Remote Detection of the Fluorescence Spectrum of Natural Pollens Floating in the Atmosphere Using a Laser-Induced-Fluorescence Spectrum (LIFS) Lidar

Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors

In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.

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.

Impact of different classification schemes on discrimination of proteins with noise-contaminated spectra using laboratory-measured fluorescence data

Biological agents are important to detect and identify with respect to environmental contamination and public health. Noise contamination in fluorescent spectra is one of the contributors to the uncertainties of identification. In order to investigate the noise-tolerant capability provided by laboratory-measured excitation-emission matrix (EEM) fluorescence spectra that are used as a database, fluorescence properties of four proteinaceous biotoxin samples and ten harmless protein samples were characterized by EEM fluorescence spectra, and the predicting performance of models trained by laboratory-measured fluorescence data was tested and verified from validation data with noise-contaminated spectra. By means of peak signal of noise (PSNR) as an indicator of noise levels, the potential impact of noise contaminations on the characterization and discrimination of these samples was evaluated quantitatively. Different classification schemes utilizing multivariate analysis techniques of Principal Component Analysis (PCA), Random Forest (RF), and Multi-layer Perceptron (MPL) coupled with feature descriptors of differential transform (DT), Fourier transform (FT) and wavelet transform (WT) were conducted under different PSNR values. We systematically analyzed the performance of classification schemes by the case study at 20 PSNR and by statistical analysis from 1-100 PSNR. The results show that the spectral features with EEM-WT decreased the demanding number of input variables while retaining high performances in sample classification. The spectral features with EEM-FT presented the worst performance although having the largest number of features. The distributions of feature importance and contribution were found sensitive to noise contaminations. The classification scheme of PCA prior to MPL with EEM-WT as input presented an improvement in lower PSNR. These results indicate that robust features extracted by corresponding techniques are critical to enhancing the spectral differentiation capabilities among these samples and play an important role in eliminating the noise effect. The study of classification schemes for discriminating protein samples with noise-contaminated spectra presents tremendous potential for future developments in the rapid detection and identification of proteinaceous biotoxins based on three-dimensional fluorescence spectrometry.

Identification of fluorescent aerosol observed by a spectroscopic lidar over northwest China

Bioaerosols play a significant role in climate change and variation of ecological environment. To investigate characterization of atmospheric bioaerosols, we conducted lidar measurement for observing bioaerosols close to dust sources over northwest China in April, 2014. The developed lidar system can not only allowed us to measure the 32-channel fluorescent spectrum between 343 nm to 526 nm with a spectral resolution of 5.8 nm but also simultaneously detect polarisation measurements at 355 nm and 532 nm, as well as Raman scattering signals at 387 nm and 407 nm. According to the findings, the lidar system was able to pick up the robust fluorescence signal emitted by dust aerosols. Especially the polluted dust, the fluorescence efficiency could reach 0.17. In addition, the efficiency of single-band fluorescence typically rises as the wavelength goes up and the ratio of fluorescence efficiency of polluted dust, dust, air pollutant and background aerosols is about 4:3:8:2. Moreover, our results demonstrate that simultaneous measurements of depolarization at 532 nm and fluorescence could better distinguish fluorescent aerosols than those at 355 nm. This study enhances the ability of laser remote sensing for real-time detecting bioaerosol in the atmosphere.

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.

Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications

The article presents the history of the development and the current state of the apparatus for the detection of interferents and biological warfare simulants in the air with the laser-induced fluorescence (LIF) method. The LIF method is the most sensitive spectroscopic method and also enables the measurement of single particles of biological aerosols and their concentration in the air. The overview covers both the on-site measuring instruments and remote methods. The spectral characteristics of the biological agents, steady-state spectra, excitation-emission matrices, and their fluorescence lifetimes are presented. In addition to the literature, we also present our own detection systems for military applications.

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.

Chlorophyll-a Pigment Measurement of Spirulina in Algal Growth Monitoring Using Portable Pulsed LED Fluorescence Lidar System

Chlorophyll-a measurement is important in algal growth and water quality monitoring in natural waters. A portable pulsed LED fluorescence lidar system based on the preliminary algal organic matter and pigments excitation–emission matrix (EEM) of commercialized AZTEC Spirulina powder at varying concentrations was developed. Fluorescence peaks from EEMs showed increasing intensity as the Spirulina concentration increases. Using this information, an LED fluorescence lidar with a wavelength of 385 nm, pulse width of 10 ns, and repetition frequency of 500 kHz was constructed for chlorophyll detection at 680 nm. Turbidity measurements were also conducted at 700 nm emission wavelength at the same excitation wavelength. Range-resolved fluorescence lidar signals from the portable pulsed LED fluorescence lidar system are highly correlated with the standard methods such as optical density at 680 nm (R2 = 0.87), EEM fluorescence chlorophyll-a pigment at 680 nm (R2 = 0.89), and corrected chlorophyll-a concentration (R2 =0.92). The F680/F700 lidar ratio was measured to provide a linear relationship of chlorophyll-a and turbidity in waters. The F680/F700 measurement showed strong correlations with Spirulina concentration (R2 = 0.94), absorbance at 680 nm (R2 = 0.84), EEM chlorophyll-a pigment at 680 nm (R2 = 0.83), and corrected chlorophyll-a concentration (R2 = 0.86). Results revealed that this new technique of chlorophyll-a measurement can be used as an alternative to other standard methods in algal growth monitoring.

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

Fluorescence lidars have the potential to identify aerosols, but it seems that the basic data of the fluorescence spectrum of various aerosols appear to be inadequate for practical use in application of fluorescence lidar monitoring. We collected the fluorescence spectrum data of 61 powders with different substances as pseudo-aerosols and organized them as EEM (Excitation-Emission-Matrix) fluorescence data. Our interest was also in the artificial substances that are discarded around our surroundings and become aerosols. Four applications of the EEM fluorescence to fluorescence lidars were discussed; designing fluorescence lidars, reconstructing aerosol fluorescence spectrums measured by fluorescence lidar, searching for new substances for fluorescence lidar measurement, and developing a database of EEM fluorescence for identifying aerosol types measured by fluorescence lidar. All EEM fluorescence data and application software were stored in one USB memory and run in the BIOS (Basic Input/Output System) independent of a computer OS (Operating System) for ease of use. Aerosol identification software worked well in general, but we have also talked a bit about improvements.

Using a simple spectrophotometer to analyze cypress hydrolat composition

The Pure Dew (Cypress Hydrolat), which could be extracted from the waste material after the extracting essential oil from Taiwan cypress, has a good bactericidal effect. However, due to the high cost on quality control and concentration measurement of the Pure Dew, its application was restricted. This research tries to find suitable spectral frequencies through which the absorbance detected by the spectrometer could be used as the index of the pure dew concentration. This study used Gas Chromatography-Mass Spectrophotometer (GC-MS) to analyze the composition of Taiwan cypress hydrolat. After obtaining the composition, the raw liquor of cypress hydrolat was diluted to 100, 50, 25 and 0% v/v with pure water. The test samples were then tested by a simple spectrophotometer. After the spectrographic detection of absorbance using a simple spectrophotometer, it is confirmed that the spectrum of wavelength between 205-350 nm is the most representative. The absorptance and the pure dew concentration was roughly in linear relation which suggested that a simple spectrophotometer can be used to develop a low-cost and high.

Dual-channel mobile fluorescence lidar system for detection of tryptophan

We present a dual-channel mobile lidar system based on laser-induced fluorescence (LIF) for real-time standoff detection and concentration distribution analysis of tryptophan. The system employs an ultraviolet laser excitation source and signal detectors for receiving fluorescence signals within two different wavelength bands. The performed experiments measured tryptophan aerosols at two different standoff distances. Moreover, distilled water and ethanol solutions were also detected for comparison. The results show that the system can detect LIF signals of tryptophan, give early warnings, locate the diffusion sources, and monitor the variation of the aerosol concentration distribution in real time.

Fluorescence Database of Aerosol-Candidate-Substances for Fluorescence Lidar Application

A database containing spectrum and cross-section of the fluorescence of substances has been made. In test of forest environment monitoring by our Laser-Induced Fluorescence Spectrum (LIFS) lidar, the database showed that the origin substance of the aerosol observed by the lidar was cedar pollen, and the concentration was calculated using the cross-section. In the urban atmosphere monitoring, three substances stored in the database were proposed to be the origins of the aerosol. Based on these experiments, we discuss the usefulness of the fluorescence database in lidar observations.

Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar

Laser-induced fluorescence (LIF) technology has been widely applied to monitor vegetation growth status and biochemical concentrations. Thus, it is important to accurately acquire the fluorescence information for the quantitative monitoring of vegetation growth status. In this study, firstly, the incidence angle's effect on chlorophyll fluorescence intensity was analyzed by using the FluorMODleaf model. Then, comprehensive experimental data on the angle dependence of the fluorescence intensity to vegetation leaf surface were collected. Numerical and experimental results showed that proposed corrected cosine expression could be used to describe the relationship between the incidence angle and the fluorescence intensity in the LIF-Lidar. Lastly, fluorescence signals at 685 and 740 nm extracted at different incident angles of excitation lights were fitted with the corrected cosine expression. The coefficient of determination (R²) of the fitting results reached a maximum value of 0.93 for Salix babylonica.

The Potential of Reflectance and Laser Induced Luminescence Spectroscopy for Near-Field Rare Earth Element Detection in Mineral Exploration

New energy, transport, computer and telecommunication technologies require an increasing supply of rare earth elements (REEs). As a consequence, adequate and robust detection methods become essential for the exploration and discovery of new deposits, the improved characterization of existing deposits and the future recycling of today’s high-tech products. Within this paper, we investigate the potential of combining passive reflectance (imaging and point sampling) with laser stimulated luminescence (point sampling) spectroscopic measurements across the visible, near and shortwave infrared for REE detection in non-invasive near-field mineral exploration. We analyse natural REE-bearing mineral samples from main REE-deposits around the world and focus on challenges such as the discrimination of overlapping spectroscopic features and the influence of the mineral type on detectability, feature position and mineral matrix luminescence. We demonstrate that the cross-validation of results from both methods increases the robustness and sensitivity, provides the potential for semi-quantification and enables the time- and cost-efficient detection of economically important REE, including Ce, Pr, Nd, Sm, Eu, Dy, Er, Yb and potentially also Ho and Tm.