The gridded desertification risk data of Iranian plateau in 2019 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Iranian plateau in 2019.
XU Wenqiang
Guided by the theory of plate tectonics, paleogeography, petroliferous basin analysis and sedimentary basin dynamics, we have collected a large number of data and achievements of geological research and petroleum geology in recent years, including strata, sedimentation, paleontology, paleogeography, paleoenvironment, paleoclimate, structure, oil and gas (potash) geology and other basic materials, especially paleomagnetism, Paleogene Based on the data of detrital zircon and geochemistry, combined with the results of typical measured stratigraphic sections, the lithofacies and climate paleogeographic pattern of Cretaceous were restored and reconstructed, and two lithofacies paleogeographic maps of early and late Cretaceous of Pan tertiary and two climate paleogeographic maps of early and late Cretaceous of Pan tertiary were obtained, aiming at discussing the influence of paleogeography, paleostructure and paleoclimate In order to reveal the geological conditions and resource distribution of oil and gas formation, and provide scientific basis and technical support for China's overseas and domestic oil and gas exploration deployment.
LI Yalin
The data set is a 2015 heat wave risk data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of year. Heat wave risk refers to the probability or loss possibility of harmful consequences caused by the interaction between heat wave hazard (possible heat wave events in the future), heat wave exposure (total population, livelihood and assets in the area where heat wave events may occur) and heat wave vulnerability (the tendency of the disaster bearing body to suffer adverse effects when affected by heat wave events) . The risk assessment method of heat wave is "hazard-exposure-vulnerability". The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.
YANG Fei YIN Cong
The data set is a 2015 heat wave hazard, exposure and vulnerability data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of yearly. Heat wave hazard is an index to measure the severity of heat wave event, which is expressed by surface temperature; heat wave exposure refers to the degree that human, livelihood and economy may be adversely affected, which is expressed by nighttime lighting data, and population density. The population older than 65 and younger than 5 years old constitute vulnerable groups; heat wave vulnerability is a measure of increased / reduced risk in the environment. The distance from road / hospital and ambulance station / water body, NDVI, impervious layer and slum area are used to represent the vulnerability of high temperature heat wave. The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.
YANG Fei YIN Cong
The data of farmland distribution on the Qinghai-Tibet Plateau were extracted on the basis of the land use dataset in China (2015). The dataset is mainly based on landsat 8 remote sensing images, which are generated by manual visual interpretation. The land use types mainly include the cultivated land, which is divided into two categories, including paddy land (1) and dry land (2). The spatial resolution of the data is 30m, and the time is 2015. The projection coordinate system is D_Krasovsky_1940_Albers. And the central meridian was 105°E and the two standard latitudes of the projection system were 25°N and 47°N, respectively. The data are stored in TIFF format, named “farmland distribution”, and the data volume is 4.39GB. The data were saved in compressed file format, named “30 m grid data of farmland distribution in agricultural and pastoral areas of the Qinghai-Tibet Plateau in 2015”. The data can be opened by ArcGIS, QGIS, ENVI, and ERDAS software, which can provide reference for farmland ecosystem management on the QTP.
LIU Shiliang SUN Yongxiu 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
The birds along elevation gradients in Gangrigabu Mountains were investigated by point count method. With a 400-meter elevational gradient, elevation zones were set up in the survey area. Five elevation zones were built in the north slope from TongMai Town to Galong Temple in Bome County, and 8 elevation zones were built in the south slope from Jiefang Bridge to Galongla in Medog County. So that we can make clear about the pattern and maintenance mechanism of bird diversity along elevation gradients in this region. The data of bird diversity and distribution will be used to further explore the key scientific issues such as the impact of climate change on bird diversity and adaptation strategies, and the response and protection strategies of bird species diversity under the global climate change.
YANG Xiaojun
This data set includes apatite and zircon (U-Th) / He ages, apatite fission-track (AFT) ages of the Yalong River thrust belt, which will be continuously updated in the future. The first part is the apatite and zircon He and apatite fission-track data from the Yunongxi fault, a branch fault in the hinterland of the Yalong River thrust belt. The second part of the data is from the Jinping Shan-Muli fault, a branch of the Yalong River thrust belt, including apatite and zircon He ages data. The data results are concentrated, which well constrain the evolution of the Yalong River thrust belt and provide a high-quality chronological basis for exploring its role in the process of plateau expansion.
ZHANG Huiping
From October to November 2020, we used both live traps and camera traps to collect mammal diversity and distributions along the elevational gradients at the Yarlung Zangbo Grand Canyon National Nature Reserve. We set trap lines for small mammals inventory, with a total of 8000 live trap nights. We collected 526 individuals and1052 tissue samples of small mammals during the field sampling. We also retrived images of 130 camera traps placed between May 2020 and October 2020. We obtained 4218 pictures of wild animals,25 species of large and medium mammals were recorded.. The camera traps were reset in the same locations after renew batteries and memory cards. Small mammal data consist of richness, abundance, traits, environmental gradients etc, and could be used to model relationship between environmental gradients and traits concatenated by richness matrix. Camera trap data could inventory endangered species in the region, and provide information to identify biodiversity hotspots and conservation priorities.
LI Xueyou
This dataset includes the observation data from 01 Jan. 2019 through 31 Dec. 2018, collected by lysimeters, which are located at 115.788 E, 40.349 N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost data were replaced by -6999; 2) deleting the abnormal data; 3) deleting the outlier data; 4) keeping the consistent date and time format (e.g.2018-6-10 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xls.
LIU Shaomin ZHU Zhongli XU Ziwei
This dataset includes the observation data from 01 Jan. 2019 through 31 Dec. 2019, collected by lysimeters, which are located at 115.788E, 40.349N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost data were replaced by -6999; 2) deleting the abnormal data; 3) deleting the outlier data; 4) keeping the consistent date and time format (e.g. 2019-01-01 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xls.
LIU Shaomin ZHU Zhongli XU Ziwei
1) Data content: Paleomagnetic data, magnetic index data, major element percentage data and chemical weathering index can establish the paleomagnetic age framework of the Dahonggou section and restore the precipitation change and chemical weathering history in geological history. 2) Data sources and processing methods The data source is experimental data. Paleomagnetic data: a cylindrical sample of 2x2x2cm was drilled with a small gasoline drill and measured with a low-temperature superconducting magnetometer in a magnetic shielding room. Magnetic data: the samples collected in the field were ground into fine particles by mortar and put into 2x2x2 non-magnetic plastic box, and tested by kappa bridge susceptibility meter, pulse magnetometer and rotating magnetometer. Mass percentage content and chemical weathering index data of major elements in the whole sample and particle size fraction: firstly, the whole sample and particle size fraction sample were pretreated with acetic acid and hydrogen peroxide to remove carbonate and organic matter, and then pressed into a round cake with a diameter of about 4cm and a thickness of about 8mm by a pressure apparatus, and finally XRF fluorescence analysis was carried out. 3) Data quality The sample collection and experimental processing are carried out according to strict standards, and the data quality is reliable. 4) Data application achievements and Prospects Three SCI papers were published using this set of data, one of which is Ni.
NIE Junsheng
This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2019. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux (3 duplicates, -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2019-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin XIAO Qing XU Ziwei BAI Junhua
This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2018. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux (3 duplicates, -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin XIAO Qing XU Ziwei BAI Junhua
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to October 24 in 2019. The site (115.7923° E, 40.3574°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&EC150) was 0 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. There were lots of negative values of H2O density in winter where filling by -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin XIAO Qing XU Ziwei BAI Junhua
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2018. The site (115.7923° E, 40.3574°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&EC150) was 0 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. There were lots of negative values of H2O density in winter where filling by -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin XIAO Qing XU Ziwei BAI Junhua
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 3 in 2019. The site (115.7880° E, 40.3491° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin XU Ziwei
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2018. The site (115.7880° E, 40.3491° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin XU Ziwei
This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2019. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin XU Ziwei
This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2017. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2017-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Yang et al. (2015) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin XU Ziwei
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2019. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin XU Ziwei
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2018. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin XU Ziwei
The data set records the surface water quality assessment data set of the Yangtze River mainstream (2008.3-2020.6). The data are collected from Yushu ecological environment bureau. The data set contains 226 files, including: water quality assessment of surface water in June 2010, water quality assessment of surface water in July 2010, water quality assessment of surface water in August 2010, water quality assessment of surface water in August 2011, and water quality assessment of surface water in April 2012. Each data table has seven fields: Field 1: monitoring section Field 2: classification of water environment functional areas Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period of last year
Department of Ecology and Environment of Qinghai Province
The data set records the water quality evaluation results of the monitoring sections of the Yangtze River, Yellow River and Huangshui (2010-2012). The data is collected from Yushu ecological environment bureau. The data set contains 18 files, which are: water quality assessment of national control section of Yangtze River in April 2010, water quality assessment of national control section of Yangtze River in May 2010, water quality assessment of national control section of Yangtze River in September 2010, water quality assessment of national control section of Yangtze River in October 2010, etc. the data table structure is the same. There are seven fields in each data table Field 1: monitoring section Field 2: classification of water environment functional areas Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period of last year
Ecological Environment Bureau of Yushu Prefecture
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 records the comparison of natural and man-made disaster losses in Qinghai Province from 2011 to 2018. The data is collected from the Department of natural resources of Qinghai Province. The data set contains 12 data tables, which are: comparison of natural and man-made disasters in 2011, natural and man-made disasters in 2012, natural and man-made disasters in 2013, and natural and man-made disasters in 2014 The structure of the data table is the same, including two fields: Field 1: disaster causes Field 2: Proportion It is classified according to human factors and natural factors
Department of Natural Resources 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 monthly air quality report of Yushu prefecture (2017.7-2019.12). The data is collected from Yushu Ecological Environment Bureau, and the data set contains six files, which are: monitoring data report form of Yushu environmental monitoring station from January to December in 2019, monthly air quality report of Yushu, monthly air quality report of Yushu (July 2017), monthly air quality report of Yushu (August 2017), monthly air quality report of Yushu (September 2017) Yushu Monthly air quality report (October 2017). The data table contains 10 fields: Field 1: City Field 2: site name Field 3: time Field 4: sulfur dioxide μ g / m3 Field 5: PM10 μ g / m3 Field 6: nitrogen dioxide μ g / m3 Field 7: NOx μ g / m3 Field 8: PM2.5 μ g / m3 Field 9: carbon monoxide mg / m3 Field 10: ozone 8h μ g / m3
Ecological Environment Bureau of Yushu Prefecture
This data set records the statistical bulletin of national economic and social development of Yushu prefecture in Qinghai Province in 2019. The data set statistics is from Qinghai Forestry and grass Bureau. The data set contains a word file, which is the statistical bulletin of national economic and social development of Yushu prefecture in 2019. The communique covers the development of agriculture and animal husbandry, investment in fixed assets, domestic trade and tourism, social security, finance and finance, education and postal services, population and people's living conditions, etc. The data of 2019 in this bulletin are all preliminary statistical data; The GDP, the added value of various industries and the per capita GDP are calculated according to the "Regulations on the division of three industries" formulated by the National Bureau of statistics in 2012, the absolute number is calculated according to the current price, and the growth rate is calculated according to the comparable price; the statistical caliber of fixed assets investment is the fixed assets investment project with a planned investment of 5 million yuan or more.
Qinghai Provincial Bureau of Statistics
The data set records the public data of atmospheric environmental monitoring in Yushu city from July 2016 to June 2017. The data is collected from the ecological environment bureau of Yushu prefecture. The data set contains 11 documents, which are: atmospheric environment publicity of Yushu City in July 2016, atmospheric environment publicity of Yushu City in August 2016, atmospheric environment publicity of Yushu City in September 2016 Yushu City in June 2017 atmospheric environment publicity, etc. The data content includes: the total monitoring days, the total effective monitoring days, the proportion of grade I, the proportion of grade I to the total monitoring days, the proportion of grade II, the proportion of grade II to the total monitoring days, and the excellent and good rate of monthly air quality.
Ecological Environment Bureau of Yushu Prefecture
The data set records the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in 2016. Data statistics from the Department of natural resources of Qinghai Province, the data set contains 16 data tables, which are: water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2016, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2016, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the third quarter of 2016, and water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the fourth quarter of 2016 Water quality of drinking water sources In the second half of 2016, the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province, the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second half of 2016, the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2017, and the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2017 The quality of drinking water sources, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the third quarter of 2017, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the fourth quarter of 2017, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2018, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2018, and the quality of centralized drinking water sources in Qinghai Province in the third quarter of 2018 Water quality of centralized drinking water sources in county-level cities and towns, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the fourth quarter of 2018, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2020, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2020, and water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the third quarter of 2020, The data table has the same structure. Each data table has six fields: Field 1: serial number Field 2: city name Field 3: water source name Field 4: water source type Field 5: compliance type
Department of Natural Resources of Qinghai Province
The data set records the information disclosure data (2018) of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level in Xining city. The data statistics are from the Department of ecological environment of Qinghai Province, and the data set contains three documents, which are respectively: information disclosure form of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level of Xining City in the first quarter of 2018, information disclosure form of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level of Xining City in the second quarter of 2018, information disclosure form of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level of Xining City in the second quarter of 2018 In the second half of 2018, the structure of the data sheet is the same. There are 10 fields in each data table Field 1: serial number Field 2: name of water source Field 3: water level Field 4: water source type Field 5: monitoring unit Field 6: number of monitoring indicators Field 7: monitoring frequency Field 8: evaluation criteria Field 9: pass rate Field 10: public period
Department of Ecology and Environment of Qinghai Province
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 dynamic statistical data of groundwater level in the monitoring area of Ping'an district (Ping'an County) of Xining city from 2014 to 2018. The data is collected from the Department of natural resources of Qinghai Province, and the data set contains five data tables, which are: the groundwater level dynamic of Haidong monitoring area in 2014, the groundwater level dynamic statistical table of Ping'an monitoring area in 2015, the groundwater level dynamic statistical table of Ping'an monitoring area in 2016, the groundwater level dynamic statistical table of Ping'an monitoring area in 2017, and the groundwater level dynamic statistical table of Ping'an monitoring area in 2018 Sketch Map. The data table has the same structure and contains four fields Field 1: year Field 2: n16 Field 3: n34 Field 4: N46
Department of Natural Resources of Qinghai Province
The data set records the monthly air quality report of Xining from 2018 to 2020 in Qinghai Province. The dataset contains 146 files, which are: Qinghai environmental protection - Xining air quality monthly report - January 2012, Qinghai environmental protection - Xining air quality monthly report - March 2012, Qinghai environmental protection - Xining air quality monthly report - April 2012, Qinghai environmental protection - Xining air quality monthly report - may 2012, Qinghai environmental protection - Xining air quality monthly report - June 2012, Qinghai environmental protection - Xining air quality monthly report - June 2012 Quality monthly report - July 2012, Qinghai environmental protection - Xining air quality monthly report - August 2012, Qinghai environmental protection - Xining air quality monthly report - September 2012, Qinghai environmental protection - Xining air quality monthly report - October 2012 Qinghai environmental protection - Xining air quality monthly report - June 2020, etc. The data provides the proportion of excellent days and the change of excellent days.
Department of Ecology and Environment of Qinghai Province
The data set records the air quality quarterly report of Xining in Qinghai Province from 2008 to 2016 (Qinghai environmental protection). The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 20 PDF files, 4 data tables and 7 word documents, which are respectively: Qinghai environmental protection - Xining air quality quarterly report - the first quarter of 2012, Qinghai environmental protection - Xining air quality quarterly report - the second quarter of 2012, Qinghai environmental protection - Xining air quality quarterly report - the third quarter of 2012, Qinghai environmental protection - Xining air quality quarterly report - In the fourth quarter of 2012, the data table structure is the same. The data table contains three fields: Field 1: level Field 2: days Field 3: proportion in total monitoring days (%)
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
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 monitoring data set of surface water quality in Xining city of Qinghai Province was collected from July, 2015 to July, 2015. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 15 data tables, which are: surface water quality of Xining City in July 2015, surface water quality of Xining City in November 2015, surface water quality of Xining City in January 2016, and surface water quality of Xining City in February 2016. The data table structure is the same. There are six fields in each data table, such as the monitoring section water quality table of Xining surface water in July 2015 Field 1: serial number Field 2: section name Field 3: executive standard level Field 4: actual water quality grade Field 5: over standard items
Department of Ecology and Environment of Qinghai Province
The data set records the statistical data of groundwater levels in Beichuan, Xichuan and Nanchuan of Xining City (2012-2018). The data is collected from the Department of natural resources of Qinghai Province. The data set contains 31 data tables, including the groundwater level of Nanchuan in Xining City in 2011, the groundwater level of Beichuan in Xining City in 2011, the groundwater level of Xichuan and xinachuan in Xining City in 2011, and the groundwater level of Beichuan in Xining City in 2012. The data are grouped by year, and the unit is meter (m). The data table has the same structure and contains five fields Field 1: year G9103: Field Field 3: G31 Field 4: G23 Field 5: G27
Department of Natural Resources of Qinghai Province
The data set records the ambient air quality of Xining, and the data statistics are from the Department of ecological environment of Qinghai Province. The data set includes one data table, which is the ambient air quality of Xining from 2007 to 2008, and the data table structure is the same. There are three fields in each data table Field 1: level Field 2: days Field 3: proportion of test days The classification of ambient air functional areas, standard classification, pollutant items, average time and concentration limits, monitoring methods, effectiveness provisions of data statistics, implementation and supervision in the data sheet are in line with the relevant provisions of the ambient air quality standard (gb3095-2012).
Department of Ecology and Environment of Qinghai Province
The data set records the main distribution of sudden geological disasters in Qinghai Province from 2011 to 2018. The data are collected from the Department of ecological environment of Qinghai Province. The data set contains seven tables, which are: the main distribution of sudden geological disasters in 2011, 2012, 2014, 2015 and 2016 Distribution statistics table, 2017 Qinghai Province sudden geological disasters distribution table, 2018 Qinghai Province sudden geological disasters distribution table, the data table structure is the same. Each data table has five fields, such as the statistical table of the main distribution of sudden geological disasters in Qinghai Province in 2016 Field 1: county (city) Field 2: landslide Field 3: collapse Field 4: debris flow Field 5: loess collapsibility
Department of Ecology and Environment of Qinghai Province
The data set records the comparison of direct economic losses caused by geological disasters in Qinghai Province from 2011 to 2018. The data is collected from the Department of ecological environment of Qinghai Province, and the data set contains 8 data tables, which are: direct economic losses caused by sudden geological disasters in 2011, direct economic losses caused by sudden geological disasters in 2012, comparison chart of direct economic losses caused by sudden geological disasters in 2013 and comparison chart of direct economic losses caused by geological disasters in 2014 The statistical table of direct economic losses caused by sudden geological disasters in Qinghai Province in 2015, the statistical table of direct economic losses caused by sudden geological disasters in Qinghai Province in 2016, the comparison of direct economic losses caused by sudden geological disasters in Qinghai Province in 2017, and the comparison chart of direct economic losses caused by sudden geological disasters in Qinghai Province in 2018 have the same data table structure. Each data table has two fields, such as the comparison chart of direct economic losses caused by sudden geological disasters in Qinghai Province in 2013 Field 1: disaster type Field 2: direct economic loss
Department of Ecology and Environment of Qinghai Province
The data set records the frequency statistics of typical geological disasters in Qinghai Province from 2011 to 2016. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains six data tables, which are: the frequency of sudden geological disasters in 2011, 2012, 2013, 2014 and 2015 Statistical table, 2016 Qinghai Province sudden geological disasters frequency statistical table, data table structure is the same. There are two fields in each data table, such as the occurrence frequency of sudden geological disasters in 2011: Field 1: Location Field 2: frequency ratio
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring situation of waste water and waste gas pollution of key enterprises under provincial control in Qinghai Province from 2013 to 2015. The monitoring results of waste water of Qinghai Province in the first quarter of 2013 and Qinghai provincial control enterprises in the fourth quarter of 2013 are included in the PDF file 。 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 monitoring results of sewage treatment plants in Qinghai Province from 2014 to 2015. Data statistics from the Qinghai Provincial Department of ecological environment data set contains six documents, which are: the monitoring results of Qinghai sewage treatment plant in the first quarter of 2014, the monitoring results of Qinghai sewage treatment plant in the second quarter of 2014, the monitoring results of Qinghai sewage treatment plant in the third quarter of 2014, the monitoring results of Qinghai sewage treatment plant in the fourth quarter of 2014, and the monitoring results of Qinghai sewage treatment plant in the first quarter of 2015 The monitoring results of the treatment plant and the supervision monitoring of the sewage treatment plant in Qinghai Province in the third quarter of 2015. The structure of the data table is the same, and the monitoring area covers Xining city and its three counties, Ping'an County, Ledu County, Gonghe County and Delingha city. 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 recorded the content of hexavalent chromium in Xiejia village downstream of Tianjiazhai Township, Huangzhong County, Qinghai Province from 2005 to 2017. The data is from the official website of the Department of ecological environment of Qinghai Province. The data set contains five Excel data tables, which are: the hexavalent chromium content table of downstream riverside spring of Xiejia village, Tianjiazhai Township, Huangzhong County from 2005 to 2011, the hexavalent chromium content table of downstream riverside spring of Xiejia village, Tianjiazhai Township, Huangzhong County from 2005 to 2014, the hexavalent chromium content table of downstream riverside spring of Xiejia village, Tianjiazhai Township, Huangzhong County from 2005 to 2015, the hexavalent chromium content table of downstream riverside spring of Xiejia village, Tianjiazhai Township, Huangzhong County from 2006 to 2016, and 2 006-2017 hexavalent chromium content table of riverside spring downstream of Xiejia village, Tianjiazhai Township, Huangzhong County. The data table structure 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 monthly water quality monitoring and evaluation data of Huangshui river monitoring section from January 2008 to June 2020. The data set consists of 146 Excel / PDF data files. They are water quality assessment.xls in January 2008, water quality assessment.xls in February 2008 Water quality assessment of national control section of Huangshui River in June 2020.xls. Data monitoring points include: Jintan and zhamalong section of Huangshui mainstream; Xiaoxia bridge section; Minhe bridge section. The detection indicators include: water environment function zoning category, water quality category, main pollution indicators, water quality status, water quality status in last month, and water quality status in the same period of last year. The data table has the same structure and contains 7 fields Field 1: section name Field 2: water environment function zoning category Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period last year
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring results of Huangnan medical waste disposal center in the first half of 2020. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains a PDF file: the monitoring results of Huangnan medical waste disposal center in the first half of 2020. The monitoring was entrusted by Huangnan environmental monitoring station and implemented by Qinghai Hongjing Environmental Protection Industry Development Co., Ltd. The test items include: (1) Wastewater: water temperature, pH value, ammonia nitrogen, total chlorine (total residual chlorine), chemical oxygen demand, five-day biochemical oxygen demand, suspended solids, fecal coliform group, a total of 8 items. (2) Organized waste gas: ammonia, hydrogen sulfide, odor concentration, non methane hydrocarbon, a total of 4. (3) Monitoring frequency: wastewater: 1 day, once a day. Organized waste gas: 1 day, 3 times a day.
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring situation of provincial key pollutant discharge units in Huangnan Prefecture of Qinghai Province in 2019. The data set is compiled from the Department of ecological environment of Qinghai Province. The data set contains two PDF files, including the supervision monitoring of provincial key pollutant discharge units in Huangnan Prefecture in the first half of 2019 and the supervision monitoring of provincial key pollutant discharge units in Huangnan Prefecture in the second half of 2019. The monitoring report was commissioned by the environment and Forestry Bureau of Tongren County and implemented by Qinghai Jinyun Environmental Technology Co., Ltd. the monitoring report includes the water temperature, flow rate, pH value, chroma, chemical oxygen demand, five-day biochemical oxygen demand, ammonia nitrogen, ammonia nitrogen at the inlet and outlet of the sewage treatment plant Total phosphorus, total nitrogen, lead, cadmium, total chromium, mercury, arsenic, suspended solids, hexavalent chromium, petroleum, animal and vegetable oils, anionic surfactants, fecal coliform, alkyl mercury and so on, a total of 64 samples were tested.
Department of Ecology and Environment of Qinghai Province
This data set records the statistical bulletin of national economic and social development of Huangnan Prefecture in Qinghai Province in 2019. The data is collected from the Statistics Bureau of Qinghai Province. The data set contains a word file, which is the statistical bulletin of national economic and social development of Huangnan Prefecture of Qinghai Province in 2019. The Gazette covers GNP, GNP public revenue, total population and its changes in the whole state, the total consumer price index in the whole state, the planting and animal husbandry in the whole state, the development of industrial and construction industries in the whole state, the completion of fixed assets investment in the whole state, and the total retail sales of social consumer goods in the whole state. Information statistics and comparative data are provided in the following aspects: economic situation, total value of goods import and export, added value of wholesale and retail industry, cultural tourism, health and sports, residents' income, consumption and social security, environment and emergency management.
Bureau of Statistics Qinghai Provincial
The data set records the supervisory monitoring of key pollution sources controlled by the state in Huangnan Prefecture in 2016. The data set is compiled from the Department of ecological environment of Qinghai Province. The data set contains four data tables, which are respectively the statistics of the first, second, third and fourth quarter of 2016 national control key pollution sources supervision monitoring in Huangnan Prefecture. The data table structure is the same. There are 17 fields in each data table (only the top 6 fields are listed), for example, the monitoring situation of national key pollution sources in the first quarter of 2016: 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
Department of Ecology and Environment of Qinghai Province
The data set records the monitoring data statistics of Huangnan wastewater treatment plant in Qinghai Province from 2017 to 2018. The data is from the Department of ecological environment of Qinghai Province. The data set contains 11 data tables, which are: the audit table of monitoring data of Huangnan sewage treatment plant in the second quarter of 2017, the audit of monitoring data of Huangnan sewage treatment plant in the third quarter of 2017, the audit of waste water monitoring data of Huangnan state-controlled enterprises in the fourth quarter of 2017, the audit of waste gas monitoring data of Huangnan state-controlled enterprises in the first quarter of 2017, and the audit of Huangnan sewage treatment plant in the first quarter of 2017 Audit of monitoring data of plant management, audit of monitoring data of sewage treatment plant in November 2017, audit of monitoring data of waste water from pollution sources of provincial controlled enterprises in the third quarter of 2017, audit of monitoring data of waste water in the first quarter of 2018, audit of monitoring data of waste gas in the second quarter of 2018, audit of monitoring data of sewage treatment plant in the second quarter of 2018 and audit of monitoring data of waste water in the third quarter of 2018 Water treatment plant monitoring data audit, Huangnan state-controlled enterprise waste gas monitoring data audit in the fourth quarter of 2018, Huangnan sewage treatment plant monitoring data audit in the fourth quarter of 2018. The data table structure is different. Sewage treatment plant monitoring data audit table: a total of 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
Department of Ecology and Environment of Qinghai Province
This data set records the bulletin of environmental status of Qinghai Province from 1998 to 2019. The data set contains 22 files, which are: Qinghai Province environmental situation bulletin in 1998, Qinghai Province environmental situation bulletin in 1999 Qinghai Province environmental situation communique in 2019, etc. The contents of the communique include water quality monitoring of 61 sections in the main stream of the Yangtze River, the main stream of the Yellow River, the main stream of the Lancang River, the main stream of the Heihe River, the Qinghai Lake Basin, the Huangshui River Basin and the Qaidam inland river basin, the proportion of days for reaching the standard of ambient air quality in the whole province, the year-on-year comparison of ambient air monitoring factors in cities (towns), the overall situation of acoustic environment quality and ecological environment quality in urban areas, as well as relevant guarantee measures And supporting measures.
Department of Ecology and Environment of Qinghai Province
The data set records the typical geological disasters in Qinghai Province from 1999 to 2017. The data are collected from the Department of ecological environment of Qinghai Province, and the data set includes seven tables: the content of hexavalent chromium in spring 1 of Xinghuo village, Haiyan County, 1999-2011, the content of hexavalent chromium in spring 1 of Xinghuo village, Haiyan County, Qinghai Province, 1999-2012, the content of hexavalent chromium in spring 1 of Xinghuo village, Haiyan County, Qinghai Province, 2002-2013, the content of hexavalent chromium in spring 1 of Xinghuo village, Haiyan County, Qinghai Province, 2002-2014, and 2006-2015 The structure of the data sheet is the same as the table of hexavalent chromium content in spring 1, Xinghuo village, Haiyan County, Qinghai Province in 2006-2016, and the table of hexavalent chromium content in spring 1, Xinghuo village, Haiyan County, Qinghai Province in 2006-2017. Each data table has two fields, Field 1: year Field 2: content
Department of Ecology and Environment of Qinghai Province
The data set records the information disclosure form of county-level centralized drinking water quality monitoring (2019-2020) in Haixi Prefecture. The data is collected from the data set of Qinghai Provincial Department of ecological environment, including nine data tables: information disclosure form of county-level centralized drinking water quality monitoring in the first quarter of 2019 in Haixi Prefecture, and information disclosure form of county-level centralized drinking water quality monitoring in the second quarter of 2019 in Haixi Prefecture Information disclosure form of quality monitoring, information disclosure form of centralized drinking water quality monitoring at county level in the third quarter of 2019, information disclosure form of centralized drinking water quality monitoring at county level in the fourth quarter of 2019, information disclosure form of centralized drinking water quality monitoring at county level in the first half of 2019, and information disclosure form of centralized drinking water quality monitoring at county level in the second half of 2019 In the first quarter of 2020, the county-level surface water centralized drinking water source water quality information disclosure form, the county-level surface water centralized drinking water source water quality information disclosure form in the second quarter of 2020, and the county-level groundwater centralized drinking water source water quality information disclosure form in the first half of 2020 The table structure is the same. There are 11 fields in each data table Field 1: serial number Field 2: name of water source Field 3: water level Field 4: water source type Field 5: water quality category requirements Field 6: monitoring unit Field 7: monitoring factors Field 8: monitoring frequency Field 9: is it up to standard Field 10: over standard factor Field 11: remarks
Department of Ecology and Environment of Qinghai Province
The data set records the operation of the pollution source monitoring center in Haixi Prefecture of Qinghai Province from July 2018 to September 2019. The data is collected from the Department of ecological environment of Haixi Prefecture. The data set contains 42 text files, recording the weekly report of Haixi pollution source monitoring center from July 2018 to September 2019, and each file records the content of the weekly report once. Including the video monitoring system operation, online monitoring system operation, new online monitoring system construction acceptance, online monitoring system construction acceptance, online monitoring data analysis and transmission efficiency. Data coverage time range: July 16, 2018 to September 1, 2019.
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
The data set records the monitoring results of Haixi sewage treatment plant in Qinghai Province from 2013 to 2016. The data is collected from the Department of ecological environment of Qinghai Province. The data set includes 8 data tables, 3 PDF files and 5 compressed documents, which are respectively the supervision monitoring results of Haixi sewage treatment plant in Qinghai Province in the second quarter of 2015, the supervision monitoring results of Haixi sewage treatment plant in Qinghai Province in the third quarter of 2015 and the supervision monitoring results of Haixi sewage treatment plant in Qinghai Province in the fourth quarter of 2015 In the fourth quarter of 2016, the supervision monitoring results of Haixi sewage treatment plant in Qinghai Province, the supervision monitoring results of Haixi sewage treatment plant in the first quarter of 2016, the supervision monitoring data of Dulan sewage treatment plant in the second quarter of 2017, the supervision monitoring results of Golmud sewage treatment plant in the second quarter of 2017, and the supervision monitoring results of Wulan sewage treatment plant in the second quarter of 2017 The monitoring results of Haixi sewage treatment plant in Qinghai Province in the fourth quarter of 2013, the second quarter of 2014 and the third quarter of 2014. The data table contains 10 fields: Field 1: Administrative Region Field 2: name of sewage treatment plant Field 3: monitoring date Field 4: name of executive standard Field 5: monitoring items Field 6: outlet concentration (mg / L) Field 7: standard limit (mg / L) Field 8: emission unit Field 9: evaluation conclusion Field 10: excess multiple
Department of Ecology and Environment of Qinghai Province
The data set records the generation of hazardous waste in Haixi Prefecture of Qinghai Province, the standardized management indicators of business units and the list of spot check and assessment. The data were collected from the Department of ecological environment of Haixi Prefecture. The data set includes two data tables, which are: the standardized management index and spot check list of hazardous waste generating units in Haixi Prefecture in 2019, and the standardized management index and spot check list of hazardous waste operating units in Haixi Prefecture in 2019. The data table structure is the same. Each data table has six fields as follows: Field 1: serial number Field 2: company name Field 3: Score Field 4: Comprehensive Evaluation Field 5: Region Field 6: Backup
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
This data set records the supervisory monitoring data of state-controlled waste gas and wastewater enterprises in Haixi Prefecture of Qinghai Province from 2013 to 2018. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 46 worksheets and 19 compressed files, which are respectively the results of the supervision monitoring of the state-controlled waste gas enterprises in Haixi Prefecture of Qinghai Province in the fourth quarter of 2013, and the results of the supervision monitoring of the state-controlled waste water enterprises in Haixi Prefecture of Qinghai Province in the fourth quarter of 2013. 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 monitoring status of centralized drinking water quality in Haixi Prefecture of Qinghai Province from January 2019 to June 2020. The data were collected from the ecological environment bureau of Haixi Prefecture. The data set includes six data tables, which are: information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the first quarter of 2019, information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the second quarter of 2019, information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the third quarter of 2019, and information disclosure data of centralized drinking water quality monitoring in Haixi Prefecture in the second quarter of 2019 The structure of information disclosure data and data table is the same for the fourth quarter of 2020, the first quarter of 2020 and the second quarter of 2020. Each data table has a total of 11 fields, such as the information disclosure table of prefecture level centralized drinking water quality monitoring in the second quarter of 2020 in Haixi prefecture (only 6 fields are listed) Field 1: serial number Field 2: name of water source Field 3: water level Field 4: water source type Field 5: water quality category requirements Field 6: testing unit Field 7: monitoring items Field 8: monitoring frequency Field 9: exceedance factor Field 10: is it up to standard Field 11: remarks
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
The data set records the information disclosure form of surface water quality monitoring in Haixi Prefecture of Qinghai Province from January 2019 to June 2020. The data were collected from the ecological environment bureau of Haixi Prefecture. The data set contains 18 data tables, which are respectively the information disclosure table of surface water quality monitoring in January, February, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 of 2019, and the information disclosure table of surface water quality monitoring in January, February, 3, 4, 5 and 6 of 2020. Each data table has 11 fields, such as the information disclosure table of surface water quality monitoring in January 2019 Field 1: serial number Field 2: Region Field 3: water body Field 4: section name Field 5: section level Field 6: monitoring unit Field 7: monitoring frequency Field 8: water quality objectives Field 9: is it up to standard Field 10: over standard factor Field 11: remarks
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
The data set records the air quality of Delingha and Golmud in Qinghai Province in 2020. The data were collected from the ecological environment bureau of Haixi Prefecture. The data set contains five data tables, which are: air quality statistics of Delingha and Golmud in Haixi Prefecture in March, April, may, June and July 2020, with the same structure. Each data table has 10 fields, such as the air quality statistics of Delingha and Golmud in June 2020 Field 1: Region Field 2: major pollutants Field 3: PM10 Concentration Field 4: PM2.5 concentration Field 5: SO2 concentration Field 6: NO2 concentration Field 7: ambient air quality composite index Field 8: effective monitoring days Field 9: Standard Days Field 10: percentage of days up to standard
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
This data set records some monitoring data of Hainan sewage treatment plant in Qinghai Province from 2015 to 2018. The data is collected from the ecological environment bureau of Hainan prefecture, and the data set contains six data tables, which are: the monitoring data audit of Hainan sewage treatment plant in Qinghai Province in the second quarter of 2015, the monitoring data audit of Hainan sewage treatment plant in Qinghai Province in the third quarter of 2015, the monitoring data audit of Hainan sewage treatment plant in Qinghai Province in the first quarter of 2015, and the monitoring data audit of Hainan sewage treatment plant in Qinghai Province In the fourth quarter of 2017, the monitoring data of Hainan sewage treatment plant in Qinghai Province - the fourth quarter of 2018, and the monitoring data of Hainan sewage treatment plant in Qinghai Province - the first quarter of 2018, the data table structure is the same. Sewage treatment plant monitoring data audit table: a total of 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
Ecological Environment Bureau of Hainan Prefecture
The data set records the monitoring reports of key pollution sources in Hainan Province from 2013 to 2014. The data is collected from the Department of ecological environment of Qinghai Province, and the data set contains two data files, which are: Supervision Monitoring Report of key pollution sources controlled by Hainan Province in Qinghai Province - 2013, and supervision monitoring report of key pollution sources controlled by Hainan Province in Qinghai Province - 2014. The monitoring sites cover six enterprises including Qinghai Xuefeng yak Dairy Co., Ltd. and Hainan Sifang Thermal Power Co., Ltd. the monitoring items are: smoke and dust, sulfur dioxide, nitrogen oxide export emission concentration and emission, pH, ammonia nitrogen, BOD5, total phosphorus, total nitrogen, nitrate nitrogen, suspended solids, chemical oxygen demand; the monitoring frequency is one day, four times in a row; the monitoring frequency is one day, four times in a row;
Ecological Environment Bureau of Hainan Prefecture
This data set records the data audit of waste gas monitoring of Hainan provincial control enterprises in Qinghai Province in 2018. The data is collected from the Department of ecological environment of Qinghai Province, and the data set contains five data tables, which are: waste gas monitoring data audit of Hainan state controlled enterprises in Qinghai Province - the first quarter of 2018, waste gas monitoring data audit of Hainan state controlled enterprises in Qinghai Province - the second quarter of 2018, waste water monitoring data audit of Hainan state controlled enterprises in Qinghai Province - the second quarter of 2018, waste water monitoring data audit of Hainan state controlled enterprises in Qinghai Province Waste water monitoring data audit - the fourth quarter of 2018, waste water monitoring data audit - the first quarter of 2018 of Hainan provincial control enterprises in Qinghai Province 2, the data table structure is the same, the waste gas monitoring data audit table has 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.
Ecological Environment Bureau of Hainan Prefecture
The data set records the monthly air quality monitoring reports of Hainan Prefecture in Qinghai Province in 2015 and 2017-2020. The data were collected from Hainan Ecological Environment Bureau. The data set contains four data tables, which are monthly air quality reports of Hainan in 2020.5, 2020.6, 2020.7 and 2020.8, with the same structure. It also contains 34 text files. Each data table has five fields, for example, Hainan air quality monthly report in May 2020: Field 1: ranking Field 2: Region Field 3: major pollutants Field 4: ambient air quality composite index The data records are divided into five counties: Guinan, guide, Tongde, Xinghai and Gonghe
Ecological Environment Bureau of Hainan Prefecture
The data set records the monthly air quality monitoring reports of Hainan Prefecture in Qinghai Province in 2015 and 2017-2020. The data were collected from Hainan Ecological Environment Bureau. The data set contains four data tables, which are monthly air quality reports of Hainan in 2020.5, 2020.6, 2020.7 and 2020.8, with the same structure. It also contains 34 text files. Each data table has five fields, for example, Hainan air quality monthly report in May 2020: Field 1: ranking Field 2: Region Field 3: major pollutants Field 4: ambient air quality composite index The data records are divided into five counties: Guinan, guide, Tongde, Xinghai and Gonghe
Ecological Environment Bureau of Hainan Prefecture
The data set records the monitoring report of key pollution sources controlled by the state in Hainan Province from 2013 to 2014. Data statistics from the Qinghai Provincial Department of ecological environment data set, including four data files, respectively: the first quarter of 2014 national control monitoring report of Hainan prefecture, Qinghai Province, national control key pollution source supervision and monitoring report - 2013, Hainan state, Qinghai Province, national control key pollution source supervision and monitoring report - 2014 (1), Hainan state, Qinghai Province, national control key pollution source supervision and monitoring report - 2014 （2）。 The monitoring report is entrusted by the Environmental Protection Bureau of Hainan Tibetan Autonomous Prefecture. The monitoring sites include Gonghe County sewage treatment plant, guide county sewage treatment plant, Qinghai Saishitang Copper Co., Ltd. and Gonghe County Jinhe Cement Co., Ltd. the monitoring items include pH, chemical oxygen demand, five-day biochemical oxygen demand, chromaticity, ammonia nitrogen, total phosphorus, total nitrogen, total chromium, arsenic, mercury, cadmium and chromium（ The monitoring frequency was 2 times / day, one day;
Department of Ecology and Environment of Qinghai Province
This data set records the statistical bulletin of national economic and social development of Hainan Prefecture in Qinghai Province in 2019. The data is collected from the Statistics Bureau of Qinghai Province. The data set contains a word file, which is the statistical bulletin of national economic and social development of Hainan Province in 2019. The communique covers the annual gross domestic product of the whole city, the annual public revenue, the registered residence population and its changes, the total consumption price index of the whole city, the planting and animal husbandry, the industry and the construction industry, the annual fixed assets investment, the total retail sales of the total social consumer goods, and the total value of the total import and export of the whole city in the whole year. Information statistics and comparative data on the added value of wholesale and retail industry, cultural tourism, health and sports, residents' income, consumption and social security, environment and emergency management, etc.
Qinghai Provincial Bureau of Statistics
The data set records the typical geological disasters in Qinghai Province from 2015 to 2018. The data set contains seven data tables, which are: the second quarter monitoring data audit of 2015, the third quarter monitoring data audit of 2015, the first quarter monitoring data audit of 2015, the second quarter monitoring data audit of 2017, and the third quarter monitoring data audit of 2015 In the fourth quarter of 2017, the monitoring data of state-controlled enterprises in Hainan Province were reviewed; in the third quarter of 2018, the monitoring data of state-controlled enterprises in Hainan Province were reviewed; in the fourth quarter of 2018, the monitoring data of state-controlled enterprises in Hainan Province were reviewed. The data table has the same structure. 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
Environmental Protection Bureau of Hainan Tibetan Autonomous Prefecture, Qinghai Province
This data set records some monitoring data of guide sewage treatment plant in Hainan Province from 2013 to 2018. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains six data files, which are: the monitoring results of guide sewage treatment plant in Hainan prefecture of Qinghai Province in the fourth quarter of 2014, the monitoring data audit of guide sewage treatment plant in Hainan prefecture of Qinghai Province in the fourth quarter of 2015, the monitoring data audit of guide sewage treatment plant in Hainan prefecture of Qinghai Province in the first quarter of 2016, and the monitoring data audit of guide sewage treatment plant in Hainan prefecture of Qinghai Province in the first quarter of 2016 Sewage treatment plant in the first half of 2019, guide county sewage treatment plant in Hainan Province in the second half of 2019, guide county sewage treatment plant in Hainan Province in 2013. The monitoring result table of sewage treatment plant contains 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
This data set records some monitoring data of Gonghe sewage treatment plant in Hainan Province from 2013 to 2019. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains six data files, which are: the monitoring results of Gonghe sewage treatment plant in Hainan prefecture of Qinghai Province in the fourth quarter of 2014, the monitoring data audit of Gonghe sewage treatment plant in Hainan prefecture of Qinghai Province in the fourth quarter of 2015, the monitoring data audit of Gonghe sewage treatment plant in Hainan prefecture of Qinghai Province in the first quarter of 2016 Online comparative monitoring [2013] No. 023-4-3 of Hexian wastewater treatment plant, the first half of 2019 of Gonghe wastewater treatment plant in Hainan Province, and the second half of 2019 of Gonghe wastewater treatment plant in Hainan Province. There are 15 fields in the monitoring result table of sewage treatment plant 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
Department of Ecology and Environment of Qinghai Province
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
This data set records the statistical bulletin of national economic and social development of Haidong city in Qinghai Province in 2019. The data is collected from the Statistics Bureau of Qinghai Province. The data set contains a word file, which is the statistical bulletin of national economic and social development of Haidong in Qinghai Province in 2019. The Gazette covers the annual gross domestic product of the whole city, the completion of the regional public budget revenue, the household registration population and its changes throughout the year, the annual total consumption price index of the whole city, the planting and animal husbandry, the industrial and construction industries, the annual fixed assets investment of the whole City, the total retail sales of social consumer goods, and the total value of the total import and export of the whole city in the whole year. Information statistics and comparative data on the added value of wholesale and retail industry, cultural tourism, health and sports, residents' income, consumption and social security, environment and emergency management, etc.
Qinghai Provincial Bureau of Statistics
This data set records the statistical bulletin of national economic and social development of Qinghai Haibei Tibetan Autonomous Prefecture in 2019. The data is collected from the Statistics Bureau of Qinghai Province. The data set contains a word file, which is the statistical bulletin of national economic and social development of Qinghai Haibei Tibetan Autonomous Prefecture in 2019. The Gazette covers the whole year's GNP, the whole state's completion of the regional public budget revenue, the household registration population and its changes, the annual total consumption price index of the whole state, the whole state's planting and animal husbandry, the whole state's industrial and construction industry, the whole state's fixed assets investment, the whole state's total retail sales of social consumer goods, the total import and export value of the whole country, and the whole country. Statistics and comparative data on the added value of wholesale and retail industry, cultural tourism, health and sports, residents' income, consumption and social security, environment and emergency management.
Qinghai Provincial Bureau of Statistics
This data set records the statistical bulletin of national economic and social development of Guoluo Tibetan Autonomous Prefecture in Qinghai Province in 2019. The data is collected from the Statistics Bureau of Qinghai Province. The data set contains a word file, which is the statistical bulletin of national economic and social development of goluo Tibetan Autonomous Prefecture in Qinghai Province in 2019. The contents of the communique cover the total economic volume and structure of the whole Prefecture, the development of agriculture and animal husbandry, the development of industry, the investment in fixed assets, the trade and price situation, the financial situation, the development of transportation, posts and telecommunications and tourism, the environmental protection and forestry, the development of education, science and technology, the culture and health of the whole Prefecture The state of the enterprise, the state of the population, people's life and social security development, etc.
Qinghai Provincial Bureau of Statistics Qinghai Provincial Bureau of Statistics
This data set records the bulletin of land greening status of Qinghai Province from 2014 to 2018. The data is collected from Qinghai Forestry and grass Bureau. The data set contains five word files, which are: 2014 Qinghai provincial land greening Status Bulletin, 2015 Qinghai provincial land greening Status Bulletin, 2016 Qinghai provincial land greening Status Bulletin, 2017 Qinghai provincial land greening Status Bulletin, 2018 Qinghai provincial land greening Status Bulletin. The contents of the communique released the continuous and in-depth implementation of the nationwide voluntary tree planting activities, the new achievements in the improvement of the quality and increment of forestry projects, the new achievements in the greening of key areas, the new progress in the greening work of departments, the strong promotion of forestry reform, the vigorous development of forestry industry, the construction of nature reserves and the protection of wild animals and plants, and the protection of wetland resources The management and management of forest resources, the management and protection of forest resources, the prevention and control of desertification, the management of forestry science and technology and the promotion of technology, etc
Qinghai Forestry and Grassland Bureau
The data set records the reasons why the state key monitoring enterprises in Qinghai province did not carry out the monitoring of pollution sources in 2014. The data is collected from the Department of ecological environment of Qinghai Province, and the data set contains four documents, which are: the reasons why the national key monitoring enterprises of Qinghai province did not carry out the supervision monitoring of pollution sources in the first, second, third and fourth quarters of 2014. According to Huangzhong County, Huzhu County, Minhe County, Gonghe County, Xinghai County, Tianjun County, Delingha County, Dachaidan County, Datong County, Ledu County and Golmud City of Qinghai Province, the specific reasons for the failure of export monitoring in "unmonitored wastewater", "unmonitored waste gas" and "unmonitored heavy metal wastewater" are given in the data set. The data table has the same structure and contains five fields Field 1: monitoring category Field 2: location city Field 3: enterprise name Field 4: reason not monitored Field 5: remarks
Department of Ecology and Environment of Qinghai Province
The data set records the statistical table of groundwater level dynamic changes in various monitoring areas of Qinghai Province from 2015 to 2018. The data are recorded from the Department of natural resources of Qinghai Province, and the data set contains four data tables, which are: the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2015, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2016, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2017, and the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2018 The data table has the same structure and contains 7 fields Field 1: "geographic location" Field 2: "basic balance area (km2)" Field 3: "percentage of monitoring area (%)" Field 4: "weak descent area (km2)" Field 5: "percentage (%) of monitored area" Field 6: "strong uplift area (km2)" Field 7: "percentage (%) of monitored area"
Department of Natural Resources of Qinghai Province
The data set records the statistical bulletin of national economic and social development of Golmud City in Qinghai Province in 2019. The data statistics are from Qinghai Province. According to the data set, there is a word file, which is respectively the statistical bulletin of national economic and social development of Golmud City in 2019. The Gazette covers the annual gross domestic product of the whole city, the completion of the regional public budget revenue, the household registration population and its changes throughout the year, the annual total consumption price index of the whole city, the planting and animal husbandry, the industrial and construction industries, the annual fixed assets investment of the whole City, the total retail sales of social consumer goods, and the total value of the total import and export of the whole city in the whole year. Information statistics and comparative data on the added value of wholesale and retail industry, cultural tourism, health and sports, residents' income, consumption and social security, environment and emergency management, etc.
Qinghai Provincial Bureau of Statistics
The data set records the typical geological disasters in Qinghai Province from 2011 to 2018. The data set includes 10 data tables, which are: typical geological disasters in 2011, 2012, 2013, 2013, distribution, 2014, etc The data structure of typical geological disasters in 2018 is the same. Each data table has five fields, such as the typical geological disasters in 2011: Field 1: Location Field 2: disaster type Field 3: time of occurrence Field 4: scale Field 5: hazards and losses
ZHAO Hu
This data set records the main data bulletin of the third national agricultural census in Qinghai Province, and the data is from the Qinghai Provincial Bureau of statistics. The data set contains five word document files, which are: the main data bulletin of the third national agricultural census of Qinghai Province (No.1), the main data bulletin of the third national agricultural census of Qinghai Province (No.2), the main data bulletin of the third national agricultural census of Qinghai Province (No.3), the main data bulletin of the third national agricultural census of Qinghai Province (No.4), and the main data bulletin of the third national agricultural census of Qinghai Province (No.4) Main data bulletin of the third National Agricultural Census (No.5). The survey objects include agricultural operators, households who live in rural areas and have the right to (contract) land or agricultural means of production, agricultural operators, villagers' committees and Township People's governments. The main contents of the census are agricultural production capacity and output, rural infrastructure and basic social services, and farmers' living conditions. Agricultural census adopts the method of comprehensive survey, and the census personnel check and fill in all census objects one by one.
Qinghai Provincial Bureau of Statistics
The data set records the Geological Environment Bulletin of Qinghai Province from 2011 to 2019. The data set contains 9 PDF data files, which are collected from the Department of natural resources of Qinghai Province. Qinghai provincial government order No. 72 "geological environment protection, social and environmental protection for the people of Qinghai Province" is the basis for the comprehensive protection of the geological environment, According to the geological environment survey and monitoring data, the provincial natural resources department publishes the annual Geological Environment Bulletin and publishes the annual geological environment status of our province to the public. The main contents of the Geological Environment Bulletin of Qinghai province include: the distribution characteristics, causes, harm degree and prevention and control of geological disasters in the whole province; the development and utilization of groundwater resources and dynamic changes, groundwater pollution; the protection and restoration of mine geological environment. The Geological Environment Bulletin of Qinghai Province is jointly compiled by the geological exploration management office of Qinghai Provincial Department of natural resources and the geological environment monitoring station of Qinghai Province.
Department of Natural Resources of Qinghai Province
The data set records the water quality of centralized drinking water sources in prefecture level cities of Qinghai Province from January 2016 to August 2020. The data is collected from the Department of natural resources of Qinghai Province. The data set contains 48 Excel data sheets, which are: water quality of centralized drinking water sources in prefecture level cities of Qinghai Province in January 2016, water quality of centralized drinking water sources in prefecture level cities of Qinghai Province in February 2016 In August 2020, the water quality of centralized drinking water sources in prefecture level cities of Qinghai Province has the same data table structure. There are six fields in each data table, such as the water quality of centralized drinking water sources in prefecture level cities of Qinghai Province in February 2016 Field 1: serial number Field 2: city name Field 3: water source name Field 4: water source type Field 5: compliance Field 6: over standard index and over standard multiple
Department of Natural Resources of Qinghai Province
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 dynamic statistical data of groundwater level in the monitoring area around Qinghai Lake from 2014 to 2018. The data are collected from the Department of natural resources of Qinghai Province. The data set includes five data tables, which are: the dynamic diagram of groundwater level in the monitoring area around Qinghai Lake in 2014, the dynamic statistical table of groundwater level in the monitoring area around Qinghai Lake in 2015, the diagram of groundwater level in the monitoring area around Qinghai Lake in 2016, the diagram of groundwater level in the monitoring area around Qinghai Lake in 2017, and the diagram of groundwater level in the monitoring area around Qinghai Lake in 2017 The groundwater level monitoring data of the same area around Qinghai Lake in 18 years. The groundwater level dynamic diagram (water level dynamic statistical table) of the monitoring area around Qinghai Lake contains 8 fields Field 1: year Field 2: qing2 Field 3: qing3 Field 4: qing4 Field 5: qing6 Field 6: qing7 Field 7: qing8 Field 8: qing9
Department of Natural Resources of Qinghai Province
This data set records the land strategy of Qinghai Province from 2019 to 2020. The data set contains four PDF files, which are collected from the Department of natural resources of Qinghai Province. They are the first phase of Qinghai land economic strategy in 2019, the second phase of Qinghai land economic strategy in 2019, the third phase of Qinghai land economic strategy in 2019, the fourth phase of Qinghai land economic strategy in 2019, the fifth phase of Qinghai land economic strategy in 2019, the sixth phase of Qinghai land economic strategy in 2019, the first phase of Qinghai land economic strategy in 2020, and Qinghai land economic strategy in 2020 No. 2, 2005. Qinghai land economics is a bimonthly magazine founded in 2002, The organizer is Qinghai provincial land and resources science and Technology Information Center, which publicizes national policies and laws, carries out academic and theoretical research, exchanges grass-roots practical experience, displays the land features of Qinghai, and guides the development of land and resources. It is received by the staff and scientific workers of the national land and resources system, large and medium-sized mining enterprises, scientific research institutes and people from all walks of life who are concerned about land and resources I'm a gentleman.
Department of Natural Resources 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
The data set records the surface water quality assessment of the Yellow River in Qinghai Province, covering the period from January 2008 to June 2020. The data is collected from the official website of Qinghai Provincial Department of ecological environment. The data set contains 90 PDF files, 24 text files and 25 data table files. The data table structure is the same. The data monitoring points are Tangnaihai, Dahejia and Guanting Each data table has 7 fields: Field 1: section name Field 2: water environment function zoning category Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period last year
Department of Ecology and Environment of Qinghai Province
The data set records the monthly report of ambient air quality of national rural air monitoring from May 2011 to February 2013 at Huzhu County observation station in Qinghai Province, and the data is collected from the official website of Qinghai Provincial Department of ecological environment. The dataset contains 7 word documents, 6 txt files and 9 PDF documents. Data contents include: total monitoring days of air quality, air quality status, percentage of air quality in total monitoring days, comparison of air quality with that of last month. Monitoring factors mainly include: SO2, NOx, NO2, no, PM10, data unit: percentage (%), grade (I, II, III, IV, V, etc.)
Department of Ecology and Environment of Qinghai Province
The data set records the dynamic statistical data of groundwater level in the monitoring area of Golmud City, Qinghai Province from 2012 to 2018, and the statistics are classified according to the year and quantity. The data were collected from the official website of the Department of natural resources of Qinghai Province. The data set contains seven data tables, which are the dynamic statistics of groundwater level in Golmud monitoring area in 2012, 2013, 2014, 2015, 2016, 2017 and 2018, with the same structure. For example, the data table in 2012 has five fields: Field 1: year Field 2: Potassium view5 Field 3: View 4 Field 4: View 39 Field 5: Potassium view 1
ZHAO Hu
This data set records the division of Qinghai Province from 2000 to 208 according to urban and rural areas, as well as economic types and quantitative statistics. The data are collected from the statistical yearbook: Qinghai statistical yearbook, and the accuracy is the same as the statistical yearbook extracted from the data. The data set contains three data tables, which are 2005-2006, 2006-2007 and 2007-2008 year-end employment statistics by urban and rural areas. The data table structure is the same. Each data table has five fields, such as the number of employed persons at the end of the year by urban and rural areas in 2005-2006: Field 1: towns in 2005 Field 2: 2005 rural Field 3: towns in 2006 Field 4: villages in 2006 Field 5: 2005 total Field 6: 2006 total
ZHAO Hu
There are 428 large and medium-sized landslides in the Bangladesh China India Myanmar economic corridor. The number of landslides in Myanmar is the largest, reaching 304, accounting for 71% of the total landslides, followed by China and India. The number of landslides is 71 and 52, accounting for 17% and 12% of the total landslides, respectively. There is only one landslide in Bangladesh. According to the material composition of landslide, it can be divided into rock landslide and soil landslide. There are 343 rock landslides in this area, accounting for 80% of the total number of landslides, and 85 soil landslides, accounting for 20% of the total number of landslides. Rock landslides are mainly distributed in the north of China, India and Myanmar, while soil landslides are mainly distributed in the middle and south of Myanmar. A total of 1569 debris flows were interpreted in the Bangladesh China India Myanmar corridor, including 574 gully debris flows and 995 slope debris flows. In the eastern part of the study area, debris flows are mainly distributed on both sides of Lancang River, Nujiang River, Mojiang River and Honghe River, and they are distributed in the north-south direction along these rivers. In the central part of the study area, debris flows are distributed in the ruokai mountain area. Compared with the gully type debris flow, the scale and harm of slope debris flow are much smaller. In this study, the correlation analysis of debris flow is mainly aimed at the gully type debris flow.
ZOU Qiang
The China Mongolia Russia economic corridor starts from China in the East, passes through Mongolia in the west to Russia, and crosses the Mongolian Plateau, West Siberian plain and Eastern European Plain. There are great differences in natural environment and complex geological conditions in the region. Driven by regional differences in structure, earthquake, meteorology, hydrology and ecology, landslides are widely distributed in China Mongolia Russia economic corridor. Based on remote sensing images, the landslide and debris flow disasters in China Mongolia Russia economic corridor are interpreted. Statistics show that there are 396 landslide disasters in China Mongolia Russia economic corridor, and the landslide disaster area is between 0.0006km2 ~ 8.57km2. The watershed area within 100km on both sides of the railway line, with a total area of 1.43 × 106km2, has identified 1336 debris flow gullies in the China Mongolia Russia economic corridor.
ZOU Qiang
1) These data main included the GPR-surveyed ice thickness of six typical various-sized glaciers in 2016-2018; the GlabTop2-modeled ice thickness of the entire UIB sub-basins, discharge data of the hydrological stations, and related raw & derived data. 2) Data sources and processing methods: We compared the plots and profiles of GPR-surveyed ice bed elevation with the GlabTop2-simulated results and selected the optimal parametric scheme, then simulated the ice thickness of the whole UIB basin and assessed its hydrological effect. These processed results were stored as tables and tif format， 3) Data quality description: The simulated ice thickness has a spatial resolution of 30 m, and has been verified by the GPR-surveyed ice thickness for the MD values were less than 10 m. The maximum error of the GPR-measured data was 230.2 ± 5.4 m, within the quoted glacier error at ± 5%. 4) Synthesizing knowledge of the ice thickness and ice reserves provides critical information for water resources management and regional glacial scientific research, it is also essential for several other fields of glaciology, including hydrological effect, regional climate modeling, and assessment of glacier hazards.
ZHANG Yinsheng
The sand drift potential (DP, in vector units (VU)) is calculated by DPi=∑U^2 [U-Ut]*fu where i represents 16 directions: N, NNE, NE, NEE, E, EES, ES, ESS, S, SSW, WS, WWS, W, WWN, NW and NNW; U is the effective sand-moving wind speed at the standard height of 10 m; Ut is the threshold wind velocity, i.e., the minimum wind velocity at the standard height to cause sand particle rolling; and fu is the fraction of time when the wind speed is higher than Ut. The 2 m s-1 bin is adopted in the effective sand-moving wind (sand-moving wind >6 m s-1 at the height of 10 m) directions, corresponding to the mean wind speeds of 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 and 34 m s-1, to sum all the above results to obtain the final DP in the wind direction. The divisor used in calculating the frequency of effective sand-moving winds from different directions is the total hour number of Julian years (8760 hours for common years or 8784 hours for leap years). The wind speed and wind direction data from 2000 to 2008 were hourly estimates of 10 m u-component of wind and 10 m v-component wind with a horizontal resolution of 0.25°×0.25° generated with the fifth generation of ECMWF atmospheric ReAnalysis of the global climate (ERA5).
LI Guoshuai
The major deserts in China include the Taklamakan Desert, Gurban Tunggut Desert, Qaidam Desert, Kumtag Desert, Badain Jaran Desert, Tengger Desert, Ulan Buh Desert, Hobq Desert, MU US Desert, Hunshandake Desert, Hulunbuir Sands, and Horqin Sands. All the desert boundaries were derived from Google Earth Pro® via manual interpretation. We delineated the desert boundaries using the Digital Global Feature Imagery and SpotImage (2011, 10 m resolution) collections of Google Earth Pro®, whose spatial resolution is finer than 30 m. The acquisition time of most images was in 2011.
LI Guoshuai LI Xin
By archaeological investigation and excavation in the Tibet Plateau and neighbouring areas, we discovered Jinchankou site, Zongri site, Xinancheng cemetery, bangga site and Tshem gzhung kha thog. 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 and analysed stone artifacts, animal remains and plant fossil, and obtained a batch of dating data of radiocarbon and optically stimulated luminescence dating;identification and isotopic composition and quality indicators of animal remains and plant fossil. At the same time, the relevant animal and plant remains and isotopes in the Tibet Plateau and neighbouring areas are sorted out. This dataset provide important basic data for understanding when and how human lived in the Tibet Plateau and neighbouring areas during the Neolithic Age and historical period.
DONG Guanghui YANG Xiaoyan Lü Hongliang MA Minmin MA Minmin
By archaeological investigation and excavation in the Tibet Plateau and neighbouring areas, we discovered Jinchankou site, Zongri site, Xinancheng cemetery, bangga site and Tshem gzhung kha thog. 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 and analysed stone artifacts, animal remains and plant fossil, and obtained a batch of dating data of radiocarbon and optically stimulated luminescence dating;identification and isotopic composition and quality indicators of animal remains and plant fossil. At the same time, the relevant animal and plant remains and isotopes in the Tibet Plateau and neighbouring areas are sorted out. This dataset provide important basic data for understanding when and how human lived in the Tibet Plateau and neighbouring areas during the Neolithic Age and historical period.
DONG Guanghui YANG Xiaoyan Lü Hongliang MA Minmin MA Minmin
By archaeological investigation and excavation in the Tibet Plateau and neighbouring areas, we discovered Jinchankou site, Zongri site, Xinancheng cemetery, bangga site and Tshem gzhung kha thog. 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 and analysed stone artifacts, animal remains and plant fossil, and obtained a batch of dating data of radiocarbon and optically stimulated luminescence dating;identification and isotopic composition and quality indicators of animal remains and plant fossil. At the same time, the relevant animal and plant remains and isotopes in the Tibet Plateau and neighbouring areas are sorted out. This dataset provide important basic data for understanding when and how human lived in the Tibet Plateau and neighbouring areas during the Neolithic Age and historical period.
DONG Guanghui YANG Xiaoyan Lü Hongliang MA Minmin MA Minmin
The Yarlung Zangbo suture zone and its neighbouring areas are crucial for the research of the evolution of Neo-Tethys. The earliest evidence of the formation of Neo-Tethys remains to be discovered and the sequence of the basin evolution in many areas, such as the eastern and western Xizang, is far from well established. The limits in stratigraphy and sedimentology have greatly restricted the investigation of the evolution of Neo-Tethys as well as the collision between Indian-Eurasian plates. This exploration team conducted a joint investigation on the Mesozoic strata in some key areas in eastern, central, and southern Xizang, with emphasis on their stratigraphy, palaeontology, and sedimentology, to retrieve potential stratigraphic and sedimentary evidence of the opening, evolution, and retreat of the Neo-Tethys in the region. This dataset is a collection of the exploration, which includes diaries and photos of the strata, geological structure, profiles, and fossils that have been collected in the above-mentioned areas. It consists of three items. The explored strata include the Jurassic-Cretaceous in Luolong and Basu counties, eastern Xizang, in Renbu and Lang counties, central Xizang, and the radiolarite strata in Ngari.
LI Jianguo LUO Hui LI Xianghui
Previous works have been undertaking on the geochemistry of some basic rocks within the Yaelung Zangbo suture zone, and it was proposed that the Jurassic-Cretaceous seamount type basement basic rocks are preserved in Zhongba and Renbu area. In this work, we conducted analysis of trace element for seventeen basic rock samples underlain below limestone blocks in Lhaze area within the central Yarlung Zangbo suture zone, southern Tibet. Results show that the rocks are dominated by basalts and have a feature of EMORB type with NMORB and OIB types. Combined with the overlain carbonates, these rocks are proposed the basement of paleo-seamounts, the Permian vestige preserved within the Yarlung Zangbo suture zone. This recognition of paleo-seamount implies that the Neotethys Ocean could have been already opened at the time (before the Middle Permian epoch).
LI Xianghui
With a depositional history spanning the early Aptian to early Albian (~19 mys), the Lower Cretaceous Xinminpu Group in Jiuquan, NW China provides abundant animal and plant fossils. This area is the represented study area of the Lower Cretaceous in China. The absence of a comprehensive stratigraphical framework for this area however limits the degree to which the unit can be interpreted relative to coeval sections and the geological records in genera. This investigation from July 9 to July 11 focuses on the Lower Cretaceous in this area, and collected abundant fossil and rock samples for biostratigraphic and stratigraphic analysis. This dataset includes: stratigraphic column of the Chijinpu Formation in the East Xiagou outcrop, and photographs of outcrops and represented fossils; stratigraphic column of the Xiagou Formation in the Changma outcrop and a photo of bird-bearing outcrop.
WANG Bo
This data set mainly includes daily surface evapotranspiration products in Heihe River Basin (HRB) from 2010 to 2016, with a resolution of 100 meters. Based on multi-source remote sensing data (MODIS Landsat TM/ETM+ data) and regional meteorological data (China meteorological forcing dataset, CMFD), sensitivity parameters of the theoretically robust surface energy balance system (SEBS) model were determined through global sensitivity analysis, and then the parameterization scheme of the model was optimized to improve the estimation accuracy. At the same time, combined with spatial and temporal data fusion algorithm of remote sensing image. Finally, the High-Temporal and Landsat-Like surface evapotranspiration (ET) (HiTLL ET) was obtained over the Heihe Basin. It was validation by the EC measurements from the flux observation stations and ETMap, and the estimation results are consistent with the observation and the spatial and temporal distribution pattern of ETMap. This data set can provide data support for the study of water consumption law and scientific effective management of watershed water resources within HRB, especially for woodland and grassland in the upper stream regions, oasis farmland and desert vegetation in the midstream and downstream regions.
MA Yanfei LIU Shaomin
By archaeological investigation and excavation in Tibetan Plateau, we discovered 8 Paleolithic-Epipaleolithic sites, including Baishiya karst cave, Jiangjunfu site, Meilongdapu cave, Xiaqulong site and so on. 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 and analysed stone artifacts, animal remains and plant fossil, and obtained a batch of dating data of uranium series, radiocarbon and optically stimulated luminescence. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the Paleolithic.
CHEN Fahu ZHANG Xiaoling
This dataset is the high-resolution downscaled results of three global circulation models (CCSM4, HadGEM2-ES, and MPI-ESM-MR) from CMIP5. The regional climate model applied is the WRF model. The domain of this dataset covers the five countries of Central Asia. Its horizontal resolution is 9km. The future (reference) period is 2031-2050 (1986-2005), which includes the 10 years under 1.5-2℃ global warming. The carbon emission scenario is RCP4.5. The variances are annual mean temperature at 2m and precipitation (cumulus and grid-scale precipitation). This dataset can be used to project the climate in Central Asia.
QIU Yuan
This dataset includes inland water data of five countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) in the great lakes region of Central Asia, including the distribution of rivers, canals and lakes. The linear and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series produced by the United States, Australia, Canada, and the United Kingdom. The DCW database has not been updated since 1992, and has been provided free of charge since 2006.
XU Xiaofan TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan TAN Minghong
Gwadar deepwater port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from the Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and the Red Sea in the West. It is a port with a strategic position far away from Muscat, the capital of Oman. This data set is an extreme drought risk assessment data set. From the four aspects of extreme drought risk, exposure, vulnerability, and stability, the Palmer drought index, elevation, water system, land use, population density, GDP density, inter field water capacity, and other data are used to comprehensively assess the extreme drought risk of the region. The spatial resolution of the data is 30 meters and the time is 2015.
WU Hua
Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data is the land cover data of Gwadar and its surrounding areas. The data is from globeland30 with a spatial resolution of 30 meters and a data format of TIFF. The classification images used in the development of globeland30 data set mainly include Landsat's TM5, ETM +, oli multispectral images and HJ-1 multispectral images. Using the Pok based classification method, the total volume accuracy is 83.50%, and the kappa coefficient is 0.78.
WU Hua
Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data includes the median values of 343 landsat8 data in each 30 meter grid of Gwadar Port Area and its surrounding area from 2014 to 2015. The data includes 12 bands with a spatial resolution of 30 meters, of which the thermal infrared band is 100 meters and the resampling resolution is 30 meters.
WU Hua
Gwadar deepwater port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from the Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and the Red Sea in the West. It is a port with a strategic position far away from Muscat, the capital of Oman. This data is the measured meteorological data of Gwadar Port meteorological station (62.329494e, 25.233308n). The data time range is 2014-2015, and the data time resolution is one day.
WU Hua
This data set is based on the spatial distribution data set of extreme precipitation disaster risk (2019) and vulnerability spatial distribution data set (2019) in Yangon deep water port area, combined with GDP and population distribution data of Yangon deep water port area, and through the definition of "risk = exposure × vulnerability × risk", the risk of extreme precipitation disaster in Yangon deepwater port area is calculated. The data set can provide a reference for the local disaster prevention and reduction work. By analyzing the distribution and causes of high risk, we can put forward engineering measures or non engineering measures to achieve the purpose of disaster reduction and prevention, and reduce the loss of people's lives and property caused by extreme precipitation disasters.
LI Yi
The area of the data set is the central urban area of Yangon deep water port. The data set is based on the spatial distribution data set of extreme precipitation disaster vulnerability (2019) and refers to its evaluation index system. When evaluating the vulnerability of extreme precipitation disaster in Yangon deepwater port area, the disaster reduction ability and sensitivity index are considered. The disaster reduction ability is negatively correlated with vulnerability, and the sensitivity is positively correlated with vulnerability. Disaster reduction capacity considers the density of impervious surface, road network and emergency rescue facilities; sensitivity considers the local land cover types, including farmland, urban and road crisscross. When extreme precipitation disaster occurs, high vulnerability areas will suffer more serious losses, and the reconstruction is more difficult.
GE Yong LI Qiangzi LI Yi
The evaluation area of the data set is the central urban area of Yangon deepwater port. The data set is based on the extreme precipitation disaster risk spatial distribution data set (2019) and its evaluation index system. The data set considers both precipitation risk and terrain risk. Among them, precipitation risk index includes extreme precipitation intensity index and extreme precipitation frequency index, both of which are obtained from GPM precipitation data. Terrain risk mainly considers elevation index. Finally, the risk assessment results of extreme precipitation disaster are obtained. The probability and intensity of extreme precipitation disaster in high risk area are higher than those in low risk area.
GE Yong LI Qiangzi LI Yi
The data set includes the road condition, water system condition and land use situation of Yangon deep water port central city. The road dataset includes both roads and railways, while the water system dataset includes rivers and lakes. The road data set and water system data set are vector data, and the land use data set is grid data with 10m resolution. The classification system of land use is: 10. Forest forest; 20. Cultivated land; 21. Paddy filed paddy field; 22. Dry farmland; 30. Water body; 31. River river river; 32. Lake Lake (including reservoirs and ponds); 33. Wetland; 40. Artificial surface; 43. Mining area; 50. Bareland Bare soil, bare rock, desert and so on, based on the limited sample accuracy analysis of the data, the classification accuracy is about 90%.
GE Yong LI Qiangzi LI Yi
Remote sensing image refers to the film or photo recording the electromagnetic wave size of various ground objects, mainly divided into aerial photo and satellite photo. The 1-5m remote sensing data set of Yangon deep water port area is from gaofen-2 satellite, with the highest resolution of 1m and the lowest resolution of 5m, including a total of 7 regional images. There are four images in each region, which are band composite images of 5m level and 1m level. The accuracy of 5m level image can meet the needs of most research purposes, and the amount of data is smaller; the accuracy of 1m level image is higher, which can be used for synthesis, verification and other purposes, but the amount of data is larger than 5m level data. In practical use, we can choose 5m or 1m images according to the needs of researchers.
GE Yong LI Qiangzi LI Yi
The 10m level elevation data set of Yangon deep water port area is the DEM data of the main urban area of Yangon deep water port. DEM is the abbreviation of digital elevation model, which is the important original data of watershed terrain and feature recognition. The data set can reflect the local topographic features of the main urban area of Yangon deep-water port with 10m resolution. Therefore, a large amount of surface morphological information can be extracted from the data set, which includes the slope, aspect and the relationship between cells of the basin grid. It can provide accurate topographic data and reliable verification data for the study of the main urban area of Yangon deep-water port.
GE Yong LI Qiangzi LI Yi
Coupled Model Intercomparison Project Phase 5 (CMIP5) provides a multiple climate model environment, which can be used to predict the future climate change in the key nodes in the Belts and Road to deal with the environmental and climate problems. Key nodes in the Belt and Road are taken as the study regions of this dataset. The ability of 43 climate models in CMIP5 to predict the future climate change in the study regions was assessed and the optimal models under different scenarios were selected according to the RMSE between the prediction results and real observations. This dataset is composed of the prediciton results of precipitation and near-surface air temperature between 2006 and 2065 using the optimal models in monthly temporal frequncy. The spatial resolution of the dataset has been downscaled to 10 km using statistical downscaling method. Data of each period has three bands, namely maximum near-surface air temperature, minimum near-surface air temperature and precipitation. In this data set, the unit of precipitation is kg / (m ^ 2 * s), and the unit of near-surface air temperature is K. This dataset provides data basis for solving environmental and climate problems of the key nodes in the Belts and Road.
LI Xinyan LING Feng
On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of Hambantota, indicators related to the disaster danger of storm surge in each unit are extracted and calculated using ten meters grid as evaluation unit. Based on statistical method, the tide level of every 20 years, 50 years and 100 years is estimated. The comprehensive index of storm surge disaster danger is constructed, and the danger index of storm surge is obtained by using the weighted method, which can be used to evaluate the danger level of storm surge in each assessment unit. The data set includes 20-year, 50-year and 100-year hazard assessment results of the port area of Hambantota.
DONG Wen
The spatial distribution data set of disaster prevention and mitigation facilities in hambantota and Colombo (2016-2018) is obtained by extracting classification information from high-resolution remote sensing images. Based on the fusion of 1-2m remote sensing image data, combined with POI data, the distribution information of hospital, fire protection and refuge facilities were extracted respectively. On this basis, the relevant layers and poi layers of OSM were superimposed with the extracted results and images. Through visual inspection, errors were found and the extracted results were corrected. Finally, hambantuota was formed Vector layer data of disaster prevention and mitigation related facilities in the node and Colombo area.
DONG Wen
The spatial distribution data set of infrastructures such as traffic and water system in the areas of hambantota and Colombo (2016-2018) is obtained by extracting classification information from high-resolution remote sensing images. Based on the 1-2m remote sensing image data, the distribution information of road, water, coastline, and coastal facilities are extracted respectively. On this basis, the road, and other layers of OSM are superimposed with the extracted results and images. Through visual inspection, errors are found and the extracted results are corrected. Finally, the hambantota node area dataset is formed road, water system, coastline, and coastal facilities distribution layer of the region. This data set contains the data information of two key node regions of hambantota and Colombo.
DONG Wen
The meter resolution remote sensing image data of hanbantota area is composed of data fusion and splicing of different satellites. Multispectral remote sensing images with resolution between 0.5 m and 1 m from 2018 to 2019 are selected, and cloud free data with similar time are selected, and the result data set is formed by cutting and splicing according to the research area. The spatial resolution of the data is about 0.6 meters. The data is mainly used to study the high-precision extraction of disaster bearing body elements, such as port facilities, roads and so on. The extracted thematic elements will be used as the basic data of storm surge exposure and vulnerability analysis.
DONG Wen
The elevation data set of Hambantota port area with 5-meter resolution is obtained from the stereo image pair data of ZY-3 satellite. ZY-3 carried four optical cameras, including an emmetropic panchromatic TDI CCD camera with a ground resolution of 2.1m, a forward and backward panchromatic TDI CCD camera with a ground resolution of 3.5m, and an emmetropic multispectral camera with a ground resolution of 5.8m. Among them, the three line array stereo image pairs formed by push broom imaging of forward looking and back looking panchromatic cameras can be used for DEM extraction. Through the retrieval of the transit information and data of ZY-3 from 2018 to 2019, the cloudless stereo images of hambantota area are selected for DEM extraction. The steps including defining ground control points, connection points, setting DEM extraction parameters and editing results.
DONG Wen
One belt, one road level, is set up. The data set is based on the 100 meter risk assessment data set and the 100m level vulnerability assessment dataset. The risk assessment data set of 34 nodes and 100 meters in the key area of the whole area is calculated based on the international definition of risk, risk (R) = hazard (H) * vulnerability (V). The data set assessed one belt, one road, the extreme precipitation risk under extreme precipitation events, and provided the basis for local government departments' decision-making. At the same time, it could make early warning before the flood disaster, so that we could gain valuable time to take measures to prevent and reduce disasters and reduce the loss of lives and property of people caused by floods.
GE Yong LI Qiangzi LI Yi
Based on the global surface water data (wod) from 1984 to 2018, the extreme precipitation frequency index and extreme precipitation intensity index were selected. Combined with the spatial analysis method in ArcGIS, the risk level of flood disaster in 34 key nodes under extreme precipitation conditions was constructed and evaluated. One belt, one road, 34 key nodes, is evaluated for the risk of flooding in the key areas of the "one belt" Road area under extreme precipitation events, which provides a basis for local government departments to make decisions and early warning before floods occur, so that we can gain valuable time for disaster prevention and mitigation measures to reduce the lives of the people brought by floods. Loss of property.
GE Yong LI Qiangzi LI Yi
Evapotranspiration over the Qinghai Tibet Plateau is calculated by etwatch, a land surface evapotranspiration remote sensing model based on multi-scale and multi-source data. Etwatch adopts the method of combining the residual term method with P-M formula to calculate evapotranspiration. Firstly, according to the characteristics of the data image, the suitable model is selected to retrieve the evapotranspiration on a sunny day; the remote sensing model is often lack of data because the weather conditions can not obtain a clear image. In order to obtain the daily continuous evapotranspiration, the penman Monteith formula is introduced, and the evapotranspiration results on a sunny day are regarded as the "key frame", and the surface impedance information of the key frame is used as the basis to construct the surface impedance Based on the daily meteorological data, the time series data of evapotranspiration are reconstructed. Through the data fusion model, the high spatial and temporal resolution evapotranspiration data set is constructed by combining the low and medium resolution evapotranspiration temporal variation information with the high resolution evapotranspiration spatial difference information, so as to generate the 8 km resolution evapotranspiration of the Qinghai Tibet Plateau Data sets (1990-2015).
WANG Xiaofeng
The Sixth Zhabuye Section locates at the northern part of the Zhabuye salt lake in Zhongba County, Tibet. The Middle Permian carbonates of the Xiala Formation outcropped very well in the region. This section has a thickness of 200 meters and was divided into 6 units. The lower 50 meters of the section is composed of limestones and chert layers. The chert layers in the above 100 meters decreased significantly, but they reappear in the top 35 meters. This section has abundant fusulines and smaller foraminifers. The fusulines has two assemblages, respectively Neoschwagerina majulensis-Kahlerina pachytheca assemblage in the lower and Chusenella quasifera-Codonofusiella nana assemblage in the upper. The foraminifers are divided into three assemblages, respectively Glomomidiellopsis specialisaeformis-Pachyphloia multiseptata assemblage, Lysites biconcavus-Shanita amosi assemblage and Lasiodiscus tenuis-Neoendothyra reicheli assemblage.
ZHANG Yichun
The Xiadong section locates at the Xiadong village region in Tsochen County, Tibet. The Permian strata in this region includes Largar, Angjie and Xiala formations. The Xiadong Section locates at the north of the Xiadong Village. The section is composed of entirely carbonates with abundant fusulines, smaller foraminifers and corals. The column exhibit the occurrences of fusulines and smaller foraminifers and their biostratigraphy. According to the fusulines, the age of the Xiala Formation at this section is middle Permian age. The fusulines can be subdivided into two assemblages, respectively Chenella changanchiaoensis-Neoschwagerina cheni in the lower and Nankinella-Chusenella assemblage in the upper. The foraminifers are divided into four assemblages, respectively Lasiodiscus tenuis-Palaeotextularia angusta elongata assemblage, Agathammina pusilla-A.vachardi assemblage, Hemigordiopsis-Midiella assemblage and Pachyphloi-Nodosinelloides assemblage.
ZHANG Yichun
The Mujiucuo section locates at the west of the Mujiucuo salt lake in Xainza County, Tibet. The Permian sequences oupcropped very well at the section. The Permian sequence at the section was divided into five formations, respectively Yunzhub, Largar, Angjie, Xiala and Mujiucuo formations. The Yunzhub Formation is composed of sandstone. The upper part of this formation contains limestone interlayers with 8 species of brachiopods. The brachiopods are grouped into the Costatumulus-Bandoproductus assemblage. The Xiala Formation is composed of entirely carbonates. The purplish limestone in the base of Xiala Formation consists of 6 species of brachiopods. They are grouped into the Alispiriferella-Retimarginifera celeteria assemblage. The overlying bed only contains one species Permophricodothyris elegantula. The brachiopods from these beds overall show a Gondwanan type in palaeobiogeography. It suggests that the Lhasa Block located not far away from the Gondwana margin. According to the constraints by fusulines and conodonts, the ages of beds 83, 86 and 87 are Middle Permian whereas those of beds 88 and 89 are late Permian. Brachiopods are found in many beds in the Xiala Formation. They are divided into two separate assemblages, respectively Echinauris opuntia-Neoplicatifera in the lower and Spinomarginifera lopingensis-Chonetinella cymatilis in the upper. Compared with the brachiopods from the Yunzhub Formation and basal Xiala Formation below, both assemblages from the middle and upper part of the Xiala Formation exhibit a pronounced palaeobiogeographical changes.
XU Haipeng ZHANG Yichun
The Mujiucuo section locates at the west of the Mujiucuo salt lake in Xainza County, Tibet. The Permian sequences oupcropped very well at the section. The beds from 83 to 89 consists mainly of bioclastic limestone with abundant fusulines and foraminifers. After careful examination, 13 species of fusulines and 37 species of smaller foraminifers are identified at the section. In terms of the occurrences of those fusulines and smaller foraminifers, the fusulines are subdivided into the lower Nankinella-Chusenella assemblage of Middle Permian and upper Codonofusiella schubertelloides zone of Late Permian. Similarly, the smaller foraminifers are also divided into lower Agathammina vachardi-Hemigordiopsis subglobosa assemblage and upper Glomomidiellopsis xainzaensis-Midiella reicheli assemblage. The dominance of Miliolinids in the section suggests an overall warm-water depositional settings.
ZHANG Yichun
One belt, one road, 34 key nodes, is used to assess the risk of flooding in the key areas of the "one belt" Road area under extreme precipitation events. It provides a basis for local government departments to make decisions and early warning before the flood. Thus, we can gain valuable time to take measures to prevent and reduce disasters and reduce the lives of the people. Loss of property. The data set takes one belt, one road, 34 key nodes, and the ratio of cultivated land to land, the proportion of urban land, the proportion of interlaced zone, the density of road network and the impervious surface. Based on the spatial analysis method in ArcGIS, the weights of each index are assigned. The vulnerability of 34 key nodes under extreme precipitation conditions is evaluated, and the vulnerability is determined by natural breakpoint method. Sex is divided into five levels, which represent no vulnerability, low vulnerability, medium vulnerability, high vulnerability and extremely high vulnerability.
GE Yong LI Qiangzi LI Yi
25 members consisting of researchers from Nanjing Institute of Geology and Palaeontology, CAS and Nanjing University, reporters from Beijing News, technicians from China Unicom, drivers and kitchener undertook the investigation on the Palaeozoic strata and faunas from various regions in northern Tibet from 30 August to 3 October. The expedition areas include areas in northern Selingco, Rejuechaka and Rongma region in northern Nyima County, Wenbu area in southern Nyima County. The objective of the expedition includes: (1) the origin of the Permian limestone blocks within the Bangong-Nujiang suture zones; (2) the Permian-Triassic strata, faunas and floras in the Rejuechaka region, northern Tibet; (3) the Ordovician cephalopods in the Rongma area, Nyima County; (4) the Permian sequence and faunas in the Wenbu area, southern Nyima County. This album contains the full record of the investigation and geological phenomenon. The links in the album can directly link to the video in internet.
ZHANG Yichun
25 members consisting of researchers from Nanjing Institute of Geology and Palaeontology, CAS and Nanjing University, reporters from Beijing News, technicians from China Unicom, drivers and kitchener undertook the investigation on the Palaeozoic strata and faunas from various regions in northern Tibet from 5 September to 2 October. The expedition areas include areas in northern Selingco, Rejuechaka and Rongma region in northern Nyima County, Wenbu area in southern Nyima County. In northern Selingco region, the expedition focused on the faunas from the exotic limestone blocks within the Bangong-Nujiang suture zone. In the Rejuechaka region, the expedition attention was paid on the Permian-Triassic successions, sea-level changes, and Permian and Triassic faunas and floras. In the Rongma area, the Ordovician cephalopod and Permian microfossils within the Longmu Co-Shuanghu suture zone was investigated. In the Wenbu area, the research attention was paid on the stratigraphic transition from the ice-houce to green-house conditions during the early Permian time. This document record the full information about the field investigation.
ZHANG Yichun
The dataset contains the identification lists of fusulines, smaller foraminifers, brachiopods and conodonts from three sections at the Mujiucuo area, Xainza County, Tibet. The Permian strata has very good outcrops at the Mujiucuo region. The Permian strata is composed of the Yunzhub, Largar, Angjie, Xiala and Mujiucuo formations in ascending order. The Yunzhub Formation contains only abundant brachiopods, they show a typical Gondwanan cool-water type. The middle part of the Xiala Formation is composed of medium-bedded limestone with abundant foraminifers, fusulines and brachiopods. The upper part of the Xiala Formation contains abundant conodonts, smaller foraminifers and fusulines. According to the ages of the fossils, the limestone from the northwest Mujiucuo section corresponds to the bed 86 of the Mujiucuo section. The limestones from the short western Mujiucuo section corresponds to bed 89 of the Mujiucuo section. The limestone and dolomites from the No. 2 short section comes from the lower part of the Mujiucuo Formation.
ZHANG Yichun
The western and northeastern Yunnan is located in the southeast of the Qinghai Tibet Plateau. Previous genetic studies have shown that there are substantial genetic imprints of late Paleolithic human in this region, and these ancient genetic imprints are likely to spread further to the Qinghai Tibet Plateau. Therefore, the genetic study of the population in this area is helpful to clarify the migration history of early human settlement in the Qinghai Tibet Plateau. In this study, we studied the genetics of Dai people in different areas of Yunnan Province. The mitochondrial DNA hypervariable regions of 264 Dai individuals were sequenced by Sanger sequencing. Based on phylogenetic analysis, we control the quality of these data to ensure that there is no sample contamination and other quality problems. According to the revised Cambridge Reference Sequence, the variants were recorded. According to the phylogenetic tree of mitochondrial DNA in the world population (PhyloTree.org), each sample was allocated into certain haplogrop. Based on the published mtDNA data of Dai people in other areas, the maternal genetic structure and formation mechanism of Dai population were systematically studied. The results showed that there was a close genetic relationship among the Dai populations in different regions, and the haplogroups (F1a, M7B and B5a) shared by these populations could be traced back to southern China, suggesting that the Dai population might have originated in southern China and migrated southward to the mainland and Southeast Asia in the Iron or Bronze age. The genetic differentiation of the Dai population in different regions is consistent with the phenomenon that their language and culture have some differences, which indicates that the Dai people and the surrounding populations in the southward migration.
KONG Qingpeng
To investigate the paternal genetic structure of Tibetans from Shigatse, 434 male samples were collected from Shigatse, Tibet. Firstly, SNP genotyping was performed to allocate samples into haplogroups. To further evaluate the genetic diversity of the major Y-chromosomal haplogroup in Tibetan populations from Lhasa, eight commonly used Y-chromosomal STR (short tandem repeat) loci (DYS19, DYS388, DYS389I, DYS389II, DYS390, DYS391, DYS392, and DYS393) were genotyped using fluorescence-labeled primers with an ABI 3130XL Genetic Analyzer (Applied Biosystems, USA). The results indicated that haplogroup O-M175 displayed highest frequency in Shigatse Tibetans (47.00%, the majority of its sublineages were O2-M122), followed by haplogroups D-M174 (40.78%, with most of the samples belonging to D-P47 (20.97%) and D-N1(16.82%)). Another relatively rare lineages in Shigatse Tibetans were C-M217 (1.84%), R1a1- M17 (1.61%), N1-LLY22G (5.76%), Q-M242 (0.69%). In combination with the data from Lhasa that released in 2019, our Y chromosome data of Tibetans from different locations on the Tibetan Plateau will be very helpful to understanding the paternal genetic structure of Tibetans. Moreover, the genetic history of Tibetans can also be dissected by phylogeographic and coalescent analyses.
KONG Qingpeng QI Xuebin
We obtained the whole genome variation data of 30 Tibetan individuals. The SNP typing of 30 samples was carried out by DNA array method, and about 700000 loci (including nuclear genome, mitochondrial DNA and Y chromosome) of each sample were obtained. First, after extracting genomic DNA, DNA amplification, enzymatic fragmentation, precipitation and re suspension were carried out. After the sample was incubated overnight and hybridized with beadchip, the DNA was annealed to obtain a site-specific 50 mer probe, covalently coupled with an Infinium bead type. Then Infinium XT was used to extend the enzyme base to give the allele specificity, and then fluorescent staining was carried out. The fluorescence intensity of the beads was detected by iSCAN system, and the Illumina software automatically performed the analysis and genotype recognition. Finally, the SNP typing results of each sample were obtained. Based on the above data, relevant biological information analysis (mainly including chip site quality control analysis, Y chromosome and mitochondrial DNA haplotype analysis) was carried out. This data is helpful to analyze the genetic structure of Tibetan population from the perspective of nuclear genome, Y chromosome and mitochondrial DNA. By comparing with the data of people around the plateau, we can trace the migration and settlement history of the plateau population comprehensively.
KONG Qingpeng
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
Net primary productivity (NPP), as the basis of ecosystem material and energy cycle, can reflect the carbon sequestration capacity of vegetation at regional and global scales, and is an important indicator to evaluate the quality of terrestrial ecosystem. Based on the principle of light use efficiency model, the productivity model of ecosystem in national barrier area was established by coupling remote sensing, meteorology, vegetation and soil type data. In the selection of parameters, the photosynthetic effective radiation (APAR) was calculated from GIMMS NDVI 3gv1.0 data, vegetation map of China, total solar radiation and temperature and humidity data. Compared with the soil water molecular model, the regional evapotranspiration model can simplify the parameters and enhance the operability of the model. The net primary productivity (NPP) of terrestrial vegetation in 1990-2015 over the Qinghai Tibet Plateau was estimated based on the parameterized model with par and actual light use efficiency as input variables of CASA model.
WANG Xiaofeng
High spatial and temporal resolution remote sensing image plays a very important role in land use change detection, disaster monitoring and bio-geochemical parameter estimation.Currently, Landsat multi-spectral series satellite data (including Landsat TM, ETM+ and OLI multi-spectral bands) is one of the most widely used multi-spectral data.Taking the One Belt And One Road key node area as the research area, and based on the data of Landsat TM/ETM+/OLI series with good quality from 2000 to 2016, python was used to clip the data in the research area with the masks .To solve the partial data missing problem, MODIS imagery on the missing date and Landsat-MODIS data pair of adjacent phases are combined for spatio-temporal fusion to obtain Landsat-like data.Finally, the high spatial and temporal resolution remote sensing images of 34 key node area during 2001 to 2016 lasted for 8 to 16 days was obtained.
YIN Zhixiang LING Feng
Soil mapping and attribution dataset of all nodes area in pan-third pole is an important information to provide extremely valuable reference for the weather process, drought and hydrological monitoring, which reflects the soil resources, soil fertility, soil environment, soil biology and other soil databases. The base of this data is from the world soil database (HWSD), which belongs to the FAO soil portal V1.2) authoritative public data are the main data, and some supplementary data such as ISRIC soil data published by ISRIC and other project data collected by the world soil data center (ISRIC). Finally the data of soil information in the expected 31 pan third pole key node regionsand 3 projects is obtained. As the research basis of soil, this data set provides the basic information of soil regionalization for the project. This data has the characteristics of grid visualization and attribute table visualization, which can be viewed through many ways. The grid visualization data provides general data on soil types and distribution. And the attribute information is included in the excel, including detailed soil parameters (including organic carbon, pH, water storage, soil depth, cation exchange capacity and clay content of soil, total exchangeable nutrients, lime and gypsum content, sodium exchange rate, salinity, structural grade and particle size, etc.).
SHANG Cheng LING Feng
