Incorporating uncertainties in emission inventories into air quality modeling

In modeling ambient ozone concentrations, NO_x emissions estimated based on emission inventories are used as input to air quality models. A concern regarding the quality of ozone predictions is the uncertainty inherent in emission inventories. This work aims at developing new methodologies for quantifying uncertainty in NO_x emission inventories and propagating the uncertainty through a photochemical grid air quality model. Time series techniques were used to develop new methodologies for developing probabilistic emission inventories. These methodologies were applied to a case study for NO_x emissions for each of 32 units of 9 coal-fired power plants in the Charlotte domain. In order to investigate how much of an effect does correlation between emissions from different units has on the developed probabilistic inventory, two different approaches were used. Univariate time series techniques were applied in the first where each unit is assumed to be dispatched independently of the all other units. Multivariate time series techniques were applied in the second in order to account for the inter-unit dependence in developing the inventory. A methodology for accounting for intra-unit dependence between variables used in estimating the inventory was also developed. Uncertainties in the developed inventories were propagated through an air quality model. Uncertainties in the maximum 1-hour and 8-hour ozone predictions were estimated at different locations in the modeling domain. Forty-three grid cells were estimated to have a probability greater than 0.9 that the maximum hourly ozone concentration exceeds the 120 ppb 1-hour ozone standard while 1654 grid cells showed a probability greater than 0.9 of exceeding the 80 ppb 8-hour standard. The results of the case study demonstrate that the range of hourly variability in power plant emissions is sufficient large to justify a quantitative analysis of uncertainty, and that the range of uncertainty in air quality predictions is large enough to imply ambiguity regarding development of control strategies. To cope with uncertainty, control strategies can be developed to achieve attainment with an acceptable degree of confidence, such as 90 or 95 percent.