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Rising atmospheric levels of fine particulate matter reduce the degree of linear polarisation of light

Many insects utilise optical information in linearly polarised light for navigation, with the degree of linear polarisation (DoLP) determining whether the ‘visibility’ of such optical information is available to them. However, changes in degree of linear polarisation in response to increased atmosph...

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Bibliographic Details
Published in:Communications earth & environment 2024-09, Vol.5 (1), p.517-10, Article 517
Main Authors: Cho, Yoori, Jeong, Sujong, Chang, Dong Yeong, Lelieveld, Jos, Pozzer, Andrea, Park, Chang-Eui, Joo, Jaewon, Park, Chan-Ryul
Format: Article
Language:English
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Summary:Many insects utilise optical information in linearly polarised light for navigation, with the degree of linear polarisation (DoLP) determining whether the ‘visibility’ of such optical information is available to them. However, changes in degree of linear polarisation in response to increased atmospheric levels of fine particulate matter (PM2.5) are poorly understood. We present analyses based on both ground-based monitoring and particulate matter modelling, establishing a quantitative relationship between PM2.5 mass concentration and the DoLP. We apply this relationship to a global PM2.5 projection for 2050 and estimate the increase in number and spatial extent of low visibility days for honeybees. We find an increase by up to 20% in the geographical extent of low visibility days in 2050, with an augmented frequency of low visibility days across an area exceeding 0.75 million km2 in India and 2 million km2 in China. More frequent and widespread low visibility conditions can reduce the ability of insects to navigate, especially in hotspot regions.The degree of linear polarisation of light in the atmosphere, one factor used by honeybees for navigation, decreases with rising atmospheric particulate matter concentrations, which are projected to rise to 2050, according to ground-based in-situ light polarisation measurements in Korea and global particulate matter modelling.
ISSN:2662-4435
2662-4435
DOI:10.1038/s43247-024-01682-3