Deciphering isoprene variability across dozen of Chinese and overseas cities using deep transfer learning
Year: 2026
Type: article
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.
Institutions: Sichuan University, Hong Kong Baptist University, University of Birmingham, Nanjing University, Shanghai Academy of Environmental Sciences, Jinan University, Chinese Research Academy of Environmental Sciences, Nankai University, Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangdong Province Environmental Monitoring Center, University of California, Irvine
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,725
OpenAlex ID: W7123558079