Collection of excitation-emission-matrix fluorescence of aerosol-candidate-substances and its application to fluorescence lidar monitoring

Investigating the impact of attenuated fluorescence spectra on protein discrimination

The optical remote sensing techniques are promising for the real-time detection, and identification of different types of hazardous biological materials. However, the received fluorescent spectra from a remote distance suffer from the atmospheric attenuation effect upon the spectral shape. To investigate the influence of atmospheric attenuation on characterizing, and classifying biological agents, the laboratory-measured fluorescence data of fourteen proteins combined with the atmospheric transmission factors of the MODTRAN model were conducted with different detection ranges. The multivariate analysis techniques of principal component analysis (PCA) and linear discriminant analysis (LDA), and the predictors of Random Forest and XGBoost were employed to assess the separability and distinguishability of different spectra recorded. The results showed that the spectral-shift effect on attenuated spectra varied as a function of the detection range, the atmospheric visibility, and the spectral distribution. According to the PCA and LDA analysis, the distribution of decomposed factors changed in the spectral explanatory power with the increasing attenuation effect, which was consistent with the hierarchical clustering results. Random Forest exhibited higher performance in classifying protein samples than that of XGBoost, while the two methods performed similarly in identifying harmful and harmless subgroups of proteins. Fewer subgroups decreased the sensitivity of the classification accuracy to the attenuation effect. Our analysis demonstrated that combining atmospheric transport models to build a fluorescence spectral database is essential for fast identification between spectra, and reduced classification criteria could facilitate the compatibility of spectral database and classification algorithms.

Experimental development of a gated UV-induced spectroscopic lidar for the daytime study of plant ecology and photosynthesis: multi-modal measurement of fluorescence of trees growing in a field and Mie-Raman-fluorescence of the surrounding atmosphere

A gated UV-induced spectroscopic lidar operational during daylight was developed to better understand the plant growth status in real time and the influence from the surrounding atmosphere chemical environment. Initial indoor experiments and short-range (100 m) field measurements were very positive. The lidar worked as a vegetation fluorescence lidar, as well as an atmospheric Mie-Raman-fluorescence lidar. A UV (355 nm) laser was effective to induce fluorescence and Raman scattering, and a synchronous detection technique made it possible to detect weak signals, even in daytime. Tree spectra containing chlorophyll fluorescence of tree leaves offered information about the growth status of trees. Atmospheric spectra containing aerosol Mie scattering, N 2, O 2, H 2 O Raman scattering, and pollutant fluorescence helped us to learn about atmospheric circumstances surrounding trees. The multi-modal information is useful for comprehensive understanding of plant ecology.

Storage method of multi-channel lidar data based on tree structure

The multi-channel lidar has fast acquisition speed, large data volume, high dimension, and vital real-time storage, which makes it challenging to be met using the traditional lidar data storage methods. This paper presents a novel approach to storing the multi-channel lidar data based on the principle of the tree structure, the adjacency linked list, the binary data storage. In the proposed system, a tree structure is constructed by the four-dimensional structure of the multi-channel lidar data, and a data retrieval method of the multi-channel lidar data file is given. The results show that the proposed tree structure approach can save the storage capacity and improve the retrieval speed, which can meet the needs for efficient storage and retrieval of multi-channel lidar data, and improve the data storage utilization and the practicality of multi-channel lidar system.

Initial experimental multi-wavelength EEM (Excitation Emission Matrix) fluorescence lidar detection and classification of atmospheric pollen with potential applications toward real-time bioaerosols monitoring

Fluorescence has the potential to identify the types of substances associated with aerosols. To demonstrate its usefulness in environmental studies, we investigated the use of Excitation-Emission-Matrix (EEM) fluorescence in lidar bioaerosol monitoring. First, the EEM fluorescence of cedar, ragweed, and apple pollens as typical bioaerosols found around our surroundings were measured using a commercial fluorescence spectrometer. We found that the patterns of fluorescence changed depending on the pollen type and excitation wavelength and it meant that studying these EEM fluorescence patterns was a good parameter for identifying pollen types. Then, we setup a simple EEM fluorescence lidar to confirm the usefulness in lidar bioaerosol monitoring. The lidar consisted of three laser diodes and one light emitting diode with output at 520 nm, 445 nm, 405 nm and 325 nm, respectively, an ultra violet camera lens as a receiver, and a fluorescence spectrum detection unit. Comparing the lidar simulation results with the EEM fluorescence dataset supported the possibility of performing bioaerosol monitoring using the EEM fluorescence lidar. Based on the results and the current technology, a feasible design of a bioaerosol detection EEM fluorescence lidar is proposed for future rel-time remote sensing and mapping of atmospheric bioaerosols.