On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis

One of the greatest environmental risks in the cement industry is particulate matter emission (i.e., PM(2.5) and PM(10)). This paper aims to develop descriptive-analytical solutions for increasing the accuracy of predicting particulate matter emissions using resample data of Kerman cement plant. Pho...

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Detalles Bibliográficos
Autores principales: Borhani, F., Shafiepour Motlagh, M., Ehsani, A. H., Rashidi, Y., Maddah, S., Mousavi, S. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643923/
https://www.ncbi.nlm.nih.gov/pubmed/36405244
http://dx.doi.org/10.1007/s13762-022-04645-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643923/
https://www.ncbi.nlm.nih.gov/pubmed/36405244
http://dx.doi.org/10.1007/s13762-022-04645-3

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