Femtosecond-laser-fabricated black silicon has been widely used in the fields of solar cells, photodetectors, semiconductor devices, optical coatings, and quantum computing. However, the responsive spectral range limits its application in the near- to mid-infrared wavelengths. To further increase the optical responsivity in longer wavelengths, in this work, silicon (Si) was co-hyperdoped with nitrogen (N) and selenium (Se) through the deposition of Se films on Si followed by femtosecond (fs)-laser irradiation in an atmosphere of NF3. The optical and crystalline properties of the Si:N/Se were found to be influenced by the precursor Se film and laser fluence. The resulting photodetector, a product of this innovative approach, exhibited an impressive responsivity of 24.8 A/W at 840 nm and 19.8 A/W at 1060 nm, surpassing photodetectors made from Si:N, Si:S, and Si:S/Se (the latter two fabricated in SF6). These findings underscore the co-hyperdoping method’s potential in significantly improving optoelectronic device performance.

Large-scale high spatial resolution aboveground biomass (AGB) maps play a crucial role in determining forest carbon stocks and how they are changing, which is instrumental in understanding the global carbon cycle, and implementing policy to mitigate climate change. The advent of the new space-borne LiDAR sensor, NASA's GEDI instrument, provides unparalleled possibilities for the accurate and unbiased estimation of forest AGB at high resolution, particularly in dense and tall forests, where Synthetic Aperture Radar (SAR) and passive optical data exhibit saturation. However, GEDI is a sampling instrument, collecting dispersed footprints, and its data must be combined with that from other continuous cover satellites to create high-resolution maps, using local machine learning methods. In this study, we developed local models to estimate forest AGB from GEDI L2A data, as the models used to create GEDI L4 AGB data incorporated minimal field data from China. We then applied LightGBM and random forest regression to generate wall-to-wall AGB maps at 25 m resolution, using extensive GEDI footprints as well as Sentinel-1 data, ALOS-2 PALSAR-2 and Sentinel-2 optical data. Through a 5-fold cross-validation, LightGBM demonstrated a slightly better performance than Random Forest across two contrasting regions. However, in both regions, the computation speed of LightGBM is substantially faster than that of the random forest model, requiring roughly one-third of the time to compute on the same hardware. Through the validation against field data, the 25 m resolution AGB maps generated using the local models developed in this study exhibited higher accuracy compared to the GEDI L4B AGB data. We found in both regions an increase in error as slope increased. The trained models were tested on nearby but different regions and exhibited good performance.

Drinking water treatment residuals (DWTR) are produced in very large quantities worldwide and many applications are investigated to reuse this type of material for combined economic and environmental incentives. This review focuses on the versatile properties of DWTR as a low-cost and low-toxicity adsorbent for wastewater treatments for a variety of contaminants such as trace metals and metalloids, nutrients, dyes, gases, and pesticides. Most studies report the best laboratory conditions of adsorption in consideration of the main controlling factors of solution pH, dosages of adsorbent, concentrations of adsorbates, and contact times with solutions of well-controlled chemical composition. Kinetic studies and common adsorption models are also generally reported. The adsorptive properties of DWTRs are also utilized for soil remediation and amendment to control the levels of essential nutrients such as phosphorus and nitrogen. Abundant literature exists to propose the inclusion of DWTR in various construction materials such as cement, bricks, geopolymers, and agricultural applications. It is somehow puzzling to realize that real-world applications are so limited in comparison to the large number of studies and publications on the subject of using DWTR. It also appears that adsorption results are mostly obtained using well-controlled conditions with synthetic solutions of selected contaminants and rarely with real wastewater samples. The transfer of laboratory results to real systems for industrial and commercial applications has been so far very limited, possibly due to limited legislation and to some reluctance from those organizations to use DWTR as a potential substitute in their respective process. Drinking water treatment residuals (DWTRs) show versatile adsorption properties for the removal of contaminants from wastewater and soil. Most adsorption studies are based on synthetic well-controlled solutions instead of real wastewater samples.

气泡释放是天然水体释放CH4的主要途径之一,准确量化水体气泡释放量对于辨析其"汇、源"特性至关重要.自然水体释放气泡的不连续性、不确定性使得监测其过程较为困难.本研究针对水体气泡释放监测难题,通过改进倒置漏斗型气泡通量监测装置提出了一种气泡释放过程连续监测方法.本方法测量对象为定长时间监测水域释放气泡的体积,经室内外实验验证,其理论量程为3.6～132 mL/(m2·min),测量结果能够较好的表征10～40 m水深缓流水体气泡体积通量变化特征.运用该方法于2021年6-11月对三峡水库支流香溪河库湾开展CH4气泡通量连续监测,并分析不同环境因子对其产生的影响.结果表明:监测期间,研究水域CH4气泡通量变化范围为0.02～8.13 mg/(m2·d),且各采样点间CH4气泡通量呈现较高的时空变异性;CH4气泡通量与水温、水体pH呈显著正相关关系,与水深及水体电导率呈显著负相关关系.其中,水深可能是决定水体是否通过气泡形式释放CH4的重要影响因素,水深超过38.35 m后水体可能不再通过气泡形式释放CH4.然而,这一水深阈值是否同样适用于三峡水库其它支流还需要更多实验验证.本研究对于揭示三峡水库典型支流气泡态CH4排放过程具有重要意义.

N and Se co-hyperdoped silicon (Si:N/Se) is prepared using deposited Se films on Si followed by femtosecond (fs)-laser irradiation in the atmosphere of NF3. The optical and crystallinity characteristics of the Si:N/Se are determined by the precursor Se film and laser fluence. The photodetector fabricated from the Si:N/Se shows remarkable responsivity of 24.8 and 19.8 A/W at the wavelength of 840 and 1060 nm, respectively, outperforming the photodetectors fabricated from Si:N, Si:S and Si:S/Se (the latter two are fabricated in SF6). The better photoelectric characteristics of Si:N/Se further facilitate the application of the co-hyperdoping method in optoelectronic devices.

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

In spite of recent advances, a significant disparity between automatically reconstructed building information models (BIMs) and real scenes persists, particularly within complex indoor environments. With this objective in mind, a knowledge-driven as-built BIM reconstruction method is proposed, which employs laser scanning point clouds and panorama images. Initiation of the method involves the segmentation of 3D data into individual rooms through the application of wall constraints. Subsequently, geometric regularization of the rooms is performed, accompanied by the establishment of topological relations through the maintenance of rigid consistency among rooms. Finally, building frames and components embedded within walls are reconstructed and assembled to formulate comprehensive BIMs. The method was evaluated using indoor point cloud datasets (ISPRS and WUT), showing that it outperforms state-of-the-art techniques with room segmentation accuracy and completeness of 0.98 and 0.88, respectively, and achieving millimetre level accuracy on average.

Abstract. In urban areas, the None-Line-Of-Sight (NLOS) and Multipath (MP) signals are the major issues degrading the GNSS position accuracy. Signal reception type should be identified before correcting the NLOS or MP induced errors. Signal features, i.e., signal strength, change rate of received signal strength, difference between delta pseudo-range and pseudo-range rate, have been explored in signal reception type classification. In this letter, with the aim to improve the signal classification accuracy, we propose a new GNSS NLOS/MP/LOS signals classification method using the correlator-level measurements. Firstly, vector tracking (VT) is employed to generate correlator-level measurements; secondly, a deep learning method, Convolutional Neural Network (CNN), is employed to automatically extract the features and identify the signal reception type, correlators’ outputs calculated at different code phases are employed as the inputs of the CNN. Field test is carried out for assessing the performance of the proposed method, and the CNN method obtains state-of-art performance compared with the K-nearest Neighbors Algorithm (kNN) and Support Vector Machine (SVM) methods.

In a smartphone, the global navigation satellite system (GNSS) receiver is dominant in providing position information for users. However, GNSS signals are vulnerable to the environment, i.e., city canyons and tunnels. Pedestrian dead reckoning (PDR) exploring human walking gaits is another effective method to determine pedestrian position. Accelerometers and gyroscope sensors are available in smartphones to detect pedestrians’ steps and carry out PDR. Aiming to improve smartphone position accuracy, we propose two different factor graph optimization (FGO)-PDR/GNSS integration models. First, the position from PDR and GNSS is integrated using FGO. Second, given the negative influences of the heading angle errors and the inconsistency between a smartphone and the pedestrian walking direction, PDR step length and GNSS are integrated using FGO. Android smartphones were utilized to collect different datasets and evaluate the proposed methods; experimental results indicated the superior performance of the proposed methods.

It is essential to understand the mechanism of algal bloom and develop effect measures to control the hazard in aquatic environment, such as large reservoirs. In this study, a series of experiments, along with field observation from 2007 to 2016, were carried out to identify the hydrodynamic parameters that drive the algal bloom in the Three Gorges Reservoir (TGR), China, and their threshold values were determined. The results show that algae concentration was markedly diluted with a short retention time, and the threshold value of the retention time to avoid algal bloom was approximately less than 3 days. With strong stratification, the algae concentration was able to approach to the level of algal bloom in 10 days, even when the water temperature is lower than 12 °C. The ratio of mixing depth to euphotic depth (Zm/Ze) had significant negative correlations with both algae concentration and algae specific growth rate (SGR). The field monitoring data indicated that Zm/Ze is an important hydrodynamic parameter which sensitively affects algae growth and concentration. This study made the first attempt to determine Zm/Ze >2.8 to restrain algal bloom in the TGR. Our findings shed light on the influence of critical depth on the algal bloom in the TGR, and the results can serve to control algal bloom in reservoirs through discharge operation.

Nowadays, the Global Navigation Satellite System (GNSS) is widely used in many applications. As a new system, China's BeiDou navigation system (BDS) is emerging in recent years. The last satellite of the third generation global BeiDou navigation system (BDS-3) has been successfully launched on June 23 in 2020, which means that BDS can offer navigation services worldwide. We evaluate the quality of BDS signals and analyze the performance of BDS positioning based on the smartphone in Espoo, Finland. The static and kinematic experiments were implemented in the parking lot of the Finish Geospatial Research Institute (FGI) and a highway route in Espoo, respectively. Experimental results show that BDS has good satellite visibility and geometric distribution. The signal carrier-to-noise density ratio (C/N0) of BDS-2 reaches 34.22 dB-Hz, which is comparable to the Global Positioning System (GPS). However, the signal carrier-to-noise density of BDS-3 is slightly lower than BDS-2, which is due to the significant number of BDS-3 satellites at low elevation angles. The horizontal precision of BDS positioning in the static and kinematic experiment is comparable to GPS in the east direction and slightly inferior to GPS in the north direction. However, the BDS shows poor precision in the up direction. In addition, the integration of BDS with other GNSS systems can significantly improve the positioning precision. This study intends to provide a reference for further research on the BDS global Positioning, Navigation, and Timing (PNT) services, particularly for LBS and smartphone positioning.

中国已建各类水库近10万座,水库建设改变了原河流水动力条件,影响了水体物质场、能量场、化学场和生物场,究竟是"削减了水体净化功能"还是"强化了水体去污能力"目前尚不明确.本文系统总结了开放水体脱氮过程研究进展,主要包括:(1)厌氧反硝化(Denitrification)、厌氧氨氧化(Anaerobic ammonium oxidation)、好氧反硝化(Aerobic denitrification)和厌氧甲烷氧化(Anaerobic methane oxidation)等是目前开放水体脱氮的4个典型过程;(2)潜流带、沉积物、溶解氧极小层及悬浮颗粒等是开放水体脱氮的主要发生区域;(3)溶解氧、碳氮比、硝酸盐浓度、温度、pH值是影响开放水体脱氮效率的直接因素.建议通过开展"出入库氮形态持续观测及氮负荷平衡计算"、"水库不同载体脱氮机制原位研究方法构建"、"水库脱氮机制及氮移出通量研究"和"自然河流与水库脱氮效率对比研究"等方面的研究来回答"水库强化水体脱氮能力"这一科学假设,以期为发掘水库的脱氮除污功能、深入认识水库的生态环境影响提供一个新的研究思路.

Vector tracking (VT) is proposed and demonstrated as a superior method to obtain more robust navigation solutions. In VT, instead of individually tracking the signals, VT accomplishes signal tracking and navigation solutions estimation through a central navigation filter, mutual aiding between the channels is realized in this manner. Commonly, a Kalman Filter (KF) is employed as the center navigation filter to estimate the navigation solutions, the estimated navigation solutions are then fed back to calculate the signal tracking parameters. However, KF works in a recursive manner, relationships between the current state and all the past states are ignored, which might degrade the estimation of the navigation solutions. In this paper, we proposed a Graph Optimization (GO) based on VT. GO optimized the state estimation utilizing all the past information instead of KF, the state transformation, and measurement model were all added to the GO as the constraints to optimize the state estimation. An experiment was carried out for assessing the proposed GO-VT, statistical analysis of the navigation solutions and the corresponding comparisons demonstrated the superiority of the proposed GO-VT method.

Traditionally, farmers in China have relied on excrement to supplement organic matter in soils, and to fertilize crops for human and animal consumption. Few studies have quantitatively analyzed the potential of returning livestock-poultry and human excrement in rural China to farmland as nutrient resources for crop growth. This study clarifies the temporal and spatial changes, distribution characteristics, nutrient contents, and econometric analysis of excrement in rural China from 1980 to 2019. Meanwhile, its potential as a nutrient resource for crop growth to substitute the utilization of chemical fertilizers was also assessed. Excrement production showed a large increase (138.49 %) in Northeast China, whereas the Northern, Northwest, Southwest and Southeast regions of China and the middle and lower reaches of the Yangtze River experienced an increase of 51.75, 33.63, 30.47, 24.24 and 24.10 %, respectively. The spatial distribution of both the rural excrement and its nutrients was concentrated in Southwest and Northern China and lower reaches of the Yangtze River, as the nutrients in these areas accounted for 69.67–86.76 % of the total. In this study, new evidence and more comprehensive fundamental data are provided for waste treatment and resource recycling in agriculture. Promoting the return of excrement to farmland has the potential to reduce the use of chemical fertilizers, lessen the emission of odorous gas, and remedy environmental stress, thereby assisting to the development of organic agriculture. Recommendations of sustainable development are made for the future development of returning excrement to farmland in this study. The introduction of practical policies on renewable resources is recommended to ensure the comprehensive utilization of excrement.

This work examined the production of crop straw and livestock-poultry manure (CSLM), both important resources for biogas and soil management, in five multi-ethnic regions of China (i.e., Yunnan, Tibet, Liangshan, Garze, and Aba) from 2007 to 2016, all of which mainly inhabited by Tibetan, Qiang, and Yi people. The CSLM production was evaluated with breakdown from multiple dimensions, based on ge-ography, species source, nutrient composition, etc. The total production of crop straw of the studied regions increased from 18.44 Mt in 2007 to 21.90 Mt in 2016, and that of manure increased from 223.56 Mt to 309.02 Mt. Further analysis aimed to evaluate to what extent the nutrients from CSLM can substitute or supplement chemical fertilizers. In this regard, the content of nutrients in CSLM was quantified in the form of N, P2O5 and K2O, and then compared with the present application of chemical fertilizers in the studied regions. The results suggested that the reuse of waste from agriculture and animal husbandry by returning them to the fields would effectively reduce the use of chemical fertilizers and help combat poverty in the remote and ethnic minority areas, and should be advocated to realize sustainable agricultural development. Introducing practical policies on renewable resources is necessary to financially incentivize farmers, ensure the comprehensive utilization of CSLM, and reduce environmental pollution from CSLM, particularly in the relatively underdeveloped multi-ethnic regions. (C) 2020 Elsevier Ltd. All rights reserved.

This work presents a data acquisition framework and the technical details of a permanent terrestrial laser scanning (TLS) measurement station for high spatial and temporal resolution forest observation that was developed in the Finnish Geospatial Research Institute. The TLS measurement station was established to provide hyper-temporal time series of three-dimensional point cloud data for long term monitoring of a boreal forest. Time series data acquisition framework consists of regular 14-minute scans performed by a RIEGL VZ-2000i laser scanner in every 30 minutes, resulting in the collection of 48 scans per day. The entire framework includes the setting up of the laser scanner, the initialization of daily project, the scanning data acquisition over a preset time window, the storage management of the collected data at a local measurement computer, and the transfer of data from the measurement computer to network-attached storage (NAS) for further data processes. The operability of the proposed TLS measurement station was first piloted at a test area of about 32,500 m2 in Southern Finland (60°09'N, 24°32'E). A set of several long monitoring experiments were performed over the whole growing season from the beginning of April to the end of October in 2019. As preliminary results, the time series outputs have captured detailed information on the phenological changes in the test site with sub-centimetre accuracy. For instance, it was possible to visualize plant dynamics phenomena, such as the sprouting of leaves in spring and their falling in autumn.

Two-dimensional (2D) semiconductors of graphene, as well as transition-metal dichalcogenides, have performed strong interaction with light. Here the strong light-matter interaction between monolayer tungsten disulphide (WS2) excitons and microcavity photons at room temperature is well studied by the introduction of a gain material embedded dielectric optical microcavity structure. A Rabi splitting of about 36 meV is observed in angle-resolved reflectance spectra at room temperature, which agrees well with the theoretical results simulated by using the transfer matrix method. Since the cavity structures and 2D semiconductors can be prepared, the cavity and the gain materials, respectively, can be optimized separately in this platform. An all-dielectric Fabry-Pérot microcavity provides a simple but effective way to study the room temperature strong coupling between cavity photons and 2D excitons.

The objectives of this study were to investigate the adsorption and transfer behaviors of phenanthrene (PHE) and bisphenol A (BPA) in purple paddy soils amended with dissolved organic matter (DOM) derived from rice and canola straw in the West Sichuan Plain of China. In the pristine soil, PHE was preferentially adsorbed on both pristine clayey (L) and sandy (T) paddy soils than BPA, indicating that the retention/adsorption by soils is closely dependent on the chemical properties of organic pollutants (OPs). The noticeably higher adsorption of PHE and BPA on smaller size fraction of the soils (L2 and T2) were observed, possibly due to their higher surface areas and higher content in organic matters with higher aromaticity and hydrophobicity in this soil fraction. The DOMs derived from rice (RDOM) and canola (CDOM) straws possessed remarkable differences in E-2/E-3 and SUV254 measurements, which reflected that their chemical composition might be different. When CDOM was introduced in the studied soil T1, adsorption of BPA was doubled, but the augment in adsorption was much less impressive with RDOM, showing the nature of derived DOM played an important role. The study also demonstrated that in the fine fraction of clayey soil (L2), the retention of a same OP (PHE) was remarkably dropped when CDOM or RDOM was introduced, whereas in a sandy soil of the same size fraction (T2), the phenomenon was the opposite, suggesting a potential risk that, in certain types of soil, the introduction of straw derived DOMs may enhance the mobility of some OPs. The humification time of straw seems not to affect the adsorptions of OPs in most studied systems. Adsorption kinetics of PHE and BPA in the adsorption systems with derived DOMs were well fitted to the two-step first-order model with r(adj)(2) values of 0.994-0.998. Results of this study will provide further comprehensive fundamental data for risk assessment and control of organic pollutants (OPs) in farmland ecosystems.

Dry flue gas desulfurization is an increasingly attractive technique in SO2 emission control. However, the low efficiency in dry desulphurization is the bottleneck of this technology. To find a high-performance desulfurizer is an urgent task. This research utilized a steam jet mill digestion to prepare a desulfurizer at steam temperature of 220 degrees C and pressure of 0.45 MPa, and compared this product with the conventional digestion desulfurizer. Our results show that the digestion in steam jet mill can transform all the calcium oxide into calcium hydroxide. The calcium hydroxide had good fluidity and with honeycomb morphological characteristics. The experiments of dry flue gas desulfurization demonstrated that under the relative humidity of 15, 30 and 45%, the total dead times were 340, 640 and 720 min, the working time for keeping a 100% desulfurization efficiency were 120, 420 and 580 min, and the total sulfur fixation were 124.05, 274.58 and 332.09 mg. Compared with the desulfurizer by conventional dry digestion, the desulfurizer prepared in this research had a significantly superior performance. This experiment provides a new method for high-performance desulfurizer via quicklime digestion, which is an important step in pushing forward the application of dry flue gas desulfurization.