Refining emission rate estimates using a coupled receptor–dispersion modeling approach

Receptor modeling techniques like chemical mass balance are used to attribute pollution levels at a point to different sources. Here we analyze the composition of particulate matter and use the source profiles of sources prevalent in a region to estimate quantitative source contributions. In dispersion modeling on the other hand the emission rates of various sources together with meteorological conditions are used to determine the concentrations levels at a point or in a region. The predictions using these two approaches are often inconsistent. In this work these differences are attributed to errors in emission inventory. Here an algorithm for coupling receptor and dispersion models is proposed to reduce the differences of the two predictions and determine the emission rates accurately. The proposed combined approach helps reconcile the differences arising when the two approaches are used in a stand-alone mode. This work is based on assuming that the models are perfect and uses a model-to-model comparison to illustrate the concept.