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Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar,

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Presentation on theme: "Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar,"— Presentation transcript:

1 Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Dalhousie University Lok Lamsal, Dalhousie University  NASA Goddard Workshop on Space Technology for Public Health Actions in the Context of Climate Change Adaptation 20 June 2011

2 Large Health Effects of Fine Particulate Matter (PM 2.5 ) Regulation of fine particulate matter achieved the largest estimated benefits of all U.S. Federal Regulations ~ 1 year increase of life expectancy for decreasing long-term exposure of PM 2.5 by 10 ug/m 3 (e.g. moving from Southeast US to most of Canada) Long-term exposure to urban outdoor PM 2.5 causes 800,000 deaths/yr (Cohen et al., 2004)

3 PM (Aerosol) Concentrations Sensitive to Climate Change

4 Large Regions Have Insufficient Measurements for Air Pollution Exposure Assessment Locations of Publicly-Available Long-Term PM 2.5 Monitoring Sites Monitor locations can be driven by compliance objectives ~1 site / 10,000 km 2 in continental US & southern Canada Lee et al., ACPD, 2011

5 Satellite Observations Complement Ground-Based Measurements

6 Column Observations of Aerosol and NO 2 Strongly Influenced by Boundary Layer Concentrations S(z) = shape factor C(z) = concentration Ω = column NO 2 Aerosol Extinction O3O3 Martin, AE, 2008 0.300.360.43 0.52 0.62 2.2 4.7 Aerosol O 3 NO 2 0.75 9.6 Normalized GEOS-Chem Summer Mean Profiles over North America Strong Rayleigh Scattering Weak Thermal Contrast Vertical Profile Affects Boundary-Layer Information in Satellite Obs O 3 Wavelength (μm)

7 Aerosol Remote Sensing: Analogy with Visibility Effects of Aerosol Loading 7.6 ug m -3 22 ug m -3 Pollution haze over East Coast Waterton Lakes/ Glacier National Park

8 Combined Aerosol Optical Depth (AOD) from MODIS and MISR Instruments for 2001-2006 Combined MODIS/MISR r = 0.63 (vs. in-situ PM 2.5 ) van Donkelaar et al., EHP, 2010

9 Calculate AOD/PM 2.5 with Chemical Transport Model (GEOS-Chem) Simulation Aaron van Donkelaar

10 Significant Agreement with Coincident In situ Measurements Satellite Derived In-situ Satellite-Derived [ μ g/m3] In-situ PM 2.5 [μg/m 3 ] Annual Mean PM 2.5 [ μ g/m 3 ] (2001-2006) r MODIS τ 0.40 MISR τ 0.54 Combined τ 0.63 Combined PM 2.5 0.77 van Donkelaar et al., EHP, 2010

11 Evaluation with measurements outside Canada/US Global Climatology (2001-2006) of PM 2.5 Better than in situ vs model (GEOS-Chem): r=0.52-0.62, slope = 0.63 – 0.71 Number sitesCorrelationSlopeBias (ug/m 3 ) Including Europe2440.830.861.15 Excluding Europe840.830.91-2.5 van Donkelaar et al., EHP, 2010

12 Error in Satellite-Derived PM 2.5 has Three Primary Sources Satellite Error of 0.1 + 20% vs independent observations Implication for satellite PM 2.5 determined by η Satellite-derived PM 2.5 = η · AOD Model Affected by aerosol optical properties, concentrations, vertical profile, relative humidity Most sensitive to vertical profile [van Donkelaar et al., 2006] Sampling Biases Satellite retrievals are at specific time of day for cloud-free conditions

13 τ a (z)/τ a (z=0) Altitude [km] Evaluate Simulated (GEOS-Chem) Vertical Profile with Satellite (CALIPSO) Observations Coincidently sample model and CALIPSO extinction profiles –Jun-Dec 2006 Compare % within boundary layer Model (GC) CALIPSO (CAL) Optical depth above altitude z Total column optical depth

14 Error Estimate Estimate error from bias in profile and AOD ±(1 μg/m 3 + 15%) Contains 68% (1 SD) of North American data Total uncertainty 25% (with sampling) Global population-weighted mean uncertainty 7 μg/m 3 van Donkelaar et al., EHP, 2010 Satellite-Derived [ μ g/m3] In-situ PM 2.5 [μg/m 3 ]

15 van Donkelaar et al., EHP, 2010

16

17 80% of global population exceeds WHO guideline of 10 μg/m 3 35% of East Asia exposed to >50 μg/m 3 in annual mean ~1 year life expectancy lost for 10 μg/m 3 Estimate health effects of PM 2.5 exposure PM 2.5 Exposure [μg/m 3 ] Long-term Exposure to Outdoor Ambient PM 2.5 van Donkelaar et al., EHP, 2010 100 90 80 70 60 50 40 30 20 10 0 AQG IT-3 IT-2 IT-1 Population [%] 5 10 15 25 35 50 100 WHO Guideline & Interim Targets

18 Emerging Applications Estimate global burden of disease (WHO) attributable to air pollution (Cohen et al. in prep) Significant association of PM 2.5 and health at low PM 2.5 levels (Crouse et al., EHP, in prep) Satellite dataset dominant contributor to national PM 2.5 model (Hystad et al., EHP, in press) Estimate global mortality from PM 2.5 (Evans et al. in prep) Air pollution and adverse birth outcomes: An international analysis of WHO Global Survey on Maternal and Perinatal Health (Fleischer et al., ISEE, 2011) Cigarette smoking is a negative confounder in epidemiological studies of long-term ambient air pollution and mortality outcomes in Canada (Villeneuve et al., OEM, 2011)

19 USA Today: Hundreds Dead from Heat, Smog, Wildfires in Moscow 9 Aug 2010: “Deaths in Moscow have doubled to an average of 700 people a day as the Russian capital is engulfed by poisonous smog from wildfires and a sweltering heat wave, a top health official said Monday.” MODIS/Aqua: 7 Aug 2010

20 Spatial and Temporal Variation in Satellite-Based PM 2.5 during Moscow 2010 Fires van Donkelaar et al., AE, submitted

21 Application of Satellite-based Estimates to Moscow Smoke Event Before Fires During Fires van Donkelaar et al., submitted MODIS-based In Situ PM 2.5 In Situ PM 2.5 from PM 10 r 2 =0.85, slope=1.06

22 General Approach to Estimate Surface NO 2 Concentration NO2 Column S → Surface Concentration Ω → Tropospheric column In Situ GEOS-Chem Coincident ModelProfile Method: Solar backscatter Scattering by Earth surface and atmosphere   Idealized NO 2 absorption spectrum  

23 Ground-Level Afternoon NO 2 Inferred From OMI for 2005 Lok Lamsal Spatial Correlation vs In Situ for North America = 0.78

24 Challenges Remote Sensing: Improved algorithms to increase accuracy, resolution, and observe other pollutants Modeling: Develop representation of processes Measurements: More needed for evaluation Encouraging Prospects for Satellite Remote Sensing to Inform Air Pollution Exposure Acknowledgements: Health Canada NSERC NASA Health Applications: Close interaction to develop appropriate applications


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