Park Chanhyeok, Yu Jaehyung, Park Bum-Jin, Wang Lei, Lee Yun Gon
Department of Astronomy, Space Science and Geology, Chungnam National University, Daejeon, 34134, Korea.
Department of Geological Sciences, Chungnam National University, Daejeon, 34134, Korea.
Environ Sci Pollut Res Int. 2023 Jan;30(1):2260-2272. doi: 10.1007/s11356-022-22242-2. Epub 2022 Aug 5.
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.
本研究使用光谱仪和高光谱成像仪分析了暴露于颗粒物（以下简称PM）的松树的光谱变化，以得出检测松针暴露于PM排放的最有效光谱指数。我们发现，近红外（以下简称NIR）波段的光谱变化与PM浓度变化系统性地一致，柴油组的变化更大，而较大的尘埃颗粒在可见光和NIR波段均显示出光谱变化。这是因为针上的PM吸附是NIR波段变化的主要来源，可见光和NIR光谱的组合可以检测到PM吸收。选择了14个波段对暴露于PM的松树进行分类，准确率为82%，kappa系数为0.61。鉴于该指数同时使用了可见光和NIR波段，它将能够检测到PM吸附。这些发现可转化为实际应用，用于监测城市地区的空气污染。
