Simulation of Absorbing Aerosol Index & Understanding the Relation of NO2 Column Retrievals

with Ground-based Monitors

Randall Martin (Dalhousie, Harvard-Smithsonian)

with contributions from

Melanie Hammer, Shailesh Kharol, Jeff Geddes, Aaron van Donkelaar (Dalhousie U)

TEMPO Science Team Meeting22 May 2014

Michael Brauer (UBC), Dan Crouse (Health Canada), Greg Evans (U Toronto), Mike Jerrett (Berkeley), Lok Lamsal (NASA), Rob Spurr (RT Solutions),

Yushan Su (Ontario MoE), Omar Torres (NASA)

Growing Use of Remote Sensing for Exposure AssessmentLooking backward: Use of (A) remote sensing data to supplement (B) available routine air quality monitoring

Looking forward: Use of (B) available routine air quality monitoring to supplement (A) remote sensing data

Wu J, et al (2006). Exposure assessment of PM air pollution before,during, and after the 2003 Southern California wildfires.

Henderson SB, et al (2008). Use of MODIS products to simplify and evaluate a forest fire plumedispersion model for PM10 exposure assessment.

Significant Association of Satellite-derived Long-term PM2.5 Exposure with Cardiovascular Mortality at Low PM2.5 & Associations with Diabetes and Hypertension

Crouse et al., EHP, 2012; Brook et al., Diabetes Care, 2013; Chen et al., EHP, 2013; Chen et al., Circulation, 2013

Some Groups Using Remote Sensing for Exposure Assessment: WHO, World Bank, OECD, Environmental Performance Index, Global Burden of Disease

Develop Assimilation System of Suite of TEMPO Observations to Estimate PM2.5 Composition, Ground-level

Ozone, and Ground-level NO2

• Absorbing Aerosol Index (aerosol composition) • NO2 (ozone and aerosol composition)• Aerosol optical depth• Ozone profile• SO2 (aerosol composition)• HCHO (ozone and aerosol composition)• Vegetation (VOC emissions)

Assimilation System Could Also be Useful for AMF Calculation

Simulation of Absorbing Aerosol Index (AAI)

GEOS-Chem Simulation of Aerosol Composition Coincident with OMI

LIDORT Radiative Transfer Model

Simulated Absorbing Aerosol IndexTOMS UV Surface

Reflectance (from Omar Torres)

OMI Viewing Geometry

A measure of the aerosol-induced spectral dependence of back-scattered UV

Example observed AAI showing a smoke plume over the United States

354 35410 10

388 388

AAI=-100 log logRayleigh aerosol Rayleigh

I II I

Initial GEOS-Chem & LIDORT Simulation of OMI Absorbing Aerosol Index (July 2008)

Will be Useful to Interpret AAI from TEMPO

Melanie Hammer

OMI

GEOS-Chem & LIDORT

-2.5 -1.5 -0.5 0 0.5 1.5 2.5

OMI Cloud Fraction < 5%

General Approach to Estimate Surface Concentration

S → Surface Concentration

Ω → Tropospheric column

Coincident Model (GEOS-Chem) Profile

OM

MO S S

Daily OMI NO2 Column

Concentration

Alti

tude

Also uses OMI to inform subpixel variation following Lamsal et al. (2008, 2013)

Bias in Satellite-Derived NO2 Trend (2005-2011)

Kharol et al., in prep

In Situ OMI-Derived

Slope with BEHR ~0.5

y = 0.40x + 0.02

r = 0.73n = 102

Why is Satellite-Derived Surface NO2 Biased vs In Situ?

Kharol et al., in prep

In situ (2005-2011) OMI NASA V2.1 (2005-2011)

Molybdenum converter measurements corrected for NOz following Lamsal et al. (2008, 2010)

Urban areas included

NO

2 Mix

ing

Rat

io (p

pbv)

y = 0.40x + 0.09r = 0.80n = 215

In situ sampled at OMI overpass time

Slope with BEHR over US ~0.5

Use Land Use Regression (LUR) Datasets to Examine Effects of Monitor Placement

Kharol et al., in prepLUR from Jerrett et al. 2009

Toronto Hamilton

Monitor Placement Contributes to Bias Versus Area Average

Kharol et al., in prep

LUR

NO

2 at M

easu

rem

ent S

ite

A

rea

Aver

age

LUR

NO

2

Consistent Relative Trends in Ground-level NO2 Indicate Both Observe Changes in Large-Scale Processes

In situOMI

Kharol et al., in prep

Remote Sensing Offers Observational Estimate of Area-Average Concentrations & Changes in Surface NO2

ΔNO2 (ppbv yr-1)

Trend

Shailesh Kharol

2005 to 2011

Concentration

NO2 (ppbv)

Lamsal et al. (2013)

Conclusions

• Initial simulation of Absorbing Aerosol Index

• Spatial bias in surface NO2 from satellite and in situ monitors partially arises from monitor placement

• Ambiguity remains about long-term area-average NO2 in urban areas

• Consider for TEMPO validation a dense collection (>10) of long-term monitors of ground-level NO2 and column NO2 within a TEMPO footprint for multiple urban areas

Acknowledgements: NSERC, Environment Canada, Health Canada