*4.1. Data Used*

We obtained the air pollutant concentrations from Ministry of Ecology and Environment of the People's Republic of China. Meteorological variables are obtained from China meteorological administration. The concentrations of carbon monoxide (*CO*), nitrogen dioxide (*NO*2), ozone (*O*3), particulate matter (PM) and sulfur dioxide (*SO*2) are monitored. The data basis consists daily values corresponding to the 2-year period between January 2016 and December 2017.

Furthermore, the air monitoring stations do not monitor the level of meteorological variables. Thus, we select the nearest meteorological stations to represent the levels of the meteorological variables in the air monitoring stations. However, the distances of some air monitoring stations and meteorological stations are too far. Consequently, only three air monitoring stations, Ma Lian Kou, Sha Po Tou and Ma Yuan are selected for this research. Figure 2 and Table 1 showed the geographical regions of Ningxia and the locations of air monitoring stations.

**Figure 2.** The locations of air monitoring and meteorological stations, where the purple and blue solid point represent the air monitoring stations and meteorological stations in Ningxia, respectively.

**Table 1.** Coordinates of the air monitoring stations in Ning Xia.


Ma Lian Kou is located at the foot of Helan mountain in Yinchuan city and belongs to the northern Yellow river diversion irrigation area. Sha Po Tou is located in the central arid zone, near the Tengger Desert and on the Bank of the Yellow River. Ma Yuan is located in Guyuan city and belongs to the southern mountainous area. A descriptive statistics of these parameters in three monitoring stations for the studied period is presented in Table 2, including minimum, mean, median and maximum levels of the 12 input parameters and the concentrations of air pollutants during 2016–2017. Figures 3–5 show the diurnal variations of air pollutant concentrations in three selected monitoring stations.


**Table 2.** Basic descriptive statistics of the observed concentrations. Minimum, maximum, mean and standard deviation of six output parameters during 2016–2017 used in this study, all in μg/m3.

**Figure 3.** The variations of the concentrations for *CO* and *NO*<sup>2</sup> at Ma Lian Kou, Sha Po Tou and Ma Yuan in 2016.

**Figure 4.** The variations of the concentrations for *O*<sup>3</sup> and *SO*<sup>2</sup> at Ma Lian Kou, Sha Po Tou and Ma Yuan in 2016.

**Figure 5.** The variations of the concentrations for *PM*2.5 and *PM*<sup>10</sup> at Ma Lian Kou, Sha Po Tou and Ma Yuan in 2016.

In Ma Lian Kou, the concentrations of *CO* fluctuated enormously, with maxima of 38.24 μg/m3 in January 2016, and 4.59 μg/m3 in August 2016, respectively. These maxima correspond to the winter months. Concentration minima of *CO* took place during the summer months. Its values were 0.0722 μg/m<sup>3</sup> and 0.1053 μg/m<sup>3</sup> in April and August 2016.

Similarly, the concentrations of *NO*<sup>2</sup> fluctuated significantly with several maxima of 54 and 51 μg/m<sup>3</sup> in December 2016, respectively. These maxima correspond to the days of highest energy consumption in homes due to heating and a greater density of cars on the roads during the winter season. Likewise, the minima in the concentrations corresponded to the spring months.

The concentrations of *O*<sup>3</sup> also fluctuated considerably, with maxima of 213 μg/m<sup>3</sup> in June 2016, and 208 μg/m3 in May 2016, respectively. These maxima corresponded to the summer months. Concentration minima of *O*<sup>3</sup> took place in September 2016, its values were 13.375 μg/m3 and 20.3 μg/m3 in September. This trend is general throughout the studied years, since the formation of *O*<sup>3</sup> is associated with photochemical reactions, which requires the presence of strong sunlight as a catalyst.

In a similar way, the concentrations of *PM*2.5 went up and down slightly but remained quite stable at around 70 μg/m<sup>3</sup> with two spikes at 307 μg/m3 in May 2016 and March 2016, and a minimum of 6.9 μg/m3 in August 2016 and 11.4 μg/m<sup>3</sup> in September 2016.

Similarly, the concentrations of *PM*<sup>10</sup> went up and down slightly but remained quite stable at around 33 μg/m<sup>3</sup> with two spikes at 195 μg/m<sup>3</sup> in May 2016 and September 2016, and a minimum of 6.1 μg/m3 in August 2016. *SO*<sup>2</sup> went up and down slightly but remained quite stable at around 32 μg/m<sup>3</sup> with two spikes at 182 μg/m3 and 143 μg/m3 in January 2016.

It is shown that the trends of air pollutant concentrations at the three monitoring stations are generally different, and the concentrations of air pollutants in Ma Yuan are the lowest. Therefore, the differences of air pollutant concentrations are closely related to geographical locations.

The meteorological variables such as ground surface minimum temperature, maximum and mean temperature, minimum relative humidity and maximum relative humidity, air minimum temperature, maximum and mean temperature, minimum, mean, maximum wind speed, sunshine duration supplied by the China meteorological data service center, their units were ◦C, m/s, and hour, respectively. The meteorological variables were recorded on a daily basis. Table 3 show that the minimum of air temperature (*Tmin*) ranged from −15 in January to 27 ◦C in July, while the maximum of air temperatures (*Tmax*) varied from −10 ◦C in January to 41 ◦C in July. The average sun shine of duration (ssd) was 6.7 h with the minimum and maximum values of 0h and 14 h appearing in November and June, respectively at Ma Lian Kou station.


**Table 3.** Basic descriptive statistics of the measured meteorological variables at the three stations.

GST, RHU, TEM, WIN, SSD represent ground surface temperature, relative humidity, air temperature, wind speed and sunshine of duration respectively.

#### *4.2. Selection of the Influential Factors*

The selection of input parameters is generally based on the prior knowledge of the formation of the air pollutants and the correlation analysis. Through the descriptive analysis of the air pollutant concentrations and the meteorological variables, we can select the most important input parameters and understand which are the dominant factors for the formation and diffusion of air pollutants. Generally, the levels of air pollutant concentrations are associated with emission sources, the formation of secondary pollutants and wind speed, air temperature and ground surface temperature, etc. It is well known that air pollutants and weather conditions are associated with each other in a complex relationship. With the increase of air temperature, the stronger the atmospheric convection activity, the more unstable the air stratification, which is conducive to the diffusion and dilution of pollutants. The air pollutant concentrations were closely related to the change of meteorological factors. Furthermore, relative humidity shows significant negative effect on the concentrations of *O*3, *PM*2.5 and *SO*2, because precipitation will wash out the atmospheric particles. It can be seen from Table 4 that there is a strong negative correlation between wind speed and air pollutant concentration, significant negative effect demonstrates the fact that low concentrations are linked with high wind speed in Ningxia. It is shown in Figure 4a that the concentration of *O*<sup>3</sup> was higher in hot summer due to the high radiation and temperature, and lower in winter. The ground surface temperatures have the strongest correlations with the air pollutant concentrations, which is due to the enhancement of ultraviolet radiation, the increase of temperature, the enhancement of the decomposition of oxygen molecules, and the increase of the photochemical reaction rate of *O*<sup>3</sup> formation, resulting in the increase of air pollutant concentrations. The obtained results show that there are strong relationships between ground surface temperatures have and the concentrations of the majority of pollutants in the region of Ningxia. Moreover, air pollutant concentrations have a close relationship with the concentrations at previous time. There is a high possibility of mutual conversion between *PM*2.5 and other pollutants, especially *PM*10. *PM*2.5 and *PM*<sup>10</sup> are negatively correlated with air temperature. Furthermore, the concentrations of *NO*<sup>2</sup> may have a notable influence on the concentrations of *O*3. High levels of particulate matter in Ningxia are mostly caused by sand storms and construction activities near the monitoring stations. High temperature can result in enhanced re-suspension of road dust. Meteorological variables are used for the prediction of air pollutant concentrations.

We only consider variables with a coefficient of correlation greater than 0.30 as input dataset [35]. According to the correlation coefficient matrix shown in Table 4, there is a negative relationship between the concentrations of *NO*<sup>2</sup> and air temperature and wind speed, respectively. Hence the combinations of other air pollutant concentrations, the air pollutant concentrations one day in advance and meteorological variables for each air pollutant concentration are chosen as the input dataset. And we obtained the selected meteorological variables for every pollutant in Table 5.

**Table 4.** The Pearson correlation coefficients between the meteorological variables and air pollutant concentrations in 2016.


\*\*\* and \*\* indicate that the Pearson correlation coefficient test is significant at the level of 1% and 5%, respectively. *x<sup>a</sup>* represents the variable *x* one day in advance.


**Table 5.** The selected influential variables for every pollutant, where *x<sup>a</sup>* represent the air pollutant concentration the day in advance.
