Analysis of a warehouse fire smoke plume over Paris with an N&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Raman lidar and an optical thickness matching algorithm

Shang, Xiaoxia; Chazette, Patrick; Totems, Julien

doi:10.5194/amt-11-6525-2018

Cited by 8 publications

(7 citation statements)

References 39 publications

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“…An increasing interest in pollen has arisen in the aerosol lidar community (Noh et al, 2013;Sicard et al, 2016). In our previous study (Bohlmann et al, 2019) we showed on the basis of an 11 d birch pollination period that lidar measurements can detect the presence of pollen grains in the atmosphere and that non-spherical pollen grains can generate strong depolarization (we found a mean depolarization ratio of 0.26 for the birch-spruce pollen mixture).…”

Section: Introductionmentioning

confidence: 50%

Optical characterization of pure pollen types using a multi-wavelength Raman polarization lidar

et al. 2020

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Abstract. We present a novel algorithm for characterizing the optical properties of pure pollen particles, based on the depolarization ratio values obtained in lidar measurements. The algorithm was first tested and validated through a simulator and then applied to the lidar observations during a 4-month pollen campaign from May to August 2016 at the European Aerosol Research Lidar Network (EARLINET) station in Kuopio (62∘44′ N, 27∘33′ E), in Eastern Finland. With a Burkard sampler, 20 types of pollen were observed and identified from concurrent measurements, with birch (Betula), pine (Pinus), spruce (Picea), and nettle (Urtica) pollen being the most abundant, contributing more than 90 % of the total pollen load, regarding number concentrations. Mean values of lidar-derived optical properties in the pollen layer were retrieved for four intense pollination periods (IPPs). Lidar ratios at both 355 and 532 nm ranged from 55 to 70 sr for all pollen types, without significant wavelength dependence. An enhanced depolarization ratio was found when there were pollen grains in the atmosphere, and an even higher depolarization ratio (with mean values of 0.25 or 0.14) was observed with the presence of the more non-spherical spruce or pine pollen. Under the assumption that the backscatter-related Ångström exponent between 355 and 532 nm should be zero for pure pollen, the depolarization ratio of pure pollen particles at 532 nm was assessed, resulting in 0.24±0.01 and 0.36±0.01 for birch and pine pollen, respectively. Pollen optical properties at 1064 and 355 nm were also estimated. The backscatter-related Ångström exponent between 532 and 1064 nm was assessed to be ∼0.8 (∼0.5) for pure birch (pine) pollen; thus the longer wavelength would be a better choice to trace pollen in the air. Pollen depolarization ratios of 0.17 and 0.30 at 355 nm were found for birch and pine pollen, respectively. The depolarization values show a wavelength dependence for pollen. This can be the key parameter for pollen detection and characterization.

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Section: Introductionmentioning

confidence: 50%

Optical characterization of pure pollen types using a multi-wavelength Raman polarization lidar

et al. 2020

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“…The simulator includes a direct model and an inverse model modules (the block diagram is shown in Fig. S1 in the supplement); Similar ones have already been used for forest and aerosol studies (Shang et al, 2018;Shang and Chazette, 2015).…”

Section: Simulatormentioning

confidence: 99%

Airborne pollen observations using a multi-wavelength Raman polarization lidar in Finland: characterization of pure pollen types

Shang

Giannakaki

Bohlmann

et al. 2020

Preprint

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Abstract. We present a novel algorithm for characterizing the optical properties of pure pollen particles, based on the depolarization values obtained in lidar measurements. The algorithm was first tested and validated through a simulator, and then applied to the lidar observations during a four-month pollen campaign from May to August 2016 at the European Aerosol Research Lidar Network (EARLINET) station in Kuopio (62°44′ N, 27°33′ E), in Eastern Finland. Twenty types of pollen were observed and identified from concurrent measurements with Burkard sampler; Birch (Betula), pine (Pinus), spruce (Picea) and nettle (Urtica) pollen were most abundant, contributing more than 90 % of total pollen load, regarding number concentrations. Mean values of lidar-derived optical properties in the pollen layer were retrieved for four intense pollination periods (IPPs). Lidar ratios at both 355 and 532 nm ranged from 55 to 70 sr for all pollen types, without significant wavelength-dependence. Enhanced depolarization ratio was found when there were pollen grains in the atmosphere, and even higher depolarization ratio (with mean values of 25 % or 14 %) was observed with presence of the more non-spherical spruce or pine pollen. The depolarization ratio at 532 nm of pure pollen particles was assessed, resulting to 24 ± 3 % and 36 ± 5 % for birch and pine pollen, respectively. Pollen optical properties at 1064 nm and 355 nm were also estimated. The backscatter-related Ångström exponent between 532 and 1064 nm was assessed as ~ 0.8 (~ 0.5) for pure birch (pine) pollen, thus the longer wavelength would be better choice to trace pollen in the air. The pollen depolarization ratio at 355 nm of 17 % and 30 % were found for birch and pine pollen, respectively. The depolarization values show a wavelength dependence for pollen. This can be the key parameter for pollen detection and characterization.

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“…To evaluate the MAPIR profiles in Sect. 5.3.2, the Klett inversion method (Klett, 1981) is applied to retrieve the aerosol backscatter coefficients and aerosol extinction coefficients at 355 and 532 nm, using lidar ratios of ∼ 45 and ∼ 35 sr, which are derived using Raman inversion for nighttime lidar measurements (method description in Ansmann et al, 1990;Shang et al, 2018). The volumetric depolarization ratio (VDR) and linear particle depolarization ratio (PDR) at 355 and 532 nm are also derived following the procedure described in Chazette et al (2012).…”

Section: Al Dhaid Lidarmentioning

confidence: 99%

“…In addition, the radiative transfer model requires some microphysical properties of the aerosols. We have chosen to maintain the parameters used in previous versions of MAPIR (Vandenbussche et al, 2013): a lognormal particle size distribution (PSD) with a median radius of 0.6 µm and a geometric standard deviation of 2, corresponding to an effective radius of 2 µm, and the spatially invariant and timeconstant refractive index of the GEISA-HITRAN dust-like data set, gathered by Massie (1994) and Massie and Goldman (2003) from measurements by Volz (1972Volz ( , 1973 and Shettle and Fenn (1979) on transported Saharan dust.…”

Section: The Forward Modelmentioning

confidence: 99%

The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation

et al. 2019

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The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm retrieves vertical dust concentration profiles from cloud-free Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared (TIR) radiances using Rodgers' optimal estimation method (OEM). We describe the new version 4.1 and evaluation results. Main differences with respect to previous versions are the Levenberg-Marquardt modification of the OEM, the use of the logarithm of the concentration in the retrieval and the use of Radiative Transfer for TOVS (RTTOV) for in-line radiative transfer calculations. The dust aerosol concentrations are retrieved in seven 1 km thick layers centered at 0.5 to 6.5 km. A global data set of the daily dust distribution was generated with MAPIR v4.

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Analysis of a warehouse fire smoke plume over Paris with an N<sub>2</sub> Raman lidar and an optical thickness matching algorithm

Cited by 8 publications

References 39 publications

Optical characterization of pure pollen types using a multi-wavelength Raman polarization lidar

Optical characterization of pure pollen types using a multi-wavelength Raman polarization lidar

Airborne pollen observations using a multi-wavelength Raman polarization lidar in Finland: characterization of pure pollen types

The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation

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