Deciphering Isoprene Variability Across Dozen of Chinese and Overseas Cities Using Deep Transfer Learning

Abstract. Isoprene, the globally most abundant volatile organic compound, significantly impacts air quality. Determining isoprene concentration variations and their drivers is a persistent challenge. Here, we developed a robust machine learning framework to simulate isoprene concentrations, without requiring localized emission inventories and explicit chemistry. Temperature, radiation, and surface pressure were the primary drivers of short-term isoprene variations across Chinese cities. On climatic timescales, urban greenspace expansion and climate warming drove isoprene increases by 341 pptv in Hong Kong during 1990–2023, but traffic emission reductions in London counteracted the isoprene rise that climate warming would have otherwise caused (-755 pptv vs. +31 pptv). Driven by rising temperatures and isoprene levels, ozone would increase by up to 1.7-fold by 2100 under the high-emission scenario. However, ambitious reduction in nitrogen oxides would alleviate this growth to 1.2-fold. The study has the potential to inform air quality management in a warming climate.

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Published
2025-10-14
DOI
https://doi.org/10.5194/egusphere-2025-4644
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Deciphering Isoprene Variability Across Dozen of Chinese and Overseas Cities Using Deep Transfer Learning

Xiaopu Lyu, Tengyu Liu, Guoliang Shi, Li Zhou et al.
Advanced Chemical Sensor Technologies
article

Deciphering Isoprene Variability Across Dozen of Chinese and Overseas Cities Using Deep Transfer Learning

Xiaopu Lyu, Tengyu Liu, Guoliang Shi, Li Zhou, Mei Li, Chengxu Tong, Alex Guenther, Fumo Yang, Zongbo Shi, Song Liu, Jian Gao, Nan Wang, Hongli Wang, Nan Chen, Xin Huang, Xin-Yi Liu, Yu Zou, Chenglei Pei
article en

Abstract

Abstract. Isoprene, the globally most abundant volatile organic compound, significantly impacts air quality. Determining isoprene concentration variations and their drivers is a persistent challenge. Here, we developed a robust machine learning framework to simulate isoprene concentrations, without requiring localized emission inventories and explicit chemistry. Temperature, radiation, and surface pressure were the primary drivers of short-term isoprene variations across Chinese cities. On climatic timescales, urban greenspace expansion and climate warming drove isoprene increases by 341 pptv in Hong Kong during 1990–2023, but traffic emission reductions in London counteracted the isoprene rise that climate warming would have otherwise caused (-755 pptv vs. +31 pptv). Driven by rising temperatures and isoprene levels, ozone would increase by up to 1.7-fold by 2100 under the high-emission scenario. However, ambitious reduction in nitrogen oxides would alleviate this growth to 1.2-fold. The study has the potential to inform air quality management in a warming climate.

China Meteorological Administration (CN), Hong Kong Baptist University (HK), Jinan University (CN), University of California, Irvine (US), Nankai University (CN), Sichuan University (CN), Guangdong Province Environmental Monitoring Center (CN), Shanghai Academy of Environmental Sciences (CN), Chinese Research Academy of Environmental Sciences (CN), University of Birmingham (GB), Nanjing University (CN)
Hong Kong Baptist University, National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 72%
Advanced Chemical Sensor Technologies
DOIOpen Access PDFOpenAlex
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