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Population aging is a global trend and a world-wide challenge to many governments. The high-quality life of the elderly cannot be separated from the support of basic services and facilities. Therefore, it is necessary to conduct analysis and research on the accessibility of urban community services and facilities. It is worth noting that the research in this article is based on master thesis (Bai, 2013). Drawing on the data, methods, and conclusions from the master's thesis, in this paper, an approach is designed for measuring spatial accessibility to services/facilities by a spatially dispersed ageing population in urban areas. The local government area (LGA) of Monash in the Melbourne metropolitan area has been selected the case study area. This study combines GIS-based spatial analytical procedures and two-step floating catchment area (2SFCA) method, in conjunction with mesh block level 2011 population data, service/facility data and transportation network data, to measure and map spatial variations in potential accessibility to services and facilities deemed essential to the aged population. Service facilities considered in the study include bus stops, train stations, bank facilities, shopping centres, post offices, churches, parks, public libraries, community centres, pharmacies, GP clinics and hospitals in Monash LGA. The research results can be used to optimize the spatial planning and layout of public facilities for ageing population.

Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22*107 kwh and 2.47*108 kwh, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies.

As commercial 5G systems rapidly expand, indoor positioning using 5G signals holds great potential for serving a large number of users. In this paper, an effective fingerprint solution is proposed for indoor positioning with 5G signal base station by exploring the multi-beam property. Multi-beam channel state information (CSI) and multi-beam reference signal received power (RSRP) are used as the observations for fingerprinting. To assess the effectiveness of the proposed scheme, field tests were conducted across various indoor environments. The results showed that the positioning accuracy is improved by more than 45% compared with the single beam by using the multi-beam characteristics of the 5G signal. Based on the multi-beam RSRP, the proposed scheme can achieve a positioning error of 67% below 1 meter. In the awareness of the two typical indoor deployments of 5G systems, i.e., the digital indoor distribution (DID) and the distributed antenna system (DAS), the paper also compared the 5G positioning performance in these two scenarios. The field tests showed that, the multi-beam in DID has more features than in DAS, which lead to a better positioning performance than that in DAS.

Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

Retrieval of atmospheric aerosol optical depth (AOD) has been a challenge for Earth satellite observations, mainly due to the difficulty of estimating surface reflectance with the combined influence of land–atmosphere coupling. Current major satellite AOD retrieval products have low spatial resolution under complex surface processes. In this study, we further improved the surface reflectance by modeling the error correction based on the previous AOD retrieval and obtained more accurate AOD retrieval results. A lookup table was constructed using the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to enable high-precision AOD retrieval. The accuracy of the algorithm's retrieval was verified by observations of the Aerosol Robotic Network (AERONET). From the validation results, we find that among the nine Multi-angle Imaging SpectroRadiometer (MISR) angles, the retrieved AOD has the best retrieved results with the AOD observed at the An angle (Taihu: R = 0.81, relative mean bias (RMB) = 0.68; Xuzhou-CUMT: R = 0.73, RMB = 0.78). This study will help to further improve the retrieval accuracy of multi-angle AOD at large spatial scales and long time series. The retrieved AOD based on the improved method has the advantages of fewer missing pixels and finer spatial resolution compared to the MODIS AOD products and our previous estimates.

Continents are formed by the amalgamation of numerous micro-terranes and island arcs, so they have spatially varying lithosphere strengths. The crème brûlée (CB) model and the jelly sandwich (JS) model have been commonly used to describe continental lithosphere strength–depth variations. Depending on the strength of the continental lower crust, the CB and JS models can be further subdivided into two subclasses in which the I subclass (CB-I and JS-I) and II subclass (CB-II and JS-II), respectively, have a strong or weak lower crust. During the continental collision, lithosphere deformation is the byproduct of the comprehensive interaction of multiple terranes. Here we used 2-D thermomechanical numerical models that contain three continental terranes to systematically explore the effects of terranes with various strengths on continental deformation and studied the effects of different rheological assumptions on terrane deformation. We found four types of lithosphere deformation patterns: collision, subduction, thickening and delamination, and replacement. These simulation patterns are seen in observed deformation patterns and structures in East Asia, suggesting they are likely to be naturally occurring modes of intracontinental orogenesis.

This study examines the climatic characteristics of 202 Jianghuai cyclones and their linkage with precipitation during the Meiyu period from 1961 to 2020. The results show that cyclones mainly originate from the eastern and western Hubei Province. Additionally, we explore the statistical characteristics of intensity, radius and their positive correlation. In studying the decadal variation of cyclones, we find a similar evolution between the cyclones and Meiyu precipitation. Therefore, we further investigate the correlation between the Jianghuai cyclones and the precipitation during the Meiyu period. There is a positive correlation coefficient of 0.77 between them. Notably, the percentage of precipitation affected by cyclone activities can reach up to 47 %. The anomalous increase in precipitation caused by cyclones north of 27° N can reach a maximum of 7 mm d−1. When a cyclone exists, a significant negative geopotential height anomaly at the 500 hPa level over Mongolia can be traced back to day −4. The abnormally enhanced WPSH (western Pacific subtropical high), southwesterly low-level jet and negative geopotential height are the dominant factors causing abnormal precipitation during Jianghuai cyclones. Before and after the cyclone develops, water vapor flux and divergence from low latitudes increase abnormally, providing sufficient water vapor conditions for the generation of cyclone precipitation.

Laying ducks in cage environments face various stressors, including the fear of novelty, which negatively affects their behaviour and performance. The reasons behind the variation in behaviour under identical stress conditions are not well understood. This study investigated how different behaviours affect production performance, immune response, antioxidant capabilities, adrenal gene expression, and serum metabolite profiles in caged laying ducks subjected to the same stressor. Overall, 42-week-old laying ducks (N = 300) were selected, fed for 60 days, and simultaneously underwent behavioural tests. Based on their behavioural responses, 24 ducks were chosen and categorised into two groups: high-active avoidance (HAA) and low-active avoidance (LAA). The study utilised phenotypic, genetic, and metabolomic analyses, coupled with bioinformatics, to identify crucial biological processes, genes, and metabolites. The results indicated that ΔW (BW gain) and average daily egg weight (ADEW) were significantly lower in the HAA group compared to the LAA group (P

Abstract Drought is a primary ecological stress limiting wheat yield in water-deficient regions. Conducting targeted genetic selection of wheat cultivars can expedite the adaptation process of wheat to the climatic conditions of the region, allowing for the identification of high-yielding varieties with stable genetic traits. This study investigated the impact of the TaGW8 and TaGS3A genes, known for their contribution to wheat productivity. The effective productivity genes TaGW8-B1b/B1a and the TaGS5-3A-T genome exert a 32.8% influence on the variability of the 1000 grain weight (TGW) trait. This influence stems from both individual genes and their interactions, with at least 17.5% of TGW variability explained by the gene combinations examined in the study. Notably, the TaGS5-3A-T gene exhibits a significant positive correlation with total yield, exceeding 63%. The integration of these productivity genes, based on field phenotypic data, has resulted in an overall yield increase of selected samples by 0.8 tons/ha compared to the country's average multi-year indicator.

The past decade has been a boom in lunar exploration. China, India, Japan and other countries have successfully landed landers or rovers on the lunar surface (Wu et al., 2014, 2018, 2020; Prasad et al., 2023). Future missions to explore the Moon are focusing on the lunar south pole (Peña-Asensio et al., 2024). The solar altitude angle at the lunar south pole is extremely low, resulting in low solar irradiance and large areas often in dim light or shadows. The permanently shadowed regions (PSRs) at the lunar south pole are also likely containing substantial amounts of water ice (Li et al., 2018). Future lunar robots exploring the lunar south pole will need to operate in low light or shadowed regions, making camera sensors sensitive to the dim-light environments necessary for these robots. Common night vision sensors usually use near-infrared cameras. However, sensors based on passive infrared technology have image resolution limited by several factors, including the intensity of infrared radiation emitted by the object, the sensitivity of the camera, and the performance of the optical system. For instance, thermal imagers typically have a resolution of 388*284 pixels only. We have developed a dim-light adaptive camera (DimCam) that is ultra-sensitive to the varying illumination conditions driven by AI to achieve high-definition imaging of 1080P or above, for future lunar robots operating in shadows or dim-light regions. The DimCam integrates two starlight-level ultra-sensitive imaging sensors connected by a rigid base to provide stereo vision in low illumination environment. An AI edge computing unit is embedded inside the DimCam to adaptively denoise and enhance image quality. The AI module uses an end-to-end image denoising network to identify and remove noises in the images more accurately by utilizing depth information from the stereo sensors. Compared with traditional monocular denoising algorithms, the denoising network based on stereo vision can significantly improve denoising effects and efficiency by enhancing the signal-to-noise ratio of the data input in the front end. The superposition of overlapping scenes can be regarded as a delayed exposure. Concurrently, the residual analysis of the aligned images aids in noise identification. In addition, for pixels obscured by noise, more accurate pixel values can be restored through interpolation or replacement using depth information obtained from the stereo sensors. Subsequently, a pre-trained lightweight deep network modified from Zero-DCE (Guo et al., 2020) is used for image quality enhancement in terms of brightness and contrast, providing high-quality images even in low-light environments for subsequent applications, such as positioning and navigation of robots, 3D mapping of the surrounding environment, and autonomous driven. We have tested the DimCam in a simulated environment in the laboratory, and the results show that the DimCam has promising performances and great potential for various applications.

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

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data.

High-precision and real-time global geographic information data are fundamental and strategic resources in various fields such as safeguarding global strategic interests, studying global environmental changes, and planning for sustainable development. However, due to challenges related to ground control and obtaining reference information, the development of global geographic information resources faces significant hurdles in terms of geometric positioning, information extraction, and data mining. This paper starts with the characteristics of domestically produced remote sensing images and proposes a comprehensive technical framework centered around "uncontrolled geometric positioning, intelligent interpretation of typical elements, mining of multi-source data from abroad, and intelligent hybrid collection and compilation of Digital Elevation Models (DEMs)." The paper elaborates on the key technical challenges that need to be overcome and their corresponding solutions. It also outlines the development of relevant data products and production technical specifications. Multiple production-oriented software tools were developed, leading to the creation of a variety of data products in multiple types and scales, including global 30-meter land cover data, DEM data, core vector data, and more.

ABSTRACT: Heat stress compromises dairy production by decreasing feed intake and milk yield, and it may also alter milk composition and feed efficiency. However, little information is available for evaluating such effects across different levels of heat stress and cows enrolled in heat stress studies. The objectives of this study were to evaluate the effects of heat stress on dry matter intake (DMI), energy-corrected milk (ECM), milk composition, and feed efficiency (kg ECM/kg DMI) and to investigate the relationship between such effects and heat stress intervention and animal characteristics by using meta-analytical approaches. Data from 31 studies (34 trials) fulfilled the inclusion criteria and were used for analysis. Results showed that heat stress decreased DMI, ECM, and milk protein concentration, but did not alter milk fat concentration or feed efficiency. Meta-regression confirmed that such reductions in DMI and ECM were significantly associated with increasing temperature-humidity index (THI). Over the period of heat stress, for each unit increase in THI, DMI and ECM decreased by 4.13% and 3.25%, respectively, in mid-lactation cows. Regression models further revealed the existence of a strong interaction between THI and lactation stage, which partially explained the large heterogeneity in effect sizes of DMI and ECM. The results indicated a need for more research on the relationship between the effect of heat stress and animal characteristics. This study calls for the implementation of mitigation strategies in heat-stressed herds due to the substantial decrease in productivity.

Since China's clean air action, PM2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less) air quality has improved, while ozone (O3) pollution has become more severe. Here we apply a coupled meteorology–chemistry model (WRF-Chem: Weather Research and Forecasting model coupled to Chemistry v3.7.1) to quantify the responses of aerosol–radiation interaction (ARI) to anthropogenic emission reductions from 2013 to 2017, including aerosol–photolysis interaction (API) related to photolysis rate change and aerosol–radiation feedback (ARF) related to meteorological field change and their contributions to O3 increases over eastern China in summer and winter. Sensitivity experiments show that the decreased anthropogenic emissions play a more prominent role in the increased daily maximum 8 h average (MDA8) O3 in both summer (+1.96 ppb vs. +0.07 ppb) and winter (+3.56 ppb vs. −1.08 ppb) than the impacts of changed meteorological conditions in urban areas. The decreased PM2.5 caused by emission reductions can result in a weaker impact of ARI on O3 concentrations, which superimposes its effect on the worsened O3 air quality. The weakened ARI due to decreased anthropogenic emissions aggravates the summer (winter) O3 pollution by +0.81 ppb (+0.63 ppb), averaged over eastern China, with weakened API contributing 55.6 % (61.9 %) and ARF contributing 44.4 % (38.1 %), respectively. This superimposed effect is more significant for urban areas during summer (+1.77 ppb). Process analysis indicates that the enhanced chemical production is the dominant process for the increased O3 concentrations caused by weakened ARI in both summer and winter. This study innovatively reveals the adverse effect of weakened aerosol–radiation interaction due to decreased anthropogenic emissions on O3 air quality, indicating that more stringent coordinated air pollution control strategies should be implemented for significant improvements in future air quality.

Stable isotopes in precipitation can effectively reveal the process of atmospheric water circulation, serving as an effective tool for hydrological and water resource research, climate change, and ecosystem studies. The scarcity of stable isotope data in precipitation has hindered comprehension of the regional hydrology, climate, and ecology due to discontinuities on a temporal scale and unevenness on a spatial scale. To this end, we collated stable hydrogen and oxygen isotope data in precipitation from 842 stations in Eurasia from 1961 to 2022, totalling 51 752 data records. Stable isotopes in precipitation across various regions of Eurasia, as a whole, decrease with increasing latitude and distance from the coast. In the summer, stable isotopes in precipitation are relatively enriched, while in the winter, they are relatively depleted. In recent decades, the stable isotope values of Eurasian precipitation show an overall trend of increasing variation with the advancement of years, which is associated with global warming. Geographical location, underlying surface conditions, seasons, and atmospheric circulation are all factors that determine the characteristics of stable isotopes in precipitation. The dataset of stable isotopes in Eurasian precipitation provides a powerful tool for understanding changes in regional atmospheric water circulation and assists in conducting hydrological, meteorological, and ecological studies in related regions. The datasets are available at https://doi.org/10.17632/rbn35yrbd2.2 (Zhu, 2024).

Atmospheric ice nucleation plays an important role in modulating the global hydrological cycle and atmospheric radiation balance. To date, few comprehensive field observations of ice nuclei have been carried out at high-altitude sites, which are close to the height of mixed-phase cloud formation. In this study, we measured the concentration of ice-nucleating particles (INPs) in the immersion freezing mode at the summit of the Changbai Mountains (2623 m above sea level), northeast Asia, in summer 2021. The cumulative number concentration of INPs varied from 1.6 × 10−3 to 78.3 L−1 over the temperature range of −5.5 to −29.0 °C. Proteinaceous-based biological materials accounted for the majority of INPs, with the proportion of biological INPs (bio-INPs) exceeding 67 % across the entire freezing-temperature range, with this proportion even exceeding 90 % above −13.0 °C. At freezing temperatures ranging from −11.0 to −8.0 °C, bio-INPs were found to significantly correlate with wind speed (r = 0.5–0.8, p < 0.05) and Ca2+ (r = 0.6–0.9), and good but not significant correlation was found with isoprene (r = 0.6–0.7) and its oxidation products (isoprene × O3) (r = 0.7), suggesting that biological aerosols may attach to or mix with soil dust and contribute to INPs. During the daytime, bio-INPs showed a positive correlation with the planetary boundary layer (PBL) height at freezing temperatures ranging from −22.0 to −19.5 °C (r > 0.7, p < 0.05), with the valley breezes from southern mountainous regions also influencing the concentration of INPs. Moreover, the long-distance transport of air mass from the Japan Sea and South Korea significantly contributed to the high concentrations of bio-INPs. Our study emphasizes the important role of biological sources of INPs in the high-altitude atmosphere of northeastern Asia and the significant contribution of long-range transport to the INP concentrations in this region.

Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a +9.35 × 105 km2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (−2.19 × 105 km2), bare soil (−5.14 × 105 km2) and dark surfaces (−2.27 × 105 km2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (https://doi.org/10.57760/sciencedb.13287, Sun and Sun, 2023).

Brown carbon (BrC) is an organic carbon component with noticeable absorption in the ultraviolet and short visible wavelengths, which influences the global radiative balance. However, assessing BrC radiative effects remains a challenging task owing to the scarcity of direct BrC observations and the uncertainties regarding their chemical and optical properties. This study proposes an efficient method for estimating BrC radiative effects based on the available observational data. The light-absorbing properties of BrC obtained from aethalometer measurements and an optical separation method were combined with simulated BrC optical properties to determine mass concentrations. An optical closure study was conducted to constrain the total and other aerosol contents. Subsequently, we estimated the aerosol optical properties and concentrations. Such a state-of-the-art combination of measurements and numerical models provides primary variables for simulating radiative transfer to estimate BrC radiative effects. We used observations conducted over 4 months (from 1 July to 18 November 2021) in Nanjing (a megacity in east China) as an example. During the observational period, BrC absorption constituted 8.7 %–34.1 % of the total aerosol absorption at 370 nm. In the atmosphere, BrC plays a warming role, with its average instantaneous radiative forcing (RF) and standard deviation of 4.0 ± 2.3 W m−2 corresponding to 15 ± 4.2 % of the black carbon (BC) RF. At the surface, the BrC-induced actinic flux (AF) attenuation is comparable to that caused by BC, accounting for over 55 % of the BC effects in the UV range and almost 20 % in the visible range. The photosynthetically active radiation (PAR) attenuated by BrC is approximately 33.5 ± 9.4 % of that attenuated by BC. Furthermore, we quantified the influences of several BC and BrC microphysical and optical properties on their radiative effects. These findings provide valuable insights for understanding BrC radiative effects. Moreover, they highlight the importance of and necessity for improved observation and modeling of BrC properties.

Abstract The plasma density is one of the most fundamental quantities of any plasma yet measuring it in space is exceptionally difficult when the density is low. Measurements from particle detectors are contaminated by spacecraft photoelectrons and methods using plasma wave emissions are hampered by natural plasma instabilities which dominate the wave spectrum. Here we present a new method which calculates the density from magnetosonic waves near the lower hybrid resonance frequency. The method works most effectively when the ratio of the plasma to cyclotron frequency is fpe/fce

New particle formation (NPF) plays a crucial role in the atmospheric aerosol population and has significant implications on climate dynamics, particularly in climate-sensitive zones such as the Tibetan Plateau (TP). However, our understanding of NPF on the TP is still limited due to a lack of comprehensive measurements and verified model simulations. To fill this knowledge gap, we conducted an integrated study combining comprehensive field measurements and chemical transport modeling to investigate NPF events on the southeastern TP during the pre-monsoon season. NPF was observed to occur frequently on clear-sky days on the southeastern TP, contributing significantly to the cloud condensation nuclei (CCN) budget in this region. The observational evidence suggests that highly oxygenated organic molecules (HOMs) from monoterpene oxidation participate in the nucleation on the southeastern TP. After updating the monoterpene oxidation chemistry and nucleation schemes in the meteorology–chemistry model, the model well reproduces observed NPF and reveals an extensive occurrence of NPF across the southeastern TP. The dominant nucleation mechanism is the synergistic nucleation of sulfuric acid, ammonia, and HOMs, driven by the transport of anthropogenic precursors from South Asia and the presence of abundant biogenic gases. By investigating the vertical distribution of NPF, we find a significant influence of vertical transport on the southeastern TP. More specifically, strong nucleation near the surface leads to an intense formation of small particles, which are subsequently transported upward. These particles experience enhanced growth to larger sizes in the upper planetary boundary layer (PBL) due to favorable conditions such as lower temperatures and a reduced condensation sink. As the PBL evolves, the particles in larger sizes are brought back to the ground, resulting in a pronounced increase in near-surface particle concentrations. This study highlights the important roles of anthropogenic–biogenic interactions and meteorological dynamics in NPF on the southeastern TP.

New particle formation contributes significantly to the number concentration of ultrafine particles (UFPs, d ≤ 100 nm) and has a great impact on human health and global climate. Iodine oxoacids (HIOx, including iodic acid, HIO3, and iodous acid, HIO2) have been observed in pristine regions and proved to dominate new particle formation (NPF) at some sites. However, the knowledge of HIOx in polluted urban areas is rather limited. Here, we conducted a long-term measurements of gaseous iodine oxoacids and sulfuric acid in Beijing from January 2019 to October 2021 and also in Nanjing from March 2019 to February 2020 and investigated the contribution of HIOx to UFP number concentration in both urban environments. HIO3 is highest in summer, up to 2.85 × 106 and 2.78 × 106 cm−3 in Beijing and Nanjing, respectively, and is lowest in winter by 96 % and 75 %, respectively. HIO3 exhibits more prominent variation than H2SO4 in both urban sites. HIO3 concentration shows a clear diurnal pattern at both sites with a daily maximum at around noontime, similar to the atmospheric temperature, solar radiation, and ozone (O3) levels. HIO2 concentration has the same diurnal and seasonal trend as HIO3 but is overall about an order of magnitude lower than HIO3 concentration. Back trajectory analysis suggests that the sources for inland iodine species could be a mix of marine and terrestrial origins, with both having peak iodine emission in warm seasons. While the contribution of HIO2 to particle growth is marginal in Beijing and Nanjing, our results demonstrate that HIO3 enhances the particle survival probability of sub-3 nm particles by about 40 % (median) and occasionally by more than 100 % in NPF events, suggesting HIOx are significant contributor to UFPs in polluted urban areas. As the growth contribution from HIO3 and H2SO4 is similar on a per-molecule basis, we propose that the sum of HIO3 and H2SO4 could be used to estimate sub-3 nm particle growth of inorganic acid origin in polluted atmospheres with a significant amount of HIOx.

In the process of hoisting the memorial archway, because the balance beam directly lifts the memorial archway, it is easy to deform during the hoisting process, and the deformation will affect the follow-up work, resulting in the delay of the project progress. In order to avoid this situation, the existing conditions are used to calculate the load that the balance beam can bear. Using 3D modeling and numerical simulation software, conduct force analysis on the balanced beam, and compare the simulation results with the calculated values to determine whether the stress and deformation of the balanced beam meet the requirements. The simulation results show that the stress on the balance beam during the lifting process does not exceed the maximum yield stress of its own material, which meets the requirements of the working conditions.

Soot is a crucial component of aerosols in the atmosphere. Understanding the hygroscopicity of soot particles is important for studying their role as cloud condensation nuclei (CCN) as well as their chemical behavior and atmospheric lifetime. However, there is still a lack of comprehensive understanding regarding the factors that determine the hygroscopic properties of soot. In this work, the hygroscopic behavior of soot particles generated from different types of fuel combustion and aged with SO2 for varying durations was measured by a vapor sorption analyzer. Various characterizations of soot were conducted to understand the key factors that influence the hygroscopic properties of soot. It was found that water-soluble substances in soot facilitate the completion of monolayer water adsorption at low relative humidity and increase the number of water adsorption layers at high relative humidity. On the other hand, soot prepared from fuel burning typically lacks water-soluble inorganic ions, and their hygroscopicity is primarily influenced by organic carbon (OC) and microstructure. Furthermore, the hygroscopicity of soot can be enhanced by the formation of sulfate due to heterogeneous oxidation of SO2. These finding sheds light on the critical factors that affect soot hygroscopicity during water adsorption and allows for estimating the interaction between water molecules and soot particles in a humid atmosphere.

In water-scarce arid areas, the water cycle is affected by urban development and natural river changes, and urbanization has a profound impact on the hydrological system of the basin. Through an ecohydrological observation system established in the Shiyang River basin in the inland arid zone, we studied the impact of urbanization on the water cycle of the basin using isotope methods. The results showed that urbanization significantly changed the water cycle process in the basin and accelerated the rainfall-runoff process due to the increase in urban land area, and the mean residence time (MRT) of river water showed a fluctuating downward trend from upstream to downstream and was shortest in the urban area in the middle reaches, and the MRT was mainly controlled by the landscape characteristics of the basin. In addition, our study showed that river water and groundwater isotope data were progressively enriched from upstream to downstream due to the construction of metropolitan landscape dams, which exacerbated evaporative losses of river water and also strengthened the hydraulic connection between groundwater and river water around the city. Our findings have important implications for local water resource management and urban planning and provide important insights into the hydrologic dynamics of urban areas.

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.

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

The precision location-based services in complex environment is a challenge in the field of navigation and positioning. With the continuous development of wireless communication technology in recent years, cellular network signals such as LTE and 5G have emerged as unique advantages in navigation and positioning applications. This paper presents a time-of-arrival (TOA) estimation method based on machine learning, which can use cellular network signals to obtain accurate ranging results in low signal-to-noise ratio conditions. For this purpose, we first present the cellular network signals that can be applied in navigation and positioning. Then, we describe in detail the process of TOA estimation based on machine learning. Finally, we carried out vehicular experiments in an urban environment to test the performance of the proposed method. The test results demonstrate the feasibility of the proposed method and achieve metre-level ranging accuracy.

Modelling the movement uncertainty of human indoor trajectory consist of an essential part in promoting the performance of smart city related applications. At this stage, the existing uncertainty modelling algorithms usually take the constant sampling error or measurement error into consideration and cannot adapt well to the changeable human motion modes and complex handheld modes of smartphones. To fill this gap, this paper applied the Long Short-Term Memory (LSTM) network for continuous prediction of uncertainty error of human indoor trajectory with complex motion modes and detected indoor landmark points. The human motion information including handheld modes, walking speed, and heading information in extracted and fused with detected landmark points for reconstruction of human indoor trajectory under large-scale areas using Gradient Descent (GD) algorithm. In addition, the hybrid LSTM and Multilayer Perceptron (MLP) network is adopted for uncertainty error prediction, by considering both sampling error and measurement error in a specific time period, and the reconstructed trajectory with human motion features are modelled as the input vector for model training with the ground-truth uncertainty error as reference. Comprehensive experiments on real-world collected dataset indicate that the proposed LSTM-assisted uncertainty modelling algorithm has robust outperformance in uncertainty error prediction and uncertainty region definition compared with traditional uncertainty modelling approaches.

In recent years, InSAR technology has continuously developed and become a mainstream technology for monitoring wide-area surface deformation. In this study, Shiyan was selected as the work area, and Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) technology was used to monitor surface deformation over a long time series. The temporal and spatial distribution characteristics of the InSAR monitoring results were analyzed. The surveillance results showed that from January 2021 to March 2023, Shiyan experienced noticeable uneven settlement, with a deformation rate range of [−111.8mm/a, 20.9mm/a]. The maximum cumulative deformation variable reached −212.9mm, and more than 70 deformation zones were identified during the study period. The main causes of deformation were also analyzed. This study provides a reference for large-scale surface deformation monitoring and disaster management in Shiyan.

The electron cyclotron maser instability (ECMI) stands as a pivotal coherent radio emission mechanism widely implicated in various astrophysical phenomena. In the context of solar activity, ECMI is primarily instigated by energetic electrons generated during solar eruptions, notably flares. These electrons, upon leaving the acceleration region, traverse the solar atmosphere, forming fast electron beams (FEBs) along magnetic field lines. It is widely accepted that as these FEBs interact with the ambient plasma and magnetic fields, they give rise to radio and hard X-ray emission. Throughout their journey in the plasma, FEBs undergo modifications in their energy spectrum and velocity spatial distribution due to diverse energy loss mechanisms and changes in ambient plasma parameters. In this study, we delve into the impact of the evolving energy spectrum and velocity anisotropic distribution of FEBs on ECMI during their propagation in flare loops. Our findings indicate that if we solely consider the progressively flattened lower energy cutoff behavior as FEBs descend along flare loops, the growth rates of ECMI decrease accordingly. However, when accounting for the evolution of ambient magnetic plasma parameters, the growth rates of ECMI increase as FEBs delve into denser atmosphere. This underscores the significant influence of the energy spectrum and velocity anisotropy distribution evolution of FEBs on ECMI. Our study sheds light on a more comprehensive understanding of the dynamic spectra of solar radio emissions.

Insoluble particles influence weather and climate by means of heterogeneous freezing process. Current weather and climate models face considerable uncertainties in freezing-process simulation due to limited information regarding species and number concentrations of heterogeneous ice-nucleating particles, particularly insoluble particles. Here, for the first time, the size distribution and species of insoluble particles are analyzed in 30 shells of 12 hailstones collected from China using scanning electron microscopy and energy-dispersive X-ray spectrometry. A total of 289 461 insoluble particles were detected and divided into three species – organics, dust, and bioprotein – utilizing machine learning methods. The size distribution of the insoluble particles of each species varies greatly among the different hailstones but little in their shells. Further, a classic size distribution of organics and dust followed logarithmic normal distributions, which could potentially be adapted in future weather and climate models despite the existence of uncertainties. Our findings highlight the need for atmospheric chemistry to be considered in the simulation of ice-freezing processes.

Thermokarst processes likely result in new habitats harboring novel bacterial communities in degraded permafrost soil (PB), thermokarst lake sediments (SB), and lake water (WB). Our study aimed to investigate the paired PB, SB, and WB across the Qinghai–Tibet Plateau (QTP) by assessing the spatial pattern of diversity as well as assembly mechanisms of these bacterial communities. Each habitat had distinct bacterial assemblages, with lower α diversity and higher β diversity in WB than in SB and PB. However, up to 41 % of the operational taxonomic units (OTUs) were shared by PB, SB, and WB, suggesting that many taxa originate from the same sources via dispersal. SB and WB had reciprocal dispersal effects, and both were correlated with PB. Dispersal limitation was the most dominant assembly process shaping PB and SB, while homogeneous selection was the most dominant for WB. Bacterial communities of the three habitats correlated differently with environmental variables, but latitude, mean annual precipitation, and pH were the common factors associated with their β diversity, while total phosphorus was the common factor associated with their assembly processes. Our results imply that thermokarst processes result in diverse habitats that have distinct bacterial communities that differ in diversity, assembly mechanisms, and environmental drivers.

Tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO2) retrieved from sun-synchronous satellite instruments have provided abundant NO2 data for environmental studies, but such data are limited by retrieval uncertainties and insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO2 at an unprecedented hourly resolution during the daytime. Here we present a research product for tropospheric NO2 VCDs, referred to as POMINO–GEMS (where POMINO is the Peking University OMI NO2 algorithm). We develop a hybrid retrieval method combining GEMS, TROPOMI (TROPOspheric Monitoring Instrument) and GEOS-CF (Global Earth Observing System Composition Forecast) data to generate hourly tropospheric NO2 slant column densities (SCDs). We then derive tropospheric NO2 air mass factors (AMFs) with explicit corrections for surface reflectance anisotropy and aerosol optical effects through parallelized pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with the O2–O2 algorithm by using ancillary parameters consistent with those used in NO2 AMF calculations. The initial retrieval of POMINO–GEMS tropospheric NO2 VCDs for June–August 2021 exhibits strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO–GEMS NO2 VCDs agree with the POMINO–TROPOMI v1.2.2 product (R=0.98; NMB = 4.9 %) over East Asia, with slight differences associated with satellite viewing geometries and cloud and aerosol properties affecting the NO2 retrieval. POMINO–GEMS also shows good agreement with the following: OMNO2 (Ozone Monitoring Instrument (OMI) NO2 Standard Product) v4 (R=0.87; NMB = −16.8 %); and GOME-2 (Global Ozone Monitoring Experiment-2) GDP (GOME Data Processor) 4.8 (R=0.83; NMB = −1.5 %) NO2 products. POMINO–GEMS shows small biases against ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) NO2 VCD data at nine sites (NMB = −11.1 %), with modest or high correlation in diurnal variation at six urban and suburban sites (R from 0.60 to 0.96). The spatiotemporal variation in POMINO–GEMS correlates well with mobile car MAX-DOAS measurements in the Three Rivers source region on the Tibetan Plateau (R=0.81). Surface NO2 concentrations estimated from POMINO–GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China for spatiotemporal variation (R=0.78; NMB = −26.3 %) and diurnal variation at all, urban, suburban and rural sites (R≥0.96). POMINO–GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO2.

Studies assessing the achievability of future air quality goals in China have focused on the role of reducing China's domestic emissions, yet the influence of transboundary pollution of foreign origins has been largely underappreciated. Here, we assess the extent to which future changes in foreign transboundary pollution would affect the achievability of air quality goals in 2030 and 2060 for China. We find that in 2030, under the current-policy scenario in China, transboundary contributions to population-weighted PM2.5 in China would be reduced by 29 % (1.2 µg m−3) as foreign countries transition from the fossil-fuel-intensive to the low-carbon pathway. By 2060, the difference would be increased to 45 % (1.8 µg m−3). Adopting the low-carbon instead of the fossil-fuel-intensive pathway in foreign countries would prevent 10 million Chinese people from being exposed to PM2.5 concentrations above China's ambient air quality standard (35 µg m−3) in 2030 and 5 million Chinese people from being exposed to PM2.5 concentrations above the World Health Organization air quality guideline (5 µg m−3) in 2060. Meanwhile, China adopting the carbon-neutral pathway rather than its current pathway would also be helpful to reduce transboundary PM2.5 produced from the chemical interactions between foreign-transported and locally emitted pollutants. In 2060, adopting a low-carbon pathway in China and foreign countries coincidently would prevent 63 % of transboundary pollution and 386 000 associated premature deaths in China, relative to adopting a fossil-fuel-intensive pathway in both regions. Thus, the influence of transboundary pollution should be carefully considered when making future air quality expectations and pollution mitigation strategies.

Below-cloud wet scavenging is an important pathway to remove atmospheric aerosols. The below-cloud wet-scavenging coefficient (BWSC) is the value to describe the ability of rainfall to remove aerosols. The reported BWSCs obtained from the field measurements are much higher than the theory, but the reason for this remains unclear. Based on the long-term field measurements in the Yangtze River Delta of eastern China, we find that 28 % of the rainfall events are high-BWSC events. The high-BWSC events show the sudden decrease in the particle number concentration in all size bins near the end of rainfall. By investigating the simultaneously observed changes in carbon monoxide and aerosol chemical compositions during rainfall events, the circulation patterns, and backward trajectories, we find the cause of the high-BWSC events is the air mass changing but not the wet scavenging. The change in air masses is always followed by the rainfall processes and cannot be screened out by the traditional meteorological criteria, which would cause the overestimation of BWSC. After excluding the high-BWSC events, the observed BWSC is close to the theory and is correlated with the rainfall intensity and particle number concentrations prior to rainfall. This study highlights that the discrepancy between the observed BWSC and the theoretical value may not be as large as is currently believed. To obtain reasonable BWSCs and parameterization from field measurements, the effect of air mass changing during rainfall needs to be carefully considered.

In order to analyze the comprehensive risks of natural disasters quantitatively and improve the accuracy of natural disaster management and control, this paper expands the F indicator, Forecast, which is about real-time monitoring and early warning data of natural disasters, and forms the flood control risk trend analysis model framework based on PSR. The framework is named FPSR, i.e. Forecast-Pressure-State-Response, composed of static data and dynamic data. By establishing the four-level index system of flood control risk trend analysis in Fangshan District of Beijing, screening factors, and using analytic hierarchy process, AHP, and experts scoring to determine the weights of each factor, it constructs the flood control risk trend analysis model, FCRTAM. At last, using the real-time monitoring and early warning data of natural disasters in Beijing and the information such as disaster-causing factors, historical natural disasters, major hidden dangers, disaster-bearing bodies, disaster reduction resources (capacities), etc., from National Natural Disaster Comprehensive Risk Census in Fangshan, it analyzes the flood control situation of each town in Fangshan. The results show that the results flood control risk index calculated according to FCRTAM is basically consistent with the actual flood control situation of the towns in Fangshan, and can provide theoretical basis for flood control comprehensive risk trend analysis and the decision-making of disaster prevention and reduction in Fangshan District, which has high use value.

Based on the bidirectional evolutionary structural optimization (BESO) method, the present article proposes an optimization method for a thermal structure involving design-dependent convective boundaries. Because the BESO method is incapable of keeping track of convection boundaries, virtual elements are introduced to assist in identifying the convection boundaries of the structure. In order to solve the difficult issue of element assignment under a design-dependent convection boundary, label matrixes are employed to modify the heat transfer matrix and the equivalent temperature load vector of elements over topology iterations. Additionally, the optimization objective is set to minimize the maximum temperature of the structure in order to deal with the objective reasonableness, and the p-norm method is then used to fit the objective function to calculate sensitivity. Finally, several cases, including 2D and 3D structures under various heat transfer boundary conditions, are provided to illustrate the effectiveness and good convergence of the proposed method.

Aerosol refers to suspensions of small solid and liquid particles in the atmosphere. Although the content of aerosol in the atmosphere is small, it plays a crucial role in atmospheric and the climatic processes, making it essential to monitor. In areas with poor aerosol characteristics, satellite-based aerosol optical depth (AOD) values often differ from ground-based AOD values measured by instruments like AERONET. The use of 3km DT, 10km DT and 10km DTB algorithms in Beijing area has led to significant overestimation of AOD values, highlighting the need for improvement. This paper proposes the use of machine learning techniques, specifically support vector regression (SVR) and artificial neural network (ANN), to correct the deviation of AOD data. Our approach leverages ground-based monitoring data, meteorological reanalysis data and satellite products to train the models. Our results show that the ANN model outperforms the SVR model achieving R2, RMSE and Slope values of 0.88, 0.12 and 0.97, respectively, when applied to nearly two decades of data from 2001 to 2019. This study significantly improves the accuracy of MODIS AOD values, reducing overestimation and bringing them closer to ground-based AOD values measured by AERONET. Our findings have important applications in climate research and environmental monitoring.

To investigate the light intensity suitable for the growth of Ardisia gigantifolia Stapf, morphology, photosynthetic parameters, and indicators of oxidative stress were analyzed under different light intensities. Compared to high-irradiance treatment, medium and low-irradiance treatments promoted plant growth and restricted transpiration. Compared to medium irradiance, plants under high and low irradiance exhibited significantly lower maximal photochemical efficiency, potential photochemical efficiency, and electron transport rate, but significantly higher malondialdehyde content. This indicated that both excessive light and severe shading inhibited photosynthetic activity and induced oxidative stress, which resulted in a significant decrease in net photosynthetic rate. A. gigantifolia can adapt to different light intensities, improving light harvesting and utilizing capacity under low irradiance by increasing Chl (a+b) content and reducing Chl a/b ratio, and adapting to high irradiance by enhancing heat dissipation and activity of peroxidase. A. gigantifolia showed the best performance in growth and photosynthesis under medium irradiance treatment.

The effective density of black carbon (BC) is a crucial factor that is relevant to its aging degree and adds uncertainty in evaluating its climate effect. Here, we have developed a new method to retrieve the effective density of internally mixed BC in the atmosphere, combining field observations conducted from 15 November to 14 December 2016 in urban Beijing with the Köhler theory. The uncertainty in the retrieval method was evaluated to be within ±30 %, which was primarily caused by assumptions on both the hygroscopic parameter of organics and the proportional distribution of primary organic aerosols in different hygroscopic modes. Using the method, we find that the ambient internally mixed BC, accounting for 80 % ± 20 % of total BC aerosol particles, was retrieved with a campaign mean density of 1.1 ± 0.6 g cm−3 during the observed periods. The retrieved result was comparable with that reported in the literature. By applying a lower (0.14 g cm−3) and upper (2.1 g cm−3) limit of the retrieved BC density in the cloud condensation nuclei (CCN) number concentration (NCCN) estimation, we derived that the neglect of such variations in the BC density would lead to an uncertainty of −28 %–11 % in predicting NCCN at supersaturations of 0.23 % and 0.40 %. We also find that the NCCN was more sensitive to the variations in BC density when it was g cm−3. This illustrates the necessity of accounting for the effect of BC density on CCN activity closer to source regions where the BC particles are mostly freshly emitted. The CCN closure was achieved when introducing the retrieved real-time BC density and mixing state. This study provides a unique way of utilizing field measurements to infer ambient BC density and highlights the importance of applying variable BC density values in models when predicting CCN and assessing its relevant climate effect.

The city of Lhasa is located in the central Tibetan Plateau and is the most densely populated area. As the first continuous monitoring of atmospheric mercury (Hg) species in a city in the Tibetan Plateau, our monitoring in Lhasa showed that the concentrations of gaseous elemental Hg (GEM), gaseous oxidized Hg (GOM), and particle-bound Hg (PBM) during the subsequent Indian summer monsoon (S-ISM) period were 2.73 ± 1.48 ng m−3, 38.4 ± 62.7 pg m−3, and 59.1 ± 181.0 pg m−3, respectively. During the westerly circulation (WEC) period, the GEM, GOM, and PBM concentrations were 2.11 ± 2.09 ng m−3, 35.8 ± 43.3 pg m−3, and 52.9 ± 90.1 pg m−3, respectively. The GOM and PBM concentrations were higher than those of previous monitoring in the Tibetan Plateau and other provincial capitals in China. Typical high-value occurrence processes were studied to investigate random events with high atmospheric Hg concentrations in Lhasa. Combustion events nearby or further away may be the main contributor of the high-concentration events. The lowest GEM concentrations occurred in the afternoon, and persistently high concentrations were observed at night. The changes in GEM concentrations were consistent with the trends of other pollutant concentrations and contradictory to those of the wind speed. The high GEM concentrations at night can be attributed to the lower boundary layer height and lower wind speed. For both GOM and PBM, higher GOM concentrations occurred during the day and PBM during the night. The results of the principal component analysis indicated that local sources and wind speed are important factors influencing atmospheric Hg concentrations in Lhasa. The trajectory simulation showed that the source of the GEM in Lhasa gradually shifted from the south to the west of Lhasa from the S-ISM to the WEC periods, while both the southern and western sources were important in the late WEC period. The concentrations and change patterns of Hg species in Lhasa were significantly different than those at other monitoring sites in the Tibetan Plateau. Monitoring Hg species in Lhasa shows the possible maximum anthropogenic influences in the Tibetan Plateau and demonstrates the dramatic effect of wind on changes in urban atmospheric Hg concentrations.

Capsule endoscopy is a promising diagnostic tool for the entire gastrointestinal tract. Since a patient swallows the capsules, their size must be sufficiently small. The principal built-in components are cameras, silver-oxide batteries, light emitting diodes, and an antenna for transmitting the video. For diagnosis and treatment, the precise localization of the capsules for specific video frames is required. Recently, static magnetic localization of these capsules with an integrated permanent magnet showed promising results. However, in the state-of-the-art, relatively large magnets compared to the small capsules were used. Therefore, in this extended paper, the localization performance of a recently proposed optimized differential static magnetic localization method for different sized disc and ring magnets was evaluated. The ring magnets were designed for integration with the two batteries of commercial capsules. The magnets were evaluated in static and dynamic scenarios to evaluate the performance of the method in a patient's daily life. It was revealed that the mean position and orientation errors did not exceed 5 mm and 4∘, respectively, for all applied magnets except for the 1.5 and 3 mm long disc magnets. Moreover, the results indicated that the ferromagnetic batteries of capsule endoscopes increase the localization performance when they are centered within a diametrical ring magnet. Overall, it was revealed that the localization performance of the optimized differential method is significantly better than the state-of-the-art even when the magnet volume is significantly reduced compared to previous work. Therefore, it was concluded that 5 mm long disc magnet or a ring magnet are excellent candidates for integration into a commercial capsule for magnetic localization and yield the advantage of being passive magnetic sources.

A numerical simulation is used to investigate the flow field characteristics of a three-dimensional over-under turbine-based combined cycle circular-to-rectangular transition exhaust nozzle when only the turbojet flowpath is in operation. The effect on the exhaust nozzle performance of there being a secondary injection on the ramp of the expansion section of the turbojet flow path is then examined. Finally, the impact of variations in the secondary injection design parameters is further investigated. The results show that a secondary injection can improve the exhaust nozzle's performance by reducing the axial force on the inner wall of the flow path. However, changes in flight status can undermine this improvement. Under the baseline operating condition, a secondary injection with a larger angle and close to the ramp outlet can produce a more significant improvement in nozzle performance. In this study, the axial thrust coefficient, lift and pitch moment of the nozzle can be improved by a maximum of 9.312%, 66.007% and 10.975%, respectively.

The nonlinear rational model is a generalized nonlinear model and has been gradually applied in modelling many dynamic processes. The parameter identification of a class of nonlinear rational models is studied in this paper. This identification problem is very challenging because of the complexity of the rational model and the coupling between model inputs and outputs. To identify the nonlinear model, a bias compensated multi-innovation stochastic gradient algorithm is presented. The multi-innovation technique replacing the scalar innovation with an information vector is adopted to accelerate the traditional stochastic gradient algorithm. However, the estimate obtained by the accelerated algorithm is biased because of the correlation between the information vector and the noise. To overcome this difficulty, a bias compensation strategy is used. The bias is calculated and compensated to get an unbiased estimate. Theoretical analysis shows that the proposed algorithm can give biased estimates with linear complexity. The proposed algorithm is validated by a numerical experiment and the modelling of the propylene catalytic oxidation.

Hydroperoxide esters, formed in the reactions of carbonyl oxides (also called Criegee intermediates, CIs) with formic acid, play a crucial role in the formation of secondary organic aerosol (SOA) in the atmosphere. However, the transformation mechanism of hydroperoxide esters in the presence of stabilized Criegee intermediates (SCIs) is not well understood. Herein, the oligomerization reaction mechanisms and kinetics of distinct SCI (CH2OO, syn-CH3CHOO, anti-CH3CHOO, and (CH3)2COO) reactions, with their respective hydroperoxide esters and with hydroperoxymethyl formate (HPMF), are investigated in the gas phase using quantum chemical and kinetics modeling methods. The calculations show that the addition reactions of SCIs with hydroperoxide esters proceed through successive insertion of SCIs into hydroperoxide ester to form oligomers that involve SCIs as the repeated chain unit. The saturated vapor pressure and saturated concentration of the formed oligomers decrease monotonically as the number of SCIs is increased. The exothermicity of oligomerization reactions decreases significantly when the number of methyl substituents increases, and the exothermicity of anti-methyl substituted carbonyl oxides is obviously higher than that of syn-methyl substituted carbonyl oxides. The −OOH insertion reaction is energetically more feasible than the −CH insertion pathway in the SCI oligomerization reactions, and the barrier heights increase with increasing the number of SCIs added to the oligomer, except for syn-CH3CHOO. For the reactions of distinct SCIs with HPMF, the barrier of the −OOH insertion pathway shows a dramatic decrease when a methyl substituent occurs at the anti-position, while it reveals a significant increase when a methyl group is introduced at the syn-position and dimethyl substituent. Compared with the rate coefficients of the CH2OO + HPMF reaction, the rate coefficients increase by about 1 order of magnitude when a methyl substituent occurs at the anti-position, whereas the rate coefficients decrease by 1–2 orders of magnitude when a methyl group is introduced at the syn-position. These new findings advance our current understanding of the influence of Criegee chemistry on the formation and growth processes and the chemical compositions of SOA.

Purpose: the main purpose of this article is to develop a methodology for assessing the digital maturity gaps of industrial enterprises in order to adapt to the digital environment, to take advantage of digital technologies to improve operations and increase competitiveness.Methods: the authors applied general scientific methods (method of synthesis, generalization, content analysis, graphical interpretation of data). In assessing the levels of digital maturity gaps, correlated with the levels of digital maturity, the scaling method was used.Results: the global metamorphoses actualizing the problem of achieving digital maturity by industrial enterprises are described. The concepts of maturity, digital maturity, and digital maturity gaps of industrial enterprises in different sources are investigated. Digital maturity gaps are shown from two perspectives: as the difference between the current and target maturity levels of industrial enterprises; as the gap between awareness and implementation of Industry X.0 solutions in industrial enterprises. The landscape of digital maturity assessment methodologies at different levels is systematized: federal, regional and grassroots level of the economy. The problem and research gap of digital maturity assessment is identified, which is the lack of methodologies for assessing digital gaps. The author proposed a methodology for assessing the digital maturity gaps of industrial enterprises, which includes fourteen stages. The methodology was tested using data from twenty industrial enterprises that are leaders in digitalization in 2021. Visualization of digital maturity indicators and digital maturity gaps was carried out.Сonclusions and Relevance: as a result of testing the methodology for assessing the digital maturity gaps of industrial enterprises, it is concluded that the current state of development of the national industrial ecosystem is characterized by significant heterogeneity in the development of individual industries, unequal development of markets and enterprises. In order to successfully complete the digital transformation and increase the level of digital maturity to the maximum, it is recommended that industrial enterprises integrate digital platform solutions and digital behaviors based on the concept of digital strategizing into their operating model.