Search

Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

At present, GIS data, which contains a large amount of semantic information, has become the main data source for map cartography. In this paper, based on the existing multi-scale database, we study a database-based automatic cartography method to meet the needs of multi-scale map cartography, and replace the traditional process by the intelligent processing of software to shorten the cycle of cartography, improve the productivity of map cartography, and at the same time, reduce the error rate caused by manual work, and improve the overall quality of map production.

Abstract The decays of the χb1(1P), χb2(1P), χb1(2P) and χb2(2P) mesons into the Υ(1S)μ+μ − final state are observed with a high significance using proton-proton collision data collected with the LHCb detector and corresponding to an integrated luminosity of 9 fb −1. The newly observed decays together with the Υ(2S) → Υ(1S)π+π − and Υ(3S) → Υ(2S)π+π − decay modes are used for precision measurements of the mass and mass splittings for the hidden-beauty states.

Due to the important strategic position of Pumped Storage Power Plants (PSPP) in global energy upgrading, conducting in-depth research on the various operating conditions of pump turbine units is important for their safe and stable operation. This study sought to clarify the gas–liquid phase motion and the nonlinear chaotic characteristics of the process of exhaust and pressurization in pump mode; with the simplified objective model proposed here, a visualization of the process is achieved using data-driven methods, and the nonlinear characteristics of gas–liquid phase motion during the process are theoretically demonstrated. A method that combines data-driven and chaotic analysis is proposed to qualitatively and quantitatively analyze the force and torque time-series signals of the runner under different exhaust rates. The results indicate that the chaotic characteristics of the force signals and torque signals of the runner are not in a single linear relationship with the exhaust rates. Therefore, this research also provides guidance on exhaust rates with the aim of informing actual engineering practice, the purpose of which is to reduce the vibration amplitude caused by repetitive torque and improve the stability of the unit operations.

A multi-group of strong atmospheric waves (wave packet nos. 1–5) over China associated with the 2022 Hunga Tonga–Hunga Ha′apai (HTHH) volcano eruptions were observed in the mesopause region using a ground-based airglow imager network. The horizontal phase speed of wave packet nos. 1 and 2 is approximately 309 and 236 m s−1, respectively, which is consistent with Lamb wave L0 mode and L1 mode from theoretical predictions. The amplitude of the Lamb wave L1 mode is larger than that of the L0 mode. The wave fronts of Lamb wave L0 and L1 below the lower thermosphere are vertical, while the wave fronts of L0 mode tilt forward above the lower atmosphere, exhibiting internal wave characteristics which show good agreement with the theoretical results. Two types of tsunamis were simulated; one type of tsunami is induced by the atmospheric-pressure wave (TIAPW), and the other type of tsunami is directly induced by the Tonga volcano eruption (TITVE). From backward ray-tracing analysis, the TIAPW and TITVE were likely the sources of wave packet nos. 3 and 4–5, respectively. The scale of tsunamis near the coast is very consistent with the atmospheric AGWs observed by the airglow network. The atmospheric gravity waves (AGWs) triggered by TITVE propagate nearly 3000 km inland with the support of a duct. The atmospheric-pressure wave can directly affect the upper atmosphere and can also be coupled with the upper atmosphere through the indirect way of generating a tsunami and, subsequently, tsunami-generating AGWs, which will provide a new understanding of the coupling between ocean and atmosphere.

The effects of selenite (0, 15, 30, 45 mg L-1) on physiological characteristics and medicinal components of Paeonia ostii were analyzed. The results showed that selenite application promoted the activity of superoxide dismutase and the contents of soluble sugar, proline, carotenoids, total flavonoids, and total polyphenols, and decreased the contents of reactive oxygen species, relative electrical conductivity, and malondialdehyde. In addition, selenite also increased chlorophyll content, improved electron transfer ability, PSI and PSII performance, and the coordination between PSI and PSII, which significantly improved photosynthetic capacity. Moreover, selenite treatment also greatly increased the contents of gallic acid, catechin, albiflorin, paeoniflorin, benzoic acid, and paeonol in Moutan cortex radicis (MCR). These results showed that selenite effectively protected the photosynthetic apparatus from photooxidative damage by enhancing antioxidant capacity, improving photosynthetic capacity, and increasing the content of the medicinal compounds in MCR.

Our study utilizes a global reanalysis of near-surface daily air temperature data spanning the years from 1949 to 2019 to construct climate networks. By employing community detection for each year, we reveal the evolving community structure of the climate network within the context of global warming. Our findings indicate significant changes in measures such as network modularity and the number of communities over the past 30 years. Notably, the community structure of the climate network has undergone a discernible transition since the early 1980s. We attribute this transition to the substantial increase in isolated nodes since the 1980s, primarily concentrated in equatorial ocean regions. Additionally, we demonstrate that nodes experiencing amplified isolation tend to diminish connectivity with other nodes globally, particularly those within the same oceanic basin, while showing a significant strengthening of connections with the Eurasian and North African continents. We deduce that the mechanism driving amplified isolation in the climate network may be comprehended through the weakening of tropical circulations, such as the Hadley cell and Walker circulation, in response to increasing greenhouse gases.

In addressing the practical application of direct air capture (DAC) technology to achieve net-zero greenhouse gas emissions by 2050, the intricate climatic environment presents a significant challenge in selecting capture adsorbents with excellent adsorption performance, high moisture tolerance and stability. This study seeks to assess the efficacy of a temperature vacuum swing adsorption (TVSA) system for DAC, integrated with two distinct pre-dehumidification methods: condensation dehumidification and solid desiccant dehumidification. Initially, to balance enhanced adsorption capacity with the energy required for regeneration in the presence of water vapor, the optimal techno-economic performance is achieved at a 40 % relative humidity (RH). This corresponds to 15.49 GJ/t of heat consumption and 194.08 $/tonCO2 of CO2 the levelized cost of DAC (LCOD). Following dynamic simulation, the dehumidification system effectively controlled the outlet RH. Furthermore, economic analysis indicates that reducing environmental air to 40 % RH is more suitable and cost-efficient for both integrated systems. The optimal LCODs are 200.29 $/tonCO2 for the solid desiccant dehumidification system and 197.16 $/tonCO2 for the condensation dehumidification system, which could effectively save 7.09 % and 5.61 % of LCOD, respectively, compared to systems without a dehumidification process.

Abstract Silicon-based quantum emitters are candidates for large-scale qubit integration due to their single-photon emission properties and potential for spin-photon interfaces with long spin coherence times. Here, we demonstrate local writing and erasing of selected light-emitting defects using femtosecond laser pulses in combination with hydrogen-based defect activation and passivation at a single center level. By choosing forming gas (N2/H2) during thermal annealing of carbon-implanted silicon, we can select the formation of a series of hydrogen and carbon-related quantum emitters, including T and Ci centers while passivating the more common G-centers. The Ci center is a telecom S-band emitter with promising optical and spin properties that consists of a single interstitial carbon atom in the silicon lattice. Density functional theory calculations show that the Ci center brightness is enhanced by several orders of magnitude in the presence of hydrogen. Fs-laser pulses locally affect the passivation or activation of quantum emitters with hydrogen for programmable formation of selected quantum emitters.

Cooking organic aerosol (COA) is one of the major constituents of particulate matter in urban areas. COA is oxidized by atmospheric oxidants such as ozone, changing its physical, chemical and toxicological properties. However, atmospheric chemical lifetimes of COA and its tracers such as oleic acid are typically longer than those that have been estimated by laboratory studies. We tackled the issue by considering temperature. Namely, we hypothesize that increased viscosity of COA at ambient temperature accounts for its prolonged atmospheric chemical lifetimes in wintertime. Laboratory-generated COA particles from cooking oil were exposed to ozone in an aerosol flow tube reactor for the temperature range of −20 to 35 °C. The pseudo-second-order chemical reaction rate constants (k2) were estimated from the experimental data by assuming a constant ozone concentration in the flow tube. The estimated values of k2 decreased by orders of magnitude for lower temperatures. The temperature dependence in k2 was fit well by considering the diffusion-limited chemical reaction mechanism. The result suggested that increased viscosity was likely the key factor to account for the decrease in chemical reactivity at the reduced temperature range, though the idea will still need to be verified by temperature-dependent viscosity data in the future. In combination with the observed global surface temperature, the atmospheric chemical lifetimes of COA were estimated to be much longer in wintertime (> 1 h) than in summertime (a few minutes) for temperate and boreal regions. Our present study demonstrates that the oxidation lifetimes of COA particles will need to be parameterized as a function of temperature in the future for estimating environmental impacts and fates of this category of particulate matter.

Land-cover change has been identified as an important cause or driving force of global climate change and is a significant research topic. Over the past few decades, global land-cover mapping has progressed; however, long-time-series global land-cover-change monitoring data are still sparse, especially those at 30 m resolution. In this study, we describe GLC_FCS30D, a novel global 30 m land-cover dynamics monitoring dataset containing 35 land-cover subcategories and covering the period 1985–2022 in 26 time steps (maps were updated every 5 years before 2000 and annually after 2000). GLC_FCS30D has been developed using continuous change detection and all available Landsat imagery based on the Google Earth Engine platform. Specifically, we first take advantage of the continuous change-detection model and the full time series of Landsat observations to capture the time points of changed pixels and identify the temporally stable areas. Then, we apply a spatiotemporal refinement method to derive the globally distributed and high-confidence training samples from these temporally stable areas. Next, local adaptive classification models are used to update the land-cover information for the changed pixels, and a temporal-consistency optimization algorithm is adopted to improve their temporal stability and suppress some false changes. Further, the GLC_FCS30D product is validated using 84 526 globally distributed validation samples from 2020. It achieves an overall accuracy of 80.88 % (±0.27 %) for the basic classification system (10 major land-cover types) and 73.04 % (±0.30 %) for the LCCS (Land Cover Classification System) level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series datasets used for validation from the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that GLC_FCS30D offers significant stability in terms of variation across the accuracy time series and achieves mean accuracies of 79.50 % (±0.50 %) and 81.91 % (±0.09 %) over the two regions. Lastly, we draw conclusions about the global land-cover-change information from the GLC_FCS30D dataset; namely, that forest and cropland variations have dominated global land-cover change over past 37 years, the net loss of forests reached about 2.5 million km2, and the net gain in cropland area is approximately 1.3 million km2. Therefore, the novel dataset GLC_FCS30D is an accurate land-cover-dynamics time-series monitoring product that benefits from its diverse classification system, high spatial resolution, and long time span (1985–2022); thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D dataset is available via https://doi.org/10.5281/zenodo.8239305 (Liu et al., 2023).

SUMMARY: Fowl adenovirus (FAdV), Avian influenza (AI), and Newcastle disease (ND) are prominent viral infectious diseases that pose a significant threat to the global poultry industry, resulting in substantial economic loss. To mitigate the occurrence of hydropericardium syndrome (HPS), a triple vaccine was developed by combining the nanoparticle-like fibre-2 protein with the inactivated H9N2 AI virus antigen derived from the A/chicken/Shandong/SZ/2008 strain, as well as the inactivated Newcastle disease virus (NDV) antigen obtained from the N7a strain, which was rescued from the NDV strain PLK-N-06. This particular strain was isolated during a chicken outbreak of ND and identified as velogenic. The immunological effect was evaluated by specific pathogen-free chicken challenge test. There was no significant difference in the specific antibody level in specific pathogen-free chickens between the triple vaccine and the monovalent vaccine (P > 0.05), which was significantly higher than that of the control group (P < 0.0001). The protective effect of triple vaccine against the 3 viruses could reach 100% after being challenged. No obvious lesions of FAdV, AI, or ND were observed in the target tissues and organs of the triple vaccine immunization group. Viral shedding test results showed that no three kinds of viruses were detected in larynx and cloaca on the fifth day after challenge. In conclusion, the immune protection effect against the 3 pathogens does not interfere with each other and can provide complete protection. The triple vaccine can be used as a candidate vaccine to prevent chickens from the three kinds of diseases, has high clinical application value, and can produce greater economic benefits.

Comprehensive observations of hydroxyl (OH) and hydroperoxy (HO2) radicals were conducted in October 2019 at a coastal continental site in the Pearl River Delta (YMK site, 22.55∘ N, 114.60∘ E). The daily maximum OH and HO2 concentrations were (4.7–9.5) × 106 and (4.2–8.1) × 108 cm−3, respectively. The synchronized air mass transport from the northern cities and the South China Sea exerted a time-varying influence on atmospheric oxidation. Under a typical ocean-atmosphere (OCM), reasonable measurement model agreement was achieved for both OH and HO2 using a 0-D chemical box model incorporating the regional atmospheric chemistry mechanism version 2-Leuven isoprene mechanism (RACM2-LIM1), with daily averages of 4.5 × 106 and 4.9 × 108 cm−3, respectively. Land mass (LAM) influence promoted more active photochemical processes, with daily averages of 7.1 × 106 and 5.2 × 108 cm−3 for OH and HO2, respectively. Heterogeneous uptake had certain effects on HOx chemistry, but the influence of the halogen mechanism was limited by NOx level. Intensive photochemistry occurred after precursor accumulation, allowing local net ozone production comparable with surrounding suburban environments (5.52 ppb h−1 during the LAM period). The rapid oxidation process was accompanied by a higher diurnal nitrous acid (HONO) concentration (> 400 ppt). After a sensitivity test, HONO-related chemistry elevated the ozone production rate by 33 % and 39 % during the LAM and OCM periods, respectively. The nitric acid (P(HNO3)) and sulfuric acid (P(H2SO4)) formation rates also increased simultaneously (∼ 43 % and ∼ 48 % for LAM and OCM sectors, respectively). In the ozone-prediction test, simulated O3 decreased from ∼ 75 ppb to a global background (∼ 35 ppb) without the HONO constraint, and daytime HONO concentrations were reduced to a low level (∼ 70 ppt). For coastal cities, the particularity of the HONO chemistry tends to influence the ozone-sensitive system and eventually magnifies the background ozone. Therefore, the promotion of oxidation by elevated precursors deserves a lot of attention when aiding pollution mitigation policies.

A high-resolution, spatially explicit forest age map is essential for quantifying forest carbon stocks and carbon sequestration potential. Prior attempts to estimate forest age on a national scale in China have been limited by sparse resolution and incomplete coverage of forest ecosystems, attributed to complex species composition, extensive forest areas, insufficient field measurements, and inadequate methods. To address these challenges, we developed a framework that combines machine learning algorithms (MLAs) and remote sensing time series analysis for estimating the age of China's forests. Initially, we identify and develop the optimal MLAs for forest age estimation across various vegetation divisions based on forest height, climate, terrain, soil, and forest-age field measurements, utilizing these MLAs to ascertain forest age information. Subsequently, we apply the LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, with the time since the last disturbance serving as a proxy for forest age. Ultimately, the forest age data derived from LandTrendr are integrated with the result of MLAs to produce the 2020 forest age map of China. Validation against independent field plots yielded an R2 ranging from 0.51 to 0.63. On a national scale, the average forest age is 56.1 years (standard deviation of 32.7 years). The Qinghai–Tibet Plateau alpine vegetation zone possesses the oldest forest with an average of 138.0 years, whereas the forest in the warm temperate deciduous-broadleaf forest vegetation zone averages only 28.5 years. This 30 m-resolution forest age map offers crucial insights for comprehensively understanding the ecological benefits of China's forests and to sustainably manage China's forest resources. The map is available at https://doi.org/10.5281/zenodo.8354262 (Cheng et al., 2023a).

​​​​​​​Teflon bag chambers have long been used for investigating atmospheric chemical processes, including secondary organic aerosol formation. The wall-loss process of gas-phase species in Teflon bag chambers has typically been investigated at around room temperature. Recent laboratory studies started employing Teflon bag chambers at sub-273 K conditions for simulating wintertime and upper-tropospheric environments. However, temperature dependence in vapor-wall-loss processes of semi-volatile organic compounds (SVOCs) in a Teflon bag chamber has not been well investigated. In this study, we experimentally investigated wall-loss processes of C14–C19 n-alkanes in a 1 m3 Teflon bag for the temperature range of 262 to 298 K. Enhanced wall losses of the tested n-alkanes were observed following the decrease in temperature. For instance, 65 %​​​​​​​ of C14 n-alkane was lost to the wall 15 h after injection at room temperature, while the corresponding value was 95 % at 262 K. The experimental data were analyzed using a two-layer kinetic model, which considers both absorption of gas-phase species to the surface layer of the Teflon wall and diffusion to the inner layer. The experimental data demonstrated that absorption of gas-phase species by the surface layer was enhanced at lower temperatures. The temperature dependence in absorption was well accounted for using the equilibrium-dissolution model of organic compounds to the Teflon surface by considering reduced saturation vapor pressure at lower temperatures. On the contrary, diffusion of n-alkanes from the surface to the inner layer slowed down at reduced temperatures. Mechanistic studies on these processes will need to be conducted in the future to quantitatively predict the influence of temperature-dependent wall-loss processes of SVOCs on laboratory experimental results.

Plant phenology refers to cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is crucial for understanding the ecosystem response to global change and modeling the material and energy balance of terrestrial ecosystems. Utilizing 24 552 in situ phenological observations of 24 representative woody plant species from the Chinese Phenology Observation Network (CPON), we have developed maps delineating species phenology (SP) and ground phenology (GP) of forests over China from 1951 to 2020. These maps offer a detailed spatial resolution of 0.1∘ and a temporal resolution of 1 d. Our method involves a model-based approach to upscale in situ phenological observations to SP maps, followed by the application of weighted average and quantile methods to derive GP maps from the SP data. The resulting SP maps for the 24 woody plants exhibit a high degree of concordance with in situ observations, manifesting an average deviation of 6.9 d for spring and 10.8 d for autumn phenological events. Moreover, the GP maps demonstrate robust alignment with extant land surface phenology (LSP) products sourced from remote sensing data, particularly within deciduous forests, where the average discrepancy is 8.8 d in spring and 15.1 d in autumn. This dataset provides an independent and reliable phenology data source for China on a long-time scale of 70 years, and contributes to more comprehensive research on plant phenology and climate change at both regional and national scales. The dataset can be accessed at https://doi.org/10.57760/sciencedb.07995 (Zhu and Dai, 2023).

Map verification plays a crucial role in ensuring the accuracy and reliability of map data, which in turn establishes the foundation for map transparency. In China, the number of approved map inspections has been increasing annually, with a diverse range of map products being applied for inspection. Taking the example of the 'number of maps accepted for inspection' by the Ministry of Natural Resources of the People’s Republic of China, there has been a consistent upward trend in the number of accepted inspections. From 2017 to 2021, the average annual growth rate of accepted inspections exceeds 18.85%. Not only are new types, uses, and formats of map products emerging, but there is also an increase in the utilization of electronic global maps. However, challenges such as the inefficient information transfer during the inspection application process and the limited automation of technical inspections persist. Furthermore, issues related to the variability of inspection results based on individual experiences need to be addressed. To tackle these problems, this paper proposes a comprehensive technical framework for map verification based on the principles of "data sharing, business collaboration, and intelligent assistance". The objective is to establish a map verification service mechanism that promotes cross-departmental coordination and intelligent cooperation. Implementing this framework will facilitate the smooth operation of national map verification services and expedite the sharing and openness of geospatial data.

High-resoulution downscaling of surface climate metrics like urban surface temperature, is a crucial and ongoing research challenge in urban climatology and environmental studies. In this study we propose a groundbreaking Physics-Inspired Neural Architecture for Modeling (PINAM) called DeepUrbanModeller(DUM), designed specifically for urban microclimate temperature estimation. DeepUrbanModeller(DUM) harnesses process-based modelling and satellite remote sensing, and draws upon high-accuracy 3D point clouds to deliver precise estimations of urban Land Surface Temperature (LST) at ultra-high resolutions. By incorporating high-accuracy land surface geometric data sourced from 3D point clouds and guided by the principles of atmospheric physics linked to surface temperature, DeepUrbanModeller(DUM) creates a data-driven framework, informed by physical laws, to accurately model high-resolution temperature distributions a task challenging for numerical simulations or conventional machine learning. The DeepUrbanModeller(DUM) design integrates two key components: Global Physical Feature Interpretation (GPFI) and Local Urban Surface Insight (LUSI). The GPFI captures broader urban physical parameters, ensuring the estimates comply with relevant physical laws. The LUSI enhances estimation performance at high-resolution levels by utilizing a newly proposed Urban Detail Orientation Index (UDOI) derived from 3D point clouds. Experimental results demonstrate the DeepUrbanModeller(DUM)’s superior capability in estimating urban LST on a detailed 30-by-30 meter grid, achieving an estimation error of less than 0.2 Kelvin compared to satellite measurements, a performance surpassing traditional methodologies.

Reconstructing the geometric curves of highways holds significant value for various tasks, such as highway design, traffic simulation, and road network planning. An essential step in highway curve reconstruction is fitting highway curve models to the extracted road markings from laser scanning point clouds. Existing methods for highway curve fitting typically handle one curve at a time. However, a stretch of highway often contains multiple curves. In this paper, we introduce a novel method capable of fitting multiple highway curves simultaneously. The method leverages a reinforcement learning (RL) algorithm to achieve this goal. Specifically, we design a unique RL environment that empowers the RL algorithm to fit multiple highway curves. Our experimental results demonstrate the superiority of our method over other methods.

The functional differences among various components of a city drive the circulation of logistics, human flows, and energy flows within the city. Qualitatively and quantitatively analyzing the spatial interactions among urban functional districts contributes to understanding the spatial structure of it and formulating more efficient urban planning strategies. Based on taxi trips, this paper compares spatial interactions between the Xudong Commercial District and the Zhongnan Road Commercial District in Wuhan, respectively, with other functional districts. Firstly, the spatial interaction based on taxi trips between these two commercial districts, acting as either the origins or destinations for taxi trips, with various functional districts was visualized. Subsequently, we analyzed the intensity and distance of the spatial interactions based on taxi trips during each typical time period (8:00-9:00, 18:00-19:00, and 20:00-21:00 on weekdays; and 9:00-10:00, 19:00-20:00, and 22:00-23:00 on weekends). These results can serve as reference for further research on the influence range, development planning, and transportation optimization of these commercial districts.

From 2000 to 2020, three versions of the 30-meter global land cover data products (GlobeLand30) have been made accessible to the public. The dataset with 30-meter spatial resolution and 10-year temporal resolution are widely used for monitoring environment changes and resource management at global, regional and local scales. By analysing and extracting information and knowledge from the GlobeLand30 data, we have planned and designed a global land cover knowledge atlas, which aims to expand the available knowledge base for scientific research, management decision-making, and knowledge dissemination.

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2–temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

When hydrometeors fall from an in-cloud saturated environment toward the ground, especially in arid and semiarid regions, below-cloud processes may heavily alter the isotopic composition of precipitation through equilibrium and non-equilibrium fractionations. If these below-cloud processes are not correctly identified, they can lead to misinterpretation of the precipitation isotopic signal. To correctly understand the environmental information recorded in the precipitation isotopes, qualitatively analyzing the below-cloud processes and quantitatively calculating the below-cloud evaporation effect are two important steps. Here, based on 2 years of synchronous observations of precipitation and water vapor isotopes in Xi'an, China, we compiled a set of effective methods to systematically evaluate the below-cloud evaporation effect on local precipitation isotopic composition. The ΔdΔδ diagram is a tool to effectively diagnose below-cloud processes, such as equilibration or evaporation, because the isotopic differences (δ2H; d-excess) between the precipitation-equilibrated vapor and the observed vapor show different pathways. By using the ΔdΔδ diagram, our data show that evaporation is the major below-cloud process in Xi'an, while snowfall samples retain the initial cloud signal because they are less impacted by the isotopic exchange between vapor and solid phases. Then, we chose two methods to quantitatively characterize the influence of below-cloud evaporation on local precipitation isotopic composition. One is based on the raindrop's mass change during its falling (hereafter referred to as method 1), and the other is dependent on the variations in precipitation isotopic composition from the cloud base to the ground (hereafter referred to as method 2). By comparison, we found that there are no significant differences between the two methods in evaluating the evaporation effect on δ2Hp, except for snowfall events. The slope of the evaporation in proportion to the variation in δ2H (Fi/Δδ2H) is slightly larger in method 1 (1.0 ‰ %−1) than in method 2 (0.9 ‰ %−1). Additionally, both methods indicate that the evaporation effect is weak in autumn and heavy in spring. Through a sensitivity test, we found that in two methods, relative humidity is the most sensitive parameter, while the temperature shows different effects on the two methods. Therefore, we concluded that both methods are suited to the investigation of the below-cloud evaporation effect, while in method 2, other below-cloud processes, such as supersaturation, can still be included. By applying method 2, the diagnosis of below-cloud processes and the understanding of their effects on the precipitation isotopic composition will be improved.

Abstract Silicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on-chip. Scaling and integration are the most fundamental challenges facing quantum science and technology. We report an all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near-unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the all-silicon quantum emissive center. Our work opens immediate avenues for large-scale integrated cavity quantum electrodynamics and quantum light-matter interfaces with applications in quantum communication and networking, sensing, imaging, and computing.

The nearby radio galaxy M87 is a very high-energy (VHE) gamma-ray emitter established by observations with ground-based gamma-ray detectors. Here we report the long-term monitoring of M87 from 2021 to 2024 with the Large High Altitude Air Shower Observatory (LHAASO). M87 has been detected by LHAASO with a statistical significance ∼ 9 σ . The observed energy spectrum extends to 20 TeV, with a possible hardening at ∼20 TeV and then a clear softening at higher energies. Assuming that the intrinsic spectrum is described by a single power law up to 20 TeV, a tight upper bound on the extragalactic background light intensity is obtained. A strong VHE flare lasting 8 days, with a rise time of ${\tau }_{r}^{\mathrm{rise}}=1.05\pm 0.49$ days and decay time of ${\tau }_{d}^{\mathrm{decay}}=2.17\pm 0.58$ days, was found in early 2022. A possible GeV flare is seen also in Fermi Large Area Telescope data during the VHE flare period. The variability time as short as 1 day seen in the LHAASO data suggests a compact emission region with a size of ∼3 × 10 ^15 δ cm ( δ being the Doppler factor of the emitting region), corresponding to a few Schwarzschild radii of the central supermassive black hole in M87. The continuous monitoring of the source reveals a duty cycle of ∼1% for VHE flares with a flux above 10 ^−11 erg cm ^−2 s ^−1 .

The first source catalog of the Large High Altitude Air Shower Observatory (LHAASO) reported the detection of a very high energy gamma-ray source, 1LHAASO J1219+2915. This Letter presents a further detailed study of the spectral and temporal behavior of this pointlike source. The best-fit position of the TeV source (R.A. = 185.°05 ± 0.°04, decl. = 29.°25 ± 0.°03) is compatible with NGC 4278 within ∼0.°03. Variation analysis shows an indication of variability on a timescale of a few months in the TeV band, which is consistent with low-frequency observations. Based on these observations, we report the detection of TeV γ -ray emissions from this low-luminosity active galactic nucleus. The observation by LHAASO's Water Cherenkov Detector Array during the active period has a significance level of 8.8 σ with a best-fit photon spectral index Γ = 2.56 ± 0.14 and a flux f _1–10 TeV = (7.0 ± 1.1 _sta ± 0.35 _syst ) × 10 ^−13 photons cm ^−2 s ^−1 , or approximately 5% of the Crab Nebula. The discovery of VHE gamma-ray emission from NGC 4278 indicates that compact, weak radio jets can efficiently accelerate particles and emit TeV photons.

SUMMARY: This study aimed to determine the effects of different cage stocking densities on the production performance, serum biochemical indices, intestinal morphology and immune levels of Hy-Line Brown and Jinghong laying hens. A total of 432 Hy-Line Brown (n = 216) and Jinghong (n = 216) commercial laying hens were selected and randomly divided into 3 groups according to stocking density: high density (H, 4 birds/cage), medium density (M, 3 birds/cage) and low density (L, 2 birds/cage). In both breeds, the laying rates and Haugh units in the L group were significantly higher than those in the H group (P < 0.05), and the feed-to-egg ratio (feed conversion ratio [FCR]) in the L group was significantly lower than that in the H group (P < 0.05). Additionally, in both breeds, serum triglyceride (TG) activities and corticosterone (CORT) concentrations in the L group were significantly lower than those in the H group. The duodenal villus-to-crypt ratio (VCR) in the L group was significantly higher than that in the H group in both breeds. There were significant interaction effects of breed and stocking density on the laying rate, FCR, Haugh unit, serum TG level, glutathione peroxidase (GSH-Px) level, CORT level, duodenal Villus height (VH), duodenal VCR and cecal VCR (P < 0.05). Overall, the results of this study indicate that a low stocking density increases the production performance and increases the intestinal mucosal barrier function of laying hens compared to a high stocking density.

Synthetic jet has been confirmed as a novel flow control technology. However, the existing application research on synthetic jet in axial flow compressor is still confined to cascade or low-speed axial flow compressors, and rarely high-speed axial flow compressor. The effects of three vital parameters (i.e., the action position, frequency, peak velocity) on the aerodynamic performance and stability margin are systematically studied, with a high-speed compressor rotor as the object of numerical simulation. An optimal excitation position is determined, corresponding to the core position of the compressor top blockage, as indicated by the results, which increases the stability margin and efficiency of the compressor by 13.2% and 1.15% respectively. The excitation frequency has a threshold ranging from 300Hz to 600Hz. Only when the frequency of synthetic jet exceeds this threshold can it suppress the tip leakage flow. Besides, the jet peak velocity may not have a threshold. However, the higher the peak velocity, the greater the mixing loss between the jet and the mainstream of the compressor rotor will be, thus limiting the further increase of the compressor efficiency.

In the present study, Fourier theory is applied to establish the expression of rigid-body poses of a spherical four-bar crank slider rigid-body guidance mechanism. According to an analysis of the harmonic components of the trajectory curve and rigid-body rotation angle, it has a certain relationship with the geometric parameters of the mechanism. On this basis, the rigid-body poses are normalized by preprocessing. Then, the rotation angle of the curve around the y axis and z axis is determined, respectively. The theoretical formulas used for calculating the real sizes and the installation position parameters of the desired spherical four-bar crank slider rigid-body guidance mechanism are established. Besides this, a genetic optimization algorithm and theoretical formulas are applied to solve the dimensional synthesis of motion generation for the spherical four-bar crank slider mechanism. The effectiveness of the proposed method is illustrated by an example. The maximum Euclidean distance error of the rigid-body position of the results with the highest similarity is 0.0086, and the average Euclidean distance error is 0.0044. The maximum error of the rigid-body orientation is 0.0179, and the average error is 0.0065.

Abstract The high‐resolution stereo camera mounted on the Mars Express (MEX) spacecraft of the European Space Agency is specifically designed for planetary photogrammetry and mapping missions. High‐precision geometric calibration is required to maintain the geometric quality of satellite imaging. This study aimed to develop a Martian geometric calibration model based on the detector look angle and offset matrix to compensate for systematic errors caused by the camera system parameters, Doppler shift measurements, and other parameters of the interior and exterior orientation during positioning. Moreover, the difficulty in identifying high‐precision geometric ground control points from extra‐terrestrial imagery was addressed by proposing an iterative Mars Orbiter Laser Altimeter extraction algorithm based on the similarity measure of terrain relief. Using this algorithm, constraint conditions were established, and a calibration model was introduced to achieve in‐orbit geometric calibration of the MEX satellite. The proposed method is effective in reducing the distortion of the camera in calibration images from 12 pixels to

Carbon capture, utilization and storage (CCUS) is considered as a promising technology to effectively mitigate CO2 emissions. Currently process design on CCUS is mainly focused on temperature swing adsorption (TSA) process and steam purge technology has the advantages of direct heat exchange and reduced carbon dioxide concentration in regeneration step. The paper initially proposes a steam-assisted temperature swing adsorption (S-TSA) process for carbon capture, in which heat pump plays a role to provide the driving force of desorption. The performance of the integrated system is evaluated in terms of feed, purge and cooling gas flowrates and evaporation and flash temperature. Results show that when the feed gas flowrates increase from 44 NL·min−1 to 56 NL·min−1, CO2 purity increases from 90.24% to 90.89% and recovery rate decreases from 95.88% to 85.72%. Heat and power consumption of the integrated system increase significantly with the rise of purge flowrates. If the waste heat inside the carbon capture system is utilized, the heat consumption can be reduced by a maximum of 14.91% at flash temperature of 75 °C. The significance of each flowrate and temperature to simulation results is analyzed through the orthogonal design and multivariate analysis of variance. It shows that performance of the integrated system is significantly influenced by feed flowrate and flash temperature, and cooling flowrate has the minimal impact. It demonstrates that the feed flowrate and desorption temperature should be given priority when choosing appropriate operating conditions for a real carbon capture system.

Abstract We present overall process for developing terahertz (THz) corrugated structure and its beam-based measurement results. 0.2-THz corrugated structures were fabricated by die stamping method as the first step demonstration towards GW THz radiation source and GV/m THz wakefield accelerator. 150- $$\upmu$$ μ m thick disks were produced from an OFHC (C10100) foil by stamping. Two types of disks were stacked alternately to form 46 mm structure with $$\sim$$ ∼ 170 corrugations. Custom assembly was designed to provide diffusion bonding with a high precision alignment of disks. The compliance of the fabricated structure have been verified through beam-based wakefield measurement at Argonne Wakefield Accelerator Facility. Both measured longitudinal and transverse wakefield showed good agreement with simulated wakefields. Measured peak gradients, 9.4 MV/m/nC for a long single bunch and 35.4 MV/m/nC for a four bunch trains, showed good agreement with the simulation.

Wetlands, often called the “kidneys of the earth”, play an important role in maintaining ecological balance, conserving water resources, replenishing groundwater and controlling soil erosion. Wetland mapping is very challenging because of its complicated temporal dynamics and large spatial and spectral heterogeneity. An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping by combining an automatic sample extraction method, existing multi-sourced products, satellite time-series images and a stratified classification strategy. This approach allowed for the generation of the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal tidal wetland sub-categories (mangrove, salt marsh and tidal flats), which was developed using Google Earth Engine platform. We first combined existing multi-sourced global wetland products, expert knowledge, training sample refinement rules and visual interpretation to generate large and geographically distributed wetland training samples. Second, we integrated the Landsat reflectance time-series products and Sentinel-1 synthetic aperture radar (SAR) imagery to generate various water-level and phenological information to capture the complicated temporal dynamics and spectral heterogeneity of wetlands. Third, we applied a stratified classification strategy and the local adaptive random forest classification models to produce the wetland dataset with a fine classification system at each 5∘×5∘geographical tile in 2020. Lastly, GWL_FCS30, mosaicked by 961 5∘×5∘ regional wetland maps, was validated using 25 708 validation samples, which achieved an overall accuracy of 86.44 % and a kappa coefficient of 0.822. The cross-comparisons with other global wetland products demonstrated that the GWL_FCS30 dataset performed better in capturing the spatial patterns of wetlands and had significant advantages over the diversity of wetland sub-categories. The statistical analysis showed that the global wetland area reached 6.38 million km2, including 6.03 million km2 of inland wetlands and 0.35 million km2 of coastal tidal wetlands, approximately 72.96 % of which were distributed poleward of 40∘ N. Therefore, we can conclude that the proposed method is suitable for large-area wetland mapping and that the GWL_FCS30 dataset is an accurate wetland mapping product that has the potential to provide vital support for wetland management. The GWL_FCS30 dataset in 2020 is freely available at https://doi.org/10.5281/zenodo.7340516 (Liu et al., 2022).

Although evidence of the hydrological response of watersheds to climate change is abundant, reliable assessments of water yield (WY) over mountainous regions, such as the Upper Brahmaputra River (UBR) basin, remain unclear. Here, we examine long-term WY changes during 1982–2013 in the UBR basin, based on multi-station runoff observations. We find that there are significant shifts in hydrological regimes in the late 1990s; WY increases in the range of ∼10 % to ∼80 %, while the directions reverse from increasing to decreasing. Additionally, the double mass curve (DMC) technique is used to assess the effects of climate, vegetation, and cryosphere on WY changes. Results show that cryosphere and climate together contribute to over 80 % of the increase in WY across the entire UBR basin, while the role of vegetation is negligible. The combined effects, however, are either offsetting or additive, thus leading to slight or substantial magnitude increases, respectively. The downward WY trend has primarily been regulated by decreased precipitation in recent years. However, we find that meltwater may alleviate the resulting water shortage in some basins. Therefore, the combined effects of climate and cryosphere on WY should be considered in future water resources management over mountainous basins, particularly involving co-benefits between upstream and downstream regions.

Condensable particulate matter (CPM) emitted from stationary combustion and mobile sources exhibits high emissions and a large proportion of organic components. However, CPM is not generally measured when conducting emission surveys of PM in most countries, including China. Consequently, previous emission inventories have not included emission rates for CPM. Here, we construct an emission inventory of CPM in China with a focus on organic aerosols (OAs) based on collected CPM emission information. Results show that OA emissions are enhanced twofold after the inclusion of CPM in a new inventory for China for the years 2014 and 2017. Considering organic CPM emissions and model representations of secondary OA (SOA) formation from CPM, a series of sensitivity cases have been simulated here using the three-dimensional Community Multiscale Air Quality (CMAQ) model to estimate the contributions of CPM emissions to atmospheric OA and fine PM (PM2.5, particulate matter with aerodynamic diameter not exceeding 2.5 µm) concentrations in China. Compared with observations at a Beijing site during a haze episode from 14 October to 14 November 2014, estimates of the temporal average primary OA (POA) and SOA concentrations were greatly improved after including the CPM effects. These scenarios demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to the POA (51 %–85 %​​​​​​​), SOA (42 %–58 %), and total OA concentrations (45 %–75 %). Furthermore, the contributions of CPM emissions to total OA concentrations were demonstrated over the 2 major cities and 26 other cities of the Beijing–Tianjin–Hebei region (hereafter referred to as the “BTH2 + 26 cities”) in December 2018, with average contributions of up to 49 %, 53 %, 54 %, and 50 % for Handan, Shijiazhuang, Xingtai, and Dezhou, respectively. Correspondingly, the inclusion of CPM emissions also narrowed the gap between simulated and observed PM2.5 concentrations over the BTH2 + 26 cities. These results improve the simulation performance of atmospheric OA and PM2.5 and may also provide important implications for the sources of OA.

We present the first catalog of very-high-energy and ultra-high-energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory. The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array from 2021 March to 2022 September and 933 days of data recorded by the Kilometer Squared Array from 2020 January to 2022 September. This catalog represents the main result from the most sensitive large coverage gamma-ray survey of the sky above 1 TeV, covering decl. from −20° to 80°. In total, the catalog contains 90 sources with an extended size smaller than 2° and a significance of detection at >5 σ . Based on our source association criteria, 32 new TeV sources are proposed in this study. Among the 90 sources, 43 sources are detected with ultra-high energy ( E > 100 TeV) emission at >4 σ significance level. We provide the position, extension, and spectral characteristics of all the sources in this catalog.

Abstract Infantile neuroaxonal dystrophy (INAD) is a rare paediatric neurodegenerative condition caused by mutations in the PLA2G6 gene, which is also the causative gene for PARK14-linked young adult-onset dystonia parkinsonism. INAD patients usually die within their first decade of life, and there are currently no effective treatments available. GLP1 receptor (GLP-1R) agonists are licensed for treating type 2 diabetes mellitus but have also demonstrated neuroprotective properties in a clinical trial for Parkinson’s disease. Therefore, we evaluated the therapeutic efficacy of a new recently licensed GLP-1R agonist diabetes drug in a mouse model of INAD. Systemically administered high-dose semaglutide delivered weekly to juvenile INAD mice improved locomotor function and extended the lifespan. An investigation into the mechanisms underlying these therapeutic effects revealed that semaglutide significantly increased levels of key neuroprotective molecules while decreasing those involved in pro-neurodegenerative pathways. The expression of mediators in both the apoptotic and necroptotic pathways were also significantly reduced in semaglutide treated mice. A reduction of neuronal loss and neuroinflammation was observed. Finally, there was no obvious inflammatory response in wild-type mice associated with the repeated high doses of semaglutide used in this study.

The current large-scale parallel barrier of ocean general circulation models (OGCMs) makes it difficult to meet the computing demand of high resolution. Fully considering both the computational characteristics of OGCMs and the heterogeneous many-core architecture of the new Sunway supercomputer, swNEMO_v4.0, based on NEMO4 (Nucleus for European Modelling of the Ocean version 4), is developed with ultrahigh scalability. Three innovations and breakthroughs are shown in our work: (1) a highly adaptive, efficient four-level parallelization framework for OGCMs is proposed to release a new level of parallelism along the compute-dependency column dimension. (2) A many-core optimization method using blocking by remote memory access (RMA) and a dynamic cache scheduling strategy is applied, effectively utilizing the temporal and spatial locality of data. The test shows that the actual direct memory access (DMA) bandwidth is greater than 90 % of the ideal band-width after optimization, and the maximum is up to 95 %. (3) A mixed-precision optimization method with half, single and double precision is explored, which can effectively improve the computation performance while maintaining the simulated accuracy of OGCMs. The results demonstrate that swNEMO_v4.0 has ultrahigh scalability, achieving up to 99.29 % parallel efficiency with a resolution of 500 m using 27 988 480 cores, reaching the peak performance with 1.97 PFLOPS.

The sequence diagram in the atlas shall represent the latest macro overview achievements of the country, closely focusing on the major national development strategies, and select topics directly related to economic construction and people's life, including thematic information such as sea area, railway, highway, population, tourism, forest, nature reserve, surface coverage, urban agglomeration and strategic layout. According to the selected thematic content, the relevant thematic information is collected and analyzed, and the available thematic information data is extracted through data normalization and spatial processing, and then spatially matched with the thematic geographic base map to form a thematic map database. This paper studies how to deal with the thematic data in the sequence diagram and how to build the database, including thematic data analysis, thematic data processing, thematic database construction and quality requirements.

In the face of the problems that have emerged along with the rapid industrialization and urbanization in China in recent years, the country proposes to compile the national main functional area plan, divides the national land space into four types of main functional areas: optimized development, key development, restricted development and prohibited development, and carries out the compilation of the national main functional area plan at two levels: national and provincial. The spatial evaluation indexes of the country and the results of the main functional area planning are expressed graphically in the form of thematic maps to make them visual, intuitive and easier to understand. In this paper, we study the topic selection of the National Atlas of Main Functional Area Planning, and mainly introduce the research results of the atlas in topic selection research, content structure design and layout design based on the comprehensive analysis of national and provincial information and following the principles of topic selection.

Landslide dams are caused when landslide materials block rivers. After the occurrence of large-scale landslides, it is necessary to conduct a large-scale investigation of barrier lakes and a rapid risk assessment. Remote sensing is an important means to achieve this goal. However, at present, remote sensing is only used for the monitoring and extraction of hydrological parameters, without predicting the potential hazard of the landslide dam. The key parameters of the barrier dam, such as the dam height and the maximum volume, still need to be obtained based on a field investigation, which is time consuming. Our research proposes a procedure that is able to calculate the height of the landslide dam and the maximum volume of the barrier lake using a single remote-sensing image and a pre-landslide DEM. The procedure includes four modules: (a) determining the elevation of the lake level, (b) determining the elevation of the bottom of the dam, (c) calculating the highest height of the dam and (d) predicting the lowest crest height of the dam and the maximum volume. Finally, a sensitivity analysis of the parameters used during the procedure and an analysis of the influence of the image resolution is carried out. This procedure is mainly demonstrated through the Baige landslide dam and the Hongshiyan landslide dam. A single remote-sensing image and a pre-landslide DEM are used to predict the height of each dam and the key parameters of the dam break, which are in good agreement with the measured data. This procedure can effectively support quick decision making regarding hazard mitigation.

Abstract In this paper, we introduce a testbed for evaluating and comparing climate modeling systems at cloud resolving scales using hindcasts of the June 2012 North American derecho. To demonstrate its utility for model intercomparison, the testbed is applied to two models: the regionally‐refined Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) at 6.5, 3.25 and 1.625 km grid spacing and the Weather Research and Forecasting (WRF) model with 3.2 and 1.6 km grid spacing. We find the simulation results to be highly sensitive to the initial conditions (ICs), initialization time, and model configurations, with ICs from the Rapid Refresh producing the best simulation. Significant improvement is identified in both models as horizontal grid spacing is refined. While a propagation delay of approximately 2 hr is found in both models, SCREAM at 1.625 km simulates the observed bow echo structure of the derecho well and predicts strong surface gusts that exceed 30 m/s. In comparison, WRF has difficulty producing surface wind over 25 m/s, with wind gusts in WRF 42%–46% lower than in SCREAM. However, WRF has a lower bias in simulating cloud top temperature and extent, but overestimates precipitation intensity. Both models reproduce the observed outgoing longwave radiation spatial patterns well (Pearson correlation >0.88), but, compared with NEXRAD observations, simulate generally larger areas of composite radar reflectivity >40 dBZ and underestimate the precipitating area by ∼47%.

Abstract Cobalt metal is considered as an essential trace element for the animals. Present investigation was undertaken in the peri-urban area to analyze the cobalt availability in animal food chain by using different indices. Cow, buffalo and sheep samples along with forage and soil samples were collected from the three different sites of District Jhang and analyzed through atomic absorption spectrophotometer. Cobalt values differed in soil samples as 0.315-0.535 mg/kg, forages as 0.127-0.333 mg/kg and animal samples as 0.364-0.504 mg/kg. Analyzed cobalt concentration in soil, forage and animal samples was found to be deficient in concentration with respect to standard limits. Soil showed the minimum cobalt level in Z. mays while maximum concentration was examined in the forage C. decidua samples. All indices examined in this study has values lesser than 1, representing the safer limits of the cobalt concentration in these samples. Enrichment factor (0.071-0.161 mg/kg) showed the highly deficient amount of cobalt enrichment in this area. Bio-concentration factor (0.392-0.883) and pollution load index (0.035-0.059 mg/kg) values were also lesser than 1 explains that plant and soil samples are not contaminated with cobalt metal. The daily intake and health risk index ranged from 0.00019-0.00064 mg/kg/day and 0.0044-0.0150 mg/kg/day respectively. Among the animals, cobalt availability was maximum (0.0150 mg/kg/day) in the buffaloes that grazed on the C. decidua fodder. Results of this study concluded that cobalt containing fertilizers must be applied on the soil and forages. Animal feed derived from the cobalt containing supplements are supplied to the animals, to fulfill the nutritional requirements of livestock.

Accurate prediction of PM2.5 concentration is the premise and guarantees to effectively control PM2.5 concentration and avoid the adverse effects of high PM2.5 concentration on human health. However, given the existing statistical models can only predict the pollutant concentration at the monitoring sites, the spatial distribution of PM2.5 concentration cannot be predicted, which greatly limits the application of the model in PM2.5 concentration prediction. This study combined the PM2.5 spatial distribution data predicted using the Moderate Resolution Imaging Spectroradiometer 3 km aerosol optical depth (AOD) and meteorological factors into a spatiotemporal autoregressive (STAR) model to predict the regional PM2.5 concentration and quantify the short-term spatial distribution change of PM2.5 in Beijing–Tianjin–Hebei region (JingJinJi) one day in advance. Five simulation functions were used to simulate the STAR model, and the 2014 data of JingJinJi were used to verify its accuracy. Results showed that the STAR model had the best prediction performance when gradient boosting decision tree was used as the simulation function compared with other simulation functions. The coefficient of determination (R2), root mean square prediction error (RMSE), index of agreement (IA), and mean absolute error (MAE) of the STAR model were 0.85, 27.08 µg/m3, 0.96, and 20 µg/m3, respectively. The spatial distribution prediction results of PM2.5 showed that the +1-day PM2.5 spatial distribution prediction results were in good agreement with the PM2.5 spatial distribution results predicted by AOD to provide accurate spatiotemporal distribution data for reducing air pollution and air pollution early warning.

Inspired by the immense success of deep neural network in image processing and object recognition, learning-based image super resolution (SR) methods have been highly valued and have become the mainstream direction of super resolution research. Base on the recent proposed state-of-art convolution neural network (CNN) super-resolution methods, this paper proposed a generative adversarial network for single satellite image Super Resolution reconstruction. It built on a trained deep residual network to generate preliminary SR images, combined with a discriminative network learns to differentiate preliminary SR images and High resolution samples. The experiments results show that our method can use existing model parameters to refine SR image performance.

Land cover data is one of the principle sources to understand changes of the earth, develop sustainable globe, conduct environment change studies, land source management, and many other societal benefit areas. Globeland30 is the first multi-temporal (for years 2000 and 2010) global land cover datasets at 30-meter resolution. Based on this dataset, an online geo-statistical system has been developed named “GlobeLand30 GSA”. It helps users from different technical expertise backgrounds to visually explore and quantitatively analyse global land cover data. Different from other Web geoprocessing services and applications or desktop GIS software, this system has developed algorithms to correct multi-type errors in computing global land cover statistics, and applied optimal strategies of pre-organizing geography and auxiliary datasets in order to provide rapid response to multiple co-occurrence user-requests. This paper presents the system architecture and the developed data processing strategies, and also the related accuracy and efficiency assessment of GlobeLand30 GSA. The result shows that the system can provide accurate and efficient land cover statistical analysis for various size and shape of user requested areas, and is capable of handling as much as 200 concurrent user-requests with reasonable response time using a low-end desktop computer system environment.

Sustainable aviation fuels (SAFs) have different compositions compared to conventional petroleum jet fuels, particularly in terms of fuel sulfur and hydrocarbon content. These differences may change the amount and physicochemical properties of volatile and non-volatile particulate matter (nvPM) emitted by aircraft engines. In this study, we evaluate whether comparable nvPM measurement techniques respond similarly to nvPM produced by three blends of SAFs compared to three conventional fuels. Multiple SAF blends and conventional (Jet A-1) jet fuels were combusted in a V2527-A5 engine, while an additional conventional fuel (JP-8) was combusted in a CFM56-2C1 engine. We evaluated nvPM mass concentration measured by three real-time measurement techniques: photoacoustic spectroscopy, laser-induced incandescence, and the extinction-minus-scattering technique. Various commercial instruments were tested, including three laser-induced incandescence (LII) 300s, one photoacoustic extinctiometer (PAX), one micro soot sensor (MSS+), and two cavity-attenuated phase shift PMSSA (CAPS PMSSA) instruments. Mass-based emission indices (EIm) reported by these techniques were similar, falling within 30 % of their geometric mean for EIm above 100 mg per kg fuel (approximately 10 µg PM m−3 at the instrument); this geometric mean was therefore used as a reference value. Additionally, two integrative measurement techniques were evaluated: filter photometry and particle size distribution (PSD) integration. The commercial instruments used were one tricolor absorption photometer (TAP), one particle soot absorption photometer (PSAP), and two scanning mobility particle sizers (SMPSs). The TAP and PSAP were operated at 5 % and 10 % of their nominal flow rates, respectively, to extend the life of their filters. These techniques are used in specific applications, such as on board research aircraft to determine particulate matter (PM) emissions at cruise. EIm reported by the alternative techniques fell within approximately 50 % of the mean aerosol-phase EIm. In addition, we measured PM-number-based emission indices using PSDs and condensation particle counters (CPCs). The commercial instruments used included TSI SMPSs, a Cambustion differential mobility spectrometer (DMS500), and an AVL particle counter (APC), and the data also fell within approximately 50 % of their geometric mean. The number-based emission indices were highly sensitive to the accuracy of the sampling-line penetration functions applied as corrections. In contrast, the EIm data were less sensitive to those corrections since a smaller volume fraction fell within the size range where corrections were substantial. A separate, dedicated experiment also showed that the operating laser fluence used in the LII 300 laser-induced incandescence instrument for aircraft-engine nvPM measurement is adequate for a range of SAF blends investigated in this study. Overall, we conclude that all tested instruments are suitable for the measurement of nvPM emissions from the combustion of SAF blends in aircraft engines.

Residents’ activities have a significant interaction with urban socioeconomic environment. Taxi trajectory data has been widely used to mine human activity patterns to identify urban functional districts. However, previous studies merely chose several spatiotemporal statistics of taxi pick-up and drop-off points. This paper compares seven time series statistics of taxi pick-up and drop-off points, and selects the best combination to identify urban functional districts. The basic analysis units are not only constructed based on the OpenStreetMap data, but also optimized with the fine-grained clean rasterized pixels, generated from preprocessed taxi trajectory data through the improved head/tail breaks method. The experiment conducted in Wuchang District, Wuhan, shows that the combination of the average statistics of pick-up points, the average statistics of drop-off points, and the ratio statistics of pick-up and drop-off difference achieves the best identification precision of 83.65%, the F1-score of 82.2%, and the recall score of 81.48%. The proposed approach has good scalability and can be transplant to other identification applications.

The Poiqu River basin is an area of concentration for glaciers and glacial lakes in the central Himalayas, where 147 glacial lakes were identified, based on perennial remote sensing images, with lake area ranging from 0.0002 to 5.5 km2 – a total of 19.89 km2. Since 2004, the retreat rate of glacier has reached as high as 5.0 km2 a−1, while the growth rate of glacial lake has reached 0.24 km2 a−1. We take five typical lakes as our case study and find that the retreat of glacier area reaches 31.2 %, while the glacial lake area has expanded by 166 %. Moreover, we reconstruct the topography of the lake basin to calculate the water capacity and propose a water balance equation (WBE) to explore the lake evolution. By applying the WBE to the five lakes, we calculate the water supplies of the last few years and compare this with the results of field surveys, which are in agreement, within an error of only 1.86 % on average. The WBE also reveals that the water supplies to the lake depend strongly on the altitude. Lakes at low altitudes are supplied by glacier melting, and lakes at high altitudes are supplied by snowmelts. The WBE is not only applicable for predicting future changes in glacial lakes under climate warming conditions but is also useful for assessing water resources from rivers in the central Himalayas.