Liliana San-Pedro

The present meta-analysis evaluates the parameters determining ammonia nitrogen (NH4+) removal in landfill leachates treated by electro-oxidation/electro-reduction (EO/ER). Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, 32 articles were analyzed to construct a database with 201 entries, which was evaluated in two stages. In the first stage, a preliminary decision tree model was trained and interpreted using SHapley Additive ExPlanations (SHAP) to rank 10 variables according to their impact. Six variables exhibited a high impact (chloride concentration, current density, chemical oxygen demand (COD), electrode area/treated volume ratio (pAV), treatment time, and anode type), two showed a marginal impact (initial NH4+ concentration and electrode gap), and two had null impact (pH and number of previous treatments). COD and pAV variables, integrated into this study but sparsely researched in the literature, demonstrated a high impact on the prediction. In the second stage, the algorithms (random forest, gradient boosting, k-nearest neighbors, and Cubist), hyperparameters, and the number of variables (from 1 to 10) were optimized. It was found that the Cubist algorithm optimized with 8 variables achieved the minimum error with a Root Mean Square Error (RMSE) of 19.7%. Unlike black-box models, Cubist allows for the identification and interpretation of the conditions and equations used for prediction, enabling the extraction of operational recommendations for various scenarios

The massive arrival of pelagic sargassum on the Gulf of Mexico coast has become an environmental and socioeconomic challenge, generating high management costs and affecting tourism, fisheries, and coastal ecosystems. In this context, its valorization through anaerobic digestion represents a sustainable alternative for renewable energy production. This study assessed its valorization through anaerobic digestion as a renewable energy route. Pelagic sargassum (Sargassum natans/Sargassum fluitans) was collected, mechanically pretreated, and digested in batch mode using ruminal fluid as inoculum. Two inoculum:substrate ratios (2:1 and 3:1, v/v) were operated for 7 days, and daily cumulative biogas production was recorded. The 3:1 ratio reached 10.6 mL of cumulative biogas, approximately twice the 5.0 mL obtained at 2:1, and its production curve did not plateau by day 7, suggesting ongoing activity. Elemental analysis of the sargassum showed a low C/N ratio (6.9:1) and high moisture (~95%), both of which constrain performance. Boyle’s model was used to estimate theoretical CH₄ and CO₂ yields and as expected, largely overpredicted the experimental volumes because it assumes ideal conversion. These results indicate that ruminal fluid enhances early-stage biogas formation but also highlight process limitations associated with biomass quality and short retention time. Future work should include extended digestion, co-digestion strategies to adjust the C/N ratio, and full monitoring of pH, soluble COD, VFAs, and volatile solids consumption.

Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.

This study aims to produce activated carbon (AC) from different biomass sources using chemical activation; characterize the AC using Fourier Transform Infrared (FTIR) Spectroscopy, Scanning Electron Microscopy (SEM), proximate and ultimate analysis; and compare the results from the AC produced. The biomass blends of rice husk & groundnut shell (RH-GS), cocoa nut shell & saw dust (CS-SD) and sugar cane bagasse & banana peel (SB-BP) were used for the preparation of AC. The samples were first characterized using proximate and ultimate analysis, SEM, and FTIR spectroscopy. The results of the BG-SD sample characterization showed that the blend produced the best results in terms of absorptive capacity. It is suggested that surface area difficulties with the ACs be investigated in order to improve their porosity and adsorption capacities.

Landfill leachates are highly contaminated liquids and complex to treat. Two of the processes which are promising for the treatment are the advanced oxidation and adsorption methods. With the combination of the Fenton and adsorption methods, practically all the organic load of leachates can be removed; however, this combination of processes is limited due to the soon clogging of adsorbent material, which leads to high operation costs. In the present work, the results of the regeneration of clogged activated carbon are shown after the application of the Fenton/adsorption process in leachates. This research consisted of four stages: sampling and leachate characterization, clogging of the carbon through the Fenton/adsorption process, carbon regeneration through the oxidative Fenton process, and lastly, evaluation of regenerated carbon adsorption through jar and column tests. In the experiments, HCl 3 M was used, and different concentration of hydrogen peroxide (0.15 M, 0.2 M, 0.25 M) were tested at different times (16 h and 30 h). The activated carbon regeneration through the Fenton process and the optimal peroxide dosage was 0.15 M for 16 h. The regeneration efficiency was obtained from comparing the adsorption efficiency between regenerated and virgin carbon, reaching 98.27% and can be applied up to 4 times without losing regeneration efficiency. These results prove that it is possible to restore the clogged activated carbon adsorption capacity during the Fenton/adsorption process.

Landfill leachate treatment is of utmost importance for pollution prevention. For this reason, in this study the performance of different types of activated carbons was monitored for applying in the Fenton-adsorption treatment of landfill leachate. Six carbon of different mesh and nature were studied, all of granular type. Only with the Fenton treatment a COD removal of >75% was achieved. Batch experiments were done to conduct adsorption isotherm studies and an adsorption column test was subsequently performed for Fenton-treated leachate. Granular coconut activated carbon revealed a better performance in removal of chemical oxygen demand (COD) and color during batch experiments. However, the activated carbon Gama L 8 × 30 (mesoporous) achieved removals >99% of COD and color in the adsorption column, removing 19.26 kg COD/kg carbon. Finally, it was found that the adsorption is carried out in multilayers with both carbons, having an adjustment with the models Temkin (80%) and Freunlich (96%) for granular coconut 12 × 40 and the Gama L 8 × 30, respectively.