SAMIRA Combined use of In-situ, Earth Observation and Modelling Data in Air Quality Mapping Jana oubalov Jan Horlek, Roman Juras
1. Introduction of SAMIRA project for AQ mapping 2. Data fusion methodology 3. Data sources for AQ mapping 4. Earth observation data treatment 5. Data fusion results
Introduction of SAMIRA project Project of European Space Agency (ESA) SAMIRA Satellite based Monitoring Initiative for Regional Air quality (CZ, PL, RO, NO) Improving of regional air quality assessment through synergetic use of data from 3 sources: In-situ measurements Earth observations
Chemical transport modelling One of the goals: development of more accurate air quality mapping for PM, NO2, SO2 using data fusions methods (residual kriging) Source: Schneider et al. (2012). ETC/ACM Technical Paper 2012/9.
1. Introduction of SAMIRA project for AQ mapping 2. Data fusion methodology 3. Data sources for AQ mapping 4. Earth observation data treatment 5. Data fusion results
Regression Interpolation Merging Mapping Implementation R code Usage of existing packages and functions for linear model and variogram model fitting etc. Temporal resolution:
Annual Daily Hourly Spatial resolution: Czech domain: 1 x 1 km computational and final European domain: 5 x 5 km computational, 1 x 1 km final
Uncertainty estimation Using leave-one-out cross-validation Data fusion estimate calculated for each insitu measurement point from all available information except from the point in question Procedure repeated for all measurement points in the available set
Statistical indicators: bias, RMSE bias =
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1. Introduction of SAMIRA project for AQ mapping 2. Data fusion methodology 3. Data sources for AQ mapping 4. Earth observation data treatment 5. Data fusion results
Input data Czech domain
Czech Air Quality Information System database (in-situ data) Satellite data (OMI, GOME, SEVIRI) CAMx model Altitude European domain
EEAs AQ e-reporting database (in-situ data) Satellite data (OMI, GOME, SEVIRI, MODIS)
WRF-Chem model Altitude Pollutants: NO2, SO2, PM10, PM2.5 In-situ and modelling data
Satellite data OMNO (NO2) OMSO (SO2) 1. Introduction of SAMIRA project for AQ mapping
2. Data fusion methodology 3. Data sources for AQ mapping 4. Earth observation data treatment 5. Data fusion results Data processing of two NO2 satellite data products
Red circles represent GOME2, blue circles represent OMI OMSO2 data, black dots represent merged data and black crosses represent merged data considering only positive values. Gap filling procedure for NO2: Input (OMNO2)
For gap filling of each day, data for 4 days in 4 years needed. Gap filling procedure for NO2 : Output Daily data processed by gapfill procedure, Europe 1. Introduction of SAMIRA project for AQ mapping
2. Data fusion methodology 3. Data sources for AQ mapping 4. Earth observation data treatment 5. Data fusion results Current results Czech domain
Daily NO2 (year 2014) Daily SO2 (year 2014) Daily/hourly PM10 (several days/hours in 09/2014) Daily/hourly PM2.5 (several days/hours in 09/2014) European domain Daily NO2 (09/2014)
Annual NO2 (2014) Daily/hourly PM10 (several days/hours in 09/2014) SEVIRI AOD Daily/hourly PM2.5 (several days/hours in 09/2014) SEVIRI AOD Daily PM10 MODIS AOD (09/2014) Results: NO2 daily RMSE and bias
Resulting maps CZ NO2 daily Results: NO2 annual RMSE and bias Data fusion for NO2 annual average 2014 comparison of different variants Resulting maps EU NO2 annual
Data fusion for NO2 annual average 2014 - based on AQ e-reporting insitu data, OMNO satellite data and EMEP chemical transport model Results: PM10 daily, hourly RMSE, bias
Resulting maps EU and CZ PM10 daily Results: PM2.5 RMSE and bias Resulting maps EU PM2.5 hourly, daily daily
hourly Results: EU PM10 daily with MODIS AOD data Resulting maps EU PM10 daily with MODIS
Next steps in the project Fill in the gaps in final maps Assess uncertainties Include NO2 and SO2 EO data from TROPOMI (Sentinel-5P) (KNMI/ESA)
Next steps in the project Provide near real time air quality maps Conclusion Inclusion of the satellite data improves the mapping results of NO2 for rural areas, both for Czech and
European domains, both for daily and annual data. For the annual data, this inclusion improves NO2 mapping results for urban areas as well. Inclusion of the satellite AOD data (available in limited days only) improves the PM10 mapping results (both for Czech and European domains), both in rural and urban background areas.
More info about the project: https://samira.nilu.no
