1 / 12

Recent Developments in Satellite Remote Sensing of Air Quality Parameters

Recent Developments in Satellite Remote Sensing of Air Quality Parameters. Randall Martin, Dalhousie and Harvard-Smithsonian Chulkyu Lee, Aaron van Donkelaar, Lok Lamsal, Dalhousie University Nick Krotkov, Ralph Kahn, Rob Levy, Ed Celarier, Eric Bucsela, NASA

shima
Download Presentation

Recent Developments in Satellite Remote Sensing of Air Quality Parameters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Recent Developments in Satellite Remote Sensing of Air Quality Parameters Randall Martin, Dalhousie and Harvard-Smithsonian Chulkyu Lee, Aaron van Donkelaar, Lok Lamsal, Dalhousie University Nick Krotkov, Ralph Kahn, Rob Levy, Ed Celarier, Eric Bucsela, NASA Folkert Boersma, Ruud Dirksen, KNMI Andreas Richter, University of Bremen

  2. Air Quality Parameters Outline • Emissions, Trends, Long-range transport, Surface concentrations, O3-NOx-VOC sensitivity, Lifetime • Synergy from multiple species • NO2 (NOx lifetime): Lok Lamsal • AOD  PM2.5: Aaron van Donkelaar • SO2 (evaluation and emissions): Chulkyu Lee

  3. NOx Lifetime Drives the Seasonal Variation in Tropospheric NO2 Over Eastern US Weak (<10%) Seasonal Variation in NOx Emissions or NO2/NOx NOx Lifetime (τ) OMI NO2 (DP_GC) Tropospheric NO2 (1015 molecules cm-2) Bottom-up OMI GEOS-Chem GEOS-Chem Simulation Gas phase (NO2+OH) OMI time Daily Average τGC Heterogeneous (N2O5 Hydrolysis) OMI time Lamsal et al., JGR, submitted τOMI τGC

  4. PM2.5 and SO2 Emissions

  5. Global Climatology (2001-2006) of PM2.5 from MODIS & MISR AODand GEOS-Chem AOD/PM2.5 Relationship Evaluation for US/Canada r=0.78 slope=1.02 n=1073 Evaluation with measurements outside Canada/US van Donkelaar et al., EHP, in prep

  6. Insight into Aerosol Source/Type with Precursor Observations Operational OMI PBL SO2 data corrected with local air mass factor improves agreement of OMI SO2 versus aircraft observations (INTEX-B) Orig: slope = 1.6, r=0.71 New: slope = 0.95, r=0.92 OMI Improved SO2 Vertical Columns for 2006 Lee et al., JGR, in press

  7. Anthropogenic Sources Dominate Annual Mean SO2 Column Volcanic SO2 Emissions 10% of Anthropogenic Source GEOS-ChemSimulations for 2006 Total SO2 Column Anthropogenic SO2 Column Fraction from Anthropogenic Chulkyu Lee

  8. Use OMI and SCIAMACHY SO2 Columns to Map SO2 Emissions Tropospheric SO2 column ~ ESO2 Over Land day OH, cloud SO42- DMS SO2 ~day Deposition Emission Phytoplankton Combustion, Smelters, Volcanoes Top-Down Emissions

  9. Global Anthropogenic Sulfur Emissions Over Land for 2006Volcanic SO2 Columns (>10 DU) Excluded From Inversion Top-Down (OMI) 47.0 Tg S/yr Bottom-Up in GEOS-Chem (EDGAR2000, NEI99, EMEP2005, Streets2006) Scaled to 2006 r = 0.77 54.6 Tg S/yr SO2 Emissions (1011 molecules cm-2 s-1) Chulkyu Lee Cloud Radiance Fraction < 0.2

  10. Anthropogenic Emissions Differences (2006) Show Some Consistency Top-Down Minus Bottom-Up Emissions Top-down (OMI) – Bottom-up (GC) -7.6 Tg S/yr Top-down (SCIAMACHY)– Bottom-up (GC) -2.6 Tg S/yr ΔSO2 Emissions (1011 molecules cm-2 s-1) Cloud Radiance Fraction < 0.2 Chulkyu Lee

  11. Indirect Validation of OMI and SCIAMACHY SO2 with Surface MeasurementsInfer Surface SO2 from OMI and SCIAMACHY Using GEOS-Chem SO2 Profiles Year 2006 Cloud Radiance Fraction < 0.2 slope=0.79 r=0.81 slope=0.91 r=0.86 In Situ (at OMI) In Situ (at SCIA) GEOS-Chem: r=0.83, slope=0.81(at OMI) and 0.84(at SCIA) Chulkyu Lee

  12. Challenges Encouraging Prospects for Applying SO2 Observations to Constrain Anthropogenic Emissions • Better understanding of differences between OMI and SCIAMACHY • Reduce uncertainty in simulated SO2 lifetime • Develop adjoint-based inversion OMI NO2 Columns Provide Information into NOx Loss Processes Wintertime Observations Reflect Heterogeneous Processes

More Related