Search Results (1,849)

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

Urban parking around the world faces similar challenges of inadequate space, pollution, and carbon emissions. Although various smart parking technologies have been tested and implemented, they primarily aim to reduce the time spent searching for parking, without considering the impact on air quality. In this study, the air quality in three urban garages was investigated with portable instruments at the entrance and exit gates and inside the garages. Garage emissions measured include CO_2, PM_2.5, PM₁₀, NO_2, and total VOCs. The results suggested that the PM_2.5 levels in these garages tend to be higher than the ambient levels. The emissions also exhibit seasonal variations, with the highest concentrations occurring in the summer, which are 20.32 µg/m³ in Campus Green, 14.25 µg/m³ in CCM, and 15.23 µg/m³ in Washington Park garages, respectively. PM_2.5 measured from these garages is strongly correlated (with an R² of 0.64) with ambient levels. CO₂ emissions are higher than ambient levels but within the indoor air quality limit. This suggests that urban garages in Cincinnati tend to enrich ambient air concentrations, which can affect garage users and garage attendants. Portable sensors are capable of long-term emission monitoring and are compatible with other technologies in smart garage development. With portable air sensors becoming increasingly accessible and affordable, there is an opportunity to integrate these devices with smart garage management systems to enhance the sustainability of parking garages. Full article

In boron neutron capture therapy (BNCT), the intracellular localization and concentration of boron-10 atoms significantly influence therapeutic efficacy. Although various boronic-acid-targeted fluorescent probes have been developed to evaluate BNCT agents, most of these probes emit at short wavelengths and are, therefore, incompatible with common nuclear-staining reagents such as Hoechst 33342 and 4′,6-diamidino-2-phenylindole (DAPI). While our previously reported probe, BS-631, emitted fluorescence above 500 nm, it exhibited limitations in terms of reaction rate and fluorescence intensity. To address these issues, we developed a boronic-acid-targeted fluorescent probe with a longer emission wavelength, rapid reactivity, and strong fluorescence intensity. Herein, we designed and synthesized BTTQ, a probe based on a 2-(2-hydroxyphenyl)benzothiazole core structure. BTTQ exhibited immediate fluorescence upon reaction with 4-borono-L-phenylalanine (BPA), with an emission wavelength of 567 nm and a sufficiently high fluorescence quantum yield for detection. BTTQ quantitatively detected BPA with high sensitivity (quantification limit of 10.27 µM), suitable for evaluating BNCT agents. In addition, BTTQ exhibited selective fluorescence for BPA over metal cations. Importantly, BTTQ enabled fluorescence microscopic imaging of intracellular BPA distribution in living cells co-stained with Hoechst 33342. These results suggest that BTTQ is a promising fluorescent probe for the evaluation of future BNCT agents. Full article

In recent years, biofuels and bioenergy derived from algae have gained increasing attention, fueled by the growing demand for renewable energy sources and the urgent need to lower CO₂ emissions. This research examines the generation of bioethanol and biomethane using freshly harvested and sedimented algal biomass. Employing a factorial experimental design, various trials were conducted, with ethanol yield as the primary optimization target. The findings indicated that the sodium hydroxide concentration during pretreatment and the amylase dosage in enzymatic hydrolysis were key parameters influencing the ethanol production efficiency. Under optimized conditions—using 0.3 M NaOH, 25 μL/g starch, and 250 μL/g cellulose—fermentation yielded ethanol concentrations as high as 2.75 ± 0.18 g/L (45.13 ± 2.90%), underscoring the significance of both enzyme loading and alkali treatment. Biomethane potential tests on the residues of fermentation revealed reduced methane yields in comparison with the raw algal feedstock, with a peak value of 198.50 ± 25.57 mL/g volatile solids. The integrated process resulted in a total energy recovery of up to 809.58 kWh per tonne of algal biomass, with biomethane accounting for 87.16% of the total energy output. However, the energy recovered from unprocessed biomass alone was nearly double, indicating a trade-off between sequential valorization steps. A comparison between fresh and dried feedstocks also demonstrated marked differences, largely due to variations in moisture content and biomass composition. Overall, this study highlights the promise of integrated algal biomass utilization as a viable and energy-efficient route for sustainable biofuel production. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Cattle manure accounts for approximately one-third of the total livestock manure produced in the Republic of Korea and is typically composted. To elucidate its feasibility as a renewable resource, this study evaluated the conversion of cattle manure into a solid biofuel and the nutrient recovery potential of its combustion residues. Solid fuel was prepared from cattle manure collected in Gyeongsangbuk-do, Korea, and its fuel characteristics and ash composition were analyzed after combustion. Combustion tests conducted using a dedicated solid fuel boiler showed that an average lower heating value of 13.27 MJ/kg was achieved, meeting legal standards. Under optimized combustion, CO and NO_x emissions (129.9 and 41.5 ppm) were below regulatory limits (200 and 90 ppm); PM was also within the 25 mg/Sm³ standard. The bottom ash contained high concentrations of P₂O₅ and K, and its heavy metal content was below the regulatory threshold, suggesting its potential reuse as a fertilizer material. Although the Zn concentration in the fly ash exceeded the standard, its quantity was negligible. Therefore, the solid fuel conversion of cattle manure can become a viable and environmentally sustainable solution for both bioenergy production and nutrient recycling, contributing to improved waste management in livestock operations. Full article

Antimicrobial resistance (AMR) has emerged as a planetary health emergency, driven not only by the clinical misuse of antibiotics but also by diverse environmental dissemination pathways. This review critically examines the role of environmental compartments—water, soil, and air—as dynamic reservoirs and transmission routes for antibiotic-resistant bacteria (ARB) and resistance genes (ARGs). Recent metagenomic, epidemiological, and mechanistic evidence demonstrates that anthropogenic pressures—including pharmaceutical effluents, agricultural runoff, untreated sewage, and airborne emissions—amplify resistance evolution and interspecies gene transfer via horizontal gene transfer mechanisms, biofilms, and mobile genetic elements. Importantly, it is not only highly polluted rivers such as the Ganges that contribute to the spread of AMR; even low concentrations of antibiotics and their metabolites, formed during or after treatment, can significantly promote the selection and dissemination of resistance. Environmental hotspots such as European agricultural soils and airborne particulate zones near wastewater treatment plants further illustrate the complexity and global scope of pollution-driven AMR. The synergistic roles of co-selective agents, including heavy metals, disinfectants, and microplastics, are highlighted for their impact in exacerbating resistance gene propagation across ecological and geographical boundaries. The efficacy and limitations of current mitigation strategies, including advanced wastewater treatments, thermophilic composting, biosensor-based surveillance, and emerging regulatory frameworks, are evaluated. By integrating a One Health perspective, this review underscores the imperative of including environmental considerations in global AMR containment policies and proposes a multidisciplinary roadmap to mitigate resistance spread across interconnected human, animal, and environmental domains. Full article

Carbon dioxide (CO₂) emissions due to human activities are responsible for approximately 80% of the drivers of global warming, resulting in a 1.1 °C increase above pre-industrial temperatures. This study quantified the CO₂ assimilation and productivity of the brown macroalgae Lessonia spicata in the central Pacific coast of Chile, across seasonal and daily cycles, under different environmental stressors, such as temperature and solar irradiance. Measurements were performed using an infra-red gas analysis (IRGA) instrument which had a chamber allowing for precise quantification of CO₂ concentrations; additional photophysiological and biochemical responses were also measured. CO₂ assimilation, along with the productivity and biosynthesis of proteins and lipids, increased during the spring, coinciding with moderate temperatures (~14 °C) and high photosynthetically active radiation (PAR). Furthermore, the increased production of photoprotective and antioxidant compounds, including phenolic compounds, and carotenoids, along with the enhancement of non-photochemical quenching (NPQ), contribute to the effective photoacclimation strategies of L. spicata. Principal component analysis (PCA) revealed seasonal associations between productivity, reactive oxygen species (ROSs), and biochemical indicators, particularly during the spring and summer. These associations, further supported by Pearson correlation analyses, suggest a high but seasonally constrained photoacclimation capacity. In contrast, the reduced productivity and photoprotection observed in the summer suggest increased physiological vulnerability to heat and light stress. Overall, our findings position L. spicata as a promising nature-based solution for climate change mitigation. Full article

Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for the rapid dispersion of hazardous air pollutants (HAPs). In this study, we conducted spatiotemporal data collection and analysis for the first time in Korea using real-time measurements obtained through mobile extractive differential optical absorption spectroscopy (Me-DOAS) mounted on a solar occultation flux (SOF) vehicle. The measurements were conducted in the Saha Sinpyeong–Janglim Industrial Complex in Busan, which comprises the Sasang Industrial Complex and the Sinpyeong–Janglim Industrial Complex. BTEX compounds were selected as target volatile organic compounds (VOCs), and real-time measurements of both BTEX and fine particulate matter (PM) were conducted simultaneously. Correlation analysis revealed a strong relationship between PM₁₀ and PM_2.5 (r = 0.848–0.894), indicating shared sources. In Sasang, BTEX levels were associated with traffic and localized facilities, while in Saha Sinpyeong–Janglim, the concentrations were more influenced by industrial zoning and wind patterns. Notably, inter-compound correlations such as benzene–m-xylene and p-xylene–toluene suggested possible co-emission sources. This study proposes a GIS-based, three-dimensional air quality management approach that integrates variables such as traffic volume, wind direction, and speed through real-time measurements. The findings are expected to inform effective pollution control strategies and future environmental management plans for industrial complexes. Full article

Accurate methane emission estimates are essential for climate policy, yet current field methods often struggle with spatial constraints and source complexity. Ground-based mobile approaches frequently miss key plume features, introducing bias and uncertainty in emission rate estimates. This study addresses these limitations by using small unmanned aerial systems equipped with precision gas sensors to measure methane alongside co-released tracers. We tested whether arc-shaped flight paths and alternative ratio estimation methods could improve the accuracy of tracer-based emission quantification under real-world constraints. Controlled releases using ethane and nitrous oxide tracers showed that (1) arc flights provided stronger plume capture and higher correlation between methane and tracer concentrations than traditional flight paths; (2) the cumulative sum method yielded the lowest relative error (as low as 3.3%) under ideal mixing conditions; and (3) the arc flight pattern yielded the lowest relative error and uncertainty across all experimental configurations, demonstrating its robustness for quantifying methane emissions from downwind plume measurements. These findings demonstrate a practical and scalable approach to reducing uncertainty in methane quantification. The method is well-suited for challenging environments and lays the groundwork for future applications at the facility scale. Full article

The priming effect (PE), a microbially mediated process, critically regulates the balance between carbon sequestration and mineralization. This study used soils from different soil depths (0–20 cm, 20–40 cm, and 40–60 cm) under Picea schrenkiana forest in the Tianshan Mountains as the research object. An indoor incubation experiment was conducted by adding three concentrations (1% SOC, 2% SOC, and 3% SOC) of ¹³C-labelled glucose. We applied ¹³C isotope probe-phospholipid fatty acid (PLFA-SIP) technology to investigate the influence of readily labile organic carbon inputs on soil priming effect (PE), microbial community shifts at various depths, and the mechanisms underlying soil PE. The results indicated that the addition of ¹³C-labeled glucose accelerated the mineralization of soil organic carbon (SOC); CO₂ emissions were highest in the 0–20 cm soil layer and decreased trend with increasing soil depth, with significant differences observed across different soil layers (p < 0.05). Soil depth had a positive direct effect on the cumulative priming effect (CPE); however, it showed negative indirect effects through physico-chemical properties and microbial biomass. The CPE of the 0–20 cm soil layer was significantly positively correlated with ¹³C-Gram-positive bacteria, ¹³C-Gram-negative bacteria, and ¹³C-actinomycetes. The CPE of the 20–40 cm and 40–60 cm soil layers exhibited a significant positive correlation with cumulative mineralization (CM) and microbial biomass carbon (MBC). Glucose addition had the largest and most significant positive effect on the CPE. Glucose addition positively affected PLFAs and particularly microbial biomass. This study provides valuable insights into the dynamics of soil carbon pools at varying depths following glucose application, advancing the understanding of forest soil carbon sequestration. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

Human activities at unprecedented levels have exacerbated the greenhouse effect and escalated the frequency of extreme weather. In response, the Chinese government has pledged to reach “carbon peak” by 2030 and achieve “carbon neutrality” by 2060. Leveraging the GOSAT L3 and L4B CO₂ datasets, this study investigated the spatiotemporal and vertical characteristics of atmospheric carbon dioxide (CO₂) concentration across China, alongside quantifying the relative importance of key influencing factors. The results show that there is a distinct regional disparity in CO₂ column concentration, with eastern China having a higher concentration level (406.85 × 10⁻⁶) than the western regions (400.92 × 10⁻⁶). Vertically, the concentration of CO₂ (390–420 × 10⁻⁶) reaches its peak at the near-surface layer (975 hPa) and then decreases with increasing altitude. High values of CO₂ levels in the mid-lower layer are concentrated in eastern China, while those in the upper layer are mainly located in southern China. In addition, CO₂ concentration shows seasonal variations, with the highest concentration occurring in spring (406.39 × 10⁻⁶) and the lowest in summer. Biospheric emissions and fossil fuel combustion emerge as the two most significant factors affecting CO₂ variation, with relative importance of 24% and 22%, respectively. Full article

This study aims to characterize the emissions of ammonia (NH₃) and methane (CH₄) from naturally ventilated dairy barns located in cold regions during the winter season, thereby providing a scientific basis for optimizing dairy barn environmental management. The target barn was selected at a commercial dairy farm in Ulanchab, Inner Mongolia, China. Environmental factors, including temperature, humidity, wind speed, and concentrations of NH₃, CH₄, and CO₂, were monitored both inside and outside the barn. The ventilation rate and emission rate were calculated using the CO₂ mass balance method. Additionally, NH₃ and CH₄ emission prediction models were developed using the adaptive neural fuzzy inference system (ANFIS). Correlation analyses were conducted to clarify the intrinsic links between environmental factors and NH₃ and CH₄ emissions, as well as the degree of influence of each factor on gas emissions. The ANFIS model with a Gaussian membership function (gaussmf) achieved the highest performance in predicting NH₃ emissions (R² = 0.9270), while the model with a trapezoidal membership function (trapmf) was most accurate for CH₄ emissions (R² = 0.8977). The improved ANFIS model outperformed common models, such as multilayer perceptron (MLP) and radial basis function (RBF). This study revealed the significant effects of environmental factors on NH₃ and CH₄ emissions from dairy barns in cold regions and provided reliable data support and intelligent prediction methods for realizing the precise control of gas emissions. Full article