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Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr−1 in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N2O to the total enhanced effective radiative forcing of greenhouse gases was 6.4 % for 1750–2022. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N2O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N2O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions have increased 40 % (or 1.9 Tg N yr−1) in the past 4 decades (1980–2020). Direct agricultural emissions in 2020 (3.9 Tg N yr−1, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1) . For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr−1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr−1. For the 2010–2019 period, the annual BU decadal-average emissions for both natural and anthropogenic sources were 18.2 (10.6–25.9) Tg N yr−1 and TD emissions were 17.4 (15.8–19.20) Tg N yr−1. The once top emitter Europe has reduced its emissions by 31 % since the 1980s, while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N2O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the importance of reducing anthropogenic N2O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose the establishment of a global network for monitoring and modeling N2O from the surface through to the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023).

Complex geometric distortions and nonlinear radiation differences between optical and synthetic aperture radar (SAR) images present challenges for the matching of sufficient and evenly distributed corresponding points. To address this problem, this paper proposes a deep convolutional network based on an attention mechanism for matching optical and SAR images. In order to obtain robust feature points, we employ phase consistency instead of image intensity and gradient information for feature detection. A deep convolutional network (DCN) is designed to extract high-level semantic features between optical and SAR images, providing robustness to geometric distortion and nonlinear radiation changes. Notably, incorporating multiple inverted residual structures in the DCN facilitates efficient extraction of local and global features, promoting feature reuse, and reducing the loss of key features. Furthermore, a dense feature fusion module based on coordinate attention is designed, focusing on the spatial positional information of effective features, integrating key features into deep descriptors to enhance the robustness of deep descriptors to nonlinear radiometric differences. A coarse-to-fine strategy is then employed to enhance accuracy by eliminating mismatches. Experimental results demonstrate that the proposed network performs better than the manually designed descriptors-based methods and the stateof- the-art deep learning networks in both matching effectiveness and accuracy. Specifically, the number of matches achieved is approximately 2 times greater than that of other methods, with a 10% improvement in F-measure.

Existing road extraction methods based on deep learning often struggle with distinguishing ground objects that share similar spectral information, such as roads and buildings. Consequently, this study proposes a dual encoder-decoder deep neural network to address road extraction in complex backgrounds. In the feature extraction stage, the first encoder-decoder designed for extracting road features. The second encoder-decoder utilized for extracting building features. During the feature fusion stage, road features and building features are integrated using a subtraction method. The resultant road features, constrained by building features, enhance the preservation of accurate road feature information. Within the feature fusion stage, road feature maps and building feature maps designated for fusion are input into the convolutional block attention module. This step aims to amplify the features of different channels and extract key information from diverse spatial positions. Subsequently, feature fusion is executed using the element-by-element subtraction method. The outcome is road features constrained by building features, thus preserving more precise road feature information. Experimental results demonstrate that the model successfully learns both road and building features concurrently. It effectively distinguishes between easily confused roads and buildings with similar spectral information, ultimately enhancing the accuracy of road extraction.

Photonic crystal (PhC) structures with subwavelength periods are widely used for diffractive optics, including high reflectivity membranes with nanoscale thickness. Here, we report on a design procedure for 2D PhC silicon nitride membrane mirrors providing optimal crystal geometry using simulation results obtained with rigorous coupled-wave analysis. The Downhill Simplex algorithm, a robust numerical approach to finding local extrema of a function of multiple variables, is used to optimize the period and hole radius of PhCs with both hexagonal and square lattices, as the membrane thickness is varied. Following these design principles, nanofabricated PhC membranes made from silicon nitride have been used as input couplers for an optical cavity, resulting in a maximum cavity finesse of 33 000, corresponding to a reflectivity of 0.999 82. The role played by the spot size of the cavity mode on the PhC was investigated, demonstrating the existence of an optimal spot size that agrees well with predictions. We find that, compared to the square lattice, the hexagonal lattice exhibits a spectrally wider reflective range, less sensitivity to fabrication tolerances, and higher reflectivity for membranes thinner than 200 nm, which may be advantageous in cavity optomechanical experiments. Finally, we find that all of the cavities that we have constructed exhibit well-resolved polarization mode splitting, which we expect is due primarily to a small amount of anisotropic stress in the silicon nitride and PhC asymmetry arising during fabrication.

Cavity optomechanical sensors can offer exceptional sensitivity; however, interrogating the cavity motion with high accuracy and dynamic range has proven to be challenging. Here, we employ a dual optical frequency comb spectrometer to readout a microfabricated cavity optomechanical accelerometer, allowing for rapid simultaneous measurements of the cavity’s displacement, finesse, and coupling at accelerations up to 24 g (236 m/s2). With this approach, we have achieved a displacement sensitivity of 2 fm Hz−1/2, a measurement rate of 100 kHz, and a dynamic range of 7.6 × 105, which is the highest we are aware of for a microfabricated cavity optomechanical sensor. In addition, comparisons of our optomechanical sensor coupled directly to a commercial reference accelerometer show agreement at the 0.5% level, a value that is limited by the reference’s reported uncertainty. Furthermore, the methods described herein are not limited to accelerometry but rather can be readily applied to nearly any optomechanical sensor where the combination of high speed, dynamic range, and sensitivity is expected to be enabling.

Investigations of quantum thermal machines and Liouvillian exceptional points have rarely crossed each other. Here, the authors realize experimentally a quantum Otto engine using a single trapped ion, and show that crossing a Liouvillian exceptional point during the cycle increases the engine performance.

Various continuous-wave (CW) electron gun technologies are reviewed, including DC, superconducting radio frequency RF (SRF), hybrid DC-SRF and normal-conducting RF. Also, the SLAC Linac Coherent Light Source II (LCLS-II) normal-conducting RF gun and injector are described, and the performance to date, including the bunch emittance achieved and the dark current observed, is presented.

We could get many helpful information and results from satellite remote sensing images and aerial images, including disaster monitoring, grid hidden danger identification, and electricity consumption management. In the recent years, novel computer vision and deep neural network have got a lot of attention in many fields because of mimicking mammalian cognitive mechanism as much as possible. With the in-depth of mammalian cognitive and motor mechanisms research, people trend to adopt these reliable and efficient methods for power grid management and maintenance.For utilizing computing resources and improving analysing efficiency flexibly, we propose an assessing and verification framework based on bio-inspired perception and understanding, which summarizes the most appropriate image scale in the electric facilities place recognition. The proposed framework consists of different scenes aerial images datasets, several electric facilities place recognition methods, and credible evaluating methods mimicking mammals. Firstly, we gather satellite remote images and aerial images of sufficient electric power facilities in the United States via Google Earth and other public datasets. Then, several typical place recognition methods are adopted to testing recognition ability of multi-scale perception results, like SAD, NetVLAD, and GIST descriptor. To get more reliable result, multi-units and multi-scenes experiments are implemented roundly. After all experiments and evaluations, we could get that the most appropriate image scale is 1000 m size and the highest recognition accuracy of electric power facilities location is 500 m. Conclusion in our article shows the recommended perception form and scale closest to human cognition in the power grid management and maintenance.

Natural disasters cause considerable losses to people’s lives and property. Satellite images can provide crucial information of the affected areas for the first time, conducive to relieving the people in disaster and reducing the economic loss. However, the traditional satellite image analysis method based on manual processing drains workforce and material resources, which slowed the government’s response to the disaster. Aiming at the natural disasters like floods and earthquakes that often happen in the south of China, we propose a dual-stage damage assessment method based on LEDNet and ResNet. Our method detects the changes between the satellite images captured before and after a disaster of the same area, segments the buildings, and evaluates the damage level of affected buildings. In addition, we calculate influence maps based on the damage scale to the building and estimate the damage situation for electrical facilities. We used images related to earthquakes and floods in the xBD dataset to train the network model. Moreover, qualitative and quantitative evaluations demonstrated that our method has higher accuracy than the xBD baseline.

Vegetation management is important to the power transmission and distribution networks. The encompassed towering tree is always the key factor of the high impedance faults(HIFs).LiDAR is an efficient way to detect trees with 3D point cloud. The classical tree detection algorithm can handle the tree with high and distinct trunk,but limited to the tree with messy trunks. While the deeplearning based detection algorithms are also suffered from the terrain noise points. In this paper, we propose an efficient LiDAR reconstruction system which can efficiently reconstruct the point cloud of surrounding vegetation without the ground plane noise. We also use different weight strategies to improve the localization accuracy. We have conducted our system on the real power network environment and the height detection result shows that our algorithm has a better accuracy and robustness compared with the classical methods.

Decabromodiphenyl ether (BDE-209), a major component of brominated flame retardants, has been detected in considerable amounts in the soil. Given that BDE-209 is toxic, ubiquitous, and persistent, it may cause toxic effects on vegetables planted in contaminated soil. In this study, Chinese cabbage seedlings cultivated in the soil contaminated by BDE-209 (5 - 20 mg kg-1) for 60 d were investigated to evaluate the phytotoxicity of BDE-209 in terms of growth, physiological responses, photosynthetic function, and antioxidant capacity. The results showed that BDE-209-induced phytotoxicity was reflected in the growth suppression, the decrease of chlorophyll content and soluble protein content, and especially in the reduced photosynthetic parameters (net photosynthetic rate and stomatal conductance). BDE-209 showed direct toxicities to plasma membranes causing their elevated permeability. In addition, BDE-209 induced the overproduction of reactive oxygen species (ROS), membrane lipid peroxidation and protein carbonylation, manifesting in the increased content of O2*-, H2O2, malondialdehyde, and carbonyl. Chinese cabbage seedlings activated the antioxidant defence system (superoxide dismutase and peroxidase) to scavenge the ROS and counter-balance the oxidative stress caused by BDE-209, while the toxicity could not be effectively alleviated. Our study will provide valuable information for further understanding of the phytotoxicity of polybrominated diphenyl ethers.

Massive floating macroalgal blooms in the ocean result in many ecological consequences. Tracking their drifting pattern and predicting their biomass are essential for effective marine management. In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM), was developed. Based on the tracking, replication, and extinction of Lagrangian particles, FMGDM is capable of determining the dynamic growth and drift pattern of floating macroalgae, with the position, velocity, quantity, and represented biomass of particles being updated synchronously between the tracking and the ecological modules. The particle tracking is driven by ocean flows and sea surface wind, and the ecological process is controlled by the temperature, irradiation, and nutrients. The flow and turbulence fields were provided by the unstructured grid Finite-Volume Community Ocean Model (FVCOM), and biological parameters were specified based on a culture experiment of Ulva prolifera, a phytoplankton species causing the largest worldwide bloom of green tide in the Yellow Sea, China. The FMGDM was applied to simulate the green tide around the Yellow Sea in 2014 and 2015. The model results, e.g., the distribution, and biomass of the green tide, were validated using the remote-sensing observation data. Given the prescribed spatial initialization from remote-sensing observations, the model was robust enough to reproduce the spatial and temporal developments of the green tide bloom and its extinction from early spring to late summer, with an accurate prediction for 7–8 d. With the support of the hydrodynamic model and biological macroalgae data, FMGDM can serve as a model tool to forecast floating macroalgal blooms in other regions.

As the urban population grows and the global economy develops, municipal solid waste management (MSWM) has become an increasingly prominent global issue. MSWM is particularly difficult in developing countries due to its high cost and time-consuming nature. The Environment-based design (EBD) can potentially contribute to global MSWM by reducing costs and increasing efficiency, especially in helping developing countries identify critical issues in MSWM. This paper uses and demonstrates the effectiveness and efficiency of the EBD method to analyze and identify critical issues in MSWM, using the city of Nairobi as a case study. This paper contains the first two of EBD's three activities: environment analysis and conflict identification, during the design problem period, design knowledge, and design solutions simultaneously and interdependently evolve as a part of the environment. The comparison with the existing literature confirms that the conclusions reached are, to some extent, reliable, time-saving, and less costly, which will offer a possibility to solve the problem of MSWM in impoverished areas.

MxFIT is a FER-like iron deficiency induced transcriptional factor in Malus xiaojinensis. Here, we described the heterologous expression of MxFIT in Escherichia coli BL21 (DE3) host cells. The E. coli harboring the recombinant construct pET-MxFIT was efficiently induced to express the MxFIT protein at a high level and the optimal profile for MxFIT expression was investigated. By inoculating a New Zealand rabbit with purified MxFIT-His fusion protein, a high specific anti-MxFIT antiserum was achieved. Western blot analysis showed MxFIT protein expression was induced in roots when iron supply was limiting and was inhibited when iron supply was excessive. In leaves, there was almost no expression irrespective of iron supply. The localization of MxFIT on transverse section of root indicated that MxFIT participated in iron deficiency response. Over-expression of MxFIT in transgenic tobacco suspension cells showed that MxFIT increased iron absorption under insufficient iron supply. This study provides a basis for further investigating the underlying mechanism of high iron absorption efficiency in M. xiaojinensis.

The streamflow of the Yellow River (YR) is strongly affected by human activities like irrigation and dam operation. Many attribution studies have focused on the long-term trends of streamflows, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified with fully mechanistic models. This study aims to (1) demonstrate whether the mechanistic global land surface model ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) is able to simulate the streamflows of this complex rivers with human activities using a generic parameterization for human activities and (2) preliminarily quantify the roles of irrigation and dam operation in monthly streamflow fluctuations of the YR from 1982 to 2014 with a newly developed irrigation module and an offline dam operation model. Validations with observed streamflows near the outlet of the YR demonstrated that model performances improved notably with incrementally considering irrigation (mean square error (MSE) decreased by 56.9 %) and dam operation (MSE decreased by another 30.5 %). Irrigation withdrawals were found to substantially reduce the river streamflows by approximately 242.8±27.8×108 m3 yr−1 in line with independent census data (231.4±31.6×108 m3 yr−1). Dam operation does not change the mean streamflows in our model, but it impacts streamflow seasonality, more than the seasonal change of precipitation. By only considering generic operation schemes, our dam model is able to reproduce the water storage changes of the two large reservoirs, LongYangXia and LiuJiaXia (correlation coefficient of ∼ 0.9). Moreover, other commonly neglected factors, such as the large operation contribution from multiple medium/small reservoirs, the dominance of large irrigation districts for streamflows (e.g., the Hetao Plateau), and special management policies during extreme years, are highlighted in this study. Related processes should be integrated into models to better project future YR water resources under climate change and optimize adaption strategies.

Cardiac paragangliomas are an extremely rare cause of cardiac masses. We present a case of a cardiac paraganglioma presenting as a highly vascular mass on coronary angiography in a patient with angina, we discuss the challenges in its diagnostic evaluation and management.

Due to technical and cost limitations, the monitoring of emissions from ships sailing in open water within ship emission control areas (ECAs) is relatively rare. The present study adopts a monitoring method involving an unmanned aerial vehicle (UAV) that takes off from a patrol boat to measure the concentrations of SO2 and CO2 within the plumes of sailing ships. Our method aims to provide a low-cost, remote approach for estimating the fuel sulfur content (FSC) of sailing ships in open water, which overcomes the limitations of ground-based and small-aircraft-based methods. The selected monitoring area was the Yangtze River estuary, a domestic ECA with an FSC limit of 0.5 % (m∕m) implemented by the Chinese government. A total of 27 sailing ships were monitored, 12 of which were found to have an FSC of >0.5 % (m∕m). Moreover, the FSCs of the sailing ships were found to be higher than those of berthing ships in the study area. Based upon the online monitoring results, four of the monitored ships were intercepted by maritime law enforcement, and fuel samples were collected and analyzed in a laboratory; the results confirmed that all four FSCs were >0.5 % (m∕m). Among them, one offending ship was tracked down on 15 July 2019; this was the first time that a sailing ship had been caught for having failed the FSC regulations in China. Overall, the present study provides scientific support for evaluating the effectiveness of ECA policies and recommends that emissions from sailing ships be monitored more often in open water in the future.

In this study, spectroscopic approaches were used to evaluate light-induced changes in chlorophyll protein 43 (CP43). Upon illumination, we observed chlorophyll (Chl) a bleaching with the complete opening of the tetrapyrrole ring. Electronic transition frequencies to different energy levels of the second singlet electronic state (S2) were changed. In addition, we observed altered rotational and vibrational transitions of Chl a and the CP43 apoprotein as well as variations in the microenvironment surrounding Chl a. CP43 contains three β-carotene (β-Car) and five Chl a molecules, which are involved in the energy transfer from β-carotene to Chl a. We found that these five Chl a molecules bleached more readily than others under constant illumination, with energy transfer from β-Car to Chl a being continuously lost as illumination continued.

To elucidate the toxicity of 2,2ʹ,4,4ʹ-tetrabromodiphenyl ether (BDE-47) on photosynthetic primary processes, in vivo and in vitro treatments of BDE-47 were performed. The 20-d treatment in vivo (5 - 20 μg dm-3) suppressed the reproduction of duckweed (Lemna minor) and led to decline in chlorophyll (Chl) content of fronds. The most obvious features of BDE-47-treated fronds included a Chl a fluorescence rise at the J phase and a depression at the G phase, whereas significant fluorescence rises at the L, K, and J phases were found on the ΔVt curve of thylakoid membranes treated with 10 - 15 mg dm-3 BDE-47 for 4 h (in vitro). In both in vivo and in vitro experiments, the BDE-47 treatments significantly reduced the density of the active reaction centers (RC/CSo), affected the efficiency and speed of photosynthetic electron transfer [the maximum quantum yield of photosystem (PS) II photochemistry - Fv/Fm, quantum yield for electron transport (at t = 0) - φEo, electron transport flux per excited cross section - ETo/CSo, and net rate of reaction centers closure at 300 and 100 μs - dV/dto and dVG/dto, respectively], and increased energy dissipation [quantum yield for energy dissipation (at t = 0) - φDo, dissipated energy flux per reaction center - DIo/RC, and issipated energy flux per excited cross section - DIo/CSo]. The BDE-47 at 5 - 15 mg dm-3 had no impact on the minimum (initial) fluorescence (Fo) and total electron carriers per reaction center (Sm) of the thylakoid membranes, but PS II units were less tightly grouped (a positive L-band). On the contrary, there was no positive L-band on the difference between relative fluorescence intensities of the normalized induction curves from 50 μs to 300 μs (WK) of each BDE-47 treatment and control (ΔWK), and Fo and Sm increased after the treatment with BDE-47. The above results indicate that BDE-47 not only affected the permeability of thylakoid membranes, but also relaxed the structure of PS II, thereby affecting the function of PS II. In addition, BDE-47 could induce secondary damage to the PSs in duckweed fronds.

Cytosine methylation plays an important role in plant development by regulating gene expressions. However, few studies have investigated methylation changes during the tissue differentiation and development of perennial plants. Here, the fluorescence-labeled methylation-sensitive amplified polymorphism method was used with eight primer combinations to detect methylation in leaves and xylem obtained at the stages of inflorescence emergence (IE), ovary start growth, and fruit maturity (FM) in two pecan (Carya illinoinensis) cvs. Pawnee and Stuart. The results show that the total methylation in the xylem was generally higher than in the leaves at each stage. Substantial methylation variations were observed at the amplified sites in pecan tissues at the various stages. The methylation patterns changed between the leaf and xylem, with frequencies from 44.97 to 67.01 % over the three stages in the two cultivars, among which the variation frequency between the tissues at the FM stage was the highest for each cultivar. The frequencies of methylation variation between the leaf samples at any two stages ranged from 31.86 to 45.88 %, with higher variation frequencies between the xylem samples (40.90 - 59.44 %) for each cultivar, which is consistent with the comparative results of polymorphism rates between the leaf and xylem over the three stages. Cluster analysis and principal coordinate analysis suggest that the xylem at the IE and FM stages had relatively distant epigenetic relationships with other tissue samples as a whole. This study reveals the patterns of methylation variation and methylation relationships among pecan tissues undergoing different developmental processes, implying the important roles of methylation in tissue differentiation and development of trees. These results lay a theoretical foundation for elucidating the regulatory mechanisms of methylation involved in tree development.

It has been found that diacylglycerol acyltransferase-2 (DGAT2) plays a crucial role in the synthesis of triglycerides (TGs) in some mammals, but its role in buffalo lactation is unclear. In the present study, the DGAT2 full-CDS cDNA sequence of Binglangjiang buffalo was isolated, and the physicochemical characteristics and structure of its encoding protein were characterized. Furthermore, the differential expressions of this gene in 10 tissues of lactating and non-lactating buffalo were analyzed by real-time quantitative PCR (RT-qPCR). The results showed that the coding region (CDS) of this gene was 1086 bp in length, encoding a peptide composed of 361 amino acid residues. The deduced amino acid sequence shared more than 98.6 % identity with that of cattle, zebu, yak, and bison in the Bovidae family. Buffalo DGAT2 protein is a slightly hydrophobic protein with a transmembrane region, which functions in membrane of endoplasmic reticulum. Besides, this protein belongs to the LPLAT_MGAT-like family and contains a conserved domain of DAGAT that has a function in the synthesis of TGs. The multi-tissue differential expression analysis demonstrated that DGAT2 was expressed in the heart, liver, mammary gland, and muscle in both non-lactating and lactating buffalo. And its expression level in the heart, liver, and mammary gland during lactation was significantly higher than that during non-lactation. The results indicate that buffalo DGAT2 may be involved in milk fat synthesis. This study can establish a foundation for further elucidating mechanisms of the buffalo DGAT2 gene in milk fat synthesis.

With the rapid development of drone technology and digital camera technology, the method of obtaining high-precision coordinates based on UAV aerial photogrammetry technology is popular. The plane coordinate accuracy of the aerial image of the drone has been able to meet the needs of practical applications, but the elevation accuracy is generally low. Aiming at the low elevation accuracy of UAV aerial photogrammetry, a multi-face function fitting method based on Vondrak filter optimization was proposed. The improved fitting model was used to obtain the elevation correction value of the aerial image, thereby obtaining high-precision image elevation data. In this paper, based on the traditional multi-face function fitting method, some known points were used to model and find the difference between the measured elevation value and the measured elevation. The Vondrak filter was used to smooth the fitting result. Finally, a small number of known elevation points were used for checking, so that the obtained elevation was compared with the actual elevation. The experimental comparison showed that the improved multi-face function fitting method used Vondrak filter was improved by 34.76% compared with the quadric surface fitting, and improved by 14.48% compared with the optimized cubic surface fitting method. Research shows that the multi-faceted function method based on Vondrak filtering is superior to the traditional elevation correction method. The experiment verifies the effectiveness and feasibility of the improved method, and provides some reference value for the research of aerial image elevation correction model.

Chlorophylls are vital for photosynthesis, allowing plants to absorb energy from light for photosynthesis. More than one hundred species of chlorophyll have been identified. Among them, chlorophylls a, b, c (c1, c2, and c3), d, and f exist in oxygenic photosynthetic organisms (e.g., higher plants, algae, and cyanobacteria), whereas anoxygenic photosynthetic bacteria possess bacteriochlorophylls a, b, c, d, e, and g. These chlorophylls have different chemical structures and properties that enable photosynthetic organisms to perform photosynthesis in different environments. All of the chlorophylls are biosynthesized from 3,8-divinyl-protochlorophyllide a by a series of enzymes. The synthetic pathways of chlorophylls have now been basically clarified. This review succinctly summarizes the structures, properties, and synthetic pathways of the chlorophylls.

Air pollution from ship exhaust gas can be reduced by the establishment of emission control areas (ECAs). Efficient supervision of ship emissions is currently a major concern of maritime authorities. In this study, a measurement system for exhaust gas from ships based on an unmanned aerial vehicle (UAV) was designed and developed. Sensors were mounted on the UAV to measure the concentrations of SO2 and CO2 in order to calculate the fuel sulfur content (FSC) of ships. The Waigaoqiao port in the Yangtze River Delta, an ECA in China, was selected for monitoring compliance with FSC regulations. Unlike in situ or airborne measurements, the proposed measurement system could be used to determine the smoke plume at about 5 m from the funnel mouth of ships, thus providing a means for estimating the FSC of ships. In order to verify the accuracy of these measurements, fuel samples were collected at the same time and sent to the laboratory for chemical examination, and these two types of measurements were compared. After 23 comparative experiments, the results showed that, in general, the deviation of the estimated value for FSC was less than 0.03 % (m/m) at an FSC level ranging from 0.035 % (m/m) to 0.24 % (m/m). Hence, UAV measurements can be used for monitoring of ECAs for compliance with FSC regulations.

Experiments were performed at the Fermilab Accelerator Science and Technology facility to elucidate the effects of long-range wakefields in TESLA-type superconducting rf cavities. In particular, we investigated the higher-order modes (HOMs) generated in the eight, nine-cell cavities of a cryomodule (CM) due to off-axis steering with correctors located ∼4 m upstream of the CM. We have observed correlated submacropulse centroid slews of a few hundred microns and centroid oscillations at ∼240 kHz in the rf beam-position-monitor data after the CM. The entrance energy into the CM was 25 MeV, and the exit energy was 100 MeV with 125 pC/bunch and 400 pC/bunch in 50-bunch pulse trains. These experimental results were evaluated for machine learning training aspects which will be used to inform the commissioning plan for the Linac Coherent Light Source-II injector CM. With an entrance beam energy of

Abstract In China, irrigation is widespread in 40.7% cropland to sustain crop yields. By its action on water cycle, irrigation affects water resources and local climate. In this study, a new irrigation module, including flood and paddy irrigation technologies, was developed in the ORCHIDEE‐CROP land surface model which describes crop phenology and growth in order to estimate irrigation demands over China from 1982 to 2014. Three simulations were performed including NI (no irrigation), IR (with irrigation limited by local water resources), and FI (with irrigation demand fulfilled). Observations and census data were used to validate the simulations. Results showed that the estimated irrigation water withdrawal ( W) based on IR and FI scenarios bracket statistical W with fair spatial agreements ( r=0.68±0.07; p

The Yongxing air–sea flux tower (YXASFT), which was specially designed for air–sea boundary layer observations, was constructed on Yongxing Island in the South China Sea (SCS). Surface bulk variable measurements were collected during a 1-year period from 1 February 2016 to 31 January 2017. The sensible heat flux (SHF) and latent heat flux (LHF) were further derived via the Coupled Ocean–Atmosphere Response Experiment version 3.0 (COARE3.0). This study employed the YXASFT in situ observations to evaluate the Woods Hole Oceanographic Institute (WHOI) Objectively Analyzed Air–Sea Fluxes (OAFlux) reanalysis data products.First, the reliability of COARE3.0 data in the SCS was validated using direct turbulent heat flux measurements via an eddy covariance flux (ECF) system. The LHF data derived from COARE3.0 are highly consistent with the ECF with a coefficient of determination (R2) of 0.78. Second, the overall reliabilities of the bulk OAFlux variables were diminished in the order of Ta (air temperature), U(wind speed), Qa (air humidity) and Ts (sea surface temperature) based on a combination of R2 values and biases. OAFlux overestimates (underestimates) U (Qa) throughout the year and provides better estimates for winter and spring than in the summer–autumn period, which seems to be highly correlated with the monsoon climate in the SCS. The lowest R2 is between the OAFlux-estimated and YXASFT-observed Ts, indicating that Ts is the least reliable dataset and should thus be used with considerable caution. In terms of the heat fluxes, OAFlux considerably overestimates LHF with an ocean heat loss bias of 52 w m−2 in the spring, and the seasonal OAFlux LHF performance is consistent with U and Qa. The OAFlux-estimated SHF appears to be a poor representative, with enormous overestimations in the spring and winter, while its performance is much better during the summer–autumn period. Third, analysis reveals that the biases in Qa are the most dominant factor on the LHF biases in the spring and winter, and that the biases in both Qa and U are responsible for controlling the biases in LHF during the summer–autumn period. The biases in Ts are responsible for controlling the SHF biases, and the effects of biases in Ts on the biases in SHF during the spring and winter are much greater than that in the summer–autumn period.

Soil moisture is a key variable of land surface hydrology, and its correct representation in land surface models is crucial for local to global climate predictions. The errors may come from the model itself (structure and parameterization) but also from the meteorological forcing used. In order to separate the two source of errors, four atmospheric forcing datasets, GSWP3 (Global Soil Wetness Project Phase 3), PGF (Princeton Global meteorological Forcing), CRU-NCEP (Climatic Research Unit-National Center for Environmental Prediction), and WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data), were used to drive simulations in China by the land surface model ORCHIDEE-MICT(ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature). Simulated soil moisture was compared with in situ and satellite datasets at different spatial and temporal scales in order to (1) estimate the ability of ORCHIDEE-MICT to represent soil moisture dynamics in China; (2) demonstrate the most suitable forcing dataset for further hydrological studies in Yangtze and Yellow River basins; and (3) understand the discrepancies of simulated soil moisture among simulations. Results showed that ORCHIDEE-MICT can simulate reasonable soil moisture dynamics in China, but the quality varies with forcing data. Simulated soil moisture driven by GSWP3 and WFDEI shows the best performance according to the root mean square error (RMSE) and correlation coefficient, respectively, suggesting that both GSWP3 and WFDEI are good choices for further hydrological studies in the two catchments. The mismatch between simulated and observed soil moisture is mainly explained by the bias of magnitude, suggesting that the parameterization in ORCHIDEE-MICT should be revised for further simulations in China. Underestimated soil moisture in the North China Plain demonstrates possible significant impacts of human activities like irrigation on soil moisture variation, which was not considered in our simulations. Finally, the discrepancies of meteorological variables and simulated soil moisture among the four simulations are analyzed. The result shows that the discrepancy of soil moisture is mainly explained by differences in precipitation frequency and air humidity rather than differences in precipitation amount.

The application of different leaf physical models will result in the low accuracy in the process of the vegetation information inversion of the expressway, as well as poor efficiency in the process. The GLIBERTY model attempts to simulate the leaf reflectance and transmittance by the use of hierarchical computing theory, through the analysis of the optical properties of the single cell and the introduction of monolayer cells and cells of infinite layer between the reflectance and transmittance mathematical relationship, based on the radiative transfer theory. The study established the completed GLIBERTY model calculation theory system after the impaction of leaf structure parameters. The results show that the GLIBERTY model can accurately simulate the spectrum information of broadleaf and needle even in the case of less input parameters.

In this paper, a novel approach is proposed for solving conditional nonlinear optimal perturbations (CNOPs), called the adaptive cooperative coevolution of parallel particle swarm optimization (PSO) and the Wolf Search algorithm (WSA) based on principal component analysis (ACPW). Taking Fitow (2013) and Matmo (2014), two tropical cyclone (TC) cases, CNOPs solved by the ACPW algorithm are used to investigate the sensitive regions identified by TC adaptive observations with the fifth-generation Mesoscale Model (MM5). Meanwhile, the 60 and 120 km resolutions are adopted. The adjoint-based method (short for the ADJ method) is also applied to solve CNOPs, and the result is used as a benchmark. To evaluate the advantages of the ACPW algorithm, we run the PSO, WSA and ACPW programs 10 times and then compare the maximum, minimum and mean objective values as well as the RMSEs. The analysis results prove that the hybrid strategy and cooperative coevolution are useful and effective. To validate the ACPW algorithm, the CNOPs obtained from the different methods are compared in terms of the patterns, energies, similarities and simulated TC tracks with perturbations. The results of our study may be summarized as follows: The ACPW algorithm can capture similar CNOP patterns as the ADJ method, and the patterns of TC Fitow are more similar than TC Matmo.At the 120 km resolution, similarities between the CNOPs of the ADJ method and the ACPW algorithm are more than those at the 60 km resolution.Compared to the ADJ method, although the CNOPs of the ACPW method produce lower energies, they can have improved benefits gained from the reduction of the CNOPs not only across the entire domain but also in the identified sensitive regions.The sensitive regions identified by the CNOPs from the ACPW algorithm have the same influence on the improvements of the skill of TC-track forecasting as those identified by the CNOPs from the ADJ method.The ACPW method is more efficient than the ADJ method. All conclusions prove that the ACPW algorithm is a meaningful and effective method for solving CNOPs and can be used to identify sensitive regions of TC adaptive observations.

A balloon-borne instrument was designed to measure the electric field in thunderstorms. One case of thunderstorm was observed in the Pingliang region (35.57° N, 106.59° E; and 1620 m above sea level, a.s.l.) of a Chinese inland plateau, through penetration by the balloon-borne sounding in the early period of the mature stage. Results showed that the sounding passed through seven predominant charge regions. A negative charge region with a depth of 800 m located near the surface, and a positive charge region appeared in the warm cloud region; their mean charge densities were −0.44 ± 0.136 and 0.43 ± 0.103 nC m−3, respectively. Five charge regions existed in the region colder than 0 °C, and charge polarity alternated in a vertical direction with a positive charge at the lowest region. The mean charge densities for these five regions were 0.40±0.037 nC m−3 (−9.5 to −4 °C), −0.63±0.0107 nC m−3 (−18 to −14 °C), 0.35±0.063 nC m−3 (−27 to −18 °C), −0.36±0.057 nC m−3 (−34 to −27 °C), and 0.24±0.06 nC m−3 (−38 to −34 °C). We speculated that the two independent positive charge regions in the lower portion are the same charge region with a weak charge density layer in the middle. The analysis showed that the real charge structure of the thunderstorm is more complex than the tripole model, and the lower dipole is the most intensive charge region in the thunderstorm.

The understanding of the reemergence of pressure induced superconductivity in alkali-metal intercalated FeSe is hampered by sample complexities. Here, Sun et al. report the electronic properties of (Li1–xFe x )OHFe1–ySe single crystal not only in the reemerged superconducting state but also in the normal state.

We present a continuous in situ hydro-meteorology observational dataset from a set of instruments first deployed in December 2012 in the south of Sri Lanka, facing toward the north Indian Ocean. In these waters, simultaneous records of wind and wave data are sparse due to difficulties in deploying measurement instruments, although the area hosts one of the busiest shipping lanes in the world. This study describes the survey, deployment, and measurements of wind and waves, with the aim of offering future users of the dataset the most comprehensive and as much information as possible. This dataset advances our understanding of the nearshore hydrodynamic processes and wave climate, including sea waves and swells, in the north Indian Ocean. Moreover, it is a valuable resource for ocean model parameterization and validation. The archived dataset (Table 1) is examined in detail, including wave data at two locations with water depths of 20 and 10 m comprising synchronous time series of wind, ocean astronomical tide, air pressure, etc. In addition, we use these wave observations to evaluate the ERA-Interim reanalysis product. Based on Buoy 2 data, the swells are the main component of waves year-round, although monsoons can markedly alter the proportion between swell and wind sea. The dataset (Luo et al., 2017) is publicly available from Science Data Bank (https://doi.org/10.11922/sciencedb.447).

For the Linac Coherent Light Source II (LCLS-II) project at SLAC, a 1.3 GHz superconducting rf (SRF) linac is being constructed that will generate 4 GeV electron bunches at a high repetition rate to drive x-ray free electron lasers. The LCLS-II electron source, which comprises the first three meters of the electron injector, includes two normal-conducting, continuous-wave rf cavities: a one-cell, 185.7 MHz gun and a two-cell, 1.3 GHz buncher. It also includes a gun load-lock system that allows photocathodes to be changed under vacuum. The components in this beam-line section were designed and built by Lawrence Berkeley National Laboratory based on experience from their advanced photoinjector experiment program. In combination with the SLAC UV laser system, the electron source is designed to produce beam rates up to 1 MHz with average currents up to 30 μA initially. The source was installed in mid-2018, well in advance of the SRF linac, which is now nearing completion. The source was commissioned over a two-year period, and this paper presents results including electron beam and dark current characterization.

The seasonal mean and synoptic fluctuation of the wintertime Taiwan Warm Current (TWC) were investigated using a well-validated finite volume community ocean model. The spatial distribution and dynamics of the synoptic fluctuation were highlighted. The seasonal mean of the wintertime TWC has two branches: an inshore branch between the 30 and 100 m isobaths and an offshore branch between the 100 and 200 m isobaths. The Coriolis term is much larger than the inertia term and is almost balanced by the pressure gradient term in both branches, indicating geostrophic balance of the mean current. Two areas with significant fluctuations of the TWC were identified during wintertime. One of the areas is located to the north of Taiwan with velocities varying in the cross-shore direction. These significant cross-shore fluctuations are driven by barotropic pressure gradients associated with the intrusion of the Taiwan Strait Current (TSC). When a strong TSC intrudes to the north of Taiwan, the isobaric slope tilts downward from south to north, leading to a cross-shore current from the coastal area to the offshore area. When the TSC intrusion is weak, the cross-shore current to the north of Taiwan is directed from offshore to inshore. The other area of significant fluctuation is located in the inshore area between the 30 and 100 m isobaths. The fluctuations are generally strong both in the alongshore and cross-shore directions, in particular at the latitudes 26.5 and 28° N. Wind affects the synoptic fluctuation through episodic events. When the northeasterly monsoon prevails, the southwestward Zhe-Min coastal current dominates the inshore area associated with a deepening of the mixed layer. When the winter monsoon is weakened or the southwesterly wind prevails, the northeastward TWC dominates in the inshore area.

Methane (CH4) has a 28-fold greater global warming potential than CO2 over 100 years. Atmospheric CH4 concentration has tripled since 1750. Anthropogenic CH4 emissions from China have been growing rapidly in the past decades and contribute more than 10 % of global anthropogenic CH4 emissions with large uncertainties in existing global inventories, generally limited to country-scale statistics. To date, a long-term CH4 emission inventory including the major sources sectors and based on province-level emission factors is still lacking. In this study, we produced a detailed annual bottom-up inventory of anthropogenic CH4 emissions from the eight major source sectors in China for the period 1980–2010. In the past 3 decades, the total CH4 emissions increased from 24.4 [18.6–30.5] Tg CH4 yr−1 in 1980 (mean [minimum–maximum of 95 % confidence interval]) to 44.9 [36.6–56.4] Tg CH4 yr−1 in 2010. Most of this increase took place in the 2000s decade with averaged yearly emissions of 38.5 [30.6–48.3] Tg CH4 yr−1. This fast increase of the total CH4 emissions after 2000 is mainly driven by CH4 emissions from coal exploitation. The largest contribution to total CH4 emissions also shifted from rice cultivation in 1980 to coal exploitation in 2010. The total emissions inferred in this work compare well with the EPA inventory but appear to be 36 and 18 % lower than the EDGAR4.2 inventory and the estimates using the same method but IPCC default emission factors, respectively. The uncertainty of our inventory is investigated using emission factors collected from state-of-the-art published literatures. We also distributed province-scale emissions into 0.1° × 0.1° maps using socioeconomic activity data. This new inventory could help understanding CH4 budgets at regional scale and guiding CH4 mitigation policies in China.