The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A neural network forecast for daily average PM concentrations in Belgium
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Citations
Cites
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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
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Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults.
Pope, C. A., Thun, M. J., Namboodiri, M. M., Dockery, D. W., Evans, J. S., Speizer, F. E., & Heath, C. W. (1995) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/ajrccm.151.3.7881654 · Cited by 2,911
Samet, J. M., Zeger, S. L., Dominici, F., Curriero, F. C., Coursac, I., Dockery, D. W., Schwartz, J. D., & Zanobetti, A. (2000) · PubMed · Cited by 704
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Vogelezang, D. H. P., & Holtslag, A. A. M. (1996) · Boundary-Layer Meteorology · doi:10.1007/bf02430331 · Cited by 561
The national morbidity, mortality, and air pollution study
Dominici, F., Zanobetti, A., Zeger, S. L., Schwartz, J. D., & Samet, J. M. (2000) · Cited by 356
Ordieres-Meré, J. B., González, E. P. V., Capuz, R. S., & Salazar, R. (2004) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2004.03.010 · Cited by 231
Prediction of maximum of 24-h average of PM10 concentrations 30h in advance in Santiago, Chile
Pérez, P., & Reyes, J. (2002) · Atmospheric Environment · doi:10.1016/s1352-2310(02)00419-3 · Cited by 118
Cited by
A review of artificial neural network models for ambient air pollution prediction
Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2019.06.014 · Cited by 459
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction
Wen, C., Liu, S., Yao, X., Peng, L., Li, X., Hu, Y., & Chi, T. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.11.086 · Cited by 425
Recursive neural network model for analysis and forecast of PM10 and PM2.5
Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., & Di Carlo, P. (2017) · Atmospheric Pollution Research · doi:10.1016/j.apr.2016.12.014 · Cited by 305
Metadata
- Authors
- HOOYBERGHS, J., MENSINK, C., DUMONT, G., FIERENS, F., & BRASSEUR, O.
- Venue
- Atmospheric Environment
- Publisher
- Elsevier BV
- Published
- 2005-06
- Volume
- 39
- Issue
- 18
- Pages
- 3279-3289
- ISSN
- 1352-2310 (ISSN-L)
- OpenAlex
- W2009329344
- Cited by
- 306
- References
- 10
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A neural network forecast for daily average PM concentrations in Belgium. https://app.cochat.ai/papers/10.1016/j.atmosenv.2005.01.050
- The paper
- HOOYBERGHS, J., MENSINK, C., DUMONT, G., FIERENS, F., & BRASSEUR, O. (2005). A neural network forecast for daily average PM concentrations in Belgium. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2005.01.050
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.
