The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States
Open accessAbstract
In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O 3 with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R 2 (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R 2, 0.93, and performance was weakest for the western mountainous regions ( R 2 of 0.86) and New England ( R 2 of 0.87). For the cross validation by season, our model had the best performance during summer with an R 2 of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O 3 over space and time, especially in health studies at an intra-urban scale.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Pattern Recognition and Machine Learning
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Kriging: a method of interpolation for geographical information systems
OLIVER, M. A., & WEBSTER, R. (1990) · International journal of geographical information systems · doi:10.1080/02693799008941549 · Cited by 2,194
Long-term ozone exposure and mortality
Jerrett, M., Burnett, R. T., Pope, C. A., Ito, K., Thurston, G., Krewski, D., Shi, Y., Calle, E., & Thun, M. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0803894 · Cited by 1,602
Health effects of air pollution
Bernstein, J. A., Alexis, N., Barnes, C., Bernstein, I. L., Nel, A., Peden, D., Diaz-Sanchez, D., Tarlo, S. M., Williams, P. B., & Bernstein, J. A. (2004) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2004.08.030 · Cited by 925
Anenberg, S. C., Horowitz, L. W., Tong, D. Q., & West, J. J. (2010) · Environmental Health Perspectives · doi:10.1289/ehp.0901220 · Cited by 747
Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., et al. (2019) · Environment International · doi:10.1016/j.envint.2019.104909 · Cited by 733
Self-citationEstimating the National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone
Fann, N., Lamson, A. D., Anenberg, S. C., Wesson, K., Risley, D., & Hubbell, B. J. (2012) · Risk Analysis · doi:10.1111/j.1539-6924.2011.01630.x · Cited by 555
Crouse, D. L., Peters, P. A., Hystad, P., Brook, J. R., van Donkelaar, A., Martin, R. V., Villeneuve, P. J., Jerrett, M., Goldberg, M. S., Pope, C. A., et al. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1409276 · Cited by 552
The effects of meteorology on ozone in urban areas and their use in assessing ozone trends
Camalier, L., Cox, W., & Dolwick, P. (2007) · Atmospheric Environment · doi:10.1016/j.atmosenv.2007.04.061 · Cited by 522
Cited by
Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States
Liang, D., Shi, L., Zhao, J., Liu, P., Sarnat, J. A., Gao, S., Schwartz, J., Liu, Y., Ebelt, S. T., Scovronick, N., et al. (2020) · The Innovation · doi:10.1016/j.xinn.2020.100047 · Cited by 285
Self-citationShi, L., Steenland, K., Li, H., Liu, P., Zhang, Y., Lyles, R. H., Requia, W. J., Ilango, S. D., Chang, H. H., Wingo, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27049-2 · Cited by 239
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Metadata
- Authors
- Requia, W. J., Di, Q., Silvern, R., Kelly, J. T., Koutrakis, P., Mickley, L. J., Sulprizio, M. P., Amini, H., Shi, L., & Schwartz, J.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-08-18
- Volume
- 54
- Issue
- 18
- Pages
- 11037-11047
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W3072681182
- Cited by
- 264
- References
- 49
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- green
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- Requia, W. J., Di, Q., Silvern, R., Kelly, J. T., Koutrakis, P., Mickley, L. J., Sulprizio, M. P., Amini, H., Shi, L., & Schwartz, J. (2020). An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States. Environmental Science & Technology. https://doi.org/10.1021/acs.est.0c01791
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