Search Results (9)

The stainless steelmaking process generates substantial volumes of metal hydroxide sludge during neutralization after pickling. The sludge contains valuable materials, including fluorspar (CaF₂) and alloy-containing compounds. So far, life cycle assessment (LCA) of resource recovery and reuse pathways for pickling sludge remains absent in the literature. This study conducted a comprehensive LCA of a real-world Swedish case study in which the sludge is thermally processed into a usable product (Hydrofluss) and utilized in the argon oxygen decarburization (AOD) process in stainless steelmaking. Using the Product Environmental Footprint (PEF) 3.1 method and ecoinvent v3.11 in SimaPro v10.3.01, the results showed that using 1 ton of Hydrofluss can reduce the total life-cycle climate change impact by 91.6 kg CO₂eq, corresponding to a 6% reduction compared with a reference scenario relying on natural fluorspar and primary ferroalloys. The sensitivity analysis indicated that replacing fossil heating oils with renewable HVO100 in the Hydrofluss recovery process could substantially enhance the climate benefit of the studied system, resulting in a six-fold (540.3 kg) CO₂eq emission reduction. Beyond climate change, this study highlights the need for multi-impact LCAs to provide a more holistic understanding of the environmental implications of resource recovery and utilization systems. In a broader context, the findings can contribute to the development of more sustainable and circular resource flows and associated business models for stainless steelmaking. Full article

Single image dehazing remains a fundamental challenge in computer vision due to the ill-posed nature of the inverse problem and the spatial heterogeneity of real atmospheric haze. Existing convolutional approaches suffer from two structural deficiencies: bounded receptive fields that fail to model large-scale haze gradients, and isotropic kernels insensitive to the directional patterns of atmospheric scattering. This paper proposes ACTA-AOD, a lightweight end-to-end dehazing network that addresses both limitations within a unified framework built upon the AOD-Net K-parameterization. The network integrates two complementary modules: (1) W-EMSAv2, a windowed efficient multi-scale attention module that reduces attention complexity from $\mathcal{O}(N^{2}C)$ to $\mathcal{O}(N{M}^{2}C/4)$ while preserving full-spectrum spatial information through pixel-shuffle reconstruction; and (2) the ACTA Fusion module, which combines structural-reparameterization-based asymmetric convolution with cross-dimensional Triple Attention for direction-sensitive local detail recovery at zero inference-time overhead. On the RESIDE benchmark, ACTA-AOD achieves peak signal-to-noise ratio (PSNR) of 26.02 dB and structural similarity index measure (SSIM) of 0.910 on indoor synthetic data, and 26.13 dB/0.910 on outdoor synthetic data, surpassing the AOD-Net baseline by $+3.41$ dB (indoor) and $+3.58$ dB (outdoor) in PSNR, and exceeding the strongest learning-based baseline (AECRNet, CVPR 2021) by $+1.17$ dB (indoor) and $+1.75$ dB (outdoor). The model processes images at 81 frames per second on a single GPU. Ablation studies and stratified robustness evaluation across five haze density levels confirm the complementary, synergistic contribution of each module. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

Stainless steel pickling sludge (SSPS) is classified as hazardous solid waste, while Argon Oxygen Decarburization (AOD) slag is challenging to utilize due to the leaching toxicity of Cr. This study introduces a novel in-line co-processing technique for AOD slag and SSPS, parallel to the steelmaking process, aimed at metal recovery, sulfur fixation, and slag detoxification: pre-treatment-AOD slag bath approach. The transformations and migrations of sulfur and metal elements, such as Fe and Cr, in the co-processed mixture were analyzed using thermogravimetric–mass spectrometry (TG-MS) and scanning electron microscopy–energy dispersive spectroscopy (SEM-EDS). The results indicated that sulfur in SSPS could be transformed from CaSO₄ to CaS under controlled low pre-reduction temperatures (below 800 °C), facilitating its stabilization in the slag and achieving a sulfur fixation rate of over 99%. Metal elements, including iron and chromium, first formed a small portion of spinel (FeCr₂O₄) during the pre-reduction phase, then Fe-Cr or Fe-Cr-C-based alloy particles were rapidly formed at high temperatures and in the presence of reducers in the slag bath (1550 °C), aggregating and growing spontaneously, ultimately achieving a metal recovery rate of over 95%. Furthermore, a reaction model for SSPS briquettes in the AOD slag bath was established to further reveal the mechanisms of sulfur, iron, and chromium stabilization and migration, thereby providing a basis for the harmless disposal of both materials. The product alloys are expected to be used as additives in stainless steel production, while the harmless slag could be safely utilized in the preparation of cementitious auxiliary materials. Full article

Acute oak decline is a high-impact disease causing necrotic lesions on the trunk, crown thinning and the eventual death of oak. Four bacterial species are associated with the lesions—Brenneria goodwinii, Gibbsiella quercinecans, Rahnella victoriana and Lonsdalea Britannica—although an epi-/endophytic lifestyle has also been suggested for these bacteria. However, little is known about their environmental reservoirs or their pathway to endophytic colonisation. This work aimed to investigate the ability of the four AOD-associated bacterial species to survive for prolonged periods within rhizosphere soil, leaves and acorns in vitro, and to design an appropriate method for their recovery. This method was trialled on field samples related to healthy and symptomatic oaks. The in vitro study showed that the majority of these species could survive for at least six weeks within each sample type. Results from the field samples demonstrated that R. victoriana and G. quercinecans appear environmentally widespread, indicating multiple routes of endophytic colonisation might be plausible. B. goodwinii and L. britannica were only identified from acorns from healthy and symptomatic trees, indicating they may be inherited members of the endophytic seed microbiome and, despite their ability to survive outside of the host, their environmental occurrence is limited. Future research should focus on preventative measures targeting the abiotic factors of AOD, how endophytic bacteria shift to a pathogenic cycle and the identification of resilient seed stock that is less susceptible to AOD. Full article

Small satellite platforms are experiencing increasing interest from the space community, because of the reduced cost and the performance available with current technologies. In particular, the hardware composing the attitude and orbit control system (AOCS) has reached a strong maturity level, and the dimensions of the components allow redundant sets of sensors and actuators. Thus, the software shall be capable of managing these redundancies with a fault tolerant structure. This paper presents an attitude and orbit determination system (AODS) architecture, with embedded failure detection and isolation functions, and autonomous redundant component management and reconfiguration for basic failure recovery. The system design and implementation has been sized for small satellite platforms, characterized by limited computing capacities, and reduced autonomy level. The discussion describes the system architecture, with particular emphasis on the failure detection and isolation blocks at the component level. The set of functions managing failure detection at system level is also described in the paper. The proposed system is capable of reconfiguring and autonomously recalibrating after various failures had occurred. Attention is also dedicated to the achieved performance, satisfying stringent requirements for a small satellite platform. In these regards, the simulation results used to verify the performance of the proposed system at the model-in-the-loop (MIL) level are also reported. Full article

The quality of Gravity Recovery and Climate Experiment (GRACE) observation is the prerequisite for obtaining the high-precision GRACE temporal gravity field model. To study the influence of new-generation GRACE Level-1B Release 03 (RL03) data and the new atmosphere and ocean de-aliasing (AOD1B) products on recovering temporal gravity field models and precise orbit determination (POD) solutions, we combined the global positioning system and K-band ranging-rate (KBRR) observations of GRACE satellites to estimate the effect of different data types on these solutions. The POD and monthly gravity field solutions are obtained from 2005 to 2010 by SHORDE software developed by the Shanghai Astronomical Observatory. The post-fit residuals of the KBRR data were decreased by approximately 10%, the precision of three-direction positions of the GRACE POD was improved by approximately 5%, and the signal-to-noise ratio of the monthly gravity field model was enhanced. The improvements in the new release of monthly gravity field model and POD solutions can be attributed to the enhanced Level-1B KBRR data and the AOD1B model. These improvements were primarily due to the enhanced of KBRR data; the effect of the AOD1B model was not significant. The results also showed that KBRR data slightly improve the satellite orbit precision, and obviously enhance the precision of the gravity field model. Full article

Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on a large scale. However, AOD data acquired by satellites are often missing, and this has gradually become a popular topic. In recent years, a large number of AOD recovery algorithms have been proposed. Many AOD recovery methods are not application-oriented. These methods focus mainly on to the accuracy of AOD recovery and neglect the AOD recovery ratio. As a result, the AOD recovery accuracy and recovery ratio cannot be balanced. To solve these problems, a two-step model (TWS) that combines multisource AOD data and AOD spatiotemporal relationships is proposed. We used the light gradient boosting (LightGBM) model under the framework of the gradient boosting machine (GBM) to fit the multisource AOD data to fill in the missing AOD between data sources. Spatial interpolation and spatiotemporal interpolation methods are limited by buffer factors. We recovered the missing AOD in a moving window. We used TWS to recover AOD from Terra Satellite’s 2018 AOD product (MOD AOD). The results show that the MOD AOD, after a 3 × 3 moving window TWS recovery, was closely related to the AOD of the Aerosol Robotic Network (AERONET) (R = 0.87, RMSE = 0.23). In addition, the MOD AOD missing rate after a 3 × 3 window TWS recovery was greatly reduced (from 0.88 to 0.1). In addition, the spatial distribution characteristics of the monthly and annual averages of the recovered MOD AOD were consistent with the original MOD AOD. The results show that TWS is reliable. This study provides a new method for the restoration of MOD AOD, and is of great significance for studying the spatial distribution of atmospheric pollutants. Full article

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides widespread Aerosol Optical Depth (AOD) datasets for climatological and environmental health research. Since MODIS AOD clearly lacks coverage in orbit-scanning gaps and cloud obscuration, some applications will benefit from data recovery using multi-temporal AOD. Aimed at qualitatively describing the relationship between multi-temporal AOD, AOD loadings and Normalized Difference Vegetation Index (NDVI) have been considered based on the mechanism of satellite AOD retrieval. Accordingly, the NDVI-based Weighted Linear Regression (NWLR) has been proposed to recover AOD by synthetically weighing AOD similarity, spatial proximity, and NDVI similarity. To evaluate the performance of AOD recovery, simulated experiments applying gap and window masks were conducted in South Asia and Beijing, respectively. The evaluation results demonstrated that the linear regression R2 achieved 0.8 and the absolute relative errors remained steady. Further validation was conducted between the recovered and actual AODs using 56 Aerosol Robotic Network (AERONET) sites in East and South Asia from 2013 to 2015, which demonstrated that over 41% of recovered AODs fell within the expected error (EE) envelope. Additional validation conducted in South Asia and Beijing showed that recovery by NWLR did not expand satellite-derived AOD errors, and the accuracy of recovered AOD was consistent with the accuracy of the original Aqua MODIS Deep Blue (DB) AOD. The recovery results illustrated that AOD coverage was improved in most regions, especially in North China, Mongolia, and South Asia, which could provide better support in aerosol spatio-temporal analysis and aerosol data assimilation. Full article