Introduction: Air quality modeling heavily relies on accurate air pollutant emissions inventories. In regions such as São Paulo State, where the vehicle fleet is significant, vehicles are typically considered the main source of emissions. However, industrial activities also play a crucial role and should not be disregarded due to their contribution to pollutant emissions. Recognizing the importance of accurate local emissions data, using this information in an air quality model can better represent the pollutant concentrations in the region. To use this data as input into an air quality model, it must be processed appropriately to align with the model input format. Therefore, the development of tools for processing local pollutant emissions data facilitates the execution of air quality models.

Objective: In this direction, the main objective of this study is to present a tool for processing emissions from stationary sources, and apply it in a WRF-Chem (Weather Research and Forecasting with Chemistry) simulation for the State of São Paulo, Brazil, ultimately discussing the impact of this source on the region’s air quality.

Method: The SSEP (Stationary Source Emissions Preprocessor) model employs the NCL (NCAR Command Language) programming language to spatialize point-source emissions. It uses the ESMF (Earth System Modeling Framework) interpolation function, which interpolates data within a grid. The file structure is built from a source grid – the input containing spatial information of the modeled domain. This data generates the target grid with emission data and spatialized metadata in netCDF format. The interpolation method used is ‘neareststod,’ which identifies the nearest grid point to the emission source locations, distributing the emitted value into the grid cell. For model execution, the number of emitting points, daily profiles, vertical levels, and the number of species considered in the mechanism must be determined. SSEP enables the fractionation of volatile organic compounds (VOCs) and particulate matter (PM) by adjusting fractions and selecting the species of interest for the region. In the case study, 21 chemical species were considered, encompassing atmospheric pollutants such as nitrogen oxides (NOx), sulfur oxides (SOx), hydrocarbons (HC), and PM.

Findings: The results indicate that industrial sources make a significant contribution to particulate matter concentrations and are the primary anthropogenic source of SO2. Despite their lower contribution to NOx, as most of it originates from vehicular sources, emissions from these industrial sources are notably high in specific areas of the state, thus constituting an important local source to consider. The SSEP model demonstrated suitability in processing information from stationary source inventories, allowing adjustments in fractionation or chemical species to be made. Another important aspect is the ability to adapt outputs to a format compatible with various air quality models, which would streamline the input of emission data into them.

Keywords: inventory, air quality model, emission of atmospheric pollutants