Analyses of colloidal, truly dissolved, and DGT-labile metal species and phosphorus in mining area surrounded by tailing dams using self-organising maps.

The knowledge of size-distribution and lability of metals and nutrients in freshwater systems is important for estimation of the ecological effects of mining. However, it is still limited in several mining areas such as the Quadrilátero Ferrífero (Brazil) which was severely polluted by the collapse of the Fundão tailings dam in November 2015. In this study, results of an investigation from 2014 using a neural network named self-organising map (SO-Map) into the conditions of selected trace metals that are of particular importance to mining areas (Cr, Cu, Co, Mn, Ni, Pb, Zn) are presented. Additionally, P was considered by its high importance as a nutrient and sites later affected by the dam burst were also included by chance. Water samples were collected at six sites in dry and rainy seasons and filtered and ultrafiltered for determination of total dissolved (<0.45 µm) and truly dissolved (<1 kDa) fractions. Diffusive gradients in thin films (DGT) devices were deployed in situ for determination of the DGT-labile fraction. All data were analysed using SO-Map and Spearman's rank correlation. Phosphorus in the Carmo River occurred mainly in the truly dissolved and DGT-labile fractions. The higher amounts of this element in the river water (up to 263 µg L-1 of total P) might be related to untreated sewage discharge. Moreover, the concentrations of other trace metals (Mn, Cu, Co, Ni, Zn) were high, even under the "natural" conditions (before the dam failure) due to natural and anthropogenic factors such as local lithology and mining.

Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach.

The coronavirus disease 2019 (COVID-19) is still spreading fast in several tropical countries after more than one year of pandemic. In this scenario, the effects of weather conditions that can influence the spread of the virus are not clearly understood. This study aimed to analyse the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021. Rank correlation tests and a neural network named self-organising map (SOM) were used to investigate similarities in the dynamics of the disease in the cities and check possible relationships among the variables. Two periods were analysed (quarantine and post-quarantine) for all cities together and individually. The data were classified in seven groups based on city, date and biome using SOM. The virus transmission was most affected by mobility variables, especially in the post-quarantine. The meteorological variables presented different behaviours on the virus transmission in different biogeographical regions. The wind speed was one of the factors connected with the highest contamination rate recorded in Leticia. The highest new daily cases were recorded in Bogota where cold/dry conditions (average temperature <14 °C and absolute humidity >9 g/m3) favoured the contagions. In contrast, Barranquilla, Cartagena and Leticia presented an opposite trend, especially with the absolute humidity >22 g/m3. The results support the implementation of better local control measures based on the particularities of tropical regions.