Jing Wei

The GlobalHighAirPollutants (GHAP) dataset provides long-term, full-coverage, high-resolution, and high-quality global ground-level air pollutant data over land. Generated from big data using artificial intelligence, it accounts for the spatiotemporal heterogeneity of air pollution. Currently, GHAP includes PM2.5 (1 km), NO2 (1 km), CO (1 km), and O3 (10 km), with plans to expand to more species. Data is accessible via platforms like Zenodo and Google Earth Engine.

Similarly, the ChinaHighAirPollutants (CHAP) dataset offers long-term, full-coverage, high-resolution, and high-quality ground-level air pollutant data specifically for China. This dataset is also developed using artificial intelligence on extensive big data, including ground-based measurements, satellite remote sensing products, and atmospheric reanalysis. CHAP covers seven major air pollutants: PM1, PM2.5, PM10, O3, NO2, SO2, and CO, along with PM2.5 chemical composition.