Open source chemistry-transport models are used for air pollution forecasting on a regional scale using meteorological fields produced by regional weather forecasting models and global emission databases for anthropogenic emissions. Many cities have compiled local emission inventories to drive air pollution forecasting. We present preliminary results from an ongoing project to develop a regional air quality forecasting model with the goal to achieve a level of accuracy that allows for scenario assessment of the effectiveness of traffic management policies to reduce air pollution.
After including local emission inventory for road transport, we observed higher variability between different locations within the city in agreement with ground observations. The forecast accuracy for PM2.5 also improved compared to the baseline case based on global emission inventory. Next steps in the adaptation of the model would involve developing local temporal disaggregation functions for annual emissions based on the historical air pollution monitoring data.