Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide due to global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O 3 , NO 2 , NO, PM 2.5 , and PM 10 , for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes.

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This paper deals with power flow optimization with security constraints, focusing on the problem of shortterm hydroelectric scheduling, called predispatch. Since the energy demand varies throughout the day, the generation must satisfy daily targets, established by long-term scheduling models. This study considers that the hydroelectric plants and transmission systems must provide an optimal flow of energy under security constraints that allow meeting energy demands for normal operating conditions and when disturbances happen. Algebraic techniques are used to exploit the sparse structure of the problem, targeting the design of an interior point algorithm, efficient in terms of robustness and computational time. Case studies compare the proposed approach with a general purpose optimization solver for quadratic problems and an algorithm for the predispatch problem that does not consider security constraints. The results show the benefits of using the method proposed in the paper, obtaining optimal power flow that is suitable to consider contingencies, with numerical stability and appropriate computational time.

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