Search Results (41)

Accurate detection of the atmospheric boundary layer (ABL) is important for weather forecasting, urban air quality monitoring, and agricultural and ecological protection. In this study, we propose a new method for enhancing ABL height detection accuracy by integrating multi-channel polarized lidar signals at 355 nm and 532 nm wavelengths. Radiosonde observations and ERA5 reanalysis are used to validate the lidar-derived results. By calculating the gradients of signals of different wavelengths and weighted fusion, the position of the top of the boundary layer is identified, and corresponding weights are assigned to signals of different wavelengths according to the signal-to-noise ratio of the signals to obtain a more accurate atmospheric boundary layer height. This method can effectively mitigate the influence of noise and provides more stable and accurate ABL height estimates, particularly under complex aerosol conditions. Three case studies of ABL height detection over the Beijing region demonstrate the effectiveness and reliability of the proposed method. The fused ABLHs were found to be consistent with the sounding data and ERA5. This research offers a robust approach to enhancing ABL height detection and provides valuable data support for meteorological studies, pollution monitoring, and environmental protection. Full article

Public health crises triggered by viral infections pose severe threats to individual health and disrupt global socioeconomic systems. Against the backdrop of global pandemics caused by highly infectious diseases such as COVID-19 and Ebola virus disease (EVD), the development of innovative prevention and treatment strategies has become a strategic priority in the field of biomedicine. Neutralizing antibodies, as biological agents, are increasingly recognized for their potential in infectious disease control. Among these, nanobodies (Nbs) derived from camelid heavy-chain antibodies exhibit remarkable technical advantages due to their unique structural features. Compared to traditional neutralizing antibodies, nanobodies offer significant cost-effectiveness in production and enable versatile administration routes (e.g., subcutaneous injection, oral delivery, or aerosol inhalation), making them particularly suitable for respiratory infection control and resource-limited settings. Furthermore, engineered modification strategies—including multivalent constructs, multi-epitope recognition designs, and fragment crystallizable (Fc) domain fusion—effectively enhance their neutralizing activity and suppress viral immune escape mechanisms. Breakthroughs have been achieved in combating pathogens such as the Ebola virus and SARS-CoV-2, with mechanisms involving the blockade of virus–host interactions, induction of viral particle disintegration, and enhancement of immune responses. This review comprehensively discusses the structural characteristics, high-throughput screening technologies, and engineering strategies of nanobodies, providing theoretical foundations for the development of novel antiviral therapeutics. These advances hold strategic significance for addressing emerging and re-emerging infectious diseases. Full article

Methane (CH₄) is the second-largest greenhouse gas contributing to global climate warming. As of 2022, methane emissions from the oil and gas industry amounted to 3.586 million tons, representing 13.24% of total methane emissions and ranking second among all methane emission sources. To effectively control methane emissions in oilfield regions, this study proposes a multi-source remote sensing data fusion method based on the concept of data fusion, targeting high-emission areas such as oil and gas fields. The aim is to construct an XCH₄ remote sensing dataset that meets the requirements for high resolution, wide coverage, and high accuracy. Initially, XCH₄ data products from the GOSAT satellite and the TROPOMI sensor are matched both spatially and temporally. Subsequently, variables such as longitude, latitude, aerosol optical depth, surface albedo, digital elevation model (DEM), and month are incorporated. Using a local random forest (LRF) model for fusion, the resulting product combines the high accuracy of GOSAT data with the wide coverage of TROPOMI data. On this basis, ΔXCH₄ is derived using GF-5. Combined with the GFEI prior emission inventory, the high-precision fusion dataset output by the LRF model is redistributed grid by grid in oilfield areas, producing a 1 km resolution XCH₄ grid product, thereby constructing a high-precision, high-resolution dataset for oilfield regions. Finally, the challenges that emerged from the study were discussed and summarized, and it was envisioned that, in the future, with the advancement of satellite technology and algorithms, it would be possible to obtain more accurate and high-resolution datasets of methane concentration and apply such datasets to a wide range of fields, with the expectation that significant contributions could be made to reducing methane emissions and combating climate change. Full article

Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized satellite data by synergizing a chemical transport model with ground-based remote sensing data. The method enables the rapid acquisition of columnar mass concentrations for seven aerosol components on a global scale, including black carbon (BC), brown carbon (BrC), organic carbon (OC), ammonium sulfate (AS), aerosol water (AW), dust (DU), and sea salt (SS). We first establish a remote sensing model based on the multiple solution mixing mechanism (MSM²) to obtain aerosol chemical components using AERONET ground-based measurements. We then employ a cross-layer adaptive fusion (CAF)-Transformer model to learn the spatial distribution characteristics of aerosol components from the MERRA-2 model. Furthermore, we optimize the retrieval model by transfer learning from the ground-based composition data to achieve satellite remote sensing of aerosol components. Residual analysis indicates that the retrieval model exhibits robust generalization capabilities for components such as BC, OC, AS, and DU, achieving a coefficient of determination of 0.7. Moreover, transfer learning effectively enhances the consistency between satellite retrievals and ground-based remote sensing results, with an average improvement of 0.23 in the correlation coefficient. We present annual and seasonal means of global distributions of the retrieved aerosol component concentrations, with a major focus on the spatial and temporal variations of BC and DU. Additionally, we analyze three typical atmospheric environmental cases, wildfire, dust storm, and particulate pollution, by comparing our retrievals with model data and other datasets. This demonstrates the ability of satellite remote sensing to identify the location, intensity, and impact range of environmental pollution events. Satellite-retrieved aerosol component data offers high spatial resolution and efficiency, particularly providing significant advantages for near-real-time monitoring of regional atmospheric environmental events. Full article

Carbon dioxide is one of the most influential greenhouse gases affecting human life. CO₂ data can be obtained through three methods: ground-based, airborne, and satellite-based observations. However, ground-based monitoring is typically composed of sparsely distributed stations, while airborne monitoring has limited coverage and spatial resolution; they cannot fully reflect the spatiotemporal distribution of CO₂. Satellite remote sensing plays a crucial role in monitoring the global distribution of atmospheric CO₂, offering high observation accuracy and wide coverage. However, satellite remote sensing still faces spatiotemporal constraints, such as interference from clouds (or aerosols) and limitations from satellite orbits, which can lead to significant data loss. Therefore, the reconstruction of satellite-based CO₂ data becomes particularly important. This article summarizes methods for the reconstruction of satellite-based CO₂ data, including interpolation, data fusion, and super-resolution reconstruction techniques, and their advantages and disadvantages, it also provides a comprehensive overview of the classification and applications of super-resolution reconstruction techniques. Finally, the article offers future perspectives, suggesting that ideas like image super-resolution reconstruction represent the future trend in the field of satellite-based CO₂ data reconstruction. Full article

The Qinghai-Tibetan Plateau (QTP) is the largest permafrost-covered area in the world, and it is critical to understand accurately and dynamically the cyclical changes in atmospheric aerosols in the region. However, due to the scarcity of researchers in this field and the complexity of analyzing the spatial and temporal dynamics of aerosols, there is a gap in research in this area, which we hope to fill. In this study, we constructed a new fusion algorithm based on the V5.2 algorithm and the second-generation deep blue algorithm through the introduced weight factor of light and dark image elements. We used the algorithm to analyze the spatial and temporal changes in aerosols from 2009–2019. Seasonal changes and the spatial distribution of aerosol optical depth (AOD) were analyzed in comparison with the trend of weight factor, which proved the stability of the fusion algorithm. Spatially, the AOD values in the northeastern bare lands and southeastern woodland decreased most significantly, and combined with the seasonal pattern of change, the AOD values in this region were higher in the spring and fall. In these 11 years, the AOD values in the spring and fall decreased the most, and the aerosol in which the AOD decreases occurred should be the cooling-type sulfate aerosol. In order to verify the accuracy of the algorithm, we compared the AOD values obtained by the algorithm at different time intervals with the measured AOD values of several AERONET stations, in which the MAE, RMSE, and R between the AOD values obtained by the algorithm and the measured averages of the 12 nearest AERONET stations in the QTP area were 0.309, 0.094, and 0.910, respectively. In addition, this study also compares the AOD results obtained from the fusion algorithm when dynamically weighted and mean-weighted, and the results show that the error value is smaller in the dynamic weighting approach in this study. Full article

Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial–temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial–temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification. Full article

The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the “traces of human activities” for the implementation of the United Union’s 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) that is equipped on SDGSAT-1 can provide nighttime light (NL) data with a 10 m panchromatic (PAN) band and red, green, and blue (RGB) bands of 40 m resolution, which can be used for a wide range of applications, such as in urban expansion, population studies of cities, and economics of cities, as well as nighttime aerosol thickness monitoring. The 10 m PAN band can be fused with the 40 m RGB bands to obtain a 10 m RGB NL image, which can be used to identify the intensity and type of night lights and the spatial distribution of road networks and to improve the monitoring accuracy of sustainable development goal (SDG) indicators related to city developments. Existing remote sensing image fusion algorithms are mainly developed for daytime optical remote sensing images. Compared with daytime optical remote sensing images, NL images are characterized by a large amount of dark (low-value) pixels and high background noises. To investigate whether daytime optical image fusion algorithms are suitable for the fusion of GI NL images and which image fusion algorithms are the best choice for GI images, this study conducted a comprehensive evaluation of thirteen state-of-the-art pansharpening algorithms in terms of quantitative indicators and visual inspection using four GI NL datasets. The results showed that PanNet, GLP_HPM, GSA, and HR outperformed the other methods and provided stable performances among the four datasets. Specifically, PanNet offered UIQI values ranging from 0.907 to 0.952 for the four datasets, whereas GSA, HR, and GLP_HPM provided UIQI values ranging from 0.770 to 0.856. The three methods based on convolutional neural networks achieved more robust and better visual effects than the methods using multiresolution analysis at the original scale. According to the experimental results, PanNet shows great potential in the fusion of SDGSAT-1 GI imagery due to its robust performance and relatively short training time. The quality metrics generated at the degraded scale were highly consistent with visual inspection, but those used at the original scale were inconsistent with visual inspection. Full article

Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM_2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM_2.5, thereby impeding accurate exposure risk assessment. Here, this study presents a novel and flexible framework that couples stacking and flexible spatiotemporal data fusion (FSDAF) approaches. By integrating multiple models and data sources, this framework aims to generate hourly (24-h) gap-free PM_2.5 estimates for the Beijing–Tianjin–Hebei (BTH) region in 2018. This study effectively reconstructed data at least three times more effectively than the original AOD-derived PM_2.5, achieving the Pearson coefficient (r), the coefficient determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) values of 0.91, 0.84, 19.38 µg/m³, and 12.17 µg/m³, respectively, based on entire samples. Such strong predictive performance was also exhibited in spatial-based (r: 0.92–0.93, R²: 0.85–0.87, RMSE: 18.13 µg/m³–20.18 µg/m³, and MAE: 11.21 µg/m³–12.52 µg/m³) and temporal-based (r: 0.91–0.98, R²: 0.82–0.96, RMSE: 3.8 µg/m³–21.89 µg/m³, and MAE: 2.71 µg/m³–14.00 µg/m³) validations, indicating the robustness of this framework. Additionally, this framework enables the assessment of annual and seasonal PM_2.5 concentrations and distributions, revealing that higher levels are experienced in the southern region, while lower levels prevail in the northern part. Winter exhibits the most severe levels, followed by spring and autumn, with comparatively lower levels in summer. Notably, the proposed framework effectively mitigates bias in calculating population-weighted exposure risk by filling data gaps with calculated values of 51.04 µg/m³, 54.17 µg/m³, 56.24 µg/m³, and 55.00 µg/m³ in Beijing, Tianjin, Hebei, and the BTH region, respectively. Full article

To enhance the real-time detection accuracy of ship exhaust plumes and further quantify the degree of darkness, this study proposes a multi-feature fusion approach that combines the YOLOv5s-CMBI algorithm for ship exhaust plume detection with the Ringerman Blackness-based grading method. Firstly, diverse datasets are integrated and a subset of the data is subjected to standard optical model aerosolization to form a dataset for ship exhaust plume detection. Subsequently, building upon the YOLOv5s architecture, the CBAM convolutional attention mechanism is incorporated to augment the network’s focus on ship exhaust plume regions while suppressing irrelevant information. Simultaneously, inspired by the BiFPN structure with weighted bidirectional feature pyramids, a lightweight network named Tiny-BiFPN is devised to enable multi-path feature fusion. The Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to counteract the impact of feature scale disparities. The EIoU_Loss is employed as the localization loss function to enhance both regression accuracy and convergence speed of the model. Lastly, leveraging the k-means clustering algorithm, color information is mined through histogram analysis to determine clustering centers. The Mahalanobis distance is used to compute sample similarity, and the Ringerman Blackness-based method is employed to categorize darkness levels. Ship exhaust plume grades are estimated by computing a weighted average grayscale ratio between the effective exhaust plume region and the background region. Experimental results reveal that the proposed algorithm achieves improvements of approximately 3.8% in detection accuracy, 5.7% in recall rate, and 4.6% in mean average precision (mAP0.5) compared to the original model. The accuracy of ship exhaust plume darkness grading attains 92.1%. The methodology presented in this study holds significant implications for the establishment and application of future ship exhaust plume monitoring mechanisms. Full article

Current COVID-19 vaccines are effective countermeasures to control the SARS-CoV-2 virus pandemic by inducing systemic immune responses through intramuscular injection. However, respiratory mucosal immunization will be needed to elicit local sterilizing immunity to prevent virus replication in the nasopharynx, shedding, and transmission. In this study, we first compared the immunoprotective ability of a chimpanzee replication–deficient adenovirus–vectored COVID-19 vaccine expressing a stabilized pre–fusion spike glycoprotein from the ancestral SARS-CoV-2 strain Wuhan–Hu–1 (BV-AdCoV-1) administered through either aerosol inhalation, intranasal spray, or intramuscular injection in cynomolgus monkeys and rhesus macaques. Compared with intranasal administration, aerosol inhalation of BV-AdCoV-1 elicited stronger humoral and mucosal immunity that conferred excellent protection against SARS-CoV-2 infection in rhesus macaques. Importantly, aerosol inhalation induced immunity comparable to that obtained by intramuscular injection, although at a significantly lower dose. Furthermore, to address the problem of immune escape variants, we evaluated the merits of heterologous boosting with an adenovirus–based Omicron BA.1 vaccine (C68–COA04). Boosting rhesus macaques vaccinated with two doses of BV-AdCoV-1 with either the homologous or the heterologous C68–COA04 vector resulted in cross–neutralizing immunity against WT, Delta, and Omicron subvariants, including BA.4/5 stronger than that obtained by administering a bivalent BV-AdCoV-1/C68–COA04 vaccine. These results demonstrate that the administration of BV-AdCoV-1 or C68–COA04 via aerosol inhalation is a promising approach to prevent SARS-CoV-2 infection and transmission and curtail the pandemic spread. Full article

The accurate determination of aerosol optical depth (AOD) is of great importance for climate change research and environmental monitoring. To understand the applicability of the MODIS aerosol product inversion algorithm in Gansu Province, this work uses ground-based solar photometer AOD observation data to validate the MODIS C6 version of the AOD product. Additionally, the retrieval accuracy of MODIS C6 Deep Blue (DB) algorithm AOD products and Deep Blue and Dark Target Fusion (DB–DT combined) algorithm AOD products for Gansu Province when setting different spatial sampling windows is compared and analyzed. Meanwhile, the monitoring effects of these two AOD algorithms in typical polluted atmospheric conditions in Gansu Province are compared. The results show that (1) the correlation between the MODIS AOD products of the two algorithms and the ground-based observation data decreases with an increasing spatial sampling window size. When the spatial sampling window of the two algorithms is set at 30 km × 30 km, it is more representative of the AOD value in Gansu Province, thus reflecting local characteristics. (2) When the spatial sampling window is set at 30 km × 30 km, the inversion effect of the DB algorithm AOD is better than that of the DB–DT combined algorithm AOD on different underlying surfaces. (3) The seasonal variability in the inversion accuracy of the DB algorithm AOD is less than that of the DB–DT combined algorithm, and it has inversion advantages in spring, autumn and winter, while the DB–DT combined algorithm outperforms the DB algorithm only in winter. The inversion effect of the two algorithms on AOD is influenced by the spatial sampling window setting. (4) Both the DB algorithm AOD and the DB–DT combined algorithm AOD can monitor the distribution of AOD in the central and western regions of Gansu, especially for high values of AOD under polluted atmospheric conditions, which represents a good monitoring effect. However, the two algorithms perform poorly in monitoring the southeast region of Gansu, while there is a discontinuous AOD distribution in the northwest region of Gansu. Overall, the MODIS DB algorithm AOD product has higher applicability in Gansu Province. This work provides a good reference for local air pollution and climate prediction. Full article

Geosynchronous equatorial orbit (GEO) satellite-derived AOD possesses huge advantages for monitoring atmospheric aerosol with high frequency; however, the data missing existing in the satellite-derived AOD products dramatically limits this expected advantage due to cloud obscuration and aerosol retrieval algorithm. In recent years, numerous AOD fusion algorithms have been proposed, while these algorithms are mostly developed to blend daily AOD products derived from low Earth orbit (LEO) satellites and generally neglect discrepancies from different categories of products. Therefore, a spatiotemporal fusion framework based on the Bayesian maximum entropy theorem, blending GEO with LEO satellite observations and incorporating data discrepancies (GL-BME), is developed to complementarily recover the Advanced Himawari-8 Imager (AHI) AOD products over East Asia. The results show that GL-BME significantly improves the average spatial completeness of AOD from 20.3% to 67.6% with ensured reliability, and the accuracy of merged AODs nearly maintains that of original AHI AODs. Moreover, a comparison of the monthly aerosol spatial distribution between the merged and original AHI AODs is conducted to evaluate the performance and significance of GL-BME, which indicates that GL-BME could further restore the real atmospheric aerosol situation to a certain extent on the basis of dramatic spatial coverage improvement. Full article

Thin and ultrathin carbon films reduce the laser energy required for copper powder fusion in selective laser melting (SLM). The low absorption of infrared (IR) radiation and its excellent thermal conductivity leads to an intricate combination of processing parameters to obtain high-quality printed parts in SLM. Two carbon-based sacrificial thin films were deposited onto copper to facilitate light absorption into the copper substrates. Graphite-like (3.5 µm) and ultra-thin (25 nm) amorphous carbon films were deposited by aerosol spraying and direct current magnetron sputtering, respectively. The melting was analyzed for several IR (1.06 µm) laser powers in order to observe the coating influence on the energy absorption. Scanning electron microscopy showed the topography and cross-section of the thermally affected area, electron backscatter diffraction provided the surface chemical composition of the films, and glow-discharge optical emission spectroscopy (GDOES) allowed the tracking of the in-deep chemical composition of the 3D printed parts using carbon film-covered copper. Ultra-thin films of a few tens of nanometers could reduce fusion energy by about 40%, enhanced by interferences phenomena. Despite the lower energy required, the melting maintained good quality and high wettability when using top carbon coatings. A copper part was SLM printed and associated with 25 nm of carbon deposition between two copper layers. The chemical composition analysis demonstrated that the carbon was intrinsically removed during the fusion process, preserving the high purity of the copper part. Full article

Legionella pneumophila is an intracellular pathogen that can cause severe pneumonia after the inhalation of contaminated aerosols and replication in alveolar macrophages. Several pattern recognition receptors (PRRs) have been identified that contribute to the recognition of L. pneumophila by the innate immune system. However, the function of the C-type lectin receptors (CLRs), which are mainly expressed by macrophages and other myeloid cells, remains largely unexplored. Here, we used a library of CLR-Fc fusion proteins to search for CLRs that can bind the bacterium and identified the specific binding of CLEC12A to L. pneumophila. Subsequent infection experiments in human and murine macrophages, however, did not provide evidence for a substantial role of CLEC12A in controlling innate immune responses to the bacterium. Consistently, antibacterial and inflammatory responses to Legionella lung infection were not significantly influenced by CLEC12A deficiency. Collectively, CLEC12A is able to bind to L. pneumophila-derived ligands but does not appear to play a major role in the innate defense against L. pneumophila. Full article