Ozone–NOx–VOC Sensitivity of the Lake Michigan Region Inferred from TROPOMI Observations and Ground-Based Measurements

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Ozone–NO_x–VOC Sensitivity of the Lake Michigan Region Inferred from TROPOMI Observations and Ground-Based Measurements ; day:09 ; month:12 ; year:2022 ; pages:1-38 ; extent:38
EGUsphere ; (09.12.2022), 1-38 (gesamt 38)

Creator: Acdan, Juanito Jerrold Mariano
Pierce, Robert Bradley
Dickens, Angela F.
Adelman, Zachariah
Nergui, Tsengel

DOI: 10.5194/egusphere-2022-1154

URN: urn:nbn:de:101:1-2022121504324085645187

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:22 AM CEST

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Acdan, Juanito Jerrold Mariano
Pierce, Robert Bradley
Dickens, Angela F.
Adelman, Zachariah
Nergui, Tsengel

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Ozone–NO<sub>x</sub>–VOC Sensitivity of the Lake Michigan Region Inferred from TROPOMI Observations and Ground-Based Measurements

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Other Objects (12)

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Ozone&ndash;NO<sub>x</sub>&ndash;VOC Sensitivity of the Lake Michigan Region Inferred from TROPOMI Observations and Ground-Based Measurements

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Other Objects (12)

NitroNet &ndash; A deep-learning NO<sub>2</sub> profile retrieval prototype for the TROPOMI satellite instrument

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Can TROPOMI-NO<sub>2</sub> satellite data be used to track the drop and resurgence of NO<sub>x</sub> emissions between 2019&ndash;2021 using the multi-source plume method (MSPM)?

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Composited analyses of the chemical and physical characteristics of co-polluted days by ozone and PM<sub>2.5</sub> over 2013&ndash;2020 in the Beijing&ndash;Tianjin&ndash;Hebei region

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Ozone–NO<sub>x</sub>–VOC Sensitivity of the Lake Michigan Region Inferred from TROPOMI Observations and Ground-Based Measurements

NitroNet – A deep-learning NO<sub>2</sub> profile retrieval prototype for the TROPOMI satellite instrument

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Can TROPOMI-NO<sub>2</sub> satellite data be used to track the drop and resurgence of NO<sub>x</sub> emissions between 2019–2021 using the multi-source plume method (MSPM)?

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NitroNet – A deep-learning NO<sub>2</sub> profile retrieval prototype for the TROPOMI satellite instrument

An improved Bayesian inversion to estimate daily NO<sub>x</sub> emissions of Paris from TROPOMI NO<sub>2</sub> observations between 2018–2023

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