Systems combining anaerobic bioreactors with constructed wetlands (CW) have proven to be adequate and efficient for wastewater treatment. Detailed knowledge of removal dynamics of contaminants can ensure positive results for engineering and design. Mathematical modeling is a useful approach to studying the dynamics of contaminant removal in wastewater. In this study, water quality monitoring was performed in a system composed of a septic tank (ST), an up flow anaerobic filter (UAF), and a horizontal flow constructed wetland (HFCW). Biological oxygen demand (BOD5), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), NH3, organic nitrogen (ON), total suspended solids (TSS), NO2−, and NO3− were measured biweekly during a 3-month period. First-order kinetics, multiple linear regression, and mass balance models were applied for data adjustment. First-order models were useful to predict the outlet concentration of pollutants (R2 > 0.87). Relevant multiple linear regression models were found, which could be applied to facilitate the system’s monitoring and provide valuable information to control and improve biological and physical processes necessary for wastewater treatment. Finally, the values of important parameters (μmax, Ks,  and Yx/s) in mass-balance models were determined with the aid of a differential neural network (DNN) and an optimization algorithm. The estimated parameters indicated the high robustness of the treatment system since performance stability was found despite variations in wastewater composition.

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Septic tanks (STs), up-flow anaerobic filters (UAFs), and horizontal-flow constructed wetlands (HFCWs) are cost-effective wastewater treatment technologies especially efficient in tropical and sub-tropical regions. In this study, the bacterial communities within a decentralized wastewater treatment plant (WWTP) comprising a ST, a UAF, and a HFCW were analyzed using high-throughput sequencing of the V3–V4 region of the 16S rRNA gene. Bacterial diversity and its spatial variation were analyzed at the phylum and family level, and principal component analysis (PCA) was applied to nitrogen- and organic-matter-degrading families. The highest percentage of nitrogen removal was seen in the HFCW (28% of total Kjeldahl nitrogen, TKN, and 31% of NH3-N), and our results suggest that families such as Rhodocyclaceae (denitrifying bacteria), Nitrospiraceae (nitrifying bacteria), and Rhodospirillaceae (sulfur-oxidizing bacteria) contribute to such removal. The highest percentage of organic matter removal was seen in the UAF unit (40% of biological oxygen demand, BOD5, and 37% of chemical oxygen demand, COD), where organic-matter-degrading bacteria such as the Ruminococcaceae, Clostridiaceae, Lachnospiraceae, and Syntrophaceae families were identified. Redundancy analysis demonstrated that bacterial communities in the HFCW were more tolerant to physicochemical changes, while those in the ST and the UAF were highly influenced by dissolved oxygen and temperature. Also, pollutant removal pathways carried out by specific bacterial families and microbial interactions were elucidated. This study provides a detailed description of the bacterial communities present in a decentralized WWTP located in a subtropical region.

Water quality indices (WQIs) are used for the simple assessment and classification of the water quality of surface water sources. However, considerable time, financial resources, and effort are required to measure the parameters used for their calculation. Prediction of WQIs through supervised machine learning is a useful and simple approach to reduce the cost of the analysis through the development of predictive models with a reduced number of water quality parameters. In this study, regression and classification machine-learning models were developed to estimate the ecosystem-specific WQI previously developed for the Santiago-Guadalajara River (SGR-WQI), which involves the measurement of 17 water quality parameters. The best subset selection method was employed to reduce the number of significant parameters required for the SGR-WQI prediction. The multiple linear regression model using 12 parameters displayed a residual square error (RSE) of 3.262, similar to that of the multiple linear regression model using 17 parameters (RSE = 3.255), which translates into significant savings for WQI estimation. Additionally, the generalized additive model not only displayed an adjusted R2 of 0.9992, which is the best fit of all the models evaluated, but also fitted the rating curves of each parameter developed for the original algorithm for the SGR-WQI calculation with great accuracy. Regarding the classification models, an overall proportion of 93% and 86% of data were correctly classified using the logistic regression model with 17 and 12 parameters, respectively, while the linear discriminant functions using 12 parameters correctly classified an overall proportion of 84%. The models evaluated were found to be efficient in predicting the SGR-WQI with a reduced number of parameters as complementary tools to extend the current water quality monitoring program of the Santiago-Guadalajara River.

The Tequila industry is a major producer of wastewater in the state of Jalisco, Mexico. Tequila vinasses (TV) are a residue from the distillation of fermented agave wort during tequila production. TV are difficult to treat due their high organic content, high nutrient loads, acidic pH and high discharge temperature. TV are frequently released into waterbodies or soil without any treatment, leading to environmental degradation of soil and water sources. To reduce the environmental impact of the tequila industry, cost-effective TV revalorization approaches must be developed. The goals of the present study were to assess the treatment and revalorization potential of TV using mono and mixed yeast cultures to produce single-cell protein (SCP) and to analyze yeast community composition using high-throughput sequencing during the mixed-culture fermentation of TV. The fermentation process was performed using a mixed culture of three fodder yeast species (Candida utilis, Rhodotorula mucilaginosa and Kluyveromyces marxianus) during 48 h at benchtop-scale. High-throughput sequencing was performed to assess the relative abundance of the yeast communities. Additionally, a redundancy analysis was performed to analyze the bidirectional influence between yeast communities and pollutant removal (COD, nitrogen, phosphorus, proteins, and sugars). Mixed yeast cultures displayed overall higher pollutant removal rates than monocultures, where C. utilis and K. marxianus contributed the most to pollutant removal and protein accumulation. The R. mucilaginosa population declined rapidly in mixed culture, presumably due to TV acidity and phenolic composition. However, the presence of The R. mucilaginosa in the mixed culture enhanced pollutant removal and amino acid contents. Accordingly, the protein and amino acid content within mixed cultures were significantly higher than those of monocultures, indicating that mixed cultures have a strong potential to produce protein rich biomass from TV, aiding in the transition of both the tequila and the livestock industries to a sustainable circular bioeconomy model by the reintegration of organic material flows into productive processes, reducing raw resource intake and waste generation. The present circular bioeconomy approach could represent a potential to produce 45,664 tons of protein feed yearly, based on the current tequila vinasses generated in the state of Jalisco.