Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM. I. Aerosol Optical Thickness Acquisition

Etzion, Yael; Jarmer, Thomas; Kolatt, Tsafrir; Shoshany, Maxim; Broday, David M.

doi:10.1080/02786826.2012.700429

Cited by 3 publications

(9 citation statements)

References 33 publications

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“…Hence, after a retrieval algorithm is applied, the signatures captured in a single image can account for the spatial variability of ambient fine PM concentrations. Previously, we demonstrated the derivation of the apparent aerosol optical thickness, τ a , from HS attenuated signatures . Here we validate the basic premise that different accumulation-size aerosols generate distinct hyperspectral signatures in the visible-NIR range, and demonstrate retrieval of modal concentrations of fine aerosol mixtures based on their HS signatures.…”

Section: Introductionsupporting

confidence: 73%

Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM—Modal Concentrations Retrieval

Etzion

Kolatt²,

Shoshany

et al. 2014

Environ. Sci. Technol.

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Retrieval of aerosol loading in vertical atmospheric columns is a common product of satellite and ground instruments that measure spectral extinction of solar radiation throughout the entire atmosphere. Here we study ground hyperspectral imaging of artificial light sources as a complementary method for retrieving fine aerosol concentrations along quazi-horizontal ambient open paths. Previously, we reported hyperspectral measurements of the aerosol optical thickness in the 500-900 nm range over urban-scale distances (180 m to 4 km), measuring the extinction of radiation emitted from a halogen source. Here we confirm in a laboratory-setup the basic premise that different accumulation-size aerosols generate distinct hyperspectral signatures in this spectral range. Measured hyperspectral attenuation signatures of fine aerosols were comparable to calculated Mie scattering signatures, suggesting that modal aerosol concentrations can be retrieved. A genetic algorithm was adapted to estimate the aerosol modal concentrations from its hyperspectral extinction signature. Retrievals of aerosol concentrations from measured and synthetic hyperspectral signatures indicated a robust algorithm, with an expected retrieval error of 0.2-22% for typical ambient concentrations along an urban-scale open path. The retrieval accuracy was found to depend on the relative aerosol modal concentrations, especially when there is a substantial overlap between the modal spectral signatures.

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Section: Introductionsupporting

confidence: 73%

Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM—Modal Concentrations Retrieval

Etzion

Kolatt²,

Shoshany

et al. 2014

Environ. Sci. Technol.

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“…In this study we suggest a new method for inferring ambient particle concentrations across varying urban segments at a high temporal resolution, using image quantitative characteristics rather than the commonly used radiometric analysis [14,15,17]. The method is based on the Image Effective Bandwidth (IEB), which was suggested by Fishbain et al [18] as a measure of image quality.…”

Section: Introductionmentioning

confidence: 97%

“…Thus, validation of the model over a set of different places and scenarios is extremely difficult. Etzion et al used hyper spectral imager for measuring light attenuation in specific wavelengths, where PM is known to have the highest signature [17]. A major drawback of imaging through open atmospheric paths for inferring aerosol PM size concentrations is that information about the source emission is required.…”

Section: Introductionmentioning

confidence: 97%

“…The usage of CCD imagery for deriving attenuation signatures (i.e. apparent aerosol optical thickness, ) from images captures in the Vis-NIR spectral window from visual-light and nearinfrared images was also presented [16,17]. Graves and Newsam used local image contrast in either the image or the frequency domain for the prediction of aerosol extinction coefficients along the open path between the camera and the scenery targets [16].…”

Section: Introductionmentioning

confidence: 99%

“…before it enters the atmosphere) with the light reflected by the ground. In [17] this was achieved by comparing two images and retrieving the aerosol size resolved concentrations based on the relative signal (much like Beet-Lambert attenuation). Moreover, the latter study used an expensive hyper-spectral imager, and was restricted to nighttime imaging such that the sun's insolation would not interfere with the measurements.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of image color and effective bandwidth as a tool for assessing air pollution at urban spatiotemporal scale

Etzion

Broday

Fishbain

2013

Computational Imaging XI

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Size and concentration of airborne particulate matter (PM) are important indicators of air pollution events and public health risks. It is therefore important to monitor size resolved PM concentrations in the ambient air. This task, however, is hindered by the highly dynamic spatiotemporal variations of the PM concentrations. Satellite remote sensing is a common approach for gathering spatiotemporal data regarding aerosol events but its current spatial resolution is limited to a large grid that does not fit high varying urban areas. Moreover, satellite-borne remote sensing has limited revisit periods and it measures along vertical atmospheric columns. Thus, linking satellite-borne aerosol products to ground PM measurements is extremely challenging. In the last two decades visibility analysis is used by the US Environmental Protection Agency (US-EPA) to obtain quantitative representation of air quality in rural areas by horizontal imaging. However, significantly fewer efforts have been given to utilize the acquired scene characteristics (color, contrast, etc.) for quantitative parametric modeling of PM concentrations. We suggest utilizing the image effective bandwidth, a quantitative measure of image characteristics, for predicting PM concentrations. For validating the suggested method, we have assembled a large dataset that consists of time series imaging as well as measurements from air quality monitoring stations located in the study area that report PM concentrations and meteorological data (wind direction and velocity, relative humidity, etc.). Quantitative and qualitative statistical evaluation of the suggested method shows that dynamic changes of PM concentrations can be inferred from the acquired images.

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Investigating the performance of satellite-based models in estimating the surface PM2.5 over China

Dong

Yang

et al. 2020

Chemosphere

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Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM. I. Aerosol Optical Thickness Acquisition

Cited by 3 publications

References 33 publications

Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM—Modal Concentrations Retrieval

Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM—Modal Concentrations Retrieval

Analysis of image color and effective bandwidth as a tool for assessing air pollution at urban spatiotemporal scale

Investigating the performance of satellite-based models in estimating the surface PM2.5 over China

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