Airborne particle pollution predictive model using Gated Recurrent Unit (GRU) deep neural networks-Reference-Cited by-同舟云学术

Airborne particle pollution predictive model using Gated Recurrent Unit (GRU) deep neural networks

Published:2020-05-24 Issue:3 Volume:13 Page:821-834
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Becerra-Rico Josue,Aceves-Fernández Marco A.,Esquivel-Escalante Karen,Pedraza-Ortega Jesús Carlos

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-020-00462-9.pdf

Reference26 articles.

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2. Aceves-Fernandez MA et al (2015) Design and implementation of ant colony algorithms to enhance airborne pollution models. Int J Environ Sci Toxicol Res 3(2):22–28

3. Akbarzadeh MA et al (2018) The association between exposure to air pollutants including PM10, PM2. 5, ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide concentration and the relative risk of developing STEMI: a case-crossover design. Environ Res 161:299–303

4. Bartzis JG et al (2019). Environmental data treatment to support exposure studies: the statistical behavior for NO2, O3, PM10 and PM2.5 air concentrations in Europe. https://doi.org/10.1016/j.envres.2019.108864

5. Bos PM et al (2019) Pulmonary toxicity in rats following inhalation exposure to poorly soluble particles: The issue of impaired clearance and the relevance for human health hazard and risk assessment. Regul Toxicol Pharmacol:104498

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