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<p>Biogenic volatile organic compounds (BVOCs) are critical to biosphere-atmosphere interactions, profoundly influencing atmospheric chemistry, air quality and climate, yet accurately estimating their emissions across diverse ecosystems remains challenging. Here we introduce GEE-MEGAN, a cloud-native extension of the widely used MEGAN2.1 model, integrating dynamic satellite-derived land cover and vegetation within Google Earth Engine to produce near-real-time BVOC emissions at 10-30 m resolution, enabling fine-scale tracking of emissions in rapidly changing environments. GEE-MEGAN reduces BVOC emission estimates by 31% and decreases root mean square errors by up to 48.6% relative to MEGAN2.1 in human-disturbed forest edges, and reveals summertime BVOC emissions up to 25‑fold higher than previous estimates in urban areas such as London, Los Angeles, Paris, and Beijing. By capturing fine-scale landscape heterogeneity and human-driven dynamics, GEE-MEGAN significantly improves BVOC emission estimates, providing crucial insights to the complex interactions among BVOCs, climate, and air quality across both natural and human-modified environments.</p>

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Improved modelling of biogenic emissions in human-disturbed forest edges and urban areas

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月28日

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  • Yanli Zhang,
  • Haofan Ran,
  • Alex Guenther,
  • Qiang Zhang,
  • Christian George,
  • Wahid Mellouki,
  • Guoying Sheng,
  • Ping’an Peng,
  • Xinming Wang

摘要

Biogenic volatile organic compounds (BVOCs) are critical to biosphere-atmosphere interactions, profoundly influencing atmospheric chemistry, air quality and climate, yet accurately estimating their emissions across diverse ecosystems remains challenging. Here we introduce GEE-MEGAN, a cloud-native extension of the widely used MEGAN2.1 model, integrating dynamic satellite-derived land cover and vegetation within Google Earth Engine to produce near-real-time BVOC emissions at 10-30 m resolution, enabling fine-scale tracking of emissions in rapidly changing environments. GEE-MEGAN reduces BVOC emission estimates by 31% and decreases root mean square errors by up to 48.6% relative to MEGAN2.1 in human-disturbed forest edges, and reveals summertime BVOC emissions up to 25‑fold higher than previous estimates in urban areas such as London, Los Angeles, Paris, and Beijing. By capturing fine-scale landscape heterogeneity and human-driven dynamics, GEE-MEGAN significantly improves BVOC emission estimates, providing crucial insights to the complex interactions among BVOCs, climate, and air quality across both natural and human-modified environments.