Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

Botanical filters for the abatement of indoor air pollutants

Nowadays, people spend 80-90% of their time indoors, while recent policies on energy efficient and safe buildings require reduced building ventilation rates and locked windows. These facts have raised a growing concern on indoor air quality, which is currently receiving even more attention than outdoors pollution. Prevention is the first and most cost-effective strategy to improve indoor air quality, but once pollution is generated, a battery of physicochemical technologies is typically implemented to improve air quality with a questionable efficiency and at high operating costs. Biotechnologies have emerged as promising alternatives to abate indoor air pollutants, but current bioreactor configurations and the low concentrations of indoor air pollutants limit their widespread implementation in homes, offices and public buildings. In this context, recent investigations have shown that potted plants can aid in the removal of a wide range of indoor air pollutants, especially volatile organic compounds (VOCs), and can be engineered in aesthetically attractive configurations. The original investigations conducted by NASA, along with recent advances in technology and design, have resulted in a new generation of botanical biofilters with the potential to effectively mitigate indoor air pollution, with increasing public aesthetics acceptance. This article presents a review of the research on active botanical filters as sustainable alternatives to purify indoor air.

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Wastewater-based epidemiology has emerged as a promising and efficacious surveillance system for SARS-CoV-2 and other infectious diseases in many nations. The process typically involves wastewater concentration, nucleic acid extraction, amplification of selected genomic segments, and detection and quantification of the amplified genomic segment. This methodology can similarly be leveraged to detect and quantify infectious agents, such as SARS-CoV-2, in air samples. Initially, SARS-CoV-2 was presumed to spread primarily through close personal contact with droplets generated by an infected individual while speaking, sneezing, coughing, singing, or breathing. However, a growing number of studies have reported the presence of SARS-CoV-2 RNA in the air of healthcare facilities, establishing airborne transmission as a viable route for the virus. This study presents a composite of established protocols to facilitate environmental detection, quantification, and sequencing of viruses from both wastewater and air samples.

Evaluation of the dynamics of microalgae population structure and process performance during piggery wastewater treatment in algal-bacterial photobioreactors

The dynamics of microalgae population during piggery wastewater (PWW) treatment in four open photobioreactors operated at 27days of hydraulic retention time, and inoculated with Chlorella sp. (R1), Acutodesmus obliquus (R2), Oscillatoria sp. (R3) and in the absence of inoculum (R4), were evaluated for 6months. In addition, the algal-bacterial biomass concentration, removal of organic matter, nutrients and heavy metals were also assessed. The results revealed a high diversity and rapid variations in the structure of microalgae populations, Chlorella sp. being dominant in R4 throughout most of the operational period. Steady state average biomass concentration ranged from 2445-2610mg/L in R1-R3 to 3265mg/L in R4. No significant differences were recorded in the removal efficiencies (REs) of total organic carbon (86-87%), inorganic carbon (62-71%), total nitrogen (82-85%) and total phosphorous (90-92%). Finally, Zn-REs accounted for 26% in R3, 37% in R2, and 49% in R1 and R4.

Comparative evaluation of piggery wastewater treatment in algal-bacterial photobioreactors under indoor and outdoor conditions

This work evaluated the performance of four open algal-bacterial photobioreactors operated at ≈26days of hydraulic retention time during the treatment of 10 (×10) and 20 (×20) times diluted piggery wastewater (PWW) under indoor (I) and outdoor (O) conditions for four months. The removal efficiencies (REs) of organic matter, nutrients and zinc from PWW, along with the dynamics of biomass concentration and structure of algal-bacterial population were assessed. The highest TOC-RE, TP-RE and Zn-RE (94±1%, 100% and 83±2%, respectively) were achieved indoors in ×10 PWW, while the highest TN-RE (72±8%) was recorded outdoors in ×10 PWW. Chlorella vulgaris was the dominant species regardless of the ambient conditions and PWW dilution. Finally, DGGE-sequencing of the bacterial community revealed the occurrence of four phyla, Proteobacteria being the dominant phylum with 15 out of the 23 most intense bands.

Influence of biogas flow rate on biomass composition during the optimization of biogas upgrading in microalgal-bacterial processes

The influence of biogas flow rate (0, 0.3, 0.6, and 1.2 m(3) m(-2) h(-1)) on the elemental and macromolecular composition of the algal-bacterial biomass produced from biogas upgrading in a 180 L photobioreactor interconnected to a 2.5 L external bubbled absorption column was investigated using diluted anaerobically digested vinasse as cultivation medium. The influence of the external liquid recirculation/biogas ratio (0.5 < L/G < 67) on the removal of CO2 and H2S, and on the concentrations of O2 and N2 in the upgraded biogas, was also evaluated. A L/G ratio of 10 was considered optimum to support CO2 and H2S removals of 80% and 100%, respectively, at all biogas flow rates tested. Biomass productivity increased at increasing biogas flow rate, with a maximum of 12 ± 1 g m(-2) d(-1) at 1.2 m(3) m(-2) h(-1), while the C, N, and P biomass content remained constant at 49 ± 2%, 9 ± 0%, and 1 ± 0%, respectively, over the 175 days of experimentation. The high carbohydrate contents (60-76%), inversely correlated to biogas flow rates, would allow the production of ≈100 L of ethanol per 1000 m(3) of biogas upgraded under a biorefinery process approach.

Denitrification with methane as electron donor in oxygen-limited bioreactors

The microbial population from a reactor using methane as electron donor for denitrification under microaerophilic conditions was analyzed. High numbers of aerobic methanotrophic bacteria (3 10(7) cells/ml) and high numbers of acetate-utilizing denitrifying bacteria (2 10(7) cells/ml) were detected, but only very low numbers of methanol-degrading denitrifying bacteria (4 10(4) cells/ml) were counted. Two abundant acetate-degrading denitrifiers were isolated which, based on 16S rRNA analysis, were closely related to Mesorhizobium plurifarium (98.4% sequence similarity) and a Stenotrophomonas sp. (99.1% sequence similarity). A methanol-degrading denitrifying bacterium isolated from the bioreactor morphologically resembled Hyphomicrobium sp. and was moderately related to H. vulgare (93.5% sequence similarity). The initial characterization of the most abundant methanotrophic bacterium indicated that it belongs to class II of the methanotrophs. "In vivo" 13C-NMR with concentrated cell suspensions showed that this methanotroph produced acetate under oxygen limitation. The microbial composition of reactor material together with the NMR experiments suggest that in the reactor methanotrophs excrete acetate, which serves as the direct electron donor for denitrification.