Search Results (1,014)

Municipal solid waste incineration promotes sustainable development by reducing waste, recovering resources, and minimizing environmental impact, with furnace temperature control playing a key role in maximizing efficiency. Accurate real-time temperature prediction is crucial in developing countries to optimize incineration, re-duce emissions, and enhance energy recovery for global sustainability. To address this, we propose a method integrating an improved whale optimization algorithm (IWOA) with a self-attention gated recurrent unit (SAGRU). Using the maximal information coefficient (MIC) to identify key factors, we optimize SAGRU parameters with IWOA, enhancing prediction accuracy by capturing temporal dependencies. Experimental validation from an MSWI plant in China demonstrates that the proposed model significantly enhances prediction accuracy under complex conditions. When compared with the Elman and LSTM models, the error is reduced by 0.7146 and 0.4689, respectively, highlighting its strong potential for practical applications in waste incineration temperature control. Full article

Heating and cooling (H&C) account for nearly half of the EU’s energy consumption, with significant potential for decarbonization through renewable energy sources (RES) integrated in district heating and cooling (DHC) systems. This study evaluates the environmental and social impacts of RES-powered DHC solutions implemented in three European small-scale demo sites (Bucharest, Luleå, Córdoba) under the Horizon 2020 WEDISTRICT project. Using the Life Cycle Assessment (LCA) and Social Life Cycle Assessment (S-LCA) methodologies, the research compares baseline fossil-based energy scenarios with post-implementation renewable scenarios. Results reveal substantial greenhouse gas emission reductions (up to 67%) and positive environmental trade-offs, though increased mineral and metal resource use and site-specific impacts on water and land use highlight important sustainability challenges. Social assessments demonstrate improvements in gender parity, local employment, and occupational safety, yet reveal persistent issues in wage equity, union representation, and inclusion of vulnerable populations. The findings emphasize that while renewable DHC systems offer significant climate benefits, social sustainability requires tailored local strategies and robust governance to avoid exacerbating inequalities. This integrated environmental-social perspective underscores the need for holistic policies that balance technical innovation with equitable social outcomes to ensure truly sustainable energy transitions. Full article

Bioplastics are gaining attention as eco-friendly alternatives to conventional plastics, with Polybutylene Adipate Terephthalate (PBAT) emerging as a promising biodegradable substitute for polyethylene (PE) in food packaging. Commercial PBAT is often blended with other plastics or bio-based fillers to improve mechanical properties and reduce costs, though these additives can influence its environmental footprint. Therefore, this study quantifies the environmental impacts of producing PBAT resin blends reinforced with common inorganic fillers and compares end-of-life (EoL) performance against PE. While prior studies have largely assessed virgin PBAT or PBAT/Polylactic Acid (PLA) systems, systematic LCA of commercial-style PBAT blends with inorganic fillers and screening LCA level for comparisons of composting vs. landfill remain limited. The contributions of this study are to: (i) map gate-to-gate environmental hotspots for PBAT-blend conversion, (ii) provide a screening gate-to-grave comparison of PBAT composting vs. PE landfill using ReCiPe 2016 and IPCC GWP100 methods, and (iii) discuss theoretical implications for material substitution in the context of EoL strategies. The results indicated that producing 1 kg of PBAT blend generated a single score impact of 921 mPt with Human Health and Resource categories contributing similarly, and a GWP of 8.64 kg CO₂-eq, dominated by mixing and drying processes. EoL screening showed PBAT composting offered clear advantages over landfilling PE, yielding −53.9 mPt and 11.35 kg CO₂-eq savings, effectively offsetting production emissions. In contrast, landfilling PE resulted in 288.8 mPt and 2.2 kg CO₂-eq emissions. Sensitivity analysis further demonstrated that a 30% reduction in electricity use could decrease impacts by up to 10%, underscoring the importance of energy efficiency improvements and renewable energy adoption for sustainable PBAT development. Full article

As the European Union’s energy systems are transforming towards achieving climate goals, this article examines the energy balances of EU member states. This analysis covers, among other things, the dynamics of energy dependence and strategies for decoupling economic growth from the level of emissions in the European Union (EU), with particular emphasis on Poland, which is strongly influenced by its historical reliance on coal in the energy balance. Using panel data from 1990 to 2022, the article investigates differences in energy dependence between individual countries, shaped by economic structures and national energy policies. The study results confirm significant heterogeneity between member states and emphasize that the stability and direction of decoupling economic growth from greenhouse gas (GHG) emissions are strongly dependent on the composition of the energy mix and vulnerability to external conditions. Based on scenario analysis, potential paths for Poland’s energy transition are assessed. We demonstrate that a high share of renewable energy sources (RES) significantly reduces CO₂ emissions, provided it is accompanied by infrastructure modernization and the development of energy storage. Furthermore, integrating nuclear energy as a stabilizing element of the energy mix offers an additional path to deep decarbonization while ensuring supply reliability. Finally, we demonstrate that improving energy efficiency and demand management can effectively increase energy security and reduce emissions, even in a scenario with a stable coal share. The study addresses a research gap by integrating decoupling analysis with scenario-based stochastic modeling for Poland, a country for which few comprehensive transition assessments exist. The results provide practical guidance for developing resilient, low-emission energy policies in Poland and the EU. Results are reported for 2025–2050 (with 2040 as an interim milestone). Full article

This study investigates the dynamics and sources of atmospheric mercury in Qinhuangdao (QHD), a coastal urban area significantly impacted by both marine and terrestrial sources. Sampling of gaseous elemental mercury (GEM), fine particle-bound mercury (PBM_2.5), and coarse particle-bound mercury (PBM_2.5–10) was conducted from September 2022 to August 2023. The annual mean concentrations of GEM, PBM_2.5, and PBM_2.5–10 were 2.66, 1.01, and 0.73 ng m⁻³, respectively, with PBM levels among the highest reported for coastal cities in eastern China. GEM displayed a pronounced midday peak (12:00–14:00) with correlations to temperature (R² = 0.25–0.65) and a significant winter association with SO₂ (R² = 0.52), suggesting the combined influence of surface re-emission and coal combustion. Seasonal variations in the GEM/CO ratio (spring: 7.12; winter: 2.62) further reflected the shift between natural and combustion-related sources. PBM_2.5 exhibited elevated concentrations (1.0–1.4 ng m⁻³) under westerly winds (~3 m s⁻¹), indicating inputs from traffic, shipping, and light industries, while PBM_2.5–10 (0.5–1.1 μg m⁻³) was strongly linked to coal-handling activities at QHD port and soil resuspension. Backward trajectory analysis showed continental air masses dominated in winter (53–100%) and maritime air masses in summer (30–50%), whereas high Hg/Na ratios in PM_2.5 (3.22 × 10⁻⁴) and PM_2.5–10 (2.17 × 10⁻⁴), far exceeding typical marine aerosol values (10⁻⁷–10⁻⁵), indicated negligible marine contributions to PBM. These findings provide new insights into the processes driving mercury pollution in coastal urban environments and highlight the critical role of port-related activities in regional mercury management. Full article

This study presents a novel multi-objective optimization (MOO) model for the design of an off-grid hybrid renewable energy system (HRES) to support sustainable agriculture and rural development in Sub-Saharan Africa (SSA). Based upon a case study selected in Linia (Chad), three system architectures are compared under different levels of the reliability requirements (LPSP = 1%, 5%, and 10%). A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to optimize the Levelized Cost of Energy (LCOE), CO₂ emissions mitigation, and social impact, referring to the Human Development Index (HDI) enhancement and the job creation (JC) opportunity, using the MATLAB R2024b environment. The calculation results show that among the three configuration schemes, the PV–Wind–Battery configuration obtains the optimal techno–economic–environmental coordination, with the lowest LCOE (0.0948 $/kWh) and the largest CO₂ emission reduction (9.58 × 10⁸ kg), and the Wind–Battery system gets the most social benefit. The method developed provides users with a decision-support method for renewable energy systems (RES) integration into rural agricultural settings, taking into consideration financial cost, environmental sustainability, and community development. This information is important for policymakers and practitioners advocating for decentralized, socially inclusive clean energy access initiatives in underserved regions. Full article

With accelerated urbanization and the pursuit of the “dual carbon” goals, shared bicycles have re-emerged as a green travel option. This study focuses on the central urban area of Nanjing and develops a carbon emissions reduction (CER) estimation model for shared bicycles. By analyzing spatio-temporal dimensions, it systematically assesses carbon reduction benefits and highlights the synergy with metro-connected travel. Key findings are as follows: (1) shared bicycles primarily support short-distance commuting, with a daily cycling pattern exhibiting a bi-modal distribution and a pronounced peak period demand; (2) cycling trips concentrate in densely populated and commercially vibrant zones, with a spatial pattern of central aggregation and multi-point diffusion; (3) each kilometer cycled by a shared bicycle reduces carbon emissions by about 96.19 g, with daily reductions of around 42.72 t and annual reductions up to 15,591.04 t; (4) the CER benefits of bicycle–metro integration are especially pronounced, contributing nearly 45.00% during peak periods; and (5) factors such as travel mode shifts, metro station layouts, and the development of electric vehicles continue to influence the CER benefits of shared bicycles. This work provides scientific evidence to inform urban green travel policies and transportation infrastructure optimization in cities. Full article

The South Korean government plans to increase the share of renewable energy (RE) used in public buildings by 10% from the current 30% to 40% by 2030. This article seeks to estimate the public willingness to pay (WTP) for this increase. To this end, a contingent valuation was applied, with 1000 households randomly selected and surveyed through one-on-one interviews. The payment vehicle and WTP elicitation method were determined to be income tax per household and the one-and-one-half-bound model, respectively. The annual WTP per household was estimated to be KRW 2712 (USD 2.04) with statistical significance. When expanded to the population, this produces an annual value of KRW 60.15 billion (USD 45.23 million). The increase in the RE use share can not only reduce greenhouse gas emissions but also result in savings on electricity bills. The sum of these two can be considered as benefits, and the sum of the construction and maintenance costs incurred due to the increase can be considered as costs. The cost–benefit analysis indicates that the present value of net benefits and the benefit-to-cost ratio were estimated to be KRW 667.3 billion (USD 501.7 million) and 1.48, respectively. Consequently, the increase is socially desirable and should be implemented immediately. Full article

Accurate air quality prediction (AQP) is crucial for safeguarding public health and guiding smart city management. However, reliable assessment remains challenging due to complex emission patterns, meteorological variability, and chemical interactions, compounded by the limited coverage of ground-based monitoring networks. To address this gap, we propose Vision-AQ (Visual Integrated Operational Network for Air Quality), a novel multi-modal deep learning framework that classifies Air Quality Index (AQI) levels by integrating environmental imagery with pollutant data. Vision-AQ employs a dual-input neural architecture: (1) a pre-trained ResNet50 convolutional neural network (CNN) that extracts high-level features from city-scale environmental photographs in India and Nepal, capturing haze, smog, and visibility patterns, and (2) a multi-layer perceptron (MLP) that processes tabular sensor data, including ${PM}_{2.5}$, ${PM}_{10}$, and AQI values. The fused representations are passed to a classifier to predict six AQI categories. Trained on a comprehensive dataset, the model achieves strong predictive performance with high accuracy, precision, recall and F1-score of 99%, with 23.7 million parameters. To ensure interpretability, we use Grad-CAM visualization to highlights the model’s reliance on meaningful atmospheric features, confirming its explainability. The results demonstrate that Vision-AQ is a reliable, scalable, and cost-effective approach for localized AQI classification, offering the potential to augment conventional monitoring networks and enable more granular air quality management in urban South Asia. Full article

The purpose was to determine the ability of 18-fluorodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET-CT) scans performed within 24 h of percutaneous image-guided ablation of primary and metastatic malignancies to predict ablation effectiveness and local tumor progression (LTP). This single-center retrospective review included patients who underwent image guided ablation (microwave ablation (MWA), cryoablation, or irreversible electroporation (IRE)) between August 2018 and February 2024 for primary and metastatic malignancies. The primary outcome measure encompassed correlating post-ablation 18F-FDG PET-CT findings with LTP development per tumor, assessed using the chi-square test. The secondary outcome measure was local tumor progression-free survival (LTPFS) per tumor, evaluated using the Kaplan–Meier survival curves, and potential confounders were identified in multivariable analysis utilizing Cox proportional hazards regression models. A total of 132 patients, who underwent 159 procedures for 224 tumors, were included. During follow-up, LTP developed in 120 out of 224 tumors (53.6%). The presence of residual nodular 18F-FDG avidity on PET-CT within 24 h after the ablation significantly correlated with the development of LTP at follow-up imaging (p < 0.001). The positive predictive value of nodular 18F-FDG avidity was 86.7%. In multivariable analysis, the hazard ratio (HR) for 18F-FDG avidity was 2.355 (95% CI 1.614–2.647; p < 0.001). The presence of 18F-FDG avidity on PET-CT within 24 h after the ablation was highly correlated with development of LTP and decreased LTPFS. The detection of residual tumor tissue may allow early re-treatments, especially in tumors with nodular uptake, contributing to increased LTPFS. Full article

Agricultural biowaste poses a major environmental challenge when improperly disposed of. An alternative to this is their utilization for producing natural fibers (NFs) to manufacture biocomposites, promoting a circular economy. However, the fact that a product is classified as renewable does not necessarily imply that its environmental performance is superior when compared to its conventional market counterpart. For this reason, this study conducted a Life Cycle Assessment (LCA) of biocomposites reinforced with coconut fiber and a polyester resin matrix, using a “cradle-to-gate” approach. Six scenarios were evaluated, grouped into S1 (2–5% fiber) and S2 (20–30% fiber), with and without chemical treatment, plus a reference scenario without fiber utilization. The IPCC 2021 GWP 100 and ReCiPe Midpoint (H) 2016 methods were applied. The results show that the scenarios without chemical treatment (RF-CCT) were environmentally more optimal, reducing CO₂ emissions by up to 7.4% (RF-CCT/H) and 1.70 kg CO₂-eq (RF-CCT/L) compared to conventional practices. The main reasons for these reductions are the avoidance of emissions associated with disposal, decreased reliance on conventional materials, and the omission of chemical treatment, which in turn mitigates critical impacts such as ozone depletion potential (ODP) linked to N₂O emissions from fertilizers (93% contribution) and terrestrial/marine toxicity. Full article

Rammed earth (RE) dwellings are characterized by accessible materials, low cost, and environmental sustainability. However, their poor seismic resistance limits their application. To address this issue, three conventional technical approaches have been developed: (1) adding cement to improve strength; (2) improving structural integrity using reinforced concrete ring beams and columns; and (3) embedding vertical steel bars in order to provide resistance against horizontal seismic actions. While effective, these methods rely on energy-intensive materials with high carbon emissions. In this study, we analyze the seismic damage characteristics and construction mechanisms of RE walls. The results reveal that the horizontal joints in RE walls significantly weaken their resistance to horizontal seismic actions. To mitigate this, three types of undulating joints are proposed and six specimens tested. The maximum horizontal loads of the specimens with local subsidence-type joints are 132.44 kN and 135.41 kN, respectively, which are approximately 50% higher than specimens with horizontal joints, whose maximum horizontal loads are 80.7 kN and 85.83 kN, respectively, while the maximum horizontal loads of the specimens with horizontally concatenated gentle arc-type joints are 151.17 kN and 173.58 kN, respectively, and they exhibit nearly double the shear capacity of the specimens with horizontal joints. Building on these findings and test results, we also include recommendations for integrating elegant RE wall texture design with seismic-resistant undulating joint technology. Full article

Climate change and labour shortage are re-shaping farming methods. Agricultural tasks are often hard, tedious and repetitive for operators, and farms struggle to find specialized operators for such works. For this and other reasons (i.e., the increasing costs of agricultural labour) more and more farmers have decided to switch to autonomous (or semi-autonomous) field robots. In the past decade, an increasing number of robots has filled the market of agricultural machines all over the world. These machines can easily cover long and repetitive tasks, while operators can be employed in other jobs inside the farms. This paper reviews the current state-of-the-art of autonomous robots for agricultural operations, dividing them into categories based on main tasks, to analyze their main characteristics and their fields of applications. Seven main tasks were identified: multi-purpose, harvesting, mechanical weeding, pest control and chemical weeding, scouting and monitoring, transplanting and tilling-sowing. Field robots were divided into these categories, and different characteristics were analyzed, such as engine type, traction system, application field, safety sensors, navigation system, country of provenience and presence on the market. The aim of this review is to provide a global view on agricultural platforms developed in the past decade, analyzing their characteristics and providing future perspectives for next robotic platforms. The analysis conducted on 59 field robots, those already available on the market and not, revealed that one fifth of the platforms comes from Asia, and 63% of all of them are powered by electricity (rechargeable batteries, not solar powered) and that numerous platforms base their navigation system on RTK-GPS signal, 28 out of 59, and safety on LiDAR sensor (12 out of 59). This review considered machines of different size, highlighting different possible choices for field operations and tasks. It is difficult to predict market trends as several possibilities exist, like fleets of small robots or bigger size platforms. Future research and policies should focus on improving navigation and safety systems, reducing emissions and improving level of autonomy of robotic platforms. Full article

This study characterized the aerosol changes during the April 2023 Hongseong wildfire in Chungcheongnam-do, Korea, using physical, optical, and chemical data from the Anmyeon-do Global Atmosphere Watch station. The observation period was divided into three distinct phases: immediately after the wildfire (Period I), during precipitation (Period II), and the re-entry of wildfire smoke after precipitation (Period III). During Periods I and III, the PM₁₀ mass concentrations were 75.7 ± 31.2 and 98.2 ± 55.6 µg/m³, respectively, which were approximately 2.4 and 3.1 times higher than the 2023 annual average (31.8 µg/m³) at the Anmyeon-do site. Aerosol scattering coefficients increased by factors of 4.0 and 6.9, and absorption coefficients by 5.5 and 4.2, respectively. Source apportionment using real-time data from a Monitor for Aerosols and Gases in ambient Air (MARGA) instrument combined with PCA demonstrated that aerosol emissions during Periods I and III were predominantly influenced by biomass burning sources. Analysis of PM₁₀ and PM_2.5 filter samples showed biomass burning markers, such as K⁺ and C₂O₄²⁻, increased by 5.5–31.4 times compared with those in Period II. Elevated levels of combustion-related elements, including S, K, V, and Pb, further confirmed the influence of wildfire smoke on air quality during the affected periods. Full article

With the gradual shift towards the use of electric vehicles (EV), electricity demand is expected to increase especially in energy communities. Therefore, it is important to investigate how energy is generated as the provenance of electricity supply is directly linked to climate change. There are only a few studies that investigated the internet of energy and energy provenance, but this area of research is important to prevent the rebound effect of CO₂ emission due to the lack of a transparent approach that verifies the source of electricity consumed for charging EVs. The energy system is a complex network, which results in difficulty verifying the source of electricity as related to the generation of energy. Identifying the provenance of electricity is challenging since electricity is a non-physical element. Moreover, the volatility of a Renewable Energy Source (RES), such as solar and wind power farms, in relation to the complex electricity distribution system makes tracking and tracing challenging. Disruptive technologies, such as Distributed Ledger Technologies (DLT), have been previously adopted to trace the end-to-end stages of products. Likewise, artificial intelligence (AI) can be adopted for the optimization, control, dispatching, and management of energy systems. Therefore, this study develops a decentralized intelligent framework enabled by AI-based DLT and smart contracts deployed to accelerate the development of the internet of energy towards energy provenance in energy communities. The framework supports the tracing and tracking of RES type and source consumed for charging EVs. Findings from this study will help to accelerate the production, trading, distribution, sharing, and consumption of RES in energy communities. Full article