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Sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) are the key components of PM2.5, but their simulations are still facing high uncertainty. This study aims to evaluate and investigate the modeling uncertainty of these aerosols over the Pearl River Delta (PRD) region based on Monte Carlo simulations of a Nested Air Quality Prediction Modeling System (NAQPMS) during 2015. Emission inventory as one of the most important uncertainty sources was perturbed according to their uncertainties to derive 50 ensemble simulations with a 15 km horizontal resolution. The surface observations of sulfate, nitrate, ammonium, OC and BC from 10 sites in PRD region for one year were used to evaluate the performance of the ensemble simulations. The results suggested that the ensemble mean could well reproduce the spatial and temporal variations of nitrate, ammonium, OC and BC with the correlation coefficients above 0.74 and their mean biases less than 2 μg m-3. However, the model has poor skills in the sulfate modeling with the correlation coefficients 0.26 and remarkable underestimation in winter. Further analysis for such modeling uncertainties suggested that the uncertainties in emissions can explain most of the modeling uncertainties in OC and BC, but the mean biases in sulfate and ammonium modeling especially during the wintertime are probably caused by the uncertainty in heterogeneous reaction modeling.

Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations