Search Results (14,254)

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

The mechanical properties and real-time damage evolution of sustainable concrete (SC) containing 100% recycled concrete aggregate (RCA) under the combined action of hybrid steel and polyolefin fibers were studied. Inspired by solving the massive effects on the environment from construction waste, as well as to improve the lower mechanical performance of lower-grade RCA, the effect of combining high-stiffness hooked-end steel fibers and flexible macro-polyolefin fibers within RCA was investigated. Six different mix designs were considered: plain, single-fiber (100% steel and 100% polyolefin) and three hybrid composites with varying fractions of the steel/polyolefin fibers (25/75, 50/50, and 75/25). Compressive, tensile and flexural strengths were determined by mechanical testing. During compressive testing, the damage evolution was monitored using low-cost acoustic emission (AE) as a non-destructive technique. Cumulative hits analysis, amplitude distributions, and the statistical b-value parameter were used for damage characterization. The results show that steel fiber significantly increased compressive strength (an increase of up to 13.8%), and the 50/50 hybrid mix showed a high synergistic effect, yielding the highest tensile (4.86 MPa) and flexural (25.54 MPa) strengths. AE analysis identified different damage fingerprints: Based on amplitude analysis, steel-fiber composites exhibited high-amplitude events (which may be attributable to fiber pull-out); polyolefin-fiber composites generated medium-amplitude events (may have resulted from distributed microcracking); and hybrid mixes displayed a mixed amplitude distribution. The b-value analysis provided insight into progressive damage and revealed that the hybrid fibers induce stable, diffuse damage that prevents the brittle failure of plain recycled aggregate concrete (RAC). The results show that hybrid fiber reinforcement can be a reliable approach to enhance the mechanical performance and crack resistance of RAC. Furthermore, low-cost acoustic emission (AE) serves as an effective non-destructive method for monitoring damage progression within the material. Full article

Simultaneous multi-ion detection is important for interpreting leaching, corrosion, hydration, and solidification processes in cement-based materials, because these processes are controlled by coupled ion migration, binding, and precipitation–dissolution reactions. Conventional methods such as pore-solution extraction, ion chromatography, inductively coupled plasma optical emission spectroscopy, and single-ion potentiometric measurements provide useful chemical information, but they generally rely on discrete sampling or isolated ion channels and therefore have limited ability to capture time-aligned multi-ion evolution. In this study, an IoT-based in situ multi-ion detection system was developed by integrating ion-selective electrodes for Cl⁻, Ca²⁺, F⁻, and H⁺ with an ADS1115 analog-to-digital converter, an ESP32 microcontroller, and a voltage amplification module. The system achieved minimum resolvable concentrations of 10⁻⁵ M for Cl⁻ and F⁻ and 10⁻⁴ M for Ca²⁺, while maintaining pH measurement over the range of 2–12. Ten consecutive measurements at 0.01 M showed relative standard deviations below 0.12%, indicating good short-term repeatability under laboratory calibration conditions. Interference and temperature tests showed that Br⁻ and NO₃⁻ affected the chloride channel at high concentrations, Ca²⁺ reduced free F⁻ activity through Ca–F precipitation equilibrium, and the temperature drift of Cl⁻ and F⁻ electrodes changed direction with concentration, whereas the Ca²⁺ response decreased monotonically with increasing temperature. When applied to phosphogypsum–cement hardened pastes, the system captured rapid Ca²⁺ release, low-level F⁻ fluctuation controlled by Ca–F interaction, non-monotonic Cl⁻ release, and alkaline pH evolution on the same time axis. Compared with existing single-ion or offline methods, the proposed system provides synchronized in situ evidence for interpreting coupled ion leaching in cement-based solid-waste systems.: Full article

The Chishui River Basin, a core production area for Chinese sauce-aroma Baijiu (exemplified by Moutai), supports sorghum cultivation critical to the liquor’s distinctive quality. The soil environment quality within this region, therefore, directly impacts the safety and quality of both raw material and the final distilled spirit. To underpin the safe production and sustainable development of this iconic beverage, it is essential to assess soil heavy metal contamination in the soils and quantify the contributions from various sources. In this study, 172 surface soil samples were collected from typical sorghum planting bases in the Renhuai area. Concentrations of eight heavy metals (loids) (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The contamination status was evaluated using the geostatistical inverse distance weighting interpolation, the Nemerow pollution index (P_N), and the potential ecological risk index (RI). Source identification and quantification were performed using the positive matrix factorization receptor model (PMF). Results revealed significant enrichment of Cd and Hg in the soil, with mean concentrations 2.07 times and 2.54 times the soil background values for Guizhou Province, respectively. Pollution index results (P_i, P_N) indicated that soil Cd contamination is relatively severe, whereas contamination from other elements is minimal. Overall, approximately 86.5% of the study area was classified as clean or only slightly polluted. Cd poses a moderate ecological risk and was the primary contributor to the total ecological hazard. Other elements exhibited lower risk, resulting in a slight overall potential ecological risk. The soil environmental quality in certified organic sorghum bases was generally favorable. PMF analysis identified three principal sources: historic industrial emissions and traffic-related sources (contributing 46%), weathering of carbonate rocks combined with agricultural activities (37%), and natural background coupled with organic fertilizer application (17%). In conclusion, while the overall soil heavy metal pollution level in the sorghum planting areas is low, the notable enrichment and higher ecological risk of Cd necessitate enhanced dynamic monitoring and targeted risk control measures to ensure long-term soil health and product safety. Full article

Rural electrification in Sub-Saharan Africa faces a trilemma: cutting carbon emissions, making it economically viable, and achieving fair access to energy for all. This paper develops a multi-objective framework that optimises carbon revenue, net present value (NPV), total energy supply, cooking fuel (firewood and LPG), health costs, and benefit to society. The model uses continuous decision variables: daily energy allocation among four sources (solar, generator, firewood, LPG) to three population groups (men, women, children). The case study is a rural community of 7000 people in Nigeria (Tier 1 energy consumers). Six policy scenarios are considered: baseline, high carbon price, low carbon price, microfinance, government subsidy and community cooperative. This study compared algorithms and identified a hybrid Non-dominated Sorting Genetic Algorithm and Particle Swarm Optimisation II as the most suitable algorithm for solving the formulated optimisation problem. It was found that NPV and unit cost of energy would increase to $175,500 and 26.4 ¢/kWh, respectively, by increasing the price of carbon from $8/ton to $12/ton. Firewood generates health savings and carbon revenue in the range of $4100–$12,270/year. Prices below $8/ton do not induce optimal reconfigurations in the system. The best energy supply (2825 kWh/day) and the lowest unsatisfied demand occur in the government subsidy scenario with the greatest disparity index, displaying an equity-efficiency trade-off. The framework shows that sustainable access to energy can be unlocked using strategic integration of carbon finance, valuation of health benefits and equity constraints. Full article

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

A novel sodium-oxide-fluxed slag is applied in the aluminothermic reduction of manganese ore. The slag’s high Al₂O₃ solubility facilitates the recycling of Al₂O₃ through hydrometallurgical processes, where NaAlO₂ serves as a water-leachable compound. Aluminothermic reduction is gaining renewed interest as an alternative processing route for the circular economy. In addition, CO₂ emissions in aluminium production via the electrochemical Hall–Héroult process can be reduced if the process electricity is sourced from non-fossil fuels. The unique Na₂O-fluxed MnO₂ ore formulation includes a small quantity of carbon reductant to ensure rapid pre-reduction to MnO. This approach negates the need for a pre-roasting step. Feed mixture variations with different collector metal additions (Si, Cr, Cu) were made to improve alloy–slag separation efficiency. The collector metals may influence the chemistry of the slag. This work compares the phase chemistry of slags formed during aluminothermic reduction to equilibrium phase chemistries calculated for the Na₂O-SiO₂-Al₂O₃-MnO-CaO system. The slag phase morphology consists of distinct alumina-rich strands (1.5% to 2.1%) embedded within a Na₂O-SiO₂-Al₂O₃-MnO-CaO glass matrix. The alumina-rich strands appear molten, indicating that the processing temperatures were higher than their liquidus temperatures (1537 °C to 1655 °C), as high as 1921 °C and 2053 °C. These findings contribute to sustainable practices in the circular economy through the production of low-carbon ferro-manganese complex alloys. Full article

Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained by low signal-to-noise ratios (SNRs) and limited AE dataset sizes. To address these challenges, this study proposes an attention-based deep learning method for AE arrival picking. The proposed method introduces an attention mechanism into the PhaseNet framework to suppress noise feature transmission in the skip connections. In addition, a kernel density estimation (KDE)-based label smoothing strategy was adopted to alleviate label imbalance and account for arrival-time uncertainty. The results demonstrate that the proposed method reduced the mean absolute error (MAE) by 10.58%, 92.92%, and 98.25% compared with PhaseNet, STA/LTA, and AR-AIC, respectively. The proposed method exhibited superior picking accuracy, robustness, and computational efficiency relative to the other methods, providing a reliable foundation for AE event localization and high-precision AE monitoring in hydraulic fracturing experiments. Full article

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article

This study investigates the side-impact crashworthiness of a low-floor electric bus with traction batteries integrated into the inter-window pillars of the body structure. A finite-element model of the bus body was developed in Ansys and used to evaluate six impact scenarios involving conventional diesel and battery-integrated configurations. The analysis included evaluation of von Mises stresses, structural safety margins, deformation fields, strain energy, and transient nodal velocity response. The battery-integrated configuration demonstrated improvements in key crashworthiness indicators across the investigated impact scenarios, with both the average maximum deformation and the averaged equivalent stress reduced by approximately one quarter compared with the conventional configuration. The stress state of the inter-window pillars remained below the local structural failure levels observed in the conventional configuration, with the maximum pillar stress criterion reduced by more than half. Simultaneously, lower transient nodal velocities indicated reduced transmission of impact momentum toward the occupant compartment and more efficient redistribution of impact energy through the body structure. The results demonstrate the feasibility of using battery-integrated inter-window pillars as multifunctional structural members that simultaneously serve as energy storage and enhance the side-impact crashworthiness of low-floor electric buses. Full article

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article

In cold arid regions, the relationships between dissolved organic matter (DOM) characteristics and heavy metal distributions across ice, water, and sediment interfaces remain insufficiently resolved. This study characterized DOM spectral features and examined their associations with measured metal distributions in a typical frozen eutrophic lake using excitation–emission matrices coupled with parallel factor analysis (EEMs-PARAFAC), ultraviolet-visible absorption spectroscopy (UV-Vis), and Fourier-transform infrared spectroscopy (FTIR). Protein-like substances dominated ice DOM, whereas water and sediment-derived DOM contained more humified fluorescent components. Fluorescence indices confirmed a primarily biological origin across all media, with ice showing the highest autochthonous microbial contribution (BIX = 1.23) but the lowest humification (HIX = 0.26), suggesting a greater contribution of recently produced protein-like fluorescent DOM in the ice samples. Water DOM showed the highest average HIX (1.88), followed by sediment-derived DOM (0.61) and ice DOM (0.26). The measured hydrochemical conditions, including weak alkalinity, elevated total dissolved solids (TDS), and locally low dissolved oxygen, provide environmental context for differences in metal distributions. Exploratory Spearman analysis at 17 matched water stations identified the strongest DOM–metal associations for HIX-As (rho = 0.474, p = 0.054) and FI-Zn (rho = 0.471, p = 0.056), indicating that DOM optical properties provide testable indicators of metal-distribution patterns but should be combined with direct binding and speciation measurements for mechanistic confirmation. Because ice was collected in January 2021, whereas water and sediment were collected in October 2020, cross-medium differences are interpreted as between-campaign associations rather than synchronous partitioning. These findings provide a basis for targeted winter monitoring and future binding, speciation, and freeze-concentration experiments in shallow eutrophic lakes. Full article

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative asset management depend on auditable digital representations of turbine behaviour. This study develops a Digital Shadow-based power-curve modelling framework on fourteen years of Supervisory Control and Data Acquisition records from an operational Vestas V52 onshore turbine (850 kW, Dundalk Institute of Technology, Ireland; 457,429 ten-minute records spanning 2006–2020) and benchmarks seven methods under identical preprocessing on a strict chronological hold-out (training 2006–2017; testing 2018–2020; n = 52,388). A parallel random 75/25 split is reported only as a within-distribution diagnostic; it quantifies an optimistic R² inflation of 0.003–0.027 depending on architecture. The Artificial Neural Network attains the best chronological performance (R² = 0.9924, BCa 95% confidence interval 0.9910–0.9931, RMSE = 19.79 kW); only the ANN and a one-dimensional Convolutional Neural Network with twenty-four-step wind-speed lags (R² = 0.9921) deliver clear positive skill against the IEC-style manufacturer power curve. Split-conformal calibration of a Quantile Regression Forest raises empirical 90% prediction-interval coverage from 0.534 to 0.904 at a width inflation from 30 to 51 kW. The framework qualifies as a Digital Shadow and is positioned, through a Horizon Europe Technology Readiness Level audit and an explicit mapping to ISO 50001:2018 Plan–Do–Check–Act energy management and Renewable Energy Community governance under Directive (EU) 2018/2001, as an auditable monitoring layer for cooperative onshore wind operations. The empirical evidence base is a single turbine; multi-turbine, multi-site replication is the natural follow-on validation. Full article

The construction sector contributes substantially to global greenhouse gas emissions, making material substitutions a key strategy for advancing sustainability transitions. Mass timber has emerged as a low-carbon alternative to mineral-based construction materials, offering biogenic carbon storage and compatibility with prefabricated and industrialized building systems. This study aims to systematically synthesize the economic, cost, and market evidence on mass timber construction by reviewing 143 peer-reviewed publications, with the objective of clarifying what is empirically known and where uncertainties remain. The reviewed literature reveals three core findings. First, economic outcomes are mixed: while several studies report regional value creation, supply-chain upgrading, and alignment with circular-economy principles, others highlight persistent constraints such as limited manufacturing capacity and uneven policy support. Second, construction cost findings vary substantially, ranging from cost parity or modest savings relative to conventional systems to premiums of approximately 10–15%, shaped by regional pricing, labor availability, transportation distance, regulatory conditions, and supply-chain maturity. Third, market-oriented studies consistently identify slow diffusion, limited practitioner experience, and risk-averse investment environments as key barriers to adoption. Overall, the review shows that economic performance is not yet consistently established and underscores the need for more standardized, context-sensitive, and methodologically consistent evaluation frameworks to support informed decision-making and the sustainable scaling of mass timber construction. Full article