Search Results (1,717)

The growing need for efficient and safe high-energy lithium-ion batteries (LIBs) in electric vehicles and grid storage necessitates advanced internal monitoring solutions. This work presents a comprehensive simulation model of a novel integrated optical sensor based on ethylene carbonate-filled photonic crystal fiber (EC-PCF). The proposed design synergistically combines a chirped fiber Bragg grating (FBG) and an extrinsic Fabry–Pérot interferometer (EFPI) on a multiplexed platform for the multifunctional sensing of refractive index (RI), temperature, strain, and pressure (via strain coupling) within LIBs. By matching the RI of the PCF cladding to the battery electrolyte using ethylene carbonate, the design maximizes light–matter interaction for exceptional RI sensitivity, while the cascaded EFPI enhances mechanical deformation detection beyond conventional FBG arrays. The simulation framework employs the Transfer Matrix Method with Gaussian apodization to model FBG reflectivity and the Airy formula for high-fidelity EFPI spectra, incorporating critical effects like stress-induced birefringence, Transverse Electric (TE)/Transverse Magnetic (TM) polarization modes, and wavelength dispersion across the 1540–1560 nm range. Robustness against fabrication variations and environmental noise is rigorously quantified through Monte Carlo simulations with Sobol sequences, predicting temperature sensitivities of ∼12 pm/°C, strain sensitivities of ∼1.10 pm/$\mathsf{\mu }$$\mathsf{\epsilon }$, and a remarkable RI sensitivity of ∼1200 nm/RIU. Validated against independent experimental data from instrumented battery cells, this model establishes a robust computational foundation for real-time battery monitoring and provides a critical design blueprint for future experimental realization and integration into advanced battery management systems. Full article

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO₂, 20,500 tons of NO_x, 4250 tons of SO₂, 656 tons of PM₁₀, and 603 tons of PM_2.5, with anchorage activities contributing 497,000 tons of CO₂, 7010 tons of NO_x, 1520 tons of SO₂, 232 tons of PM₁₀, and 214 tons of PM_2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies. Full article

In recent years, one of the major problems facing humanity has been the contamination of the environment by various organic pollutants, with some of them exhibiting environmental persistence or pseudo-persistence. For this reason, it is necessary today, more than ever, to find new and effective methods for degrading these persistent pollutants. Transition metal selenides (TMSes) have emerged as a versatile and promising class of catalysts for the degradation of organic pollutants through various advanced oxidation processes (AOPs). The widespread use of these materials lies in the desirable characteristics they offer, such as unique electronic structures, narrow band gaps, high electrical conductivity, and multi-valent redox behavior. This review comprehensively examines recent progress in the design, synthesis, and application of these TMSes—including both single- and composite systems, such as TMSes/g-C₃N₄, TMSes/TiO₂, and heterojunctions. The catalytic performance of these systems is being highlighted, regarding the degradation of organic pollutants such as dyes, pharmaceuticals, antibiotics, personal care products, etc. Further analysis of the mechanistic insights, structure–activity relationships, and operational parameter effects are critically discussed. Emerging trends, such as hybrid AOPs combining photocatalysis with PMS or electro-activation, and the challenges of stability, scalability, and real wastewater applicability are explored in depth. Finally, future directions emphasize the integration of multifunctional activation methods for the degradation of organic pollutants. This review aims to provide a comprehensive analysis and pave the way for the utilization of TMSe catalysts in sustainable and efficient wastewater remediation technologies. Full article

This study presents the preparation of iron and nitrogen co-doped sludge-based biochar (FeCN-MSBC) and iron oxide-doped biochar (FeO-MSBC) by ball milling municipal sludge with different iron precursors (K₃Fe(CN)₆ and Fe₂O₃), followed by pyrolysis. These biochars were utilized to activate persulfate (PMS) for the degradation of phenolic pollutants. The results demonstrate that FeCN-MSBC, formed by the introduction of K₃Fe(CN)₆, contains Fe/N phases, with surface Fe sites exhibiting a lower oxidation state, which significantly enhances PMS activation efficiency. In contrast, FeO-MSBC, due to the aggregation of Fe₂O₃/Fe₃O₄, shows relatively lower catalytic activity. The FeCN-MSBC/PMS system degrades pollutants via a synergistic mechanism involving non-radical pathways mediated by ¹O₂ and electron transfer processes (ETP) catalyzed by surface Fe. Electrochemical oxidation and quenching experiments confirm that ETP is the dominant pathway. FeCN-MSBC, prepared at a pyrolysis temperature of 600 °C and an Fe loading of 3 mmol/g TSS, exhibited the best performance, achieving a phenol degradation rate constant (k_obs) of 0.127 min⁻¹, 4.5 times higher than that of undoped biochar (MSBC). FeCN-MSBC/PMS maintained high efficiency across a wide pH range and in complex water matrices, exhibiting excellent stability over multiple cycles, demonstrating strong potential for practical applications. This study provides an effective strategy for simultaneous Fe and N doping in sludge-derived biochar and offers mechanistic insights into Fe/N synergistic activation of PMS for practical water treatment. Full article

Advanced oxidation processes (AOPs) utilizing peroxymonosulfate (PMS) have recently gained attention for effectively removing organic dyes. Biochar, a carbon-based material, can act as a catalyst carrier for PMS activation. This study developed a nitrogen-doped biochar catalyst (NCMR800–2) from waste Chinese medicine residue (CMR) through one-step pyrolysis to efficiently remove Rhodamine B (RhB) from wastewater. Results indicate that NCMR800–2 rapidly achieved complete removal of 20 mg/L Rhodamine B (RhB), the primary focus of this study, within 30 min, while maintaining high degradation efficiencies for other pollutants and significantly outperforming the unmodified material. The material demonstrates strong resistance to ionic interference and operates effectively across a wide pH range. Quenching experiments and in situ testing identified singlet oxygen (¹O₂) as the primary active species in RhB degradation. Electrochemical analysis showed that nitrogen doping significantly enhanced the electrical conductivity and electron transfer efficiency of the catalyst, facilitating PMS decomposition and RhB degradation. Liquid chromatography–mass spectrometry (LC-MS) identified intermediate products in the RhB degradation process. Seed germination experiments and TEST toxicity software confirmed a significant reduction in the toxicity of degradation products. In conclusion, this study presents a cost-effective, efficient catalyst with promising applications for removing persistent organic dyes. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 6.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 9.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 12.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and 15.3 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

Indoor air quality (IAQ) is a critical determinant of health, comfort, and productivity, and is strongly connected to building energy demand due to the role of ventilation and air treatment in HVAC systems. This review examines recent applications of Artificial Intelligence (AI) and Machine Learning (ML) for IAQ prediction across residential, educational, commercial, and public environments. Approaches are categorized by predicted parameters, forecasting horizons, facility types, and model architectures. Particular focus is given to pollutants such as CO₂, PM2.5, PM10, VOCs, and formaldehyde. Deep learning methods, especially the LSTM and GRU networks, achieve superior accuracy in short-term forecasting, while hybrid models integrating physical simulations or optimization algorithms enhance robustness and generalizability. Importantly, predictive IAQ frameworks are increasingly applied to support demand-controlled ventilation, adaptive HVAC strategies, and retrofit planning, contributing directly to reduced energy consumption and carbon emissions without compromising indoor environmental quality. Remaining challenges include data heterogeneity, sensor reliability, and limited interpretability of deep models. This review highlights the need for scalable, explainable, and energy-aware IAQ prediction systems that align health-oriented indoor management with energy efficiency and sustainability goals. Such approaches directly contribute to policy priorities, including the EU Green Deal and Fit for 55 package, advancing both occupant well-being and low-carbon smart building operation. Full article

The synthesis of precious metal nanoparticles (PM-NPs) is an important field of research that has expanded significantly in recent decades due to their numerous applications. Therefore, research has been directed toward developing green methods for the synthesis of such nanoparticles that are simple, safe, eco-friendly, efficient, and sustainable. In this context, the use of marine algae biomass for the green synthesis of PM-NPs can be a viable large-scale alternative, as algae are easy to cultivate, have a rapid growth rate, and are widely distributed across many regions of the globe. The reduction of precious metal ions takes place at the surface of algae biomass particles, and the characteristics of the resulting precious metal nanoparticles depend on the experimental conditions (pH, amount of algae biomass, contact time, etc.), as well as on the type of algae biomass and the speciation form of the metal ions in the solution. All these factors significantly influence the properties of precious metal nanoparticles, and their understanding allows the development of synthesis strategies that can be applied on a large scale. The aim of this review is to provide a comprehensive overview of the way in which PM-NPs can be synthesized using algae biomass. The importance of experimental conditions (such as pH, contact time, amount of biomass, type of algal biomass, temperature, etc.) on the synthesis efficiency, as well as the elementary steps involved in the synthesis, is also discussed in this study. Particular attention has been paid to the analytical methods used for characterizing PM-NPs, as they provide crucial data regarding their structure and composition. These aspects are essential for identifying the practical applications of PM-NPs. Full article

Urban expansion intensifies population exposures to fine particulate matter (PM_2.5). Trees mitigate pollution by dry deposition, in which particles settle on plants. However, city-scale models frequently overlook differences in tree species and structure. This study assesses PM_2.5 removal by individual city-owned street trees in Mississauga, Canada, throughout the 2019 leaf-growing season (May to September). Using a modified i-Tree Eco framework, we evaluated the removal of PM_2.5 by 200,560 city-owned street trees (245 species) in Mississauga from May to September 2019. The model used species-specific deposition velocities (V_d) from the literature or leaf morphology estimates, adjusted for local winds, a 3 m-resolution satellite-derived Leaf Area Index (LAI), field-validated, crown area modelled from diameter at breast height, and 1 km² resolution PM_2.5 data geolocated to individual trees. About twenty-eight tons of PM_2.5 were removed from 200,560 city-owned trees (245 species). Coniferous species (14.37% of trees) removed 25.62 tons (92% of total), much higher than deciduous species (85.63%, 2.18 tons). Picea pungens (18.33 tons, 66%), Pinus nigra (3.29 tons, 12%), and Picea abies (1.50 tons, 5%) are three key species. Conifers’ removal efficiency originates from the faster deposition velocities, larger tree size, and dense foliage, all of which enhance particle deposition. This study emphasizes species-specific approaches for improving urban air quality through targeted tree planting. Prioritizing coniferous species such as spruce and pine can improve pollution mitigation, providing actionable strategies for Mississauga and other cities worldwide to develop green infrastructure planning for air pollution. Full article

Urban clusters face escalating atmospheric challenges, with elevated PM_2.5 concentrations representing a critical environmental constraint. This study investigates spatiotemporal evolution mechanisms of PM_2.5 within China’s Taiyuan-Yuci-Xinzhou (TYX) urban cluster. Daily PM_2.5 concentrations (July–September 2000–2020) characterized spatiotemporal distributions. The Urban Forest Effects (UFORE) model integrated multisource data—remote sensing, leaf area index (LAI), wind speed, and precipitation—within a Geographically and Temporally Weighted Regression (GTWR) framework, quantifying PM_2.5 dry deposition across land cover types and identifying drivers. Key findings reveal the following: (1) PM_2.5 concentrations followed an initial-rise-then-decline trajectory, with pollution hotspots concentrated in Taiyuan City and neighboring industrial corridors; (2) Construction lands sprawl markedly elevated PM_2.5 levels, whereas green areas and agricultural lands expansion promoted deposition; water areas exhibited no significant effect; (3) Wind speeds and precipitation positively modulated PM_2.5 in green areas and agricultural lands, contrasting with NDVI’s negative influence; elevation showed null correlation, while agricultural lands deposition correlated positively with PM_2.5; (4) GDP displayed an inverted U-curve association with PM_2.5; positive correlations emerged with AVSI, TIOV, NOP, and EC indices, but negative linkage with TOC. This clarifies land cover impacts on urban atmospheric particulates, providing empirical foundations for pollution control. Full article

In this study, a novel LFP–catalytic microfiltration membrane (LFP@PVDF) was fabricated by loading a lithium-ion battery material LiFePO₄ (LFP) onto polymeric micro-porous polyvinylidene fluoride (PVDF) using a filter press coating method. The successful loading of LFP material onto the LFP@PVDF catalytic membrane was confirmed by the characterization of the material using FTIR, SEM, EDX, and XRD analysis. To evaluate the catalytic performance of the LFP@PVDF membrane, the reactive black 5 (RB5) dye-containing solution was used with or without the peroxymonosulfate (PMS) activator in a dead-end filtration under room conditions. The influence of parameters such as LFP loading, initial RB5 dye concentration, persulfate dosage, and solution pH on the performance of the persulfate oxidation process was comprehensively examined. It was found that the LFP@PVDF membrane/persulfate activation system can effectively remove RB5 dye with an efficiency of 97.3%. The RB5 dye removal by LFP@PVDF membranes with varying experimental conditions was found to fit the pseudo-second-order kinetic model. Quenching experiments showed that the reactive species ${\mathrm{HO}}^{•}$, ${{\mathrm{SO}}_4}^{•-}$ and ¹O₂ were responsible for the dye removal. The LFP@PVDF membrane/persulfate activation system appeared to be a promising approach for the removal of organic contaminants. Full article

Particulate matter (PM) suspended in the air has posed significant potential threats to human health. However, current air filters designed to intercept PM are confronted with several challenges, including a complicated preparation process, monotonous protective performance, and uncomfortable wearability. Herein, a novel jet-splitting electrospinning strategy was demonstrated to simply fabricate a hierarchically structured PLA membrane with a high filtration performance, antibacterial performance, and rapid heat dissipation for effective and comfortable air filtering. Formulating a cationic antibacterial surfactant in the PLA solution to tailor the splitting of charged jets enables the simultaneous formation of nanofibers, submicron-fibers, and beads in the hierarchical filtration network by the single-jet electrospinning. Benefiting from the synergistic effect of multi-scale fibers and beads, the hierarchically structured filter exhibited an excellent filtration efficiency of 99.979% and high quality factor of 0.45 Pa⁻¹ against PM_0.3, with a remarkably low pressure drop of 18.7 Pa. Furthermore, the hierarchical structure endowed the filter with excellent stability in filtration performance, even under 20-cyclic and 480 min long-term tests, high-humidity tests with sodium chloride aerosol particles, and the 20-cycle PM_2.5 smoke tests. Simultaneously, the filter also demonstrated remarkable antibacterial performance and an excellent heat dissipation property—all achieved due to its PLA formulation and the hierarchical structure. Full article

Laboratory buildings represent some of the highest energy-consuming infrastructure due to stringent environmental requirements and the continuous operation of specialized equipment. Ensuring both energy efficiency and indoor air quality (IAQ) in such spaces remains a central challenge for sustainable building design and operation. Recent advances in Internet of Things (IoT) systems allow for real-time monitoring of multivariate environmental parameters, including CO₂, total volatile organic compounds (TVOC), PM_2.5, temperature, humidity, and noise. However, these datasets are often noisy or incomplete, complicating conventional monitoring approaches. Supervised anomaly detection methods are ill-suited to such contexts due to the lack of labeled data. In contrast, unsupervised machine learning (ML) techniques can autonomously detect patterns and deviations without annotations, offering a scalable alternative. The challenge of identifying anomalous environmental conditions and latent operational states in laboratory environments is addressed through the application of unsupervised models to 1808 hourly observations collected over four months. Anomaly detection was conducted using Isolation Forest (300 trees, contamination = 0.05) and One-Class Support Vector Machine (One-Class SVM) (RBF kernel, ν = 0.05, γ auto-scaled). Standardized six-dimensional feature vectors captured key environmental and energy-related variables. K-means clustering (k = 3) revealed three persistent operational states: Empty/Cool (42.6%), Experiment (37.6%), and Crowded (19.8%). Detected anomalies included CO₂ surges above 1800 ppm, TVOC concentrations exceeding 4000 ppb, and compound deviations in noise and temperature. The models demonstrated sensitivity to both abrupt and structural anomalies. Latent states were shown to correspond with occupancy patterns, experimental activities, and inactive system operation, offering interpretable environmental profiles. The methodology supports integration into adaptive heating, ventilation, and air conditioning (HVAC) frameworks, enabling real-time, label-free environmental management. Findings contribute to intelligent infrastructure development, particularly in resource-constrained laboratories, and advance progress toward sustainability targets in energy, health, and automation. Full article

With the intensification of population ageing and the increasingly diverse service needs of older adults, existing digital elderly care platforms generally exhibit fragmentation in functional integration, understanding of needs, and service coordination, making it difficult to effectively respond to the complex challenges faced by urban ageing populations. To fill this gap, this study starts from a service design perspective and adopts Constructivist Grounded Theory (CGT) to construct a theoretical model, proposing a three-tier framework that encompasses seven core user needs, four platform response mechanisms, and three categories of service outcomes. A questionnaire survey was subsequently conducted in the Pearl River Delta region of China, collecting 352 responses, of which 322 were valid. Through Exploratory Factor Analysis (EFA), correlation analysis, and multiple regression analysis, the structural stability and predictive validity of the proposed “User Needs-Platform Mechanisms-Service Outcomes” (UN-PM-SO) model were verified. The research results confirm that the theoretical model constructed in this study has good logical consistency and empirical support. Based on this model, a series of concrete design framework recommendations are further proposed, aiming to guide the sustainable and inclusive development of future smart elderly care platforms. The findings of this study not only respond to the urgent global demand for age-friendly digital infrastructure but also demonstrate the sustainable value of smart elderly care platform design in terms of social inclusion, resource efficiency, and environmental friendliness, providing a feasible and theory-based design logic and governance pathway for promoting social sustainability. Full article