Investigating the importance of landslides as a sediment source in a tectonically active catchment using the geochemical fingerprinting approach

Description

This Excel file contains two worksheets (Sheet), with details as follows:① Worksheet 1 (named: sediment source): A total of 82 sets of sediment source sample data are included, divided into 4 sample groups: 27 sets of Forest samples, 21 sets of farmland samples, 21 sets of landslide samples, and 13 sets of abandoned farmland samples. Each set of sample data contains 18 elemental indicators: Al, Ca, Fe, K, Mg, Na, Ba, Co, Cr, Cu, Li, Mn, Ni, P, Pb, Sr, V, and Zn.② Worksheet 2 (named: suspended sediment): A total of 17 sets of suspended sediment sample data are included, with all detection indicators completely consistent with those in the sediment source worksheet.

Steps to reproduce

In this data set, the collected catchment surface sediment samples and suspended sediment samples were naturally air-dried and sieved through a 2 mm mesh, followed by digestion. Elemental concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS). Quality assurance and quality control (QA/QC) were strictly performed throughout the ICP-MS analysis in accordance with national standards HJ 1315–2023 and GB/T 44343–2024. Certified reference materials (CRMs) were tested alongside the samples, and the relative deviations between measured and certified values were less than 10%. At least two procedural blanks were included in each batch, and the concentrations of all target elements in blanks were below the detection limit. One parallel sample was analyzed for every 20 samples or per batch containing fewer than 20 samples, with relative deviations of parallel measurements within ±25%. One certified reference material was also added every 20 samples or per batch of less than 20 samples, and the relative errors between measured and certified values were within ±25%.

Institutions

• Neijiang Normal University

  Sichuan, Neijiang

Categories

Funders

• National Natural Science Foundation of China

  Grant ID: 42207413
• Natural Science Foundation of Sichuan Province

  Grant ID: 2022NSFSC1045

Licence