Articles | Volume 26, issue 1
https://doi.org/10.5194/acp-26-635-2026
https://doi.org/10.5194/acp-26-635-2026
Research article
 | 
14 Jan 2026
Research article |  | 14 Jan 2026

Deciphering isoprene variability across dozen of Chinese and overseas cities using deep transfer learning

Song Liu, Xiaopu Lyu, Fumo Yang, Zongbo Shi, Xin Huang, Tengyu Liu, Hongli Wang, Mei Li, Jian Gao, Nan Chen, Guoliang Shi, Yu Zou, Chenglei Pei, Chengxu Tong, Xinyi Liu, Li Zhou, Alex B. Guenther, and Nan Wang

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Short summary
We studied the invisible gas isoprene, which trees and vehicles release into the air and which can worsen urban smog. Using advanced computer learning trained on measurements from many cities, we uncovered how temperature, sunlight, and city greening shape isoprene levels. Comparing Hong Kong and London, we found climate warming boosts isoprene and future ozone pollution, but strong cuts in anthropogenic emission could limit this impact.
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