Abstract
This study analyzed spectral variations of the particulate matter (PM hereafter)-exposed pine trees using a spectrometer and a hyperspectral imager to derive the most effective spectral indices to detect the pine needle exposure to PM emission. We found that the spectral variation in the near-infrared (NIR hereafter) bands systemically coincided with the variations in PM concentration, showing larger variations for the diesel group whereas larger dust particles showed spectral variations in both visible and NIR bands. It is because the PM adsorption on needles is the main source of NIR band variation, and the combination of visible and NIR spectra can detect PM absorption. Fourteen bands were selected to classify PM-exposed pine trees with an accuracy of 82% and a kappa coefficient of 0.61. Given that this index employed both visible and NIR bands, it would be able to detect PM adsorption. The findings can be transferred to real-world applications for monitoring air pollution in an urban area.
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The data and materials used during the current study are available from the corresponding on reasonable request.
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This work was supported by the National Research Foundation (NRF) of Korea grant by the Korean Government (NRF-2020R1A2C2005439).
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Chanhyeok Park carried out the experiments design, the experimental works, the data collection, the variable measurement, data analysis, and manuscript drafting. Jaehyung Yu supervised the project and edited the paper. He is corresponding author, as well. Bum-Jin Park supervised the project. Lei Wang edited the paper and analyzed data. Yun Gon Lee analyzed data and conducted statistical analysis.
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Park, C., Yu, J., Park, BJ. et al. Imaging particulate matter exposed pine trees by vehicle exhaust experiment and hyperspectral analysis. Environ Sci Pollut Res 30, 2260–2272 (2023). https://doi.org/10.1007/s11356-022-22242-2
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DOI: https://doi.org/10.1007/s11356-022-22242-2