## 3 Results and Discussion
### Evaluation of WRF-Chem Model Performance  
October was the month when the concentrations of PM\({}_{2.5}\), NO\({}_{2}\) and O\({}_{3}\) (the major pollutants in Haizhu) were all at elevated levels. Therefore, we used the observed data in October to evaluate the performance of the WRF-Chem simulation. (1) Meteorological simulation evaluation  
The simulated surface meteorological parameters (wind speed, wind direction, temperature, and RH) in Guangzhou were validated with the observed data (Table S6 and Figure 4). The temperature, RH, and wind speed in the simulation were generally in line with the observations (Table S6), and the simulated wind directions covered the observed wind directions (Figure 4, wind direction). Similar model behaviour was reported in some previous studies [9; 70; 71]. The over-prediction of the wind speed in the present study is probably due to the underestimation of the roughness in the cities. For example, the constructions in the urban area are not adequately represented in our model simulation although a very fine resolution of 1 km is already used. Overall, the simulation generally reproduced the meteorological condition in 2015. (2) Chemical simulation evaluation. Simulated pollutant concentrations were also compared to observed data, including O\({}_{3}\) daily 8 h maximum concentration and other pollutants' daily-averaged concentrations (CO, PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), and NO\({}_{2}\)). Metrics of evaluation on model performance are shown in Table S7. The ranges of the mean-bias (MB) and root-mean-square-error (RMSE) on pollutant concentrations were \(-8.07\) ug/m\({}^{3}\) to \(24.34\) ug/m\({}^{3}\), \(9.81\) ug/m\({}^{3}\) to \(48.35\) ug/m\({}^{3}\), respectively, except CO. The MB and RMSE of CO were \(-0.32\) mg/m\({}^{3}\) and \(0.43\) mg/m\({}^{3}\), respectively. Compared to previous studies [13; 70; 71; 72], the model in this paper showed similar behaviour. Figure 5 shows that the general characteristics of the routine pollutants were captured by the simulation. Monthly averaged simulated concentrations of NO\({}_{2}\), PM\({}_{10}\), SO\({}_{2}\), CO, O\({}_{3}\)-8h, and PM\({}_{2.5}\) at the locations of the monitoring sites were \(147.6\%\), \(101.1\%\), \(112.0\%\), \(65.1\%\), \(91.4\%\), and \(80.9\%\) of the observations, respectively. Concentrations of NO\({}_{2}\), PM\({}_{10}\), and SO\({}_{2}\) were overpredicted in the simulation, and concentrations of CO, O\({}_{3}\)-8h, and PM\({}_{2.5}\) were underestimated. It is noticeable that there was an observed peak in pollution on 14 October which was underestimated in the simulation. Concentrations of NO\({}_{2}\), PM\({}_{10}\), SO\({}_{2}\), CO, O\({}_{3}\)-8h, and PM\({}_{2.5}\) in the simulation were \(39\%\), \(49\%\), \(42\%\), \(52\%\), \(49\%\), and \(65\%\) less than that of the observation on 14 October, respectively. The most probable reason for the discrepancy is the meteorological condition was not well captured. Other possible causes include the uncertainty of the emission inventory, the uncertainty of the chemical scheme in simulating the formation of secondary air pollutants, etc. The uncertainty of emission inventories results from the uncertainty in the activity level, the emission factor, and the different grid size [73; 74; 75]. The simulation of secondary air pollutants (O\({}_{3}\), secondary inorganic and organic aerosol)  
Figure 4: Comparison of simulated and observed meteorological parameters in Guangzhou, 2015. (Hourly statistical data in October 2015). is a hot research topic and still contains some uncertainty as reported in [PERSON] et al. (2012) [76], [PERSON] et al., (2018) [77], etc.