Theoretical Basis for Converting Satellite Observations to Ground-Level PM 2.5 Concentrations Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing Training (ARSET) – Air Quality A project of NASA Applied Sciences

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

What are we looking for? 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 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 0-15.4 Good Very Unhealthy Unhealthy Unhealthy for Sensitive Groups Moderate

What Do Satellites Provide? Sun Column Satellite Measurement Ozone Rayleigh Scattering 10km Water vapor + other gases (absorption) Seven MODIS bands are utilized to derive aerosol properties: 0.47, 0.55, 0.65, 0.86, 1.24, 1.64, and 2.13 µm 10X10 km2 Res. Aerosol Surface Satellite retrieval issues - inversion (e.g. aerosol model, background).

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

MODIS-Terra True Color Images May 11, 2007 May 12, 2007 May 13, 2007 May 14, 2007 May 15, 2007 May 16, 2007

MODIS-Terra AOD May 11, 2007 May 12, 2007 May 13, 2007 May 14, 2007

AOD – PM Relation Composition Size distribution Vertical profile  – particle density Q – extinction coefficient re – effective radius fPBL – % AOD in PBL HPBL – mixing height Composition Size distribution Vertical profile

Support for AOD-PM2.5 Linkage 2 4 6 8 10 12 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)

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.

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!

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 data

Simple Models from Early Days Chu et al., 2003 Wang et al., 2003 13

AOT-PM2.5 Relationship Gupta, 2008

Limitation: Major Unsolved Issue All three Statistical models (TVM, MVM, ANN) underestimate high PM2.5 loadings

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

What Satellites can provide for vertical information? - CALIPSO Aug 17 Aug 18 Aug 19 Aug 20 Aug 21 Aug 22 Aug 23 Aug 25 Aug 28 5 km (courtesy of Dave Winker, P.I. CALIPSO)

Advantages of using reanalysis meteorology along with satellite TVM MVM

Spatial Comparison MODIS-Terra, July 1, 2007 TVM MVM ANN Spatial Comparison Satellite-derived PM2.5 fills the gap in surface measurements All three methods underestimate the higher PM2.5 concentrations.

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?

Exposure Modeling Approach Basic idea: PM2.5 = f(AOD, meteorological variables, land use variables, etc.) + ε Census / traffic data land use MODIS AOD NLDAS meteorology Statistical prediction model EPA air monitors Predicted PM2.5 surface

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

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. Satellite-derived PM2.5 = x satellite AOD

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

Comparison of two approaches Statistical model Scaling 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 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)

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

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

Summary Statistics PM2.5 (ug/m3) PBL (m) RH (%) TMP (K)

U-Wind (m/sec) V-Wind (m/sec) Forest (%) AOD

Model Fitting Results Max Obs. Per Day 101 Model Days 137 (37.5%) Total Obs. 4,477 Local R2 values vary in time – daily model is necessary. No residual spatial autocorrelation was found in 78% of the daily GWR models. >=10 matched data records is needed to stabilize the model

Model Performance Evaluation Mean Min Max Model R2 0.86 0.56 0.92 CV R2 0.70 0.22 0.85 Putting all the data points together, we see unbiased estimates Model CV

Spatial Pattern of Model Bias Model Fitting Cross Validation Negative and positive model / CV residuals are randomly distributed.

Predicted Daily Concentration Surface

Model Predicted Mean PM2.5 Surface Note: annual mean calculated with137 days

Comparison with CMAQ General patterns agree, details differ

Caveats Data coverage MISR has the least coverage MODIS GOES

Data Quality - MISR Source: Kahn et al. 2010 urban biomass burning dusty r = 0.92, mean absolute diff = 26% r = 0.93, mean absolute diff = 32% r = 0.87, mean absolute diff = 51% Expected error globally: ±(0.05 or 0.20t) In the US: ±(0.05 or 0.15t) Source: Kahn et al. 2010 Source: Liu et al. 2004

Data Quality – MODIS DDV Product Expected error: ±(0.05+0.15t) Source: Levy et al. 2010

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

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?