Year: 2025
Type: article
Abstract: This study systematically develops a spatiotemporal distribution dataset (YRB-S5P-TGD) of four trace gases (CH4, HCHO, NO2, and CO) in the Yellow River Basin from 2018 to 2024, based on ESA Sentinel-5P satellite L2 offline data streams and GEE L3 standardized datasets. The dataset integrates the high-frequency global coverage advantage of S5P satellites with the cloud computing capabilities of the GEE platform. The study adopts a standardized processing workflow: (1) Data screening and quality control: Offline data streams are prioritized, with low-confidence observations filtered using quality flags; (2) Spatiotemporal aggregation and resampling: Monthly/annual mean vertical column concentrations are calculated, and the original resolution is upscaled to a 1 km grid using bilinear resampling; (3) Regional cropping and standardized output: GeoTIFF-format datasets are generated based on the Yellow River Basin boundary to ensure compatibility with mainstream GIS analysis requirements. By implementing rigorous and unified quality control standards, the dataset effectively avoids biases potentially introduced during reprocessing, supporting multi-scale analyses ranging from monthly variations to interannual trends. This dataset constitutes a valuable supplement to the systematic observation data of trace gases in the Yellow River Basin. Its high spatiotemporal resolution delivers critical data support for research such as precise source apportionment of regional air pollution and greenhouse gas flux estimation.
Source: China Scientific Data
Authors: Shiyu CAO, Ji LIANG, Keting FENG, Lian YANG
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4416984622