Sludge is a by-product of municipal wastewater treatment plants (WWTPs) and its management contributes significantly to the operating costs. Large WWTPs usually have anaerobic sludge digesters to valorize sludge as methane and to reduce its mass. However, the low methane market price opens the possibility for generating other high value-added products from the organic matter in sludge, such as polyhydroxyalkanoates (PHAs). In this work, the economic feasibility of retrofitting two types of WWTPs to convert them into biofactories of crude PHAs was studied. Two cases were analyzed: (a) a large WWTP with anaerobic sludge digestion; and (b) a small WWTP where sludge is only dewatered. In a two-stage PHA-production system (biomass enrichment plus PHAs accumulation), the minimum PHAs cost would be 1.26 and 2.26 US$/kg PHA-crude for the large and small WWTPs, respectively. In a single-stage process, where a fraction of the secondary sludge (25%) is directly used to accumulate PHAs, the production costs would decrease by around 15.9% (small WWTPs) and 19.0% (large WWTPs), since capital costs associated with bioreactors decrease. Sensitivity analysis showed that the PHA/COD (Chemical Oxygen Demand) yield is the most crucial parameter affecting the production costs. The energy, methane, and sludge management prices also have an essential effect on the production costs, and their effect depends on the WWTP’s size.

This study’s aim is to generate a complete profile of reverse osmosis concentrate (ROC), including physicochemical characteristics, environmental impact, and technologies for ROC treatment, alongside element recovery with potential valorization. A systematic literature review was used to compile and analyze scientific information about ROC, and systematic identification and evaluation of the data/evidence in the articles were conducted using the methodological principles of grounded data theory. The literature analysis revealed that two actions are imperative: (1) countries should impose strict regulations to avoid the contamination of receiving water bodies and (2) desalination plants should apply circular economies. Currently, synergizing conventional and emerging technologies is the most efficient method to mitigate the environmental impact of desalination processes. However, constructed wetlands are an emerging technology that promise to be a viable multi-benefit solution, as they can provide simultaneous treatment of nutrients, metals, and trace organic contaminants at a relatively low cost, and are socially accepted; therefore, they are a sustainable solution.

In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru.

The effect of free ammonia (NH₃or FA), free nitrous acid (HNO₂or FNA), and total alkalinity (TA) on the performance of a partial nitrification (PN) sequencing batch reactor (SBR) treating anaerobically pretreated pig slurry was studied. The SBR was operated under alternating oxic/anoxic (O/A) conditions and was fed during anoxic phases. This strategy allowed using organic matter to partially remove nitrite (NO2-) and nitrate (NO3-) generated during oxic phases. The desiredNH4+toNO2-ratio of 1.3 g N/g N was obtained when an Ammonium Loading Rate (ALR) of 0.09 gNH4+-N/L·d was applied. The system was operated at a solid retention time (SRT) of 15–20 d and dissolved oxygen (DO) levels higher than 3 mg O₂/L during the whole operational period. PN mainly occurred caused by the inhibitory effect of FNA on nitrite oxidizing bacteria (NOB). Once HNO₂concentration was negligible,NH4+was fully oxidized toNO3-in spite of the presence of FA. The use of biomass acclimated to ammonium as inoculum avoided a possible effect of FA on NOB activity.

The objective of this study was to design a university-level course focused on Nature-Based Solutions (NBS) for water pollution control. The work unfolded in three phases: the initial planning, course delivery, and assessment of learning outcomes. In the planning phase, a set of competencies was outlined using the Developing a Curriculum Method (DACUM), resulting in defined learning outcomes and a structured course outline. Subsequently, the course was conducted over a two-week period, employing a hybrid format including both online and in-person sessions. The assessments of the learning outcomes included one test, an assignment, a satisfaction survey, and the post-course feedback. As a result of the planning phase, four competencies, seven learning outcomes and four course units were defined. The participant cohort encompassed a diverse group of 50 individuals, including undergraduate and postgraduate students, professionals working in industry and institutions, and professors. The assessment of the learning outcomes showed good results. However, issues regarding the mathematical calculations and field-trip experience were noted, suggesting areas for course enhancement. The participants expressed high satisfaction levels across the various course components. Notably, 70% of the participants indicated the application of the acquired knowledge in their professional endeavors. These findings underscore the successful implementation of the course, establishing it as a pioneering university-level program in NBS for water pollution control.