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- Title
Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations.
- Authors
Tao, Chenliang; Peng, Yanbo; Zhang, Qingzhu; Zhang, Yuqiang; Gong, Bing; Wang, Qiao; Wang, Wenxing
- Abstract
Narrowing surface ozone disparities between urban and nonurban areas escalate health risks in densely populated urban zones. A comprehensive understanding of the impact of ozone photochemistry on this transition remains constrained by current knowledge of aerosol effects and the availability of surface monitoring. Here we reconstructed spatiotemporal gapless air quality concentrations using a novel transformer deep learning (DL) framework capable of perceiving spatiotemporal dynamics to analyze ozone urban–nonurban differences. Subsequently, the photochemical effect on these discrepancies was analyzed by elucidating shifts in ozone regimes inferred from an interpretable machine learning method. The evaluations of the model exhibited an average out-of-sample cross-validation coefficient of determination of 0.96, 0.92, and 0.95 for ozone, nitrogen dioxide, and fine particulate matter (PM 2.5), respectively. The ozone sensitivity in nonurban areas, dominated by a nitrogen-oxide-limited (NO x -limited) regime, was observed to shift towards increased sensitivity to volatile organic compounds (VOCs) when extended to urban areas. A third "aerosol-inhibited" regime was identified in the Jiaodong Peninsula, where the uptake of hydroperoxyl radicals onto aerosols suppressed ozone production under low NO x levels during summertime. The reduction of PM 2.5 could increase the sensitivity of ozone to VOCs, necessitating more stringent VOC emission abatement for urban ozone mitigation. In 2020, urban ozone levels in Shandong surpassed those in nonurban areas, primarily due to a more pronounced decrease in the latter resulting from stronger aerosol suppression effects and less reduction in PM 2.5. This case study demonstrates the critical need for advanced spatially resolved models and interpretable analysis in tackling ozone pollution challenges.
- Subjects
SHANDONG Sheng (China); TRANSFORMER models; PHOTOCHEMICAL smog; OZONE generators; DEEP learning; PARTICULATE matter; ZONING; VOLATILE organic compounds; NITROGEN oxides; CARBONACEOUS aerosols
- Publication
Atmospheric Chemistry & Physics, 2024, Vol 24, Issue 7, p4177
- ISSN
1680-7316
- Publication type
Article
- DOI
10.5194/acp-24-4177-2024