The spatial-temporal pattern and influencing factors of negative air ions in urban forests, Shanghai, China

Negative air ions are natural components of the air we breathe Forests are the main continuous natural source of negative air ions （NAI）. The spatio-temporal patterns of negative air ions were explored in Shanghai, based on monthly monitoring in 15 parks from March 2009 to February 2010. In each park, sampling sites were selected in forests and open spaces. The annual variation in negative air ion concentrations （NAIC） showed peak values from June to October and minimum values from December to January. NAIC were highest in summer and autumn, intermediate in spring, and lowest in winter. During spring and summer, NAIC in open spaces were significantly higher in rural areas than those in suburban areas. However, there were no significant differences in NAIC at forest sites among seasons. For open spaces, total suspended particles （TSP） were the dominant determining factor of NAIC in sum- mer, and air temperature and air humidity were the dominant determining factors of NAIC in spring, which were tightly correlated with Shanghai＇s ongoing urbanization and its impacts on the environment. R is suggested that urbanization could induce variation in NAIC along the urban-rural gradient, but that may not change the temporal variation pattern. Fur- thermore, the effects of urbanization on NAIC were limited in non-vegetated or less-vegetated sites, such as open spaces, but not in well-vegetated areas, such as urban forests. Therefore, we suggest that urban greening, especially urban forest, has significant resistance to theeffect of urbanization on NAIC.

Spatial-temporal pattern and formation mechanism of county urbanization on the Chinese Loess Plateau

Urbanization is a comprehensive and complex socioeconomic phenomenon that plays an influential role in promoting global socioeconomic development.The Loess Plateau region is an important part of the China’s ecological security pattern,and occupies an important position in the implementation of China’s new-type urbanization strategy and the realization of the urban dream.The characteristics of the staged changes and regional differentiation of urbanization in the area from 1990 to 2018 were studied with focus on regions and subregions by selecting 341 county-level administrative units on the Chinese Loess Plateau as the research area,and employing partition analysis and geographic detector methods.This revealed the formation mechanism of the spatial differentiation pattern of urbanization on the Loess Plateau.We found that the urbanization of the Loess Plateau,previously in a slow growth phase,entered the accelerated development phase,presenting a macro pattern of high rates of urbanization in central and eastern areas and low rates in western areas.The formation of the regional differentiation patterns of urbanization on the Loess Plateau were the combined results of natural geographical and socioeconomic factors.Among these factors,the interaction of any two factors had a stronger impact on regional urbanization patterns than a single factor,which was specifically manifested as nonlinear or bi-factor enhancement effects.The findings of this paper may provide a theoretical reference and scientific basis for the scientific promotion of healthy urbanization on the Chinese Loess Plateau and the ecologically fragile areas of developing countries around the world.

Spatial-temporal Patterns and Driving Forces of Water Retention Service in China

Overwhelming water-deficiency conditions and an unbalanced water supply and demand have been major concerns of both the Chinese government and the general public during recent decades. Studying the spatial-temporal patterns and impact factors that influence water retention in China is important to enhance the management of water resources in China and other similar countries. We employed a revised Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST) model and regression analyses to investigate the water retention service in China. The results showed that the southeastern China generally performed much better than Northwest China in terms of the spatial distribution of water retention. In general, the efficacy of the water retention service in China increased from 2000 to 2014; although some areas still had a downward trend. Water retention service increased significantly(P < 0.05) in aggregate in the Qinghai-Tibet Plateau, and the Da Hinggan Mountains and Xiao Hinggan Mountains. However, the service in southwestern China showed a decreasing trend(P < 0.05), which would have significant negative impact on the downstream population. This study also showed that in China the changes in water retention service were primarily due to climate change(which could explain 83.49% of the total variance), with anthropogenic impact as a secondary influence(likewise the ecological programs and socioeconomic development could explain 9.47% and 1.06%, respectively). Moreover, the identification of water retention importance indicated that important areas conservation and selection based on downstream beneficiaries is vital for optimization protection of ecosystem services, and has practical significance for natural resources and ecosystem management.

Spatial-temporal Pattern and Differences of Land Use Changes in the Three Gorges Reservoir Area of China during 1975-2005

Regional land use changes are an important part of global changes.The research on land use changes in the Three Gorges Reservoir Area of China attracts a lot of attention owing to the Three Gorges Dam building.The Three Gorges Reservoir Area becomes one of the important research areas.This study analyzed the transforming processes and traits of each land use type and the regional differences of land use changes during the past 30 years,summarized the distribution of different land use types in different buffer zones and regresses the equation areas and different buffer distances based on buffer analyses and regression analyses,and then analyzed the transforming rules in different buffer distances,got the optimal influence distances.The research results indicate that,(1) cultivated land lies at the northwest of the reservoir and was decreasing,however,the construction land was increasing,especially the urban construction land,a large number of land was flooded because of the reservoir water level rise;(2) urban area was sprawling quickly in developed and neighboring areas,and a great deal of cultivated land and a considerable amount of grassland were occupied;in the earlier time,rural settlements occupied lots of cultivated land and a sum of forestry land in the later time;(3) the optimum influenced distances for cultivated land and forestry land were 10-35 km,and for urban and rural settlements were in 5-20 km.Overall,this research can reflect the spatial-temporal characteristics of land use changes during the 30 years,and it is helpful for urban planning and land use planning in the reservoir area.

Tupu methods of spatial-temporal pattern on land use change:a case study in the Yellow River Delta

Comprehensive study on land-use change of spatial pattern and temporal process is the key component in LUCC study nowadays. Based on the theories and methods of Geo-information Tupu (Carto-methodology in Geo-information, CMGI), integration of spatial pattern and temporal processes of land-use change in the Yellow River Delta (YRD) are studied in the paper, which is supported by ERDAS and ARC/INFO software. The main contents include: (1) concept models of Tupu by spatial-temporal integration on land-use change, whose Tupu unit is synthesized by "Spatial·Attribute·Process" features and composed of relatively homogeneous geographical unit and temporal unit; (2) data sources and handling process, where four stages of spatial features in 1956, 1984, 1991, and 1996 are acquired; (3) integration of series of temporal-spatial Tupu, reconstruction series of "Arising" Tupu, spatial-temporal Process Tupu and the spatial temporal Pattern Tupu on land-use change by remap tables; (4) Pattern Tupu analysis on land-use change in YRD during 1956-1996; and (5) spatial difference of the Pattern Tupu analysis by dynamic Tupu units. The various landform units and seven sub-deltas generated by the Yellow River since 1855 are different. The Tupu analysis on land-use in the paper is a promising try on the comprehensive research of "spatial pattern of dynamic process" and "temporal process of spatial pattern" in LUCC research. The Tupu methodology would be a powerful and efficient tool on integrated studies of spatial pattern and temporal process in Geo-science.

Characterizing urban expansion of Korla City and its spatial-temporal patterns using remote sensing and GIS methods

Cities provide spatial contexts for populations and economic activities. Determining the spatial-temporal patterns of urban expansion is of particular significance for regional sustainable development. To achieve a better understanding of the spatial-temporal patterns of urban expansion of Korla City, we explore the urban expansion characteristics of Korla City over the period 1995-2015 by employing Landsat TM/ETM＋ images of 1995, 2000, 2005, 2010, and 2015. Urban land use types were classified using the supervised classification method in ENVI 4.5. Urban expansion indices, such as expansion area, expansion proportion, expansion speed, expansion intensity, compactness, and fractal dimension, were calculated. The spatial-temporal patterns and evolution process of the urban expansion （e.g., urban gravity center and its direction of movement） were then quantitatively analyzed. The results indicated that, over the past 25 years, the area and proportion of urban land increased substantially with an average annual growth rate of 15.18%. Farmland and unused land were lost greatly due to the urban expansion. This result might be attributable to the rapid population growth and the dramatic economic development in this area. The city extended to the southeast, and the urban gravity center shifted to the southeast as well by about 2118 m. The degree of urban compactness tended to decrease and the fractal dimension index tended to increase, indicating that the spatial pattern of Korla City was becoming loose, complex, and unstable. This study could provide a scientific reference for the studies on urban expansion of oasis cities in arid land.

Spatial-temporal Patterns and Evolution History of Rural Settlements from the Perspective of Inheritance——A Case Study of Lianzhou City,Guangdong Province

Formation and evolution of rural settlement patterns in Lianzhou City,Guangdong Province were analyzed from the perspective of space and time,on the basis of its gazetteer and relevant historical data.The results show that Lianzhou was first founded in the sixth year of Yuanding Period of the Western Han Dynasty,and its development could be roughly classified into 6 stages according to the construction of south–north traffic lines and regional development progress,and it witnessed the fastest development in the Ming and Qing Dynasty.In terms of spatial distribution,rural settlements in the local area show spatial continuity,Lianzhou Town is the core of rural settlement growth in the city,and towns with the most concentrated rural settlements in all stages are located in central-west and northeast parts of the city,and those with lower density of rural settlements are mostly located in minority regions in the north and mountainous areas in the east.On the basis of the above facts,the paper studies the influence of natural geological conditions,immigrant,traffic,economic development and ethnic composition on the establishment and development of rural settlements in Lianzhou City.

Spatial-temporal patterns and evolution of carbon storage in China's terrestrial ecosystems from 1980 to 2020

Analyzing the changes in carbon storage in terrestrial ecosystems caused by land use changes is a crucial part of exploring the carbon cycle. In addition, enhancing carbon storage in terrestrial ecosystems is an effective and environmentally friendly measure to sequester anthropogenic carbon emissions, which is significant for achieving carbon neutrality and curbing global climate change. This paper uses land use data and carbon density tables with the In VEST model to obtain a carbon storage distribution map of China. It further applies land use response elasticity coefficients, Theil index multi-stage nested decomposition, and spatial autocorrelation analysis to examine the spatial-temporal patterns, causes of changes, and evolution characteristics of carbon storage in terrestrial ecosystems from 1980 to 2020. The results show that the temporal changes in China's carbon storage generally present an inverted S-curve, with an initial rapid decline followed by a slower decrease. Spatially, it features high levels in the northeast, low levels in the northwest, and a uniform distribution in the central and southern regions.The disturbance of land use type changes on terrestrial ecosystem carbon storage has been effectively mitigated. The significant reduction in grassland area in the Southwest region is the main source of carbon storage loss during the study period, and the encroachment of construction land on arable land in large urban agglomerations is one of the important causes of carbon storage loss. The Theil index multi-stage nested decomposition results indicate that the overall difference in carbon storage in China has decreased, while differences among cities within provinces and among counties within cities have increased. The influence of natural factors on the distribution of carbon storage is weakening, whereas the impact of human activities is becoming more profound, enhancing its influence on the spatial distribution of carbon storage in China. From 1980 to 2000, the carbon density in coastal metropolises generally showed a declining trend. From 2000 to 2020, the carbon density in the central urban areas of eastern coastal city clusters gradually showed an upward trend and continued to expand outward, revealing to some extent the“Environmental Kuznets Curve” characteristic in the development process of urban carbon storage. Therefore, in future ecological construction, the government should fully consider the impact of land management planning on carbon storage in different regions, promote the efficient use and standardized management of land, and strive to cross the “Environmental Kuznets Curve” inflection point of carbon storage as soon as possible.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Spatial-temporal pattern changes of main agriculture natural disasters in China during 1990-2011

China is a disaster prone country, and a comprehensive understanding of change of disasters is very important for China＇s agricultural development. In this study, statistical tech niques and geographic information system tools are employed to quantify the main agricul ture disasters changes and effects on grain production in China during the period of 1990-2011. The results show that China＇s grain production was severely affected by disas ters including drought, flood, hail, frost and typhoon. The annual area covered by these dis asters reached up to 48.7x106 ha during the study period, which accounted for 44.8% of the total sown area, and about 55.1% of the per unit area grain yield change was caused by disasters. In addition, all of the disasters showed high variability, different changing trends, and spatial distribution. Drought, flood, and hail showed significantly decreasing trends, while frost and typhoon showed increasing trends. Drought and flood showed gradual changes and were distributed across the country, and disasters became more diversified from north to south. Drought was the dominated disaster type in northern China, while flood was the most important disaster type in the southern part. Hail was mainly observed in central and northern China, and frost was mainly distributed in southern China. Typhoon was greatly limited to the southeast coast. Furthermore, the resilience of grain production of each province was quite different, especially in several major grain producing areas, such as Shandong, Liaoning, Jilin and Jiangsu, where grain production was seriously affected by disasters. One reason for the difference of resilience of grain production was that grain production was marginalized in developed provinces when the economy underwent rapid development. For China＇s agricul tural development and grain security, we suggest that governments should place more em phasis on grain production, and invest more money in disaster prevention and mitigation, especially in the major grain producing provinces.

Spatial-temporal patterns of China＇s interprovincial migration, 1985-2010

Migration plays an increasing role in China＇s economy since mobility rose and economic restructuring has proceeded during the last three decades. Given the background of most studies focusing on migration in a particular period, there is a critical need to analyze the spatial-temporal patterns of migration. Using bicomponent trend mapping technique and interprovincial migration data during the periods 1985-1990, 1990-1995, 1995-2000, 2000- 2005, and 2005-2010 we analyze net-, in-, out-migration intensity, and their changes over time in this study. Strong spatial variations in migration intensity were found in China＇s interprovincial migration, and substantial increase in migration intensity was also detected in eastern China during 1985-2010. Eight key destinations are mostly located within the three rapidly growing economic zones of eastern China （Pearl River Delta, Yangtze River Delta and Beijing-Tianjin-Hebei Metropolitan Region）, and they are classified into three types： mature, emerging, and fluctuant origins, while most key origins are relatively undeveloped central and western provinces, which are exactly in accordance with China＇s economic development patterns. The results of bicomponent trend mapping indicate that, in a sense, the migration in the south was more active than the north over the last three decades. The result shows the new changing features of spatial-temporal patterns of China＇s interprovincial migration that Fan and Chen did not find out in their research. A series of social-economic changes including rural transformation, balanced regional development, and labor market changes should be paid more attention to explore China＇s future interprovincial migration.

Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis

The study of temporal and spatial variations of nitrate in groundwater under different soil nitrogen environments is helpful to the security of groundwater resources in agricultural areas.In this paper,based on 320 groups of soil and groundwater samples collected at the same time,geostatistical analysis and multiple regression analysis were comprehensively used to conduct the evaluation of nitrogen contents in both groundwater and soil.From May to August,as the nitrification of groundwater is dominant,the average concentration of nitrate nitrogen is 34.80 mg/L;The variation of soil ammonia nitrogen and nitrate nitrogen is moderate from May to July,and the variation coefficient decreased sharply and then increased in August.There is a high correlation between the nitrate nitrogen in groundwater and soil in July,and there is a high correlation between the nitrate nitrogen in groundwater and ammonium nitrogen in soil in August and nitrate nitrogen in soil in July.From May to August,the area of low groundwater nitrate nitrogen in 0-5 mg/L and 5-10 mg/L decreased from 10.97%to 0,and the proportion of high-value area(greater than 70 mg/L)increased from 21.19%to 27.29%.Nitrate nitrogen is the main factor affecting the quality of groundwater.The correlation analysis of nitrate nitrogen in groundwater,nitrate nitrogen in soil and ammonium nitrogen shows that they have a certain period of delay.The areas with high concentration of nitrate in groundwater are mainly concentrated in the western part of the study area,which has a high consistency with the high value areas of soil nitrate distribution from July to August,and a high difference with the spatial position of soil ammonia nitrogen distribution in August.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

The Spatial-Temporal Distribution Patterns of Dyke Swarms in Central Asia and their Tectonic Significance: Case Studies in Eastern Tianshan and Western Junggar

Dykes are a special kind of intrusive rocks which were formed by deep magma intruded into the existing brittle fractures in the crust.Dykes swarms in different tectonic environments are very significant to re-construct the

Spatial-temporal patterns of major ion chemistry and its controlling factors in the Manasarovar Basin, Tibet

The Manasarovar Basin in southern Tibet, which is considered a holy land in Buddhism, has drawn international academic attention because of its unique geographical environment. In this study, based on actual measurements of major ion concentrations in 43 water samples collected during the years 2005 and 2012, we analyzed systemically the spatialtemporal patterns of water chemistry and its controlling factors in the lake and inflowing rivers. The results reveal that the water in the Manasarovar Basin is slightly alkaline, with a pH ranging between 7.4-7.9. The amounts of total dissolved solids （TDS） in lake and river waters are approximately 325.4 and 88.7 mg/l, respectively, lower than that in most of the surface waters in the Tibetan Plateau. Because of the long-term effect of evaporative crystallization, in the lake, Na^＋ and HCO3^- have the highest concentrations, accounting for 46.8% and 86.8% of the total cation and anion content. However, in the inflowing rivers, the dominant ions are Ca^2＋ and HCO3^-, accounting for 59.6% and 75.4% of the total cation and anion content. The water exchange is insufficient for such a large lake, resulting in a remarkable spatial variation of ion composition. There are several large inflowing rivers on the north side of the lake, in which the ion concentrations are significantly higher than that on the other side of the lake, with a TDS of 468.9 and 254.9 mg/l, respectively. Under the influence of complicated surroundings, the spatial variations in water chemistry are even more significant in the rivers, with upstreams exhibiting a higher ionic content. The molar ratio between （Ca^2＋＋Mg^2＋） and （Na^＋＋K^＋） is much higher than 1.0, revealing that the main source of ions in the waters is carbonate weathering. Although natural processes, such as rock weathering, are the major factors controlling main ion chemistry in the basin, in the future we need to pay more attention to the anthropogenic influence.