SAtellite based Monitoring Initiative for Regional Air quality Presented by I.S.Stachlewska

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1 SAtellite based Monitoring Initiative for Regional Air quality Presented by I.S.Stachlewska ESA-ESRIN funded project, started May 2016 (3 years period) lead by NILU, Norway with partners from Poland, The Czech Republic, and Romania CAMS 2nd GA, Warsaw, May 201&

2 Motivation PM2.5 concentrations in 2011 were responsible for about premature deaths in Europe originating from long term exposure. Horálek et al. (2016), ETC/ACM Technical Paper 2015/5.

3 Project Objectives Overall Goal: Improve quality and availability of satellite-based air quality information in Eastern Europe and Norway Improve MSG-SEVIRI based AOD Develop robust methods for AOD to PM conversion Generate air quality maps for PM, O3, NO2, and SO2 Add value to existing monitoring networks (in-situ, models, satellite) by combining them using data fusion methods Facilitate and improve use of satellite data at the local scale by developing appropriate downscaling methods Improve local air quality forecasting Gather user feedback and perform independent product validation

4 Countries, Sites covered Oslo Warsaw Prague Silesia Bucharest Gorj Countries: Norway, Poland, The Czech Republic, Romania Capitals of all four participating countries Bucharest Oslo Prague Warsaw Special interest sites: Silesia Region (Czech Republic/Poland) Gorj country (Romania)

5 AOD retrieval from SEVIRI Lead: University of Warsaw AOD Aerosol Optical Depth Proxy for PM (but: complex relationship!) SEVIRI: Spinning Enhanced Visible and Infrared Imager, onboard geostationary MSG platform AOD target: 15 min resolution Zawadzka & Stachlewska: AOD over Poland September 2016, in prep. Atmos. Environ.

6 AOD to PM conversion UW Lidar measurements Zawadzka et al. (2013), Impact of urban pollution emitted in Warsaw on aerosol properties, Atmos. Environ. 69. INOE Lidar measurements

7 Data Fusion Goal: Combine satellite datasets related to air quality with ground observations and model information Add value to observations and satellite/model data Gap filling observations Constraining model/sat data Example of annual interpolated maps produced by the EEA-funded ETC/ACM. These maps are produced from station observations (Airbase), model data (EMEP), and auxiliary data. Note that no satellite information is currently used.

8 Data Fusion Satellite observations Station observations Model output Value-added fused data product Satellite-based annualaverage surface NO2 field for Europe, combining station data (Airbase), model data (EMEP/CHIMERE), and satellite-based tropospheric NO2 column (OMI) (from Schneider et al., 2012)

9 Downscaling of satellite data Current EO products for air quality applications tend to have coarse spatial resolution For example, for NO2 we currently have OMI: 13 km x 24 km GOME-2: 80 km x 40 km Soon: S5P at 7 km x 7 km However, AQ models are routinely run at spatial resolutions of down to 1 km OMI-derived long-term mean NO2 concentration over Oslo, Norway and even less

10 Downscaling of satellite data Use satellite data together with high-resolution model information as a proxy dataset (providing information about the sub-pixel spatial patterns - geo-statistics) to create a value-added product for small regions. Approximate 7 km by 7 km grid of Sentinel5P products for atmospheric composition

11 Connections to CAMS Copernicus Atmospheric Monitoring Service (CAMS) Global total column nitrogen dioxide as modeled by the C-IFS within the MACC-III/CAMS system European Sulphur Dioxide concentrations modeled by the CAMD regional model EURAD-IM Using initial and boundary conditions from CAMS for the modelling component Project could be seen as a potential downstream service for CAMS Creating value-added products based to some extent on CAMS data

12 SAMIRA product validation Independent validation of the SAMIRA products concentration fields to be accomplished by comparison against station datasets (e.g. Airbase, EMEP, AERONET, EARLINET) The Airbase network of air quality monitoring stations in Europe

13 Users/beneficiaries Regional and national environmental protection agencies Ministry of Environment National Institutes of Public Health City councils Real estate agents and insurance companies General public

14 Conclusions Project has just started Future plans: inclusion of Bulgaria PM10 in EU-28 (2012) from Guerreiro et al (2014). The graph is based on the 90.4 percentile of daily mean concentration values corresponding to the 36th highest daily mean for each Member State. For each country, the lowest and highest value observed (in μg/m3) are given; the average value is given as a dot. Preparation for the exploitation of the current/upcoming Sentinels providing atmospheric information (aerosols, trace gases)

15 Relationship to Sentinel 3 (SLSTR) Sentinel-3A (SLSTR) - B - Launch date AOD (heritage from AATSR) km swath, dual view Possible applications of Sentinel3/SLSTR data - Intercomparison of SLSTR AOD against SEVIRI AOD - Testing AOD to PM conversion - Possibly testing direct use of SLSTR AOD in data fusion-based mapping

16 Relationship to Sentinel 5p Sentinel-5P (TROPOMI) - Launch High-resolution (7 7 km2) data for reactive gases such as NO2, SO2, and others Possible applications of Sentinel-5p data - Directly use high-resolution TROPOMI products as an input to data fusionbased mapping - Use TROPOMI reactive gas products (for downscaling algorithm (both as input and reference) Spatial variability of NO2 over Europe, measured with OMI s spatial zoom mode (~13 km x 12 km at nadir)