Uncertainties in the simulated intercontinental transport of air pollutants in the springtime from emission and meteorological inputs

Abstract

The uncertainty in the quantitative estimates of long-range transport of pollutants in air quality models is large. In this study, based on the Global Nested Air Quality Prediction Modeling System (GNAQPMS-SM), using Latin hypercube sampling (LHS) method, we conducted a base simulation and an ensemble of 1° × 1° simulations in April 2017 to assess the uncertainty in model estimates of O3 and PM2.5 intercontinental transport from uncertainties in input parameters (emissions, temperature, wind field). The results show that the base simulation has biases in some regions, and when the uncertainty of input parameters is taken into account, the observations mostly lie within the uncertainty range of the 50 simulated group samples. The uncertainties of input parameters are reasonably quantified in terms of their propagation during computation in GNAQPMS-SM. The best performance occurs with the multiple linear regression (MLR) method, which is used to fit ensemble simulation results. The uncertainty range of monthly O3 transport from North America (NAM) to Europe (EUR) at a 1.2 km altitude is 4.91–5.44 ppb, from EUR to China is 0.78–0.96 ppb, and from China to NAM is 0.76–0.85 ppb. Correspondingly, the MLR result of each is 5.12 ppb, 0.80 ppb, and 0.77 ppb, respectively. The MLR result is thought to be closer to the actual transport amounts, and most of them lie within the uncertainty range. The appreciable differences between base simulation and MLR results reflect the uncertainties in the input parameters. Finally, taking O3 transport from China to Japan as an example, the maximum absolute value of the daily average Spearman correlation coefficient |ρ‾|max between input parameters and contributions is calculated to identify the key uncertainty sources in several regions of O3 transport to Japan. Meteorological and air pollutant observations and the performance of meteorological simulations should be strengthened.