A DEEP LEARNING APPROACH WITH WAVELETS TO FORECASTING TROPOSPHERIC OZONE IN A TROPICAL METROPOLITAN REGION

Author:

Carmo Junior Clovis,Winkler Ingrid,Nascimento Erick Giovani Sperandio

Publisher

Editora Blucher

Reference18 articles.

1. "1 PEDRUZZI, Rizzieri et al. Performance evaluation of a photochemical model using different boundary conditions over the urban and industrialized metropolitan area of Vitória, Brazil. Environmental Science and Pollution Research, [S.L.], v. 26, n. 16, p. 16125-16144, 10 abr. 2019. Springer Science and Business Media LLC.

2. 2 WHO 2013. Review of evidence on health aspects of air pollution-REVIHAAP project: final technical report. World Health Organization Regional Office for Europe. https://www.euro.who.int/en/health-topics/environment-and-health/air-quality/publications/2013/review-of-evidence-on-health-aspects-of-air-pollution-revihaap-project-final-technical-report Accessed May 3, 2021

3. 3 RYBARCZYK, Yves et al. Machine Learning Approaches for Outdoor Air Quality Modelling: a systematic review. Applied Sciences, [S.L.], v. 8, n. 12, p. 2570, 11 dez. 2018. MDPI AG.

4. 4 CONAMA Environmental National Committee, New national air quality standards, Res. 491/ 2018. www2.mma.gov.br/port/conama/legiabre.cfm?codlegi=740. Accessed on: 23 Apr. 2021

5. 5 KITAGAWA, Yasmin Kaore Lago et al. ASSESSMENT OF PRIMARY AIR POLLUTANTS IN A TROPICAL METROPOLITAN REGION BY COMBINING LOCAL AND GLOBAL EMISSIONS INVENTORIES. Air Pollution XXVII, [S.L.], v. 236, p. 99-110, 4 set. 2019. WIT Press.

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