Identification_Information: 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: 1.00 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: 20000101 Ending_Date: 20161231 Currentness_Reference: publication date Status: Progress: Complete Maintenance_and_Update_Frequency: As needed 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: Country 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: New York Postal_Code: 10964 Country: United States 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: Browse_Graphic_File_Name: https://sedac.ciesin.columbia.edu/downloads/maps/aqdh/aqdh-pm2-5-concentrations-contiguous-us-1-km-2000-2016/sedac-logo.jpg Browse_Graphic_File_Type: JPEG Spatial_Data_Organization_Information: Direct_Spatial_Reference_Method: Raster Raster_Object_Information: Raster_Object_Type: Grid Cell Row_Count: 2891 Column_Count: 4355 Vertical_Count: 1 Spatial_Reference_Information: 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: 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: United States 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 Distribution_Liability: 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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