High-resolution emission source mapping: a bottleneck for understanding urban climate and air quality

Recent advances in high-resolution urban climate and air quality modeling have enabled more explicit representation of fine-scale meteorology and pollutant transport within cities. However, the emission data used to drive these models have not kept pace. This mismatch can introduce structural errors and uncertainties that propagate into simulated pollutant concentrations, chemical regimes, and population exposure, particularly in neighborhoods characterized by steep emission gradients and nonlinear chemistry. These limitations are especially pronounced during extreme and compound heat–air pollution events, when emissions can deviate substantially from climatological patterns, and in settings such as the wildland–urban interface, where conventional inventories often omit or simplify key sources. Recent developments in emission source mapping, such as source-resolving emission estimation, improved chemical speciation, integration of indoor and biogenic emissions, and high-resolution observational constraints, demonstrate the feasibility of more process-informed emission inventories. We argue that high-resolution urban emission inventories should be treated as critical scientific infrastructure for next-generation air quality and climate applications. We outline key requirements for such inventories, including spatial and temporal fidelity, sectoral and chemical details, and transparent uncertainty characterization. We also propose a roadmap centered on benchmarking, reproducible data pipelines, uncertainty quantification, and timely incorporation of emerging emission sources. Without parallel progress in emission datasets, the growing capabilities of urban atmospheric models will remain fundamentally constrained, limiting their reliability for scientific understanding and decision support.