Wastewater treatment plants (WWTPs) are complex systems that must maintain high levels of performance to achieve adequate effluent quality to protect the environment and public health. Artificial intelligence and machine learning methods have gained attention in recent years for modeling complex problems, such as wastewater treatment. Although artificial neural networks (ANNs) have been identified as the most common of these methods, no study has investigated the development and configuration of these models. We conducted a systematic literature review on the use of ANNs to predict the effluent quality and removal efficiencies of full-scale WWTPs. Three databases were searched, and 44 records of the 667 identified were selected based on the eligibility criteria. The data extracted from the papers showed that the majority of studies used the feedforward neural network model with a backpropagation training algorithm to predict the effluent quality of plants, particularly in terms of organic matter indicators. The findings of this research may help in the search for an optimum design modeling process for future studies of similar prediction problems.

The objective of this study is to carry out economic and risk assessment of eucalyptus plantations located in Minas Gerais, considering the influence of the cost of forest insurance, evidencing its impact on the project. The costs of implementation and driving come from eucalyptus plantations located in the Jequitinhonha Valley, Minas Gerais. The cost of forest insurance was obtained through a simulation of a quote from an insurer. For the economic evaluation, the Net Present Value (NPV), the Internal Rate of Return (IRR) and the Equivalent Periodic Benefit (EPB) were used. The risk sensitivity analysis was performed by the Monte Carlo method. The economic analysis was considered viable by all indicators used in all three situations. However, by the analysis of investment risk, we have a probability of 11.5% chance of NPV being below zero in situation 1, 18.5% in situation 2 and 25% in situation 3. The cost of Forest insurance increases the likelihood of negative value for economic indicators, as it increases the cost of the project, but it was input that less influenced the final value of the NPV. The government grant reduces the likelihood of investment risk as it lowers the cost of forest insurance.

Multivariate techniques, like cluster analysis (CA) and principal component analysis (PCA) were used to evaluate the spatial and temporal variability of surface water quality in a large neotropical hydroelectric reservoir (Nova Ponte Reservoir). The dataset, obtained during the period of 1995–2011 from the Energy Company of Minas Gerais State (CEMIG), contain 14 parameters surveyed quarterly in 14 sites, at different depths. The CA grouped the 14 sites in three main groups: lotic sites, half of the photic zone sites and bottom sites. Statistical tests showed that only three parameters (total dissolved solids, nitrate and chemical oxygen demand) have no significant difference between cluster groups. The PCA results showed temporal changes of the water quality in all groups, illustrating modifications to the importance of the parameters over time. PCA also revealed the major causes of water deterioration from 1995 to 2005 were related to agricultural and livestock activities. Currently, the water quality parameters related to organic pollution are also highlighted. Generally, this study shows that possible optimization of the monitoring network should consider temporal variation of water quality parameters.

This work aimed to evaluate the spatial-temporal variability of precipitation in the Minas Gerais section of the São Francisco River basin, an area of substantial socio-environmental relevance for the country and which has presented recent events of water scarcity. Multivariate and non-parametric statistical analyses were applied to the monthly precipitation data from 131 pluviometric stations, covering a period from 1989 to 2018. The results indicated distinct homogeneous pluviometric regions with greater spatial variability in rainfall patterns in the southern regions of the basin. Results from the temporal analysis indicated seasonality in the rainfall patterns for all seasons, with the rainy period predominantly occurring between October and March for the entire Minas Gerais section of the São Francisco River basin. No rainfall trend was identified in 78% of the stations, with the other stations (22%) showing a trend toward a reduction in rainfall volume.