Mapping spatio-temporal changes of energy consumption-related NOx emissions at grid scale in the Pearl River Delta region using nighttime light data

Monitoring nitrogen oxide (NO x ) emissions in developing regions faces persistent challenges due to fragmented and inconsistent municipal/county-level energy statistics, hindering precise spatiotemporal assessment. To overcome this limitation, this study pioneered a downscaling framework integrating cross-calibrated DMSP-OLS and NPP-VIIRS nighttime light data with sparse energy consumption records. This approach establishes a robust power-function correlation, enabling the creation of kilometer-scale resolution emission maps. Applying this method to China's Pearl River Delta (PRD) revealed distinct patterns in NO x emissions between 2010 and 2020. Emissions increased from 1.31 × 10 6 t in 2010 to 1.50 × 10 6 t in 2020, with the most significant growth occurring in coastal areas. Spatially, emission hotspots expanded southeastward, shifting from inland cores like Guangzhou-Foshan towards coastal cities such as Dongguan and Shenzhen, while low-emission areas persisted in peripheral regions including Huizhou and Zhaoqing. Concurrently, the geographic center of emissions migrated approximately 4.2 kilometers southeast, a shift attributed to industrial relocation driven by port-oriented development policies. Validated against official energy statistics with exceptional accuracy (R² = 0.989, MRE = 0.82 %), this methodology provides high-fidelity, spatially explicit emission inventories for data-scarce environments, delivering critical evidence to design targeted pollution control strategies and inform regional low-carbon transitions.