Articles | Volume 24, issue 7
https://doi.org/10.5194/acp-24-4177-2024
https://doi.org/10.5194/acp-24-4177-2024
Research article
 | 
08 Apr 2024
Research article |  | 08 Apr 2024

Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations

Chenliang Tao, Yanbo Peng, Qingzhu Zhang, Yuqiang Zhang, Bing Gong, Qiao Wang, and Wenxing Wang

Data sets

Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data. Chenliang Tao https://doi.org/10.5281/zenodo.10071408

Model code and software

Air-Transformer Chenliang Tao https://github.com/myles-tcl/Air-Transformer

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Short summary
We developed a novel transformer framework to bridge the sparse surface monitoring for inferring ozone–NOx–VOC–aerosol sensitivity and their urban–nonurban discrepancies at a finer scale with implications for improving our understanding of ozone variations. The change in urban–rural disparities in ozone was dominated by PM2.5 from 2019 to 2020. An aerosol-inhibited regime on top of the two traditional NOx- and VOC-limited regimes was identified in Jiaodong Peninsula, Shandong, China.
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