June 2022 Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000-2016) PURPOSE To provide daily and annual Nitrogen Dioxide (NO2) 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. DESCRIPTION The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1 km x 1 km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73. ACCESSING THE DATA The data may be downloaded at https://sedac.ciesin.columbia.edu/data/set/aqdh-no2-concentrations-contiguous-us-1-km-2000-2016/data-download DATA FORMAT This archive contains the data in raster, tabular, and vector formats. The data files are compressed zipfiles. Downloaded files need to be uncompressed in a single folder using either WinZip (Windows file compression utility) or similar application. Users should expect an increase in the size of downloaded data after decompression. DATA UNITS The unit is parts per billion (ppb). SPATIAL EXTENT Contiguous United States, 1 km (about 30 arc-seconds). DISCLAIMER 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 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. 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. RECOMMENDED CITATION(S) Data Set: Qian Di1, Yaguang Wei1, Alexandra Shtein1, Carolynne Hultquist2, Xiaoshi Xing2, Heresh Amini1,11, Liuhua Shi1,10, Itai Kloog3, Rachel Silvern4, James T. Kelly5, M. Benjamin Sabath6, Christine Choirat6, Petros Koutrakis1, Alexei Lyapustin7, Yujie Wang8, Loretta J. Mickley9, and Joel Schwartz1. 2022. Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 – 2016). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/f8eh-5864. Accessed DAY MONTH YEAR. 1 Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Harvard University, Boston, MA, United States 2 The Center for International Earth Science Information Network (CIESIN), Earth Institute, Columbia University, Palisades, NY, United States 3 Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel 4 Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States 5 U.S. Environmental Protection Agency, Office of Air Quality Planning & Standards, Research Triangle Park, NC, United States 6 Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, MA, United States 7 NASA Goddard Space Flight Center, Greenbelt, MD, United States 8 University of Maryland, Baltimore County, Baltimore, MD, United States 9 John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States 10 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States 11 Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 1165, Denmark Scientific Publication: Di, Q., H. Amini, L. Shi, I. Kloog, R. Silvern, J. T. Kelly, M. B. Sabath, C. Choirat, P. Koutrakis, A. Lyapustin, Y. Wang, L. J. Mickley, and J. Schwartz. 2019. Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging. Environmental Science & Technology 2020 54 (3): 1372-1384. https://doi.org/10.1021/acs.est.9b03358. Two R code files are provided as a part of the data set dissemination, under the same DOI (https://doi.org/10.7927/f8eh-5864) and open access license: Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). ACKNOWLEDGEMENT The work for journal paper was supported by U.S. EPA grants RD-834798, RD-835872, and 83587201, and HEI grant 4953-RFA14-3/16-4. Research described in this article was also conducted under contract to the Health Effects Institute (HEI), an organization jointly funded by the U.S. EPA (Assistance Award No. CR-83467701) and certain motor vehicle and engine manufacturers. The computations were run on the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University. The data work from RDS to GeoTIFF with QA/QC is supported by NIH/NIEHS grant R01ES032418.