forestry and Grassland Administration, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, ChinaUrban 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 (PM<sub>1</sub>; 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, PM<sub>1</sub> 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 O<sub>3</sub>. From night to early morning, BVOCs have a positive, balanced relationship with O<sub>3</sub>, SOAs are affected by wind speed or deposition, BVOCs have no obvious relationship with O<sub>3</sub>, 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.https://www.mdpi.com/1999-4907/14/8/1617urban forestssecondary organic aerosolsbiogenic volatile organic compoundscausal inferencehigh-frequency monitoringtemporal effects |
| spellingShingle | Yuchong Long Wenwen Zhang Ningxiao Sun Penghua Zhu Jingli Yan Shan Yin Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference Forests urban forests secondary organic aerosols biogenic volatile organic compounds causal inference high-frequency monitoring temporal effects |
| title | Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference |
| title_full | Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference |
| title_fullStr | Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference |
| title_full_unstemmed | Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference |
| title_short | Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference |
| title_sort | sequential interaction of biogenic volatile organic compounds and soas in urban forests revealed using toeplitz inverse covariance based clustering and causal inference |
| topic | urban forests secondary organic aerosols biogenic volatile organic compounds causal inference high-frequency monitoring temporal effects |
| url | https://www.mdpi.com/1999-4907/14/8/1617 |
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