Spatial-aware source estimation in building downwash environments

Abstract

This paper introduces a concept called ”spatial-aware” source-term estimation (STE), which utilizes the acquired knowledge of how the forward model (FM) predictions compare against observations spatially to enhance the overall quality of source estimation. We evaluated the effectiveness of the proposed method against a wind tunnel experiment simulating low-stack plume dispersion in a building downwash environment with known source locations and varying building aspect ratios and building angles relative to wind direction, selected to achieve the balance of physical complexity and environmental controllability. Specifically, we adopted AERMOD as the FM, constructed spatially-resolved error models by comparing the FM predictions against measured concentrations for one configuration, referred to as the training case. Then the error models were applied to the rest of the configurations, referred to as test cases, for estimating emission rates. The results show that the spatial-aware STE can improve the estimation results, due to closer agreement between the error models and error distribution in terms of shapes and/or magnitudes. For example, using the vertical measurements around the H/W = 1/2 building with wind direction perpendicular to the building as the training case and applying the spatially-resolved error models, the estimated emission rates for all test cases with same building aspect ratio differ from the true emission rate by less than 30%, compared to 63.2%–236% without applying any error models. We argue that parallel efforts, i.e., improving the accuracy of FMs and quantifying the FM performance are needed to further advance the science and applications of source estimation.