Theoretical Basis for Converting Satellite

Observations to Ground-Level PM 2.5 Concentrations

Applied Remote SEnsing Training (ARSET) – Air Quality

A project of NASA Applied Sciences

Training Workshop in Partnership with BAAQMD

Santa Clara, CASeptember 10 – 12, 2013

Estimate ground-level PM2.5 mass concentration (µg m-3) using satellite

derived AOD

OBJECTIVEObjective

2

Sensitive groups should avoid all physical activity outdoors; everyone else should avoid prolonged or heavy exertion

Sensitive groups should avoid prolonged or heavy exertion; everyone else should reduce prolonged or heavy exertion

Sensitive groups should reduce prolonged or heavy exertion

Unusually sensitive people should consider reducing prolonged or heavy exertion

None

Cautionary Statements

201-300

151-200

101-150

51-100

0-50

Index

Values

PM10

(ug/m3)

PM2.5

(ug/m3)

Category

355-424

255-354

155-254

55-154

0-54

150.5-250.4

65.5-150.4

40.5-65.4

15.5-40.4

0-15.4Good

Very Unhealthy

Unhealthy

Unhealthy for Sensitive Groups

Moderate

What are we looking for?

3

Surface

Aerosol

Rayleigh Scattering

Water vapor + other gases (absorption)

Ozone

10km

Satellite

Sun

ColumnSatelliteMeasurement

Satellite retrieval issues - inversion (e.g. aerosol model, background).

Seven MODIS bands are utilized to derive aerosol properties: 0.47, 0.55, 0.65, 0.86, 1.24, 1.64, and 2.13 µm10X10 km2 Res.

dzAOD ext

What Do Satellites Provide?

4

Measurement Techniques

AOD – Column integrated value (top of the atmosphere to surface) - Optical measurement of aerosol loading – unitless. AOD is a function of shape, size, type and number concentration of aerosols

PM2.5 – Mass per unit volume of aerosol particles less than 2.5 µm in aerodynamic diameter at surface (measurement height) level

5

May 11, 2007 May 12, 2007 May 13, 2007

May 14, 2007 May 15, 2007 May 16, 2007

MODIS-Terra True Color Images

6

May 11, 2007 May 12, 2007 May 13, 2007

May 14, 2007 May 15, 2007 May 16, 2007

MODIS-Terra AOD

7

AOD – PM Relation

• – particle density• Q – extinction

coefficient• re – effective radius

• fPBL – % AOD in PBL

• HPBL – mixing height

AODH

f

Q

rC

PBL

PBLe 3

4

Composition

Size distribution

Vertical profile

8

Support for AOD-PM2.5 Linkage

• Current satellite AOD is sensitive to PM2.5 (Kahn et al. 1998)

• Polar-orbiting satellites can represent at least daytime average aerosol loadings (Kaufman et al., 2000)

• Missing data due to cloud cover appear random in general (Christopher et al., 2010)

2 4 6 8 10 12

9

Assumption for Quantitative Analysis

When most particles are concentrated and well mixed in the boundary layer, satellite AOD contains a strong signal of ground-level particle concentrations.

10

Modeling the Association of AOD With PM2.5

• The relationship between AOD and PM2.5 depends on parameters hard to measure:– Vertical profile– Size distribution and composition– Diurnal variability

• We develop statistical models with variables to represent these parameters– Model simulated vertical profile– Meteorological & other surrogates– Average of multiple AOD measurements

No textbook solution! 11

Methods Developed So Far• Statistical models

– Correlation & simple linear regression– Multiple linear regression with effect modifiers– Linear mixed effects models– Geographically weighted regression– Generalized additive models– Hierarchical models combining the above– Bayesian models– Artificial neural network

• Data fusion models– Combining satellite data with model

simulations• Deterministic models

– Improving model simulation with satellite data12

Simple Models from Early Days

13

Chu et al., 2003

Wang et al., 2003

13

Gupta, 2008

AOT-PM2.5 Relationship

14

Limitation: Major Unsolved Issue

All three Statistical models (TVM, MVM, ANN) underestimate high PM2.5 loadings

15

Vertical Distribution

Engel-Cox et al., 2006Al-Saadi et al., 2008

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Aug 17Aug 18Aug 19

Aug 20Aug 21Aug 22Aug 23

Aug 25Aug 28

5 km

(courtesy of Dave Winker, P.I. CALIPSO)

What Satellites can provide for vertical information? - CALIPSO

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TVM MVM

Advantages of using reanalysis meteorology along with satellite

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TVM

MVM ANN

Spatial Comparison

MODIS-Terra, July 1, 2007

Satellite-derived PM2.5 fills the gap in surface measurements

All three methods underestimate the higher PM2.5 concentrations.

19

Questions to Ask: Issues

How accurate are these estimates ?

Is the PM2.5-AOD relationship always linear?

How does AOD retrieval uncertainty affect estimation of air quality

Does this relationship change in space and time?

Does this relationship change with aerosol type?

How does meteorology drive this relationship?

How does vertical distribution of aerosols in the atmosphere affect these estimates?

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MODIS AODCensus / traffic data

Predicted PM2.5 surface

land use

EPA air monitorsStatistical prediction model

NLDAS meteorology

Exposure Modeling Approach

Basic idea:

PM2.5 = f(AOD, meteorological variables, land use variables, etc.) + ε

21

Requirements for this job

• PCs with large hard drives and good graphics card

• Internet to access satellite & other data

• Some statistical software (SAS, R, Matlab, etc.)

• Some programming skill• Knowledge of regional air pollution

patterns• Ideally, GIS software and working

knowledge

22

Satellite-derived PM2.5 = x satellite AOD

Scaling approach

• Basic idea: let an atmospheric chemistry model decide the conversion from AOD to PM2.5. Satellite AOD is used to calibrate the absolute value of the model-generated conversion ratio.

ModelAOD

PM

5.2

23

Scaling approach can be applied wherever there are satellite retrievals, but prediction accuracy can vary a lot.

24

Comparison of two approaches

Statistical model

• Flexible modeling structure

• Ability to provide estimates at high spatial resolution with the help of other covariates

• Higher accuracy

• Requires extensive ground data support

• Models are often not generalizable to other regions

Scaling model

• Mathematically more elegant

• Applicable in areas with extensive ground data support

• Requires ability to conduct CTM runs

• Resolution of predictions dependant on satellite data

• Lower accuracy (no error terms allowed in the model)

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Study Domain Design

Number of monitoring sites: 119 Exposure modeling domain: 700 x 700 km2

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Geographically Weighted Regression Model

GWR allows model parameters to vary in space to better capture spatially varying AOD-PM relationship – major advantage over global regression models.

Model Structure

Datasets (2003): PM2.5 – EPA / IMPROVE daily measurements AOD – MODIS collection 5 (10 km)

Meteorology – NLDAS-2 (14 km)Land use: NLCD 2001

erForest_Cov Wind_Speed Temp

RH PBL AOD ~][PM

y)(x, 6y)(x, 5y)4(x,

),(3y)(x, 2),(1),(0y)(x,2.5

yxyxyx

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PM2.5 (ug/m3) PBL (m)

RH (%) TMP (K)

Summary Statistics

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U-Wind (m/sec) V-Wind (m/sec)

Forest (%) AOD

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Model Fitting Results

>=10 matched data records is needed to stabilize the model

No residual spatial autocorrelation was found in 78% of the daily GWR models.

Local R2 values vary in time – daily model is necessary.

Max Obs. Per Day 101

Model Days 137 (37.5%)

Total Obs. 4,477

30

Model Performance Evaluation

Mean Min Max

Model R2

0.86 0.56 0.92

CV R2 0.70 0.22 0.85

Mo

de

l

CV

Putting all the data points together, we see unbiased estimates

31

Spatial Pattern of Model Bias

Model Fitting Cross Validation

Negative and positive model / CV residuals are randomly distributed.

32

Predicted Daily Concentration Surface

33

Model Predicted Mean PM2.5 Surface

Note: annual mean calculated with137 days 34

Comparison with CMAQ

General patterns agree, details differ35

Caveats• Data coverage

– MISR has the least coverage

MODIS

GOES

36

Data Quality - MISR

r = 0.93, mean absolute diff = 32%

urban biomass burning dusty

r = 0.92, mean absolute diff = 26%

Source: Kahn et al. 2010Source: Liu et al. 2004

r = 0.87, mean absolute diff = 51%

Expected error globally: ±(0.05 or 0.20t) In the US: ±(0.05 or 0.15t) 37

Data Quality – MODIS DDV Product

Source: Levy et al. 2010

Expected error: ±(0.05+0.15t)

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Data Quality - GOES

Source: Prados et al. 2007r = 0.79 over 10 northeastern sites, slope = 0.8

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The Use of Satellite Models

• Currently for research– Spatial trends of PM2.5 at regional to national

level– Interannual variability of PM2.5

– Model calibration / validation– Exposure assessment for health effect studies

• In the near future for research– Spatial trends at urban scale– Improved coverage and accuracy– Fused statistical – deterministic models

• For regulation?40