Folkert Boersma with inputs from J. P. Veefkind, R.J. van der A, H. J. Eskes, and B. Mijling First TROPOMI NO2 measurement on 17 November 2017!

Transcription

1 What can we learn about African air pollution from space? Folkert Boersma with inputs from J. P. Veefkind, R.J. van der A, H. J. Eskes, and B. Mijling First TROPOMI NO2 measurement on 17 November 2017!

2 Few air pollution measurements from the ground in Africa 6 AERONET sta,ons for aerosol op,cal thickness 2 MAX- DOAS sites (Bujumbura, Nairobi) 2 O 3 sonde launch sites (Irene, Nairobi) IDAF sites in West Africa Aircra< campaigns AMMA DACCIWA IAGOS/MOZAIC CARIBIC Need more informa.on to understand the air pollu.on mix, origin, trends and what to do about it Slide 2

3 How satellites may help Satellite data tell us which ac,ons are working to impove air quality, and where addi,onal measures are necessary s,ll. WMO workshop on African air quality, Pretoria, South Africa, 4 December 2017 Slide 3

4 How satellite retrievals work Global coverage every day! Peters/QA4ECV, IUP Bremen Strength of molecular fingerprint in satellite spectra provides column density Measure for op,cal thickness Ra,o of Earth- to- Sun spectrum Wavelength (nm) Slide 4

5 What satellite retrievals represent Spa,al footprint (owen larger than a city) Snapshot for the overpass,me Ver,cally integrated concentra,on data sets! 40 km Ver,cal sensi,vity depends on knowledge on state of the atmosphere Slide 5

6 What have learned from satellite measurements over Africa? 1. Source categories Surface NOx sources: biomass burning, fuel combus,on Satellites provide spa,otemporal constraints on surface emissions and secondary pollutants (DJF ) Surface VOC sources: biomass burning, and natural emissions Tropospheric O3: from oxida,on of VOCs in presence of NOx

7 What have learned from satellite measurements over Africa? 2. Emission estimates Bocom- up Top- down GFED4s biomass burning (MODIS burned area) Large contribu,on from soil NO x emissions to NO 2 column average van der Werf et al., 2017 Vinken et al., 2014

8 What have learned from satellite measurements over Africa? 3. Trend studies Duncan et al., 2016 OMI NO 2 column (deseasonalized) Hilboll et al., : Lagos +2.7±1 %/yr (GOME, SCIAMACHY) : Cairo +6.4±1 %/yr No clear trends for other major ci,es or biomass burning areas Discrimina,on between emissions difficult Aerosol perturbs retrievals Retrieval improvement and valida,on s,ll necessary! WMO workshop on African air quality, Pretoria, South Africa, 4 December 2017 Slide 8

9 Potential model for African air quality: Marco Polo / PANDA 4. Inform model forecasts WP2 WP3 Meterological data Satellite data Monitoring of air quality Emission estimates Ground-based data WP4 Emission inventories WP5 Assessment & forecasting WP7 User service Users FP7- Space WP6 Validation of observations and emissions Development chain: 1. Satellite retrievals 2. Emission estimates 3. Ensemble model forecasts 4. Quality control WMO workshop on African air quality, Pretoria, South Africa, 4 December 2017 Slide 9

10 Ensemble air quality forecasts Air quality ensemble forecast based on 9 models: - Chimere (KNMI) - C- IFS (ECMWF) - WRF- Chem (SMS) - SILAM (FMI) - WRF- Chem (MPI) - EMEP (NILU) - Lotos- Euros (TNO) - WRF- CMAQ (Nanjing Univ) Development chain: - WARMS- CMAQ (SMS) 1. Satellite retrievals 2. Emission estimates 3. Ensemble model forecasts Ground data (> Quality control sta,ons) from CNEMC WMO workshop on African air quality, Pretoria, South Africa, 4 December 2017 Slide 10

11 Tremendous potential TROPOMI NO 2 (launch 13 Oct 2017) Antwerp WMO workshop on African air quality, Pretoria, South Africa, 4 December 2017 Slide 11

12 Tremendous potential TROPOMI to better resolve these emissions EDGAR v Results from ESA s GlobEmissions Project DECSO + OMI

13 Tremendous potential TROPOMI to better resolve these emissions EDGAR v DECSO + OMI

14 Conclusions Ground- based informa,on on African air pollu,on is sparse Need more, also for valida,on of satellite measurements Satellite measurements provide valuable constraints on various emissions, trends, and secondary effects Such emissions inform model analyses and predic,ons of air quality Stakeholders / funding / data policy Collabora,on with local groups Funding: my ERC- proposal was rejected in 2016 Grant n o Satellite data, documenta,on, read sowware available for free via and TROPOMI data will become publicly available in 2018 WMO workshop on African air quality, Pretoria, South Africa boersma@knmi.nl

15 OMI: GOME- 2: SCIAMACHY: NO 2, HCHO, and CO data and algorithm informa,on to be found at: hcp:// Slide 15