Performance verification of a LIF-LIDAR technique for stand-off detection and classification of biological agents

Wojtanowski, J.; Zygmunt, Marek; Muzal, M.; Knysak, P.; Młodzianko, Andrzej; Gawlikowski, A.; Drozd, Tadeusz; Kopczyński, Krzysztof; Mierczyk, Zygmunt; Kaszczuk, M.; Traczyk, M.; Gietka, Andrzej; Piotrowski, W.; Jakubaszek, Marcin; Ostrowski, Roman

doi:10.1016/j.optlastec.2014.08.013

Cited by 29 publications

(11 citation statements)

References 11 publications

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“…Differentiation of biological particles from non-biological particles can be achieved by Laser induced fluorescence-LIDAR (LIF-LIDAR). Biological particles (having endogenous fluorophores such as tryptophan, tyrosine, NAD-NADH, riboflavins, dipicolinic acid, etc) are illuminated by UV laser radiation (laser-induced fluorescence) and the fluorescence signals are detected by LIDAR receiving channel(s) in standard range-resolved regimes 81 . UV wavelength is an important factor in determining the range and efficiency of UV LIF LIDAR.…”

Section: Standoff Detection Technologiesmentioning

confidence: 99%

Biological Warfare Agents and their Detection and Monitoring Techniques (Review Paper)

Pal¹,

Sharma²,

Sharma³

et al. 2016

Def. Sc. Jl.

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Recently, threat from biological warfare agents (BWAs) has emerged as the foremost national and global security challenge because of their simple and cheap production, easy dispersal, complicated detection, expensive protection and psychological, economical and social impact. Early detection and identification of BWAs during intentional biological event is essential to initiate corrective emergency responses for management of such incidents. Efforts are being made across the globe for development of state of the art technologies and systems for detection and identification of BWAs. However, till date there is no single system which can detect all the bio-threat agents. In the present review, we describe the currently available techniques and systems for detection and identification of these agents. The basic identification techniques including biological culture, immunological methods, nucleic acid based detection, MALDI-TOF MS, cellular fatty acid profiling and flow cytometry based detection are presented. Detection of BWAs with bio-sensors, surface plasmon resonance, biological detectors, and stand-off detection systems is also summarized. However, despite of availability of several techniques and tools, no full proof system is available for detection/identification of all the BWAs.

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Section: Standoff Detection Technologiesmentioning

confidence: 99%

Biological Warfare Agents and their Detection and Monitoring Techniques (Review Paper)

Pal¹,

Sharma²,

Sharma³

et al. 2016

Def. Sc. Jl.

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show abstract

“…The number of articles from the lidar community dealing with this topic has increased in recent years (Sassen, 2008;Noh et al, 2013;Sicard et al, 2016a;Bohlmann et al, 2019;Shang et al, 2020). The second property of pollen to emit characteristic fluorescence spectra is known since a few years now (Sugimoto et al, 2012;Sharma et al, 2015;Wojtanowski et al, 2015;Rao et al, 2017;Richardson et al, 2019) and can be detected by the technique of the so-called laser induced fluorescence 5 lidars. Some authors even detected fluorescence effects of aerosol of biogenic origin with the water vapor channel of an elastic/Raman lidar system (Immler et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Measurement report: Characterization of the vertical distribution of airborne Pinus pollen in the atmosphere with lidar-derived profiles: a modelling case study in the region of Barcelona, NE Spain

Sicard

Jorba

Ho³

et al. 2021

Preprint

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Abstract. This paper investigates the mechanisms involved in the dispersion, structure and mixing in the vertical column of atmospheric pollen. The methodology used employs observations of pollen concentration obtained from Hirst samplers (we will refer to as surface pollen) and vertical distribution (polarization-sensitive lidar) as well as nested numerical simulations with an atmospheric transport model and a simplified pollen module developed especially for this study. The study focuses on the predominant pollen type, Pinus, of the intense pollination event which occurred in the region of Barcelona, Catalonia, NE Spain, during 27–31 March, 2015. First, conversion formulas are expressed to convert lidar-derived total backscatter coefficient and model-derived mass concentration into pollen grains concentration, the magnitude measured at the surface by means of aerobiological methods, and for the first time ever, a relationship between optical and mass properties of atmospheric pollen, through the estimation of the so-called specific extinction cross-section, is quantified in ambient conditions. Second, the model horizontal representativeness is assessed through comparison between nested pollen simulations at 9, 3 and 1 km horizontal resolution and observed meteorological and aerobiological variables at seven sites around Catalonia. Finally, hourly observations of surface and column concentration in Barcelona are analysed with the different numerical simulations at increasing horizontal resolution and varying sedimentation/deposition parameters. We find that the 9 or 3 km simulations are less sensitive to the meteorology errors hence they should be preferred for specific forecasting applications. The largest discrepancies between measured surface (Hirst) and column (lidar) concentrations occur during nighttime: only residual pollen is detected in the column whereas it is present at the surface. The main reason is related to the lidar characteristics which has a lowest useful range bin at ~225 m, above the usually very thin nocturnal stable boundary layer. At the hour of the day of maximum insolation, the pollen layer does not extend up to the top of the planetary boundary layer according to the observations (lidar), probably because of gravity effects; however, the model simulates the pollen plume up to the top of the planetary boundary layer, resulting in an overestimation of the pollen load. Besides the large size and weight of Pinus grains, sedimentation/deposition processes have only a limited impact on the model vertical concentration in contrast to the emission processes. For further modelling research, emphasis is put on the accurate knowledge of plant/tree spatial distribution, density and type, as well as on the establishment of reliable phenology functions.

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“…The second property of pollen to emit characteristic fluorescence spectra has been known for a few years. Such spectra have been detected by the technique of the so-called laser-induced fluorescence lidars (Sugimoto et al, 2012;Sharma et al, 2015;Wojtanowski et al, 2015;Rao et al, 2017;Saito et al, 2018;Richardson et al, 2019). Very recently, fluorescence returns produced by atmospheric pollen excited at 355 nm were also measured with broadband filters and combined with multi-wavelength, multidepolarization Raman retrievals (Veselovskii et al, 2020(Veselovskii et al, , 2021.…”

Section: Introductionmentioning

confidence: 99%

Measurement report: Characterization of the vertical distribution of airborne <i>Pinus</i> pollen in the atmosphere with lidar-derived profiles – a modeling case study in the region of Barcelona, NE Spain

Sicard

Jorba

et al. 2021

Atmos. Chem. Phys.

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Abstract. This paper investigates the mechanisms involved in the dispersion, structure, and mixing in the vertical column of atmospheric pollen. The methodology used employs observations of pollen concentration obtained from Hirst samplers (we will refer to this as surface pollen) and vertical distribution (polarization-sensitive lidar), as well as nested numerical simulations with an atmospheric transport model and a simplified pollen module developed especially for this study. The study focuses on the predominant pollen type, Pinus, of the intense pollination event which occurred in the region of Barcelona, Catalonia, NE Spain, during 27–31 March 2015. First, conversion formulas are expressed to convert lidar-derived total backscatter coefficient and model-derived mass concentration into pollen grains concentration, the magnitude measured at the surface by means of aerobiological methods, and, for the first time ever, a relationship between optical and mass properties of atmospheric pollen through the estimation of the so-called specific extinction cross section is quantified in ambient conditions. Second, the model horizontal representativeness is assessed through a comparison between nested pollen simulations at 9, 3, and 1 km horizontal resolution and observed meteorological and aerobiological variables at seven sites around Catalonia. Finally, hourly observations of surface and column concentration in Barcelona are analyzed with the different numerical simulations at increasing horizontal resolution and varying sedimentation/deposition parameters. We find that the 9 or 3 km simulations are less sensitive to the meteorology errors; hence, they should be preferred for specific forecasting applications. The largest discrepancies between measured surface (Hirst) and column (lidar) concentrations occur during nighttime, where only residual pollen is detected in the column, whereas it is also present at the surface. The main reason is related to the lidar characteristics which have the lowest useful range bin at ∼ 225 m, above the usually very thin nocturnal stable boundary layer. At the hour of the day of maximum insolation, the pollen layer does not extend up to the top of the planetary boundary layer, according to the observations (lidar), probably because of gravity effects; however, the model simulates the pollen plume up to the top of the planetary boundary layer, resulting in an overestimation of the pollen load. Besides the large size and weight of Pinus grains, sedimentation/deposition processes have only a limited impact on the model vertical concentration in contrast to the emission processes. For further modeling research, emphasis is put on the accurate knowledge of plant/tree spatial distribution, density, and type, as well as on the establishment of reliable phenology functions.

show abstract

Performance verification of a LIF-LIDAR technique for stand-off detection and classification of biological agents

Cited by 29 publications

References 11 publications

Biological Warfare Agents and their Detection and Monitoring Techniques (Review Paper)

Biological Warfare Agents and their Detection and Monitoring Techniques (Review Paper)

Measurement report: Characterization of the vertical distribution of airborne Pinus pollen in the atmosphere with lidar-derived profiles: a modelling case study in the region of Barcelona, NE Spain

Measurement report: Characterization of the vertical distribution of airborne <i>Pinus</i> pollen in the atmosphere with lidar-derived profiles – a modeling case study in the region of Barcelona, NE Spain

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Performance verification of a LIF-LIDAR technique for stand-off detection and classification of biological agents

Cited by 29 publications

References 11 publications

Biological Warfare Agents and their Detection and Monitoring Techniques (Review Paper)

Biological Warfare Agents and their Detection and Monitoring Techniques (Review Paper)

Measurement report: Characterization of the vertical distribution of airborne Pinus pollen in the atmosphere with lidar-derived profiles: a modelling case study in the region of Barcelona, NE Spain

Measurement report: Characterization of the vertical distribution of airborne &lt;i&gt;Pinus&lt;/i&gt; pollen in the atmosphere with lidar-derived profiles – a modeling case study in the region of Barcelona, NE Spain

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Measurement report: Characterization of the vertical distribution of airborne <i>Pinus</i> pollen in the atmosphere with lidar-derived profiles – a modeling case study in the region of Barcelona, NE Spain