115 lines
4.4 KiB
Text
115 lines
4.4 KiB
Text
National Environmental Public Health Tracking Network
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Downscaler PM 2.5 Metadata — Census Tract Data
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Publication Date
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01/11/2017
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Background
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The Downscaler PM25 dataset provides the output from a Bayesian space-time
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downscaling fusion model called Downscaler (DS) that combines PM25 monitoring data
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from the US EPA Air Quality System (AQS) repository of ambient air quality data (e.g.,
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National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS))
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and simulated PM25 data from the deterministic prediction model, Models-3/Community
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Multiscale Air Quality (CMAQ). The files contain estimates of the mean prediction and
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associated standard error for each of the 2010 U.S. Census Tracts within the contiguous
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U.S. for each day of the modeling year.
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The data are intended for use by professionals comparing air quality and health
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outcomes, through techniques such as case crossover analysis. Other uses may be
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developed at a later time. The standard errors of the predictions should be taken into
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account when using the results.
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Data Values
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The dataset includes nine variables:
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STATEFIPS: State FIPS code
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COUNTYFIPS: County FIPS code
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CTFIPS: Census tract FIPS code
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LATITUDE: Latitude of census tract centroid (degrees)
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LONGITUDE: Longitude of census tract centroid (degrees)
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YEAR: Year of prediction
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DATE: Date (day-month-year) of prediction
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DS_PM_PRED: Mean estimated 24-hour average PM25 concentration in pg/m?
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DS _PM_STDD: Standard error of the estimated PM2.5 concentration
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Geographic Scale
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All census tracts in the contiguous United States
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& Scope
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Time Period January 1, 2001 to December 31, 2014
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Raw Data The air quality monitoring data from the NAMS/SLAMS network were downloaded from
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Processing the Air Quality System (AQS) database. Only Federal Reference Method (FRM) samplers
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were included in the dataset. Data from all Pollutant Occurrence Codes (POC) were used.
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The data was downloaded covering January 1, 2001 through December 31, 2014. The
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CMAQ data was created from version 4.7.1 of the model using Carbon Bond Mechanism-
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05 (CB-05). The CMAQ data are daily 24-hour average PM25 concentrations calculated on
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a 12 km x 12 km grid for the continental United States. The CMAQ emissions data are
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based on 2008 NEI version 2, with specific updates including data from regional planning
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organizations and year-specific data for some larger point sources, including continuous
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emissions monitoring data for NOx and SO2 sources. The onroad mobile source
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emissions were generated using MOVES 2010B, except for California, in which data
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provided by the California Air Resources Board was interpolated to each year. In
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addition, the meteorological data used are from the Weather Research and Forecasting
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Model (WRF) version 3.2 at 12 km simulation. The WRF simulation included the physics
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options of the Pleim-Xiu land surface model (LSM), Asymmetric Convective Model
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version 2 planetary boundary layer (PBL) scheme, Morrison double moment
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microphysics, Kain- Fritsch cumulus parameterization scheme and the RRTMG long-wave
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and shortwave radiation (LWR/SWR) scheme. The DS combines the actual monitoring
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data and the estimated PM25 concentration surface (CMAQ) to predict PM25 through
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space and time. It attempts to find an optimal linear relationship between CMAQ output
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and measurement data to predict new "measurements" at each spatial point in the area
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of interest. Fitted parameters are based on sampling from distributions (built into the
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code by the developers) rather than an objective function minimum, which allows
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calculation of a standard error associated with each prediction.
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Additional processing of the data was conducted to standardize variable names across all
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years of data and to expand FIPS variable into separate statefips, countyfips, and ctfips
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variables.
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Additional
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Information
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Berrocal, V., Gelfand, A. E. and Holland, D. M. (2011). Space-time fusion under error in
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sitll aiodel output: an application to modeling air quality
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Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler
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under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975
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Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for
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output from numerical models. J. of Agricultural, Biological,and Environmental Statistics
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15, 176-197
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