Evaluation and Diagnosis of Regional Ammonia Emission Inventory in the Pearl River Delta Using Multisite NH₃ Observations and Model Simulations

Ammonia (NH₃) has attracted increasing attention for its reduction potential in fine particulate matter mitigation, yet current NH₃ emission inventories involve substantial uncertainties. Previous bottom-up NH₃ inventories are usually constrained by satellite observations, deposition measurements, or isotopic analysis and still lack careful validation at fine regional scales. This study develops a novel diagnostic framework combining multisite NH₃ observations across the Pearl River Delta (PRD) with the Community Multiscale Air Quality (CMAQ) model simulations and machine learning techniques to evaluate and refine a regional NH₃ inventory. Our analysis indicates that the inventory overestimates agricultural emissions, particularly during the wet period, while underestimating nonagricultural sources. Underrepresented precipitation effects were a key driver of overestimated agricultural emissions (∼19% during the wet period). Conversely, a natural experiment during the Spring Festival holiday provided strong evidence that vehicle emissions are a key underestimated nonagricultural source. Adjusting the inventory based on these findings (agricultural sources reduced 39% (31%) during wet (dry) periods, nonagricultural sources increased 70%) improved NH₃(g) simulations across the PRD. Our study highlights the value of multisite observations in validating NH₃ inventory and the critical need to better characterize underestimated (e.g., vehicles) and missing sources (e.g., urban landscaping) in current inventories.