Tertiary wastewater treatment could provide a reliable source of water for reuse. Amongst these types of wastewater treatment, deep-bed filtration of secondary effluents can effectively remove particles and organic matter; however, NH₄⁺ and NO₂⁻ are not easily removed. This study examined the feasibility of stimulating microbial activity using hydrogen peroxide (H₂O₂) as a bio-specific and clean oxygen source that leaves no residuals in the water and is advantageous upon aeration due to the solubility limitations of the oxygen. The performance of a pilot bio-filtration system at a filtration velocity of 5–6 m/h, was enhanced by the addition of H₂O₂ for particle, organic matter, NH₄⁺, and NO₂⁻ removal. Hydrogen peroxide provided the oxygen demand for full nitrification. As a result, influent concentrations of 4.2 ± 2.5 mg/L N-NH₄⁺ and 0.65 ± 0.4 mg/L N-NO₂ were removed during the short hydraulic residence time (HRT). In comparison, filtration without H₂O₂ addition only removed up to 0.6 mg/L N-NH₄⁺ and almost no N-NO₂⁻. A DNA metagenome analysis of the functional genes of the media biomass reflected a significant potential for simultaneous nitrification and denitrification activity. It is hypothesized that the low biodegradability of the organic carbon and H₂O₂ addition stimulated oxygen utilization in favor of nitrification, followed by the enhancement of anoxic activity.

Soil aquifer treatment (SAT), as a gravity-based wastewater reuse process, is limited by oxygen availability to the microbial community in the soil. Using oxygen from enzymatic degradation of H₂O₂ to generate hyper-oxygen conditions can exceed solubility limitations associated with aeration, but little is known about the effect of hyper-oxygen conditions on the microbial community and the dominant bio-reactions. This study examined the impact of H₂O₂ addition on the community structure and process performance, along with SAT depth. Overall, two soil columns were incrementally fed synthetic secondary effluents to simulate infiltration through SAT. The experimental column received 14 mg/L hydrogen peroxide to double the level of natural oxygen available. The microbial kinetics of nitrifiers and heterotrophs were evaluated. We found that all of the H₂O₂ was degraded within the top 10 cm of the column, accompanied by a higher removal of COD (23 ± 0.25%) and ammonia (31 ± 3%) in comparison to the reference column. Higher nitrogen removal (23 ± 0.04%) was obtained for the whole process using H₂O₂. Analysis of nitrifiers indicated that ammonia-oxidizing bacteria were most influenced, obtaining higher concentration and abundance when exposed to H₂O₂. DNA sequencing analysis of samples exposed to H₂O₂ revealed significant community structure and diversity differences among heterotrophs. This study shows that not only aerobic, but also anoxic, microbial activity and process performance in a SAT system could be accelerated in existing infrastructure with H₂O₂, which could significantly decrease the associated environmental footprint.

This study presents a detailed dataset on the growth responses of 80 young olive trees from three varieties: Haouzia and Menara (Moroccan) and Languedoc (French), under varying levels of water stress. The experiment was organised into eight groups: four experimental (A1, B1, C1, D1) and four control (A2, B2, C2, D2), with each group comprising 10 olive trees (4 Languedoc, 3 Menara, and 3 Haouzia). The olive trees were subjected to four distinct irrigation regimes, with groups A1 and A2 receiving 100 % of the water requirement, groups B1 and B2 receiving 50 %, groups C1 and C2 receiving 25 %, and groups D1 and D2 receiving no irrigation (0 %). Weekly data collection included trunk diameter, tree height, number of branches, SPAD values, leaf temperature, canopy cover, and individual tree images. This dataset offers valuable information for prospective researchers working on the impacts of drought stress on different varieties of olive trees, facilitating comparative analysis at varying stress levels. It is also useful for farmers, providing insights that can help optimize irrigation strategies and improve drought resilience in olive tree cultivation. The dataset can also be used to develop predictive artificial intelligence models for monitoring and analyzing drought stress in olive trees.

Modern large language models have achieved remarkable growth and performance across domains, yet their intense use of resources and high computational costs present challenges to scalability and sustainability. Current attempts to surpass baseline (naïve) AutoDL (Automated Deep Learning) models often rely on complex manipulations to yield marginal accuracy gains while demanding deep domain knowledge and computational intensity. To address known inefficiencies in computation and implementation, this study proposes a strategic approach for enhancing processing without compromising model accuracy or performance through a simplified, scalable methodology. We present a novel AutoDL weight optimization model method that analyzes the most accurate deep learning starting point and achieves the highest outcomes while considering the additional “presetting” analysis overhead. Using 20 real-world datasets, we conducted experiments across three models, six weight configurations, and ten seeds, totaling 62,400 epochs. In all experiments, the optimized model outperformed baselines, achieving higher accuracy across every dataset while reducing preprocessing to only two epochs per seed. These results demonstrate that minimal preprocessing—limited to two epochs per seed—can substantially lower computational demand while maintaining precision. As the global demand for AI deployment accelerates, this conservation-oriented approach will be critical to sustaining innovation within resource and infrastructural constraints, enabling advances in computational sustainability and responsible AI development, tangible savings across multiple dimensions of resource consumption, and broader access to deep learning technologies.