January 2024

Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1.10 (2000 – 2016)


PURPOSE

To provide daily 8-hour maximum and annual ground-level Ozone (O3) 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 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains estimates of ozone concentrations at a high resolution spatially (1-km grid cells) and temporally (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically-weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages. In version 1.10, we have enhanced the completeness of daily O3 predictions by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, we used inverse distance weighting interpolation to fill the missing grid cells. Other missing daily O3 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily 8-hour maximum and annual O3 predictions allow public health researchers to respectively estimate the short- and long-term effects of O3 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for O3. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.

The RDS files, containing a matrix of data values corresponding to geographic coordinates, are native to the R statistical computing environment that is widely used in public health research and applications. The GeoTIFF data format is widely used in earth science data and GIS communities.


ACCESSING THE DATA

The data may be downloaded at https://sedac.ciesin.columbia.edu/data/set/aqdh-o3-concentrations-contiguous-us-1-km-v1-10-2000-2016/data-download


DATA FORMAT

This archive contains data in RDS and GeoTIFF 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 UNIT

The unit for O3 is parts per billion (ppb).


SPATIAL EXTENT

Contiguous United States (1-km grids, 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.


CITATION(S)

Data Set:

Weeberb J. Requia1,3, Yaguang Wei1, Alexandra Shtein1, Xiaoshi Xing2, Edgar Castro1, Qian Di1,4, Rachel Silvern5, James T. Kelly6, Petros Koutrakis1, Loretta J. Mickley5, Melissa P. Sulprizio 5, Heresh Amini7, Carolynne Hultquist2,8, Liuhua Shi9, Yasmine Daouk2, and Joel Schwartz1,10. 2024. Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/5tht-jg22. Accessed DAY MONTH YEAR.

1 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
2 Center for International Earth Science Information Network (CIESIN), Columbia Climate School, Columbia University, Palisades, NY, United States
3 School of Public Policy and Government, Fundação Getúlio Vargas, Brasília, Distrito Federal, Brazil
4 Vanke School of Public Health, Tsinghua University, Beijing, China
5 John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
6 U.S. Environmental Protection Agency (EPA), Office of Air Quality Planning & Standards (OAQPS), Research Triangle Park, NC, United States
7 Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
8 School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
9 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
10 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States

Scientific Publication:

Requia, W. J., Q. Di, R. Silvern, J. T. Kelly, P. Koutrakis, L. J. Mickley, M. P. Sulprizio, H. Amini, L. Shi, and J. Schwartz. 2020. An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-level Ozone in the Contiguous U.S. Environmental Science & Technology 54(18):11037-11047. https://doi.org/10.1021/acs.est.0c01791.

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).


ACKNOWLEDGEMENTS

This work was supported by U.S. EPA grants RD-834798, RD-835872, and 83587201, and Health Effects Institute (HEI) grant 4953-RFA14-3/16-4 and assistance award CR-83467701. The HEI is an organization jointly funded by the U.S. EPA and certain motor vehicle and engine manufacturers. The computations were run on the Odyssey cluster supported by the Faculty of Arts & Sciences (FAS) Division of Science, Research Computing Group at Harvard University. The data conversion work from RDS to GeoTIFF with QA/QC was supported by the National Institutes of Health, National Institute of Environmental Health Sciences (NIH/NIEHS) grant R01ES032418. The contents are solely the responsibility of the grantees and do not necessarily represent the official views of the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the data or documents. The authors also thank Gregory Yetman (CIESIN) for his help with the data conversion process.


POTENTIAL USE CASES

It is anticipated for this work to be used for conducting new studies on individual and combined health risks of total O3 concentration, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of O3.

This data can be useful for the environmental health community to estimate the health impacts of O3 more accurately over space and time, especially in health studies at an intra-urban scale.


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