Multipath is a major source of unmodeled errors that limits the ability of Global Navigation Satellite Systems (GNSS) to achieve highly accurate and reliable positioning solutions. Currently, the Multipath Hemispherical Map model (MHM) is a commonly used method for mitigating multipath errors. However, the MHM model neglects the spatial correlation within each grid cell, leading to insufficient accuracy in multipath error correction. To address this, we propose a Multipath Hemispherical Map model integrated with least-squares collocation (MHM-L). The MHM-L model incorporates the Gaussian model (MHM-LG), the Hirvonen model (MHM-LH), and the Markov model (MHM-LM) to take the spatial correlation of multipath errors within the grid into account. First, the contributions of Double Difference (DD) and single difference residuals to multipath mitigation are systematically analyzed. Based on this comparison, all subsequent experiments and analyses use DD residuals. Experiments were conducted on both an open rooftop and a partially obstructed deformation monitoring area. Experimental results indicate that, under open-sky conditions, the three covariance function models slightly outperform the MHM model. In contrast, under partially obstructed environments, these three covariance function models achieve positioning accuracy improvements of over 30%. Among them, the MHM-LM model achieved the highest positioning accuracy among three covariance function models. Statistical significance tests further confirm that the improvements achieved by the three covariance function models over the traditional MHM model are statistically significant, and that the positioning performance differences among the three covariance function models are also significant.

Real-Time Precise Orbit Determination (RTPOD) of Low Earth Orbit (LEO) satellites relies primarily on onboard GNSS observations and may suffer from degraded performance when observation geometry weakens or tracking conditions deteriorate within satellite formations. To enhance the robustness and accuracy of RTPOD under such conditions, a cooperative Extended Kalman Filter (EKF) framework that fuses onboard GNSS and inter-satellite link (ISL) range measurements is established, integrated with an iterative Detection, Identification, and Adaptation (DIA) quality control algorithm. By introducing high-precision ISL range measurements, the strategy increases observation redundancy, improves the effective observation geometry, and provides strong relative position constraints among LEO satellites. This constraint strengthens solution stability and convergence, while simultaneously enhancing the sensitivity of the DIA-based quality control to observation outliers. The proposed strategy is validated in a simulated real-time environment using Centre National d’Etudes Spatiales (CNES) real-time products and onboard observations of the GRACE-FO mission. The results demonstrate comprehensive performance enhancements for both satellites over the experimental period. For the GRACE-D satellite, which suffers from about 17% data loss and a cycle slip ratio several times higher than that of GRACE-C, the mean orbit accuracy improves by 39% (from 13.1 cm to 8.0 cm), and the average convergence time is shortened by 44.3%. In comparison, the GRACE-C satellite achieves a 4.2% mean accuracy refinement and a 1.3% reduction in convergence time. These findings reveal a cooperative stabilization mechanism, where the high-precision spatiotemporal reference is transferred from the robust node to the degraded node via inter-satellite range measurements. This study demonstrates the effectiveness of the proposed method in enhancing the robustness and stability of formation orbit determination and provides algorithmic validation for future RTPOD of LEO satellite formations or large-scale constellations.

Length of day (LOD), a critical component of Earth orientation parameters (EOP), represents variations in Earth's rotation rate. It is very difficult to predict accurately due to the effects of atmosphere, ocean, hydrology, the Earth's internal interactions and so on. The international Earth rotation and reference systems service (IERS) EOP C04 series, derived from four space geodetic observations, could offer high accuracy and smooth EOP product. However, this product typically has a latency of about 30 days. It is not adequate for fields requiring strict real-time data processing and applications, such as precise tracking and navigation of interplanetary spacecraft, global navigation satellite system (GNSS) meteorology, real-time precision orbit determination of artificial satellites, real-time kinematic (RTK) positioning and so on. To address the aforementioned issues, we propose an algorithm for predicting LOD that adopts a convolutional long-short-term memory (ConvLSTM) method with different base sequence lengths based on the LOD series from the IERS EOP C04, effective angular momentum (EAM) datasets and GNSS near-real-time (NRT) LOD data from the International GNSS Services (IGS) Rapid Products. Compared to the most accurate models used by participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), when GNSS NRT data is not used, the proposed model improves LOD ultra-short-term (1-10 days) prediction accuracy by 29.72% and medium- to long-term (60-360 days) prediction accuracy by 11.86%. After incorporating GNSS NRT data, the short-term (10-30 days) LOD prediction accuracy improves by 55.07%. It is shown that the ConvLSTM model, integrated with GNSS NRT data and EAM datasets, could significantly enhance the forecast accuracy of LOD across various time spans. This advancement enriches the Earth's rotation prediction models and holds potential benefits for real time applications such as real-time satellite orbit determination, extreme weather analysis, RTK technology and so on.

Agriculture is a key economic pillar for many countries around the world. However, pests and diseases have always been an intractable issue in agricultural production, causing significant economic losses. Therefore, timely identification of crop pests and diseases is crucial. The accuracy of existing crop pest and disease detection technologies still needs improvement, particularly when farmers capture plant images under varying lighting conditions and with different devices. To address the issue where these factors affect image quality and thus impact recognition accuracy, a crop pest and disease detection system based on the improved YOLOv9 algorithm has been developed. The YOLOv9 model incorporates attention mechanism modules SE and convolution operations modules AKConv, which enhance the system's focus on leaf features and its ability to extract those features, making pest and disease detection easier. Additionally, this system is suitable for real-time environments, is easy to implement, and can be deployed on various terminals and connected devices. Ablation and comparative experiments demonstrate that the system performs well in detecting crop pests and diseases.

No. 3 BeiDou navigation satellite system (No. 3 BDS) has been open to worldwide users since 2020. Its navigation service benefits more and more people from different countries. The satellite M26 is one of the standby satellites of BDS, which was sent into its operation orbit by the end of the year 2023. Its functions include stabilizing BDS, replacing the nearly retired satellite, and carrying out assessments of the new concept of GNSS. Due to the importance of satellite M26, authors have gathered the key orbit elements information; the orbit altitude, eccentricity and orbit inclination have been completely monitored and analyzed since its launch into working orbit. Based on the orbit elements, space environment effects for the first 271-day stay in orbit of M26 are simulated and space collision probability between M26 and other objects is studied. This work will partially support M26 to quickly join in the routine operation of BDS.

Global navigation satellite system (GNSS) monitoring stations established in canyon environments inevitably face challenges such as multipath effects, diffracted signals, and non-line-of-sight (NLOS) reception due to obstructions. To address this problem, existing methods employ a stochastic model based on GNSS receiver signal-to-noise ratio (SNR) to detect NLOS reception. However, the method only accounts for NLOS reception concerning elevation angles of satellites, neglecting NLOS reception from obstructions at side edges. To overcome this limitation, this paper proposes a threshold of azimuth angle. When the threshold is considered, it does not only improve NLOS detection, but also increases the position dilution of precision (PDOP) value, potentially affecting positioning precision. Thus, we introduced a PDOP stochastic model and proposed a method that integrates both the azimuth angle threshold and PDOP considerations. Experimental results show that the precision of the stochastic model based on equivalent elevation angles improves by approximately 24.25% after considering the threshold of azimuth angle. Furthermore, the precision increases by an additional 8.69% when the PDOP stochastic model is also taken into account.

The multipath effect is a critical factor that prevents the Global Navigation Satellite System (GNSS) from achieving millimeter-level positioning accuracy. A multipath hemispherical map (MHM) is a popular approach to achieving real-time multipath error mitigation. The premise of the constructed MHM model is that the residuals in the grid only contain multipath errors and noise without any outliers. However, when there are numerous obvious outliers in each grid, the traditional quality control method is unable to detect them effectively. Therefore, we propose a multipath hemispherical map with strict quality control (MHM-S) to mitigate multipath errors. This method first uses the maximum phase delay to eliminate obvious outliers. Then, the 3-sigma rule and F-test are applied to remove the remaining few outliers in the grid. After applying the proposed MHM-S method, the experimental results show that when the PRN20 satellite is affected by outliers, the standard deviation (STD) reduction rate of the MHM-S residuals is 12.03% compared with the residual STDs of the MHM model. In addition, we evaluate the capabilities of MHM-S with carrier phase observation (MHM-SC) and carrier phase and pseudo-range observation (MHM-SCP) models in multipath error mitigation. Especially in the east direction, the positioning accuracy of the MHM-SCP model is improved by 48% compared with the MHM-SC model.

With the application and promotion of space geodesy, the popularization of remote sensing technology, and the development of artificial intelligence, a more accurate and stable Terrestrial Reference Frame (TRF) has become more urgent. For example, sea level change detection, crustal deformation monitoring, and driverless cars, among others, require the accuracy of the terrestrial reference frame to be better than 1 mm in positioning and 0.1 mm/a in velocity, respectively. However, the current frequently used ITRF2014 and ITRF2020 do not satisfy such requirements. Therefore, this paper analyzes the coordinate residual time series data of linear TRFs and finds there are still some unlabeled jumps and time-dependent periodic signals, especially in the GNSS coordinate residuals, which can lead to incorrect station epoch coordinates and velocities, further affecting the accuracy and stability of the TRF. The unlabeled jumps could be detected by the sequential t-test analysis of regime shifts (STARS) combined with the generalized extreme Studentized deviate (GESD) algorithms introduced in our earlier paper. These nonlinear time-dependent periodic signals could be modeled better by singular spectrum analysis (SSA) with respect to least squares fitting; the fitting period is no longer composed of semi-annual and annual items, as with ITRF2014. The periods of continuous coordinate residual time series data longer than 5 years are obtained by FFT. The results show that there are no period signals for individual SLR/VLBI sites, and there are still other period terms, such as 34 weeks, 20.8 weeks and 17.3 weeks, in addition to semi-annual and annual items for some GNSS sites. Moreover, after SSA corrections, the re-calculated TRF and the corresponding EOP could be obtained, based on data from the Chinese Earth Rotation and Reference System Service (CERS) TRF and the Earth Orientation Parameter (EOPs) multi-technique determination software package (CERS TRF&EOP V2.0) developed by the Shanghai Astronomical Observatory (SHAO). Their accuracy could be evaluated with respect to the ITRF2014 and the IERS 14 C04, respectively. The results show that the accuracy and stability of the newly established a nonlinear TRF and EOP based on SSA have been greatly improved and better than a linear TRF and EOP. SSA is better than least squares fitting, especially for those coordinate residual time series with varying amplitude and phase. For GPS, comparing with the ITRF2014, the station coordinate accuracy of 10.8% is better than 1 mm, and the station velocity accuracy of 4.4% is better than 0.1 mm/year. There are 3.1% VLBI stations, for which coordinate accuracy is better than 1 mm and velocity accuracy is better than 0.1 mm/year. However, there are no stations with coordinates and velocities better than 1 mm and 0.1 mm/year for the SLR and DORIS. The WRMS values of polar motion x, polar motion y, LOD, and UT1-UTC are reduced by 2.4%, 3.2%, 2.7%, and 0.96%, respectively. The EOP’s accuracy in SOL-B, in addition to LOD, is better than that of the JPL.

Multipath effect is the main factor limiting the high-precision positioning of Global Navigation Satellite System (GNSS) because it cannot be eliminated by double-difference observations or existing empirical models. Although the receiver antenna technology can reduce the multipath effect, it also brings the cost burden and cannot effectively solve the short-delay multipath error of carrier phase observation. Sidereal filtering (SF) is a common method used to mitigate multipath effect in static positioning mode. However, the coordinate time series may be contaminated by outliers, and satellite orbits may be affected by maneuvers. These will lead to inaccurate estimation of the Multipath Repetition Period (MRP), and even deteriorate the multipath mitigation effect of the SF. To solve these problems, a Sidereal Filtering method with the Satellite Maneuver Detection and Robustness (SFSMDR) is proposed. In this method, based on the Orbital Repeat Time Method (ORTM), the Multipath Repetition Period with the Satellite Maneuvers Detection (MRPSMD) is determined. Furthermore, considering the robustness of median filtering to outliers, the coordinate time series of the first day were preprocessed by the median filtering. After applying the proposed SFSMDR method, experimental results show that the proposed SFSMDR can effectively detect satellite maneuvers. In the absence of satellite maneuvers, there is no need to re-estimate MRP, which can save computational cost to a certain extent. When the coordinate time series on the first day is contaminated by outliers, the positioning accuracy of the MRPSMD for the east and north directions, is decreased by 2.26% and 95.73%, respectively. In contrast, the positioning accuracy of the SFSMDR is increased by 46.21% and 52.71%, respectively.

Precise Orbit Determination (POD) of Satellite Laser Ranging (SLR)-tracked satellites is stepping towards millimeter-level precision, which requires further refinement of satellite perturbation models and consideration of more detailed perturbation force models that were not previously taken into accounted. Atmospheric drag is one of the main perturbative forces acting on a Low Earth Orbit (LEO) satellite, however, the atmospheric wind speed associated with atmospheric drag has not been considered in International Laser Ranging Service (ILRS) regular data processing. Therefore, in order to analyze the magnitude of atmospheric wind speed influence on POD of SLR satellites, this paper applies HWM14 (Horizontal Wind Model 14) atmospheric wind speed model to perform POD of LARES, Ajisai, Starlette, Stella, Larets satellites and analyses the results. The results show that after applying HWM14 atmospheric wind speed model, the SLR observation residual WRMS (Weighted Root Mean Square) value for LARES, Starlette, Stella, Larets satellites reach 1.04 cm, 2.13 cm, 1.73 cm, 3.36 cm, respectively, which are reduced by 0.10 cm, 0.30 cm, 0.00 cm, 0.05 cm, respectively. However, for Ajisai, one has an almost the same orbit residual WRMS value of 2.88 cm. The 3D (three-dimensional) orbit overlapping arcs error for LARES is 17.53 cm, which is reduced by 1.75 cm; for Starlette it is 19.12 cm, which is reduced by 0.96 cm; for Stella there is almost same orbit overlapping arcs error of 34.9 cm. The accuracy of orbit forecast for 1 day and 3 days for LARES reaches 147.57 cm and 163.96 cm, respectively, which is improved by 0.04 cm and 20.73 cm in the T (Tangential) direction, respectively. The accuracy of orbit forecast for 1 day and 3 days for Stella reaches 191.77 cm and 308.52 cm, respectively, which is improved by 1.95 cm and 6.74 cm in the T direction, respectively. The accuracy of orbit forecast for 1 day and 3 days for Larets reaches 6.18 m and 145.26 m, respectively, which is improved by 7.81 m and 473.61 m in the T direction, respectively. Above results display that considering HWM14 atmospheric wind speed model has a certain improvement for POD of LARES, Starlette, Stella and Larets. Therefore, the influence of atmospheric wind speed should be considered in the SLR POD and regular data processing.

Multipath effect is one of the major challenges for Global Navigation Satellite Systems (GNSS) to achieve millimeter-level high-precision positioning and orbit determination. Wavelet Transform (WT) and Sidereal Filtering (SF) can effectively extract and mitigate multipath errors. Therefore, they are widely used in ground deformation monitoring and high precise GNSS applications. In view of this, the selection of refined wavelet decomposition levels and thresholds is critical for better extraction and mitigation of multipath errors. In this paper, we systematically analyze the performance of multipath error mitigation in Single-Difference (SD) and Double-Difference (DD) residuals based on different wavelet decomposition levels, thresholds and threshold functions. The results show that both DD SF and SD SF can effectively mitigate multipath errors. Compared to the traditional positioning, the positioning accuracy using the adopted SD SF in the east, north and up direction are improved by about 30.42

The multipath effect is one of the primary challenges preventing the Global Navigation Satellite System (GNSS) from achieving millimeter-level high-precision positioning. The Multipath Hemispherical Map (MHM) model can effectively mitigate multipath errors. Galileo satellites require data from the tenth day to correct multipath errors on the target day. However, it may not be possible to obtain ten-day data of the Galileo satellites due to the short observation time. In this study, we used measured data from consecutive days instead of the tenth day to construct a reliable MHM model of Galileo satellites. The experimental results show that the RMS reduction rate of consecutive 5-day MHM model can reach 19%, 5%, and 6% in the east, north, and upward directions, respectively. After 5-day MHM model, the RMS reduction rates of multi-day MHM models remains basically stable. Therefore, the constructed MHM model with long Orbital Repetition Period (ORP) requires at least 5 days to obtain reliable multipath correction.

Carrier-phase smoothing code (CPSC) is a code-smoothing technology that uses carrier-phase changes to reduce code noise in Global Navigation Satellite System (GNSS) appliances. Although CPSC performs well in reducing noise and is easy to implement, it is a trade-off between the reduction of noise and the increase of the variation of ionospheric errors. The width of the smoothing window needs to be large to reduce noise. However, a wider smoothing window increases the variation of ionospheric errors. To circumvent this dilemma, the grid ionospheric model (GIM) was used to estimate the variation of ionospheric errors between consecutive epochs, and a noise estimation method is proposed for low-cost single-frequency receivers. Furthermore, an optimal carrier phase smoothing code (OCPSC) algorithm with an adaptive width smoothing window is proposed to reduce the noise of Global Positioning System (GPS) data. We found that the OCPSC is more robust and its positioning performance is better overall for low-cost single-frequency receivers than is the traditional CPSC. In a static mode, when applying the OCPSC algorithm, the positioning accuracy can be improved by 0.15 m (6%) and 0.06 m (6%) in the horizontal and vertical directions, respectively. These improvements are 0.08 (3%) m and 0.06 m (1%) when a kinematic mode is applied. The research presented here demonstrates that the OCPSC algorithm effectively addresses the trade-off between the reduction of noise and the increase of the variation of ionospheric errors. In the OCPSC algorithm, the grid ionospheric model is used to estimate the variation of ionospheric errors between consecutive epochs, and a noise estimation method is used to estimate noise for low-cost single-frequency receivers. The OCPSC algorithm was applied to static and dynamic experiments. All results in this paper show that, compared with traditional CPSC, the OCPSC algorithm provides more-accurate positioning results for low-cost single-frequency receivers. Because future smartphones will integrate GNSS chips, the proposed OCPSC algorithm has great potential to be applied widely to the market of multiconstellation single-frequency Precise Point Positioning with low-cost smartphones.

This article proposes a new sea surface wind speed (SSWS) retrieval modeling algorithm based on the empirical orthogonal function (EOF) analysis for observations acquired by the global navigation satellite system reflectometry (GNSS-R). As a nonparametric modeling algorithm, it is simpler compared with the nonlinear methods. The influence of wind speed and incident angle on the modeling error is analyzed for the first time using a spectrum analysis. Three types of data from 80% CYGNSS 2019–2020 observations [delay Doppler map average (DDMA) and leading edge slope (LES)], signal incident angle, and the European Centre for Medium-Range Weather Forecasts Reanalysis V5 (ERA5) reference wind speed are used in the EOF analysis to establish two retrieval models. The remaining 20% of the data are used for accuracy evaluation after getting the final wind speed by the minimum variance (MV) estimator. As a result, when using three 0–20-m/s wind speeds of ERA5, Advanced Scatterometer (ASCAT), and the Modern-Era Retrospective Analysis for Research and Applications V2 (MERRA2) as contrasts, the root mean squared errors (RMSEs) are 1.51, 1.45, and 1.43 m/s, respectively. Compared with CYGNSS wind product, the performance of this algorithm is closer to the L2 Climate Data Record (CDR) V1.1 product than V1.0. The results demonstrate that the EOF algorithm has a good performance in retrieving SSWS and can better retain the influence of the incident angle on the observations.

Solar radiation pressure (SRP) is an extremely critical perturbative force that affects the GNSS satellites’ precise orbit determination (POD). Its imperfect modelling is one of the main error sources of POD, whose magnitude is even to10 −9 m/s 2 . The shadow factor (i.e., eclipse factor) is one crucial parameter of SRP, generally estimated by the cylindrical model, the conical model, or shadow models considering the Earth’s oblateness and the atmospheric effect, such as the Perspective Projection Method atmosphere (PPMatm) model and Solar radiation pressure with Oblateness and Lower Atmospheric Absorption, Refraction, and Scattering Curve Fit (SOLAARS-CF) model. This paper applies the former four shadow models to determine the corresponding precise orbit using BeiDou satellites’ ground-based observation, and then compared and assessed the orbit accuracy through Satellite Laser Ranging (SLR) validation and Inter-Satellite Link (ISL) check. The results show that the PPMatm model’s accuracy is equivalent to the SOLAARS-CF model. Compared with the conical shadow model, SLR validations show the orbit accuracy from the PPMatm and SOLAARS-CF model can be generally improved by 2–10 mm; ISL range check shows that the Root Mean Square (RMS) can be decreased by 2–7 mm. These results show that the shadow model in GNSS POD should fully consider the Earth’s oblateness and the atmospheric effect, especially for the perturbative acceleration higher than 10 –10 m/s 2 . Graphical Abstract

针对中国区域卫星激光测距(SLR)观测站测距精度评估和稳定性问题,该文利用上海天文台SHORD-Ⅱ(ShangHai ORbit Determination-Ⅱ)软件对2010年1月—2021年6月lageos1/2卫星观测数据进行精密定轨,并分析了国内长春站、北京站、上海站和昆明站的距离偏差、时间偏差和测站残差.结果表明,国内测站有效观测数均达90％以上,观测精度较高,同时测站的长期系统偏差趋于稳定.对于lageos1卫星,各测站经过系统偏差修正后,长春站、北京站、上海站、昆明站残差平均值分别为4.14、4.70、2.01、3.90mm;对于lageos2卫星,各测站经过系统偏差修正后,长春站、北京站、上海站、昆明站残差平均值分别为5.63、5.25、1.85、4.30mm,残差平均值均优于1cm,与国际激光测距服务(ILRS)国际测站观测精度相当.

With the rapid development of interferometric synthetic aperture radar (InSAR) measurement technology, its measurement accuracy requirements are increasing. Atmospheric delay errors must be corrected, especially in the case of crustal deformation monitoring, the 20% variation of tropospheric water vapor among InSAR pairs generally produces range from 10 cm to 14 cm deformation errors. Such errors can be of the same magnitude as the annual changes in crustal deformation, or even greater, masking crustal deformation information and seriously affecting the results of crustal deformation monitoring. Therefore, in order to obtain a more accurate InSAR atmospheric delay correction model, this paper calculated and integrated atmospheric delays that were estimated by different sources, including the 37 pressure levels of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF)) numerical weather prediction model, ECMWF Reanalysis v5 (ERA5), and Global Navigation Satellite System (GNSS) measurement data from the crustal movement observation network of China, based on the variance component estimation (VCE) weighting method. The results showed that the integrated model, based on the VCE method, is better than the generic atmospheric correction online service (GACOS) model for InSAR measuring of crustal deformation. The precision in monitoring crustal deformations was improved by approximately 5 mm, the correlation coefficient of atmospheric delay errors and crustal deformations improved from 0.287 to 0.347, and accuracy improved by approximately 25%. However, the improvement in accuracy was limited because of system error decoherence that was induced by atmospheric noise caused by abundant vegetation or snow cover. Therefore, in order to achieve more accurate results, we recommend the adoption of the multi-source integrated atmospheric delay correction model, based on the VCE method, for InSAR high-precision measuring of crustal deformation and seismic activities.

This study investigated the bioaccumulation and transfer of heavy metals including Cd, Cr, Cu, Mn, Ni, Pb and Zn in soil-crop system in Lhasa, and assessed the health risks of the edible part of the crops. The results showed that the average values of Cd, Cr, Cu, Mn, Ni, Pb and Zn were 0.15, 44.55, 24.68, 532.40, 22.47, 38.18 and 73.99 mg kg(-1) in natural soil, and 0.16, 46.93, 38.45, 559.13, 23.23, 40.03 and 83.29 mg kg(-1) in cultivated soil, respectively. Highland barley and wheat had the strongest ability to accumulate Zn in grain, the BCF values were 0.24 and 0.27, respectively, significant differences in the distribution of metal contents in crop root, stem, leaf and grain were observed. Root presented larger accumulation capacity in most metals, Zn and Cu was easily transferred in the plant organs, most metals in this study presented difficult to migrate from root to grain. The transfer peak of most metals in soil-crop system appeared from stem to leaf. The concentrations of Cr and Mn in crop grains could be predicted according to the multiple linear regression models. THQ and HI values of heavy metals in edible parts of both highland barley and wheat were below the safety threshold of 1, indicating no detrimental effects posed to adults health. This study helps to understand the accumulation and transfer of heavy metals in soil-crop system in plateau region.

Background Kashin-Beck disease (KBD) is one of the major endemic diseases in China, which severely impacts the physical health and life quality of people. A better understanding of the spatial distribution of the health loss from KBD and its influencing factors will help to identify areas and populations at high risk so as to plan for targeted interventions. Methods The data of patients with KBD at village-level were collected to estimate and analyze the spatial pattern of health loss from KBD in Bin County, Shaanxi Province. The years lived with disability (YLDs) index was applied as a measure of health loss from KBD. Spatial autocorrelation methodologies, including Global Moran’s I and Local Moran’s I, were used to describe and map spatial clusters of the health loss. In addition, basic individual information and environmental samples were collected to explore natural and social determinants of the health loss from KBD. Results The estimation of YLDs showed that patients with KBD of grade II and patients over 50 years old contributed most to the health loss of KBD in Bin County. No significant difference was observed between two genders. The spatial patterns of YLDs and YLD rate of KBD were clustered significantly at both global and local scales. Villages in the southwestern and eastern regions revealed higher health loss, while those in the northern regions exhibited lower health loss. This clustering was found to be significantly related to organically bound Se in soil and poverty rate of KBD patients. Conclusions Our results suggest that future treatment and prevention of KBD should focus on endemic areas with high organically bound Se in soil and poor economic conditions. The findings can also provide important information for further exploration of the etiology of KBD.

Drinking water is considered to be an important exposure pathway for humans to ingest trace elements; human urine samples are widely accepted as biometric substrates that can reflect human exposure to trace elements. The current study aimed at investigating the concentrations of trace elements including selenium (Se), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) in drinking water and human urine in plateau region of China, determining the association among trace elements in drinking water and urine, and analyzing their associations with age and gender. The results showed that the majority of trace element concentrations were in the range of the World Health Organization (WHO 2011) guideline values, in both urine samples of male and female, and the median values were descending in the order: Zn > Cu > As > Se > Cr > Ni > Mn > Pb > Cd > Co. Selenium contributed to the excretion of As, Cr, Cu, Cd, and Zn in human body, group of 31–40 years appeared to present the greatest excretion ability in most of the trace elements. Weak positive correlations were observed between age and Mn in female urine samples, and negative correlations were observed between age and Se, As, Co, and Cu in male urine samples and between age and Co in female urine samples, respectively. Significant positive correlation was observed in As between drinking water and the whole human urine. In the same family, female seemed to show higher proportions of urinary As levels than male. This study will provide elementary information regarding trace element levels in drinking water and human urine in residents in plateau region of China and is helpful to provide reference for dietary nutrient trace element intake and effective control for local resident.