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Comparison of two methods for indirect measurement of atmospheric dust deposition: Street-dust composition and vegetation-health status derived from hyperspectral image data

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Abstract

This study presents a statistical comparison between the in situ measurements of the elemental composition of street dust and a forest health status classification derived from aerial hyperspectral image data (HyMap). Combining these two methods allowed us to indirectly pinpoint at a high spatial resolution the atmospheric dust emissions and its effects in a study area around the open-pit lignite mine in Sokolov, Czech Republic. The results reveal a statistically significant relationship between increased Al, Na, Li and Sr levels in street dust and decreased forest health status, and the highest number of statistically significant correlations within a 100 m distance from the street-dust sampling points. Differences in lithological composition were unable to sufficiently explain these changes, therefore anthropogenic factors like dust emissions from coal mining and coal combustion, as well as urbanisation and other industries might be the reason for this link. Such studies are a crucial step in developing new high spatial resolution methods for determining atmospheric dust deposition and their effects.

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Acknowledegments

The study was funded from the 7th framework programme project EO-MINERS (Grant Agreement No. 244242), by the Slovenian Research Agency (Research Core Funding No. P1-0025) and by the Czech Science Foundation (Grant No. 17-05743S). Authors would also like to thank the Editor and anonymous reviewers for their valuable comments.

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  1. Geological Survey of Slovenia, Dimičeva ulica 14, 1000, Ljubljana, Slovenia

    Gorazd Žibret

  2. Czech Geological Survey, Klárov 3, Prague 1, 118 21, Czech Republic

    Veronika Kopačková

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  1. Gorazd Žibret
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  2. Veronika Kopačková
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Correspondence to Gorazd Žibret.

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Žibret, G., Kopačková, V. Comparison of two methods for indirect measurement of atmospheric dust deposition: Street-dust composition and vegetation-health status derived from hyperspectral image data. Ambio 48, 423–435 (2019). https://doi.org/10.1007/s13280-018-1093-0

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