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Many studies rely on air pollution modeling such as land use regression (LUR) or atmospheric dispersion (AD) modeling in epidemiological and health impact assessments. Generally, these models are only validated using one validation dataset and their estimates at select receptor points are generalized to larger areas. The primary objective of this paper was to explore the effect of different validation datasets on the validation of air quality models. The secondary objective was to explore the effect of the model estimates’ spatial resolution on the models’ validity at different locations. Annual NOx and NO2 were generated using a LUR and an AD model. These estimates were validated against four measurement datasets, once when estimates were made at the exact locations of the validation points and once when estimates were made at the centroid of the 100m×100m grid in which the validation point fell. The validation results varied substantially based on the model and validation dataset used. The LUR models’ R2 ranged between 21% and 58%, based on the validation dataset. The AD models’ R2 ranged between 13% and 56% based on the validation dataset and the use of constant or varying background NOx. The validation results based on model estimates at the exact validation site locations were much better than those based on a 100m×100m grid. This paper demonstrated the value of validating modeled air quality against various datasets and suggested that the spatial resolution of the models’ estimates has a significant influence on the validity at the application point.


Mary MacKillop Institute for Health Research

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Journal Article

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