Model-Data Fusion Approaches for Exposure Estimation

Charles StanierAssistant Professor

University of Iowa

CENTER FOR GLOBAL AND REGIONAL ENVIRONMENTAL RESEARCH

Oct 12, 2010 CMAS Conference 2

– Gregory Carmichael, Chemical and Biochemical Engineering– Sinan Sousan, Chemical and Biochemical Engineering– Naresh Kumar, Geography– R. William Field, Epidemiology– Jacob Oleson, Biostatistics– Jaemeen Baek, Center for Global and Regional Environmental

Research– Scott Spak, Center for Global and Regional Environmental Research– Sang Rin Lee, Center for Global and Regional Environmental Research– Daniel Krewski & Michelle Turner, University of Ottawa, R. Samuel

McLaughlin Centre for Population Health Risk Assessment– Adam Beranek Collins, Chemical and Biochemical Engineering

• This research has been supported by a grant from the U.S. Environmental Protection Agency's Science (USEPA) to Achieve Results (STAR) program grant R833865.  

Oct 12, 2010 CMAS Conference 3

• With respect to statistical methods for infusing air quality data and models into health studies– Data fusion (AQS / MODIS / CMAQ)

• We want the spatial resolution of the model (4-12 km)• Without the inaccuracies of the model• Simple approach - use computational efficient weighted

averaging techniques– Optimal Interpolation

– Model evaluation using statistical metrics to select model parameters/settings

Chemical MassBalance Model

Ground ObservationData

3D Air QualityModel

Health Data- Census Tracts

Spatial-Temporal Analysis

Emissions

Meteorology

MODIS aerosol optical depth data

Data assimilation

I – Traditional Source-Resolved Exposure Estimation Techniques

CMAQ

Forward Model Inputs (Met Data; Initial &

Boundary Conditions; Emissions Hourly PM2.5 separated

into sources.

Hourly PM2.5 by size and chemical composition Source-Oriented

Modeling

Source-Receptor Analysis STN / IMPROVE

Speciated PM

Measurement Data CMB (and source profiles)

Daily PM2.5 separated into

sources

I – Traditional Source-Resolved Exposure Estimation Techniques

CMAQ

Forward Model Inputs (Met Data; Initial &

Boundary Conditions; Emissions Hourly PM2.5 separated

into sources.

Hourly PM2.5 by size and chemical composition

II – Model-Measurement Hybrid (Data Assimilation)

Source-Oriented Modeling

Source-Receptor Analysis STN / IMPROVE

Speciated PM

Measurement Data CMB (and source profiles)

Daily PM2.5 separated into

sources

Observational Data

STN/ IMROVE/ FRM Networks

SatelliteAerosol Optical Depth (AOD)

CMAQCMAQAdjoint

(if 4dvar)

Target for Optimization (low model-

measurement error)

Data Assimilation (4dvar or OI)

Emissions

InitialConditions

Control Variables Adjustment

Hourly PM2.5 separated into sources.

Hourly PM2.5 by size and chemical composition

Forward Model Inputs (Met Data; Boundary

Conditions)

Reflect both Model and Observations

I – Traditional Source-Resolved Exposure Estimation Techniques

CMAQ

Forward Model Inputs (Met Data; Initial &

Boundary Conditions; Emissions Hourly PM2.5 separated

into sources.

Hourly PM2.5 by size and chemical composition

II – Model-Measurement Hybrid (Data Assimilation)

Source-Oriented Modeling

Source-Receptor Analysis STN / IMPROVE

Speciated PM

Measurement Data CMB (and source profiles)

Daily PM2.5 separated into

sources

Observational Data

STN/ IMROVE/ FRM Networks

SatelliteAerosol Optical Depth (AOD)

CMAQCMAQAdjoint

(if 4dvar)

Target for Optimization (low model-

measurement error)

Data Assimilation (4dvar or OI)

Emissions

InitialConditions

Control Variables Adjustment

Hourly PM2.5 separated into sources.

Hourly PM2.5 by size and chemical composition

Forward Model Inputs (Met Data; Boundary

Conditions)

Reflect both Model and Observations

• When we assimilate, we try to think in terms of the underlying processes

• Emissions• Regional Transport• Boundary Layer Height• Boundary Condition• Chemical Processes…

– This can help select strategies for temporal and spatial binning, whether we want the nudging to be short-lived or persistant, etc.

Evaluation - statistics

CMAQ evaluation - statistics

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PMPM2.52.5 performance evaluation performance evaluation

11

North East

Mean Fractional Bias (FB)

South Central

Mean Fractional Bias (FB)

Compared to STN sites: Average to excellent performance.

• MODIS OPTIMAL INTERPOLATION WORK12

OIPosterior

PM2.5

IMPROVESTN

CMAQ

10kmMODIS

Temporal Averagin

gSettings

Evaluation

MODIS AOD CMAQ AOD (Case a)

13

OI results for May: OI results for May: Case a: Case a: Average and correct all hoursAverage and correct all hours Case b: Case b: Average overpass hours and correct all Average overpass hours and correct all hourshours

AOD0.22

AOD0.07

Posterior CMAQ-derived AOD

CMAQ-derived AOD logarithmic scaling factors

1

16

4

0.3

OI Result for May 2002OI Result for May 2002Case: 2a, lmxlmy: 1 (Average and correct all hours)Case: 2a, lmxlmy: 1 (Average and correct all hours)

Posterior CMAQ PM2.5

14

MFB (Pacific)

IMPROVE: -19% to 85%STN : -4% to 91%

MFB (Mountain)IMPROVE: -86% to 67%STN : -41% to 95%

MFB (Midwest)IMPROVE: -49% to 17%STN : -12% to 24%

MFB (North East)IMPROVE: -31% to -21%STN : -24% to 21%

MFB (South Central)IMPROVE: -76% to 1%STN : -59% to 9%

MFB (South Atlantic)IMPROVE: -56% to -7%STN : -38% to 6%

3-D Modeling Settings• Weather Research and Forecasting (WRF) model 3.1.1• SMOKE 2.5 • CMAQ 4.7 with aero 5 and CB05 mechanism• Preliminary results shown in WRF-CMAQ comparison is based

on MM5 – CMAQ 4.6 modeling

36km resolution domain

12km resolution domain• Seattle• Los Angeles and Phoenix• Northeastern US

4km resolution domain over Chicago

WRF evaluation sections

7700 met stations

WRF settings [1]

• North American Regional Reanalysis (NARR) data is used for initial and boundary data

• NARR data is a high resolution (every 3 hour data with a 32km resolution) reanalysis including assimilated precipitation

• 3 days spin up time and 15 days run• Objective analysis of bindary and initial data (OBSGRID)• Grid nudging with NARR data

– Analysis nudging for 36 and 12km domains– Interval: every three hours– Nudging on over the planetary boundary layer

WRF settings [2]• Microphysics, radiative transfer , land surface model

– Morrison double-moment scheme for microphysics– RRTMG scheme for longwave and shortwave physics – Pleim-Xiu land surface model with two soil layers– ACM2 (Asymmetric convective model) PBL scheme– Kain-Fritsch scheme

• Observation nudging with Automated Data Processing (ADP) surface and upper air measurements– Observation data was screened using OBSGRID– 12km and 4km– Interval: every hour– Radius of influence: 50km for 12 and 4km resolution

Other studies• Gilliam et al (2010) • MM5 settings (Baker, 2004)

- Explicit moisture : Reisner I mixed phase

- Cumulus: Kain-Fritsch 2- PBL: Pleim-Chang (ACM)- Radiation: RRTM- Multi-layer soil model:

Pleim-Xu- No shallow convection- Analysis nudging on above

PBL (4-D Data assimilation0- No moist physics table

WRF evaluation – statistical benchmarks

Wind speed RMSE ≤ 2m/s

Bias ≤ ±0.5m/s

IOA ≥ 0.6

Wind direction Gross Error ≤30 deg

Bias ≤ ±10 deg

Temperature Gross Error ≤ 2 K

Bias ≤ ± 0.5 K

IOA ≥ 0.8

Humidity Gross Error ≤ 2 g/kg

Bias ≤ ±1 g/km

IOA ≥ 0.6Emery et al. 2001

WRF – extended statistical benchmarks

Legends in figures 1st 2nd 3rd 4th

Wind speed RMSE ws_rmse <= 2.0 <= 2.5 <= 3.0 > 3.0Wind speed IOA ws_ioa >= 0.6 >= 0.5 >= 0.4 < 0.4

Wind direction gross error wd_error <= 30 <= 40 <= 50 > 50

Temperature gross error tp_error <= 2.0 <= 4.0 <= 6.0 > 6.0

Temperature IOA tp_ioa >= 0.8 >= 0.6 >= 0.4 < 0.4

Relative humidity gross error rh_error <= 2.0 <= 2.5 <= 3.0 > 3.0

Relative humidity IOA rh_ioa >= 0.6 >= 0.5 >= 0.4 < 0.4

• Distribution of evaluation statistics is simplified as histograms to understand overall trends better

WRF evaluation – Northeastern US with a 12km resolution (Feb. 2002)

• All sections are in the 2nd bin except the section 20 and 28, which are costal regions

Comparing WRF (currently MM5) statistics and CMAQ mean fractional error

WG-CG WB-CG

WG-CB WB-CB

S28

S8

S20

S15

S22S13

S9

Needed forQuantification of skill for

health studyTo guide assimilation

strategy To identify model

weaknessesCompensating for transport, removal, ventilation?

EmissionsBCs?

MM5 vs. CMAQ performances [1](STN Sites. Jan. 2002)

S8

S8S22

S28

S20

S24S4,18

S3, 9,10S11

S22 S8

S28S20

S15S5

S13

S8

S22

S6S13

S15S3

S28S24

S8S22

S5

S6

S8

S13

Conclusions & Recommendations• Use statistical approaches (with benchmarks) to evaluate model-

measurement skill• Divide into geographical regional• Consider different timescales (sensitive to different types of errors)• Consider stratification of data into clean, moderate and polluted periods• Often require specific analysis by season and for urban and rural areas• OI of MODIS can work, but there are issues to be worked out• Current nationwide 2002 WRF run has most trouble in Mountain West and

in northern New England

Met error (RH / T / WS / WD )

CT

M e

rror

• Additional slides

PMPM2.52.5 performance evaluation performance evaluation For both IMPROVE and STN networks in 2002 (Independent of location)

26

Excellent - Good

Average

Problematic

Compared to IMPROVE sites the model show less PM2.5 estimate correlation than STN sites.

Major PM2.5 species that contribute to model biases are OC, sulfate, and nitrate.

PM2.5 bias is due to OC and nitrate.1BOYLAN, et al., 2006a. PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmospheric Environment 40, 4946-4959.

WRF evaluation – Northeastern US with a 12km resolution (Jun. 2002)

• Performance of wind direction simulation in section 20 and 28 is better than that in February.

WRF evaluation - Chicago with a 4km resolution

MM5 vs. CMAQ performances [2] (STN Sites. Jan. 2002)

MM5 vs. CMAQ performances [3] (IMPROVE. Jan. 2002)

MM5 vs. CMAQ performances [4] (IMPROVE. Jan. 2002)

October 12, 2010 CMAS Conference 32

Techniques are Aimed at Helping to Solve the Component Toxicity Problem

Smith, Jerrett et al. Lancet (2009)

Smith, Jerrett et al. Lancet (2009)

Smith, Jerrett et al. Lancet (2009)

Exposure estimates are means across metropolitan statistical areas

Krewski et al. (2009) Extended followup ACSLall et al. (2004) Atmos. Environ.