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Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

Author

Listed:
  • Yslene Kachba

    (Department of Production Engineering, Federal University of Technology—Parana (UTFPR), Ponta Grossa 84017-220, Brazil)

  • Daiane Maria de Genaro Chiroli

    (Department of Production Engineering, Federal University of Technology—Parana (UTFPR), Ponta Grossa 84017-220, Brazil)

  • Jônatas T. Belotti

    (Department of Computational Science, Federal University of Technology—Parana (UTFPR), Ponta Grossa 84017-220, Brazil)

  • Thiago Antonini Alves

    (Department of Mechanical Engineering, Federal University of Technology—Parana (UTFPR), Ponta Grossa 84017-220, Brazil)

  • Yara de Souza Tadano

    (Department of Mathematics, Federal University of Technology—Parana (UTFPR), Ponta Grossa 84017-220, Brazil)

  • Hugo Siqueira

    (Department of Computational Science, Federal University of Technology—Parana (UTFPR), Ponta Grossa 84017-220, Brazil
    Department of Electric Engineering, Federal University of Technology—Parana (UTFPR), Ponta Grossa 84017-220, Brazil)

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’ 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’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—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—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.

Suggested Citation

  • Yslene Kachba & Daiane Maria de Genaro Chiroli & Jônatas T. Belotti & Thiago Antonini Alves & Yara de Souza Tadano & Hugo Siqueira, 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America," Sustainability, MDPI, vol. 12(7), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2621-:d:337153
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    References listed on IDEAS

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    1. Luna, Ivette & Ballini, Rosangela, 2011. "Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 708-724.
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    Cited by:

    1. Paulo S. G. de Mattos Neto & Manoel H. N. Marinho & Hugo Siqueira & Yara de Souza Tadano & Vivian Machado & Thiago Antonini Alves & João Fausto L. de Oliveira & Francisco Madeiro, 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    2. Jônatas Belotti & Hugo Siqueira & Lilian Araujo & Sérgio L. Stevan & Paulo S.G. de Mattos Neto & Manoel H. N. Marinho & João Fausto L. de Oliveira & Fábio Usberti & Marcos de Almeida Leone Filho & Att, 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants," Energies, MDPI, vol. 13(18), pages 1-22, September.
    3. Elias Amancio Siqueira-Filho & Maira Farias Andrade Lira & Attilio Converti & Hugo Valadares Siqueira & Carmelo J. A. Bastos-Filho, 2023. "Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models," Energies, MDPI, vol. 16(7), pages 1-27, March.
    4. Hyeon-Ju Oh & Jongbok Kim, 2020. "Monitoring Air Quality and Estimation of Personal Exposure to Particulate Matter Using an Indoor Model and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    5. Maroto Estrada, Pedro & de Lima, Daniela & Bauer, Peter H. & Mammetti, Marco & Bruno, Joan Carles, 2023. "Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach," Applied Energy, Elsevier, vol. 329(C).
    6. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    7. Adiqa Kausar Kiani & Wasim Ullah Khan & Muhammad Asif Zahoor Raja & Yigang He & Zulqurnain Sabir & Muhammad Shoaib, 2021. "Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    8. Huafang Huang & Xiaomao Wu & Xianfu Cheng, 2020. "The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm," IJERPH, MDPI, vol. 17(12), pages 1-13, June.

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