Effect of elemental composition assigned to antrotopic pollution on the quality of the water and sediment of the Marrecas river (PR, Brazil) as highlighted by multivariate statistical analyses.

In recent years, several environmental pollutants have been monitored in surface waters and sediments. However, few studies apply multivariate statistics to identify the main components and correlate them temporally and spatially. In this sense, the present study sought to monitor the quality of water and sediments in the Rio Marrecas/Brazil, through the analysis of physicochemical parameters and trace elements, as well as to identifying sources of contamination, using multivariate statistics. For this purpose, sampling was carried out in nine locations for a period of 12 months. The Total Reflection X-ray Fluorescence (TXRF) technique was used to quantify the 15 elements identified in water and sediment samples. Through multivariate statistical analyses, the most significant elements, their correlations and possible pollutant sources were defined, and the pollution index (HPI) and assessment index (HEI) of heavy metals were applied. The parameters pH and BOD5 do not comply with Brazilian legislation. Based on PCA and Spearman correlation, there was strong evidence of contamination of the water naturally, composed of the elements Ti, V, Mn, Fe, and of anthropogenic origin composed of the elements Ca, Ni, Cu, Zn. These findings provide insights to determine the impacts of heavy metals on human health and the environment.

Spectral deconvolution associated to the Gaussian fit as a tool for the optimization of photovoltaic electrocoagulation applied in the treatment of textile dyes.

The objective of this study was to investigate the color removal in a binary mixture of azo dyes from the photovoltaic electrocoagulation (EC) technique, using spectral deconvolution and the Gaussian fit for qualitative and quantitative determination of the physical color parameter. Initially, a conventional energy source was used to feed the EC reactor and the experimental design was conducted according to the Rotational Central Compound Design (RCCD). The spectral deconvolution method associated to the Gaussian fit aided in the description of the composition of the sample matrix, In the first step, through the Analysis of Variance, the RCCD and the three-dimensional surface response graphs, the optimized operating conditions were identified, which corresponded to 1320 A m-2 with an reaction time of 16.6 min, and an expected removal of 98.40% for Scarlet Red (SR) dye and 1160 A m-2 with a run time of 15.7 min and 97.9% removal for Turquoise Blue (TB) dye. Using the photovoltaic module as the power source of the EC reactor, a maximum removal of 97 ± 0.43% for TB dye and 98% ± 0.81 for SR was obtained. The results encourage the applicability of photovoltaic module-fed EC technology as a promising alternative for the treatment of effluents containing textile dyes, as well as the use of the spectral deconvolution method associated with the Gaussian fit, for the reliability and precision of the results.