A new model to downscale urban and rural surface and air temperatures evaluated in Shanghai, China

A simple model, TsT2m (Surface Temperature and near surface air Temperature (at 2 m) model), is developed to downscale numerical model output (such as from ECMWF) to obtain higher temporal and spatial resolution surface and near surface air temperature. It is evaluated in Shanghai, China. Surface temperature (TS) and near surface air temperature (Ta) sub-models account for variations in land covers and their different thermal properties, resulting in spatial variations of surface and air temperature. The Net All Wave Radiation Parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature sub-model, the Objective Hysteresis Model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near surface air temperature sub-model considers the horizontal and vertical energy changes for a column of well mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land cover types values are more similar. Downscaled, higher temporal and spatial resolution air temperatures are compared to observations at 110 Automatic Weather Stations across Shanghai. After downscaling with the TsT2m model, the average forecast accuracy of near surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stres

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation

Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%

Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%

Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity

Gaseous Emissions from a Seagoing Ship under Different Operating Conditions in the Coastal Region of China

Pollution caused by ship emissions has drawn attention from various countries. Because of the high density of ships in ports, channels, and anchorages and their proximity to the densely populated areas, ship emissions will considerably impact these areas. Herein, a Chinese seagoing ship is selected and a platform is established for monitoring the ship emissions to obtain detailed characteristics of the ship’s nearshore emissions. The ship navigation and pollution emission data are obtained under six complete operating conditions, i.e., berthing, manoeuvring in port, acceleration in a channel, cruising, deceleration before anchoring, and anchoring. This study analyzes the concentrations of the main emission gases (O2, NOX, SO2, CO2, and CO) and the average emission factors (EFs) of the pollution gases (NOX, SO2, CO2, and CO) based on the engine power under different operating conditions. Results show that the change in O2 concentration reflects the load associated with the main engine of the ship. The NOX, SO2, and CO2 emission concentrations are the highest during cruising, whereas the peak CO emission concentration is observed during anchoring. The average EFs of NOX and SO2 based on the power of the main engine are the highest during cruising, and those of CO2 and CO are the highest after anchoring. The ship EFs are different during acceleration and deceleration. By comparing the EFs along the coast of China and the global EFs commonly used to perform the emission inventory calculations in China, the NOX EFs under different operating conditions is observed to be generally lower than the global EFs under the corresponding operating conditions. Furthermore, the SO2 EF is considerably affected by the sulfur content in the fuel oil and the operating conditions of the ship. The average CO2 EFs are higher than the global EFs commonly used during cruising, and the CO EFs are higher than the global EFs under all the conditions. Our results help to supplement the EFs for this type of ship under different operating conditions, resolve the lack of emission data under anchoring conditions, and provide data support to conduct nearshore environmental monitoring and assessment

The functional divergence of homologous GPAT9 genes contributes to the erucic acid content of Brassica napus seeds

Abstract Background The early allopolyploid Brassica napus was a hybrid of two Brassica species, that had undergone a whole genome duplication event followed by genome restructuring, including deletions and small scale duplications. A large number of homologous genes appeared functional divergence during species domestication. Due to the high conservation of de novo glycerolipid biosynthesis, multiple homologues of glycerol-3-phosphate acyltransferases (GPATs) have been found in B. napus. Moreover, the functional variances among these homologous GPAT-encoding genes are unclear. Results In this study, four B. napus homologous genes encoding glycerol-3-phosphate acyltransferase 9 (BnaGPAT9) were characterized. Although a bioinformatics analysis indicated high protein sequence similarity, the homologues demonstrated tissue-specific expression patterns and functional divergence. Yeast genetic complementation assays revealed that BnaGPAT9-A1/C1 homologues but not BnaGPAT9-A10/C9 homologues encoded functional GPAT enzymes. Furthermore, a single nucleotide polymorphism of BnaGPAT9-C1 that occurred during the domestication process was associated with enzyme activity and contributed to the fatty acid composition. The seed-specific expression of BnGPAT9-C1 1124A increased the erucic acid content in the transformant seeds. Conclusions This study revealed that BnaGPAT9 gene homologues evolved into functionally divergent forms with important roles in erucic acid biosynthesis

Facile Synthesis of 1T-Phase MoS₂ Nanosheets on N-Doped Carbon Nanotubes towards Highly Efficient Hydrogen Evolution

1T-phase molybdenum disulfide is supposed to be one of the non-precious metal-based electrocatalysts for the hydrogen evolution reaction with the highest potential. Herein, 1T-MoS2 nanosheets were anchored on N-doped carbon nanotubes by a simple hydrothermal process with the assistance of urea promotion transition of the 1T phase. Based on the 1T-MoS2 nanosheets anchored on the N-doped carbon nanotubes structures, 1T-MoS2 nanosheets can be said to have highly exposed active sites from edges and the basal plane, and the dopant N in carbon nanotubes can promote electron transfer between N-doped carbon nanotubes and 1T-MoS2 nanosheets. With the synergistic effects of this structure, the excellent 1T-MoS2/ N-doped carbon nanotubes catalyst has a small overpotential of 150 mV at 10 mA cm−2, a relatively low Tafel slope of 63 mV dec−1, and superior stability. This work proposes a new strategy to design high-performance hydrogen evolution reaction catalysts

Insights on the fundamental lithium storage behavior of all-solid-state lithium batteries containing the LiNi0.8Co0.15Al0.05O2 cathode and sulfide electrolyte

An insightful study on the fundamental lithium storage behavior of all-solid-state lithium battery with a structure of LiNi0.8Co0.15Al0.05O2 (NCA)/Li10GeP2S12/Li-In is carried out in this work. The relationship between electrochemical performances and particle size, surface impurities and defects of the NCA positive material is systematically investigated. It is found that a ball-milling technique can decrease the particle size and remove surface impurities of the NCA cathode while also give rise to surface defects which could be recovered by a post-annealing process. The results indicate that the interfacial resistance between the NCA and Li10GeP2S12 is obviously decreased during the ball-milling followed by a post-annealing. Consequently, the discharge capacity of NCA in the NCA/Li10GeP2S12/Li-In solid-state battery is significantly enhanced, which exhibits a discharge capacity of 146 mAh g(-1) at 25 degrees C. (C) 2016 Elsevier B.V. All rights reserved