National Environmental Public Health Tracking Network 
Downscaler PM 2.5 Metadata — Census Tract Data 


Publication Date 


01/11/2017 


Background 


The Downscaler PM25 dataset provides the output from a Bayesian space-time 
downscaling fusion model called Downscaler (DS) that combines PM25 monitoring data 
from the US EPA Air Quality System (AQS) repository of ambient air quality data (e.g., 
National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS)) 
and simulated PM25 data from the deterministic prediction model, Models-3/Community 
Multiscale Air Quality (CMAQ). The files contain estimates of the mean prediction and 
associated standard error for each of the 2010 U.S. Census Tracts within the contiguous 
U.S. for each day of the modeling year. 


The data are intended for use by professionals comparing air quality and health 
outcomes, through techniques such as case crossover analysis. Other uses may be 
developed at a later time. The standard errors of the predictions should be taken into 
account when using the results. 


Data Values 


The dataset includes nine variables: 


STATEFIPS: State FIPS code 

COUNTYFIPS: County FIPS code 

CTFIPS: Census tract FIPS code 

LATITUDE: Latitude of census tract centroid (degrees) 

LONGITUDE: Longitude of census tract centroid (degrees) 

YEAR: Year of prediction 

DATE: Date (day-month-year) of prediction 

DS_PM_PRED: Mean estimated 24-hour average PM25 concentration in pg/m? 
DS _PM_STDD: Standard error of the estimated PM2.5 concentration 


Geographic Scale 


All census tracts in the contiguous United States 


& Scope 

Time Period January 1, 2001 to December 31, 2014 

Raw Data The air quality monitoring data from the NAMS/SLAMS network were downloaded from 
Processing the Air Quality System (AQS) database. Only Federal Reference Method (FRM) samplers 


were included in the dataset. Data from all Pollutant Occurrence Codes (POC) were used. 
The data was downloaded covering January 1, 2001 through December 31, 2014. The 
CMAQ data was created from version 4.7.1 of the model using Carbon Bond Mechanism- 
05 (CB-05). The CMAQ data are daily 24-hour average PM25 concentrations calculated on 
a 12 km x 12 km grid for the continental United States. The CMAQ emissions data are 
based on 2008 NEI version 2, with specific updates including data from regional planning 
organizations and year-specific data for some larger point sources, including continuous 
emissions monitoring data for NOx and SO2 sources. The onroad mobile source 
emissions were generated using MOVES 2010B, except for California, in which data 
provided by the California Air Resources Board was interpolated to each year. In 
addition, the meteorological data used are from the Weather Research and Forecasting 
Model (WRF) version 3.2 at 12 km simulation. The WRF simulation included the physics 
options of the Pleim-Xiu land surface model (LSM), Asymmetric Convective Model 
version 2 planetary boundary layer (PBL) scheme, Morrison double moment 
microphysics, Kain- Fritsch cumulus parameterization scheme and the RRTMG long-wave 
and shortwave radiation (LWR/SWR) scheme. The DS combines the actual monitoring 
data and the estimated PM25 concentration surface (CMAQ) to predict PM25 through 


space and time. It attempts to find an optimal linear relationship between CMAQ output 
and measurement data to predict new "measurements" at each spatial point in the area 
of interest. Fitted parameters are based on sampling from distributions (built into the 
code by the developers) rather than an objective function minimum, which allows 
calculation of a standard error associated with each prediction. 


Additional processing of the data was conducted to standardize variable names across all 
years of data and to expand FIPS variable into separate statefips, countyfips, and ctfips 
variables. 


Additional 
Information 


Berrocal, V., Gelfand, A. E. and Holland, D. M. (2011). Space-time fusion under error in 
sitll aiodel output: an application to modeling air quality 


Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler 
under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975 


Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for 
output from numerical models. J. of Agricultural, Biological,and Environmental Statistics 
15, 176-197