Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)
Identification_Information
Spatial_Data_Organization_Information
Spatial_Reference_Information
Distribution_Information
Metadata_Reference_Information
Identification Information
Section Index
Citation:
Citation Information:
Originator: Di, Q., Y. Wei, A. Shtein, C. Hultquist, X. Xing, H. Amini, L. Shi, I. Kloog, R. Silvern, J. Kelly, M. B. Sabath, C. Choirat, P. Koutrakis, A. Lyapustin, Y. Wang, L. J. Mickley, and J. Schwartz
Publication Date: 20210715
Title: Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)
Edition: 1.0
Geospatial Data Presentation Form: raster, tabular, vector
Publication Information:
Publication Place: Palisades, NY
Publisher: NASA Socioeconomic Data and Applications Center (SEDAC)
Online Linkage: https://doi.org/10.7927/0rvr-4538
Larger Work Citation:
Citation Information:
Originator: Di, Q., H. Amini, L. Shi, I. Kloog, R. Silvern, J. Kelly, M. B. Sabath, C. Choirat, P. Koutrakis, A. Lyapustin, Y. Wang, L. J. Mickley, and J. Schwartz
Publication Date: 20190701
Title: An Ensemble-based Model of PM2.5 Concentration Across the Contiguous United States with High Spatiotemporal Resolution
Geospatial Data Presentation Form: journal article
Series Information:
Series Name: Environment International
Issue Identification: 130: 104909
Online Linkage: https://doi.org/10.1016/j.envint.2019.104909
Description:
Abstract:
The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set includes predictions of PM2.5 concentrations in grid cells at a resolution of 1 km for the years 2000 to 2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, as well as other predictors. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions.
Purpose:
To provide daily and annual PM2.5 concentration data in the U.S. at a resolution of 1 km (about 30 arc-seconds) for public health research to respectively estimate short- and long-term effects on human health, and for other related research.
Time Period of Content:
Time Period Information:
Range of Dates/Times:
Beginning Date: 2000/01/01
Ending Date: 2016/12/31
Currentness Reference: publication date
Spatial Domain:
Bounding Coordinates:
West Bounding Coordinate: -180.000000
East Bounding Coordinate: -65.000000
North Bounding Coordinate: 72.000000
South Bounding Coordinate: 17.000000
Keywords:
Theme:
Theme Keyword Thesaurus: SEDAC Theme
Theme Keyword: Health
Theme:
Theme Keyword Thesaurus: GCMD Science Keywords, Version 8.6
Theme Keyword: EARTH SCIENCE > ATMOSPHERE > AEROSOLS > PARTICULATE MATTER
Theme Keyword: EARTH SCIENCE > ATMOSPHERE > AIR QUALITY > PARTICULATES
Theme:
Theme Keyword Thesaurus: Data Granularity
Theme Keyword: State
Theme:
Theme Keyword Thesaurus: ISO Topic
Theme Keyword: Environment
Theme Keyword: Health
Place:
Place Keyword Thesaurus: CIESIN Location Terms, Version 3.1
Place Keyword: United States of America
Access Constraints: None
Use Constraints:
This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided
Point of Contact:
Contact Information:
Contact Organization Primary:
Contact Organization: NASA Socioeconomic Data and Applications Center (SEDAC)
Contact Address:
Address Type: mailing and physical address
Address: CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
City: Palisades
State or Province: NY
Postal Code: 10964
Country: USA
Contact Voice Telephone: +1 845-365-8920
Contact Facsimile Telephone: +1 845-365-8922
Contact Electronic Mail Address: ciesin.info@ciesin.columbia.edu
Browse Graphic:
Spatial Data Organization Information
Section Index
Direct Spatial Reference Method: Raster
Raster Object Information:
Row Count: 2891
Column Count: 4355
Vertical Count: 1
Spatial Reference Information
Section Index
Horizontal Coordinate System Definition:
Geographic:
Latitude Resolution: 0.008330
Longitude Resolution: 0.008330
Geographic Coordinate Units: Decimal degrees
Geodetic Model:
Horizontal Datum Name: WGS84
Ellipsoid Name: WGS84
Semi-major Axis: 6378137.000000
Denominator of Flattening Ratio: 298.257224
Distribution Information
Section Index
Distributor:
Contact Information:
Contact Organization Primary:
Contact Organization: NASA Socioeconomic Data and Applications Center (SEDAC)
Contact Address:
Address Type: mailing and physical address
Address: CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
City: Palisades
State or Province: New York
Postal Code: 10964
Country: USA
Contact Voice Telephone: +1 845-365-8920
Contact Facsimile Telephone: +1 845-365-8922
Contact Electronic Mail Address: ciesin.info@ciesin.columbia.edu
Resource Description: CIESIN_SEDAC_AQDH_DAPM25_US_1KM
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CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at +1 845-365-8920 or via email at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.
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Ordering Instructions: The data in Georeferenced Tagged Image File Format (.tif), RData (.rds), and Shapefile (.shp) formats are available from the NASA Socioeconomic Data and Applications Center (SEDAC).
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Computer Contact Information:
Access Instructions: Data accessible via the data set landing page.
Digital Form:
Digital Transfer Information:
Digital Transfer Option:
Online Option:
Computer Contact Information:
Access Instructions: Data accessible via the data set landing page.
Digital Form:
Digital Transfer Information:
Digital Transfer Option:
Online Option:
Computer Contact Information:
Access Instructions: Data accessible via the data set landing page.
Available Time Period:
Time Period Information:
Range of Dates/Times:
Beginning Date: 2021/07/15
Ending Date: Present
Metadata Reference Information
Section Index
Metadata Date: 2021/04/30
Metadata Review Date: 2022/08/31
Metadata Contact:
Contact Information:
Contact Organization Primary:
Contact Organization: Center for International Earth Science Information Network (CIESIN) Metadata Administration
Contact Address:
Address Type: mailing and physical address
Address: CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
City: Palisades
State or Province: New York
Postal Code: 10964
Country: USA
Contact Voice Telephone: +1 845-365-8988
Contact Facsimile Telephone: +1 845-365-8922
Contact Electronic Mail Address: metadata@ciesin.columbia.edu
Metadata Standard Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata Standard Version: FGDC-STD-001-1998
Metadata Time Convention: local time
GeoMedia
Catalog report generated Wednesday, August 31, 2022