Long-term dataset of concentrations of atmospheric POPs in the southeast Tibet (2012-2014)

This data set is the long-term concentrations of atmospheric POPs in southeast Tibet, including OCP, PCBs and PAHs. The sampling period in this study was from August 2008 to July 2014. The data was gained from the continuous air monitoring program in STORS. In this program, a low-volume air sampler (~100 L/min) was set at STORS to trap particle- and gas-phase chemicals by a glass fiber filter (GFF, diameter of 9 cm) and polyurethane foam plugs (PUFs, 7.5 cm × 6 cm diameter), respectively. Typically, ~700 m3 of air was collected over a two-week period. Total (gas + particle) phase concentrations are reported. POPs were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The air samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently. The samples were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs and PCBs and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Field blanks and procedural blanks were prepared. The recoveries ranged from 64% to 112% for OCPs, and 65% to 92% for PAHs. The reported concentrations were not corrected for recoveries.

0 2020-08-15

Digital soil mapping dataset of soil texture (soil particle-size fractions)in the Tianlaochi basin (2012-2014)

Select the soil mechanical composition data with a depth of 0-20cm on the surface of the soil, select the optimal spatial prediction mapping method for soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The classification standard of soil particle size is American classification. The source data of this data set are from the data center of cold and drought regions, soil physical properties-soil bulk density and mechanical composition data set soil sampling profile data of Tianlaochi watershed in Qilian mountain.

0 2020-03-28

Digital soil mapping dataset of soil bulk density in the Heihe river basin (2012-2014)

The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF;

0 2020-03-27

Digital soil mapping dataset of soil pH in the Heihe river basin (2012-2014)

Using digital soil mapping method to produce soil surface pH spatial distribution data products. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301).

0 2020-03-27

Digital soil mapping dataset of soil depth in the Heihe River Basin (2012-2014)

The data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). The prediction method is mainly based on the soil landscape model. The basic theory of the model is the classic soil genesis theory. The model regards the soil as the product of the comprehensive effects of climate, topography, parent material, biology and time. Scope: Heihe River Basin; Projection: Albers ﹣ conic ﹣ equal ﹣ area; Spatial resolution: 90m; Data format: ArcGIS grid; Data content: spatial distribution of soil thickness Prediction method: enhanced regression tree Environmental variables: main soil forming factors

0 2020-03-27

Digital soil mapping dataset of soil texture in the Heihe river basin (2012-2014)

The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). The prediction method is mainly based on the soil landscape model. The basic theory of the model is the classic soil genesis theory. The model regards the soil as the product of the comprehensive effects of climate, topography, parent material, biology and time. Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Data content: spatial distribution of soil clay, silt and sand content Prediction method: enhanced regression tree Environmental variables: main soil forming factors

0 2020-03-27