Air Quality Forecasts With Observation‐Based Scaling of Anthropogenic Emissions for Urban Agglomerations

Abstract

Air quality forecasts are essential to support decision‐making in urban agglomerations, where millions of people are exposed to high levels of pollution. However, these forecasts are often limited by uncertainties in anthropogenic emissions, especially in large urban agglomerations where no dedicated operational system exists. In this study, we present an observation‐based emission scaling approach aimed at improving operational forecasts. This method derives scaling factors (SF) from the ratio of observed‐to‐modeled concentrations over the previous week and applies them to anthropogenic emissions in the forecast model, assuming that in large urban agglomerations, biases between observed and modeled concentrations are primarily driven by uncertainties in anthropogenic emission inventories. The method also derives SF for anthropogenic volatile organic compound emissions based on modeled daytime biases under the assumption of a NOx‐saturated regime. We implement this method in a chemistry‐transport model using a global anthropogenic emission inventory and apply it to São Paulo for two distinct periods (February–April 2023 and July–September 2024). The results show that forecasts of CO, , and concentrations are significantly improved within a few weeks. For and , improvements are more limited by the influence of secondary aerosol formation and by pollution transport from outside of the agglomeration. Overall, our results demonstrate that observation‐based emission scaling provides an efficient and transferable methodological approach for improving operational air quality forecasts in urban agglomerations without requiring model‐specific developments.