Search Results (376)

Currently, there is a lack of systematic and quantitative analytical tools for dust emission control in open-pit iron mines. To address this research gap, this study constructs a comprehensive evaluation index system by integrating the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation (FCE) method. The framework includes four first-level indicators, 12 s-level indicators and 30 third-level indicators. The structural design was informed by laws and regulations, the relevant literature and the principle of dust hierarchical control to ensure the theoretical and empirical basis for the selection of indicators. The evaluation process was based on on-site monitoring data and production ledgers from the open-pit iron mine of the Shuichang Mine, as well as the results of multiple rounds of consultation by the Delphi method group composed of 30 experts in related industries. The results show that the comprehensive score of the mine is 87.14 points, and the overall prevention and control is effective. But the performance of each dimension is unbalanced: fundamental data on production processes scored highest, while individual exposure and protection measures were relatively weak, indicating that the personnel protection link needs to be strengthened. Sensitivity analysis further verified the structural stability of the index system and identified the ventilation and dust removal system as a key driving factor. This framework can provide quantitative decision support for mine managers, enhancing the precision and overall effectiveness of dust control through the accurate identification of weaknesses and optimized resource allocation. Full article

The conventional treatment of high-concentration volatile organic compounds (VOCs) relies on energy-intensive dilution to avoid explosion risks. This study proposes an efficient catalytic combustion process treating VOCs directly within the explosive range while recovering reaction heat using Pt/γ-Al₂O₃-based catalysts promoted with La and Ce. Catalysts (0.05–0.5 wt% Pt) were synthesized via impregnation and characterized using FE-SEM, BET, and XRD. Catalytic combustion experiments at VOC concentrations up to 13,000 ppm showed combustion initiation below 200 °C, achieving 83–99% conversions at 300 °C with complete oxidation to CO₂. Although 5 vol.% moisture significantly inhibited low-temperature activity through competitive adsorption, La and Ce promoters (10 wt%) effectively overcame this limitation by increasing surface area (up to 194.93 m²/g) and oxygen mobility. The Ce-promoted catalyst demonstrated superior water tolerance, achieving complete conversion at 200–210 °C due to its high Oxygen Storage Capacity (OSC). Bench-scale validation using a 1 Nm³/h system confirmed industrial feasibility. Operating at 220 °C with 13,000 ppm toluene for 100 h, the catalyst maintained >99.98% conversion with negligible deactivation and THC emissions below 2 ppm. The double-jacket heat exchanger effectively managed reaction heat (limiting temperature rise to ~20 °C) and recovered it as steam. Compared to Regenerative Thermal Oxidation, this Regenerative Catalytic Oxidation approach reduced emissions and energy consumption. This work demonstrates a robust “combustion-with-recovery” strategy for high-concentration VOC treatment, offering a sustainable alternative with high efficiency, stability, and safe energy-integrated operation. Full article

Blast furnace tuyeres are the primary dust emission source in ironmaking facilities (accounting for over 30% of total pollutants). High-temperature dust plumes with intense thermal energy are prone to dispersion, while China’s steel industry ultra-low emission standards (particulate matter ≤ 10 mg/m³) impose strict requirements on capture efficiency. Existing technologies often neglect crosswind interference and lack coordinated design between air volume regulation and hood structure, leading to excessive fugitive emissions and non-compliance. This study established a localized numerical model for high-temperature dust capture at blast furnace tuyeres, investigating air volume’s impact on velocity fields and capture efficiency, revealing crosswind interference mechanisms, and proposing optimization strategies (adding hood baffles, adjusting dimensions, installing ejector fans). Results show crosswind significantly reduces efficiency—only 78% at 1.5 m/s crosswind and 400,000 m³/h flow rate. The optimal configuration (2.5 m side flaps plus1.4 m baffles) achieves 99% efficiency, maintaining high performance at lower flow rates: 350,000 m³/h (1.5 m/s crosswind) and 250,000 m³/h (0.9 m/s crosswind). This study provides technical support for blast furnace tuyere dust control and facilitates ultra-low emission compliance in the steel industry. This study supports blast furnace tuyere dust control and aids the steel industry in meeting ultra-low emission standards. Notably, the proposed optimization scheme boasts simple structural adjustments, low retrofitting costs, and good compatibility with existing production lines, enabling direct industrial promotion and notable environmental and economic gains. Full article

Currently, brake particle emissions from traffic are considered one of the dominant sources of particulate matter in the atmosphere. A recent question concerns the contribution to brake particles of Battery Electric Vehicles (BEVs). The present work assesses brake particle emissions by measurements of particle number (PN) and mass (PM) of three light-duty BEVs. One front disc brake of each vehicle has been enclosed in a customized casing with appropriate ventilation for forming the aerosol. All three BEVs have been measured on a two-axis chassis dynamometer. The BEV relying more on electric braking (some 68% of the braking energy was covered by electric braking) had the lowest brake PN emissions over the (emissions) WLTC at 6.4 × 10⁹ km⁻¹ per front brake. This was less than half with respect to the other BEV (where only 52% of the braking energy was electric). PM emissions of the two vehicles were similar at 0.93 mg/km for PM < 12 μm and 0.65 mg/km for PM < 2.5 μm, both for one front brake. However, one of the measured BEVs had extraordinarily high PN emissions, some 23 times higher than the lowest-emitting BEV. The difference in PM was not as high, but was some four times higher. Full article

In order to solve the problems of high volatility and insufficient absorption effect when using chemical by-product washing oil to treat benzene-containing waste gas, this study innovatively proposed a composite solvent screening method based on the solvation free energy (ΔGsol), and reasonably predicted the absorption performance of 26 solvents for benzene. Through theoretical calculation and experimental verification, tetraethylene glycol dimethyl ether (TGDE) was finally determined to be the optimal composite component of washing oil. The absorption efficiency of the composite solvent reached 96.2%, and the regeneration efficiency was stable after 12 cycles with a mass loss of only 2.4%. Quantum computing simulation revealed that the dispersion force is dominant between benzene and the solvent, and TGDE enhances the electrostatic interaction through weak hydrogen bonds. The synergistic effect of the two improves the absorption performance. This study provides theoretical and technical support for the development of efficient and renewable benzene waste gas recovery solvent systems. Full article

Air pollution, a critical environmental threat, has worsened alongside urbanization and industrialization, particularly in rapidly developing economies like India. Despite efforts to curb emissions, the concurrent rise in energy consumption, industrial activity, and digitalization complicates the fight against air pollution. This study examines the interplay between air pollution, economic growth, clean energy transition, digitalization, and urbanization in India from 1990Q1 to 2020Q4. Using advanced econometric techniques, including multivariate quantile-on-quantile regression (MQQR) and the quantile ADF and quantile KPSS tests, we investigate the complex, non-linear relationships across these factors. Our findings suggest that while economic growth exacerbates air pollution, the clean energy transition can mitigate its impact, especially when integrated with digitalization. However, the effects of digitalization are nuanced, potentially increasing pollution unless paired with green energy policies. The study demonstrates that the combined strategies of promoting clean energy and digitalization can provide a sustainable pathway for reducing air pollution in India. This work offers novel insights into the role of digital technologies in enhancing environmental sustainability and highlights the need for policy interventions that balance economic growth with climate resilience. The results present a roadmap for India’s sustainable development, emphasizing the integration of clean energy, digital innovation, and urban planning. Full article

Black carbon (BC), as a potent light-absorbing aerosol, is mainly removed from the atmosphere through wet deposition. The efficiency of this process depends on the capacity of BC particles to serve as cloud condensation nuclei (CCN) or ice nuclei (IN). Newly emitted BC particles are typically small in size and highly hydrophobic, which limits their activation potential. However, atmospheric aging processes involving interactions with sulfates, nitrates, or organic matter enhance their hydrophilicity and nucleation capacity. Particle size serves as the critical link between aging and removal processes. Larger or coated BC particles are more readily activated and removed, while smaller particles require higher supersaturation levels. Both observations and models indicate that uncertainties in BC particle size distribution and aging processes lead to significant discrepancies in lifetime and transport estimates. This paper reviews recent research on the size dependence of wet removal of BC, evaluates current observational and modeling results, and proposes key research priorities to more accurately constrain its role in the climate system. Full article

Fine particulate matter (PM2.5) is a critical environmental and health concern in northern Thailand, where haze episodes are strongly influenced by biomass burning, meteorological variability, and complex topography. This study aims to (1) analyze and select input variables for PM2.5 prediction by integrating WRF-Chem outputs, satellite data, and ground observations, and (2) evaluate the predictive performance of four machine learning (ML) algorithms—Random Forest (RF), XGBoost, CNN3D, and ConvLSTM—during the 2024 haze season (January–May). The dataset included hourly PM2.5 observations from 54 stations, the WRF-Chem-simulated PM2.5 and meteorological variables, satellite-based fire data, and geographical data. To improve consistency with ground-based data, WRF-Chem PM2.5 values were bias-corrected for the training and validation phases prior to ML learning. Among Linear Regression, RF, XGBoost, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) tested for bias correction, RF achieved the best performance (R = 0.78, RMSE = 29.28 µg/m³); the RF-corrected WRF-Chem PM2.5 was then used as an input to the forecasting stage. Variable selection was supported by correlation, VIF, feature importance, and SHAP analyses. The results indicate that RF provided the most reliable predictions, achieving a correlation of R = 0.867 and the lowest RMSE of 27.6 µg/m³ when using the SHAP+VIF-selected input set (seven variables: PM2.5_lag1, PM2.5_lag24, T2, RH2, Precip, Burned Area, NDVI). Notably, RF remained the top performer, predicting PM2.5 more accurately than the other algorithms during high-pollution conditions, specifically Air Quality Index (AQI) “Unhealthy for Sensitive Groups” (high) and “Unhealthy” (very high). Taken together, RF set the performance bar across both stages, with XGBoost ranked second, whereas CNN3D and ConvLSTM performed considerably worse. These findings emphasize the effectiveness of ensemble tree-based algorithms combined with bias-corrected WRF-Chem outputs and strategic variable selection in supporting accurate hourly PM2.5 predictions for air quality management in biomass burning regions. Full article

Faults of the selective catalytic reduction (SCR) significantly exacerbate nitrogen oxide (NOx) emissions from heavy-duty vehicles, thereby posing a severe hazard to atmospheric environmental quality. Currently, the paucity of systematic studies on NOx emission degradation induced by typical SCR faults has severely hindered the advancement of precise emission regulation for heavy-duty vehicles in China. To address this critical gap, this study investigates the impact of typical SCR faults on NOx emission deterioration from heavy-duty vehicles. Initially, leveraging the China heavy-duty commercial vehicle test cycle as the benchmark, heavy-duty vehicle emission tests were designed and conducted under typical SCR faults. Emission datasets were acquired for three typical SCR faults—namely nozzle circuit disconnected fault, upstream temperature sensor inaccuracy fault, and urea-water replacement fault—as well as under normal operating conditions. Building upon these data, three representative scenarios were established by integrating vehicle operating condition, fuel consumption levels, and vehicle specific power states, enabling systematic quantification of the extent of NOx emission deterioration caused by each SCR fault. The findings reveal that the NOx emissions deterioration caused by urea-water replacement fault is the most severe, followed by nozzle circuit disconnected fault, and the impact of upstream temperature sensor inaccuracy fault is the least. This research provides crucial support for identifying key targets in emission control and enhancing the precision of heavy-duty vehicle emission regulation. Relevant authorities should prioritize cracking down on intentional non-compliant practices such as urea water substitution to safeguard a healthy and sustainable atmospheric environment. Full article

The heading face is one of the zones most severely affected by dust pollution in underground coal mines, and dust control becomes even more challenging during roadway excavation with continuous miners. To improve dust mitigation in environments characterized by intense dust generation, high ventilation demand, and large cross-sectional areas, this study integrates numerical simulations, laboratory experiments, and field tests to investigate the physicochemical properties of dust, airflow distribution, dust migration behavior, and a comprehensive dust control strategy combining airflow regulation, foam suppression, and dust extraction fan systems. The results show that dust dispersion patterns differ markedly between the left-side advancement and right-side advancement of the roadway; however, the wind return side of the continuous miner consistently exhibits the highest dust concentrations. The most effective purification of dust-laden airflow is achieved when the dust extraction fan delivers an airflow rate of 500 m³/min and is positioned behind the continuous miner on the return side. After optimization of foam flow rate and coverage based on the cutting head structure of the continuous miner, the dust suppression efficiency reached 78%. With coordinated optimization and on-site implementation of wall-mounted ducted airflow control, foam suppression, and dust extraction fan systems, the total dust reduction rate at the heading face reached 95.2%. These measures substantially enhance dust control effectiveness, improving mine safety and protecting worker health. The resulting reduction in dust concentration also improves visibility for underground intelligent equipment and provides practical guidance for industrial application. Full article

Ammonia (NH₃) is a major anthropogenic pollutant originating from agricultural activity, particularly livestock operations. NH₃ emissions from livestock slurry storage pose risks to environmental quality and human health. Reducing NH₃ emissions aligns with several United Nations Sustainable Development Goals (SDGs), including SDG 3, SDG 12, SDG 14, and SDG 15. This study evaluates the performance of the commercially available SOP^® LAGOON additive under real-scale farm conditions for mitigating NH₃ emissions. Two adjacent slurry storage tanks of a dairy farm in Northern Italy were monitored from 27 May to 7 September: one treated with SOP^® LAGOON and one left untreated (serving as a control). In the first month, the treated tank showed a 77% reduction in NH₃ emissions. Emissions from the treated tank remained consistently lower than those from the control throughout the monitoring period, reaching an 87% reduction relative to the baseline levels by the end of the period. The results suggest that SOP^® LAGOON is an effective and scalable strategy for reducing NH₃ emissions from liquid manure storage, with practical implications for farmers and policy makers in regard to designing sustainable manure management practices. Full article

Motor vehicle emissions are a major air quality concern in Chinese cities. However, traditional population-based emission inventory methods fail to capture the spatial and temporal variations in emissions for effective policy design. This study proposes a high-resolution approach for traffic flow prediction and vehicle emission inventory estimation, using Jinan City, China, as a case study. We leverage multi-source geospatial data and employ a two-fold random forest model to predict hourly traffic flow at a road-segment level. Speed-aligned emission factors were then combined with these data to calculate hourly and road-level vehicle emission estimates. Compared to traditional methods, our approach offers substantial improvements: (1) improved spatiotemporal resolution; (2) enhanced accuracy of traffic flow prediction; and (3) support for more effective vehicle emission control strategies. Results show that heavy-duty vehicles, particularly freight trucks operating on inter-regional corridors through Jinan, contribute 78% more to NO_X emissions than local light-duty vehicles. These transient emissions are typically overlooked in static inventories but constitute a significant source of urban pollution. This study offers valuable insights for combining geospatial data and machine learning to improve the accuracy and resolution of vehicle emission inventories, supporting urban air quality policy and planning. Full article

The new type of gold cone filter cartridge has dual functions of increasing filter area and enhancing pulsed-jet cleaning, but the issue of patchy cleaning remains to be addressed. This study further enhances the pulsed-jet cleaning performance of cone filter cartridges by employing a porous diffusion nozzle. The temporal and spatial distributions of pulse jet velocity and pressure under the condition of porous nozzles were investigated through numerical modeling. The variation law of pressure on the side wall of the filter cartridge was analyzed. The influence of jet distance of porous nozzles on pulsed-jet pressure and pulsed-jet uniformity was experimentally investigated. Dust filtration and cleaning experiments were conducted, and the filtration pressure drop, dust emission concentration, and comprehensive filtration performance were compared. It was found that the airflow jetted by the porous diffusion nozzle is more divergent than that of the common round nozzle. This results in a larger entrainment of the jet stream, a milder collision of the jet stream with the cartridge cone, and a slower overall velocity reduction. More airflow is generated into the filter cartridge and accumulated; the accumulated static pressure covers a larger range of the upper section of the filter cartridge, with a longer duration of static pressure. In the online dust filtration and cleaning experiment, compared with the condition of the common round nozzle, the porous nozzle can reduce the residual pressure drop by 27.0%, increase the filtration cleaning interval by a factor of 3.80, reduce the average dust emission concentration by 45.2%, and increase the comprehensive performance index Q_F by 5.2%. The research conclusions can provide references for the design and optimization of industrial filter cartridge dust collectors. Full article

The specification of inlet boundary conditions plays a critical role in computational fluid dynamics (CFD) simulations of pollutant dispersion from nuclear facilities, particularly in regions characterized by uneven terrain. Previous studies have often simplified such terrain by approximating it as a flat surface to reduce computational complexity. However, this approach fails to adequately capture the realistic atmospheric boundary layer dynamics inherent to uneven topographies. To address this limitation, this study conducted atmospheric dispersion tracer experiments specifically designed for nuclear facilities situated on non-uniform terrain. A novel inlet boundary condition, termed the Atmospheric Boundary Layer of Uneven Terrain (ABLUT), was developed by modifying the existing atmBoundaryLayer model in OpenFOAM. Numerical simulations were performed using both the default and the proposed ABLUT boundary conditions, incorporating different turbulence models and examining the influence of turbulent Schmidt numbers across a range of 0.3 to 1.3. The results demonstrate that the ABLUT boundary condition, particularly when coupled with a turbulent Schmidt number of 0.7 and the $SSTk-\omega$ turbulence model, yields the closest agreement with experimental tracer dispersion data. Notably, comparative analyses between the default and improved models revealed significant discrepancies in near-surface wind speed profiles, with deviations becoming increasingly pronounced at higher elevations. Numerical simulations were conducted to assess the ground-level distribution of Total Effective Dose Equivalent (TEDE) for four typical radionuclides (³$H$, ¹⁴$C$, ⁸⁵$Kr$ and ¹²⁹$I$) emitted from nuclear facilities under both higher and lower wind speed conditions. Results demonstrate that the TEDE maxima across all scenarios remain orders of magnitude below regulatory annual limits. These findings provide critical insights for enhancing the accuracy of wind field simulations in the vicinity of nuclear facilities located on uneven terrain, thereby contributing to improved risk assessment and environmental impact evaluations. Full article

Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a high-resolution, hourly emission inventory at the road-segment level for six major expressways in Shanghai, one of China’s leading port cities. The emission estimates are derived using a locally adapted COPERT V model, calibrated with HDDT GPS trajectory data and detailed road network information from OpenStreetMap. The inventory quantifies emissions of CO₂, NO_x, CO, PM, and VOCs, highlighting distinct temporal and spatial variation patterns. Weekday emissions consistently exceed those of weekends, with three prominent traffic-related peaks occurring between 5:00–7:00, 10:00–12:00, and 14:00–16:00. Spatial analysis identifies the G1503 and S20 expressways as major emission corridors, with S20 exhibiting particularly high emission intensity relative to its length. Combined spatiotemporal patterns reveal that weekday emission hotspots are more concentrated, reflecting typical freight activity cycles such as morning dispatch and afternoon return. The findings provide a scientific basis for formulating more precise emission control measures targeting HDDT operations in urban port environments. Full article