Abstract
Sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) are the key components of PM2.5, but predicting their concentrations remains a challenge because of high uncertainties in the modeling. Employing the Nested Air Quality Prediction Modeling System (NAQPMS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, this study investigated the uncertainties in Monte Carlo simulations of these aerosols in the Pearl River Delta (PRD) region during 2015. 50 ensemble simulations with a 15 km horizontal resolution were derived by perturbing the emission data for sulfate, nitrate, ammonium, OC and BC from an emission inventory, which is one of the largest sources of uncertainty. Then, surface observations of these species collected from 10 sites across the region for 1 year were used to evaluate the performance of the ensemble simulations. The high correlation coefficients (> 0.74) and low mean biases (< 2 µg m–3) between the mean values of the ensemble and the observation data suggested that the model fairly accurately reproduced spatial and temporal variations in the nitrate, ammonium, OC and BC. However, the predicted sulfate concentrations, which displayed a correlation coefficient of 0.26, were far less reliable, particularly owing to the significant underestimation during winter. Further analysis revealed that uncertainties in the emission data explained most of the discrepancies for the OC and BC, but the mean biases for the sulfate and ammonium, especially during winter, probably stemmed from uncertainties in the heterogeneous reaction modeling.
Highlights
The Pearl River Delta (PRD) region is one of three major city clusters located in southern China, and frequently experiences serious levels of haze pollution (Zhang et al, 2008; Fu et al, 2019) with high concentrations of PM2.5, leading to the sharp decline of visibility and negative health effect (Zheng et al, 2015; Liu et al, 2016)
Employing the Nested Air Quality Prediction Modeling System (NAQPMS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, this study investigated the uncertainties in Monte Carlo simulations of these aerosols in the Pearl River Delta (PRD) region during 2015. 50 ensemble simulations with a 15 km horizontal resolution were derived by perturbing the emission data for sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) from an emission inventory, which is one of the largest sources of uncertainty
Model performance has been evaluated by statistical metrics as follows: simulated mean, observed mean, mean bias (MB), correlation coefficient (R), root mean square error (RMSE), mean fractional error (MFE) and mean fractional bias (MFB)
Summary
The PRD region is one of three major city clusters located in southern China, and frequently experiences serious levels of haze pollution (Zhang et al, 2008; Fu et al, 2019) with high concentrations of PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm), leading to the sharp decline of visibility and negative health effect (Zheng et al, 2015; Liu et al, 2016). Aerosol and Air Quality Research | https://aaqr.org found in PM2.5. Among these components, the sum of sulfate, nitrate and ammonium is regarded as the secondary inorganic aerosol (SIA) (Wang et al, 2019). High-accuracy simulation of those components is very important for establishing effective control measures for air pollution, and it is a key factor for climate modeling
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