Truax Kelly, Dulai Henrietta, Misra Anupam, Kuhne Wendy, Fuleky Peter, Smith Celia, Garces Milton
Department of Earth Sciences, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA.
Savannah River National Laboratory, Aiken, SC 29831, USA.
Plants (Basel). 2023 Aug 30;12(17):3124. doi: 10.3390/plants12173124.
The ability to detect, measure, and locate the source of contaminants, especially heavy metals and radionuclides, is of ongoing interest. A common tool for contaminant identification and bioremediation is vegetation that can accumulate and indicate recent and historic pollution. However, large-scale sampling can be costly and labor-intensive. Hence, non-invasive in-situ techniques such as laser-induced fluorescence (LIF) are becoming useful and effective ways to observe the health of plants through the excitation of organic molecules, e.g., chlorophyll. The technique presented utilizes images collected of LIF in moss to identify different metals and environmental stressors. Analysis through image processing of LIF response was key to identifying Cu, Zn, Pb, and a mixture of the metals at nmol/cm levels. Specifically, the RGB values from each image were used to create density histograms of each color channel's relative pixel abundance at each decimal code value. These histograms were then used to compare color shifts linked to the successful identification of contaminated moss samples. Photoperiod and extraneous environmental stressors had minimal impact on the histogram color shift compared to metals and presented with a response that differentiated them from metal contamination.
检测、测量和定位污染物源(尤其是重金属和放射性核素)的能力一直备受关注。用于污染物识别和生物修复的一种常见工具是能够积累并指示近期和历史污染情况的植被。然而,大规模采样可能成本高昂且耗费人力。因此,诸如激光诱导荧光(LIF)之类的非侵入式原位技术正成为通过激发有机分子(如叶绿素)来观察植物健康状况的有用且有效的方法。所介绍的这项技术利用在苔藓中收集的LIF图像来识别不同的金属和环境应激源。通过对LIF响应进行图像处理分析是识别纳摩尔/厘米水平的铜、锌、铅以及这些金属混合物的关键。具体而言,来自每张图像的RGB值被用于创建每个颜色通道在每个十进制代码值处相对像素丰度的密度直方图。然后,这些直方图被用于比较与成功识别受污染苔藓样本相关的颜色变化。与金属相比,光周期和外部环境应激源对直方图颜色变化的影响最小,并且呈现出能将它们与金属污染区分开来的响应。