The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm
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Citations
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
An association between air pollution and mortality in six U.S. cities
Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G., & Speizer, F. E. (1993) · New England Journal of Medicine · doi:10.1056/nejm199312093292401 · Cited by 8,018 · cited 1×
“5) has been associated with increased cardiovascular morbidity and respiratory mortality (Dockery et al., 1993; Keller et al., 2015; Madaniyazi et al., 2015; Pope et al., 2011; Puett et al., 2009), as well as decreased birth weight (Rich et al., 2015).”
BackgroundAddressing Global Mortality from Ambient PM2.5
Apte, J. S., Marshall, J. D., Cohen, A. J., & Brauer, M. (2015) · Environmental Science & Technology · doi:10.1021/acs.est.5b01236 · Cited by 995
Rich, D. Q., Liu, K., Zhang, J., Thurston, S. W., Stevens, T. P., Pan, Y., Kane, C., Weinberger, B., Ohman-Strickland, P., Woodruff, T. J., et al. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1408795 · Cited by 174
Cited by
Toxicity prediction based on artificial intelligence: A multidisciplinary overview
Pérez Santín, E., Rodríguez Solana, R., González García, M., García Suárez, M. D. M., Blanco Díaz, G. D., Cima Cabal, M. D., Moreno Rojas, J. M., & López Sánchez, J. I. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1516 · Cited by 153
Metadata
- Authors
- Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., & Zhang, M.
- Venue
- Atmospheric Environment
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 155
- Pages
- 129-139
- ISSN
- 1352-2310 (ISSN-L)
- OpenAlex
- W2588978790
- Cited by
- 258
- References
- 5
- FWCI
- 13.85
- Type
- journal article
- Language
- en
- Open access
- closed
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- This page
- CoChat. (2026). Citation check: Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm. https://app.cochat.ai/papers/10.1016/j.atmosenv.2017.02.023
- The paper
- Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., & Zhang, M. (2017). Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2017.02.023
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
