January 2024

Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1.10 (2000 – 2016)


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.


DESCRIPTION

The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set includes predictions of PM2.5 concentration in grid cells at a resolution of 1-km for the years 2000-2016. 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, and others. 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. In version 1.10, the completeness of daily PM2.5 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily PM2.5 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 and annual PM2.5 predictions allow public health researchers to respectively estimate the short- and long-term effects of PM2.5 exposures on human health, supporting the U.S. Environmental Protection Agency (EPA) for the revision of the National Ambient Air Quality Standards for 24-hour average and annual average concentrations of PM2.5. 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-pm2-5-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 PM2.5 is micrograms (one-millionth of a gram) per cubic meter air (µg/m^3).


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:

Qian Di1,3, Yaguang Wei1, Alexandra Shtein1, Xiaoshi Xing2, Edgar Castro1, Heresh Amini5, Carolynne Hultquist2,4, Liuhua Shi6, Itai Kloog5,7, Rachel Silvern8, James Kelly9, M. Benjamin Sabath10, Christine Choirat10, Petros Koutrakis1, Alexei Lyapustin11, Yujie Wang12, Loretta J. Mickley13, Yasmine Daouk2, and Joel Schwartz1,14. 2024. Daily and Annual PM2.5 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/g2n9-ca10. 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 Vanke School of Public Health, Tsinghua University, Beijing, China
4 School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
5 Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
6 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
7 Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
8 Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States
9 U.S. Environmental Protection Agency (EPA), Office of Air Quality Planning and Standards (OAQPS), Research Triangle Park, NC, United States
10 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
11 National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC), Greenbelt, MD, United States
12 University of Maryland, Baltimore County, Baltimore, MD, United States
13 John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
14 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States

Scientific Publication:

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. 2019. An Ensemble-based Model of PM2.5 Concentration Across the Contiguous United States with High Spatiotemporal Resolution. Environment International, 130:104909. https://doi.org/10.1016/j.envint.2019.104909.

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 PM2.5 concentration, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of PM2.5. 

The high resolution PM2.5 estimates allow epidemiologists to assess the health effects of PM2.5 with higher accuracy, and the PM2.5 estimates further help correct residual exposure measurement errors. Epidemiological studies may also use these predictions to inform public health decisions, air quality standards, and assessments of long-term health impacts of PM2.5 exposure.


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