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Article

A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil—An Artificial Neural Networks Approach

by
Luciana Leirião
,
Michelle de Oliveira
,
Tiago Martins
* and
Simone Miraglia
Institute of Environmental, Chemical and Pharmaceutical Sciences, Federal University of Sao Paulo (UNIFESP), Diadema 09913030, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(8), 5458; https://doi.org/10.3390/ijerph20085458
Submission received: 9 February 2023 / Revised: 30 March 2023 / Accepted: 7 April 2023 / Published: 11 April 2023
(This article belongs to the Special Issue Environmental and Occupational Health in Brazil)
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Abstract

Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM10, NO2, and SO2 concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM10 and NO2 concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.
Keywords: air pollution; health effects; multi-pollutant model; artificial neural network air pollution; health effects; multi-pollutant model; artificial neural network

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MDPI and ACS Style

Leirião, L.; de Oliveira, M.; Martins, T.; Miraglia, S. A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil—An Artificial Neural Networks Approach. Int. J. Environ. Res. Public Health 2023, 20, 5458. https://doi.org/10.3390/ijerph20085458

AMA Style

Leirião L, de Oliveira M, Martins T, Miraglia S. A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil—An Artificial Neural Networks Approach. International Journal of Environmental Research and Public Health. 2023; 20(8):5458. https://doi.org/10.3390/ijerph20085458

Chicago/Turabian Style

Leirião, Luciana, Michelle de Oliveira, Tiago Martins, and Simone Miraglia. 2023. "A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil—An Artificial Neural Networks Approach" International Journal of Environmental Research and Public Health 20, no. 8: 5458. https://doi.org/10.3390/ijerph20085458

APA Style

Leirião, L., de Oliveira, M., Martins, T., & Miraglia, S. (2023). A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil—An Artificial Neural Networks Approach. International Journal of Environmental Research and Public Health, 20(8), 5458. https://doi.org/10.3390/ijerph20085458

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