Use of satellite data in data fusion methods for air quality mapping

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F18%3AN0000158" target="_blank" >RIV/00020699:_____/18:N0000158 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Use of satellite data in data fusion methods for air quality mapping

Popis výsledku v původním jazyce

Poster at ELSEDIMA conference, Cluj, 17-18 May 2018. Air quality mapping plays an important role in informing the public about air pollution levels in various areas. The most often used data sources for air quality mapping are in-situ measurements and chemical transport model (CTM) outputs. Next to these data sources, satellite resp. earth observation data show a large potential for the maps improvements. None of these datasets alone is ideal for mapping purposes due to either substantial data gaps, significant uncertainties and biases, or insufficient spatial resolution. Within the scope of the SAMIRA (Satellite based Monitoring Initiative for Regional Air quality) project, combination of these different data sources using data fusion, namely residual kriging methods is explored, in order to provide more accurate air quality maps. Here, we present first results of the on-going project where we have applied multiple linear regression and spatial interpolation of its residuals (residual kriging) for combining in-situ measurement data, chemical transport modelling outputs and satellite observations over the Czech Republic and a major part of Europe. We have focused on four pollutants (NO2, SO2, PM10 and PM2.5) at different temporal resolutions (annual, daily, hourly). Satellite data usually suffer from spatial and temporal data gaps. To reduce these gaps we merged data from two different sources, namely products OMNO2 and GOME-2 of AURA and MetOp satellites respectively. The gaps were further filled by spatio-temporal interpolation using the Gapfill package in R language. For mutual comparison of different mapping methods, we have used the ‘leave one out’ cross-validation method. Our first results show that including the satellite observations in air quality mapping provides slight improvement in terms of cross-validation root mean square error and bias, for some pollutants and time steps.

Název v anglickém jazyce

Use of satellite data in data fusion methods for air quality mapping

Popis výsledku anglicky

Poster at ELSEDIMA conference, Cluj, 17-18 May 2018. Air quality mapping plays an important role in informing the public about air pollution levels in various areas. The most often used data sources for air quality mapping are in-situ measurements and chemical transport model (CTM) outputs. Next to these data sources, satellite resp. earth observation data show a large potential for the maps improvements. None of these datasets alone is ideal for mapping purposes due to either substantial data gaps, significant uncertainties and biases, or insufficient spatial resolution. Within the scope of the SAMIRA (Satellite based Monitoring Initiative for Regional Air quality) project, combination of these different data sources using data fusion, namely residual kriging methods is explored, in order to provide more accurate air quality maps. Here, we present first results of the on-going project where we have applied multiple linear regression and spatial interpolation of its residuals (residual kriging) for combining in-situ measurement data, chemical transport modelling outputs and satellite observations over the Czech Republic and a major part of Europe. We have focused on four pollutants (NO2, SO2, PM10 and PM2.5) at different temporal resolutions (annual, daily, hourly). Satellite data usually suffer from spatial and temporal data gaps. To reduce these gaps we merged data from two different sources, namely products OMNO2 and GOME-2 of AURA and MetOp satellites respectively. The gaps were further filled by spatio-temporal interpolation using the Gapfill package in R language. For mutual comparison of different mapping methods, we have used the ‘leave one out’ cross-validation method. Our first results show that including the satellite observations in air quality mapping provides slight improvement in terms of cross-validation root mean square error and bias, for some pollutants and time steps.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

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