PHLET-BIG: 1-km resolution inversion of sectoral emissions based on satellite constrained by big Earth data

High-resolution emission data are essential for strategic environmental governance and accurate air quality modeling. However, fine-scale (i.e. 1-km) emission assessments remain challenging for traditional bottom-up inventories in Global South countries, including China, due to the lack of unit-level source information. Meanwhile, observation-based emission inversions are often limited in timeliness, spatial resolution, and/or sectoral discrimination. Here, we integrate a fast physics-based inversion framework, PHLET, with big Earth data to derive 1-km-resolution, sector-specific emissions from satellite observations. The resulting new framework, PHLET-BIG, achieves accurate emission positioning and sectoral attribution by incorporating spatial features linked to emission sources extracted from high-resolution Earth data.

Applying PHLET-BIG to China reveals unprecedented fine-scale distributions of NO_X emissions and their recent sectoral spatiotemporal evolution during the summers of 2018–2024. Emissions span several orders of magnitude and show a clear decoupling from population density and nighttime light at the 1-km grid scale. While national total NO_X emissions declined by 24.6% over this period, pronounced sectoral contrasts persist at individual locations, townships, and counties. PHLET-BIG enables unit-level emission tracking from space, demonstrates consistency with in situ flux observations, and reduces NO₂ modeling errors by 20–60%. This framework provides a cost-effective foundation for refined emission control strategies and fine-scale air pollution analyses.