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In the pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) many strategies have been performed in order to control viral spread in the population and known the real-time situation about the number of infected persons. In this sense, Wastewater Based Epidemiology (WBE) has been applied as an excellent tool to evaluate the virus circulation in a population. In order to obtain reliable results, three low-cost viral concentration methods were evaluated in this study, polyethylene glycol (PEG) precipitation, skimmed milk flocculation (SM) and Aluminum polychloride flocculation, for Pseudomonas aeruginosa bacteriophage PP7 as a surrogate for non-enveloped viruses and Bovine Coronavirus (BCoV) as a surrogate for enveloped virus, with emphasis for SARS- CoV-2. Our results suggest that PEG precipitation for viral concentration, for both enveloped and non-enveloped virus from wastewater is an appropriate approach since it was more sensitive compared to SM flocculation and Aluminum polychloride flocculation. This methodology can be used for WBE studies in order to follow the epidemiology of the SARS-CoV-2 pandemic, mainly in developing countries where the economic resources are frequently limited.

Groundwater arsenic in Uruguay is an important environmental hazard, hence, predicting its distribution is important to inform stakeholders. Furthermore, occurrences in Uruguay are known to variably show dependence on depth and geology, arguably reflecting different processes controlling groundwater arsenic concentrations. Here, we present the distribution of groundwater arsenic in Uruguay modelled by a variety of machine learning, basic expert systems, and hybrid approaches. A pure random forest approach, using 26 potential predictor variables, gave rise to a groundwater arsenic distribution model with a very high degree of accuracy (AUC = 0.92), which is consistent with known high groundwater arsenic hazard areas. These areas are mainly in southwest Uruguay, including the Paysandú, Río Negro, Soriano, Colonia, Flores, San José, Florida, Montevideo, and Canelones departments, where the Mercedes, Cuaternario Oeste, Raigón, and Cretácico main aquifers occur. A hybrid approach separating the country into sedimentary and crystalline aquifer domains resulted in slight material improvement in a high arsenic hazard distribution. However, a further hybrid approach separately modelling shallow (<50 m) and deep aquifers (>50 m) resulted in the identification of more high hazard areas in Flores, Durazno, and the northwest corner of Florida departments in shallow aquifers than the pure model. Both hybrid models considering depth (AUC = 0.95) and geology (AUC = 0.97) produced improved accuracy. Hybrid machine learning models with expert selection of important environmental parameters may sometimes be a better choice than pure machine learning models, particularly where there are incomplete datasets, but perhaps, counterintuitively, this is not always the case.

In Uruguay, groundwater is frequently used for agricultural activities, as well as for human consumption in urban and rural areas. As in many countries worldwide, drinking water microbiological quality is evaluated only according to bacteriological standards and virological analyses are not mentioned in the legislation. In this work, the incidence of human viral (Rotavirus A, Norovirus GII, and human Adenovirus) and bacterial (total and thermotolerant coliform and Pseudomonas aeruginosa) contamination in groundwater in the Salto district, Uruguay, as well as the possible correlation between these groups of microorganisms, was studied. From a total of 134 groundwater samples, 42 (32.1%) were positive for Rotavirus, only 1 (0.7%) for both Rotavirus and Adenovirus, and 96 (72.6%) samples were positive for bacterial indicators. Results also show that Rotavirus presence was not associated with changes in chemical composition of the aquifer water. Bacteriological indicators were not adequate to predict the presence of viruses in individual groundwater samples (well scale), but a deeper spatial-temporal analysis showed that they are promising candidates to assess the viral contamination degree at aquifer scale, since from the number of wells with bacterial contamination the number of wells with viral contamination could be estimated.

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