Detecting atmospheric oxidation in the PM<sub>2.5</sub> and ozone multilayer complex network

Abstract Reducing the levels of Fine particulate matter (PM 2.5 ) and ozone (O 3 ) is essential for further improving air quality in China. The intricate and non-linear interactions between PM 2.5 and O 3 on a national scale over long-term records are not well understood. Here, we proposed a novel multi-layer network approach to bridge the gap. Our findings reveal that the variability of O 3 precedes that of PM 2.5 by one day. The degree, weighted degree, and edge distance follow a power-law decay distribution, suggesting that their relationship is not a random process. O 3 concentration in the Yangtze River Delta (YRD) region and eastern Shandong Province significantly impacts the PM 2.5 levels in surrounding areas. Conversely, PM 2.5 concentrations in Shandong Province, Henan Province, YRD and Pearl River Delta (PRD) have a strong influence on O 3 levels. Moreover, we quantified the seasonality of the interactions and elucidated the underlying reasons. The results revealed the interactions between PM 2.5 and O 3 are intricately tied to atmospheric oxidation processes. More specifically, in summer, the atmospheric oxidation has a strong impact on the interactions in key regions, such as the Beijing–Tianjin–Hebei,YRD region and Fenwei Plain. However, the PRD region experiences a more pronounced effect from atmospheric oxidation on this relationship in winter. These findings demonstrate that it is crucial to effectively regulate atmospheric oxidation to mitigate PM 2.5 and O 3 . Our results also serve as a valuable methodological framework for understanding the characteristics of pollutants.

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Publication Details

Journal
Environmental Research Letters
Published
2024-09-09
DOI
https://doi.org/10.1088/1748-9326/ad7869
Citations
1
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.21

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article

Detecting atmospheric oxidation in the PM2.5 and ozone multilayer complex network

Zhi-Dan Zhao, Yi Tang, Yu Liu, Na Ying et al.
1 citations
Environmental Research Letters
Air Quality Monitoring and Forecasting
0.21
article

Detecting atmospheric oxidation in the PM2.5 and ozone multilayer complex network

Zhi-Dan Zhao, Yi Tang, Yu Liu, Na Ying, Zhigang Xue, Di Wang, Jingfang Fan
article en
1 citations

Abstract

Abstract Reducing the levels of Fine particulate matter (PM 2.5 ) and ozone (O 3 ) is essential for further improving air quality in China. The intricate and non-linear interactions between PM 2.5 and O 3 on a national scale over long-term records are not well understood. Here, we proposed a novel multi-layer network approach to bridge the gap. Our findings reveal that the variability of O 3 precedes that of PM 2.5 by one day. The degree, weighted degree, and edge distance follow a power-law decay distribution, suggesting that their relationship is not a random process. O 3 concentration in the Yangtze River Delta (YRD) region and eastern Shandong Province significantly impacts the PM 2.5 levels in surrounding areas. Conversely, PM 2.5 concentrations in Shandong Province, Henan Province, YRD and Pearl River Delta (PRD) have a strong influence on O 3 levels. Moreover, we quantified the seasonality of the interactions and elucidated the underlying reasons. The results revealed the interactions between PM 2.5 and O 3 are intricately tied to atmospheric oxidation processes. More specifically, in summer, the atmospheric oxidation has a strong impact on the interactions in key regions, such as the Beijing–Tianjin–Hebei,YRD region and Fenwei Plain. However, the PRD region experiences a more pronounced effect from atmospheric oxidation on this relationship in winter. These findings demonstrate that it is crucial to effectively regulate atmospheric oxidation to mitigate PM 2.5 and O 3 . Our results also serve as a valuable methodological framework for understanding the characteristics of pollutants.

Environmental Research LettersVol. 19(10)
Peking University (CN), Beijing Normal University (CN), Shantou University (CN), Chinese Research Academy of Environmental Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 51%
Air Quality Monitoring and Forecasting
0.21
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