CBQF - Contribuições em Revistas Científicas / Contribution to Journals
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Percorrer CBQF - Contribuições em Revistas Científicas / Contribution to Journals por Objetivos de Desenvolvimento Sustentável (ODS) "06:Água Potável e Saneamento"
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- Dynamics and interrelationships between antibiotic resistance, organic micropollutants and bacterial communities in full-scale rural constructed wetlandsPublication . Teixeira, A. Margarida; Matos, Diana; Coelho, Norberta; Halwatura, Lahiruni M.; Vaz-Moreira, Ivone; Castro, Paula M. L.; Aga, Diana S.; Manaia, Célia M.Constructed wetlands systems (CWs) are increasingly regarded as promising alternatives or complements to conventional wastewater treatment processes. However, the fate of chemical and biological contaminants in realworld treatment processes is understudied in this type of systems. This study aimed to fill this gap by evaluating the response of three horizontal subsurface flow CWs, in Northern Portugal, planted with Phragmites australis, in operation for >7 years, to reducing the load of fecal contamination, antibiotic resistance genes and organic micropollutants (OMPs). Influent, effluent and sediments samples (n = 36) were examined for abundance of cultivable Escherichia coli and total coliforms, total bacteria (16S rRNA gene), 10 genetic biomarkers associated with anthropogenic contamination (uidA, crAssphage, intI1, sul1, ermB, ermF, mefC, qacEΔ1, tetX and aph(3″)-Ib) by quantitative PCR, non-target LC-MS of OMPs and 16S rRNA gene-based bacterial community analysis. The three CWs showed reduction values (log-units/mL) up to 4.8 of E. coli and 3.6 of biomarkers, with the highest values observed in warmer periods. No evidence of for the accumulation microbiological contaminants in the sediments was observed. Among the 59 OMPs detected, reduction rates varied, and the concentration of the most abundant pharmaceutical compounds in the final effluent varied –reaching ng/L concentrations of ~36 000 for fenofibric acid, ~14 000 for acetaminophen, ~3000 for oxazepam and ~2000 for irbesartan, which can be considered high to discharge in the receiving environment. The bacterial community was dominated by members of the class Gammaproteobacteria, with treatment contributing to significant reduction of the relative abundance of members of the classes Clostridia, Bacilli and Actinomycetes. Compared with wastewater, sediments had significantly higher relative abundance of Alphaproteobacteria. The study confirms that CWs are an adequate alternative for the treatment of domestic wastewater in small communities, although it warns of the need for regular monitoring and adjustment of treatment conditions, especially during cooler periods.
- Integrated treatment and valorization of meat processing wastewater via microalgae-based biomass productionPublication . Sousa, Ana S. S.; Oliveira, Ana S.; Castro, Paula M. L.; Amorim, Catarina L.Meat-processing wastewater (MPWW) is rich in nutrients and organic matter. This study assessed its potential as feedstock for microalgal biomass production while enabling wastewater treatment. In batch assays, the microalgae-based consortium grew in raw MPWW, and its synergy with the native wastewater microbial community enhanced the chemical oxygen demand (COD) removal rate. If suspended solids were pre-removed from wastewater, COD removing rates improved from 828.5 ± 60.5 to 1097.5 ± 22.2 mg L?1 d?1. In a raceway system operated in fed-batch mode with sieved and sedimented MPWW, COD removal was consistently achieved across feeding cycles, despite the variability in wastewater composition, reaching rates of up to 806.3 ± 0.0 mg L?1 d?1. Total nitrogen also decreased in most cycles. Microalgal biomass, estimated from total photosynthetic pigment’s concentration, increased from 0.4 to 17.9 µg mL?1. The microalgae-based consortium became more diverse over time, harboring at the end, additional eukaryotic taxa such as protozoan grazers and fungi (e.g., Heterolobosea class and Trichosporonaceae and Dipodascaceae families), although their roles in removal processes remain unknown. This study highlights the potential use of real MPWW as feedstock for microalgal-based biomass production with concomitant carbon/nutrient load reduction, aligning its implementation with circular economy percepts.
- Treating domestic wastewater towards freshwater quality: bacterial community and antibiotic resistance profiles highlight critical steps and improvement opportunitiesPublication . Leão, Inês; Antunes, Jorge; Baptista, Inês; Jorge, Ruben; Marinheiro, Luís; Löblich, Stefan; Vaz-Moreira, Ivone; Manaia, Célia M.Ideally, wastewater treatment aims to produce water indistinguishable from freshwater, especially for reuse. This study evaluated bacterial community and antibiotic resistance variations throughout treatment and benchmarked these with freshwater sources. Samples collected from six points of a full-scale wastewater treatment plant, pilot-scale advanced treatment options (non-thermal plasma - NTP, ultrafiltration - UF, UF followed by reverse osmosis- UF+RO), two rivers and a borehole were analyzed for quality parameters (BOD5, TSS, turbidity, Escherichia coli), antibiotic resistance genes (quantitative PCR), class 1 integron variable region composition (Oxford Nanopore sequencing), and bacterial community composition (16S rRNA Illumina sequencing). Secondary treatment followed by sand filters and coagulants caused the highest reduction (~2 log-unit/volume) of all analyzed parameters and the sharpest reduction of diversity of antibiotic resistance genes within class 1 integrons’ variable region. Ultraviolet disinfection triggered minimal bacterial or genes reduction, while among advanced treatments, UF+RO caused the highest, and NTP the lowest. Principal component analysis suggested significant associations between antibiotic resistance (n=32) and genetic recombination elements (n=12) and predominant bacterial families in raw wastewater (Aeromonadaceae, Moraxellaceae, Campylobacteraceae, Lachnospiraceae). For predominant freshwater families (Comamonadaceae, Chitinophagaceae, Flavobacteriaceae) no significant associations were observed. Freshwater differed from UF-treated water by a lower antibiotic resistance abundance, higher bacterial richness (~4000 vs.1200 operational taxonomic units) and distinct predominant families - Alcaligenaceae, Sphingomonadaceae, Chitinophagaceae, and Microbacteriaceae in UF water. The findings underscore the critical role of secondary/post-secondary treatments in shaping resistance and community profiles and suggest that advanced treatment should balance water quality with bacterial diversity preservation for sustainable reuse.
- Unraveling the microbiome–environmental change nexus to contribute to a more sustainable world: a comprehensive review of artificial intelligence approachesPublication . Barbosa, Maria Inês; Silva, Gabriel; Ribeiro, Pedro; Vieira, Eduarda; Perrotta, André; Moreira, Patrícia; Rodrigues, Pedro MiguelThis review aims to explore the literature to assess the potential of artificial intelligence (AI) in environmental monitoring for predicting microbiome dynamics. Recognizing the significance of comprehending microorganism diversity, composition, and ecologically sustainable impact, the review emphasizes the importance of studying how microbiomes respond to environmental changes to better grasp ecosystem dynamics. This bibliographic search examines how AI (Machine Learning and Deep Learning) approaches are employed to predict changes in microbial diversity and community composition in response to environmental and climate variables, as well as how shifts in the microbiome can, in turn, influence the environment. Our research identified a final sample of 50 papers that highlighted a prevailing concern for aquatic and terrestrial environments, particularly regarding soil health, productivity, and water contamination, and the use of specific microbial markers for detection rather than shotgun metagenomics. The integration of AI in environmental microbiome monitoring directly supports key sustainability goals through optimized resource management, enhanced bioremediation approaches, and early detection of ecosystem disturbances. This study investigates the challenges associated with interpreting the outputs of these algorithms and emphasizes the need for a deeper understanding of microbial physiology and ecological contexts. The study highlights the advantages and disadvantages of different AI methods for predicting environmental microbiomes through a critical review of relevant research publications. Furthermore, it outlines future directions, including exploring uncharted territories and enhancing model interpretability.