Assessment of water quality based on statistical analysis of physical-chemical, biomonitoring and land use data: Manso River supply reservoir.

Preserving the quality of surface water has become increasingly difficult due to the intensification of human activities in watersheds. This study assessed the water quality of the Manso River reservoir, which supplies water to Brazil's third largest metropolitan region. The integration of >10,000 secondary data, comprising physico-chemical parameters, metals and microbiological indicators, together with biomonitoring and land use and occupation data, were analyzed by using statistical tools, the Water Quality Index (WQI) and the Trophic State Index (TSI).

Artificial neural networks for performance prediction of full-scale wastewater treatment plants: a systematic review.

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.

Municipal wastewater discharge standards for ammonia nitrogen in Brazil: technical elements to guide decisions.

In Brazil, domestic effluents represent the primary source of pressure on water resources. Water pollution can be controlled by defining, applying, and enforcing the effluent standards for wastewater discharge. Discussions are ongoing in Minas Gerais State regarding the possibility of setting a discharge standard for ammonia nitrogen in municipal wastewater, which is currently not required.

Spatial variability of surface water quality in a large Brazilian semiarid reservoir and its main tributaries.

Brazil has one of the greatest hydroelectric potential in the world with high number of reservoirs for the electricity generation. However, little is known about the influence of these environments on the water quality. The water quality monitoring data from 14 stations distributed throughout the Irapé HPP reservoir (lentic environment), and its main tributaries (lotic environment), between the years 2008 and 2018, were evaluated and compared to assess the spatial variability of water quality.

Performance of sewage treatment plants and impact of effluent discharge on receiving water quality within an urbanized area.

In Brazil, wastewater treatment coverage is low. Even when treatment is carried out, many municipalities cannot achieve adequate levels of contaminant removal, and the usual practice of releasing raw or treated domestic effluent into water bodies remains. Thus, this pollution source puts pressure on water resources, compromising downstream uses of the disposal.

Applicability of statistical analysis for performance and reliability evaluation of large-scale water treatment plants with direct filtration systems.

Water treatment plants (WTPs) are extremely important in basic sanitation services because of their association with human health by producing safe drinking water. Thus, their proper operation is of utmost relevance and has led to the development of distinct performance evaluation methodologies. Direct filtration is a leading technology applied in WTPs.

Impact of fecal contamination on surface water quality in the São Francisco River hydrographic basin in Minas Gerais, Brazil.

Proper water quality monitoring is a valuable tool for water resource management, helping to identify polluting sources and risks related to the use of water resources. One of the main types of contamination found in Brazilian water bodies is fecal contamination, which originates mainly from point source pollution through wastewater disposal. Thus, this study analyzed water quality monitoring data from the responsible environmental body (Minas Gerais Institute of Water Management, IGAM), related to the fecal contamination indicator (FCI), for the years 2000-2018.

Impacts of anthropogenic activities and calculation of the relative risk of violating surface water quality standards established by environmental legislation: a case study from the Piracicaba and Paraopeba river basins, Brazil.

The nonparametric test of Kruskal-Wallis and relative risk were used to evaluate surface water quality allowed to an identification of the most degraded water bodies in Piracicaba River and Paraopeba River basins, two important hydrographic basins in Brazil. Total manganese, dissolved iron, and fecal contamination indicator were considered the most relevant parameters for the characterization of water quality in the basins. The Peixe River, in Nova Era, and Pedras Creek, in Betim, were considered the most impacted water bodies in the Piracicaba River and Paraopeba River basins, respectively.

Adequacy analysis of drinking water treatment technologies in regard to the parameter turbidity, considering the quality of natural waters treated by large-scale WTPs in Brazil.

This paper seeks to present a performance evaluation of large-scale water treatment plants and verify the adjustment of the treatment to the parameter turbidity of natural waters. Nonparametric and multivariate statistical tools were used to analyze raw water and treated water turbidity of a large on-line monitoring databank for the period from 2013 to 2015, from six large-scale treatment plants utilizing different technologies. Cluster analysis was able to differentiate adequately groups of treatment plants with similar raw and treated water quality.

Assessment of spatial variations in the surface water quality of the Velhas River Basin, Brazil, using multivariate statistical analysis and nonparametric statistics.

The Velhas River sub-basin, which is located in the third-largest river basin in Brazil (São Francisco), is in an advanced state of degradation. In this work, the surface water quality of the Velhas River Basin was studied at 65 monitoring sites; 16 water quality parameters were sampled quarterly for 11 years (2008 to 2013). Cluster analysis (CA) and a nonparametric Kruskal-Wallis test were associated with the analysis of violations to water quality standards to interpret the water quality data set from the Velhas River Basin and assess its spatial variations.

Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin, Brazil.

Surface water quality monitoring networks are usually deployed and rarely re-evaluated with regard to their effectiveness. In this sense, this work sought to evaluate and to guide optimization projects for the water quality monitoring network of the Velhas river basin, using multivariate statistical methods. The cluster, principal components, and factorial analyses, associated with non-parametric tests and the analysis of violation to the standards set recommended by legislation, identified the most relevant water quality parameters and monitoring sites, and evaluated the sampling frequency.

Correction to: The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin, Brazil.

Unfortunately, the original version of this article was published online with error. The Tables 3 and 4 data was mixed up. The corrected Tables 3 and 4 are shown in the next page.

The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin, Brazil.

This study sought to evaluate and propose adjustments to the water quality monitoring network of surface freshwaters in the Paraopeba river basin (Minas Gerais, Brazil), using multivariate statistical methods. A total of 13,560 valid data were analyzed for 19 water quality parameters at 30 monitoring sites, over a period of 5 years (2008-2013). The cluster analysis grouped the monitoring sites in eight groups based on similarities of water quality characteristics.

Application of solar photo-Fenton toward toxicity removal and textile wastewater reuse.

Solar photo-Fenton represents an innovative and low-cost option for the treatment of recalcitrant industrial wastewater, such as the textile wastewater. Textile wastewater usually shows high acute toxic and variability and may be composed of many different chemical compounds. This study aimed at optimizing and validating solar photo-Fenton treatment of textile wastewater in a semi-pilot compound parabolic collector (CPC) for toxicity removal and wastewater reclamation.