Search Results (9,370)

This study presents an experimental 3E (energy–exergy–environmental) assessment of a PLC-controlled solar air heater (SAH) equipped with adjustable internal baffles. Unlike conventional passive systems, the proposed design enables active airflow regulation to maintain stable outlet temperatures of 54 °C and 60 °C, achieving rapid stabilization within 3–10 s under outdoor conditions. Experimental results show that increasing the baffle inclination significantly enhances convective heat transfer and thermal efficiency, while the friction factor remains primarily governed by the Reynolds number and exhibits minimal sensitivity to baffle angle. Exergy efficiency values remain relatively low (1.24–2.69%), and the sustainability index stays close to unity, reflecting the inherent thermodynamic limitations of low-temperature solar air heaters rather than deficiencies in system design. A regression-based airflow velocity model is developed to support fan-speed optimization and to clarify the trade-off between thermal enhancement and auxiliary power demand. Long-term projections based on regional solar data indicate that the proposed SAH can deliver approximately 20–22 MWh of useful heat and mitigate nearly 9 tons of CO₂ emissions over a 20-year operational lifetime. Overall, the results demonstrate that PLC-assisted dynamic baffle control provides a flexible and effective approach for improving the performance and operational stability of solar air heaters for low-temperature drying applications. Full article

Direct injection in hydrogen engines enables flexible combustion control, improves engine efficiency, and reduces the risk of abnormal combustion. However, implementing this injection strategy is challenging due to the need to provide a relatively high volumetric fuel flow rate, achieve a specified degree of mixture stratification, and account for the functional and technological limitations of the injection system. These challenges highlight the relevance and objectives of the present study. The mathematical model of a turbocharged engine cycle has been refined to account for the influence of injection parameters on combustion kinetics. On the basis of mathematical modeling, the injection pressure and injector area were determined to ensure the specified injection conditions. For the late injection strategy, a method was proposed to select the start of injection based on a specified value of the “relative ignition timing” criterion. Engine operation was simulated across the full range of operating modes for both early and late injection strategies. The results show that the late injection strategy increases the maximum indicated thermal efficiency by approximately 2%, reduces peak in-cylinder pressure by about 1 MPa, lowers maximum nitrogen oxide emissions by a factor of 1.4, and ensures knock-free operation across all modes compared to early injection. Full article

Accurate estimation of ship emissions is essential for the effective enforcement of emission control policies in inland waterways. However, existing “bottom-up” models face significant challenges owing to severe data scarcity for inland ships, particularly regarding ship static parameters. This study proposes a novel data fusion and machine learning framework to address this issue. The methodology integrates real-time SO₂ and CO₂ pollutant concentrations on the Nanjing Dashengguan Yangtze River Bridge, Automatic Identification System (AIS) data, and meteorological information. To address the scarcity of design data for inland ships, web scraping was used to extract basic parameters, which were then used to train five machine learning models. Among them, the XGBoost model demonstrated superior performance in predicting the main engine rated power. A refined activity-based emission model combines these predicted parameters, ship operational profiles, and specific emission factors to calculate real-time emission source strengths. Furthermore, the model was validated against field measurements by comparing the calculated and measured emission source strengths from ships, demonstrating high predictive accuracy with R² values of 0.980 for SO₂ and 0.977 for CO₂, and MAPE below 13%. This framework provides a reliable and scalable approach for real-time emission monitoring and supports regulatory enforcement in inland waterways. Full article

With the intensification of global climate change, frequent extreme temperature events pose increasing challenges to agricultural production. The aim of this study is to characterize the spatiotemporal evolution of county-level agricultural total factor productivity (ATFP) under extreme temperature events, reveal key driving factors and crop-specific heterogeneity, and predict potential high-risk areas, which is crucial for providing scientific basis for risk management and adaptive policy formulation in globally climate-sensitive agricultural regions. This paper selects Jiangsu Province as a typical case study, uses the DEA-Malmquist model to measure agricultural total factor productivity (ATFP), systematically analyzes the spatiotemporal dynamic evolution characteristics of ATFP at the county scale, and selects the random forest and XGBoost ensemble models with optimal accuracy through model comparison for prediction, assessing the evolution trends of ATFP under different climate scenarios. The results showed that: (1) From 1993 to 2022, the average ATFP increased from 0.7460 to 1.1063 in the province, though development showed uneven distribution across counties, exhibiting a “high in the south, low in the north” gradient pattern. (2) Mechanization, agricultural film and land inputs are the core elements driving the overall ATFP increase but there are obvious crop differences: mechanization has a more prominent role in promoting the productivity of wheat and maize, while labor inputs have a greater impact on the ATFP of rice. (3) The negative impacts of extreme climate events on agricultural production will be significantly amplified under high-emission scenarios, while moderate climate change may have a promotional effect on certain crops in some regions. Full article

Nutrient additions including nitrogen (N) and phosphorus (P) are widely considered as an important strategy for enhancing grassland productivity. However, the effects of these nutrients additions on soil nitrous oxide (N₂O) emissions and the underlying mechanisms remain debated. We conducted a two-year field experiment in an alpine grassland on Kunlun Mountain in northwestern China to assess the effects of N and P additions on N₂O emissions, in relation with nitrifying enzyme activity (NEA), denitrifying enzyme activity (DEA), and key functional genes abundance responsible for nitrification (amoA and Nitrobacter-like nxrA) and denitrification (narG, nirS, nirK and nosZ). Compared to the Control without nutrient addition (CK), N addition alone substantially increased cumulative N₂O emission (ƩN₂O) by 2.0 times. In contrast, P addition or combined N and P (N+P) addition did not significantly affect ƩN₂O, though both treatments significantly increased plant aboveground biomass. Such results indicate that P addition may mitigate N-induced N₂O emission, likely by reducing soil N availability through enhanced plant and microbial N uptake. Compared to CK, N or N+P addition significantly elevated NEA but did not affect DEA. Structural equation modeling (SEM) indicated that NEA was directly influenced by the gene abundances of ammonia-oxidizing bacteria (AOB) and Nitrobacter-like nxrA but not by ammonia-oxidizing archaea (AOA). However, SEM also revealed that soil environmental variables including soil temperature, pH, and water-filled pore space (WFPS) had a stronger direct influence on N₂O emissions than the abundances of nitrifiers. These results demonstrate that soil environmental conditions play a more significant role than functional gene abundances in regulating N₂O emissions following N and P additions in semi-arid alpine grasslands. This study highlights that the N+P application can potentially decrease N₂O emissions than N addition alone, while increasing productivity in the alpine grassland ecosystems. Full article

Immunogenic cell death (ICD) is a subtype of regulated cell death characterized by the spatiotemporally coordinated emission of damage-associated molecular patterns (DAMPs), such as calreticulin (CALR), ATP, and high-mobility group box-1 (HMGB1), which collectively prime tumor-specific T-cell responses. Autophagy, a lysosome-dependent catabolic process, is increasingly recognized as a key modifier of antitumor immunity and the tumor microenvironment (TME). In preclinical models, autophagy can not only promote ICD by sustaining endoplasmic reticulum (ER) stress, eukaryotic translation initiation factor-2α (eIF2α) phosphorylation, and secretory pathways, but it can also limit ICD by degrading DAMPs, antigenic cargo, and major histocompatibility complex (MHC) molecules. The net outcome is highly context-dependent and determined by the tumor type, the nature and intensity of the stress, and the level at which autophagy is modulated. Herein, we summarize how autophagy affects the three canonical ICD-associated DAMPs, highlight solid-tumor models in which autophagy supports ICD, and contrast them with systems wherein autophagy inhibition is required for immunogenicity. We then focus on hematological malignancies, especially multiple myeloma, where recent reports implicate the autophagy-related protein GABARAP in bortezomib-induced ICD. Finally, we discuss the translational implications, including rational combinations of autophagy modulators with ICD-inducing chemotherapies, targeted drugs, and cellular immunotherapies, and outline the remaining challenges for safely harnessing the autophagy–ICD axis in the clinical setting. Full article

This study examines the relationship between energy consumption and energy-related CO₂ emissions for a sample of 79 reporting entities, grouped into seven regions, over the period 2013–2023. The methodology uses three empirical tools: (i) Tapio elasticity to classify types of decoupling; (ii) Kaya–LMDI decomposition to identify factors that determine emissions; and (iii) a log-difference panel model to separate year- and country-specific effects. The results indicate a reduction in carbon intensity in all regions, more pronounced in Europe and North America. According to the Tapio classification, Europe is in recessive decoupling, the Middle East is on the verge of expansive decoupling, North and South America are in strong expansive decoupling, and Asia Pacific, Africa, and CIS show only weak signals of expansive decoupling. The LMDI results show that, in regions with strong decoupling, the decrease in carbon intensity contributes to reducing emissions. In those with weak decoupling, the effects are partially canceled out by population growth and energy demand. Finally, the fixed-effects panel model does not identify any structural decoupling at the regional level. Overall, this study contributes to the literature by separating long-term structural effects from annual fluctuations. On this basis, we provide clear guidelines for designing regional energy policies. Full article

Accurately predicting pollutant emission factors (EFs) from woody biomass fuels remains challenging because small-scale combustion tests are fuel-specific, time-consuming, and highly sensitive to operating conditions. This study combines controlled laboratory combustion experiments with a physics-informed artificial neural network (ANN–PINN) to estimate the emission factors of particulate matter (${\mathrm{EF}}_{PM}$), carbon monoxide (${\mathrm{EF}}_{CO}$), and nitrogen oxides (${\mathrm{EF}}_{NOx}$) using only laboratory-scale fuel characterization. Three pelletized woody biomass, Pinus radiata, Acacia dealbata, and Nothofagus obliqua, were analyzed through ultimate and proximate composition, lignin content, and TGA-derived parameters and tested in a residential pellet stove under identical control setpoints, resulting in a narrow and well-defined operating regime. A medium-depth ANN–PINN was constructed by integrating mechanistic constraints, monotonicity based on known emission trends and a weak carbon balance penalty, into a feed-forward neural network trained and evaluated using Leave-One-Out Cross-Validation. The model accurately reproduced the experimental behavior of ${\mathrm{EF}}_{CO}$ and captured structured variability in ${\mathrm{EF}}_{PM}$, while the limited nitrogen variability of the fuels restricted generalization for ${\mathrm{EF}}_{NOx}$. Sensitivities derived via automatic differentiation revealed physically coherent relationships, demonstrating that PM emissions depend jointly on fuel chemistry and aero-thermal conditions, CO emissions are dominated by mixing and temperature, and ${\mathrm{NO}}_x$ formation is primarily governed by fuel-bound nitrogen. When applied to external biomass fuels characterized independently in the literature, the ANN–PINN produced physically plausible predictions, highlighting its potential as a rapid, low-cost screening tool for assessing new biomass feedstocks and supporting cleaner residential heating technologies. The integrated experimental–PINN framework provides a physically consistent and data-efficient alternative to classical empirical correlations and purely data-driven ANN models. Full article

This work investigated the impact of vehicle-to-grid (V2G) cycling on the service life of lithium-ion cells, using real-world V2G data from commercial electric vehicle (EV) battery chargers. Commercially available cylindrical lithium-ion cells were subjected to long-term storage and V2G cycling under varying state of charge (SOC), depth of discharge (DOD), and temperature conditions. The ageing results demonstrate that elevated temperature (40 °C) is the dominant factor accelerating degradation, particularly at a high storage SOC (>80% SOC) and increased cycle depths (30–80% SOC, 30–95% SOC). A comparison between V2G cycling and calendar ageing over a similar storage period revealed that shallow V2G cycling (30–50% SOC) leads to comparable capacity fade to storage at a high SOC (≥80% SOC). The comparative analysis indicated that 62% of a full equivalent cycle (FEC) of V2G cycling can be achieved daily, without compromising the cell’s lifetime, demonstrating the viability of V2G adoption during EV idle/charging periods, which can offer potential operational benefits in terms of cost reduction and emissions savings. Furthermore, this work introduced the concept of a V2X capability metric as a novel cell-level specification, along with a corresponding experimental evaluation method. Full article

Blasting operations associated with in-pit ramp construction in open-pit mines generate gaseous emissions originating from both explosive detonation and diesel-powered drilling and loading equipment. The research object of this study is the ramp construction process in an operating open-pit quarry, and the objective is to comparatively evaluate gaseous emissions across alternative blasting scenarios to support emission-aware operational decision-making. Five realistic blasting scenarios are assessed using a combined methodology that integrates laboratory fume index data for ANFO, emulsion explosives, and dynamite with diesel-emission estimates derived from non-road mobile machinery inventory factors. Laboratory detonation tests provide standardized upper-bound emission potentials for CO_x and NO_x, while drilling and loading emissions are quantified using a fuel-based inventory approach. The results show that the dominant contribution to total mass emissions arises from diesel combustion during drilling and loading, consistent with studies on real-world non-road mobile machinery inventory factors. Detonation fumes, although chemically concentrated and relevant for short-term exposure risk, represent a smaller share of the mass-based emission budget. Among the explosive types, bulk emulsions consistently exhibit lower toxic-gas emission indices than ANFO, attributable to their more uniform microstructure and a moderated reaction temperature. Dynamite demonstrates the lowest fume potential but is operationally less scalable for large open-pit patterns due to manual loading. Uncertainty analysis indicates that both laboratory-derived fume indices and diesel emission factors introduce systematic variability: laboratory tests tend to overestimate detonation fumes, while inventory-based diesel estimates may underestimate real-world NO_x and particulate emissions. Notwithstanding these limitations, the scenario-based framework developed here provides a robust basis for comparative evaluation of blasting strategies during ramp construction. The findings support increased use of emulsion explosives and emphasize the importance of moisture management, field-integrated gas monitoring, and improved characterization of diesel-equipment duty cycles. Full article

The dynamic development of electromobility and tightening emissions regulations are making electric light commercial vehicles an increasingly important element of modern urban transport. The purpose of this article is to analyze and compare selected models of electric light commercial vehicles available on the market in terms of four key operational parameters: range, charging time, payload, and energy consumption. These parameters directly impact the efficiency of vehicle operation in real-world conditions, especially in last-mile transport. The study employed a multi-criteria decision method (MCDM), which evaluated 10 alternatives and objectively assigned criterion weights using the CRITIC method, which takes into account data variability and correlations between criteria. The article presents the interdependencies between these factors, emphasizing the need to find a compromise between maximum range and usable payload, as well as the impact of charging time on vehicle operational availability. The analysis aims to identify design and technological solutions that contribute most to improving the efficiency of electric light commercial vehicles in urban and suburban applications. Full article

Ecosystem restoration is increasingly recognised as part of the global solution for building a resilient, low-emissions economy, with its associated employment opportunities helping to provide political legitimacy for government investment. In Aotearoa New Zealand, however, little is known about the employment effects of government-funded ecosystem restoration initiatives. This study addresses that gap by analysing project-level data from 359 “Jobs for Nature” projects to examine how funding levels and contextual factors influence direct employment outcomes. Multiple regression analyses build on one-way ANOVA tests to quantify the contribution of funding and contextual factors to employment outcomes and to assess their differential impacts across various settings (regions, agencies, project types, and durations). The analysis reveals that while funding is the primary driver of employment—with each additional NZD 100,000 creating approximately 0.7 full-time equivalent (FTE) jobs—contextual factors call for a more dynamic, targeted policy approach to maximise marginal employment returns. Three key policy implications are accordingly drawn: (1) direct more funding to regions with higher socio-economic deprivation; (2) preferentially support projects of medium-term duration; and (3) evaluate and replicate the practices of high-performing funding agencies. Full article

Road dust resuspension is widely recognized as a major contributor to traffic-related particulate matter (PM) in urban environments. Nevertheless, reported emission factors exhibit substantial variability. These discrepancies stem not only from the intrinsic complexity of the resuspension process but also from limitations in measurement techniques, which often fail to adequately control or characterize the influencing parameters. As a result, the contribution of each parameter remains difficult to isolate, leading to inconsistencies across studies. This study presents an experimental protocol developed to quantify PM10 and PM2.5 emission factors associated with vehicle-induced road dust resuspension. Experiments were conducted on a dedicated test track seeded with alumina particles of controlled mass and size distribution to simulate road dust. A network of microsensors was strategically deployed at multiple upwind and downwind locations to continuously monitor particle concentration variations during vehicle passages. Emission factors were derived through time integration of the mass flow rate of resuspended dust measured by the sensor network. The estimated PM10 emission factor showed excellent agreement, within 2.5%, with predictions from a literature-based formulation, thereby validating the accuracy and external relevance of the proposed protocol. In contrast, comparisons with U.S. EPA formulas and other empirical equations revealed substantially larger discrepancies, particularly for PM2.5, highlighting the persistent limitations of current modeling approaches. Full article

In the context of advancing agricultural and rural modernization in China, digital agriculture has gained significant governmental attention. However, existing research has predominantly focused on examining the relationship from digital inclusive finance to digital agriculture, while in-depth investigations into their bidirectional coupled coordination, spatiotemporal evolution, and underlying obstacle factors remain limited. To address this research gap, this study aims to construct innovative evaluation index systems for both domains and to establish a coupling coordination degree model alongside an obstacle degree model. This methodological framework is designed to examine the bidirectional coupled coordination, reveal its spatiotemporal evolution patterns, and identify key obstacle factors across 30 Chinese provinces. Results indicate a consistent annual improvement in the coupling coordination level across provinces. Many regions have progressed from moderate or mild dysfunction to marginal or primary coordination, with coordination degrees ranging between 0.5 and 0.6 by 2022. Specifically, the eastern region recorded 0.586, the central region 0.562, and the western region 0.531. Regional disparities are identified as the primary source of variation. Key obstacles include insufficient support from digital finance to agriculture, the east–west development gap, low actual usage of digital financial services, volatility in agricultural production price indices, and high agricultural carbon emissions. Recommendations focus on bridging regional gaps, strengthening financial support, and addressing these impediments, which are crucial for promoting sustainable development. Full article

Accurately grasping the relationship between science and technology finance efficiency (STFE) and carbon emission efficiency (CEE), and further exploring their interaction and synergistic development within the network structure are of great significance for promoting regional coordinated development, economic growth, and environmental issues. This article uses the super-efficient SBM model to measure the STFE and CEE in 30 provinces of China from 2011 to 2020, and innovatively introduces the Multi-Layer Network (MN) method to explore the characteristics of their network structure, synergistic evolution, and influencing factors. The results show that (1) the evolution of the MN structure is the result of synergistic development, which mainly forms the network pattern of the Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Qinghai–Gansu region with “triple-core, multi-zone”. (2) The STFE network plays a leading role in the MN structure by influencing the CEE network structure. (3) The layers of MN are connected in a disassortative way, while the network similarity is gradually increasing. (4) The number of communities of the MN is decreasing, and the agglomeration of the community structure is gradually increasing. (5) The performance of the MN structure has better robustness than the single-layer network under different strategies and different node retention levels of destruction. (6) The economic development level, government support rate, and industrial structure upgrading are the core factors affecting the value of weighted degree and closeness centrality, while betweenness centrality is mainly affected by the urbanization level and foreign direct investment level. Full article