ABSTRACT Two wastewater monitoring areas connecting to a graph. On the x-axis is the month of the year; on the y-axis is population. Mobile device counts and flow rates both show fluctuations, but mobile device counts have larger swings. Mobile device data has the potential to capture fluctuations in populations contributing to a wastewater catchment area, which can aid in interpreting pathogen concentrations. We evaluated the use of mobile device data to characterize population dynamics as an alternative to flow rates, which are commonly used to normalize pathogen concentrations. Analyses were conducted at the daily and monthly scale across 66 sewersheds in Colorado from August 2020 to December 2024. Using K-means clustering, we identified two distinct patterns of population dynamics using mobile device data, separating high-tourism areas from other areas. There were significant fluctuations in daily and monthly device counts, with more pronounced patterns at the monthly level. Device counts and flow rates were not consistently well-correlated over time. Consistent with well-documented patterns of urban drainage, we found that rainfall and snowmelt explained periods of decoupling between device counts and flow rates. Mobile device data can be used to describe population dynamics over multiple geographical areas and over short and long timescales independent of the environmental processes that impact flow. It should be considered in efforts to improve normalization algorithms for wastewater-based epidemiology.
ABSTRACT This study evaluated water-energy-carbon performance of municipal wastewater treatment plants in South Korea by integrating the Korean Sewerage Statistics Yearbook (2018–2024) with process simulations. Greenhouse gas emissions were evaluated following the 2019 refinement of Intergovernmental Panel on Climate Change guidelines. The public data analysis showed that electricity-related emissions accounted for 44.9% of total greenhouse gas emissions, while direct emissions of nitrous oxide and methane from bioreactors accounted for 42.6%. Environmental performance of six processes was assessed using an integrated water–energy–carbon coupling index combining effluent quality, energy consumption, and carbon footprint. As a result, each process had weakness on following factors: energy consumption for anaerobic-oxic (AO), effluent quality for modified Ludzack-Ettinger, and greenhouse gas emission for AO, anaerobic-anoxic-oxic, and four-stage biological nutrient removal. Multi-linear models for estimating direct emissions with operational factors based on simulation results were built. Dissolved oxygen and sludge retention time had a negative relationship with direct emissions. Simulation results further indicated that higher effluent ammonia concentrations were associated with increased direct emissions. Simulation would be a powerful tool for finding weakness of process and guiding management on the integrated perspective.
ABSTRACT Graphical abstract showing citric-acid modification of loquat seed powder and its application as a biosorbent for methylene blue removal. The material achieved a maximum adsorption capacity of 47.53 mg/g within 9 min, followed by the Freundlich and pseudo-second-order models, which were accurately predicted by a random forest model, and retained more than 85% removal efficiency after five reuse cycles. Synthetic dye contamination poses a major environmental challenge, requiring sustainable, high-performance remediation technologies. Herein, an eco-friendly biosorbent, citric acid-modified loquat powder (LP-CA), was developed for efficient removal of methylene blue (MB) from aqueous solutions. FTIR, FE-SEM-EDS, and pHpzc analyses confirmed successful surface functionalization by introducing oxygen-containing groups, enhancing surface reactivity and yielding a pHpzc of 4.0. LP-CA reached adsorption equilibrium within 9 min and achieved a maximum adsorption capacity of 47.53 mg/g, representing a 19% improvement over the pristine biomass. Equilibrium and kinetic data were best described by the Freundlich isotherm (R2 = 0.98) and the pseudo-second-order model. Thermodynamic and activation energy analyses revealed a spontaneous, exothermic process predominantly governed by physisorption, with electrostatic attraction, hydrogen bonding, and π-interactions contributing to adsorption. A Random Forest model accurately predicted MB removal (R2 = 0.970; RMSE = 3.57 %), and feature importance analysis identified solution pH and pHpzc as the dominant variables, quantitatively validating the proposed adsorption mechanism. LP-CA retained >85% removal efficiency through the first five adsorption-desorption cycles and demonstrated practical applicability in a bag-assisted adsorption configuration. Machine learning provides a powerful framework for predictive adsorption modeling, mechanistic interpretation, and future process optimization.
ABSTRACT Exploring the spatiotemporal evolution patterns of Water Networks (WNs) is of great significance for improving water environment governance, watershed water resource management, and environmental protection. Using the Noah-LSM-GIS model, the WN features are finely extracted, and a system of 13 categories of WN elements was constructed; the SARIMA is employed to forecast the trend of evolution. The major findings are as follows: First, from 2003 to 2023, the number of nodes witnessed the sharpest increase (166.29%); the length of segmental arcs increased by 64.66%; and the area of polygons showed a V-shaped evolution trend (37.96 km² → 14.55 km² → 39.96 km²). Second, in areas with a slope larger than 46°, the elements were sparsely distributed, while the elements concentrated in areas with a 0°-5° slope; the elements showed a trend of southeastward migration and concentration. Third, concerning the sub-basins, the #14 sub-basin showed the most significant increase in nodes (318.18%). Fourth, The prediction for the next 30 years shows that node growth will exhibit the most significant increase (51.05%), followed by arcs (25.37%). The findings are expected to provide a theoretical basis and new insights for the scientific management of watershed water resources and ecological environment protection.
ABSTRACT Graphical summary of the study. Four anaerobic digestion configurations - conventional anaerobic digestion, thermal hydrolysis combined with conventional digestion, an anaerobic membrane bioreactor configuration, and the fully integrated configuration - are evaluated by plant-wide simulation across food-waste mixing ratios. Four indicators (specific methane production, sidestream load to mainstream, greenhouse gas emissions and energy footprint) are normalised and combined into a single composite Sustainability WWTP Index. This study evaluated the sustainability of four anaerobic digestion (AD) configurations, namely conventional anaerobic digester (CAD), thermal hydrolysis coupled with CAD (THP + CAD), CAD integrated with an anaerobic membrane bioreactor (AnMBR), and a fully integrated THP + CAD + AnMBR system. Process simulation and response surface methodology (RSM) were applied to assess system performance under varying food waste (FW) ratios. the sustainability WWTP Index (SWI) was developed by integrating specific methane production (SMP), sidestream load to mainstream (SLM), greenhouse gas (GHG) emissions and energy footprint (ENF). Advanced configurations enhanced methane production at moderate FW ratios. However, synergy decreased at 50% FW, where THP + CAD + AnMBR's SMP (11.5) fell below the CAD baseline (11.8). THP + CAD achieved the most consistently favourable SWI balance, maintaining a 100% above-median frequency up to 25% FW. The AnMBR-based systems exhibited higher volatility and increased the GHG footprint by approximately 4.5%. Ammonia rose with food waste addition, yet free ammonia stayed below inhibitory levels in all scenarios. Optimization indicated that for the THP + CAD + AnMBR configuration, a 0.35 bypass ratio and a 50-day SRT maximized the SWI. These findings show that AD configuration performance depends on the operating regime, which the SWI makes explicit.
