Typically, air pollutant emission rates for chemical species such as NOx are estimated using a "bottom-up" approach, which uses fuel consumption information, spatial surrogates (e.g., road density, population density, locations of known stack emissions), temporal surrogates (e.g., traffic patterns, industrial work schedules) and emission factors (mass of pollutant per mass of fuel burned) to estimate the spatiotemporal patterns of emissions across regions . With investments in technology to better understand the spatiotemporal patterns of pollutants (e.g. incorporating real-time traffic data using speed and type of vehicle) and laboratory studies to better estimate the emission factors in a wide range of conditions, these "bottom-up" estimates can be improved . These new and improved estimates can then be incorporated into a chemical transport model and evaluated against observations from ground monitors. Based on this comparison, the emission estimates can be further adjusted and improved if necessary. However, given the complexity of this cycle, "bottom-up" emission estimates typically take many years to compile by a large team of scientists, and subsequently, are delayed in time by several years from the actual emission time.