Assessing the PM<sub>2.5</sub>–O<sub>3</sub> Correlation and Unraveling Their Drivers in Urban Environment: Insights from the Bohai Bay Region, China
Understanding the correlation between PM<sub>2.5</sub> and O<sub>3</sub> is critical for complex air pollution control. This study comprehensively analyzed PM<sub>2.5</sub> and O<sub>3</sub> pollution characteristics, uncovered spatiotemporal variation...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/5/512 |
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| Summary: | Understanding the correlation between PM<sub>2.5</sub> and O<sub>3</sub> is critical for complex air pollution control. This study comprehensively analyzed PM<sub>2.5</sub> and O<sub>3</sub> pollution characteristics, uncovered spatiotemporal variations in their correlation, and investigated the driving mechanisms of their association in Dongying, a typical petrochemical city in China’s Bohai Bay region. Results showed that PM<sub>2.5</sub>–O<sub>3</sub> correlation in Dongying exhibited significant seasonal variations, spatial patterns, and concentration threshold effects from 2017 to 2023. PM<sub>2.5</sub> and O<sub>3</sub> showed strong positive correlations in summer, negative in winter, and weak positive in spring/autumn, with strongest links in western areas. The strongest positive PM<sub>2.5</sub>–O<sub>3</sub> correlation occurred in summer when PM<sub>2.5</sub> ≤ 35 μg·m<sup>−3</sup> and O<sub>3</sub> >160 μg·m<sup>−3</sup>, while the strongest negative correlation was exhibited in winter with PM<sub>2.5</sub> > 75 μg·m<sup>−3</sup> and O<sub>3</sub> ≤ 100 μg·m<sup>−3</sup>. Meteorological conditions (T > 20 °C, RH < 30%, wind speed < 1.73 m/s, O<sub>x</sub> > 125 μg·m<sup>−3</sup>) and non-sea-breeze periods enhanced the PM<sub>2.5</sub>–O<sub>3</sub> positive correlation. During the four typical pollution episodes, the positive PM<sub>2.5</sub>–O<sub>3</sub> correlation in summer was propelled by synchronous increases in O<sub>3</sub> and secondary components via shared precursors. In autumn, strong positivity resulted from secondary component–O<sub>3</sub> correlations (r > 0.7) and dominance of secondary formation in PM<sub>2.5</sub>. In winter, the negative correlation stemmed from primary emissions inhibiting photochemistry. Random forest analysis showed that O<sub>x</sub>, RH, and T drove positive PM<sub>2.5</sub>–O<sub>3</sub> correlation via photochemistry in summer, whereas winter primary emissions and NO titration caused negative correlation. This study offers guidance for the collaborative PM<sub>2.5</sub> and O<sub>3</sub> control in the petrochemical cities of the Bay region. |
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| ISSN: | 2073-4433 |