Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
This study investigates sewage treatment technologies at manned and unmanned converter stations and pumped storage power stations across various regions of China, considering the regional differences in water availability, infrastructure, and ecological conditions. Using a multi-criteria evaluation approach, this study analyzes key factors, such as economic characteristics, technical characteristics, and efficiency, to assess the most suitable sewage treatment solutions. Powered Eco-type Sewage Treatment Units and Powered Underground Units perform best in southern and eastern China, where advanced infrastructure supports high treatment demands. Conversely, Septic Tanks show the lowest performance across all the regions, particularly in remote and water-scarce areas like northeast and northwest China. For pumped storage power stations, AAO+MBR and Multi-stage A/O processes are most effective in regions with high water reuse needs. This study highlights the necessity of region-specific water management strategies and technological upgrades to ensure efficient sewage treatment and sustainable water use across China’s power grid infrastructure....
Air pollution is a significant health concern identified by the World Health Organization (WHO) as it poses serious health risks and climate impacts. WHO indicates that 99% of the global population breathes air with pollutant levels exceeding safe guidelines. Indoor particulate level (IPL) is approximately 20% higher in naturally ventilated buildings than mechanically ventilated ones. Volatile organic compounds (VOCs), found in products such as pesticides and gasoline, and pollutants including PM2.5 and PM10 contribute to these health risks. This study aims to characterize six common household pollutants, focusing on their concentrations and potential health impacts indoor environments. By understanding the characteristics of the pollutants, indoor air quality can be improved to mitigate associated health risks. The results showed that VOC showed the highest level of concentration as 23.8% was filtered while vape showed the highest concentration of PM2.5 with 83.3% filtered. No significant difference was observed among the VOC concentrations of candles, mosquito coils, and cigarettes. For PM2.5, frying and LPG had the same levels of concentration while the other groups had similar levels....
There is a lack of a systematic comparison framework that can assess models in both single- step and multi-step forecasting situations while balancing accuracy, training efficiency, and prediction horizon. This study aims to evaluate the predictive capabilities of machine learning and deep learning models in water quality time series forecasting. It made use of 22-month data with a 4 h interval from two monitoring stations located in a tributary of the Pearl River. Six models, specifically Support Vector Regression (SVR), XGBoost, K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Long Short- Term Memory (LSTM) Network, Gated Recurrent Unit (GRU), and PatchTST, were employed in this study. In single-step forecasting, LSTM Network achieved superior accuracy for a univariate feature set and attained an overall 22.0% (Welch’s t-test, p = 3.03 × 10−7) reduction in Mean Squared Error (MSE) compared with the machine learning models (SVR, XGBoost, KNN), while RNN demonstrated significantly reduced training time. For a multivariate feature set, the deep learning models exhibited comparable accuracy but with no model achieving a significant increase in accuracy compared to the univariate scenario. The KNN model underperformed across error evaluation metrics, with the lowest accuracy, and the XGBoost model exhibited the highest computational complexity. In multi-step forecasting, the direct multi-step PatchTST model outperformed the iterated multi-step models (RNN, LSTM, GRU), with a reduced time-delay effect and a slower decrease in accuracy with increasing prediction length, but it still required specific adjustments to be better suited for the task of river water quality time series forecasting. The findings provide actionable guidelines for model selection, balancing predictive accuracy, training efficiency, and forecasting horizon requirements in environmental time series analysis....
Pervaporation is a proven technology that overcomes distillation to produce high-quality ethanol. The process is modeled and simulated in the literature, but the model’s accuracy with industrial data is rarely discussed. In this work, a commercial pervaporation membrane was modeled and simulated in UniSIM® (R490) to purify ethanol from water after the azeotrope point of distillation. The pervaporation system was developed manually using a component splitter, adjust functions, and a spreadsheet. Results show that the simulation calculations were acceptable and differed from pilot-plant data by 4.6%. A correction factor was used to reduce the error further to 0.7%....
Domestic showers are critical points of human exposure to microbial biofilms, which may harbor opportunistic pathogens such as Legionella spp. and nontuberculous Mycobacterium. However, biofilm development in reverse osmosis (RO)-treated drinking water systems remains poorly understood. We tested whether shower plumbing material (flexible polymer hose versus showerhead with inline polyethersulfone filter) and seasonal water variations influence biofilm community assembly. In a controlled field study, commercial shower systems were deployed in households supplied with RO-treated tap water from the KAUST Seawater Desalination Plant; biofilm samples were collected from hoses and filters over 3–17 months. Flow cytometry and 16S rRNA gene amplicon sequencing characterized microbial abundance, diversity, and taxonomic composition. We found that alpha diversity, measured by observed OTUs, was uniformly low, reflecting ultra-low biomass in RO-treated tap water. Beta diversity analyses revealed clear clustering by material type, with hoses exhibiting greater richness and evenness than filters. Core taxa—Pelomonas, Blastomonas, and Porphyrobacter—dominated both biofilm types, suggesting adaptation to low-nutrient, chlorinated conditions. Overall, our results demonstrate that ultra-low-nutrient RO tap water still supports the formation of material-driven, low-diversity biofilms dominated by oligotrophic taxa, underscoring plumbing-material choice as a critical factor for safeguarding shower water quality. These findings advance our understanding of biofilm ecology in RO-treated systems, informing strategies to mitigate potential health risks in shower water....
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