3.1. Distribution of ρ(PM2.5) in Time and Space
According to seasonal and the climatic characteristics in Xi’an city, each season should be arranged as follows: Spring contains March, April, and May; Summer holds June, July, and August, Autumn takes September to November; and Winter lasts from December to February in the next year. The distribution situation of PM
2.5 concentrations for the different seasons in Xi’an city is illustrated in
Figure 4.
Figure 3.
Wavelet analysis platform.
Figure 3.
Wavelet analysis platform.
We can learn from the concentrations shown in the
Figure 4 that the concentration of all sites in 2013 is higher than that in 2014, which matches the information contained in
Table 1. For different seasons, the highest concentration is in Winter, when the specific mean value reaches 260 μg/m
3; Autumn is the second worst season, with a mean concentration 170 μg/m
3; and the concentration of PM
2.5 is the lowest in Summer with a mean value 75 μg/m
3.
For the statistic standard deviation for the two years, the value of 2013 is obviously higher than that in 2014, showing that the concentration of PM2.5 in 2013 is much more turbulent than that in 2014. For the standard deviation in different seasons, the value in Winter which is nearly three times that in Summer, is the highest, and the value in Autumn and Spring are located between those two extremes. This situation is similar to the seasonal distribution of PM2.5 concentration.
Figure 4.
Seasonal distribution of ρ(PM2.5) in Xi’an city. (a) Spring; (b) Summer; (c) Autumn; (d) Winter.
Figure 4.
Seasonal distribution of ρ(PM2.5) in Xi’an city. (a) Spring; (b) Summer; (c) Autumn; (d) Winter.
In 2013 (as
Figure 5 shows), the concentration of PM
2.5 is higher in January and February than any other months, and the highest center is located in Cao Tan district. The concentration value is continually decreasing in March, April and May, and the highest center is located at the High-voltage Switch Factory in March and April. The concentration in June is the lowest in the whole year, and the mean value for downtown is 60.36 μg/m
3, which is below the national standard (75 μg/m
3). The situation in July is similar to that in June, and the mean value is a little bit higher. The concentration value in August, September and October is continually increasing, and the situation of September is definitely different with that in other months—the highest center is located in Lintong district. The spatial distribution of PM
2.5 is nearly the same for November and December, and the value in December again gets near the highest concentration value. The standard deviation shows that the concentration in June is relatively stable in the whole year. In 2014 (as
Figure 6 shows), the overall conditions is similar to those in 2013, but there are several differences: the month of the lowest concentration value is July, and the lowest standard deviation month is September.
Figure 5.
Monthly distribution of ρ(PM2.5) in Xi’an city for 2013.
Figure 5.
Monthly distribution of ρ(PM2.5) in Xi’an city for 2013.
Figure 6.
Monthly distribution of ρ(PM2.5) in Xi’an city for 2014.
Figure 6.
Monthly distribution of ρ(PM2.5) in Xi’an city for 2014.
3.2. Regional Distribution Characteristic of ρ(PM2.5)
Cluster analysis is a versatile tool in this study, which can illustrate the inner relationships among behind the monitoring data of the 13 sites.
Figure 7 shows the hierarchical clustering map of the 13 sites. Through this map, we can obtain the different clusters, and figure out why the 13 sites could be put into these clusters.
Figure 7.
Hierarchical clustering map of 13 monitoring sites in Xi’an city: 1—High-voltage Switch Factory; 2—Xingqing district; 3—Textile city; 4—Xiaozhai; 5—People’s stadium; 6—High-tech Western; 7—Economic development district; 8—Chang’an district; 9—Yanliang district; 10—Lintong district; 11—Qujiang cultural group; 12—Guangyun Tan; 13—Cao Tan.
Figure 7.
Hierarchical clustering map of 13 monitoring sites in Xi’an city: 1—High-voltage Switch Factory; 2—Xingqing district; 3—Textile city; 4—Xiaozhai; 5—People’s stadium; 6—High-tech Western; 7—Economic development district; 8—Chang’an district; 9—Yanliang district; 10—Lintong district; 11—Qujiang cultural group; 12—Guangyun Tan; 13—Cao Tan.
If we define the Euclidean distance is 18, we can get three clusters from the 13 sites based on the PM
2.5 concentration monitoring data. The reason why we choose 18 as the optimal distance is that, under this condition, the coefficient of goodness of the analysis is 0.6761, which is the best we can get. We can see from the map that Textile city is Cluster 1, and High-tech Western is Cluster 2, and the rest of the sites belong to Cluster 3, which includes the High-voltage Switch Factory district, Xingqing district, Xiaozhai, People’s Stadium, Economic development district, Chang’an district, Yanliang dstrict, Lintong district, Qujiang cultural group, Guangyun Tan, and Cao Tan. Interestingly, we can learn from
Figure 1 that, Yanliang district and Lintong district are located far from the other sites, but the cluster analysis similarly puts them into one cluster. Textile city is very near the other sites, but it is put into a cluster alone. This result reveals that the regional distribution of PM
2.5 is not very related to the geographical locations, which means the climate influence on the regional distribution is not obvious. The most direct impact factor to this result could be the regional industrial production mode and activities. According to the cluster analysis result, we checked out some relevant information, and obtained the main representative industrial activities characteristics of the three clusters, which are collected in
Table 2.
We know that Cluster 1 and 2 are often the highest center of ρ(PM2.5), which is determined by their industrial activities and production structure. For Cluster 3, it is usually not the highest center of ρ(PM2.5), and the possible reasons for this fact may be that, the proportion of agriculture and tourism in this cluster is quite large. In some specific sites, like Xiaozhai, where the commercial activities are developed, the ρ(PM2.5) at the site may often be the highest center. However, the specific degree of industrial impacts on ρ(PM2.5) regional clusters still needs further study.
Table 2.
The characteristics of the regional industrial activities in Xi’an city.
Table 2.
The characteristics of the regional industrial activities in Xi’an city.
Cluster | Sites | Main industrial Activities Characteristics |
---|
1 | Textile city | Dense urban road networks, shopping malls and supermarkets dotted, textiles and clothing, aerospace technology, new materials, equipment manufacturing, modern service industry are the leading production |
2 | High-tech Western | Electrical machinery, refrigeration equipment, petroleum equipment, instrumentation |
3 | Xingqing district, Xiaozhai, People’s Stadium, Economic development district, Chang’an district, Yanliang dstrict, Lintong district, Qujiang cultural group, Guangyun Tan, and Cao Tan | Aluminum plant, profile plant, equipment factory, auto parts factory, aircraft industry, paper, flour, commercial vehicle industry, photovoltaic industry, agriculture, tourism |
3.3. Time Series Analysis of PM2.5 Concentration
Wavelet analysis is regarded as an excellent method for time series analysis.
Figure 8 and
Figure 9 describe the result of the wavelet analysis through db6 on the 4th level, and the low frequency signal result contains the yearly change characteristics.
Figure 8.
Original signal of ρ(PM2.5) and 4th level reconstructed low-frequency signal for 2013.
Figure 8.
Original signal of ρ(PM2.5) and 4th level reconstructed low-frequency signal for 2013.
The original PM
2.5 concentration signal in 2013 is shown in
Figure 8(a), and the reconstructed low-frequency signal is illustrated in
Figure 8(b). The yearly change characteristics can be obtained directly from the curve: The highest concentration months are January, February and December, and the lowest month is June. The change trend and tendency of PM
2.5 concentration in the whole year is shown very clearly in
Figure 8. Also, the extent of turbulence of the change is contained in the figure, in which the extent of turbulence from April to July is relatively small.
The situation for 2014 is revealed in
Figure 9, where we can learn that the overall yearly change characteristics for 2014 are similar to those for 2013. However, the highest concentration months in 2014 are January and February, which is a little different with the situation in 2013. This result is consistent with the information revealed in
Figure 3 and
Figure 4, which indicates that wavelet analysis is applicable and suitable for studying yearly change of PM
2.5 concentration.
Figure 9.
Original signal of ρ(PM2.5) and 4th level reconstructed low-frequency signal for 2014.
Figure 9.
Original signal of ρ(PM2.5) and 4th level reconstructed low-frequency signal for 2014.
The sudden changes for 2013 and 2014 are illustrated in
Figure 10 and
Figure 11, respectively, based on the db1 wavelet. The reconstructed signal at the 2nd level is shown in
Figure 10(a), and the reconstructed signal on the 1st level is shown in
Figure 10(b). We can obtain from the signal curve on the 2nd level that, there are seven points where the amplitude of the reconstructed coefficients are very big, which means the changes are sudden.
Figure 10.
1st level and 2nd level high-frequency signals for 2013.
Figure 10.
1st level and 2nd level high-frequency signals for 2013.
Figure 11.
1st level and 2nd level high-frequency signal for 2014.
Figure 11.
1st level and 2nd level high-frequency signal for 2014.
We can obtain the specific dates through zooming in the details in the figure—12th, 42nd, 57th, 71st, 300th, 351st, and 358th. The corresponding dates are: 12 January, 11 February, 26 February, 12 March, 27 October, 17 December, and 24 December.
There are two sudden change points in
Figure 11, and the dates are 16 January and 2 February. There are lots of factors that can induce sudden
ρ(PM
2.5) changes, for instance, fireworks, gas heating, as well as dust explosions,
etc. Fireworks can cause an instant increase of
ρ(PM
2.5) at a single site, but not in the whole city. Similarly, for gas heating, the fired coal can bring about a
ρ(PM
2.5) increase in a time period, but not on a single day. Dust explosions are a kind of accident, which happens occasionally. We have searched the historical records in Xi’an city for the two years, finding that no dust explosions happened. For the considerations above, the human induced factors are not considered in this paper, and we focus on the climate factors that may cause the sudden changes in PM
2.5 concentration, like temperature, wind speed, and barometric pressure.
Table 3.
Meteorological data of corresponding mutation day.
Table 3.
Meteorological data of corresponding mutation day.
Date(mm-dd) | ρ(PM2.5) (μg/m3) | Pollution Degree | Temperature/°C | Weather Condition | Wind Speed |
---|
2013 | 01-12 | 264 | Severe | −3–7 | Cloudy | <3 |
02-11 | 292 | Severe | −2–5 | Sleety | <3 |
02-26 | 372 | Severe | 5–13 | Cloudy | <3 |
03-12 | 260 | Severe | 7–16 | Cloudy | <3 |
10-27 | 243 | Severe | 10–19 | Cloudy | <3 |
12-17 | 278 | Severe | 9–12 | Hazy | <3 |
12-24 | 500 | Severe | −2–3 | Hazy | <3 |
2014 | 1-16 | 310 | Severe | −3–5 | Cloudy | <3 |
2-2 | 413 | Severe | 1–14 | Cloudy | <3 |
For this purpose we obtained the meteorological data for the sudden change points in
Table 3. The data comes from the historical climate records system in Xi’an city. The pollution degrees for the nine sudden change points are all severe. The observation of the meteorological data suggests that the impact of temperature on PM
2.5 concentration is not so obvious, and still needs further study. What we can decide is that, cloudy days and low wind (<3) are the main meteorological factors for the sudden changes of
ρ(PM
2.5).
3.4. Discussion
We try to explain the distribution in time and space of PM2.5 in Xi’an city geographical locations and meteorological features. Xi’an city is located in the center of China, and belongs to the Semi-humid continental monsoon climate region. A dry and dusty Spring may account for the high concentration of PM2.5 in Spring; While in Summer, there is lots of rainy weather and windy days, which can increase the sedimentation ability of the air and help the diffusion of PM2.5; In Autumn, low temperatures and winds are very common, both of which are not beneficial for the diffusion of PM2.5; The fired coal use for heating in Winter may be the leading factor for the high concentration of PM2.5 in December.
Several scholars have tried to find out the distribution of PM
2.5 in China. The distribution characteristics of PM
2.5 and PM
10 in Beijing are an example in this case [
26], in which the authors find that the order from high to low of monthly mean concentrations of PM
2.5 in Beijing is April, February, March, and January; while in this study, the order is January, February, March, and April. The reason for this difference may be the different geographical locations and meteorological factors. Studies of spatial distribution of PM
2.5 are seldom found. Arc GIS was once used to figure out the distribution map of PM
10 in the national level in China [
23]. The advantages of this method is that the concentration contours can be drawn directly on the map, and the disadvantage is that all the layer cannot match very well, which leads to some blank areas. In this paper, the strength of the method is utilizing the data as much as possible, even though the monitoring sites are distant from each other, but we cannot develop the distribution contour on a single city map.
In the exploration of the regional distribution of PM2.5, we adopt the cluster analysis method. For the convenience of analysis, we divide the 13 sites in three clusters, and analyze the correlations between the ρ(PM2.5) and the industrial activities in each cluster. The results show that Cluster 1 and Cluster 2 which with highest population and diverse industrial activities are often the center of highest ρ(PM2.5).
The characteristic
ρ(PM
2.5) yearly changes and sudden changes have been studied by the wavelet transform method, and some relevant factors have been analyzed. The results show that
ρ(PM
2.5) is high in Winter and Spring, and low in Summer and Autumn, which shows the credibility of this method. Meteorological factors are considered to explain sudden changes, and cloudy weather and low wind are the main inducements for the change. The wavelet transform had previously been used in the time series analysis of
ρ(PM
10) in Xi’an city in 2001 and 2002 [
27]. In that study the author pointed out that the reason for the high
ρ(PM
10) is the combined effect of dusty weather, city construction (low wind, temperature inversion,
etc.), and meteorological conditions. The result of this study is similar to the conclusion of PM
10 distribution, which reveals that wavelet transform is a suitable tool for the analysis. Additionally, since AQI has only been adopted by the Chinese government for a short time period, and the monitoring of PM
2.5 is not comprehensive both in time and space, it means the knowledge about it lacks depth as relevant materials are rare to find. All these can bring produce limitations in the study. Further studies should perform a regression analysis between
ρ(PM
2.5) and temperature, industrial production value, and wind speed, to figure out the specific correlation of these factors with
ρ(PM
2.5). If the data is enough, the change cycle in the time aspect of
ρ(PM
2.5) could also be studied.