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.