| dc.description.abstract | The mathematical modeling of the transport and transformation of trace species in the atmosphere is one of the scientific tools currently used to assess atmospheric chemistry, air quality, and climatic conditions. From the scientific but also from the management perspectives accurate inventories of emissions of the trace species at the appropriate spatial, temporal, and species resolution are required. There are two general methodologies used to estimate regional to global emissions: bottom-up and top-down (also known as inverse modeling). Bottom-up methodologies to estimate industrial emissions are based on activity data, emission factors (amount of emissions per unit activity), and for some inventories additional parameters (such as sulfur content of fuels). Generally these emissions estimates must be given finer sectoral, spatial (usually gridded), temporal, and for some inventories species resolution. Temporal and spatial resolution are obtained via the use of surrogate information, such as population, l nd use, traffic counts, etc. which already exists in or can directly be converted to gridded form. Speciation factors have been and are being developed to speciate inventories of NO{sub x}, particulate matter, and hydrocarbons. Top-down (inverse modeling) methodologies directly invert air quality measurements in terms of poorly known but critical parameters to constrain the emissions needed to explain these measurements values of these parameters are usually computed using atmospheric transport models. Currently there are several strong limitations of inverse modeling, but the continued evolution of top-down estimates will be facilitated by the development of denser monitoring networks and by the massive amounts of data from satellite observations. | en_US |
