Abstract

ABSTRACTThis paper is focused on the retrieval of industrial aerosol optical thickness (AOT) and microphysical properties by means of airborne imaging spectroscopy. Industrial emissions generally lead to optically thin plumes requiring an adapted detection method taking into account the weak proportion of particles sought in the atmosphere. To this end, a semi-analytical model combined with the Cluster-Tuned Matched Filter (CTMF) algorithm is presented to characterize those plumes, requiring the knowledge of the soil under the plume. The model allows the direct computation of the at-sensor radiance when a plume is included in the radiative transfer. When applied to industrial aerosol classes as defined in this paper, simulated spectral radiances can be compared to ‘real’ MODTRAN (Moderate Resolution Atmospheric Transmission) radiances using the Spectral Angle Mapper (SAM). On the range from 0.4 to 0.7 µm, for three grounds (water, vegetation, and bright one), SAM scores are lower than 0.043 in the worst case (a both absorbing and scattering particle over a bright ground), and usually lower than 0.025. The darker the ground reflectance is, the more accurate the results are (typically for reflectance lower than 0.3). Concerning AOT retrieval capabilities, with a pre-calculated model for a reference optical thickness of 0.25, we are able to retrieve plume AOT at 550 nm in the range 0.0 to 0.4 with an error usually ranging between 9% and 13%. The first test case is a CASI (Compact Airborne Spectrographic Imager) image acquired over the metallurgical industry of Fos-sur-Mer (France). First results of the use of the model coupled with CTMF algorithm reveal a scattering aerosol plume with particle sizes increasing with the distance from the stack (from detection score of 54% near the stack for particles with a diameter of 0.1 µm, to 69% away from it for 1.0 µm particles). A refinement is made then to estimate more precisely aerosol plume properties, using a multimodal distribution based on the previous results. It leads to find a mixture of sulfate and brown carbon particles with a plume AOT ranging between 0.2 and 0.5. The second test case is an AHS (Airborne Hyperspectral Scanner) image acquired over the petrochemical site of Antwerp (Belgium). The first CTMF application results in detecting a brown carbon aerosol of 0.1 µm mode (detection score is 51%). Refined results show the evolution of the AOT decreasing from 0.15 to 0.05 along the plume for a mixture of brown carbon fine mode and 0.3 µm radius of sulfate aerosol.

Highlights

  • Remote sensing tools have been widely used over the past twenty years to study atmospheric composition, in particular, aerosols (King et al 1999)

  • In order to study optically thin plumes of aerosols emitted by an industrial complex, a semi-analytical model of their radiative impact is developed and coupled with the Cluster-Tuned Matched Filter algorithm, intended to characterize their microphysical properties and optical thickness

  • For ten aerosol classes with one mode chosen as reference industrial emissions, we show that we can describe plumes formed by these particles for aerosol optical thickness (AOT) ranging from 0.0 to 0.4, the best results being obtained over dark surfaces as water, for a model designed with a reference AOT of 0.25

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Summary

Introduction

Remote sensing tools have been widely used over the past twenty years to study atmospheric composition, in particular, aerosols (King et al 1999). Existing sensors enabling to characterize aerosols from space are, for example, the Advanced Very High-Resolution Radiometer – AVHRR (Riffler et al 2010), the Moderate-resolution Imaging Spectrometer – MODIS (Levy et al 2013), or the Cloud-Aerosol Lidar with Orthogonal Polarization – CALIOP (Winker et al 2010) Their spatial resolution is ranging from 300 m to 1 km, and so does not allow studying industrial aerosols close to their emission points, which requires a finer spatial resolution to resolve the emitted plume, while these anthropogenic particles are precisely the least known component in the radiative forcing of climate change (Andreae et al 2001)

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