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Single-photon detectors, with their exceptional sensitivity, provide a reliable means for single-photon-level detection, demonstrating significant advantages in detecting weak signals in complex environments compared to traditional detectors. With the continuous advancement in semiconductor manufacturing technology, single-photon detectors based on linear array configurations have emerged and rapidly developed. This study utilizes a linear array single-photon detector in free-running mode combined with a scanning mechanism to design and implement a spectral detection and imaging system. Through the spectral scanning unit, this system successfully achieves precise spectral detection in the 890 nm to 1710 nm range, with a spectral resolution better than 2 nm. Utilizing the imaging scanning unit, the system effectively performs target spectral imaging under single and multiple wavelength conditions at 1064 nm, 1310 nm, and 1520 nm. By optimizing algorithms for data processing, the system can achieve rapid and accurate spectral detection and imaging even under low-light conditions where the average photon count per pixel is less than 3. The results of this study are expected to provide strong technical support for the application of spectral imaging technology in the field of high-speed detection and imaging.

The risk of acquiring new intramammary infections is high at the end of lactation, especially for the high milk-producing dairy animals. Resistance to bacterial infection increases following the completion of mammary gland involution after milking cessation. The serotonin precursor 5-hydroxytryptophan (5-HTP) could accelerate involution by increasing circulating serotonin levels, but ruminal microbes may degrade 5-HTP if orally administered to adult ruminants. It is unclear whether rumen-protected 5-HTP could effectively mediate circulating serotonin (5-hydroxytryptamine, 5-HT) and therefore accelerate mammary gland involution in ruminants. Goats were used as a model in the current study to investigate the effects of rumen-protected 5-HTP on behaviour, 5-HT metabolism, and mammary involution in ruminants. In the first experiment, 16 female Dazu black goats were assigned to one of four groups in a randomised block design. The treatments included a basal diet plus 0, 4, 20, or 100 mg/kg BW of rumen-protected 5-HTP. Serum was collected at 0, 3, 6, 12, and 24 h after offering the rumen-protected 5-HTP in the morning feed, and the behaviours were monitored. In the second experiment, 12 female Dazu black goats (Somatic cell count

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.

Quantifying the full-volatility organic emissions from cooking sources is important for understanding the causes of organic aerosol pollution. However, existing national cooking emission inventories in China fail to cover full-volatility organics and have large biases in estimating emissions and their spatial distribution. Here, we develop the first emission inventory of full-volatility organics from cooking in China, which covers emissions from individual commercial restaurants as well as residential kitchens and canteens. In our emission estimates, we use cuisine-specific full-volatility emission factors and provincial policy-driven purification facility installation proportions, which allows us to consider the significant impact of diverse dietary preferences and policy changes on China's cooking emissions. The 2021 emissions of volatile organic compounds (VOCs), intermediate-volatility organic compounds (IVOCs), semi-volatile organic compounds (SVOCs), and organic compounds with even lower volatility (xLVOCs) from cooking in China are 561 (317–891, 95 % confidence interval) kt yr−1, 241 (135–374) kt yr−1, 176 (95.8–290) kt yr−1, and 13.1 (7.36–21.0) kt yr−1, respectively. The IVOC and SVOC emissions from cooking account for 9 %–21 % and 31 %–62 % of the total emissions from all sources in the five most densely populated cities in China. Among all cooking types, commercial cooking dominates the emissions, contributing 54.5 %, 66.2 %, 68.5 %, and 46.7 % to the VOC, IVOC, SVOC, and xLVOC emissions, respectively. Sichuan–Hunan cuisine contributes the most to total cooking emissions among all commercial cuisines. Residential cooking emissions are also important, accounting for 22.2 %–47.1 % of the cooking organic emissions across the four volatility ranges, whereas canteens make minor contributions to each volatility range (%). In terms of spatial distribution, emission hotspots mainly occur in densely populated areas and regions with oily and spicy dietary preferences. From 2015 to 2021, national organic emissions from cooking increased by 25.2 % because of the rapid growth of the catering industry, despite being partly offset by the increased installation of purification facilities. Future control measures need to further promote the purification facilities in commercial restaurants and improve their removal efficiency as well as reduce emissions from residential cooking. Our dataset and generalizable methodology serve as valuable resources for evaluating the air quality, climate, and health impacts of cooking sources, and help to formulate effective emission control policies. Our national, multi-year, high-spatial-resolution dataset can be accessed from https://doi.org/10.6084/m9.figshare.23537673 (Li et al., 2023).

With the rapid development of GNSS, high-resolution remote sensing, InSAR, LiDAR and other earth observation technologies, advanced technical conditions are provided for the training of talents in the field of geomatics. The discipline of surveying and mapping science and technology in Chang'an University has been integrated with engineering geology and geohazards over decades, forming one of the special research directions, that is, development of earth observation techniques and the applications in geological disasters. Therefore, we have formed the "integration of science and education, international cooperation" characteristic talent training mode. The specific initiatives include six aspects, namely, concentrating featured discipline directions, building scientific research platforms and teams, recruiting and developing outstanding talents, undertaking major scientific projects, strengthening the integration of science and education, and expanding international exchanges and cooperation, which ultimately serves the goal of improving the quality of personnel training in geomatics.

Abstract Studies have found that neutrophil extracellular traps (NETs) which are the specific dying form of neutrophil upon activation have fundamental role in the rheumatoid arthritis onset and progression. The purpose of this study was to explore the therapeutic effect of Sinomenine on adjuvant-induced arthritis in mice, and the neutrophil activities regulated by Sinomenine. The rheumatoid arthritis model was established by local injection of adjuvant and the Sinomenine treatment was administered orally for 30 days, during which, arthritic scores were evaluated and the joint diameter was measured to determine disease progression. The joint tissues and serum were acquired for further tests after sacrifice. Cytometric beads assay was performed to measure the concentration of cytokines. For paraffin-embedded ankle tissues, hematoxylin and erosin staining and Safranin O-fast staining were adopted to monitor the tissue changes of joint. In order to analyze the inflammation, NETs and autophagy of neutrophils in vivo, immunohistochemistry assays were applied to detect the protein expression levels in the local joints. To describe the effect brought by Sinomenine on inflammation, autophagy and NETs in vitro, the western blotting and the immunofluorescence assays were performed. The joint symptoms of the adjuvant induced arthritis were alleviated by the Sinomenine treatment significantly in terms of the ankle diameter and scores. The improvement of local histopathology changes and decrease of inflammatory cytokines in the serum also confirmed the efficacy. The expression levels of interleukin-6, P65 and p-P65 in the ankle areas of mice were remarkably reduced by Sinomenine. Compared with the model group, the decreased expression levels of lymphocyte antigen 6 complex and myeloperoxidase in the Sinomenine treating group showed the inhibitory effect of Sinomenine on the neutrophil migration. The expression of protein arginine deiminase type 4 (PAD4), ctrullinated histone H3 (CitH3) and microtubule-associated protein 1 light chain 3B (LC3B) had the similar tendency. Upon activation of lipopolysaccharide (LPS) in vitro, Sinomenine suppressed the phosphorylation of P65, extracellular signal-regulated kinase (ERK) and P38 of neutrophil. Meanwhile, Sinomenine inhibited NETs formation induced by phorbol 12-myristate 13-acetate (PMA), which were demonstrated by the decreased expression of neutrophil elastase (NE), PAD4 and CitH3. Sinomenine also inhibited PMA-induced autophagy in vitro based on the changes of Beclin-1 and LC3B. Sinomenine has good efficacy in treating adjuvant induced arthritis via regulating neutrophil activities. Apart from inhibiting activation of nuclear factor kappa-B (NF-κB) and mitogen-activated protein kinase (MAPK) pathways, the mechanism includes suppression of NETs formation via autophagy inhibition.

Tailpipe emissions from three heavy-duty diesel vehicles (HDDVs), complying with varying emission standards and installed with diverse aftertreatment technologies, are collected at a certified chassis dynamometer laboratory. The HDDV-emitted intermediate-volatility and semi-volatile organic compound (I/SVOC) emission and the gas–particle partitioning of the I/SVOCs are investigated. Over 4000 compounds are identified and grouped into 21 categories. The dominant compound groups of particulate I/SVOCs are alkanes and phenolic compounds. For HDDVs without aftertreatment devices, i.e., diesel oxidation catalysts (DOCs) and diesel particulate filters (DPFs), the emitted I/SVOCs partition dramatically into the gas phase (accounting for ∼ 93 % of the total I/SVOC mass), with a few exceptions: hopane, four-ring polycyclic aromatic hydrocarbons (PAH4rings), and five-ring polycyclic aromatic hydrocarbons (PAH5rings). For HDDVs with DPFs and DOCs, the particulate fractions are reduced to a negligible level (i.e., less than 2 %). Nevertheless, 50 % of the total two-ring PAH mass is detected in the particle phase, which is much higher than the high-molecular-weight PAHs, arising from the positive sampling artifact of quartz filter absorbing organic vapors. The positive sampling artifact of quartz filter absorbing organic vapors is clearly observed, and uncertainties are discussed and quantified. Particulate I/SVOCs at low-speed, middle-speed, and high-speed phases are collected and analyzed separately. The emission factor (EF) distribution of the speciated organic aerosol (OA) on a two-dimensional volatility basis set (2D-VBS) space reveals that the fractions of OA with oxygen to carbon (O : C) ratios > 0.3 (0.4, 0.5) are 18.2 % (11.5 %, 9.5 %), 23 % (15.4 %, 13.6 %), and 29.1 % (20.6 %, 19.1 %) at the low-speed, middle-speed, and high-speed stages. These results help to resolve complex organic mixtures and trace the evolution of OA.

Polarimetric calibration is one of the preprocessing steps in the quantitative processing of Polarimetric synthetic aperture radar (PolSAR) data, and its accuracy will affect subsequent applications. At present, the polarimetric calibration method based on distributed targets is widely used, and this kind of method needs to extract distributed targets that satisfy certain scattering characteristics as the calibration reference ground object samples before calibrating. Therefore, the extraction accuracy of distributed targets has a great influence on the accuracy of polarimetric calibration methods based on such targets. Therefore, this paper proposes a new distribution target extraction method, which is based on the idea of KS hypothesis testing, and uses the homogeneity of the pixels in the window to determine whether it is a distribution target. To verify the effectiveness of the method, the X-band airborne PolSAR images are used as the data of the polarimetric calibration experiment. Experiments show that, compared with other extraction methods, our method can not only ensure the extraction accuracy of distributed targets, but also further improve the accuracy of polarimetric calibration.

Characterization of the spatial distributions of air pollutants on an intracity scale is important for understanding local sources, secondary formation, and human exposure. In this study, we conducted in situ mobile measurements for the chemical composition of fine particles, volatile organic compounds (VOCs), oxygenated VOCs (OVOCs), and common gas pollutants in winter in the megacity of Beijing. The spatial patterns of these pollutants under different pollution conditions were investigated. During the non-haze days all pollutants showed significant spatial variability. Large spatial variations of secondary species including OVOCs and secondary aerosol species highlight the chemical heterogeneity. In particular, the inorganic chemical composition of fine particles varied greatly on the 65 km urban highway, suggesting a wide range of particle neutralization in the megacity of Beijing. Localized sources such as vehicle, cooking, and industrial emissions led to hot spots and nonuniform distributions of primary pollutants in the city. The spatial heterogeneity of air pollutants under less polluted conditions calls for a future need of using fine-resolution models to evaluate human exposure and to develop pollution control strategies. During the haze day the spatial variabilities of secondary gaseous and particulate pollutants were largely reduced, explained by both the elevated urban background of the polluted air mass and the enhanced secondary formation by elevated precursor concentrations and heterogeneous or aqueous pathways. Although localized primary emissions were accumulated under stagnant haze conditions, the chemical composition of fine particles became relatively homogeneous because of the predominant secondary contributions. A uniform spatial pattern of particle neutralization was observed. The concentrations of hydrocarbons and less oxygenated OVOCs showed good positive spatiotemporal correlations during the haze day, while the concentrations of more oxygenated OVOCs showed good positive correlations among themselves but moderate negative correlations with the concentrations of hydrocarbons, less oxygenated OVOCs, and aerosol species. Our results indicate a spatial chemical homogeneity on the megacity scale to promote efficient secondary organic aerosol (SOA) production under haze conditions.

Abstract Background A spinal cord injury (SCI) is a devastating, life-changing event that has profoundly deleterious effects on an individual’s health and well-being. Dysregulation of neuromuscular, cardiometabolic, and endocrine organ systems following an SCI contribute to excess morbidity, mortality and a poor quality of life. As no effective treatments currently exist for SCI, the development of novel strategies to improve the functional and health status of individuals living with SCI are much needed. To address this knowledge gap, the current study will determine whether a Home-Based Multimodality Functional Recovery and Metabolic Health Enhancement Program that consists of functional electrical stimulation of the lower extremity during leg cycling (FES-LC) plus arm ergometry (AE) administered using behavioral motivational strategies, and testosterone therapy, is more efficacious than FES-LC plus AE and placebo in improving aerobic capacity, musculoskeletal health, function, metabolism, and wellbeing in SCI. Methods This single-site, randomized, placebo-controlled, parallel group trial will enroll 88 community-dwelling men and women, 19 to 70 years of age, with cervical and thoracic level of SCI, ASIA Impairment Scale grade: A, B, C, or D, 6 months or later after an SCI. Participants randomized to the multimodality intervention will undergo 16 weeks of home-based FES-LC and AE training plus testosterone undecanoate. Testosterone undecanoate injections will be administered by study staff in clinic or by a visiting nurse in the participant’s home. The control group will receive 16 weeks of home-based FES-LC and AE exercise plus placebo injections. The primary outcome of this trial is peak aerobic capacity, measured during an incremental exercise testing protocol. Secondary outcomes include whole body and regional lean and adipose tissue mass; muscle strength and power; insulin sensitivity, lipids, and inflammatory markers; SCI functional index and wellbeing (mood, anxiety, pain, life satisfaction and depressive symptoms); and safety. Discussion We anticipate that a multimodality intervention that simultaneously addresses multiple physiological impairments in SCI will result in increased aerobic capacity and greater improvements in other musculoskeletal, metabolic, functional and patient-reported outcomes compared to the control intervention. The findings of this study will have important implications for improving the care of people living with an SCI. Trial registration ClinicalTrials.gov : ( NCT03576001 ). Prospectively registered: July 3, 2018.

Remote sensing image mapping (aviation, aerospace, UAV) is the main form of current mapping. Optical image mapping has been a certain research foundation and played an important role in the mapping task. However, cloud, ice, snow, rain, and haze conditions seriously affect the interpretation and processing of optical images, making it difficult to meet the needs of rapid emergency response. Synthetic Aperture Radar (SAR) is currently the only method that can achieve all-day, all-weather rapid imaging under various weather conditions such as day, night, cloud, snow, rain, etc. With unique advantages unmatched by traditional optical remote sensing technology, it is an important technical guarantee for improving emergency response capabilities and promoting remote sensing and geographic information industries, and an earth observation technology that has been invested and vigorously developed internationally. Digital Elevation Model (DEM) plays an increasingly important role in the application of terrain information acquisition such as rapid acquisition of sudden disasters and surface deformation information acquisition. Rapid acquisition of large area high-accuracy DEM is one of the important research contents of SAR mapping. Based on interferometric SAR data combined with sensor orbit data, external DEM and other auxiliary data, DEM is finally generated through complex image registration, interference image generation, removal of flat ground effects, interference phase filtering, phase unwrapping, interference calibration and geocoding. The registration accuracy of multiple images greatly affects the accuracy of DEM. This paper proposes an improved registration method based on the conventional rough registration, pixel-level registration and sub-pixel registration. When the motion compensation of the sensor platform is unknown during imaging, it can meet the requirements of high-accuracy and large-scale rapid registration of dual-antenna polarization interferometric SAR. The experimental data is X-band airborne SAR data in Weinan region of Shaanxi Province, and DEM products with the elevation accuracy better than 0.5m is generated, which verifies the effectiveness, accuracy and fast processing ability of the method.

Synthetic aperture radar (SAR) coherent change detection (CCD) often utilizes the degree of coherence to detect changes that have occurred between two data collections. Although having shown some performances in change detection, many existing coherence estimators are still relatively limited because the change areas do not stand out well from all decorrelation areas due to the low cluster-to-noise ratio (CNR) and volume scattering. Moreover, many estimators require the equal-variance assumption between two SAR images of the same scene. However, the assumption is less likely to be met in regions of significant differences in intensity, such as the change areas. To address these problems, we proposed an improved coherence estimator that introduces the parameters about the true-variance ratio as the weights. Since these parameters are closely related to the ratio-change statistic in intensity-based change detection algorithms, their introduction frees the estimator from the need for the equal-variance assumption and enables detection results to largely combine the advantages of intensity-based and CCD methods. Experiments on simulated and real SAR image pairs demonstrate the effectiveness of the proposed estimator in highlighting the change, obviously improving the contrast between the change and the background.

Indoor positioning algorithms based on Wi-Fi or Bluetooth low energy (BLE) have been widely used for indoor navigation and Internet of Things (IoT) applications, due to their low cost. However, their positioning accuracy is between 2–5 meters, which is insufficient for many emerging applications requiring higher accuracy. New high-precision positioning technologies, such as light, acoustic, and Bluetooth Angle of Arrival (AoA)-based positioning systems, have been developed, and they can achieve decimeter-level positioning accuracy. However, these high-precision positioning technologies suffer from high costs. In this paper, an adaptive smartphone hybrid indoor positioning solution incorporating heterogeneous sensors is proposed to achieve high accuracy and reliable localization. Multiple indoor scenes are identified and the quality of pedestrian movement is detected, thus enabling the adaptive estimation and adjustment of measurement noise and process noise in the filtering procedure. High-precision sensors (i.e. Bluetooth array, acoustic sensors and light sensors) are utilized in the proposed solution as controlling anchors to improve the positioning accuracy. Low-cost sensors (i.e. Wi-Fi, BLE and inertial measurement unit (IMU) sensors), are integrated to achieve wide-area coverage. An autonomous outlier detection method is developed to improve the positioning accuracy and reliability. Experimental results show that the proposed solution can achieve accuracy of 0.98 m in the test scenario.

Based on MOD09GA/MYD09GA surface reflectance data, a new MODIS snow-cover-extent (SCE) product from 2000 to 2020 over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets – Terra-MODIS and Aqua-MODIS SCE datasets and a final daily cloud-gap-filled (CGF) SCE dataset. The first two datasets are generated mainly through optimizing snow-cover discriminating rules over land-cover types, and the latter dataset is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. The validation against 362 China Meteorological Administration (CMA) stations shows that during snow seasons the overall accuracy (OA) values of the three datasets are larger than 93 %, all of the omission error (OE) values are constrained within 9 %, and all of the commission error (CE) values are constrained within 10 %. Bias values of 0.98, 1.02, and 1.03 demonstrate on a whole that there is no significant overestimation nor a significant underestimation. Based on the same ground reference data, we found that the new product accuracies are obviously higher than standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For example, compared with the CE of 23.78 % that the MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 % versus 89.54 % of MOD10A1F product and 84.36 % of MYD10A1F product. Besides, as expected, snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates that the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China; thereby, it can better serve for hydrological, climatic, environmental, and other related studies there.

La amplificación por recombinasa y polimerasa combinada con la extracción rápida de ADN para la identificación sobre el terreno de Deinagkistrodon acutus, una especie en peligro de extinción En este estudio se aborda la utilización de la amplificación por recombinasa y polimerasa (RPA en su sigla en inglés) en combinación con la extracción rápida de ADN para identificar sobre el terreno a Deinagkistrodon acutus, una especie de serpiente que se encuentra en peligro de extinción debido a la sobreexplotación y la destrucción del hábitat. Con vistas a su conservación, es muy conveniente disponer de un método eficiente de identificación que permita combatir la captura y el comercio ilegales de la especie. Se capturaron 14 ejemplares de 12 especies de serpiente (incluida D. acutus) en las regiones montañosas del sureste de China. Se extrajo el ADN genómico con un método de lisis alcalina que llevó cinco minutos. Se diseñaron cebadores para la RPA, específicos de la especie, a partir de las secuencias de la región del código de barras de la subunidad I de la citocromo c oxidasa, y se estableció un sistema optimizado de análisis mediante RPA. Se comprobaron la especificidad y sensibilidad del ensayo, que se validó mediante la identificación de 10 muestras comerciales de la sustancia sin elaborar conocida como Qi She derivada de D. acutus. En condiciones de RPA optimizadas, se amplificó una banda única de 354 pb solo para D. acutus, pero no para las especies de serpiente relacionadas. El proceso se puede llevar a cabo en 30 minutos a temperatura ambiente. La identificación de la sustancia comercial sin tratar Qi She permitió validar la eficacia del sistema de análisis establecido. Al combinar el análisis mediante RPA con la extracción rápida de ADN, establecimos un método de identificación de D. acutus sobre el terreno con buena especificidad y sensibilidad. Podría convertirse en un instrumento eficiente para llevar a cabo la supervisión rigurosa de la captura y el comercio ilegales de D. acutus.

A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

Feature importance has been a popular approach for machine learning models to investigate the relative significance of model predictors. In this study, we developed a Wilks feature importance (WFI) method for hydrological inference. Compared with conventional feature importance methods such as permutation feature importance (PFI) and mean decrease impurity (MDI), the proposed WFI aims to provide more reliable variable rankings for hydrological inference. To achieve this, WFI measures the importance scores based on Wilks Λ (a test statistic that can be used to distinguish the differences between two or more groups of variables) throughout an inference tree. Compared with PFI and MDI methods, WFI does not rely on any performance measures to evaluate variable rankings, which can thus result in less biased criteria selection during the tree deduction process. The proposed WFI was tested by simulating monthly streamflows for 673 basins in the United States and applied to three interconnected irrigated watersheds located in the Yellow River basin, China, through concrete simulations for their daily streamflows. Our results indicated that the WFI could generate stable variable rankings in response to the reduction of irrelevant predictors. In addition, the WFI-selected predictors helped random forest (RF) achieve its optimum predictive accuracy, which indicates that the proposed WFI could identify more informative predictors than other feature importance measures.

Oxidation of aromatic volatile organic compounds (VOCs) leads to the formation of tropospheric ozone and secondary organic aerosol, for which gaseous oxygenated products are important intermediates. We show, herein, the experimental results of highly oxygenated organic molecules (HOMs) produced by the oxidation of benzene and toluene in a wide range of OH exposure and NOx conditions. The results suggest that multigeneration OH oxidation plays an important role in the product distribution, which likely proceeds more preferably via H subtraction than OH addition for early generation products from light aromatics. More oxygenated products present in our study than in previous flow tube studies, highlighting the impact of experimental conditions on product distributions. The formation of dimeric products, however, was suppressed and might be unfavorable under conditions of high OH exposure and low NOx in toluene oxidation. Under high-NOx conditions, nitrogen-containing multifunctional products are formed, while the formation of other HOMs is suppressed. Products containing two nitrogen atoms become more important as the NOx level increases, and the concentrations of these compounds depend significantly on NO2. The highly oxygenated nitrogen-containing products might be peroxyacyl nitrates, implying a prolonged effective lifetime of RO2 that facilitates regional pollution. Our results call for further investigation on the roles of high-NO2 conditions in the oxidation of aromatic VOCs.

Accurate lidar ratio (LR) and better understanding of its variation characteristics can not only improve the retrieval accuracy of parameters from elastic lidar, but also play an important role in assessing the impacts of aerosols on climate. Using the observational data of a Raman lidar in Shanghai from 2017 to 2019, LRs at 355 nm were retrieved and their variations and influence factors were analyzed. Within the height range of 0.5–5 km, about 90 % of the LRs were distributed in 10–80 sr with an average value of 41.0 ± 22.5 sr, and the LR decreased with the increase in height. The volume depolarization ratio (δ) was positively correlated with LR, and it also decreased with the increase in height, indicating that the vertical distribution of particle shape was one of the influence factors of the variations in LR with height. LR had a strong dependence on the original source of air masses. Affected by the aerosols transported from the northwest, the average LR was the largest, 44.2 ± 24.7 sr, accompanied by the most irregular particle shape. The vertical distribution of LR was affected by atmospheric turbidity, with the greater gradient of LR under clean conditions. The LR above 1 km could be more than 80 sr, when Shanghai was affected by biomass burning aerosols.

Drilling carbon fiber reinforced silicon carbide composites still forms a big challenge for machining because of their special braided structure and anisotropy. In this study, through the drilling of 2D Cf∕C–SiC composites, two kinds of brazed diamond core drills with different abrasive distributions were compared. The results showed that the drilling force and torque of the two drills decreased with the increase in the spindle speed and increased with the increase in the feed speed. Under the same drilling conditions, the drilling force and torque of the brazed diamond drill with the ordered abrasive distribution were far lower than those of the brazed diamond drill with the random abrasive distribution. Also, the quality of the holes drilled by the drill with the ordered abrasive distribution was better than that of the holes drilled by the drill with the random abrasive distribution, which is attributed to the uniform abrasive spacing provided by the drill with the ordered abrasive distribution.

Extensive uncertainties exist in hydrologic risk analysis. Particularly for interdependent hydrometeorological extremes, the random features in individual variables and their dependence structures may lead to bias and uncertainty in future risk inferences. In this study, an iterative factorial copula (IFC) approach is proposed to quantify parameter uncertainties and further reveal their contributions to predictive uncertainties in risk inferences. Specifically, an iterative factorial analysis (IFA) approach is developed to diminish the effect of the sample size and provide reliable characterization for parameters' contributions to the resulting risk inferences. The proposed approach is applied to multivariate flood risk inference for the Wei River basin to demonstrate the applicability of IFC for tracking the major contributors to resulting uncertainty in a multivariate risk analysis framework. In detail, the multivariate risk model associated with flood peak and volume will be established and further introduced into the proposed iterative factorial analysis framework to reveal the individual and interactive effects of parameter uncertainties on the predictive uncertainties in the resulting risk inferences. The results suggest that uncertainties in risk inferences would mainly be attributed to some parameters of the marginal distributions, while the parameter of the dependence structure (i.e. copula function) would not produce noticeable effects. Moreover, compared with traditional factorial analysis (FA), the proposed IFA approach would produce a more reliable visualization for parameters' impacts on risk inferences, while the traditional FA would remarkably overestimate the contribution of parameters' interaction to the failure probability in AND (i.e. all variables would exceed the corresponding thresholds) and at the same time underestimate the contribution of parameters' interaction to the failure probabilities in OR (i.e. one variable would exceed its corresponding threshold) and Kendall (i.e. the correlated variables would exceed a critical multivariate threshold).

To monitor the large area land subsidence in Dezhou city, Shandong province, China. This paper uses 23 scenes of Sentinel-1A radar images from August 2017 to February 2019, and based on small baseline subset (SBAS) technology to obtain the subsidence information in Dezhou urban area. The monitoring results show that: Dezhou city has a serious subsidence phenomenon in large coverage area. A subsidence funnel with Chenzhuang as the center was formed. The average annual subsidence rate (along the vertical direction) of the subsidence center exceeded 45 mm/yr. There was also a serious subsidence phenomenon in the eastern and northeastern parts of the urban area, and there was a tendency of forming a whole area. After detailed data analysis, it is found that the ground subsidence presents seasonal characteristics closely related to the groundwater level and is affected by large-scale engineering construction on the surface.

In this study, we investigated wide-area land subsidence in Hebei Plain using 78 C-band Sentinel-1A SAR images acquired from May 2017 to May 2018 covering two tracks. High-precision time series retrieval was performed by NSBAS technology based on a single track. The offset deformation values of two tracks calculated in this paper were −0.09 mm/year, and the multi-track deformation rate was successfully merged. Using the cross-validation of redundant observations in the multi-track overlap area to evaluate the accuracy, and it was found that 90% pixel difference between the two track overlap areas was within 9 mm. The standard deviation was 5.38 mm, and the subsidence trend of the overlap area was consistent. Twenty-four subsidence bowls were extracted, and the maximum subsidence rate reached −62 mm/year.

The Xinjiang region of China is a vast and sparsely populated area with complex topography, surrounded by basins and mountains, and its geomorphological features and water circulation process make the traditional spring water resource acquisition time-consuming, labor-consuming and inaccurate. Remote Sensing Technology has the advantages of large scale, periodicity, timeliness and comprehensiveness in target detection. In order to realize the artificial intelligence detection of springs in Xinjiang, this paper presents a method of detecting springs in remote sensing image based on the YOLOV3 network framework, based on the data set of 512 * 512 by using 0.8m remote sensing image annotation, a model of recognition of spring point based on Yolov3 network is constructed and trained. The results show that the map of spring point is 0.973, which is the basis of monitoring and protecting the natural environment in the Belt and Road Initiatives.

Optimum management of irrigated crops in regions with shallow saline groundwater requires a careful balance between application of irrigation water and upward movement of salinity from the groundwater. Few field-validated surrogate models are available to aid in the management of irrigation water under shallow groundwater conditions. The objective of this research is to develop a model that can aid in the management using a minimum of input data that are field validated. In this paper a 2-year field experiment was carried out in the Hetao irrigation district in Inner Mongolia, China, and a physically based integrated surrogate model for arid irrigated areas with shallow groundwater was developed and validated with the collected field data. The integrated model that links crop growth with available water and salinity in the vadose zone is called Evaluation of the Performance of Irrigated Crops and Soils (EPICS). EPICS recognizes that field capacity is reached when the matric potential is equal to the height above the groundwater table and thus not by a limiting hydraulic conductivity. In the field experiment, soil moisture contents and soil salt conductivity at five depths in the top 100 cm, groundwater depth, crop height, and leaf area index were measured in 2017 and 2018. The field results were used for calibration and validation of EPICS. Simulated and observed data fitted generally well during both calibration and validation. The EPICS model that can predict crop growth, soil water, groundwater depth, and soil salinity can aid in optimizing water management in irrigation districts with shallow aquifers.

The temporal and spatial distributions of regional irrigation water productivity (RIWP) are crucial for making decisions related to agriculture, especially in arid irrigated areas with complex cropping patterns. Thus, in this study, we developed a new RIWP model for an irrigated agricultural area with complex cropping patterns. The model couples the irrigation- and drainage-driven soil water and salinity dynamics and shallow groundwater movement in order to quantify the temporal and spatial distributions of the target hydrological and biophysical variables. We divided the study area into 1 km × 1 km hydrological response units (HRUs). In each HRU, we considered four land use types: sunflower fields, wheat fields, maize fields, and uncultivated lands (bare soil). We coupled the regional soil hydrological processes and groundwater flow by taking a weighted average of the water exchange between unsaturated soil and groundwater under different land use types. The RIWP model was calibrated and validated using 8 years of hydrological variables obtained from regional observation sites in a typical arid irrigation area in North China, the Hetao Irrigation District. The model simulated soil moisture and salinity reasonably well as well as groundwater table depths and salinity. However, overestimations of groundwater discharge were detected in both the calibration and validation due to the assumption of well-operated drainage ditch conditions; regional evapotranspiration (ET) was reasonably estimated, whereas ET in the uncultivated area was slightly underestimated in the RIWP model. A sensitivity analysis indicated that the soil evaporation coefficient and the specific yield were the key parameters for the RIWP simulation. The results showed that the RIWP decreased from maize to sunflower to wheat from 2006 to 2013. It was also found that the maximum RIWP was reached when the groundwater table depth was between 2 and 4 m, regardless of the irrigation water depth applied. This implies the importance of groundwater table control on the RIWP. Overall, our distributed RIWP model can effectively simulate the temporal and spatial distribution of the RIWP and provide critical water allocation suggestions for decision-makers.

The Sentinel-1 data is currently the latest free SAR data and is well suited for land subsidence monitoring based on InSAR technology due to its 6-day revisit cycle. In this paper, the urban area and surrounding areas of Handan City are used as research areas, and 29 scences of S1A data (December 2017 – December 2018) was used for time series SBAS processing. The results shows that the surface cumulative deformation ranged from -42 to 30 mm in most regions of Handan City. The maximum settlement is 69 mm, which is near the Hankuang Group Julong Company. The areas where the settlement is obvious include Wu'an City, Daishan Village, Dashe Town, Zhangxibao Town, Baijia Street, Dongxingtai Village, Gaonan Village and Hankuang Group Julong Company. Slightly elevated in the southeast of Handan City.

Nadir-viewing satellite observations of tropospheric ozone in the UV have been shown to have some sensitivity to boundary layer ozone pollution episodes, but so far they have not yet been compared to surface ozone observations collected by large-scale monitoring networks. Here we use 2013–2017 surface ozone data from China's new Ministry of Ecology and Environment (MEE) network of ∼ 1000 sites, together with vertical profiles from ozonesondes and aircraft, to quantify the ability of tropospheric ozone retrievals from the Ozone Monitoring Instrument (OMI) and to detect boundary layer ozone pollution in China. We focus on summer when ozone pollution in China is most severe and when OMI has the strongest sensitivity. After subtracting the Pacific background, we find that the 2013–2017 mean OMI ozone enhancements over eastern China have strong spatial correlation with the corresponding multiyear means in the surface afternoon observations (R=0.73), and that OMI can estimate these multiyear means in summer afternoon surface ozone with a precision of 8 ppb. The OMI data show significantly higher values on observed surface ozone episode days (>82 ppb) than on non-episode days. Day-to-day correlations with surface ozone are much weaker due to OMI noise and are stronger for sites in southern China (<34∘ N; R=0.3–0.6) than in northern China (R=0.1–0.3) because of weaker retrieval sensitivity and larger upper tropospheric variability in the north. Ozonesonde data show that much of the variability of OMI ozone over southern China in summer is driven by the boundary layer. Comparison of 2005–2009 and 2013–2017 OMI data indicates that mean summer afternoon surface ozone in southern China (including urban and rural regions) has increased by 3.5±3.0 ppb over the 8-year period and that the number of episode days per summer has increased by 2.2±0.4 (as diagnosed by an extreme value model), generally consistent with the few long-term surface records. Ozone increases have been particularly large in the Yangtze River Delta and in the Hubei, Guangxi and Hainan provinces.

Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as oversampling. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼ 16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.

Cancer, as one of the most life-threatening diseases, shows a high fatality rate around the world. When improving the therapeutic efficacy of conventional cancer treatments, researchers also conduct extensive studies into alternative therapeutic approaches, which are safe, valid, and economical. Phototherapies, including photodynamic therapy (PDT) and photothermal therapy (PTT), are tumor-ablative and function-reserving oncologic interventions, showing strong potential in clinical cancer treatment. During phototherapies, the non-toxic phototherapeutic agents can be activated upon light irradiation to induce cell death without causing much damage to normal tissues. Besides, with the rapid development of nanotechnology in the past decades, phototheranostic nanomedicine also has attracted tremendous interests aiming to continuously refine their performance. Herein, we reviewed the recent progress of phototheranostic nanomedicine for improved cancer therapy. After a brief introduction of the therapeutic principles and related phototherapeutic agents for PDT and PTT, the existing works on developing of phototheranostic nanomedicine by mainly focusing on their categories and applications, particularly on phototherapy-synergized cancer immunotherapy, are comprehensively reviewed. More importantly, a brief conclusion and future challenges of phototheranostic nanomedicine from our point of view are delivered in the last part of this article. Keywords: Theranostics, Nanoparticles, Photodynamic therapy, Photothermal therapy, Cancer treatment

Our rapidly changing world requires new sources of image based information. The quickly changing urban areas, the maintenance and management of smart cities cannot only rely on traditional techniques based on remotely sensed data, but also new and progressive techniques must be involved. Among these technologies the volunteer based solutions are getting higher importance, like crowd-sourced image evaluations, mapping by satellite based positioning techniques or even observations done by unskilled people. Location based intelligence has become an everyday practice of our life. It is quite enough to mention the weather forecast and traffic monitoring applications, where everybody can act as an observer and acquired data – despite their heterogeneity in quality – provide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowd-sourced spatial data have revolutionary transformed our general applications. Most important factors of location based decisions are the time-related quality parameters of the used data. In this paper several time-related data quality dimensions and terms are defined. The paper analyses the time sensitive data characteristics of image-based crowd-sourced big data, presents quality challenges and perspectives of the users. The data quality analyses focus not only on the dimensions, but are also extended to quality related elements, metrics. The paper discusses the connection of data acquisition and processing techniques, considering even the big data aspects. The paper contains not only theoretical sections, strong practice-oriented examples on detecting quality problems are also covered. Some illustrative examples are the OpenStreetMap (OSM), where the development of urbanization and the increasing process of involving volunteers can be studied. This framework is continuing the previous activities of the Remote Sensing Data Quality Working Group (ICWGIII/IVb) of the ISPRS in the topic focusing on the temporal variety of our urban environment.

The Heihe River Basin (HRB) is the second largest inland river basin, located in the arid region of Northwest China with a serious water shortage. Evaluation of water productivity will provide scientific implications for agricultural water-saving in irrigated areas of the arid region under climate change. Based on observed meteorological data, 23 GCMs outputs and the ERA-40 reanalysis data, an assemble statistical downscaling model was developed to generate climate change scenarios under RCP2.6, RCP4.5, RCP8.5 respectively, which were then used to drive the SWAP-EPIC model to simulate crop growth in the irrigated areas of the middle HRB for the future period from 2018 to 2047. Crop yield showed an increasing trend, while crop water consumption decreased gradually in Gaotai and Ganzhou irrigated areas. The water productivity in future 30 years showed an increasing trend in both Gaotai and Ganzhou areas, with the most significant increase under RCP4.5 scenario, which were both larger than 2 kg m−3. Compared with that of the period from 2012 to 2015, the water productivity during 2018–2047 under three RCP scenarios increased by 9.2, 14.3 and 11.8 % in the Gaotai area, and 15.4, 21.6, 19.9 % in the Ganzhou area, respectively.

The availability and accessibility of remote sensing (RS) data, cloud processing platforms and provided information products and services has increased the size and diversity of the RS user community. This development also generates a need for validation approaches to assess data quality. Validation approaches employ quality criteria in their assessment. Data Quality (DQ) dimensions as the basis for quality criteria have been deeply investigated in the database area and in the remote sensing domain. Several standards exist within the RS domain but a general classification – established for databases – has been adapted only recently. For an easier identification of research opportunities, a better understanding is required how quality criteria are employed in the RS lifecycle. Therefore, this research investigates how quality criteria support decisions that guide the RS lifecycle and how they relate to the measured DQ dimensions. Subsequently follows an overview of the relevant standards in the RS domain that is matched to the RS lifecycle. Conclusively, the required research needs are identified that would enable a complete understanding of the interrelationships between the RS lifecycle, the data sources and the DQ dimensions, an understanding that would be very valuable for designing validation approaches in RS.

GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.

In this paper, we used the Generic Atmospheric Correction Online Service for InSAR (GACOS) tropospheric delay maps to correct the atmospheric phase delay of the differential interferometric synthetic aperture radar (D-InSAR) monitoring, and we improved the accuracy of subsidence monitoring using D-InSAR technology. Atmospheric phase delay, as one of the most important errors that limit the monitoring accuracy of InSAR, would lead to the masking of true phase in subsidence monitoring. For the problem, this paper used the Sentinel-1A images and the tropospheric delay maps got from GACOS to monitor the subsidence of the Yellow River Delta in Shandong Province. The conventional D-InSAR processing was performed using the GAMMA software. The MATLAB codes were used to correct the atmospheric delay of the D-InSAR results. The results before and after the atmospheric phase delay correction were verified and analyzed in the main subsidence area. The experimental results show that atmospheric phase influences the deformation results to a certain extent. After the correction, the measurement error of vertical deformation is reduced by about 18 mm, which proves that the removal of atmospheric effects can improve the accuracy of the D-InSAR monitoring.

Towards spaceborne spotlight SAR, the antenna is regulated by the SAR system with specific regularity, so the shaking of the internal mechanism is inevitable. Moreover, external environment also has an effect on the stability of SAR platform. Both of them will cause the jitter of the SAR platform attitude. The platform attitude instability will introduce antenna pointing error on both the azimuth and range directions, and influence the acquisition of SAR original data and ultimate imaging quality. In this paper, the relations between the antenna pointing errors and the three-axis attitude errors are deduced, then the relations between spaceborne spotlight SAR imaging of the point target and antenna pointing errors are analysed based on the paired echo theory, meanwhile, the change of the azimuth antenna gain is considered as the spotlight SAR platform moves ahead. The simulation experiments manifest the effects on spotlight SAR imaging caused by antenna pointing errors are related to the target location, that is, the pointing errors of the antenna beam will severely influence the area far away from the scene centre of azimuth direction in the illuminated scene.

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

Point cloud data has been one type of widely used data sources in the field of remote sensing. Key steps of point cloud data’s pro-processing focus on gross error elimination and quality control. Owing to the volume feature of point could data, existed gross error elimination methods need spend massive memory both in space and time. This paper employed a new method which based on Kd-tree algorithm to construct, k-nearest neighbor algorithm to search, settled appropriate threshold to determine with result turns out a judgement that whether target point is or not an outlier. Experimental results show that, our proposed algorithm will help to delete gross error in point cloud data and facilitate to decrease memory consumption, improve efficiency.

China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal–spatial patterns of water resources. Based on the long-term (1960–2008) water budget data and climate projections from 28 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to historical and future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results show that there is a large spatial variation in precipitation (P) elasticity (from 1.1 to 3.2) and potential evaporation (PET) elasticity (from −2.2 to −0.1) across China. The P elasticity is larger in north-eastern and western China than in southern China, while the opposite occurs for PET elasticity. The catchment properties' elasticity of R appears to have a strong non-linear relationship with the mean annual aridity index and tends to be more significant in more arid regions. For the period 1960–2008, the climate contribution to R ranges from −2.4 to 3.6 % yr−1 across China, with the negative contribution in north-eastern China and the positive contribution in western China and some parts of the south-west. The results of climate projections indicate that although there is large uncertainty involved in the 28 GCMs, most project a consistent change in P (or PET) in China at the annual scale. For the period 2071–2100, the mean annual P is projected to increase in most parts of China, especially the western regions, while the mean annual PET is projected to increase in all of China, particularly the southern regions. Furthermore, greater increases are projected for higher emission scenarios. Overall, due to climate change, the arid regions and humid regions of China are projected to become wetter and drier in the period 2071–2100, respectively (relative to the baseline 1971–2000).

We validate the Ozone Monitoring Instrument (OMI) ozone profile (PROFOZ v0.9.3) product including ozone profiles between 0.22 and 261 hPa and stratospheric ozone columns (SOCs) down to 100, 215, and 261 hPa from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against the latest Microwave Limb Sound (MLS) v4.2x data. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the data set into before and after the occurrence of serious RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). During the pre-RA period, OMI ozone profiles agree very well with MLS data. After applying OMI averaging kernels to MLS data, the global mean biases (MBs) are within 3 % between 0.22 and 100 hPa, negative biases are within 3–9 % for lower layers, and the standard deviations (SDs) are 3.5–5 % from 1 to 40 hPa, 6–10 % for upper layers, and 5–20 % for lower layers. OMI shows biases dependent on latitude and solar zenith angle (SZA), but MBs and SDs are mostly within 10 % except for low and high altitudes of high latitudes and SZAs. Compared to the retrievals during the pre-RA period, OMI retrievals during the post-RA period degrade slightly between 5 and 261 hPa with MBs and SDs typically larger by 2–5 %, and degrade much more for pressure less than ∼ 5 hPa, with larger MBs by up to 8 % and SDs by up to 15 %, where the MBs are larger by 10–15 % south of 40° N due to the blockage effect of RA and smaller by 15–20 % north of 40° N due to the solar contamination effect of RA. The much worse comparisons at high altitudes indicate the UV1 channel of pixels that are not flagged as RA is still affected by the RA. During the pre-RA period, OMI SOCs show very good agreement with MLS data with global mean MBs within 0.6 % and SDs of 1.9 % for SOCs down to 215 and 261 hPa and of 2.30 % for SOC down to 100 hPa. Despite clearly worse ozone profile comparisons during the post-RA period, OMI SOCs only slightly degrade, with SDs larger by 0.4–0.6 % mostly due to looser spatial coincidence criteria as a result of missing data from RA and MBs larger by 0.4–0.7 %. Our retrieval comparisons indicate significant bias trends, especially during the post-RA period. The spatiotemporal variation of our retrieval performance suggests the need to improve OMI's radiometric calibration to maintain the long-term stability and spatial consistency of the PROFOZ product.

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 Ozone Monitoring Instrument (OMI) has been successfully measuring the Earth's atmospheric composition since 2004, but the on-orbit behavior of its slit functions has not been thoroughly characterized. Preflight measurements of slit functions have been used as a static input in many OMI retrieval algorithms. This study derives on-orbit slit functions from the OMI irradiance spectra assuming various function forms, including standard and super-Gaussian functions and a stretch to the preflight slit functions. The on-orbit slit functions in the UV bands show U-shaped cross-track dependences that cannot be fully represented by the preflight ones. The full widths at half maximum (FWHM) of the stretched preflight slit functions for detector pixels at large viewing angles are up to 30 % larger than the nadir pixels for the UV1 band, 5 % larger for the UV2 band, and practically flat in the VIS band. Nonetheless, the on-orbit changes of OMI slit functions are found to be insignificant over time after accounting for the solar activity, despite of the decaying of detectors and the occurrence of OMI row anomaly. Applying the derived on-orbit slit functions to ozone-profile retrieval shows substantial improvements over the preflight slit functions based on comparisons with ozonesonde validations.

The image segmentation method based on two-dimensional histogram segments the image according to the thresholds of the intensity of the target pixel and the average intensity of its neighborhood. This method is essentially a hard-decision method. Due to the uncertainties when labeling the pixels around the threshold, the hard-decision method can easily get the wrong segmentation result. Therefore, a fusion segmentation method based on fuzzy theory is proposed in this paper. We use membership function to model the uncertainties on each color channel of the color image. Then, we segment the color image according to the fuzzy reasoning. The experiment results show that our proposed method can get better segmentation results both on the natural scene images and optical remote sensing images compared with the traditional thresholding method. The fusion method in this paper can provide new ideas for the information extraction of optical remote sensing images and polarization SAR images.

We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure ∼ 50 hPa after applying OMI averaging kernels to ozonesonde data. The MBs of the stratospheric ozone column (SOC, the ozone column from the tropopause pressure to the ozonesonde burst pressure) are within 2 % with SDs of ∼ 50 hPa. The SOC MBs increase up to 3 % with SDs as great as 6 % and the TOC SDs increase up to 30 %. The comparison generally degrades at larger solar zenith angles (SZA) due to weaker signals and additional sources of error, leading to worse performance at high latitudes and during the midlatitude winter. Agreement also degrades with increasing cloudiness for pressure > ∼ 100 hPa and varies with cross-track position, especially with large MBs and SDs at extreme off-nadir positions. In the tropics and midlatitudes, the post-RA comparison is considerably worse with larger SDs reaching 2 % in the stratosphere and 8 % in the troposphere and up to 6 % in TOC. There are systematic differences that vary with latitude compared to the pre-RA comparison. The retrieval comparison demonstrates good long-term stability during the pre-RA period but exhibits a statistically significant trend of 0.14–0.7 % year−1 for pressure −1 in SOC, and −0. 33 DU year−1 in TOC during the post-RA period. The spatiotemporal variation of retrieval performance suggests the need to improve OMI's radiometric calibration especially during the post-RA period to maintain the long-term stability and reduce the latitude/season/SZA and cross-track dependency of retrieval quality.

Non-methane volatile organic compounds (NMVOCs) include a large number of chemical species which differ significantly in their chemical characteristics and thus in their impacts on ozone and secondary organic aerosol formation. It is important that chemical transport models (CTMs) simulate the chemical transformation of the different NMVOC species in the troposphere consistently. In most emission inventories, however, only total NMVOC emissions are reported, which need to be decomposed into classes to fit the requirements of CTMs. For instance, the Emissions Database for Global Atmospheric Research (EDGAR) provides spatially resolved global anthropogenic emissions of total NMVOCs. In this study the EDGAR NMVOC inventory was revised and extended in time and in sectors. Moreover the new version of NMVOC emission data in the EDGAR database were disaggregated on a detailed sector resolution to individual species or species groups, thus enhancing the usability of the NMVOC emission data by the modelling community. Region- and source-specific speciation profiles of NMVOC species or species groups are compiled and mapped to EDGAR processes (detailed resolution of sectors), with corresponding quality codes specifying the quality of the mapping. Individual NMVOC species in different profiles are aggregated to 25 species groups, in line with the common classification of the Global Emissions Initiative (GEIA). Global annual grid maps with a resolution of 0.1° × 0.1° for the period 1970–2012 are produced by sector and species. Furthermore, trends in NMVOC composition are analysed, taking road transport and residential sources in Germany and the United Kingdom (UK) as examples.

'The Tropospheric Ozone Assessment Report' (TOAR) is an activity of the International Global Atmospheric Chemistry Project. This paper is a component of the report, focusing on the present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. Utilizing the TOAR surface ozone database, several figures present the global distribution and trends of daytime average ozone at 2702 non-urban monitoring sites, highlighting the regions and seasons of the world with the greatest ozone levels. Similarly, ozonesonde and commercial aircraft observations reveal ozone’s distribution throughout the depth of the free troposphere. Long-term surface observations are limited in their global spatial coverage, but data from remote locations indicate that ozone in the 21st century is greater than during the 1970s and 1980s. While some remote sites and many sites in the heavily polluted regions of East Asia show ozone increases since 2000, many others show decreases and there is no clear global pattern for surface ozone changes since 2000. Two new satellite products provide detailed views of ozone in the lower troposphere across East Asia and Europe, revealing the full spatial extent of the spring and summer ozone enhancements across eastern China that cannot be assessed from limited surface observations. Sufficient data are now available (ozonesondes, satellite, aircraft) across the tropics from South America eastwards to the western Pacific Ocean, to indicate a likely tropospheric column ozone increase since the 1990s. The 2014–2016 mean tropospheric ozone burden (TOB) between 60°N–60°S from five satellite products is 300 Tg ± 4%. While this agreement is excellent, the products differ in their quantification of TOB trends and further work is required to reconcile the differences. Satellites can now estimate ozone’s global long-wave radiative effect, but evaluation is difficult due to limited in situ observations where the radiative effect is greatest.

The aim of this study was to evaluate the clinical and radiographic outcomes of upper thoracic (UT) versus lower thoracic (LT) upper instrumented vertebrae (UIV) for adult scoliosis by meta-analysis. We conducted a literature search in three databases to retrieve related studies up to March 15, 2017. The preliminary screened studies were assessed by two reviewers according to the selection criteria. All analyses were carried out using the statistical software package R version 2.31. Odds ratios (OR) with 95% confidence intervals (CI) were used to describe the results. The I2 statistic and Q statistic test were used for heterogeneity assessment. Egger's test was performed to detect publication bias. To assess the effect of each study on the overall pooled OR or standardized mean difference (SMD), sensitive analysis was conducted. Ten trials published between 2007 and 2015 were eligible and included in our study. Meta-analysis revealed that the UT group was associated with more blood loss (SMD=0.4779, 95%CI=0.3349-0.6209, Z=6.55, P

For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm based on the coherent matrix in the pre-processing. Then, the corresponding patches are extracted in two temporal images to measure the differences of objects. To detect changes of patches, a difference map is created by means of weighted polarization scattering difference. Finally, the result of change detection can be obtained by threshold determining. The experiments show that this approach is feasible and effective, and a reasonable choice of weights can improve the detection accuracy significantly.

A pot experiment using root separation technique was conducted to further understand the effect of root interaction played in intercropping system under different nitrogen levels. The results showed that root interaction and increasing nitrogen application increased the green leaf area per plant and chlorophyll content of soybean, but their effects gradually decreased with increasing nitrogen fertilization level. Root interaction and increasing nitrogen application can improve photosynthetic characteristics of soybean, but root interaction only had a significant effect under low nitrogen level. The number of bacteria, fungi, actinomycetes and Azotobacteria was also obviously affected by root interaction and nitrogen fertilization, and the number of Azotobacteria presented a changing trend of first increased and then decreased with increasing nitrogen fertilization level. Root interaction and increasing nitrogen application improved soybean yield and its components, but their effects gradually decreased with increasing nitrogen fertilization level. The root activity of soybean was obviously affected by root interaction, and was significantly positively correlated with green leaf area per plant, chlorophyll content, photosynthetic rate and economic yield per plant. Our results indicate that the advantage effect of root interaction and increasing nitrogen application will be partially inhibited with an increasing nitrogen fertilization level.