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Aiming at the characteristics of complex structure, strong coupling and different multi-modal safety levels of more electric aircraft starter generator system, a safety analysis method based on the operating process and a multi-modal failure rate calculation method are proposed. This paper analyses the architecture, operating process and modals of more electric aircraft starter generator system and decomposes the system into eight operating modals. Based on the construction of SafetyLab, a domestic safety analysis platform, the structural models and failure rate calculation models of eight modals of starter generator system are established, and the top event failure rate of each modal, the highest transient failure rate and the steady state failure rate of the system are calculated for a complete safety analysis, taking a typical starter generator system as an example. The method proposed in this paper helps to solve the problem of multi-modal failure rate analysis of complex systems with different equipment involved in the operating process. The multi-modal failure rate calculation method proposed in this paper is also applicable to the safety analysis of other multi-modal complex systems.

ABSTRACT Poultry is frequently contaminated by Salmonella, a pathogen leading to human health concern worldwide. This study aimed to evaluate the effect of Bacillus subtilis (BS)strain 048 (BS048) on the activation, phagocytosis, sterilization, cytokine secretion, and nitrogen oxide synthesis of HD11 chicken macrophages subjected to Salmonella enteritidis challenge, using lipopolysaccharide treatment as a negative control. The results showed: (1) BS048 had no significant effect on extracellular lactate dehydrogenase activity (p>0.05), while lipopolysaccharide treatment significantly increased extracellular lactate dehydrogenase activity (p

In recent decades, there has been a significant increase in annual area burned in California’s Sierra Nevada mountains. This rise in fire activity has prompted the need to understand how historical forest management practices affect fuel composition and emissions. Here we examined the total carbon (TC) concentration and radiocarbon abundance (Δ ^14 C) of particulate matter (PM) emitted by the KNP Complex Fire, which occurred during California’s 2021 wildfire season and affected several groves of giant sequoia trees in the southern Sierra Nevada. During a 26 h sampling period, we measured concentrations of fine airborne PM (PM _2.5 ), as well as dry air mole fractions of carbon monoxide (CO) and methane (CH _4 ), using a ground-based mobile laboratory. We also collected filter samples of PM _2.5 for analysis of TC concentration and Δ ^14 C. High correlation among PM _2.5 , CO, and CH _4 time series confirmed that our PM _2.5 measurements captured variability in wildfire emissions. Using a Keeling plot approach, we determined that the mean Δ ^14 C of PM _2.5 was 111.6 ± 7.7‰ ( n = 12), which was considerably enriched relative to atmospheric carbon dioxide in the northern hemisphere in 2021 (−3.2 ± 1.4‰). Combining these Δ ^14 C data with a steady-state one-box ecosystem model, we estimated that the mean age of fuels combusted in the KNP Complex Fire was 40 years, with a range of 29–57 years. These results provide evidence for emissions originating from woody biomass, larger-diameter fine fuels, and coarse woody debris that have accumulated over multiple decades. This is consistent with independent field observations that indicate high fire intensity contributed to widespread giant sequoia mortality. With the expanded use of prescribed fires planned over the next decade in California to mitigate wildfire impacts, our measurement approach has the potential to provide regionally-integrated estimates of the effectiveness of fuel treatment programs.

Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally at the grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analysis and the ability to generate actionable insights at finer scales. In this study, we developed a global building height map at the building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote-sensing features and building morphology features to develop height estimation models using the extreme gradient boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the three-dimensional (3D) Global Building Footprints (3D-GloBFP) dataset for the year 2020. Our evaluation results show that the height estimation models perform exceptionally well at a global scale, with R2 values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) ranging from 1.9 to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3, accounting for 23.9 % of the global total), followed by the USA (3.90×1011 m3, accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint-scale height map (3D-GloBFP) reveals the significant heterogeneity in urban built-up environments, providing valuable insights for studies on urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at https://doi.org/10.5281/zenodo.11319912 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP; Che et al., 2024c), https://doi.org/10.5281/zenodo.11397014 (Building height of Asia in 3D-GloBFP; Che et al., 2024a), and https://doi.org/10.5281/zenodo.11391076 (Building height of Europe in 3D-GloBFP; Che et al., 2024b).

This article presents a non-collision trajectory planning algorithm in three-dimensional space based on velocity potential field for robotic manipulators, which can be applied to collision avoidance among serial industrial robots and obstacles, and path optimization in multi-robot collaborative operation. The algorithm is achieved by planning joint velocities of manipulators based on attractive, repulsive, and tangential velocity of velocity potential field. To avoid oscillating at goal point, a saturated function is suggested to the attractive velocity potential field that slows down to the goal progressively. In repulsive velocity potential field, a spring damping system is designed to eliminate the chattering phenomenon near obstacles. Moreover, a fuzzy logic approach is used to optimize the spring damping coefficients for different velocities of manipulators. Different from the usual tangential velocity perpendicular to the repulsive velocity vector for avoiding the local minima problem, an innovative tangential velocity potential field is introduced that is considering the relative position and moving direction of obstacles for minimum avoidance path in three-dimensional space. In addition, a path priority strategy of collision avoidance is taken into account for better performance and higher efficiency when multi-robots cooperation is scheduled. The improvements for local minima and oscillation are verified by simulations in MATLAB. The adaptabilities of the algorithm in different velocities and priority strategies are demonstrated by simulations of two ABB robots in Robot Studio. The method is further implemented in an experimental platform with a SCARA and an ABB robot cooperation around a stationary obstacle and a moving object, and the result shows real time and effectiveness of the algorithms.

The objective of this study was to prepare a 3-dimensional nanoparticle gene delivery system (3D-NGDS) based on collagen/chitosan scaffolds, in which plasmid transforming growth factor beta 1 (TGF-β1)/calcium phosphate nanoparticles mixed with fibronectin (FN) were used to transfect mesenchymal stem cells (MSCs). Scanning electron microscopy was used to characterise the microstructure of 3-dimensional collagen/chitosan scaffolds. An analysis performed to quantify the TGF-b1 concentrations in MSC cultures revealed that the MSCs transfected with the 3D-NGDS showed remarkably high levels of TGF-b1 over long periods, retaining a concentration of TGF-b1 of approximately 10 ng/mL within two weeks, with the highest level (12.6 ng/mL) being observed on the 6th day. An immunohistochemistry analysis for collagen type II revealed that much higher production of collagen II from the 9th to 15th day was observed in the 3D-NGDS-transfected MSCs than that in MSCs transfected by the Lipofectamine 2000 method. The glycosaminoglycan content of the 3D-NGDS was comparable to those treated with TGF-β1 as well as TGF-β1 plus dexamethasone, and was significantly higher than those treated with free plasmid and Lipofectamine 2000. A remarkable type I collagen expression inhibition of the 3D-NGDS at day 21 was observed via ELISA. These results suggested that transfection with the 3D-NGDS could successfully induce MSC chondrogenic differentiation in vitro without dexamethasone. In summary, the 3D-NGDS could be developed into a promising alternative method to transfer exogenous nucleic acid to MSCs in clinical trials.

This study focused on individuals who classify themselves as lesbian, gay, bisexual, transgender, queer, intersex, asexual and any other non-heterosexual orientations (LGBTQIA+) and their general attitude towards HIV testing. The hypothesis for this study is that LGBTQIA+ individuals with lower levels of predictor variables (lower level of outness and peer openness) have significantly different attitudes towards HIV testing than those with higher levels of predictor variables. A three-part survey was passed out via quota sampling method, and 121 participants answered the survey. The result showed that there is a statistical significance between a LGBTQIA+ individual’s level of outness and their attitude towards HIV testing (p

It has been proved that polyphenols have positive effects against inflammatory response and oxidative stress. We investigated whether dietary supplementation of the holly polyphenols (HP) might relieve a liver injury induced by lipopolysaccharide (LPS) in weaned pigs. Twenty-four weaned pigs were fed diets containing 0 or HP and saline or LPS challenge in 2×2 factorial arrangement of treatments. On d 17, pigs were intraperitoneally injected with an LPS or saline. Dietary HP supplementation reduced plasma AST and GGT activities and liver MDA levels, increased plasma ALB and TP and liver GSH concentrations, and ameliorated LPS-induced liver histopathological damage. HP supplementation reduced plasma and /or liver TNF-α, IL-6 and IL-1β levels, and liver mRNA expression of IL-6, TNF-α and COX2. Moreover, HP decreased the mRNA abundance of liver MyD88, TRAF6 and NOD1. These results suggest that HP supplementation attenuates liver injury induced by LPS in weaned pigs, and inhibits inflammatory response and oxidative stress.

Except for excessive nutrient input and climate warming, the rapidly rising SO42- concentration is considered as a crucial contributor to the eutrophication in shallow lakes; however, the driving process and mechanism are still far from clear. In this study, we constructed a series of microcosms with initial SO42- concentrations of 0, 30, 60, 90, 120, and 150 mg L−1 to simulate the rapid SO42- increase in Lake Taihu, China, subjected to cyanobacteria blooms. Results showed that the sulfate reduction rate was stimulated by the increase in initial SO42- concentrations and cyanobacteria-derived organic matter, with the maximal sulfate reduction rate of 39.68 mg (L d)−1 in the treatment of 150 mg L−1 SO42- concentration. During the sulfate reduction, the produced maximal ∑S2− concentration in the overlying water and acid volatile sulfate (AVS) in the sediments were 3.15 mg L−1 and 11.11 mg kg−1, respectively, and both of them were positively correlated with initial SO42- concentrations (R2=0.97; R2=0.92). The increasing abundance of sulfate reduction bacteria (SRB) was also linearly correlated with initial SO42- concentrations (R2=0.96), ranging from 6.65×107 to 1.97×108 copies g−1. However, the Fe2+ concentrations displayed a negative correlation with initial SO42- concentrations, and the final Fe2+ concentrations were 9.68, 7.07, 6.5, 5.57, 4.42, and 3.46 mg L−1, respectively. As a result, the released total phosphorus (TP) in the overlying water, to promote the eutrophication, was up to 1.4 mg L−1 in the treatment of 150 mg L−1 SO42- concentration. Therefore, it is necessary to consider the effect of rapidly increasing SO42- concentrations on the release of endogenous phosphorus and the eutrophication in lakes.

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements.

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations, and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. It remains, nonetheless, of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The cloud-based analysis framework (i) minimizes the cost of duplicating and storing ghost copies of data and (ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin- to global-scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.

Strict air pollution control strategies have been implemented in recent decades in the North China Plain (NCP), previously one of the most polluted regions in the world, and have resulted in considerable changes in emissions of air pollutants. However, little is so far known about the long-term trends of the regional background level of NOx and SO2, along with the increase and decrease processes of regional emissions. In this study, the seasonal and diurnal variations of NOx and SO2 as well as their long-term trends at a regional background station in the NCP were characterized from 2004 to 2016. On average, SO2 and NOx mixing ratios were 5.7 ± 8.4 and 14.2 ± 12.4 ppb, respectively. The seasonal variations in SO2 and NOx mixing ratios showed a similar pattern, with a peak in winter and a valley in summer. However, the diurnal variations in SO2 and NOx mixing ratios greatly differed for all seasons, indicating different sources for SO2 and NOx and meteorological effects on their concentrations. Overall, the annual mean SO2 exhibited a significant decreasing trend of −6.1 % yr−1 (R = −0.84, P < 0.01) from 2004 to 2016, which is very close to −6.3 % yr−1 of the annual SO2 emission in Beijing, and a greater decreasing trend of −7.4 % yr−1 (R = −0.95, P < 0.01) from 2008 to 2016. The annual mean of NOx showed a fluctuating rise of +3.4 % yr−1 (R = 0.38, P = 0.40) from 2005 to 2010, reaching the peak value (16.9 ppb) in 2010, and then exhibited an extremely significant fluctuating downward trend of −4.5 % yr−1 (R = 0.95, P < 0.01) from 2010 to 2016. After 2010, the annual mean NOx mixing ratios correlated significantly (R = 0.94, P < 0.01) with the annual NOx emission in North China. The decreasing rate (−4.8 % yr−1, R = −0.92, P < 0.01) of the annual mean NOx mixing ratios from 2011 to 2016 at the Shangdianzi (SDZ) regional atmospheric background station is lower than the one (−8.8 % yr−1, R = −0.94, P < 0.01) for the annual NOx emission in the NCP and in Beijing (−9.0 % yr−1, R = −0.96, P < 0.01). This indicated that surface NOx mixing ratios at SDZ had a weaker influence than SO2 on the emission reduction in Beijing and its surrounding areas in the NCP. The increase in the number of motor vehicles led to an increase in traffic emissions for NOx. This study supported conclusions from previous studies that the measures taken for controlling NOx and SO2 in the NCP in the past decades were generally successful. However, NOx emission control should be strengthened in the future.

Among various climate drivers, direct evidence for the Intertropical Convergence Zone (ITCZ) control of sediment supply on the millennial scale is lacking, and the changes in ITCZ migration demonstrated in paleoclimate records need to be better investigated. Here, we use clay minerals and Sr–Nd isotopes obtained from a gravity core on the Ninetyeast Ridge to track the corresponding source variations and analyze the relationship between terrestrial material supply and climatic changes. On the glacial–interglacial scale, chemical weathering weakened during the North Atlantic cold-climate periods and falling sea level hindered the transport of smectite into the study area due to the exposure of Andaman and Nicobar Islands. However, the influence of the South Asian monsoon on the sediment supply was not obvious on the millennial scale. We suggest that the north–south migration of the ITCZ controlled the rainfall in Myanmar and further directly determined the supply of clay minerals on the millennium scale because the transport of smectite was highly connected with the ITCZ location; thus, the regional shift of the ITCZ induced an abnormal increase in the smectite percentage during the late Last Glacial Maximum (LGM) in our records. The smectite percentage in the studied core is similar to distinct ITCZ records but different in some periods, revealing that regional changes in the ITCZ were significantly obvious, the ITCZ is not a simple north–south displacement, and closer connections occurred between the Northern–Southern Hemisphere in the eastern Indian Ocean during the late LGM.

Abstract We present a search for the charged lepton-flavor-violating decays ϒ(1S) → ℓ ± ℓ′∓ and radiative charged lepton-flavour-violating decays ϒ(1S) → γ ℓ ± ℓ′∓ [ℓ,ℓ′ = e, μ, τ] using the 158 million ϒ(2S) sample collected by the Belle detector at the KEKB collider. This search uses ϒ(1S) mesons produced in ϒ(2S) → π + π − ϒ(1S) transitions. We do not find any significant signal, so we provide upper limits on the branching fractions at the 90% confidence level.

Abstract We report a measurement of the electric dipole moment of the τ lepton (dτ) using an 833 fb −1 data sample collected near the Υ(4S) resonance, with the Belle detector at the KEKB asymmetric-energy e + e − collider. Using an optimal observable method, we obtain the real and imaginary parts of d τ as Re(d τ ) = (−0.62 ± 0.63) × 10 −17 ecm and Im(d τ ) = (−0.40 ± 0.32) × 10 −17 ecm, respectively. These results are consistent with null electric dipole moment at the present level of experimental sensitivity and improve the sensitivity by about a factor of three.

Different entrainment–mixing processes can occur in clouds; however, a homogeneous mixing mechanism is often implicitly assumed in most commonly used microphysics schemes. Here, we first present a new entrainment–mixing parameterization that uses the grid mean relative humidity without requiring the relative humidity of the entrained air. Then, the parameterization is implemented in a microphysics scheme in a large eddy simulation model, and sensitivity experiments are conducted to compare the new parameterization with the default homogeneous entrainment–mixing parameterization. The results indicate that the new entrainment–mixing parameterization has a larger impact on the number concentration, volume mean radius, and cloud optical depth in the stratocumulus case than in the cumulus case. This is because inhomogeneous and homogeneous mixing mechanisms dominate in the stratocumulus and cumulus cases, respectively, which is mainly due to the larger turbulence dissipation rate in the cumulus case. Because stratocumulus clouds break up during the dissipation stage to form cumulus clouds, the effects of this new entrainment–mixing parameterization during the stratocumulus dissipation stage are between those during the stratocumulus mature stage and the cumulus case. A large aerosol concentration can enhance the effects of this new entrainment–mixing parameterization by decreasing the cloud droplet size and evaporation timescale. The results of this new entrainment–mixing parameterization with grid mean relative humidity are validated by the use of a different entrainment–mixing parameterization that uses parameterized entrained air properties. This study sheds new light on the improvement of entrainment–mixing parameterizations in models.

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

Using the dual-polarized array for underdetermined estimation of partially polarized (PP) signal parameters can lead to limited signal-to-ratio (SNR) and biased reconstruction of the coherency matrix. In this paper, a new non-iterative method is proposed with the tri-polarized nested array. With the sub-covariance addition, the power of different polarized components of a signal can be completely accumulated, which improves the SNR. Besides, it is proven that with the optimized tri-polarized nested array, noise variance estimation without iterations becomes possible in the underdetermined case, which is critical for unbiased coherency matrix reconstruction. The subspace-based method is adopted to estimate the direction-of-arrival (DOA), and the polarization parameters can be obtained based on the reconstructed coherency matrices. The proposed method is validated by numerical experiments and compared with other representative methods. It has relatively high accuracy and is about one order of magnitude faster than its competitor.

In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N2O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO2 and top emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH4 retrievals and a tendency for inversions to diagnose higher CH4 emission estimates than reported by NGHGIs. In particular, oil- and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N2O emission factors for direct agricultural soil emissions. Inversions provide insights into seasonal and interannual greenhouse gas fluxes anomalies, e.g., during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports (e.g., NGHGIs) could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed by this study is publicly available at https://doi.org/10.5281/zenodo.5089799 (Deng et al., 2021).

Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.

The distribution of meteorological elements has always been an important factor in determining the horizontal and vertical distribution of particles in the atmosphere. To study the effect of meteorological elements on the three-dimensional distribution structure of particles, mobile vehicle lidar and fixed-location observations were collected in the western Guangdong–Hong Kong–Macao Greater Bay Area of China during September and October in 2019 and 2020. Vertical aerosol extinction coefficient, depolarization ratio, and wind and temperature profiles were measured using a micro pulse lidar, a Raman scattering lidar, and a Doppler wind profile lidar installed on a mobile monitoring vehicle. The mechanism of how wind and temperature in the boundary layer affects the horizontal and vertical distribution of particles was analysed. The results show that particles were mostly distributed in downstream areas on days with moderate wind speed in the boundary layer, whereas they were distributed homogeneously on days with weaker wind. There are three typical types of vertical distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area (GBA): surface single layer, elevated single layer, and double layer. Analysis of wind profiles and Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) backward trajectory reveals different sources of particles for the three types. Particles concentrating near the temperature inversion and multiple inversions could cause more than one peak in the extinction coefficient profile. There were two mechanisms affecting the distribution of particulate matter in the upper and lower boundary layers. Based on this observational study, a general model of meteorological elements affecting the vertical distribution of urban particulate matter is proposed.

Water clarity serves as a sensitive tool for understanding the spatial pattern and historical trend in lakes' trophic status. Despite the wide availability of remotely sensed data, this metric has not been fully explored for long-term environmental monitoring. To this end, we utilized Landsat top-of-atmosphere reflectance products within Google Earth Engine in the period 1984–2018 to retrieve the average Secchi disk depth (SDD) for each lake in each year. Three SDD datasets were used for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red / blue band ratio algorithm was applied to map SDD for lakes (>0.01 km2) based on the first SDD dataset, where R2=0.79 and relative RMSE (rRMSE) =61.9 %. The other two datasets were used to validate the temporal transferability of the SDD estimation model, which confirmed the stable performance of the model. The spatiotemporal dynamics of SDD were analyzed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found the number of lakes with SDD m accounted for the largest proportion (80.93 %) of the total lakes, but the total areas of lakes with SDD of and >4 m were the largest, both accounting for about 24.00 % of the total lakes. During 1984–2018, lakes in the Tibetan–Qinghai Plateau region (TQR) had the clearest water with an average value of 3.32±0.38 m, while that in the northeastern region (NLR) exhibited the lowest SDD (mean 0.60±0.09 m). Among the 10 814 lakes with SDD results for more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia–Xinjiang region (MXR), more than half of the total lakes in every other region exhibited significant increasing trends. In the eastern region (ELR), NLR and Yungui Plateau region (YGR), almost more than 50 % of the lakes that displayed increase or decrease in SDD were mainly distributed in the area range of 0.01–1 km2, whereas those in the TQR and MXR were primarily concentrated in large lakes (>10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset is freely available at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Hydro.tpdc.271571, Tao et al., 2021).

China has been experiencing rapid changes in emissions of air pollutants in recent decades. Increased emissions of primary particulates and reactive gases caused severe haze in several polluted regions including the Yangtze River Delta (YRD). Measures implemented in recent years for improving air quality have reduced the emissions of NOx, SO2, etc. The emission changes in these gases are reflected by tropospheric columns from satellite observations and surface measurements of surface concentrations from urban sites. However, little is known about the long-term variations in regional background NOx and SO2. In this study, we present NOx and SO2 measurements from the Lin'an station (LAN; 30∘18' N, 119∘44′ E; 138.6 m a.s.l.), one of the Global Atmosphere Watch (GAW) stations in China. We characterize the seasonal and diurnal variations and study the long-term trends of NOx and SO2 mixing ratios observed at LAN from 2006 to 2016. We also interpret the observed variations and trends in terms of changes in meteorological conditions as well as emission of these gases. The overall average mixing ratios of NOx (NO2) and SO2 during 2006–2016 were 13.6 ± 1.2 ppb (12.5 ± 4.6) and 7.0 ± 4.2 ppb, respectively. The averaged seasonal variations showed maximum values of NOx and SO2 in December (23.5 ± 4.4 ppb) and January (11.9 ± 6.2 ppb), respectively, and minimum values of 7.1 ± 0.8 and 2.8 ± 2.3 ppb (both in July), respectively. The average diurnal variation characteristics of NOx and SO2 differed considerably from each other, though the daily average mixing ratios of both gases were significantly correlated (R2=0.29, P<0.001). The annual average mixing ratio of NOx increased during 2006–2011 and then decreased significantly at 0.78 ppb/yr (−5.16 %/yr, P<0.01). The annual 95th and 5th percentiles of hourly NOx mixing ratios showed upward trends until 2012 and 2014, respectively, before a clear decline. The annual average mixing ratio of SO2 decreased significantly at 0.99 ppb/yr (−8.27 %/yr, P<0.01) from 2006–2016. The annual 95th and 5th percentiles of hourly SO2 mixing ratios all exhibited significant (P<0.001) downward trends at 3.18 and 0.19 ppb/yr, respectively. Changes in the total NOx and SO2 emissions as well as the industrial emissions in the YRD region were significantly correlated with the changes in annual NOx and SO2 mixing ratios. The significant decreases in NOx from 2011 to 2016 and SO2 from 2006 to 2016 highlight the effectiveness of relevant control measures on the reduction in NOx and SO2 emissions in the YRD region. A decrease in annual SO2 / NOx ratio was found, suggesting a better efficacy in the emission reduction in SO2 than NOx. We found gradual changes in average diurnal patterns of NOx and SO2, which could be attributed to increasing contributions of vehicle emissions to NOx and weakening impacts of large sources on the SO2 concentration. This study reaffirms China's success in controlling both NOx and SO2 in the YRD but indicates at the same time a necessity to strengthen the NOx emission control.

By using the multi-source data of meteorology over recent decades, this study discovered a summertime “hollow wet pool” in the troposphere with a center of high water vapor over the Asian water tower (AWT) on the Tibetan Plateau (TP), which is indicated by a vertical transport “window” in the troposphere. The water vapor transport in the upper troposphere extends from the vertical transport window over the TP with significant connections among the Arctic, Antarctic and TP regions, highlighting the effect of the TP's vertical transport window of water vapor in the troposphere on global change in water vapor. The vertical transport window is built by the AWT's thermal forcing in association with the dynamic effect of the TP's “hollow heat island”. Our study improves the understanding of the vapor transport over the TP with important implication for global climate change.

With the increasing complexity of the electromagnetic environment and the continuous development of radar technology, more and more modern digital programmable radars using agile waveform will appear in the future battlefield. It is difficult to effectively identify these radar emitters with complex system only by relying on traditional recognition models. In response to the above problem, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism to deal with the problem of variable conventional characteristic parameters of radar emitter signals with agile waveform. First, we perform a distributed representation of the pulse signal data to generate high-dimensional sparse signal features. Then we design to use a dynamic Convolutional Neural Network to extract features of structural details of radar emitter signals with agile waveform at different levels, and use a Long Short-Term Memory to extract its timing features. In order to obtain the deep features that can characterize the agility of the waveform, the attention mechanism-based method is used to fuse the extracted structural features and timing features, and at the same time it can reduce the influence of noise in complex electromagnetic environment on the characteristic data of radar emitter. Finally, the deep feature is input into the Softmax layer to complete the recognition of radar emitters with agile waveform. The experimental results show that the method proposed in this paper can effectively solve the problem of the recognition of radar emitters with agile waveform, and the recognition accuracy is improved by 1.26% compared with the traditional models and other deep models.

Polymer gears are an effective solution to lightweighting, which are highly demanded in the automotive industry. Acetal is one of the most widely used polymer gear materials. In this study, two commercial grades of acetal, homopolymer (POM-H) based and copolymer (POM-C) based, were injection moulded into gears with their wear performance compared. Noticeable differences were discovered in failure mechanism, and thermal and mechanical characteristics, which led to a difference in performance prediction. The service life of over two million cycles was expected under a torque up to 10 N m, with POM-H gears having 35% better service life than POM-C. The differences in the properties of POM-H and POM-C should be considered in future industrial applications such as the replacement for metal gears.

The planetary boundary layer (PBL) governs the vertical transport of mass, momentum, and moisture between the surface and the free atmosphere, and thus the determination of PBL height (BLH) is recognized as crucial for air quality, weather, and climate analysis. Although reanalysis products can provide important insight into the global view of BLH in a seamless way, the BLH observed in situ on a global scale remains poorly understood due to the lack of high-resolution (1 or 2 s) radiosonde measurements. The present study attempts to establish a near-global BLH climatology at synoptic times (00:00 and 12:00 UTC) and in the daytime using high-resolution radiosonde measurements over 300 radiosonde sites worldwide for the period 2012 to 2019, which is then compared against the BLHs obtained from four reanalysis datasets, including ERA5, MERRA-2, JRA-55, and NCEP-2. The variations in daytime BLH exhibit large spatial and temporal dependence, and as a result the BLH maxima are generally discerned over the regions such as the western United States and western China, in which the balloon launch times mostly correspond to the afternoon. The diurnal variations in BLH are revealed with a peak at 17:00 local solar time (LST). The most promising reanalysis product is ERA5, which underestimates BLH by around 130 m as compared to radiosondes released during daytime. In addition, MERRA-2 is a well-established product and has an underestimation of around 160 m. JRA-55 and NCEP-2 might produce considerable additional uncertainties, with a much larger underestimation of up to 400 m. The largest bias in the reanalysis data appears over the western United States and western China, and it might be attributed to the maximal BLH in the afternoon when the PBL has risen. Statistical analyses further indicate that the biases of reanalysis BLH products are positively associated with orographic complexity, as well as the occurrence of static instability. To our best knowledge, this study presents the first near-global view of high-resolution radiosonde-derived boundary layer height and provides a quantitative assessment of the four frequently used reanalysis products.

Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-oriented regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds illustrate that the rain-distorted wind distributions become more nominal with SVM, hence much reducing the rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the estimation of simultaneous rain rate is attempted with some success to retrieve both wind and rain. Although additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the ML results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications.