Environmental Quality Management最新文献

Polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) are persistent organic pollutants (POPs) that pose significant environmental and health risks due to their persistence, bioaccumulation, and toxicity. In urbanizing regions like Ado-Ekiti, Nigeria, these contaminants, stemming from industrial activities, vehicular emissions, and waste disposal, accumulate in soils, threatening ecosystems and human health. This study investigates the concentrations, spatial distribution, sources, and ecological risks of PAHs and PCBs in sediment samples from three sites (A, B, and C) in Ado-Ekiti, Nigeria. Sixteen priority PAHs and 13 PCB congeners were analyzed to characterize contamination levels and patterns. The total PAH concentrations ranged from 29.12 µg/kg at Site B to 55.44 µg/kg at Site A, with high molecular weight PAHs (5–6 rings) dominating, comprising over 70% at Site A and over 50% at Sites B and C. Diagnostic ratios indicated a mix of pyrogenic and petrogenic sources. Total PCB concentrations varied from 7.65 µg/kg at Site B to 40.50 µg/kg at Site A, with dioxin-like PCBs (e.g., PCB 114, PCB 77) prevalent at Site A (>70% of total PCBs) and indicator PCBs (e.g., PCB 28, 44, 101) elevated at Site C. Spatial variability was moderate with a coefficient of variation (CV%) of 63.59%. However, individual congeners like PCB 114 exhibited CVs exceeding 170%, indicating localized contamination region. Toxic equivalency (TEQ) estimates highlighted potential carcinogenic risks, particularly at Sites A and C, surpassing international sediment quality guidelines. Ecological Risk Index values revealed moderate to high risks at Sites A and C, driven by local industrial activities, waste disposal, and land use practices. These findings underscore the need for ongoing monitoring and regulatory measures to mitigate POP contamination and protect environmental and public health in Ado-Ekiti.

The present study investigates the quality of river water of Prayagraj region, which is a prominent holy pilgrimage site of India. A total of 100 river water samples were collected from five selected sites during January 2020–November 2021 and examined for 12 physicochemical parameters. The mean values of turbidity (6 ± 3.65 NTU), total hardness (220 ± 25.69 mg L⁻¹), and dissolved oxygen (8.2 ± 2.74 mg L⁻¹) exceeded the BIS permissible limits. The physicochemical parameter dataset was employed to evaluate the weighted arithmetic water quality index (WQI). River water of the region was found to have an overall WQI of 66.39 ± 23.35, suggesting that it was unfit for human consumption, although it may be utilized for agricultural and industrial applications. Recognizing the importance of computational modeling for rapid WQI evaluation, 18 machine learning algorithms were investigated, which revealed PCA-ANN (principal component analysis-artificial neural network) as the most appropriate model for WQI prediction. PCA reduced the dimensionality of water quality data by identifying 7 PCs that explained >90% variance. The model ANN-A directly incorporated all 12 physicochemical parameters as input, achieving an exceptionally high accuracy (R² = 0.998, RMSE = 0.829, A_f = 1.088, MAPE = 0.847%). The model ANN-B utilized PC scores of only 7 significant components and could attain a commendable accuracy (R² = 0.950, RMSE = 5.193, A_f = 1.289, MAPE = 7.854%). Consequently, PCA-ANN may serve as a significant and reliable tool for policymakers for modeling WQI, since it not only reduced the data load but also generated a decent prediction accuracy.

Ellagitannins (ETs) are polyphenolic compounds present in rambutan peel, which is discarded and considered an agro-industrial waste and a rich source of ETs such as geraniin, corilagin, and ellagic acid (EA). Solid-state fermentation (SSF) is a good alternative and offers a sustainable approach to biodegrade ETs. In this work, the objective was to determine the SSF conditions to improve EA production from rambutan peel ETs using Saccharomyces cerevisiae in column type reactors. ETs were obtained by combining ultrasound and microwave technologies. A Box Hunter & Hunter experimental design was used to explore the effects of aeration, temperature, inoculum, peptone, yeast extract, and NaCl on EA production. The maximum production time of EA was determined at 24 h of culture time. NaCl, temperature, and peptone factors were significant and allowed the production of EA of 1.55 ± 0.02 mg/g. HPLC/ESI/MS was used to identify EA, geraniin, and corilagin, among others. SSF is an effective method for extracting bioactive compounds such as EA, demonstrating the biodegradation of geraniin and corilagin.

This paper deals with the analysis of the anaerobic digestion process performed by Upflow Anaerobic Filters in Two and Three Separated Stages (UAF-2SS and UAF-3SS), treating sanitary landfill leachates (SLLs). SLLs were fed to UAF-2SS and UAF-3SS at three temperatures (20°C, 27°C, and 34°C), three volumetric organic loads (VOLs) (2.25, 3.45, 4.64 kgCOD (Chemical Oxygen Demand)/m³/d)), total depth (D) fractionation in two-stages (D1/D2: 20%/80%, 50%/50%, and 80%/20%), and three-stages (D1/D2/D3: 4%/16%/80%, 10%/10%/80%, and 16%/4%/80%). The microbiological sampling demonstrated that the total depth fractionation produced the prevalence of species associated with each of the stages of anaerobic digestion. In UAF-3SS was identified the presence of hydrolytic and acidogenic bacteria in Stage 1, acidogenic and acetogenic bacteria in Stage 2, as well as acetogenic and methanogenic bacteria in Stage 3. In general, the experimental factor causing the most significant effect on the COD removal efficiency was the temperature (T), followed by the d₁/d₂ ratio and the least influence was produced by the VOL. The effect models for orders from first to third included coefficients of the variables associated to the principal and interaction effects, that were mainly representing to the biosorption process in the residual liquid-biofilm interface within UAF-2SS and UAF-3SS reactors, whose values are similar to those reported for trickling filters and tropical rivers. These results are suggesting that the first stages in the anaerobic digestion associated with hydrolysis and acidogenic reactions constitute the fundamental processes in the UAF-2SS and UAF-3SS perform by treating organic mass composing SLLs. The novelty of the research consists of providing mathematical structures whose coefficients allow to estimate with greater depth the rates at which the substrate transport processes are performed in the SLLs-biofilm interface within UAF-2SS and UAF-3SS.

The review presents the global challenges and opportunities associated with manure management in dairy cattle systems. Livestock farming, while essential for food security and economic growth, contributes significantly to environmental degradation through nutrient runoff, greenhouse gas emissions, and the dissemination of zoonotic pathogens and antibiotic-resistant genes. Developed regions, characterized by industrialized dairy operations, face challenges such as the concentration of manure in fewer locations, leading to storage and nutrient management issues. Conversely, smallholder farmers in developing countries rely on traditional methods, often exacerbating nutrient losses and environmental contamination. The environmental consequences of improper manure management include nutrient leaching, air quality degradation, and water pollution from hormones and heavy metals. Public health risks, including the transmission of zoonotic pathogens like Escherichia coli (E. coli) O157:H7, Salmonella enterica, and Listeria monocytogenes, highlight the need for sustainable practices. Advanced technologies, including anaerobic digestion, composting, and nutrient recovery methods, offer pathways to mitigate these risks while enhancing resource efficiency. Region-specific strategies, such as biogas production in high-intensity systems and education programs for smallholder farmers, are important to achieving sustainability.

The choice of appropriate training algorithms often impacts the convergence and fitness of ANN models. In this regard, three conventional (Levenberg–Marquardt, Bayesian regularization, scaled conjugate gradient) and three evolutionary (genetic algorithm, particle swarm, reptile search) algorithms were explored for their suitability in the training of feedforward backpropagation (BP) neural networks, modeling experimental datasets from airlift bioreactor (ALB) and inverse fluidized bed bioreactor (IFBB). The Selenite removal efficiency (RE_selenite) (%) in ALB was modelled as a function of influent selenite concentration (IC_selenite) (10–60 mg/L) and hydraulic retention time (HRT) (24–72 h), while RE_selenite (%) and chemical oxygen demand removal efficiency (RE_COD) (%) in IFBB considered IC_selenite (12.7–635 mg/L) and HRT (12–48 h) as the input parameters. After training and testing, the reptile search algorithm offers slightly better prediction accuracy compared to the Levenberg–Marquardt algorithm, with the lowest RMSE (ALB: 0.946; IFBB: 1.352) and MAE (ALB: 0.701; IFBB: 0.927), and reasonably high R values (ALB: 0.996, 0.983, 0.995; IFBB: 0.985, 0.952, 0.980), on training, testing, and complete datasets. The optimized neural network topologies were 2-18-1 and 2-15-2, respectively, for ALB and IFBB.