Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on uncertainties in chemical transport model predictions

Abstract

A Monte Carlo (MC) probabilistic approach is used to estimate uncertainties in the emissions outputs of the Biogenics Emission Inventory System Version 3 (BEIS3) model and subsequent ozone outputs of three Chemical Transport Models (CTMs) due to uncertainties in many key BEIS3 biogenics emissions model parameters and inputs. BEIS3 was developed by the Environmental Protection Agency to estimate emissions of biogenic substances such as isoprene, monoterpenes, oxygenated and other volatile organic compounds (OVOCs), and biogenic nitric oxide (BNO). Uncertainties are investigated for three time periods, 24–29 May, 11–15 July, and 4–9 September 1995, which are ozone episodes that have been extensively studied by others as part of emissions control planning exercises. In the MC approach, 1000 samples are drawn randomly and independently from seventeen BEIS3 parameters and inputs, whose distribution shapes and variances are determined from literature reviews. The 95% confidence range on the calculated uncertainties in isoprene and BNO emissions cover approximately an order of magnitude. On the other hand, the 95% confidence ranges on the calculated uncertainties in monoterpenes and OVOC emissions are much smaller: about ±20%. Correlations are calculated between the 1000 MC samples of pairs of variations in model parameters or inputs and variations in BEIS3 daily emissions estimates for individual grid squares. A few significant correlations are found for some of the assumed model parameters. The MC uncertainties in the CTM‐predicted 1‐ and 8‐hour averaged ozone concentrations were studied by drawing 20 random samples from the 1000 sets of BEIS3 outputs and running each CTM (MAQSIP, UAM‐V, and URM) 20 times for the three episodes. The estimated total uncertainties of ±15 to 20% are found to be nearly the same for the three CTMs over the three time periods, for 1‐ and 8‐hour averages.