Near Real-Time Air Quality Mapping Using Data Fusion Techniques and Sentinel-5P Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F19%3AN0000173" target="_blank" >RIV/00020699:_____/19:N0000173 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00020699:_____/19:N0000210

Výsledek na webu

<a href="https://meetingorganizer.copernicus.org/EGU2019/EGU2019-13440.pdf" target="_blank" >https://meetingorganizer.copernicus.org/EGU2019/EGU2019-13440.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Near Real-Time Air Quality Mapping Using Data Fusion Techniques and Sentinel-5P Data

Popis výsledku v původním jazyce

Air quality mapping plays an important role in informing the public about air pollution levels as well as in the assesment of air quality in areas not covered by measuring stations. For this purpose various data sources can be utilized, in particular in-situ measurements, air quality models and Earth observation data. Within the scope of the ESA funded project SAMIRA (SAtellite based Monitoring Initiative for Regional Air quality) we have been testing data fusion techiques that combine these data sources to provide more accurate information within air quality mapping. The methodology of the data fusion is based on multiple linear regression followed by the interpolation (kriging) of its residuals. The response in the multiple linear regression is the in-situ measurement and the explanatory variables are mainly outputs from a chemical transport model (CTM) and satellite observations. This methodology was first tested on historical data for hourly, daily and annual time steps. The final goal of the project was to utilize the method in near real-time. Therefore, we have set up a model to combine up-to-date in-situ measurements with the outputs of a pre-operational air quality forecast using the CAMx model and near real-time measurements from Sentinel-5P. Using this system, hourly air quality maps of NO2 are produced. Each map is also evaluated automatically by cross-validation to assess its uncertainties.

Název v anglickém jazyce

Near Real-Time Air Quality Mapping Using Data Fusion Techniques and Sentinel-5P Data

Popis výsledku anglicky

Air quality mapping plays an important role in informing the public about air pollution levels as well as in the assesment of air quality in areas not covered by measuring stations. For this purpose various data sources can be utilized, in particular in-situ measurements, air quality models and Earth observation data. Within the scope of the ESA funded project SAMIRA (SAtellite based Monitoring Initiative for Regional Air quality) we have been testing data fusion techiques that combine these data sources to provide more accurate information within air quality mapping. The methodology of the data fusion is based on multiple linear regression followed by the interpolation (kriging) of its residuals. The response in the multiple linear regression is the in-situ measurement and the explanatory variables are mainly outputs from a chemical transport model (CTM) and satellite observations. This methodology was first tested on historical data for hourly, daily and annual time steps. The final goal of the project was to utilize the method in near real-time. Therefore, we have set up a model to combine up-to-date in-situ measurements with the outputs of a pre-operational air quality forecast using the CAMx model and near real-time measurements from Sentinel-5P. Using this system, hourly air quality maps of NO2 are produced. Each map is also evaluated automatically by cross-validation to assess its uncertainties.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů