The project “The impact of the frozen soil environment on the construction of the Qinghai-Tibet Railway and the environmental effects of the construction” is part of the “Environmental and Ecological Science in West China” programme supported by the National Natural Science Foundation of China. The person in charge of the project is Wei Ma, a researcher at the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The project ran from January 2002 to December 2004. Data collected in this project included the following: Monitoring data of the active layer in the Beiluhe River Basin (1) Description of the active layer in the Beiluhe River Basin (2) Subsurface moisture data from the Beiluhe River Basin, 2002.9.28-2003.8.10 (Excel file) * Site 1 - Grassland moisture data * Site 2 – Removed turf moisture data * Site 3 - Natural turf moisture data * Site 4 - Gravel moisture data * Site 5 - Insulation moisture data (3) Subsurface temperature data from the Beiluhe River Basin, 0207-0408 Excel file * Temperature data for the ballast surface * Temperature data for insulation materials * Temperature data for a surface without vegetation * Temperature data for a grassland surface * Temperature data for a grit and pebble surface Data on the impact of construction on the ecological environment were obtained at Fenghuoshan, Tuotuohe, and Wudaoliang. Sample survey included plant type, abundance, community coverage, total coverage, aboveground biomass ratio and soil structure. The moisture content at different depths of the soil was detected using a time domain reflectometer (TDR). A set of soil samples was collected at a depth of 0-100 cm at each sample site. An EKKO100 ground-penetrating radar detector was used to continuously sample 1-1.5 km long sections parallel to the road to determine the upper limit depth of the frozen soil. 3. Predicted data: The temperature of the frozen soil at different depths and times was predicted in response to temperature increases of 1 degree and 2 degrees over the next 50 years based on initial surface temperatures of -0.5, -1.5, -2.5, -3.5, and -4.5 degrees. 4. The frozen soil parameters of the Qinghai-Tibet Railway were as follows: location, railway mileage, total mileage (km), frozen soil type mileage, mileage of zones with an average temperature conducive to permafrost, frozen soil with high temperatures and high ice contents, frozen soils with high temperatures and low ice contents, frozen soils with low temperatures and high ice contents, frozen soils with low temperatures and low ice contents, and melting area.
MA Wei, WU Qingbai
Correlation data of vegetation functional traits with topographic factors and pastoral animal husbandry activity factors, including: 1) observation data of main functional traits of 2-3 kinds of grassland plants in elevation, slope and slope upward; 2) correlation analysis data of vegetation functional traits and topographic factors; 3) correlation analysis data between vegetation functional traits and livestock activity intensity factors.
ZHAO Chengzhang
The ecological data of Zhangye City from 2001 to 2012 include: the reuse rate of industrial water, the comprehensive utilization rate of industrial solid, the ratio of environmental protection investment to GDP, the per capita water consumption, the share of ecological water, the use intensity of chemical fertilizer, the use intensity of pesticide, the use intensity of agricultural plastic film, and the energy consumption per unit GDP
ZHANG Dawei
According to the characteristics of the Qinghai Tibet Plateau and the principles of scientificity, systematization, integrity, operability, measurability, conciseness and independence, the human activity intensity evaluation index system suitable for the Qinghai Tibet Plateau has been constructed, which mainly includes the main human activities such as agricultural and animal husbandry activities, industrial and mining development, urbanization development, tourism activities, major ecological engineering construction, pollutant discharge, etc, On the basis of remote sensing data, ground observation data, meteorological data and social statistical yearbook data, the positive and negative effects of human activities are quantitatively evaluated by AHP, and the intensity and change characteristics of human activities are comprehensively evaluated. The data can not only help to enhance the understanding of the role of human activities in the vegetation change in the sensitive areas of global change, but also provide theoretical basis for the sustainable development of social economy in the Qinghai Tibet Plateau, and provide scientific basis for protecting the ecological environment of the plateau and building a national ecological security barrier.
ZHANG Haiyan, XIN Liangjie, FAN Jiangwen, YUAN Xiu
1) The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015m the grid resolution is 300m.2) The data of soil erosion intensity are obtained by using the Chinese soil erosion prediction model (CSLE). The formula of soil erosion prediction model includes rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover and biological measure factor, engineering measure factor and tillage measure factor. Rainfall erodibility factors are calculated from the daily rainfall data by the US Climate Prdiction Center (CPC); soil erodibility factors, engineering measures factors and tillage measures factors are obtained from the first water conservancy census data; slope length factors and slope factors are obtained by resampling after calculating 30 m elevation data; vegetation coverage and biological measures factors are obtained by combining fractional vegetation cover with land use data and rainfall erodibility proportionometer. The fractional vegetation cover is calculated by MODIS vegetation index products through pixel dichotomy. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion intensity is of great significance for studying the present situation of soil erosion in Pan third polar 65 countries and better carrying out the development policy of the area along the way.
ZHANG Wenbo
The interaction mechanism project between major road projects and the environment in western mountainous areas belongs to the major research plan of "Environment and Ecological Science in Western China" of the National Natural Science Foundation. The person in charge is Cui Peng researcher of Chengdu Mountain Disaster and Environment Research Institute, Ministry of Water Resources, Chinese Academy of Sciences. The project runs from January 2003 to December 2005. Data collected for this project: Engineering and Environmental Centrifugal Model Test Data (word Document): Consists of six groups of centrifugal model test data, namely: Test 1. Centrifugal Model Test of Soil Cutting High Slope (6 Groups) Test 2. Centrifugal Model Experiment of Backpressure for Slope Cutting and Filling (4 Groups) Test 3. Centrifugal Model Experimental Study on Anti-slide Piles and Pile-slab Walls (10 Groups) Test 4. Centrifugal Model Tests for Different Construction Timing of Slope (5 Groups) Test 5. Migration Effect Centrifugal Model Test (11 Groups) Test 6. Centrifugal Model Test of Water Effect on Temporary Slope (8 Groups) The purpose, theoretical basis, test design, test results and other information of each test are introduced in detail.
CUI Peng
Referring to the temperature-humidity index formula proposed by J.E. Oliver in 1973, the temperature-humidity index of thethe Green Silk Road Countries(GSRCs) is calculated based on the annual average temperature and relative humidity. The climate suitability assessment of human settlements of the GSRCs is carried out on the basis of the temperature-humidity index. the climate suitability of human settlements in different areas of GSRCs can be divided into five categories: Non-suitable area,Critically suitable area, Low suitable area, Moderately suitable area and High suitable area, based on the distribution characteristics of temperature-humidity index and its correlation with population distribution, according to the regional characteristics and differences of temperature and relative humidity, and referring to the physiological climate evaluation standard of temperature-humidity index.
LIN Yumei
Geographical distribution of major ecological protection and construction projects on the Tibetan plateau. There are four main projects, i.e. forest protection and construction project, grassland protection and construction project, desertification control project, soil erosion comprehensive control project. Processing method: classified summary, and the county as a unit of the regional distribution.
Da Wei
1) The dataset includes the raster data of soil erosion intensity in Pan-Third Pole 65 countries.2) The data of soil erosion intensity are obtained by using the Chinese soil loss equation (CSLE). The formula of soil erosion prediction model includes rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover and biological measure factor, engineering measure factor and tillage measure factor. Rainfall erodibility factors are calculated from the daily rainfall data by the US Climate Prdiction Center (CPC); soil erodibility factors are calculated by 250 m soil grid data; engineering measure factors are calculated based on vegetation cover, land use and rainfall erosivity ratio; tillage measure factors haven't been considered yet, and the default value is 1; slope length factors and slope factors are obtained by resampling after calculating 30 m elevation data; vegetation coverage and biological measures factors are obtained by combining fractional vegetation cover with land use data and rainfall erodibility proportionometer. The fractional vegetation cover is calculated by MODIS vegetation index products through pixel dichotomy. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion intensity is of great significance for studying the present situation of soil erosion in Pan third polar 65 countries and better implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
ZHANG Wenbo
Based on the vulnerability assessment framework of "exposure sensitivity adaptability", the vulnerability assessment index system of agricultural and pastoral areas in Qinghai Tibet Plateau was constructed. The index system data includes meteorological data, soil data, vegetation data, terrain data and socio-economic data, with a total of 12 data indicators, mainly from the national Qinghai Tibet Plateau scientific data center and the resource and environmental science data center of the Chinese Academy of Sciences. Based on the questionnaire survey of six experts in related fields, the weight of the indicators is determined by using the analytic hierarchy process (AHP). Finally, four 1km grid data are formed involving ecological exposure, sensitivity, adaptability and ecological vulnerability in the agricultural and pastoral areas of the Qinghai Tibet Plateau. The data can provide a reference for the identification of ecological vulnerable areas in the Qinghai Tibet Plateau.
ZHAN Jinyan, TENG Yanmin, LIU Shiliang
This dataset subsumes sustainable livestock carrying capacity in 2000, 2010, and 2018 and overgrazing rate in 1980, 1990, 2000, 2010, and 2017 at county level over Qinghai Tibet Plateau. Based on the NPP data simulated by VIP (vehicle interface process), an eco hydrological model with independent intellectual property of the institute of geographic sciences and nature resources research(IGSNRR), Chinese academy of Sciences(CAS), the grass yield data (1km resolution) is obtained. Grass yield is then calculated at county level, and corresponding sustainable livestock carring capacity is calculated according to the sustainable livestock capacity calculation standard of China(NY / T 635-2015). Overgrazing rate is calculated based on actual livestock carring capacity at county level.The dataset will provide reference for grassland restoration, management and utilization strategies.
MO Xingguo
The data set records the carbon dioxide emissions of 1960-2014 countries along 65 countries along the belt and road.Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.Data source:Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States.The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). The dataset contains 2 tables: CO2 emissions(kt),CO2 emissions(metric tons per capita).
XU Xinliang, Department of Energy Carbon Dioxide Information Analysis Center (CDIAC)
Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.
ZHONG Fanglei
As the “third pole” of the world, the Qinghai-Tibet Plateau (QTP) is extremely ecologically sensitive and fragile while facing increasing human activities and overgrazing. In this study, eight types of spatial data were firstly selected, including grazing intensity, Night-Time Light, population density, Gross Domestic Product (GDP) density, the ratio of cultivated land, the slope of the Normalized Difference Vegetation Index (NDVI), distance to road, and distance to town. Then, the entropy weight method was applied to determine the weight of each factor. Finally, the five-year interval human activity intensity data in 1990, 1995, 2000, 2005, 2010 and 2015 were made in the agricultural and pastoral areas of QTP through the spatial overlap method. By preparing the historical spatial datasets of human activity intensity, our study will help to explore the influence of human disturbance on the alpine ecosystems on the QTP and provide effective support for decision-making of government aiming at regional ecosystem management and sustainable development.
LIU Shiliang, SUN Yongxiu, LIU Yixuan, LI Mingqi
The Grassland Degradation Assessment Dataset in agricultural and pastoral areas of the Qinghai-Tibet Plateau (QTP) is a data set based on the 500m Global Land Degradation Assessment Data (2015), which is evaluated according to the degree of grassland degradation or improvement. In this dataset, the grassland degradation of the QTP was divided into two evaluation systems. At the first level, the grassland degradation assessment was divided into 3 types, including no change type, improvement type and degradation type. At the second level, the grassland degradation assessment on the QTP was divided into 9 types, among which the type with no change was class 1, represented by 0. There were 4 types of improvement: slight improvement (3), relatively significant improvement (6), significant improvement (9) and extremely significant improvement (12). The degradation types can be divided into 4 categories: slight degradation (-3), relatively obvious degradation (-6), obvious degradation (-9) and extremely obvious degradation (-12). This dataset covers all grassland areas on the QTP with a spatial resolution of 500m and a time of 2015. The projection coordinate system is D_Krasovsky_1940_Albers. The data are stored in TIFF format, named “grassdegrad”, and the data volume is 94.76 MB. The data were saved in compressed file format, named “500 m grid data of grassland degradation assessment in agricultural and pastoral areas of the Qinghai-Tibet Plateau in 2015”. The file volume is 2.54 MB. The data can be opened by ArcGIS, QGIS, ENVI, and ERDAS software, which can provide reference for grassland ecosystem management and restoration on the QTP.
LIU Shiliang, SUN Yongxiu, LIU Yixuan
Taking 2005 as the base year, the future population scenario was predicted by adopting the Logistic model of population. It not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted by using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation by nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data adopted the non-agricultural population. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of GDP per capita),the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.
ZHONG Fanglei
Taking 2005 as the base year, the future population scenario was predicted by adopting the logistic model of population. This model not only effectively describes the pattern of changes in population and biomass but is also widely applied in the field of economics. The urbanization rate was predicted using the urbanization logistic model. Based on the observed horizontal pattern of urbanization, a predictive model was established by determining the parameters in the parametric equation by applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data represent the non-agricultural population. The logistic model was used to predict the future gross domestic product of each county (or city), and then the economic development level of each county (or city) in each period (in terms of GDP per capita). The corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changes in China and the research area lagged behind the growth in GDP, so the changes were adjusted according to the need for future industrial structure scenarios in the research area.
YANG Linsheng, ZHONG Fanglei
The concentration data set of persistent organic pollutants in the atmosphere, lake water and fish bodies in Namco from 2012 to 2014 includes concentration time series of atmospheric gaseous organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs), atmospheric gaseous polycyclic aromatic hydrocarbons (PAHs), atmospheric particulate PAHs, dissolved persistent organic pollutants (POPs) in lake water, POPs in suspended particles of lake water and POPs in bodies of Gymnocypris namensis. The contents of the data set are all measured data. (1) The atmospheric samples were collected from the Integrated Observation and Research Station of Multisphere in Namco by the atmospheric active sampler. The flow rate of the sampler is 60 L min-1, which collects data every other day. One sample is generated every half month, and the sampling volume is approximately 600 m³. Each sample includes a glass fiber filter (GFF, 0.45 μm, Whatman) that adsorbs particulate POPs and a polyurethane foam (PUF, 7.5 x 6 cm) that collects gaseous POPs. (2) Fifteen sampling points were selected along Namco to collect surface lake water samples at a water depth of 0-1 m and with a volume of 200 L. The total suspended particulates are obtained by filtering the water samples with a 0.7 μm GFF membrane, and then the dissolved POPs in the water are collected using a solid phase extraction column packed with XAD-2. (3) Gymnocypris namensis is the most widely distributed fish in Namco. A total of 35 samples of different sizes were collected, and the concentration of POPs in the back muscle samples was analyzed. Each medium sample was prepared and analyzed by the Key Laboratory of Tibetan Environment Changes and Land Surface Processes of CAS. The sample preparation steps include Soxhlet extraction, silica-alumina column purification, removal of macromolecular impurities by a GPC column, concentration and constant volume. The analytical test instrument was a gas chromatography-mass spectrometer (GC-MS, Finnigan-Trace GC/PolarisQ) manufactured by American Thermoelectric Corporation. The column separating OCPs and PCBs was a CP-Sil 8CB capillary column (50 m × 0.25 mm × 0.25 μm), and the column separating PAHs was a DB-5MS capillary column (60 m × 0.25 mm × 0.25 μm). Sampling and laboratory analysis procedures followed strict quality control measures with lab blanks and field blanks. The detection limit of the compound is the average of the concentration of the corresponding compound in the field blank plus 3 times the standard deviation; if the compound is not detected in the field blank, the signal-to-noise ratio, 10 times the lowest concentration of the working curve, will be considered as the detection limit. Data below the detection limit are considered undetected and labeled as BDL; data marked in italics are detected by 1/2 times the detection limit. The recovery of PAHs is between 65% and 92%, the recovery of OCPs is between 64% and 112%, and the sample concentration is not corrected using recovery.
WANG Xiaoping
This dataset includes the concentrations and spatial pattern of mercury (Hg) in the soil of the southern Tibetan Plateau. Two hundred thirty nine soil samples were collected, and cold vapor atomic fluorescence spectrophotometry (CVAFS) was used to analyse the Hg contents. The limit of detection (LOD) for this method is 1.8 ng/g. The standard reference material, soil GB GSS-2, which is supplied by National Institute of Metrology P.R.China, was also analyzed for assessing the accuracy of this method, and the recoveries of this method were 84%-103%. This dataset will provide the informations of soil Hg contamination and background values over the southern Tibetan Plateau.
WANG Xiaoping
This data contains part of the economic indicators of Qinghai province and Tibet Autonomous Region. The data statistics based on provinces can be used to construct the evaluation index system for the coupling coordination relationship between urbanization and eco-environment on the Tibetan Plateau. The data of the Tibet Autonomous Region contains seven indicators, including the gross domestic product (GDP), the primary, secondary and tertiary industries, industry, construction industry, and the per capita GDP, the time span is 1951-2016. The time span of the data set of Qinghai province is from 1952 to 2015, besides the above seven indicators, there is one more indicator of Qinghai province called agriculture forwdtry animal husbandry and fishery. All data are derived from the statistical yearbook, which is calculated at current prices. The gross domestic product (GDP) for 2005-2008 has been revised based on data from the second economic census.
DU Yunyan
Temperature-humidity index (THI) was adopted to evalulate the climate suitability for the Green Silk Road. The relative humidity isone of the basic parameters to calculate THI. Refering to theTHI model of Tanget al. (2008), the multi-year average of relative humidity is calculted based on the observation data (1981-2017) of weather stations provided by National Meteorological Information Center. The multi-year average values were interpolated into the raster dataset at the resolution of 11km×1km by Kriging method based on GIS software. The climate suitability evaluation results calculated based on this dataset could highlight regional differences.
1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 5 m in the year of 2017 in Tibet. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 11 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation results and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 11 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
This data set contains data on the concentrations of persistent organic pollutants (POPs) and total suspended particulate (TSP) in the atmosphere at a station in southeastern Tibet (Lulang). The samples were collected using an atmospheric active sampler equipped with a tandem fibreglass membrane-polyurethane foam sampling head. The gaseous POPs and TSPs were collected. The sampling period for each sample was 2 weeks. The types of observed POPs include organochlorine pesticides (OCPs), polychorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). Only gaseous concentrations were detected for OCPs and PCBs, while both gaseous concentrations and particulate concentrations were detected for PAHs. All of the data contained in the data set are measurement data. The samples were collected in the field at the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The sampler was an atmospheric flow active sampler equipped with a tandem fibreglass membrane-polyurethane foam sampling head, in which the fibreglass membrane was used to collect TSPs and the polyurethane foam was used to adsorb gaseous pollutants in the atmosphere. During the sampling period, the sampler was run every other day for approximately 24 hours each time, and each sample was collected for 2 weeks. The atmospheric volume collected for each sample was 500-700 cubic metres. Both gaseous and particulate POP samples were prepared and analysed in the Key Laboratory of Tibetan Environment Changes and Land Surface Processes, CAS. The sample preparation steps included Soxhlet extraction, silica-alumina column purification, removal of macromolecular impurities by a GPC column, concentration to a defined volume, etc. The analytical test instrument was a gas chromatography/ion trap mass spectrometer (Finnigan-TRACE GC/PolarisQ) produced by Thermo Fisher Scientific. The column used to separate OCPs and PCBs was a CP-Sil 8CB capillary column (50 m × 0.25 mm × 0.25 μm), and the column used to separate PAHs was a DB-5MS capillary column (60 m x 0.25 mm x 0.25 μm). The total suspended particulate concentration in the atmosphere was determined by the gravimetric method, and the accuracy of the weighing balance was 1/100,000 g. The field samples were subjected to strict quality control with laboratory blanks and field blanks. The detection limit of a given compound was 3 times the standard deviation of the concentration of the corresponding compound in the field blank; if the compound was not detected in the field blank, the detection limit of the method was determined by the lowest concentration of the working curve. For a sample, concentrations above the detection limit of the method are corrected by subtracting the detection limit; concentrations below the detection limit of the method but higher than 1/2 times the detection limit are corrected by subtracting half the method detection limit; and concentrations below 1/2 times the detection limit are considered undetected. The recovery rate of PAH laboratory samples was between 65-120%, and that of OCPs was between 70-130%; the sample concentrations were not corrected by the recovery rate. In the table, undetected data are marked as BDL; data marked in black italics are data corrected by subtracting 1/2 the method detection limit.
WANG Xiaoping
The data set of socio-economic vulnerability parameters in the agricultural and pastoral areas of the Qinghai Tibet Plateau mainly contains the socio-economic vulnerability parameter data at county level. The data time range is from 2000 to 2015, involving 112 counties and districts in Qinghai Province and Tibet Autonomous Region. The main parameters include population density, the proportion of unit employees in the total population, the proportion of rural employees in the total population, the proportion of agricultural, forestry, animal husbandry and fishery employees in rural employees, per capita GDP, per capita savings balance of residents, per capita cultivated land area, per capita grain output, and people Average oil production, livestock stock per unit area, per capita meat production, the proportion of primary and secondary school students in the total population, and the number of hospital beds per 10000 people. The entropy weight method is used to calculate the weight of each index, and ArcGIS is used to spatialize, and finally the county scale socio-economic vulnerability parameter data is obtained. The original data is from the statistical yearbook of Qinghai Province and Tibet Autonomous Region. The data are expressed by shape file and excel file. This data set will provide reference for socio-economic vulnerability assessment and selection of typical agricultural and pastoral areas.
ZHAN Jinyan, TENG Yanmin, LIU Shiliang
1) Data content: species list and distribution data of Phrynocephalus and Eremais in Tarim Basin, including class, order, family, genus, species, and detailed distribution information including country, province, city and county; 2) Data source and processing method: Based on the field survey of amphibians and reptiles in Tarim Basin from 2008 to 2020, and recording the species composition and distribution range of Phrynocephalus and Eremias in this area; 3) Data quality description: the investigation, collection and identification of samples are all conducted by professionals, and the collection of samples information are checked to ensure the quality of distribution data; 4) Data application results and prospects: Through comprehensive analysis of the dataset, the list of species diversity and distribution can provide important data for biodiversity cataloguing in arid central Asia, and provide scientific basis for assessing biodiversity pattern and formulating conservation strategies.
GUO Xianguang
The Slope Length and Stepness Factor (LS) dataset of Pan-third pole 20 country is calculated based on the free accessed 1 arc second resolution SRTM digital elevation data (Shuttle Radar Topography Mission, SRTM; the website is http://srtm.csi.cgiar.org）. After the pre-processing such as pseudo edge removal, filtering and noise removal, the LS factor with 7.5 arc second resolution was calculated with the LS factor algorithm in CSLE model and the LS calculation tool (LS_tool) developed in this project. The LS factor data of Pan-third pole 20 countries is the fundamental data for soil erosion rate calculation based on CSLE, and it also the fuandatmental data for analyzing the erosion topographic characteristics of Pan third pole 20 countries (such as macro distribution and micro pattern of elevation, slope and slope) . The dataset if of great importance for the analysis of geomorphic characteristics and geological disaster characteristics in this area.
YANG Qinke
1) Establish the material flow analysis table and air pollutant emission table of Xining Special Steel Co., Ltd. (Xining Special Steel) in 2019 ，to provide support for the analysis and distribution of pollutant emission sources of regional iron and steel industry. 2) The data comes from the official website of Xining Special Steel, field survey and statistical data. Based on the official data and field survey results, some results are calculated by the relevant industry parameters 3) Due to the different sources of ore raw materials, the calculation is only for the steel production process in 2019 4) Xining Special Steel is a typical enterprise in the iron and steel industry of Qinghai Province. Its crude steel production is more than 90% of that of Qinghai Province. Therefore, the data represent the material flow characteristics of the iron and steel industry in Qinghai Province
LI Xiaojun
As the roof of the world, the water tower of Asia and the third pole of the world, the Qinghai Tibet Plateau is an important ecological security barrier for China and even Asia. With the rapid development of social economy, human activities have increased significantly, and the impact on the ecological environment is growing. In this paper, eight factors including cultivated land, construction land, National Road, provincial road, railway, expressway, GDP and population density were selected as the threat factors, and the attributes of the threat factors were determined based on the expert scoring method to evaluate the habitat quality of the Qinghai Tibet Plateau, so as to obtain six data sets of the habitat quality of the agricultural and pastoral areas of the Qinghai Tibet Plateau in 1990, 1995, 2000, 2005, 2010 and 2015. The production of habitat quality data sets will help to explore the habitat quality of the Qinghai Tibet Plateau and provide effective support for the government to formulate sustainable development policies of the Qinghai Tibet Plateau.
LIU Shiliang, LIU Yixuan, SUN Yongxiu, LI Mingqi
1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 30 m in the year of 2017 in Qinhai. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 11 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation result and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 11 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
ZHANG Wenbo
By archaeological investigation and excavation in Tibetan Plateau, we discovered 8 Paleolithic sites, including 151, Jiangxigou 1, Jiangxigou 2, Heimahe 1, Xiadawu, Yezere, Niamudi and Lingjiong. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified animal remains, plant macrofossil, selected some samples for radiocarbon dating and stable carbon and nitrogen isotopes. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the Paleolithic.
ZHANG Dongju , ZHANG Xiaoling, LIU Xiangjun
This dataset includes the concentrations and spatial pattern of mercury (Hg) in the foliage of the local tree species over the easteran and the southern Tibetan Plateau. Fifty-three leaf samples were collected, and cold vapor atomic fluorescence spectrophotometry (CVAFS) was used to analyse the Hg contents. The limit of detection (LOD) for this method is 1.8 ng/g. The standard reference material, foliage GB GSW-11, which is supplied by National Institute of Metrology P.R.China, was also analyzed for assessing the accuracy of this method, and the recoveries of this method were 94.6%±9.7%. This dataset will provide the informations of foliage absoprtion to Hg over the Tibetan Plateau.
WANG Xiaoping
The data content includes the spatial distribution map of the impact of agricultural development on the ecological environment in 1985, 1993, 2000, 2005, 2010 and 2015. This data set takes the impact of agricultural development on ecological environment as the evaluation objective, establishes the ecological environment evaluation index system composed of 14 indexes of 3 types of elements by using the pressure state response model, obtains the weight of each index by using the entropy weight method, and finally analyzes the correlation between each index and the corresponding evaluation level by using the matter-element analysis method to build the Qinghai Tibet Plateau base The entropy weight extension ecological environment evaluation model of pressure state response reveals the impact of agricultural activities on the ecological environment.
LI Dan
1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.
LI Guangdong
According to the method of dendrology, tree cores of Schrenk spruce (Picea schrenkiana) in central (Houxia, Urumqi) and western Tianshan Mountains (kuruning, Yili) were collected. Through the traditional method of dendrology, the sample was processed and dated according prescriptive process. We established the width chronology of Schrenk spruce (Picea schrenkiana) in central and western Tianshan Mountains. As the method of tree ring isotope, four tree cores were selected. After cleaning and air-dried, tree rings were separated five-year increments suing a scalpel under a microscope. Hg concentrations were analyzed in duplicate following the established procedures on a Leeman Hydra IIC Direct Hg Analyzer (Teledyne Leeman Labs, Hudson, NH, USA). The principle of the analytical method is cold vapor atomic absorption spectrometer after thermal decomposition and amalgamation of a gold trap following the US EPA method 7473 (USEPA 1998). The enhanced Hg pollution, especially at low-frequency, was revealed, which was consistent with the changes of global Hg deposition. In central Tianshan Mountains, Hg values showed strong anthropogenic impacts and reflected the local Hg emission loading. Compared to the ice-core Hg records on the Tibet Plateau, our outcomes presented the dramatic increasing trend after the World War Ⅱ. We suggested that tree rings in remote area can be employed to reflect the low-frequency and large-scale Hg deposition and can benefit to accurate the Hg emission inventory in China.
LIU Xiaohong
1) The data includes the soil erosion modulus of 18 watersheds with a resolution of 5 m in the year of 2017 in Thailand. 2) Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 18 watersheds of Thailand respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation result and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 18 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion intensity is of great significance for studying the present situation of soil erosion in Pan third polar region and better implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
YANG Qinke
Based on the calculated ecological environmental risk of agriculture and animal husbandry in 1985, 1990, 1995, 2000, 2010 and 2015 on the Tibetan Plateau, the fuzzy weighted Markov chain model was used to predict the ecological environmental risk without the meteorological factors.The meteorological factors data extracted from future climate model (rcp4.5) was superimposed with ecological environmental risk of agriculture and animal husbandry without the meteorological factors. The resulting risk of agriculture and animal husbandry development in 2030, 2050 and 2070 can provide scientific basis for the future development planning of agricultural and animal husbandry on the Tibetan Plateau.
LU Hongwei
Based on the ecological environmental risk data of the development of agriculture and animal husbandry in 2030, 2050 and 2070 in the Qinghai Tibet Plateau, the risk values of agriculture and animal husbandry in the six typical years of 198519901995200002010 and 2015 are calculated, and the predicted value of ecological environmental risk in 203020502070 is calculated by using the fuzzy weighted Markov chain model. The grid map of meteorological factors extracted from ArcGIS and the future climate model (rcp4.5) was superimposed to obtain the data of agricultural and animal husbandry ecological environment risk in the Tibetan Plateau in 203020502070.
LU Hongwei
Temperature-humidity index (THI) was adopted to evalulate the climate suitability for the Green Silk Road. Temperature is one of the basic parameters to calculate THI. Refering to theTHI model of Tanget al. (2008), the multi-year average of temperature is calculted based on the observation data (1981-2017) of weather stations provided by National Meteorological Information Center. The multi-year average values were interpolated into the raster dataset at the resolution of 11km×1km by Kriging method based on GIS software. The climate suitability evaluation results calculated based on this dataset could highlight regional differences.
LIN Yumei
Provide the spatial distribution of the annual emissions of BC, CH4, CO2, CO, NH3, NMVOC, NOx, OC and SO2 from agriculture, energy exploitation, industrial and fuel combustion, surface transportation, residential and commercial housing, solvent production, waste disposal and international shipping in China from 1990 to 2015, in kg/m2/yr. The spatial precision is 0.5 °, and the geographic coordinate system is WGS84. The data comes from the CEDs data set. The historical homogenized land use data of China is obtained by linear time interpolation, Chinese regional mask extraction and coordinate system transformation of the original data, and saved in geotiff file format. The methods and standards of data over the years are consistent, the coverage is complete, and the collection and processing process is traceable and reliable.
WANG Can , WANG Jiachen
Topographic relief is a comprehensive representation of regional altitude and surface cutting degree. Based on the definition and calculation formula of topographic relief under the background of China's human settlements assessment, the digital elevation model (Aster GDEM 30 m) data is resampled into 1 km, The data set includes: (1) kilometer grid spatial data of Tibetan Plateau topographic relief（ 2) Terrain suitability evaluation data of Qinghai Tibet Plateau. The data can be used to analyze the spatial difference of topographic relief of the Qinghai Tibet Plateau, which is of great significance to the study of human settlements and Natural Suitability of the Qinghai Tibet Plateau.
XIAO Chiwei, LI Peng,
1) The data is the layout of sample survey units in 65 countries of Pan-Third Polar region and western China. 2)Sample survey units were set in the pan-third pole region (70 °N-10 °S, 180 °E-180 ° W) . No samplings points were selected in the region with latitude >70 °. In the region wiht latitude of 60 ° -70 ° , sample survey units were selected in cells of 0.5 ° latitude ×1 ° Longitude (about 55km×55km-55km×38km). In the area with latitude of 40°-60°, sample survey units were selected in cells of 0.5 ° latitude×0.75 ° longitude (about 55km×63km-55km×42km). In the area with latitude <40°, sample survey units were selected in cells of 0.5 ° latitude × 0.5 ° longitude. In the Qinghai-Tibet Plateau, sample survey units were selected in cells of 0.25 ° latitude × 0.25 ° longitude. Thesample survey units deployed in the first national water conservancy survey for soil and water conservation were used in current project in five provinces including Xinjiang, Qinghai, Gansu, Sichuan and Yunnan in western China. The total number of sample survey units is 29,651, of which, 4052 are in the Qinghai-Tibet Plateau, 8771 in the western China, and 16,828 in countries outside of China. 3) The selected sample survey units is well distributed and the data quality is good.4) the layout map of sample survey units is of great significance for the study of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the area along the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
WEI Xin
The data set records the monitoring data of Xining sewage treatment plant (2013-2020). The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 39 documents, including the monitoring results of the sewage treatment plant in the second quarter of 2013, the supervision monitoring report of the wastewater from the key pollution sources of Huangyuan sewage treatment plant in the second quarter of 2019, and the audit of the monitoring data of the sewage treatment plant in the second quarter of 2014. The number of supervisory monitoring of sewage treatment plant, including 15 fields Field 1: Administrative Region Field 2: name of sewage treatment plant Field 3: receiving water body Field 4: monitoring date Field 5: name of executive standard Field 6: name of execution standard condition Field 7: Design daily capacity (T / D) Field 8: import flow (T / D) Field 9: export flow (T / D) Field 10: monitoring items Field 11: inlet concentration (mg / L) Field 12: outlet concentration (mg / L) Field 13: standard limit (mg / L) Field 14: emission unit Field 15: is it up to standard
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring report of the key industrial enterprises in Qinghai Province (2015-2020). The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 115 data files, including the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the first quarter of 2015, the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the second quarter of 2015, the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the third quarter of 2015, and the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the fourth quarter of 2015 Supervision monitoring of Huadian Datong Power Generation Co., Ltd. The key polluting enterprises involved are: Qinghai Huadian Datong Power Generation Co., Ltd., Qinghai Products Industry Investment Co., Ltd., Qilian mountain cement, Qinghai ningbei Power Generation Co., Ltd., Qinghai Qiaotou Aluminum Power Co., Ltd., Qinghai new building materials industry and Trade Co., Ltd., Qinghai Products Industry Investment Co., Ltd., Qinghai Yihua Chemical Co., Ltd., and Chalco Qinghai Branch Company, Qinghai cement, Baihe aluminum, Huaneng Xining Thermal Power Co., Ltd., Huanghe Xinye, Jihua Jiangyuan, Qinghai Huanghe Jianiang Beer Co., Ltd., Qinghai Jiangcang Energy Development Co., Ltd., Qinghai ningbei Power Generation Co., Ltd., Qinghai Tianlu Dairy Co., Ltd., Qinghai xiaoxiniu Dairy Co., Ltd., Western zinc and Xining Special Steel Co., Ltd Company, Asia silicon (Qinghai) Co., Ltd., Salt Lake Haina, yuntianhuazhong, State Power Investment Group Xi'an solar power generation Co., Ltd., upper Yellow River Hydropower Development Co., Ltd., Qinghai Electronic Materials Industry Development Co., Ltd., Qinghai Yellow River Jianiang Beer Co., Ltd., Qinghai Pharmaceutical Factory Co., Ltd., Qinghai Western Indium Industry Co., Ltd., Qinghai Hongyang Cement Co., Ltd Ltd., Qinghai Jieshen Environmental Energy Industry Co., Ltd., etc. among Monitoring point: flue gas outlet of rotary kiln. Monitoring items: particulate matter, sulfur dioxide, nitrogen oxides, flue gas flow. Monitoring frequency: three samples per production cycle under normal operation condition Contents of daily monitoring: cod, pH, COD, cod
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring report of the key industrial enterprises in Qinghai Province (2015-2020). The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 115 data files, including the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the first quarter of 2015, the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the second quarter of 2015, the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the third quarter of 2015, and the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the fourth quarter of 2015 Supervision monitoring of Huadian Datong Power Generation Co., Ltd. The key polluting enterprises involved are: Qinghai Huadian Datong Power Generation Co., Ltd., Qinghai Products Industry Investment Co., Ltd., Qilian mountain cement, Qinghai ningbei Power Generation Co., Ltd., Qinghai Qiaotou Aluminum Power Co., Ltd., Qinghai new building materials industry and Trade Co., Ltd., Qinghai Products Industry Investment Co., Ltd., Qinghai Yihua Chemical Co., Ltd., and Chalco Qinghai Branch Company, Qinghai cement, Baihe aluminum, Huaneng Xining Thermal Power Co., Ltd., Huanghe Xinye, Jihua Jiangyuan, Qinghai Huanghe Jianiang Beer Co., Ltd., Qinghai Jiangcang Energy Development Co., Ltd., Qinghai ningbei Power Generation Co., Ltd., Qinghai Tianlu Dairy Co., Ltd., Qinghai xiaoxiniu Dairy Co., Ltd., Western zinc and Xining Special Steel Co., Ltd Company, Asia silicon (Qinghai) Co., Ltd., Salt Lake Haina, yuntianhuazhong, State Power Investment Group Xi'an solar power generation Co., Ltd., upper Yellow River Hydropower Development Co., Ltd., Qinghai Electronic Materials Industry Development Co., Ltd., Qinghai Yellow River Jianiang Beer Co., Ltd., Qinghai Pharmaceutical Factory Co., Ltd., Qinghai Western Indium Industry Co., Ltd., Qinghai Hongyang Cement Co., Ltd Ltd., Qinghai Jieshen Environmental Energy Industry Co., Ltd., etc. among Monitoring point: flue gas outlet of rotary kiln. Monitoring items: particulate matter, sulfur dioxide, nitrogen oxides, flue gas flow. Monitoring frequency: three samples per production cycle under normal operation condition Contents of daily monitoring: cod, pH, COD, cod
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring report of the key industrial enterprises in Qinghai Province (2015-2020). The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 115 data files, including the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the first quarter of 2015, the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the second quarter of 2015, the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the third quarter of 2015, and the supervision monitoring of Qinghai Huadian Datong Power Generation Co., Ltd. in the fourth quarter of 2015 Supervision monitoring of Huadian Datong Power Generation Co., Ltd. The key polluting enterprises involved are: Qinghai Huadian Datong Power Generation Co., Ltd., Qinghai Products Industry Investment Co., Ltd., Qilian mountain cement, Qinghai ningbei Power Generation Co., Ltd., Qinghai Qiaotou Aluminum Power Co., Ltd., Qinghai new building materials industry and Trade Co., Ltd., Qinghai Products Industry Investment Co., Ltd., Qinghai Yihua Chemical Co., Ltd., and Chalco Qinghai Branch Company, Qinghai cement, Baihe aluminum, Huaneng Xining Thermal Power Co., Ltd., Huanghe Xinye, Jihua Jiangyuan, Qinghai Huanghe Jianiang Beer Co., Ltd., Qinghai Jiangcang Energy Development Co., Ltd., Qinghai ningbei Power Generation Co., Ltd., Qinghai Tianlu Dairy Co., Ltd., Qinghai xiaoxiniu Dairy Co., Ltd., Western zinc and Xining Special Steel Co., Ltd Company, Asia silicon (Qinghai) Co., Ltd., Salt Lake Haina, yuntianhuazhong, State Power Investment Group Xi'an solar power generation Co., Ltd., upper Yellow River Hydropower Development Co., Ltd., Qinghai Electronic Materials Industry Development Co., Ltd., Qinghai Yellow River Jianiang Beer Co., Ltd., Qinghai Pharmaceutical Factory Co., Ltd., Qinghai Western Indium Industry Co., Ltd., Qinghai Hongyang Cement Co., Ltd Ltd., Qinghai Jieshen Environmental Energy Industry Co., Ltd., etc. among Monitoring point: flue gas outlet of rotary kiln. Monitoring items: particulate matter, sulfur dioxide, nitrogen oxides, flue gas flow. Monitoring frequency: three samples per production cycle under normal operation condition Contents of daily monitoring: cod, pH, COD, cod
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring data of wastewater, waste gas and sewage treatment plants in Xining City (2013-2018). The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 50 documents, which are: the audit of waste gas monitoring data of state-controlled enterprises in the fourth quarter of 2013, the audit of waste water monitoring data of state-controlled enterprises in the fourth quarter of 2013, the audit of waste gas monitoring data of state-controlled enterprises in Xining in the fourth quarter of 2014, and the audit of waste water monitoring data of state-controlled enterprises in Xining in the fourth quarter of 2014. The number of supervisory monitoring of sewage treatment plant, including 15 fields Field 1: Administrative Region Field 2: name of sewage treatment plant Field 3: receiving water body Field 4: monitoring date Field 5: name of executive standard Field 6: name of execution standard condition Field 7: Design daily capacity (T / D) Field 8: import flow (T / D) Field 9: export flow (T / D) Field 10: monitoring items Field 11: inlet concentration (mg / L) Field 12: outlet concentration (mg / L) Field 13: standard limit (mg / L) Field 14: emission unit Field 15: is it up to standard Waste gas monitoring data audit table, a total of 16 fields Field 1: Administrative Region Field 2: enterprise name Field 3: industry name Field 4: monitoring point name Field 5: name of executive standard Field 6: monitoring date Field 7: operating load (%) Field 8: flow (m3 / h) Field 9: flue gas temperature (℃) Oxygen content: 10% Field 11: monitoring item name Field 12: measured concentration (mg / m3) Field 13: standard limit (mg / m3) Field 14: emission unit Field 15: is it up to standard Field 16: excess multiple The number of wastewater supervision monitoring, including 16 fields Field 1: Administrative Region Field 2: industry name Field 3: receiving water body Field 4: monitoring point name Field 5: name of executive standard Field 6: name of execution standard condition Field 7: monitoring date Field 8: production load (%) Field 9: monitoring point flow (T / D) Field 10: monitoring item name Field 11: pollutant concentration Field 12: standard limits Field 13: Unit Field 14: is it up to standard Field 15: excess multiple Field 16: enterprise name
Department of Ecology and Environment of Qinghai Province
The data set records the supervisory monitoring results of sewage treatment plants in Zeku County, Gangcha County, Haiyan County, Qilian County, Henan County, Jianzha county and Tongren County (2020.1-2020.6). The data is collected from the Department of ecological environment of Qinghai Province. The data set contains seven documents, namely: supervisory monitoring of Gangcha sewage treatment plant in 2020.pdf, In 2020, Haiyan County sewage treatment plant of Haibei Prefecture was monitored.pdf; in 2020, Qilian sewage treatment plant was monitored.pdf; in the first half of 2020, Jianzha county sewage treatment plant was monitored; in the first half of 2020, Tongren County sewage treatment plant was monitored; in the first half of 2020, Zeku County sewage treatment plant was monitored; in the first half of 2020, Henan county sewage treatment plant was monitored The results of supervision monitoring. The data monitoring entrusted units are Zeku County, Gangcha County, Haiyan County, Qilian County, Henan County, Jianzha county and Tongren County Environmental Bureau; Detection point: inlet and outlet of sewage treatment plant Detection items: water temperature, flow rate, pH value, chromaticity, chemical oxygen demand, five-day biochemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, lead, cadmium, chromium, sclera, arsenic, suspended solids, hexavalent chromium, petroleum, animal and vegetable oil, anionic surfactant, fecal coliform, alkyl mercury, free chlorine (free residual chlorine), a total of 22 items Detection frequency: 1. Water temperature, pH value and flow rate are sampled in 24h, measured on site, and measured once every 2h (the average value of data is measured); 2. Chemical oxygen demand powder, suspended solids, five-day biochemical oxygen demand, petroleum, animal and vegetable oil, fecal coliform group are sampled by 24h, once every 2h, and all items are collected and packed separately (the average value of data is determined) 3. The other 13 items were sampled every 2 hours and mixed samples were taken for 24 hours
Department of Ecology and Environment of Qinghai Province
The data set recorded one belt, one road, 2002-2016 years' fertilizer and pesticide consumption data in 65 countries. Fertilizer and pesticide consumption refers to the amount of plant nutrients and pesticides consumed per unit of cultivated land. Fertilizer products include nitrogen, potassium and phosphate (including phosphate rock powder), and traditional nutrients animal and plant fertilizers are not included. Data source: Food and Agriculture Organization, electronic files and web site. Fertilizer and pesticide are the main sources of agricultural chemical pollution, which pose a serious threat to the agricultural ecological environment and the sustainable development of agricultural economy. The data set reflects one belt, one road, along the line of fertilizer and pesticide use, and can provide data support for the research on agricultural ecological environment and other related research. The data set contains two data tables: fertilizer consumption (kg / ha of cultivated land) and pesticide consumption (kg / ha of cultivated land).
XU Xinliang
The waste gas monitoring data of provincial and municipal wastewater treatment units from 2020 to 2013 were collected. The data is collected from the Department of ecological environment of Qinghai Province, and the data set contains 106 data tables, which are respectively: the results of supervisory monitoring data of waste water, waste gas and sewage treatment plant of provincial controlled enterprises in Haidong city from 2013 to 2020, the results of supervisory monitoring data of waste water, waste gas and sewage treatment plant of municipal controlled enterprises in Haidong city from 2013 to 2020, and the results of supervisory monitoring data of waste water, waste gas and sewage treatment plant of state controlled enterprises in Haidong city from 2013 to 2020 The results of supervisory monitoring data of management plant, and the results of supervisory monitoring data of waste water, waste gas and sewage treatment plant of provincial enterprises in Haidong county from 2013 to 2020. The data table structure is different. The number of supervisory monitoring of sewage treatment plant, including 15 fields Field 1: Administrative Region Field 2: name of sewage treatment plant Field 3: receiving water body Field 4: monitoring date Field 5: name of executive standard Field 6: name of execution standard condition Field 7: Design daily capacity (T / D) Field 8: import flow (T / D) Field 9: export flow (T / D) Field 10: monitoring items Field 11: inlet concentration (mg / L) Field 12: outlet concentration (mg / L) Field 13: standard limit (mg / L) Field 14: emission unit Field 15: is it up to standard Waste gas monitoring data audit table, a total of 16 fields Administrative Region: 1 field Field 2: enterprise name Field 3: industry name Field 4: monitoring point name Executive standard field name: 5 Field 6: monitoring date Field 7: operating load (%) Field 8: flow (m3 / h) Field 9: flue gas temperature (℃) Oxygen content: 10% Field 11: monitoring item name Field 12: measured concentration (mg / m3) Field 13: standard limit (mg / m3) Field 14: emission unit Field 15: is it up to standard Field 16: excess multiple The number of wastewater supervision monitoring, including 16 fields Field 1: Administrative Region Field 2: industry name Field 3: receiving water body Field 4: monitoring point name Field 5: name of executive standard Field 6: name of execution standard condition Field 7: monitoring date Field 8: production load (%) Field 9: monitoring point flow (T / D) Field 10: monitoring item name Field 11: pollutant concentration Field 12: standard limits Field 13: Unit Field 14: is it up to standard Field 15: excess multiple Field 16: enterprise name
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring data of waste gas from state-controlled enterprises and thermal power enterprises in Xining city of Qinghai Province from 2013 to 2017. The data set includes 11 data tables and 3 PDF data files, which are respectively: monitoring results of Qinghai provincial waste gas control enterprises in the first quarter of 2013, supervisory monitoring data of Qinghai Huadian Datong Power Generation Co., Ltd. in the second half of 2017, pollution source monitoring data of Qinghai Huadian Datong Power Generation Co., Ltd. in the first quarter of 2017, waste gas monitoring data audit of thermal power plants in the fourth quarter of 2013, and fourth quarter of 2014 Waste gas monitoring data audit of thermal power plant. There are 16 fields in the waste gas monitoring data audit table Field 1: Administrative Region Field 2: enterprise name Field 3: industry name Field 4: monitoring point name Field 5: name of executive standard Field 6: monitoring date Field 7: operating load (%) Field 8: flow (m3 / h) Field 9: flue gas temperature (℃) Oxygen content: 10% Field 11: monitoring item name Field 12: measured concentration (mg / m3) Field 13: standard limit (mg / m3) Field 14: emission unit Field 15: is it up to standard Field 16: excess multiple
Department of Ecology and Environment of Qinghai Province
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