1. Introduction
In recent years, with China’s rapid economic development, consumption of fossil energy has also grown rapidly, and its air quality, especially in cities, has deteriorated drastically, causing a significant negative impact on people’s health as well as climate change [
1,
2,
3,
4]. It has been realized that the scope and severity of urban air pollution are affected by the nature of air pollutants and pollution sources [
5], weather conditions [
6,
7,
8], as well as properties of the land surface [
9,
10,
11]. These factors are influenced by natural factors (such as air pressure [
12], temperature [
13], wind direction and speed [
14], etc.), but human factors (such as industrial waste gas emissions [
15], domestic coal combustion [
16,
17], automobile exhaust emissions [
18], etc.) have a greater impact on the urban air quality. At the same time, human activities also affect natural factors to a certain extent, and a considerable part of the human factors come from the unreasonable consumption of primary energy and secondary energy. The energy consumption structure is closely connected to the industrial structure [
19,
20]. The current industrial structure with high consumption and low output has further resulted in the deterioration of air quality.
Shanghai is China’s largest industrial city and an energy-consuming city, with a per capita energy consumption and unit area energy consumption much higher than the national average. Its total energy consumption has increased from 106.71 million tons of standard coal in 2010 to 117.12 million tons of standard coal in 2016 [
21], while the total energy consumption nationwide was 4.36 billion tons of standard coal in 2016 [
22], which means Shanghai’s total energy consumption accounted for 3% of the total energy consumption of 338 cities in China. What comes with such high energy consumption density is the deterioration of Shanghai’s urban environment. According to the data in the Shanghai Environmental Condition Bulletin, the number of days with good air quality was only 275 in 2017, with an Air Quality Index (AQI) good rate of 75.3% [
23]. The requirement of continuous economic growth, the increasing consumption of energy and resources, and the continuous deterioration of air quality have brought tremendous pressure and severe challenges to the sustained and stable development of Shanghai’s economy and society.
In order to meet the requirements of air quality under the new circumstances, in 2012, China issued a new national ambient air quality standard (GB 3095-2012), which clarified the calculation method of AQI [
24]:
First, calculate the Individual Air Quality Index of certain pollutant (
):
In the equation above, represents the mass concentration of pollutant P; is the higher threshold of pollutant concentration near corresponding to the specified IAQI (Individual Air Quality Index) regulated by government policy; is the lower threshold of pollutant concentration near regulated by the government; is the corresponding IAQI to ; while is the corresponding IAQI to .
Then, take the largest number from all
to calculate the
:
In 2013, the first year the new ambient air quality standard was implemented, the air quality monitoring and evaluation work of Shanghai started to follow the new standards including the Ambient Air Quality Standards (GB3095-2012) and the Technical Regulation on Ambient Air Quality Index (HJ 633-2012) [
25]. This was a great opportunity to study the impact of Shanghai’s energy consumption structure on its air quality, accelerate the optimization of Shanghai’s energy consumption structure, and build an energy-saving society, which is of great significance to the construction of an international city.
Currently, studies on related fields mainly focus on three aspects. The first is the analysis of fine particle pollution and its impact on atmospheric visibility in cities. The second is the concentration feature and chemical composition of air pollutants. The third is the description of emission factors of air pollutants.
Li et al. (2019) studied the meteorological conditions of the severe haze weather that frequently occurred in North China and concluded two main reasons for the decrease in visibility [
26]. The first is the influence of meteorological conditions such as atmospheric currents, and the second is the change in the average astigmatism coefficient caused by the absorption and scattering of light due to fine particles and major air pollutants [
26]. Golly et al. [
27] (2019) conducted experiments on the chemical characterization of PM
2.5 particles in five rural areas of France, and conducted chemical analysis on the samples every 6 days, including their organic carbon (OC), elemental carbon (EC), ion species, etc. The results showed that wood combustion had made high contributions to the organic carbon (OC), and in some rural areas, the contribution rate of wood combustion to OC could be as high as 90% in winters; the contribution of terrestrial protozoa organic components was also significant in summers and autumns, with a monthly PM
2.5 contribution rate of 4.5–9.5% [
27]. Ryu et al. (2019) studied the PM (Particulate Matter) removal effect of plant evapotranspiration by using the PM removal performance of five plants and the relative humidity (RH) in a closed chamber as control parameters. The results showed that under effective transpiration, honeysuckle had higher efficiency for aerosol PM
2.5 removal [
28].
At the same time, relevant departments of different countries have also formulated different emission inventories in response to air pollution. The U.S. Environmental Protection Agency (EPA) has established the emission inventory for pollutants through direct measurements of power plants stacks, which provides emission measurements that have an error of less than 2% [
29]. The establishment of this emission inventory has provided valuable guidance to the study of the impact of energy consumption on the atmospheric environment. The European Environment Agency (EEA) has established an emission inventory for 30 countries and regions including France and Germany, which covers 8 pollutants (NO
x, SO
2, CO, NH
3, CH
4, N
2O, CO
2, NMVOC) [
30]. The study of the emission inventory in Asia started relatively late. Ohara et al. established an emission inventory of Asia from 1980–2020, in which the pollutants mainly come from energy consumption such as the combustion of fossil fuel and biomass fuel for industrial, power, transportation and civil use [
31]. This is a relatively comprehensive emission inventory for Asia so far. Meanwhile, Korea and Japan are expected to have their own emission inventory [
32,
33,
34,
35].
In current studies, there is a lack of systematic and quantitative research on the migration characteristics of urban air pollutants under the influence of energy consumption and estimation of pollutants produced by energy consumption. Therefore, it is important to analyze the characteristics of urban air pollution by relating to the energy consumption needs of Shanghai as a mega-city in its economic and social development, in order to improve its air quality as well as the life quality of its residents. This paper has adopted the Comprehensive Pollution Index Method, the Improved Grey Relational Degree Method, and the Euclid Approach Degree Method to evaluate the air quality of Shanghai, and systematically analyzed the changing pattern and correlation of fine particle pollutants (PM2.5 and PM10) in Shanghai, in order to achieve innovations as following:
(1) By introducing the pollution index analysis method, the grey correlation analysis method, and the Euclid approach degree method comprehensively, we hope to overcome their respective deficiencies and make new additions to existing research methods.
(2) By further discussing the changing pattern and correlation of the fine particle pollutants (PM2.5 and PM10), we hope to provide new evidence of the interrelationship between major atmospheric pollutants in China.
In the following parts of this paper:
Section 2 introduces the backgrounds and methods of this paper and introduces three study methods.
Section 3 uses the three methods to calculate and evaluate the air quality of Shanghai from 1 November 2017 to 31 October 2018. Based on the above assessment,
Section 4 further discusses the changing pattern and correlation of the fine particle pollutants (PM
2.5 and PM
10) in Shanghai during the study period. Finally,
Section 5 provides conclusions of this paper.
3. Results
The study period of this paper is from 1 November 2017 to 31 October 2018. The air pollutants as the study object include SO
2, NO
2, PM
10, PM
2.5, O
3 and CO. The seasons are determined based on the months: Spring (March, April, May), summer (June, July, August), autumn (September, October, November), and winter (December, January, February). See
Table 4 for the average concentration levels of various air pollutants in different seasons.
The Air Pollution Grading Indexes of different seasons obtained through the Comprehensive Pollution Grading Method are shown in
Table 5.
It can be seen from
Table 5 that from 1 November 2017 to 31 October 2018, the average air quality in Shanghai during winter (December, January, February) was heavy pollution; the average air quality during spring (March, April, May) was moderate pollution; the average air quality during summer (June, July, August) was clean; while the average air quality during autumn (September, October, November) was mild pollution. The results above indicate that the air quality of Shanghai was still not ideal and further pollution control measures are needed.
Table 6 has shown the Air Pollution Grading Index by season obtained through the Improved Grey Relational Degree Method and the Euclid Approach Degree Method introduced in Part 2.4 (please refer to
Appendix A for the MATLAB algorithm—MATLAB 2017b, MathWorks, Natick, USA).
It can be seen that the evaluation results obtained through the Improved Grey Relational Degree Method and the Euclid Approach Degree Method are consistent except for autumn. The air quality in winter has met the Level II national air quality standard stipulated in GB3095-2012; the air quality in spring has also met the Level II standard; while the air quality in summer has reached the Level I national air quality standard.
It can be seen from the calculation results obtained by the three evaluation methods above that there is some concern in the air quality of Shanghai, especially during winters when the air pollution is most severe.
4. Discussion
Based on the above calculation results, this paper further analyzes the fine particle pollution of Shanghai during the study period. The particulate pollutants in the atmosphere can be categorized into total suspended particulates (TSP), PM
10, and PM
2.5 based on the particle size [
55,
56,
57]. TSP generally refers to the particulate matters floating in the air with a particle size of less than 100
, including solid particles and liquid particles [
58,
59]. PM
10 refers to particulate matters with a particle size of 10
or less. Most PM
10 could reach the throat or even further in the respiratory tract [
60,
61]. PM
2.5 refers to particulate matters with a particle size of below 2.5
. Most PM
2.5 can settle in the respiratory tract, and a small number of PM
2.5 could even reach the pulmonary alveoli which are very difficult to get rid of and extremely harmful to the human body [
62,
63,
64]. In recent research, PM
2.5 and PM
10 have been the focus of air pollution control in China [
65,
66,
67,
68]. According to studies at home and abroad, there exist certain correlations between PM
10 and PM
2.5 [
69,
70,
71,
72]. In order to fully understand the relationship between PM
2.5 and other major pollutants in Shanghai, we have calculated the ratio of PM
2.5/PM
10, PM
2.5/SO
2, PM
2.5/NO
2, PM
2.5/CO, and PM
2.5/O
3, according to the 2017 Shanghai Environmental Bulletin [
26] and the Shanghai Air Quality Monthly Report from January–October 2018 [
73]. The results showed that the variation range of PM
2.5/PM
10 was [0.4–0.7], while the ratio of PM
2.5/SO
2, PM
2.5/NO
2, PM
2.5/CO, and PM
2.5/O
3 was low (see
Figure 2).
Hence, we will focus on the correlation between PM
2.5 and PM
10 concentration in Shanghai. The ratio of PM
2.5/PM
10 in Shanghai ranged from 0.50–0.91 during the study period [
23,
73]. The monthly ratios are shown in
Figure 3 below, which was highest in January and lowest in August. Overall speaking, the ratios were volatile, with an average value of 0.68. Among the 90 pollution days in 2017 as published in 2017 Shanghai Environmental Bulletin, there are 25.6% of the days in which fine particles (PM
2.5) was the primary air pollutant [
23].
It can be seen from the seasonal change of the PM
2.5/PM
10 ratio in
Figure 4 that the seasonal trend of this ratio is: winter > spring > summer > autumn. Meanwhile, this ratio in winter is 1.3 times of that in summer. According to the relevant literature, we found that it is due to the increased energy consumption in winter heating, less rainfall and more fog weathers in winters, which do not facilitate the movement of fine particles and results in less sedimentation. In springs, the increased wind frequency and air flow, especially the northwest wind would bring coarse particulate pollution to Shanghai. In summers, the high temperature and rising hot air do not facilitate the sedimentation of fine particles. In autumns, the cool weather and air flow help to spread and subside fine particles, and therefore the degree of fine particle pollution is lower [
74,
75,
76,
77,
78].
Through the quarterly linear regression analysis of PM2.5 and PM10 in Shanghai from 1 November 2017 to 31 October 2018, this paper has found a significant linear relationship between PM2.5 and PM10.
As shown in
Figure 5a, although the linear correlation between PM
2.5 and PM
10 varies from season to season, there is still a strong correlation between PM
2.5 and PM
10 concentrations, which is the strongest during winters and summers. In winter, the correlation coefficient reached
, while in summer, the correlation coefficient
. The corresponding regression equations are
and
, respectively. Taking winter as an example, the t-test on the three-month data of winter provided a confidence interval of [53.4543, 68.4943] with 95% confidence, and a significance probability of 0, which is less than 0.05. Therefore, we can say there is a significant linear relationship between PM
2.5 and PM
10 concentration in winter. In spring and autumn, there is also a linear relationship between PM
2.5 and PM
10 concentration, but the correlation coefficient is smaller. The regression equation in spring is
, with a correlation coefficient of
; while the regression equation in autumn is
, with a correlation coefficient of
.
The regression fitting results above show that there is a significant linear relationship between PM2.5 and PM10 concentration in winters and summers, while their linear correlation is less significant during spring and autumn, which is mainly due to temperature reasons. The cold weather in winters and hot weather in summers of Shanghai do not facilitate the spread of particle pollutants. The particles tend to float in the air, showing a significant linear correlation. On the other hand, in springs and autumns, the temperature is moderate with frequent and strong monsoon which helps to increase air flow and facilitate the diffusion and sedimentation of particle pollutants. Fine particles and coarse particles respond differently to these climate factors. Therefore, the linear correlation between the mass concentration of particulate matters PM2.5 and PM10 is less significant in springs and autumns.
Although air quality has shown improvement in the past decade, there are numerous challenges in the coming years. With the construction of the Yangtze River Delta urban agglomeration and the Yangtze River Economic Belt during the 13th Five-Year Plan period, there will be strong economic growth as well as a continuous increase in air pollutant emissions in neighboring cities and other provinces and cities at the upper and middle region of the Yangtze River. If we cannot establish an effective and coordinated regional air pollution prevention and control mechanism, it would greatly affect Shanghai’s air quality. Moreover, since the parameters we used are derived from official data from Shanghai [
21,
23,
36,
37,
38,
39,
40,
73], and the aforementioned research methods have been widely recognized in the academic world, the research design of this paper has exportability under the premise of using other reliable data sources.
5. Conclusions
This paper has evaluated the quarterly air quality of Shanghai by using the Comprehensive Pollution Index Method, the Improved Grey Relational Degree Method, and the Euclid Approach Degree Method and based on the technical norms of China’s current AQI and analysis of Shanghai’s overall climate. By analysis on the air pollutants (SO2, NO2, PM10 and PM2.5) in Shanghai from 1 November 2017 to 31 October 2018, this paper has reached the following conclusions:
(1) The air quality of Shanghai has moderate pollution in winters and springs, clean in summers, and mild pollution in autumns. The evaluation results obtained by the Improved Grey Relational Degree Method and the Euclid Approach Degree Method are basically consistent. The air quality in winter has met the Level II national air quality standard in GB3095-2012; the air quality in spring has also met the Level II standard; while the air quality in summer has reached the Level I national air quality standard. These results are consistent between the Improved Grey Relational Degree Method and the Euclid Approach Degree Method. However, in autumn, the air quality evaluation result according to the Improved Grey Relational Degree Method is Level I, while the evaluation result according to the Euclid Approach Degree Method is Level II. Therefore, there exist some concern in the air quality of Shanghai, especially during winters when the air pollution is most severe.
(2) The air pollutants in Shanghai have shown a seasonal pattern of high concentration in winters and low concentration in summers; meanwhile, the pollutant concentration is higher in the first half of the year than in the second half. This is because the first half of the year is the peak period of industrial energy consumption, and both industrial and residential heating needs in winter would inevitably cause increase in energy consumption such as the coal [
79], which would undoubtedly increase the concentration of air pollutants.
(3) By analyzing the particle pollutants of PM2.5 and PM10, this paper has found that the linear correlation between the two varies with the seasons, which is most significant during winters and summers.