Latest revision as of 20:03, 22 March 2021

Abstract

The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations&rsquo

health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations&rsquo

health related to road transport variables for S&atilde

o Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron&mdash

MLP, Extreme Learning Machines&mdash

ELM, and Echo State Neural Networks&mdash

ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity&mdash

hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3&mdash

good health and well-being, 7&mdash

affordable and clean energy, and 11&mdash

sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/su12072621 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/2071-1050/12/7/2621/pdf under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/2071-1050/12/7/2621,

https://doaj.org/toc/2071-1050 under the license cc-by

• https://www.mdpi.com/2071-1050/12/7/2621,

https://www.mdpi.com/2071-1050/12/7/2621/pdf,

https://ideas.repec.org/a/gam/jsusta/v12y2020i7p2621-d337153.html,

https://www.scilit.net/article/d626343d7df72effb2ae1b5f0c1c2c02,

https://academic.microsoft.com/#/detail/3013638355

• https://www.mdpi.com/2071-1050/12/7/2621/pdf,

http://dx.doi.org/10.3390/su12072621

under the license https://creativecommons.org/licenses/by/4.0/