The data set records the monitoring data of hexavalent chromium content in zhangshengcaijiajing, shangbaobao village, Changning Town, Datong County, Qinghai Province from 2002 to 2015. The data is from the official website of the Department of ecological environment of Qinghai Province. The data set contains five data tables, which are: hexavalent chromium content of zhangshengcaijiajing in shangbaobao village, Changning Town, Datong County from 2002 to 2011, hexavalent chromium content of zhangshengcaijiajing in shangbaobao village, Changning Town, Datong County from 2002 to 2012, hexavalent chromium content of zhangshengcaijiajing in shangbaobao village, Changning Town, Datong County from 2002 to 2013, hexavalent chromium content of zhangshengcaijiajing in shangbaobao village, Changning Town, Datong County from 2002 to 2014, and hexavalent chromium content of zhangshengcaijiajing in Datong County from 2002 to 2015 The structure of the data sheet is the same. Each data table has two fields: Field 1: year Field 2: content (mg / L)
Department of Ecology and Environment of Qinghai Province
The data set records the supervisory monitoring report of Xining Power Generation Branch of the upper Yellow River Hydropower Development Co., Ltd., covering the period from 2017 to 2018. The data is collected from the Department of ecological environment of Qinghai Province, and the data set contains four PDF files, which are: supervision monitoring of Xining Power Generation Branch of upper Yellow River Hydropower Development Co., Ltd. in the first half of 2017, supervision monitoring of Xining Power Generation Branch of upper Yellow River Hydropower Development Co., Ltd. in the second half of 2017, and supervision monitoring of Xining Power Generation Branch of upper Yellow River Hydropower Development Co., Ltd. in 2018 Supervision monitoring in the first half of the year, and supervision monitoring in the second half of 2018 of Xining Power Generation Branch of upper Yellow River Hydropower Development Co., Ltd. The basic information of monitoring points, monitoring methods, and monitoring data of Qinghai Province are released by Qinghai provincial monitoring center.
Department of Ecology and Environment of Qinghai Province
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
This data set includes the concentration and distribution data of main persistent organic pollutants in environmental media of Qilian Mountains. Samples were collected from the Qilian Mountains and its surrounding areas in May 2018. The samples were prepared by Soxhlet extraction purification concentration and determined by gas chromatography ion trap mass spectrometry. The target compounds include organochlorine pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, etc. Mirex and pcb-30 were added as recovery markers during sample pretreatment. PCNB and PCB-209 are the internal standards for sample testing. The recoveries of the samples are generally between 60% and 101%.
GONG Ping, 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) The data include the emission information of various pollutants (CO2, Co, CH4, NOx, SO2, PM2.5, PM10) from Pan third pole solid civil sources. The data are sorted according to China and other pan third polar regions. 2) This data is based on the pan third pole emission inventory of solid civil sources, and the population data of 1km * 1km (2017) provided by landscan is used for grid distribution. 3) The data format is shpfile format, which is grid emission data with high spatial resolution. 4) The data can provide data support for the study of pollutant emission in the pan third polar region.
WANG Shuxiao
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
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
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
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
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
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
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
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
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
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
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
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
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