This paper discusses and critiques methods used to estimate emissions of, a
nd create both aggregate and detailed modeling inventories for, nitrogen ox
ides (NOx), volatile organic compounds (VOC) and carbon monoxide (CO), the
main pollutants involved in ozone formation. Emissions of sulfur dioxide (S
O2) and methods to project emissions into the future are also briefly discu
ssed. Many improvements have been made in emissions inventories over the pa
st decade. For example, the required use of continuous emission monitors (C
EMS) has produced site-specific emissions estimates from almost all US elec
tric utility power plants, which are the major stationary source of NOx. Ho
wever, many data quality issues remain. For example, the overall quality of
standardized emission factors is very poor. In addition, uncertainties hav
e been introduced by use of simplistic assumptions on the existent level of
emission control. Even the use of GEMS has not eliminated uncertainty in e
missions from power plants, because methods to deal with missing data can i
ntroduce bias. Emissions data for Mexico are not comprehensive, making ozon
e modeling in US border regions difficult. Data for VOC speciation is outda
ted, and crude data is often used to disaggregate emissions to the fine lev
el of spatial and temporal detail needed for atmospheric modeling. It is di
fficult to make general statements about the importance of each of these pr
oblems, because there are no reliable estimates of the overall uncertainty
of emissions values, and because the impact of emission inventory errors is
very site specific. The Emissions Inventory Improvement Program (EIIP) ini
tiated by the US Environmental Protection Agency promises to enhance the qu
ality of future inventories, mainly through communication of best practices
among state agencies. Further inventory improvement efforts must be focuse
d on problems that most strongly influence poor prediction of ozone concent
rations. Targets for improvement could be based on comparison of photochemi
cal modeling results to observed concentrations, coupled with other techniq
ues that better explain source-receptor relationships. (C) 2000 Elsevier Sc
ience Ltd. All rights reserved.