Using the Google Earth Engine to estimate a 10 m resolution monthly inventory of soil fugitive dust emissions in Beijing, China

Soil fugitive dust (SFD) is an important contributor to ambient particulate matter (PM), but most current SFD emission inventories are updated slowly or have low resolution. In areas where vegetation coverage and climatic conditions undergo significant seasonal changes, the classic wind erosion equation (WEQ) tends to underestimate SFD emissions, increasing the need for higher spatiotemporal data resolution. Continuous acquisition of precise bare soil maps is the key barrier to compiling monthly high-resolution SFD emission inventories. In this study, we proposed taking advantage of the massive Landsat and Sentinel-2 imagery data sets stored in the Google Earth Engine (GEE) cloud platform to enable the rapid production of bare soil maps with spatial resolutions of up to 10 m. The resulting improved spatiotemporal resolution of wind erosion parameters allowed us to estimate SFD emissions in Beijing as being ~5-7 times the level calculated by the WEQ. Spring and winter accounted for >85% of SFD emissions, while April was the dustiest month with SFD emissions of PM10 exceeding 11,000 t. Our results highlighted the role of SFD in air pollution during winter and spring in northern China, and suggested that GEE should be further used for image acquisition, data processing, and compilation of gridded SFD inventories. These inventories can help identify the location and intensity of SFD sources while providing supporting information for local authorities working to develop targeted mitigation measures.