Unveiling nature's secrets: Deep learning for enhanced biogenic emission resolution
Natural ecosystems contribute significantly to releasing numerous chemical substances into the atmosphere, including various volatile and semi-volatile compounds. A significant group of these chemicals, emitted predominantly by plants, are known as Biogenic Volatile Organic Compounds (BVOCs). These...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Science Talks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772569325000155 |
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| Summary: | Natural ecosystems contribute significantly to releasing numerous chemical substances into the atmosphere, including various volatile and semi-volatile compounds. A significant group of these chemicals, emitted predominantly by plants, are known as Biogenic Volatile Organic Compounds (BVOCs). These compounds, such as carbon monoxide and nitric oxide, play a pivotal role in atmospheric processes and have become a key focus of research over the past two decades due to their influence on atmospheric chemistry.Studying BVOC emissions is essential for numerical evaluations of past, current, and future air quality and climate conditions. To support such studies, quantitative estimations of BVOC emissions are required. As a result, various ground-based measurement techniques have been developed to sample BVOC emissions at multiple scales, from the leaf level to regional and global scales.However, current BVOC measurements are often limited in space and time, as generating a fine-grained map of BVOC emissions over a large region is costly and time-consuming. Consequently, many existing BVOC emission maps may not be fully suitable for reliable atmospheric, climate, and forecasting model simulations.My research aims to explore and assess the use of novel AI-based algorithms to improve the spatiotemporal modeling of BVOC emissions. By enhancing these models, we can assist policymakers in developing more effective regulations to address climate change, reduce the environmental impact of industrial activities, and mitigate the harmful effects of emissions on human health.This technology has practical applications in agriculture, forestry, and urban planning. For instance, understanding gas emissions from crops can help farmers optimize their activities, reducing fertilizer and pesticide use while improving yields. In forestry, better management practices can minimize the environmental impact of logging. In urban planning, accurate gas emission maps can inform the design of green spaces and other urban features, helping reduce emissions and health impacts on city populations.Additionally, this research can generate dense datasets for atmospheric chemistry, climate, and air quality models. These data can help capture small-scale processes, improve our understanding of BVOC interactions with other chemical compounds, and better quantify emissions caused by abiotic stress and ozone stress.As the need to tackle atmospheric chemical shifts and climate change intensifies, BVOC emission maps are emerging as critical resources for enhancing our understanding of these compounds' impact on Earth's future. This research marks a vital advancement in pursuing a more sustainable and environmentally conscious future for both present and future generations. |
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| ISSN: | 2772-5693 |