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Air Quality Data for Health-Related Applications

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  • Collection Overview
  • Data Sets (5)
    • Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 – 2016)
    • Country Trends in Major Air Pollutants, v1 (2003 – 2018)
    • Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 – 2016)
    • Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 – 2016)
    • Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., v1 (2000 – 2016)
  • Map Gallery (6)
  • FAQs

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

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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.
Abstract:
The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set contains estimates of ozone concentrations at a high resolution in space (1 km x 1 km grid cells) and time (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.
Recommended Citation(s)*:

Requia, W. J., Y. Wei, A. Shtein, C. Hultquist, X. Xing, Q. Di, R. Silvern, J. T. Kelly, P. Koutrakis, L. J. Mickley, M. P. Sulprizio, H. Amini, L. Shi, and J. Schwartz. 2021. Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016). Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/a4mb-4t86. Accessed DAY MONTH YEAR.

ENW (EndNote & RefWorks)†
RIS (Others)

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.

ENW (EndNote & RefWorks)†
RIS (Others)

* When authors make use of data they should cite both the data set and the scientific publication, if available. Such a practice gives credit to data set producers and advances principles of transparency and reproducibility. Please visit the data citations page for details. Users who would like to choose to format the citation(s) for this dataset using a myriad of alternate styles can copy the DOI number and paste it into Crosscite's website.

† For EndNote users, please check the Research Note field for issues with importing authors that are organizations when using the ENW file format.

Available Formats:
raster, tabular, vector

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