First experiment on retrieval of tropospheric NO2 over polluted areas with 2.4-km spatial resolution basing on satellite spectral measurements

Postylyakov, O. V.; Borovski, Alexander; Makarenkov, Aleksandr A.

doi:10.1117/12.2285794

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…This relatively small fitting window was used to estimate NO 2 SCDs owing to interference from instrumental artifacts and/or other atmospheric spectral contaminants at other wavelengths where there is strong NO 2 absorption. Postylyakov et al (2017) similarly averaged spectra from a hyperspectral sensor on the Resurs-P 2 satellite to a spatial resolution of 2.4 km to provide precision of about 2.5 × 10 15 molec. cm −2 for SCD.…”

Section: Addressing Instrumental Noise In the Training Processmentioning

confidence: 99%

“…Therefore, it has been a stated and challenging goal of these ocean color missions to quantify NO 2 spatiotemporal variations with their lowerspectral-resolution measurements. NO 2 retrievals have been demonstrated with hyperspectral sensors on aircraft (Tack et al, 2017;Kuhlmann et al, 2022) and the Russian Resurs-P satellite (Postylyakov et al, 2017(Postylyakov et al, , 2019Zakharova et al, 2021); these sensors have somewhat higher spectral resolution than PACE and GLIMR.…”

Section: Introductionmentioning

confidence: 99%

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Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting

et al. 2023

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Abstract. Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

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Section: Addressing Instrumental Noise In the Training Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting

et al. 2023

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Comparison of measured and simulated NO2 integral content in the lower troposphere in Moscow region

Postylyakov

Borovski

Kirsanov

et al. 2020

IOP Conf. Ser.: Earth Environ. Sci.

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We presented preliminary results of a comparison of the NO2 integral contents (IC) measured by the DOAS technique and simulated by the COSMO-Ru7-ART chemical transport model at Zvenigorod Scientific Station (ZSS) located in 38 km west from Moscow. The comparison covers January and July of 2014 when background and polluted by Moscow air masses were observed at ZSS. The measured NO2 IC in the atmospheric boundary layer observed at ZSS does not exceed 0.5 × 1016 molec × cm−2 in background conditions of the atmosphere when non-east wind direction dominated. It grows up to 5.4 × 1016 molec × cm−2 when polluted air masses come from Moscow megacity. Simulated NO2 IC has similar behaviour. As a whole, a good agreement between measured and simulated datasets is observed. Some overestimation of the NO2 emission presents for sources located to the south and north-east from ZSS. Underestimation of the NO2 emission presents for sources located inside of Moscow megacity and located to south-west from ZSS.

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Potential sources of tropospheric nitrogen dioxide for Western Moscow Region, Russia

Shukurov

Borovski

Postylyakov

et al. 2018

24th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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First experiment on retrieval of tropospheric NO2 over polluted areas with 2.4-km spatial resolution basing on satellite spectral measurements

Cited by 14 publications

References 19 publications

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting

Comparison of measured and simulated NO2 integral content in the lower troposphere in Moscow region

Potential sources of tropospheric nitrogen dioxide for Western Moscow Region, Russia

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First experiment on retrieval of tropospheric NO2 over polluted areas with 2.4-km spatial resolution basing on satellite spectral measurements

Cited by 14 publications

References 19 publications

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting

Comparison of measured and simulated NO2 integral content in the lower troposphere in Moscow region

Potential sources of tropospheric nitrogen dioxide for Western Moscow Region, Russia

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Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting