Ponente
Descripción
Emissions inventories provide information on pollutants’ mass contributions and emission characteristics during a given period for sources located in a geographic area. Researchers further transform them into model-ready files that are input to air quality models by aggregating emission rates for each pollutant and source in a computational domain. As such, emissions inventories used in air quality models are essential instruments for air quality management during the design and evaluation of emission control strategies, air quality forecasting, and evaluation of health and environmental impacts. Since all emissions inventories are estimates obtained by a combination of 1) limited source measurements, 2) emissions modeling, and 3) assumptions based on expert judgment, uncertainties from input parameters are introduced and irremediably propagated in the emission estimates. Therefore, there is a need to better characterize uncertainties estimates in model-ready emissions inventories used in air quality modeling applications. In this study, we 1) summarize the current state of statistical methods for quantifying uncertainties in emissions inventories, and 2) identify improved approaches that can be used to estimate uncertainties in model-ready emission inventories. The results of this analysis include recommendations of analytical techniques during the application of air quality models for assessing the impacts of emissions uncertainties in air quality predictions.
