Estimating PM2.5 using Fusion of Satellite Remote Sensing, - - PowerPoint PPT Presentation

Estimating PM2.5 using Fusion of Satellite Remote Sensing, GEOS-Chem, and other Parameters

• Joel Schwartz
• Harvard University

Critical Issues in Modeling/Fusion

• Missing Data
• Surrogates for emissions have time varying

impacts

• Aerosol Optical Depth has a time varying

relationship to ground level particle concentrations

• Nonlinearities
• Many factors have multiplicative impacts on

particle concentrations: High order interactions

Aerosol Optical Depth

• Is based on direct physical measures at every

square kilometer of earth twice per day

• This provides high spatial and temporal

resolution, BUT

• It is based on scattering and absorption of

light in the entire column of air, not just ground level

• The scattering and absorption varies with

particle color and size

Key Issues in Using AOD to Model PM2.5

• The correlation between AOD and PM2.5 is

not that high

• Differences in vertical profiles and particle

composition that change extinction explain a good bit of that

• These factors have more day to day variation,

but vary spatially smoothly

• Many grid-cell-days are missing

A density plot exhibiting the daily variation of AOD slopes between 2000-2008 during the stage 1 calibrations

November 15, 2003 AOD

Low pollution day

Land Use Regression

• Offers the possibility of highly geographically

resolved estimates of exposures

• Standard Models have limited temporal

resolution

• Many are calibrated with intensive monitoring

campaigns with limited duration

• This raises issues of spatial-temporal error

1995 2000 2005 2010 0.6 1.0 1.4

BC over time in Boston

Year BC

Concentrations Change with Time

What is worse is the decline varies depending on where you are

Combining with Land use improves The model

Other Approaches

• Chemical Transport Models

– Also provide daily (or better) time resolution

• Hybrid Models (CTM + Land Use + Weather)

GEOS-Chem

• GEOS-Chem is the most widely used chemical

transport model in the world.

• It incorporates detailed emissions inventories,

meteorology, and nonlinear chemistry to estimate transport of aerosols and gases, and formation of secondary pollutants, including

• rganic aerosols.
• The individual species can be added to

estimate PM2.5

Advantages

• There is no missing data
• It gives us PM components, not just PM2.5
• Disadvantages
• It has a lower resolution (25km)
• It has a lower R2
• Uses modeled meteorology from Reanalysis data not

real

• Emission inventories are not perfect, and poorly

resolved spatially and temporally

• But: The errors from this model derive from completely

different sources

Why not combine them all?

Put it All Together

• MAIAC AOD from Aqua and Terra
• AAI, O3 and NO2 from OMI
• GEOS-Chem output
• Land use and Meteorology
• Monitoring Data
• Neural Network Algorithm
• Entire US, Daily 2000-2012

Out of Sample R2

• 0.85 for PM2.5
• 0.76 for Ozone
• Daily predictions for each of 11 million 1km

cells in the Continental US for each day Jan 1 2000-Dec 31 2012.