The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Recursive neural network model for analysis and forecast of PM10 and PM2.5
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Citations
Cites
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution
Pope III, C. A. (2002) · JAMA · doi:10.1001/jama.287.9.1132 · Cited by 8,447
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
Gardner, M. W., & Dorling, S. R. (1998) · Atmospheric Environment · doi:10.1016/s1352-2310(97)00447-0 · Cited by 3,324
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
Acute Respiratory Effects of Particulate Air Pollution
Dockery, D. W., & Pope, C. A. (1994) · Annual Review of Public Health · doi:10.1146/annurev.pu.15.050194.000543 · Cited by 2,177
Katsouyanni, K., Touloumi, G., Spix, C., Schwartz, J., Balducci, F., Medina, S., Rossi, G., Wojtyniak, B., Sunyer, J., Bacharova, L., et al. (1997) · BMJ · doi:10.1136/bmj.314.7095.1658 · Cited by 875
Turner, M. C., Krewski, D., Pope, C. A., Chen, Y., Gapstur, S. M., & Thun, M. J. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201106-1011oc · Cited by 577
Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain
Rodrı́guez, S., Querol, X., Alastuey, A., Kallos, G., & Kakaliagou, O. (2001) · Atmospheric Environment · doi:10.1016/s1352-2310(00)00496-9 · Cited by 531
Cited by
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
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
Forecasting air quality time series using deep learning
Freeman, B. S., Taylor, G., Gharabaghi, B., & Thé, J. (2018) · Journal of the Air & Waste Management Association · doi:10.1080/10962247.2018.1459956 · Cited by 319
Metadata
- Authors
- Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., & Di Carlo, P.
- Venue
- Atmospheric Pollution Research
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 8
- Issue
- 4
- Pages
- 652-659
- ISSN
- 1309-1042 (ISSN-L)
- OpenAlex
- W2570322979
- Cited by
- 305
- References
- 23
- FWCI
- 15.06
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
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- CoChat. (2026). Citation check: Recursive neural network model for analysis and forecast of PM10 and PM2.5. https://app.cochat.ai/papers/10.1016/j.apr.2016.12.014
- The paper
- Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., & Di Carlo, P. (2017). Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric Pollution Research. https://doi.org/10.1016/j.apr.2016.12.014
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.
