Estimating PM 2.5 from MODIS and MISR AOD Aaron van Donkelaar and Randall Martin March 2009

Objective Get feedback Estimate long-term, high-resolution, fine particulate matter concentrations globally, using satellite-based measurements Satellite-based Sparse networks outside North America Global coverage Multi-year coverage – ( ) Fine resolution –(0.1º x 0.1º) Fine Particulate Matter (PM 2.5 ) Dry weight of aerosol particles < 2.5 µm in diameter Infiltrates natural biological defenses Linked to lung cancer, cardiopulmonary mortality lowers life expectancy Often 24h measurements Annual EPA standard < 15 μg/m 3

τ -PM 2.5 Relation Most τ -PM 2.5 studies are –Regional –High temporal –Empirical or Hybrid –Reliant on a single instrument Relationship can be reduced to: Accuracy of PM2.5 will depend upon accuracy of both η and τ Estimated PM 2.5 = η· τ vertical structure aerosol type meteorological effects meteorology η

MODIS and MISR τ MODIS τ 1-2 days for global coverage Requires assumptions about surface reflectivity Version 5, best quality MISR τ 6-9 days for global coverage Simultaneous surface reflectance and aerosol retrieval Most recent available version, (F09_0017-F11_0021), best estimate Mean τ at 0.1º x 0.1º τ [unitless] MISR MODIS r = 0.40 vs. PM 2.5 r = 0.54 vs. PM 2.5

How to identify accurate regions? < >0.6 < >0.6 ρ 650 nm / ρ 2.1 µm ρ 470 nm / ρ 650 nm Need way to group regions of similar quality Compare with AERONET - is the nearest station best? Divide world into regions of similar surface albedo ratios using MODIS surface albedo product (MCD43) Use same regions for MISR Similar to MODIS –ρ 650 nm = (0.39 – 0.67) * ρ 2.1 um –ρ 470 nm = 0.49 * ρ 650 nm Evaluate monthly AERONET and Satellite τ agreement at overpass within each region Remove τ from regions with mean error greater than ±(0.1+20%)

Global Monthly Filter Number of Included Months

Combined τ mixes MODIS and MISR MODIS r = 0.40 (vs. PM 2.5 ) MISR r = 0.54 (vs. PM 2.5 ) Combined MODIS/MISR r = 0.63 (vs. PM 2.5 ) τ [unitless]

How do we find η? Estimated PM 2.5,24h = η· τ η= τ fτ f · PM 2.5,overpass · PM 2.5,24h τ τ fτ f PM 2.5,overpass v assimilated meteorology –GEOS-4 ( ) GEOS-Chem Global Chemical Transport Model 2º x 2.5º Aerosols include: –Dust, sea salt, sulfate, organic carbon, black carbon, SOA Explore model and satellite Combine model and satellite model

How to best represent typical fine fractions? r GEOS- Chem 0.73 MODIS0.26 MISR ( τ > 0.2) 0.39 Based on non-coincident, monthly mean values τfτf = fine fraction τ

2º x 2.5º can contain a lot of variation PM 2.5,overpass → ν ·PM 2.5,overpass τ fτ f ν · τ f - ( ν -1)· τ f,free trop Can use observed variation within a model grid: where PM 2.5,overpass, τ f and τ f,tree trop are simulated Based upon Lamsal et al., 2008 ν = τ 0.1ºx0.1º τ 2ºx2.5º

Significantly improved agreement with coincident ground measurements over NA Satellite Derived Measured Satellite-Derived [ μ g/m3] Measured PM 2.5 [μg/m 3 ] PM 2.5 [ μ g/m 3 ] r MODIS τ 0.40 MISR τ 0.54 Combined τ 0.63 Combined PM

PM 2.5 -AOD relations can be globally estimated where: PM 2.5,24h = η· τ

Annual mean measurements –Outside Canada/US –295 sites (105 non-EU) r = 0.70 (0.72) slope = 0.77 (0.81) bias = 0.86 (-0.80) μg/m 3 Significant agreement with global mean measurements

Coincident error has two sources Satellite Error limited to % by AERONET filter Implication for satellite PM 2.5 determined by η Estimated PM 2.5 = η· τ Model Affected by aerosol optical properties, concentrations, vertical profile, relative humidity Most sensitive to vertical profile [van Donkelaar et al., 2006]

τ(z)/τ surface Altitude [km] CALIPSO allows profile evaluation Coincidently sample model and CALIPSO extinction profiles –Jun-Dec 2006 Compare % within boundary layer Model (GC) CALIPSO (CAL) Optical Depth from TOA Optical Depth at surface

Coincident measurements within estimated error Combined effect of τ and vertical structure: ±(5 μg/m %) Contains 95.1% of NA data Satellite-Derived PM 2.5 [ μ g/m 3 ]

Sampling frequency varies with region Potential loss of representativeness relative to annual mean

Sampling error is regional Compare continuous and coincident model results Plot sampling-induced error in excess of ±2 μg/m 3 Sampling-Induced Error = Annual PM 2.5 – Coincident PM 2.5 ± 2 μg/m 3 Annual PM 2.5 Sampling Error [%]

Satellite PM 2.5 deviates from model All East West All East West

Satellite-model deviation not just resolution All East West All East West

Significant global deviations from model Annual mean measurements –295 sites (105 non-EU) r = 0.70 (0.72) slope = 0.77 (0.81) bias = 0.86 (-0.80) μg/m 3 r = 0.39 (0.52) slope = 0.38 (0.37) bias = 7.29 (4.20) μg/m 3

Satellite-Derived PM 2.5 [μg/m 3 ]

Pope et al. [2009] –Mean life expectancy decreases 0.61±0.20 year / 10 μg/m 3 satellite-PM population map → –estimate of lost life expectancy ~2 years lost for 30-40% of Asian population ~6 months lost for 70% of E. North American population Loss in Expected Lifetime [years] PM 2.5 Exposure [μg/m 3 ] Population [%] High global impact of PM 2.5

Summary Satellite-derived PM 2.5 asset to global air quality monitoring Quantifiable Error –Coincident: ±(5 μg/m %) –Sampling: ±(2 μg/m %) Distinct from model data Potential for health studies