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Variation of Particulate Matter and Its Correlation with Other Air Pollutants in Xi`an, China

In recent years, the high concentration of air pollutants has seriously affected people’s daily life. So, it is important and necessary to know the spreading PM2.5 concentration and its correlation with other pollutants. The data presented in this paper was received from 13 monitoring stations by a typical northern city of Xi’an in China for the whole year of 2017. The concentration distribution of PM2.5 and its variations with the season, month and day were analyzed. Also the average correlation between PM2.5 and CO, SO2, NO2, O3 and PM10 was studied. The results showed concentrating PM2.5 was consistent with the seasonal, monthly and daily trends. The concentration of PM2.5 showed a low in summer and autumn. While the concentration of PM2.5 was higher in spring and winter, and the highest concentration was in winter. The daily variation distribution of PM2.5 showed a double-peak variation characteristic. The concentration of PM2.5 reached a peak between 8:00-9:00 in the morning and 22:00-23:00 in the evening. Particles of Xi’an in atmospheric were mainly fine particles. There was a strong correlation between PM2.5 and CO, SO2, NO2, PM10, but a negative correlation between PM2.5 and O3. A multivariate linear regression model with the pollutant concentration of CO, SO2, NO2 and PM10 was established. This study helps to understand the distribution of PM2.5 and its correlation with other pollutants of Xi’an, and further supplying important support for the targeted controlling approach of fog and haze.

Therefore, all countries and related organizations had been adopted many policies and standards to reduce the concentration of atmospheric particulate matters now [8][9][10][11][12]. Most developed countries been updated and improved the relevant standards now. PM 2.5 was taken into the latest control target, and the limit concentrations for PM 2.5 were shown in Fig. 1.
Besides limited of the daily average concentration and annual average concentration for PM 2.5 , WHO also showed three transition period target values, which would provide phased targets for countries or regions. The limit of daily average concentration of PM 2.5 in developed countries was consistent (25-35 μg/m 3 ), which was lower than the limit value of developing countries. The average daily concentration of PM 2.5 was as a recommended signal and it was not compulsory [13]. The daily average concentrations were 35 and 75 μg/m 3 for PM 2.5 , 50 and 150 μg/m 3 for PM 10 in Chinese standard, respectively [12].
The study of pollution characteristics and sources for PM 2.5 in atmosphere also gradually becamethe focus of researchers at different countries. Some results had been achieved, and the main focus were on composing particulate matters [14], the limits of the standards [15], regional studies [16], human health and the correlation between particulate matters and other pollutants [17]. In addition, more scholars were also focusing on the research and development of new air filter materials to control the concentration of particulate matter [18,19]. Also some types of corresponding measures and research were carried out to solve the existing environmental pollution problems [20][21][22][23][24][25]. For example, plants could reduce many pollutants such as carbon monoxide, particles [20,21], and they also could change the climate on the environment as well as urban area [22]. Furthermore, the plants could reduce the stress and negative feelings to improve the effectiveness of work [23][24][25].
However, there were few studies on the concentration distribution characteristics of PM 2.5 in different timescales and correlating other major pollutants in the atmosphere. The research on the current domestic was only part of the region. For example, Tianjin [26], Shanghai [27], Guangzhou [28], Xi'an [14]. But there were great distinctions in different regions of China, and the characteristics of pollutants would also change every year, which would lead to more complicated distribution and source of PM 2.5 . There was why spreading PM 2.5 concentrations in different regions was different [29]. So, the typical regional research on the frequent smog in the central and western regions was even more lacking.
Xi'an was selected as an example in this paper, which was the typical city of northern China, and it is located at east longitude from 107.40°~109.49° and north latitude 33.42°~34.45°. The data was received form 13 monitoring stations of Xi'an for the whole year of 2017. The aim of the study was to provide the detailed data on pollutant concentrations, and the concentration distribution of PM 2.5 and its correlation with other pollution were taken into analysis. It would help to explore the causes of PM 2.5 pollution and its main sources.

Methods
The data was collected from January 31, 2017 to December 31, 2017 in this paper, and they was gathered from the national urban air quality real-time publishing platform. Such as the average concentration values of O 3 , CO, SO 2 , NO 2 , PM 10 and PM 2.5 were all collected from the weather forecast network (http://www.tianqihoubao. com/aqi/xian) every day. The hourly concentration values of six kinds of pollutants in 13 monitoring stations in Xi'an released by the weather network (http://www.tianqi.com/air/xian.html). According to the air quality index [12], the average value of each pollutant after the summary of each mocnitoring point was calculated. In addition, GRIMM1.109 portable aerosol particle size spectrometer was used to measure the concentration of particles in the atmosphere. Measuring range was 0.1~100.000 μg/m 3 . Counting range was 2000000 P/L, and 31 particle size channels were divided between 0.25~32 μm. Repeatability was 5%. To ensure the truth of the data statistics, some of Chinese standards were referenced to make sure the data more effectively [12,30,31]. Fig. 2 showed the annual average concentrations of PM 2.5 in 31 tested cities in China was between 18.4 and 81.0 μg/m 3 . The city with the smallest average concentration of PM 2.5 was Lasa, and the largest city was Shijiazhuang. The difference between them was 62.6 μg/m 3 . The highest average daily concentration of PM 2.5 was Xi'an, while the lowest average daily concentration of PM 2.5 was Lasa, and the city with the daily average concentration of PM 2.5 meeting the national secondary standard was only Lasa [12]. Therefore, the study of PM 2.5 in atmosphere of Xi'an was more representative.

Quarterly Variation of PM 2.5 Concentration
The data of five typical monitoring stations in Xi'an (Caotan, Fang Zhicheng, high voltage switch factory, Xiaozhai and Chang'an district) in 13 monitoring stations was selected in this paper. The statistics were divided into four seasons: spring, summer, autumn and winter. The spring was March to May, the summer was June to August, the autumn was September to November, and the winter was December to February [32]. The average concentration of PM 2.5 in different seasons was shown in Fig. 3. Fig. 3 showed the concentrations of PM 2.5 in different seasons showed different trends. The concentrations of PM 2.5 which were measured by different monitoring stations in the same season were roughly same. The concentrations of PM 2.5 were low in summer (June-August) and autumn (September-November). The concentrations of PM 2.5 were high in spring (March-May) and winter (December-February). The highest was in winter, and about 140 μg/m 3~1 58 μg/m 3 . The lowest was in summer, and about 30.0 μg/m 3~3 7.5 μg/m 3 . The seasonal average concentration was followed: winter>autumn>spring>summer. The concentrations of PM 2.5 in winter was about 2.22~2.97 times higher than spring, about 3.74~5.27 times higher than summer, about 2.11~2.56 times higher than autumn. It also showed the concentration of PM 2.5 did not exceed the national secondary standard only Xiaozhai (29.6 μg/m 3 ) and Changan District (30.2 μg/m 3 ) in summer, while the other seasons were exceeded the secondary standard [12]. The reasons were affected by heating in winter, and large of particulate matters were produced by burning of fossil fuels. In addition, it was not conducive to spread of pollutants under the low air pressure in winter, while eroding rainwater also played a certain role in elimination in summer [32]. The season in winter had a greater impact on PM 2.5 , while the season in summer had a much smaller impact on PM 2.5 . It was consistent with the conclusions given in reference [26].

Monthly Variation of PM 2.5 Concentration
The average concentration of PM 2.5 in five environmental monitoring stations was shown in Fig. 4. Fig. 4 showed the annual trends of PM 2.5 concentration which were given by the five regional monitoring stations were roughly the same, and they were not much different. The annual average values of PM 2.5 in Caotan, Fangzhi Cheng, Xiaozhai, Chang'an district and High voltage switch factory were 68.8 μg/m 3 , 71.8 μg/m 3 , 71.5 μg/m 3 , 69.4 μg/m 3 and 79.2 μg/m 3 , respectively. They were higher than the standard which was given the secondary standard limit (35 μg/m 3 ) [12]. They were 1.97 times, 2.05 times, 2.04 times, 1.98 times and 2.26 times higher than the standard, respectively. The monthly distribution of PM 2.5 showed a trend of U-shaped, and the average monthly concentration of PM 2.5 was the highest in January, while the average monthly concentration of PM 2.5 was the lowest in July. The average monthly concentration of PM 2.5 was as followed: January>February>December> November>March>October>April>May>September >August>June>July. The main reason was the level of particulate matter increased because of the need for heating in winter [14]. They were not conducive to spread pollutants under the low temperature in winter [32].

Daily Variation of PM 2.5 Concentration
The average daily concentration of PM 2.5 was from 0:00 to 23:00 on February 1, 2017, and the variation of PM 2.5 at different time of the day was shown in Fig. 5. Fig. 5 showed the daily average concentration of PM 2.5 in the five monitoring stations varied from 99 to 226 μg/m 3 . All of them were exceed the secondary limit [12]. The concentration distribution of PM 2.5 was showed a double peak variation characteristic. Concentrating PM 2.5 changed at different time of the day, and it showed that human activity had a significant effect on concentrations of PM 2.5 [14]. PM 2.5 concentration was the lowest at 0:00, the activities of human were low, while the outdoor environment was better. With the increase of people's activities in the morning, the sources of particulate matters also increased. The concentration of PM 2.5 reached a peak between 8:00 and 9:00, while there were the high peak of people's travel at that time, and the pollutant emissions also reached the peak of the day. With the passage of time, the concentration of PM 2.5 gradually decreased, which was  caused by strong solar radiation in the afternoon, high atmospheric temperature, and strong turbulent exchange and vertical diffusion between gas [14]. Concentrating PM 2.5 reached peaks between 22:00 and 23:00, which was caused by people's everyday activities. The difference in hourly concentration variation which was given by different monitoring stations was related to the locations of monitoring stations and the make-up of the industrial chain [18]. Fig. 6 showed the average of the test concentrations in 8 different months of the whole year in 2017 for analysis. Particle size of 0 to 0.54 μm accounted for 95.9%. Particle size of 0.54 to 1.0 μm accounted for 3.29%. With a particle size of 1.0 to 2.5 μm accounted for 0.591%. Particles above 2.5 μm accounted for 0.184%. Particle sizes of 0-2.5 μm accounted for most of the atmosphere during the testing, accounting for 99.8%. Particle sizes of 0-1.0 μm accounted for more than 99.2%. Particles of Xi'an in atmospheric were mainly fine particles. This conclusion was consistent with the literature [33], which were easy to enter the human respiratory tract and lungs, and they were seriously harmful to human health [4].

Correlation between PM 2.5 and Other
Major Pollutants Xiao Zhai was located in the southern suburbs of Xi'an, and it has a large population and prosperous business [14]. Therefore, it was more representative as the selected point to study. The date was selected from January 1 to December 31 in 2017. The average correlation between PM 2.5 and CO, SO 2 , NO 2 , O 3 and PM 10 was studied. Fig. 7a) showed there was a consistent change of the trend between the daily average concentration value of PM 2.5 and PM 10 . The annual average value of PM 2.5 was 71.5 μg/m 3 , while the annual average value of PM 10 was 137.8 μg/m 3 . The highest daily average concentration of PM 10 could reach 806 μg/m 3 , while the highest daily average concentration of PM 2.5 could reach 561 μg/m 3 . This showed the decrease of PM 10 emissions was critical for reducing pollution with PM [1]. The correlation coefficient between PM 2.5 and PM 10 was 0.917, which was considered to be highly relevant. Fig. 7b) showed the annual average value of PM 2.5 was 71.5 μg/m 3 , while the annual average value of CO was 1.43 mg/m 3 . The highest daily average concentration of CO could reach 5.5 mg/m 3 , while the highest daily average concentration of PM 2.5 could reach 561 μg/m 3 . Related research showed that when the concentration of CO in the atmosphere was exceeded 35 ppm, it would cause serious harm to the human body where they were located [34]. The correlation coefficient between PM 2.5 and CO was 0.871, which was considered to be highly relevant. Fig. 7c) showed the annual average value of PM 2.5 was 71.5 μg/m 3 , while the annual average value of NO 2 was 62.0 μg/m 3 . The highest daily average concentration of NO 2 could reach 155 μg/m 3 , while the highest daily average concentration of PM 2.5 could reach 485 μg/m 3 . This was because consuming fossil fuel in winter was large so the air pollutions of outdoor were large [35]. The correlation coefficient between PM 2.5 and NO 2 was 0.732, which was considered to be highly correlated. Fig. 7d) showed the annual average value of PM 2.5 was 71.5 μg/m 3 , while the annual average value of SO 2 was 17.4 μg/m 3 . The highest daily average concentration of SO 2 could reach 54.0 μg/m 3 , while the highest daily average concentration of PM 2.5 could reach 108 μg/m 3 . Related research showed that SO 2 was the main gassy pollutant to form of PM 2.5 [36]. The correlation coefficient between PM 2.5 and SO 2 was 0.694, which was considered to be moderately correlated. The correlation between the concentration of PM 2.5 and SO 2 was seasonally different. The correlation was better in spring and winter, while the correlation was less relevant in summer and autumn. The average concentration of O 3 in summer was the highest. It was opposite to the change with the average concentration of PM 2.5 . Related research showed that if ozone was increased in the troposphere, it would have a serious impact on the environment and human health [37]. The correlation coefficient between PM 2.5 and O 3 was -0.449, which was considered to be a negative correlation.

Multiple Linear Regression Equations for PM 2.5 and Other Pollutants
The correlation coefficients between PM 2.5 and each pollutant was as follows: 0.917>0.871>0.732>0.694. The corresponding pollutants affecting the magnitude of PM 2.5 were: PM 10 >CO>NO 2 >SO 2 . Therefore, among them the most relevant pollutant was PM 10 . A multiple linear regression model was established by the software Eviews as follows (1). (1) ...where: Y, X CO , X NO 2 , X SO 2 , and X PM 10 were the average daily concentration values of PM 2.5 , CO, NO 2 , SO 2 , and PM 10 , respectively.
The final formula was as follows (2): The correlation coefficient R was 0.915, and the decision coefficient R 2 was 0.914. From the point of decision coefficient, the regression equation was obvious.
The daily average concentrations of CO, SO 2 , NO 2 and PM 10 were substituted into the formula (2). Compared with the results between the tested and calculated of the PM 2.5 were shown in Fig. 8. It showed the prediction effect was good, and the overall trend was still consistent with the tested results. However, there was a large variation of PM 2.5 in spring. It was a certain deviation in the prediction effect, but the overall still had a good agreement. So it could be considered the daily average concentration of PM 2.5 which was obtained by the multiple linear regression equation (2) could be effectively predicted and monitored.

Conclusions
The data was based on five typical monitoring stations of Xi'an in China for the whole year of 2017 in this paper. The concentration distribution of PM 2.5 and its correlation with other air pollutants was analyzed, and the conclusions were as follows: 1. The concentration value of PM 2.5 was consistent with the seasonal, monthly and daily mean trends. It showed a low PM 2.5 concentration in summer (June-August) and autumn (September-November), while the concentrations of PM 2.5 were higher in spring (March-May) and winter (December-February), and the highest were in winter.
2. Variation distribution of PM 2.5 concentration showed a double-peak variation characteristic. The concentration of PM 2.5 reached a peak between 8:00-9:00 in the morning and 22:00-23:00 in the evening, and the particles of Xi'an were mainly fine particles.
4. A multivariate linear regression model with the pollutant concentration of CO, SO 2 , NO 2 and PM 10 as independent was established as follows: Y = 37.5X CO + 0.107X NO 2 + 0.763X SO 2 + 0.348X PM 10 -50.7. The present study was conducive to understanding the distribution of PM 2.5 and its correlation with other five pollutants of Xi'an. It would supply important support for the targeted controlling approach of fog and haze.