129 lines
5.3 KiB
Text
129 lines
5.3 KiB
Text
National Environmental Public Health Tracking Network
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Downscaler Ozone Metadata — Census Tract Level
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Publication Date
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01/11/2017
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Background
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The Downscaler ozone dataset provides the output from a Bayesian space-time
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downscaling fusion model called Downscaler (DS) that combines ozone 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 ozone data from the deterministic prediction model, Models-
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3/Community Multiscale Air Quality (CMAQ). The files contain estimates of the mean
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prediction and associated standard error for each of the 2010 US Census Tracts within
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the contiguous US 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 _O3 PRED: Mean estimated 8-hour average ozone concentration in parts per billion
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(ppb) within 3 meters of the surface of the earth
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DS_O3_STDD: Standard error of the estimated ozone 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 were 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 maximum 8-hour ozone concentrations calculated
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ona12kmx 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 NO, 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 3.4 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 CMAQ model results were developed
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in November 2013. The DS combines the actual monitoring data and the estimated
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ozone concentration surface (CMAQ) to predict ozone through space and time. It
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attempts to find an optimal linear relationship between CMAQ output and measurement
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data to predict new "measurements" at each spatial point in the area of interest. Fitted
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parameters are based on sampling from distributions (built into the code by the
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developers) rather than an objective function minimum, which allows calculation of a
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standard error associated with each prediction. It differs from other fusion efforts by
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not assuming the existence of a true air pollution process driving both the monitoring
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data and CMAQ output. Instead, downscaling relates air data and model output using a
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linear regression with bias coefficients (additive and multiplicative) that can vary in space
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and time. This approach to modeling provides a new answer to the “change-of-support”
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problem where we would like to predict air pollution at a certain spatial resolution, but
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must reconcile the difference between point monitoring data and areal average CVAQ
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concentrations. Model parameters are fit just to paired CMAQ and air monitoring data,
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thus CMAQ output that do not contain monitoring sites are not used in model fitting.
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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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computer model 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) is used to provide daily, predictive PM2.5 (daily average) and O3 (daily 8-hr
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maximum) surfaces for 2010.
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