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The accelerated growth of the automotive supply network has had an immeasurable impact on the environment, especially relating to reusing and disposal of materials. The appropriate management of End-of-Life Vehicles (ELV) has become an imperative item for achieving sustainable development in the field of interest and it is, therefore, a target of special attention from global economies in recent years. Therefore, the present study aims to estimate the future generation of ELVs to assist decision making and mitigate the global impact of this type of waste on the environment. For this, a hybrid forecasting model was used, based on Autoregressive Integrated Moving Average (ARIMA) methodology and on Artificial Neural Networks (ANN), with a set of temporal data extracted from Brazilian sectoral platforms. The results achieved point to a good convergence of the model, indicating better performance than a naive or trivial prediction. The efficiency obtained by the Nash-Sutcliffe coefficient was 98% and the expectation is that for the year 2030, approximately 5.2 million ELVs will be produced in Brazil, of which only 78 thousand units would be effectively recycled, considering the current vehicle recycling rate in the country. Considering the scarcity of information that supports decision-making in waste management in Brazil, this study may also contribute to the proposition of alternatives that favor the proper management of automotive waste, providing a reference for the formulation and implementation of policies related to ELVs in the country. [Display omitted] • We propose a hybrid model to estimate the future generation of ELVs in Brazil. • The model is formulated through ARIMA and ANN. • The required data and information about ELVs are obtained from Brazilian sectoral platforms. • The results achieved point to a good convergence of the model. • The expectation is that for the year 2030, approximately 5.2 million ELVs will be produced in Brazil. [ABSTRACT FROM AUTHOR]

Abstract: The impact of human activity on the sediments of Todos os Santos Bay in Brazil was evaluated by elemental analysis and 13C Nuclear Magnetic Resonance (13C NMR). This article reports a study of six sediment cores collected at different depths and regions of Todos os Santos Bay. The elemental profiles of cores collected on the eastern side of Frades Island suggest an abrupt change in the sedimentation regime. Autoregressive Integrated Moving Average (ARIMA) analysis corroborates this result. The range of depths of the cores corresponds to about 50years ago, coinciding with the implantation of major onshore industrial projects in the region. Principal Component Analysis of the 13C NMR spectra clearly differentiates sediment samples closer to the Subaé estuary, which have high contents of terrestrial organic matter, from those closer to a local oil refinery. The results presented in this article illustrate several important aspects of environmental impact of human activity on this bay. [Copyright &y& Elsevier]