Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of São Paulo.

This work aims to obtain an artificial neural network to simulate hospitalizations for respiratory diseases influenced by pollutant gaseous such as CO, PM10, PM2.5, NO2, O3, and SO2 emitted from 2011 to 2017, in the city of São Paulo. The hospitalization costs were also be calculated. MLP and RBF neural networks have been tested by varying the number of neurons in the hidden layer and the type of equation of the output function. The following pollutants and its concentration range were collected considering the supervision of Alto Tiete station set, in several neighborhoods in the city of São Paulo, from in the period 2011 to 2017: 28-63 µg/m3 of PM2.5, 52-110 µg/m3 of PM10, 49-135 µg/m3 of O3, 0.8-2.6 ppm CO, 41-98 µg/m3 of NO2, and 3-16 µg/m3 of SO2. Results showed that a RBF neural network with 6 input neurons, 13 hidden layer neurons, and 1 output neuron, using BFGS algorithm and a Gaussian function to neuronal activation, was the best fitted to the experimental datasets. So, knowing the monthly concentration of gaseous pollutions was possible to predict the hospitalization of 1464 to 3483 ± 510 patients, with costs between 570,447 and 1,357,151 ± 198,171 USD per month. This way, it is possible to use this neural network to predict the costs of hospitalizing patients for respiratory diseases and to contribute to the decision-making of how much the government should spend on health care.

Data on kinetic, energy and emission performance of biodiesel from waste frying oil.

The data presented in this article are related to the research article "Environmental and techno-economic considerations on biodiesel production from waste frying oil in São Paulo city" (Silva Filho et al., 2018) [1]. This article presents the variation of the concentration of waste frying oil (WFO) with the reaction time and temperature during the transesterification of WTOs collected in the residences and restaurants of the city of São Paulo. Then, the biodiesel samples were mixed with the S-10 diesel oil in order to obtain the B10, B20, B30, B40, B50, B75 and B100 blends, which were tested in a diesel engine and their power, fuel consumption and gas emissions (CO, CO2 and SO2) have been measured to verify their greenhouse effect and energy efficiency.