(748a) Downscaling Camx Using a Reduced Complexity Model: A Multiscale Modeling Approach for Urban Air Pollution

Exposure to air pollution is associated with an increased risk of respiratory and cardiovascular disease. Urban populations are often exposed to high air pollution levels and are at greater risk of developing these health outcomes. Unfortunately, air pollution remains a complex problem in urban areas. Emissions from sources present in urban areas result in an irregular distribution of pollutants. Many chemical and dynamic processes that affect urban air pollution also occur on a wide range of spatial and temporal scales. The nature of urban air pollution requires models capable of high spatial and temporal resolution to capture local and regional interactions. Eulerian grid chemical transport models are employed and can provide estimates of pollutant concentrations at the regional scale. These models may also estimate concentrations at some urban scales. However, the highest resolution cannot adequately capture the spatiotemporal variability of air pollutants needed to understand urban air pollution.

Therefore, in this work we propose a multiscale modeling system to overcome this limitation of regional chemical transport models. We use the Comprehensive Air Quality Model with Extensions (CAMx), a common regional scale model used in the United States, to predict concentrations at a regional scale (12 x 12 km resolution) for the Contiguous United States. We further resolved these estimates using a reduced complexity model, InMAP, that uses grid adaptations to produce a variable horizontal resolution grid. The reduced complexity model provides higher spatial resolution, down to 500-meters, in densely populated areas and lower resolution in more sparsely populated areas. This provides additional spatial resolution in the urban areas that experience more spatial heterogeneity in air pollutant concentrations. We use source-specific spatial surrogates to calculate the emissions in each grid cell. We compare the simulated concentrations to monitoring data provided by the EPA. The proposed approach is the first step toward understanding the spatial and temporal variability of air pollution in urban areas.