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Variation trends and principal component analysis of nitrogen oxide emissions from motor vehicles in Wuhan City from 2012 to 2017
Journal Pre-proofs Variation Trends and Principal Component Analysis of Nitrogen Oxide Emissions from Motor Vehicles in Wuhan City from 2012 to 2017 Daoru Liu, Qinli Deng, Zhigang Ren, Zeng Zhou, Zhe Song, Jiahui Huang, Ruibo Hu PII: DOI: Reference:
S0048-9697(19)34979-4 https://doi.org/10.1016/j.scitotenv.2019.134987 STOTEN 134987
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Science of the Total Environment
Received Date: Revised Date: Accepted Date:
5 July 2019 25 September 2019 13 October 2019
Please cite this article as: D. Liu, Q. Deng, Z. Ren, Z. Zhou, Z. Song, J. Huang, R. Hu, Variation Trends and Principal Component Analysis of Nitrogen Oxide Emissions from Motor Vehicles in Wuhan City from 2012 to 2017, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134987
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Variation Trends and Principal Component Analysis of Nitrogen Oxide Emissions from Motor Vehicles in Wuhan City from 2012 to 2017
Daoru Liu1, Qinli Deng1,*, Zhigang Ren1,*, Zeng Zhou2,*, Zhe Song1, Jiahui Huang3, Ruibo Hu1
1
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan
430070, China; 2
School of Urban Planning, Wuhan University, Wuhan 430070, China;
3
School of Civil Engineering, Technical University of Denmark, Copenhagen 2800 Kgs,
Denmark; *
Correspondence: [email protected] (Q.D.); [email protected] (Z.R.);
[email protected] (Z.Z.)
Variation Trends and Principal Component Analysis of Nitrogen Oxide Emissions from Motor Vehicles in Wuhan City from 2012 to 2017 Daoru Liu1, Qinli Deng1,*, Zhigang Ren1,*, Zeng Zhou2,*, Zhe Song1, Jiahui Huang3, Ruibo Hu1 1.
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China;
2.
School of Urban Planning, Wuhan University, Wuhan 430070, China;
3.
School of Civil Engineering, Technical University of Denmark, Copenhagen 2800 Kgs, Denmark;
* Correspondence: [email protected] (Q.D.); [email protected] (Z.R.); [email protected] (Z.Z.)
Abstract: In addition to fine particulate matter and oxysulfides, nitrogen oxides (NOx) emitted by motor vehicles are among the most important pollutants affecting air quality and public health in those urban areas where centralized heating and chemical industry absent. We utilized correlation analysis (pearson
correlation coefficient and spearman correlation coefficient) and principal component analysis (PCA) to identify the variation trends and main causes of NOx emissions from motor vehicles in Wuhan City. We considered the total number of motor vehicles (TN), ratios of motor vehicles of different emission standards (RE), rations of labeled motor vehicles (RL), and rations of motor vehicles’ fuel types (RF). The results show that: 1) with an increase in the total amount of motor vehicles, the NOx emissions of motor vehicles have been decreasing since 2015; 2) three sub-categories (the ratio of the State III emission standard, the ration of yellow label diesel vehicles, and the ration of diesel vehicles) were recognized as key indexes of PE, PL and PF, respectively, in the PCA; 3) a new parameter, the ESindex is proposed as an index to represent the variation trend of the NOx emissions of motor vehicles in Wuhan City.
Keywords: Public health; Emission standards; Correlation; Sub-factor
1. Introduction In the past three decades, China had made considerable economic progress, at the cost of immeasurable environmental damage [1, 2]. Environmental degradation endangers public health, and air pollution is one of the most serious and widely discussed issues of the recent 10 years. Fine particulate matter, sulfur oxides (SOx) and nitrogen oxides (NOx) are harmful to both the environment and public health. Fine particulate matter emissions have been effectively controlled in China since the implementation of the national policy on smog governance, and this improving trend will continue in the coming years [3-5]. Especially in areas where centralized heating is generally not provided, like central and southern areas of China, fine particulate matter is no longer the primary object of air pollution control. Wuhan
is a typical representative of these areas, and it has recorded a decrease in PM2.5 concentration for 5 consecutive years since 2013 [6]. Besides, SOx have not been the main component of air pollutants in the Wuhan area, because of the absence of chemical industry and coal combustion [6, 7]. In addition to PM2.5, NOx and ozone (O3) are the main components of air pollutants in Wuhan city [6, 8-10], and NOx is the main source of ozone generation at room temperature in air [11-13]. Research on NOx may therefore have a considerable impact on both NOx and O3 pollution control. Generally, NOx refers to NO and NO2. In non-industrial urban areas like Wuhan, most NOx comes from motor vehicles, and is produced when vehicles are driven by an internal combustion engine at high temperatures under oxygen-rich conditions [14-16]. Wang et al. [14] conducted studies in Beijing and Chongqing, utilizing a "chase" vehicle sampling strategy, in which a mobile laboratory follows target vehicles, repeatedly sampling their exhaust, to assess NOx and black carbon emission factors of 440 diesel trucks. The results show that effective multi-pollutant control strategies and in-use compliance programs are imperative to reduce the overall emissions from the transportation sector. Yamada et al. [15] utilized different techniques to study the emission properties of NO, NO2, N2O and other pollutants from diesel engines. Their results show that NO2 and N2O were strongly correlated with the equivalence proportions of the engines, and higher equivalence proportions lead to higher pollutant emissions. Wallington et al. [16] made an overview of vehicle emissions, ambient concentrations, and atmospheric chemistry of NOx. They also discussed in depth the formation of NOx in vehicle engines, technologies used to control emissions of NOx, and trends in vehicle emissions. The conclusion was that the emission of NOx is closely related to engine sizes and numbers of vehicles in urban areas. Denis et al. [17] thought NOx emissions of road vehicles were the major contributor to poor air quality in urban areas. High NOx concentrations, especially of NO2, are typically the most
problematic pollution in cities. Lin et al. [18] field-tested 185 diesel vehicles at an engine dynamometer station, for their black smoke reflectivity and NOx concentrations, to explore the correlation between these two pollutants. The results showed that the service life and engine size were the main factors affecting the emission of NOx from vehicles. Kadijk et al. [19] criticized the status quo of NOx emissions from vehicles in Europe, and expressed concern about future NOx emissions in Europe. Shorter et al. [20] utilized a chase vehicle sampling strategy to sample NOx from approximately 170 in-use New York City mass transit buses. The results showed that emissions of NOx from diesel and compressed natural gas (CNG) buses were comparable, but hybrid electric buses had approximately one-half the NOx emissions. In areas where fine particulate matter is effectively controlled, NOx are the dominant pollutants that seriously endanger the environment and public health [21]. It is therefore of great importance to study the variation trends and main causes of NOx emissions, in order to reduce NOx pollution.
2. Materials and Methods 2.1. Case Design and Factor Selection This study was carried out in Wuhan City, which is located at 29°58'-31°22' N, 113°41'-115°05' E, in the central southern part of China, at the junction of the Yangtze River and Hanjiang River, as shown in Figure.1. It is the inland transportation center of China, and a typical city where heating is not generally provided, and without chemical and petroleum industries.
Figure 1. Location of the research area. As a mega-city with a population of more than 10 million, Wuhan needs a developed transportation system and a large number of motor vehicles to maintain its normal operation. The extensive use of motor vehicles has resulted in a large amount of NOx emissions, and the intensity of NOx emissions has been increasing for more than two decades [22, 23]. We utilized data on air pollutants and motor vehicles collected and processed by the Wuhan Ecology and Environment Bureau and the Statistics Bureau of Wuhan Municipality, to study the statistical relationships between these two factors. The main factors affecting NOx emissions (NE, 104t) of motor vehicles in Wuhan City can be divided into four categories: (1) the total number of motor vehicles (TN, 104); (2) the ratios of motor vehicles of different emission standards (RE); (3) the ratios of labeled motor vehicles (RL, %); and (4) the ratios of fuel types of motor vehicles (RF).
The number of motor vehicles is the factor most directly impacting the NOx emissions of motor vehicles. Besides this, emission standards are also of great importance to NOx emissions. As mentioned in Section 2.2, the Ministry of Ecology and Environment of China proposed six grades, State 01 to State V, to evaluate the emission levels of motor vehicles. Each motor vehicle was given a label (green or yellow) according to its actual emission level. Gasoline motor vehicles whose emission level is higher than the State I emission standard, and diesel motor vehicles whose emission level is higher than the State III emission standard, are collectively called “yellow label” vehicles. According to emission limits, the emissions of a yellow label vehicle are equivalent to 5 State I vehicles, 7 State II vehicles, 14 State III vehicles, or more than 20 State IV vehicles consuming gasoline. Due to their high emissions, yellow label vehicles are especially subject to control and elimination. Fuels consumed by motor vehicles are divided into three categories: C1: gasoline, C2: diesel, C3: LNG and others. The “others” included in C3 are electric vehicles, gas-electric vehicles, and LNG-electric vehicles. As the traditional fuels for motor vehicles, gasoline and diesel dominate their fuel consumption. Table 3. Fuel types of motor vehicles from 2012 to 2017. Year 2012 2013 2014 2015 2016 2017
1
Gasoline 86.06 87.17 88.78 90.46 91.42 91.89
State 0: A motor vehicle whose emissions are higher than State I.
Proportion (%) Diesel 13.76 12.62 10.91 9.21 8.15 7.62
LNG and others 0.16 0.21 0.27 0.33 0.43 0.49
2.2. Model of Pollutant Emissions Calculation By integrating various foreign traffic emission models, and utilizing localized basic data to calculate the pollutant emissions of motor vehicles, determined by vehicle type and pollutant emission process, the Vehicle Emission Control Center of the Ministry of Ecology and Environment of China established the China Vehicle Emission Model (CVEM) [24-27]. In the CVEM, the calculation of motor vehicles’ emissions is divided into exhaust emissions and evaporation emissions, and the exhaust emissions include the cold starting process and hot running process. With consideration of the types and emission processes of motor vehicles, the CVEM gives equations to calculate their exhaust emissions as shown below.
EMEi , j CEEi , j Di
(1)
Di N i ADi
(2)
where, EMEi,j is the total amount of pollutant j emitted by i-type vehicles (g); CEEi,j is the emission factor when the vehicle type is i and the pollutant is j, and this factor is processed by the correction of speed, temperature, altitude, etc. (g/km); Di is the annual mileage of i-type vehicles (km); Ni is the registered number of i-type vehicles; and ADi is the average annual mileage of i-type vehicles (km). The emission factor CEEi,j is mainly determined by ambient conditions, driving conditions and technical properties of the vehicles, as shown in Table 1. Table 1. Key factors affecting exhaust emissions of motor vehicles in the CVEM Ambient conditions
Driving conditions
Technical properties
1. Temperature 2. Altitude 3. Fuel properties
1. Speeds on different roads 2. Proportions of vehicles on different roads 3. Annual mileage 4. Number of cold starts per day 5. Average annual mileage
1. Vehicle types 2. Emission level 3. Registered numbers 4. Gauge mileage
Based on the state regulation of the Ministry of Public Security of China, the vehicles in the CVEM are divided into 34 categories according to vehicle type, vehicle specifications, fuel types and emission standards, as shown in Table 2. Table 2. Categories of motor vehicles in the CVEM. Vehicle type
Vehicle specification
Fuel type
Emission standard2
Passenger vehicle
Mini, Small, Mid, Large
State 0 to State V
Cargo vehicle Low-speed CV4 Motorbike
Mini, Light, Mid, Heavy Tri-wheel, Low-speed General, Light
Gasoline, Diesel, LNG3 and others Gasoline, Diesel Diesel Gasoline
State 0 to State V State 0 to State V State 0 to State V
2.3. Pearson Correlation Coefficient and Spearman Correlation Coefficient The Pearson correlation coefficient (PCC) and Spearman correlation coefficient (SCC) are two indexes to evaluate the linear correlation between two random variables X and Y. If the correlation coefficient is positive, then most likely the variables are directly related and if it is negative, then they are inversely related (1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation). In this article, the PCC and SCC were used to calculate the correlation between the NOx emissions and sub-categories of RE, RL and RF, and to investigate the impact of each sub-category of RE, RL and RF on the NOx emissions. However, applicability of the PCC and KCC is different, depending on variable types [28, 29]. Generally, variables in statistical analysis are divided into continuous variables, disorderly classification variables, and orderly classification variables. The PCC is suitable for two normally-distributed continuous variables, and the KCC can be used to evaluate the linear correlation between any two kinds of variables. 2
Referring to European motor vehicle emission standards, China proposed six grades (State 0, State I, State II, State III, State IV
and State V) to evaluate the emission levels of motor vehicles. 3
LNG = Liquefied natural gas.
4
Low-speed CV = Low-speed cargo vehicle.
The PCC is defined as the quotient of covariance and standard deviations between two variables, and its mathematical expression is as follows [28]:
X
Y Y X X Y Y n
rXY
i 1
X 2
n
i 1
i
i
n
i
i 1
2
(3)
i
where, rXY is the PCC between variables X and Y, n is the sample size, Xi and Yi are individual values —
—
of dataset X and Y, X is the mean value of X, and analogously for Y. The SCC is defined as the PCC between hierarchical variables. For a sample of size n, the original data are converted into hierarchical data, and the mathematical expression for the SCC is as follows [29]:
=1-
6 di2
n n 2 1
(4)
where, ρ is the SCC, di is order differences, and n is the sample size. 2.4. Principal Component Analysis In this study, in order to comprehensively analyze the NOx emissions, many variables/factors are proposed, with each variable reflecting some information on the subject, though to varying degrees, and with correlations between the variables. When there is a certain correlation between two variables, it indicates that the two variables reflect overlapping information. Principal component analysis (PCA) is utilized to remove redundant variables (closely related variables) and establish as few new variables (principal components, PCs) as possible, so that these new variables are not related to each other, and these new variables keep the original information as far as is possible. In consideration of the unit differences of variables, variables were standardized using Z-score Standardization, as shown in Equation (5) as follows [30]:
X
X X SD X
(5) —
where X* is the standardized variables, X is the original variables, X is the mean value of X, and SD(X) is the standard deviation of X. After standardization, the original variables were transformed into standardized variables, which were utilized as calculated data in the PCA process. Based on PCA theory, PCs can be expressed as follows [31]:
PC1 C11 C12 PC C C22 2 21 PCi Ci1 Ci 2
C1 j X 1 C2 j X 2 Cij X j
(6)
where, PCi is the ith PC of the variables, Cij is the loading coefficient indicating the contribution of the jth variable to define PCi, X*j is the standardized variable. PC1 is generally required to contain the maximum possible variance and the most information of the original variables, while other PCs cover the remaining information and variability, in decreasing order of information content. Generally, the cumulative value of variation covered by the selected PCs is over 85%. For j dimensional variables, the relationship among the variable covariance matrix, i principal axes (i.e., PC1, PC2, …, PCi), and eigenvectors is given as: cPCi i PCi
where, c is the variable covariance matrix, and λi is the ith largest eigenvalue of the matrix c.
(7)
3. Results and Discussion 3.1. Variation Characteristics of NE, TN, RE, RL and RF As shown in Figure 2, the emissions of NOx had been increasing, with a growth rate of 4.30% in 2012 and 2.75% in 2014. Since 2015, the emissions of NOx have continued to decline. The growth rate in 2015 was the lowest in nearly six years, at -13.62%. Despite a substantial increase in the number of motor vehicles, the emissions of NOx from motor vehicles was decreasing. Among the reasons for this, emission standards and policy factors are extremely important. The number of motor vehicles increased by more than 1.5 million since 2012. From 2012 to 2013, the growth rate increased from 14.8% to 17.4%. After 2012, the growth rate in motor vehicles had been around 17% for the 5 years to 2016, but it then dropped to 10% in 2017.
(a)
(b) Figure 2. (a) NOx emissions of motor vehicles and growth rate, and (b) numbers and growth of motor vehicles from 2012 to 2017. Figure 3 shows the proportions of different emission standards of motor vehicles. It is obvious that proportions of motor vehicles of State 0, State I and State II emission standards had fallen for six years in a row, while those of motor vehicles of State IV and State V emission standards had risen. The proportion of motor vehicles of State III had its peak at 53.57% in 2013, and then fell continuously to 30.47% in 2017. Combining Figure 2(b) and Figure 3, it is found that the year of 2014 is a key time point. At the beginning of 2014, the government of Wuhan City began to implement the State V emission standards for motor vehicles. At the same time, the government was vigorously promoting the elimination of high-pollution and high-emission vehicles of State I, State II and State III, which greatly increased the proportion of vehicles of State IV [32]. The growth trend in NOx emissions from motor vehicles was then terminated, falling to -13.62% from 2014 to 2015.
As well as the European emission standards, each new emission standard will reduce NOx emissions by more than 30%, compared with the previous version of the emission standards [33-36]. Government-led mandatory emission standards therefore have a considerable impact on reducing emissions of NOx from motor vehicles. With more strict emission limits, the control of NOx emissions will be better [36, 37].
Figure 3. Proportions of emission standards of motor vehicles from 2012 to 2017. Since 2010, preliminary measures and mechanisms for eliminating yellow label vehicles have been formulated [38]. Under the same operating conditions, the pollutant emission level of a yellow label vehicle is 5 to 15 times that of normal vehicle, including NOx emissions [39]. As shown in Figure 4, the number of yellow label vehicles had decreased by about 90% from 2012 to 2017, which contributed a lot to the decline of NOx emissions. The proportion of green label vehicles increased every year from 2012 to 2017, and had reached 99.5% in 2017. At the same time, proportions of yellow label gasoline vehicles and yellow label diesel vehicles decreased to 0.27% and 0.23%,
respectively, in 2017. This was due to the government's compulsory measures and financial subsidies aimed at eliminating yellow label vehicles.
(a)
(b)
Figure 4. (a) Number of yellow label vehicles, and (b) proportions of labeled vehicles. As shown in Figure 5, the proportion of gasoline motor vehicles increased year by year, while the proportion of diesel motor vehicles decreased. The proportion of new-energy vehicles had an increment every year, but the variation range was limited. Fuel type is also an important factor affecting NOx emissions; with the same engine size, diesel vehicles emit three to seven times more NOx than gasoline vehicles [40, 41]. The decline of gasoline vehicles also contributed a lot to the reduction in NOx emissions.
Figure 5. Proportions of motor vehicles using different fuels (others are new-energy vehicles). 3.2. Correlation Analysis of RE, RL and RF Factors such as RE, RL and RF all affect the NOx emissions of motor vehicles to different degrees. These factors can be divided into several sub-categories, and each sub-category has its own variation characteristics. PCC and SCC were utilized to identify the sub-categories of each factor that have the
greatest correlation with the NOx emissions of motor vehicles, considering the variable types shown in Figure 6.
NOx Emissions （NE)
Variables
Variable Types
Correlation Model
Ratios of Emission Standards (RE)
Orderly Classification
SCC
Ratios of Labeled Vehicles (RL)
Continuous
PCC
Ratios of Fuel Type of Vehicles (RF)
Continuous
PCC
Figure 6. Variables and Correlation Models used in the Correlation Analysis. As shown in Table 3, a strong correlation was identified between the proportion of the State III emission standard and the NOx emissions of motor vehicles, with a correlation coefficient 0.829 (red background). Meanwhile, absolute values of correlation coefficients between the proportion of State III motor vehicles and proportions of motor vehicles of other emission standards are both over 0.75 (green background), which means there are strong correlations between them. Based on the analysis above, the proportion of the State III emission standard was determined to be the most significant subcategory of RE, and utilized for the subsequent PCA of the NOx emissions of motor vehicles.
Table 3. Correlation coefficients between NOx emissions and proportions of motor vehicles of different emission standards. Variables
Emission Standards
NOx Emission
0
I
II
III
IV
V
-0.600
-0.812*
NOx Emission
1.000
0.771
0.771
0.771
0.829*
0
0.771
1.000
1.000**
1.000**
0.943**
-0.829*
-0.986**
I
0.771
1.000**
1.000
1.000**
0.943**
-0.829*
-0.986**
II
0.771
1.000**
1.000**
1.000
0.943**
-0.829*
-0.986**
III
0.829*
0.943**
0.943**
0.943**
1.000
-0.771
-0.986**
IV
-0.600
-0.829*
-0.829*
-0.829*
-0.771
1.000
0.812*
V
-0.812*
-0.986**
-0.986**
-0.986**
-0.986**
0.812*
1.000
*Correlation is significant at the 0.05 level (two-tailed). **Correlation is significant at the 0.01 level (two-tailed).
As shown in Table 4, a strong correlation was identified between the proportion of yellow label diesel vehicles and the NOx emissions of motor vehicles, with a correlation coefficient 0.799 (red background). Meanwhile, the absolute values of the correlation coefficients between the proportion of yellow label diesel vehicles and proportions of other label vehicles are both over 0.90 (green background), which means there are strong correlations between them. Based on the analysis above, the proportion of yellow label diesel vehicles was determined to be the most significant sub-category of RL, and it was utilized for the PCA. Table 4. Correlation coefficients between NOx emissions and proportions of labeled vehicles. Variables
NOx Emission
NOx Emission
1.000
Yellow Label - Gasoline Yellow Label - Diesel Green Label
Labeled Vehicles Yellow Label - Gasoline
Yellow Label - Diesel
Green Label
0.743
0.799
-0.786
0.743
1.000
0.992**
-0.997**
0.799
0.992**
1.000
-0.999**
-0.786
-0.997**
-0.999**
1.000
**Correlation is significant at the 0.01 level (two-tailed).
As shown in Table 5, a strong correlation was identified between the proportion of diesel vehicles and the NOx emissions of motor vehicles, with a correlation coefficient 0.849 (red background). The absolute values of the correlation coefficients between the proportion of diesel vehicles and proportions of other vehicles are both over 0.90 (green background), which means there are strong
correlations between them. Based on the analysis above, the proportion of yellow label diesel vehicles was determined as the most significant sub-category of RF, and it was utilized for the PCA. Table 5. Correlation coefficients between NOx emissions and proportions of motor vehicles’ fuel types. Variables
NOx Emission
Fuels Gasoline
Diesel
Other
NOx Emission
1
-0.848*
0.849*
-0.856*
Gasoline
-0.848*
1.000
-1.000**
0.975**
Diesel
0.849*
-1.000**
1.000
-0.978**
Other
-0.856*
0.975**
-0.978**
1.000
*Correlation is significant at the 0.05 level (two-tailed). **Correlation is significant at the 0.01 level (two-tailed).
3.3. PCA of NOx Emissions of Motor Vehicles In Section 3.2, the proportion of the State III emission standard (RSES III), the proportion of yellow label diesel vehicles (RYLD), and the proportion of diesel vehicles (RD), were determined as key subcategories of RE, RL and RF, respectively. These three sub-categories, together with TN, are used as variables for the PCA. Table 6(a) shows that there are correlations between these variables. The Kaiser-Meyer-Olkin (KMO) test and Bartlett test were used to identify the correlation. The value of the KMO test was 0.635, and the significance value was below 0.001, as shown in Table 6(b). This proves the correlations between the variables, and the PCA can therefore be conducted. Table 6. Correlation test of RSES III, RYLD, RD and TN. (a) Correlation Matrix of RSES III, RYLD, RD and TN. RSES III TN RYLD RD
RSES III
TN
RYLD
RD
1.000 0.969 0.787 -0.962
0.969 1.000 0.897 -0.988
0.787 0.897 1.000 -0.923
-0.962 -0.988 -0.923 1.000
(b) KMO test and Bartlett test of RSES III, RYLD, RD and TN. KMO measure of sampling adequacy Bartlett test
0.635
Approximate Chi-square
34.826
Sig.
0.000
One principal component (PC1) was obtained in the PCA, as shown in Table 7, with an initial eigenvalue of 3.766, while the initial eigenvalues of the other components were 0.221, 0.012 and 0.001, respectively. The extraction sum of squared loadings of PC1 was 94.157%, which means that PC1 contributes more than 94% of the overall effect of RSES III, RYLD, RD and TN. Therefore, PC1 can be reliably treated as a unique index to represent the variation of the NOx emissions of motor vehicles in Wuhan City. Table 7. Component analysis of RSES III, RYLD, RD and TN. (a) Total variance explanation. Initial eigenvalues Component
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
3.766
94.157
94.157
3.766
94.157
94.157
2
0.221
5.524
99.682
/
/
/
3
0.012
0.305
99.986
/
/
/
4
0.001
0.014
100.000
/
/
/
(b) Component matrix and component coefficient. Variable
Loading Coefficient
Component Coefficient
RSES III
0.959
0.494
TN
0.994
0.512
RYLD
0.928
RD
-0.998
Equation (7)
0.478 -0.512
According to Table 7 and Equation (6), the mathematical expression PC1 can be expressed as follows:
RSESIII RYLD PC1 0.494 0.512 0.478 0.512 RD TN
(8)
ESindex PC1 0.494 RSESIII 0.512 RYLD 0.478 RD 0.512TN
(9)
4. Conclusions In this paper, data on nitrogen oxides emissions from motor vehicles in Wuhan City between 2012 and 2017 were utilized to identify their trends and principal components, using correlation analyses (KCC and SCC) and principal component analysis. Three principal conclusions were drawn, as follows. 1） The NOx emissions of motor vehicles increased from 2012 to 2014, and then declined continuously from 2015, while the total number of motor vehicles almost doubled between 2012 and 2017. 2） The ration of the State III emission standard (RSES III), the ration of yellow label diesel vehicles (RYLD), and the ration of diesel vehicles (RD), were found to be the key sub-categories of RE, RL and RF, respectively. 3） A new parameter/equation ESindex/PC1 was proposed as a unique index to represent the variation of the NOx emissions of motor vehicles in Wuhan City.
Acknowledgments: This project was funded by the National Natural Science Foundation of China (51608405, 51808410). We thank the staff at the various monitoring sites of the Wuhan Ecology and Environment Bureau for their work and their support for this study.
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Highlights： 1. Various factors affecting the NOx emissions from motor vehicles were considered; 2. The Pearson Correlation Coefficient (PCC) and Spearman Correlation Coefficient (SCC) were utilized to identify key sub-factors affecting the NOx emissions of motor vehicles;
A reliable parameter is proposed to represent/estimate the variation in the NOx emissions of motor vehicles by Principal Component Analysis.
Figure 1. Location of the research area.
(a)
(b) Figure 2. (a) NOx emissions of motor vehicles and growth rate, and (b) numbers and growth of motor vehicles from 2012 to 2017.
Figure 3. Proportions of emission standards of motor vehicles from 2012 to 2017.
(a)
(b) Figure 4. (a) Number of yellow label vehicles, and (b) proportions of labeled vehicles.
Figure 5. Proportions of motor vehicles using different fuels (others are new-energy vehicles).
NOx Emissions （NE)
Variables
Variable Types
Correlation Model
Ratios of Emission Standards (RE)
Orderly Classification
SCC
Ratios of Labeled Vehicles (RL)
Continuous
PCC
Ratios of Fuel Type of Vehicles (RF)
Continuous
PCC
Figure 6. Variables and Correlation Models used in the Correlation Analysis.
Table 1. Key factors affecting exhaust emissions of motor vehicles in the CVEM Ambient
Driving conditions
Technical properties
conditions 1. Speeds on different roads 1. Temperature
2. Proportions of vehicles on different roads
1. Vehicle types
2. Altitude
3. Annual mileage
2. Emission level
3. Fuel properties
4. Number of cold starts per day
3. Registered numbers
5. Average annual mileage
4. Gauge mileage
Table 2. Categories of motor vehicles in the CVEM. Vehicle type
5
Vehicle specification
Fuel type
Emission standard5
Referring to European motor vehicle emission standards, China proposed six grades (State 0, State I, State II, State III, State IV
and State V) to evaluate the emission levels of motor vehicles.
Passenger vehicle
Mini, Small, Mid,
Gasoline, Diesel, LNG6 and others
Large Cargo vehicle
State 0 to State V
Mini, Light, Mid,
Gasoline, Diesel
State 0 to State V
Heavy Low-speed CV7
Tri-wheel, Low-speed
Diesel
State 0 to State V
Motorbike
General, Light
Gasoline
State 0 to State V
Table 3. Correlation coefficients between NOx emissions and proportions of motor vehicles of different emission standards. Variables
Emission NOx
Emission Standards
NOx
1.000
0
I
II
III
IV
V
0.771
0.771
0.771
0.829*
-
-
0.600
0.812*
-
-
0.829*
0.986**
-
-
0.829*
0.986**
-
-
0.829*
0.986**
-
-
0.771
0.986**
Emission 0
I
II
III
IV
V
0.771
0.771
0.771
0.829*
-0.600
-0.812*
1.000
1.000**
1.000**
0.943**
1.000**
1.000
1.000**
0.943**
1.000**
1.000**
1.000
0.943**
-
-
-
0.829*
0.829*
0.829*
-
-
0.986**
0.986**
*Correlation is significant at the 0.05 level (two-tailed).
6
LNG = Liquefied natural gas.
7
Low-speed CV = Low-speed cargo vehicle.
0.943**
0.943**
0.943**
1.000
-0.771
1.000
0.812*
-
-
0.812*
1.000
0.986**
0.986**
**Correlation is significant at the 0.01 level (two-tailed).
Table 4. Correlation coefficients between NOx emissions and proportions of labeled vehicles. Labeled Vehicles Variables
NOx Yellow Label -
Green
Emission Gasoline
Yellow Label - Diesel
Label
NOx Emission
1.000
0.743
0.799
-0.786
Yellow Label -
0.743
1.000
0.992**
-0.997**
Yellow Label - Diesel
0.799
0.992**
1.000
-0.999**
Green Label
-0.786
-0.997**
-0.999**
1.000
Gasoline
**Correlation is significant at the 0.01 level (two-tailed).
Table 5. Correlation coefficients between NOx emissions and proportions of motor vehicles’ fuel types. Variables
Fuels
NOx Emission
Gasoline
Diesel
Other
1
-0.848*
0.849*
-0.856*
Gasoline
-0.848*
1.000
-1.000**
0.975**
Diesel
0.849*
-1.000**
1.000
-0.978**
Other
-0.856*
0.975**
-0.978**
1.000
NOx Emission
*Correlation is significant at the 0.05 level (two-tailed).
**Correlation is significant at the 0.01 level (two-tailed).
Table 6. Correlation test of RSES III, RYLD, RD and TN. (a) Correlation Matrix of RSES III, RYLD, RD and TN. RSES III
TN
RYLD
RD
RSES III
1.000
0.969
0.787
-0.962
TN
0.969
1.000
0.897
-0.988
RYLD
0.787
0.897
1.000
-0.923
RD
-0.962
-0.988
-0.923
1.000
(b) KMO test and Bartlett test of RSES III, RYLD, RD and TN. KMO measure of sampling adequacy
0.635
Approximate Chi-square
34.826
Sig.
0.000
Bartlett test
Table 7. Component analysis of RSES III, RYLD, RD and TN. (a) Total variance explanation. Extraction Sums of Squared Initial eigenvalues Loadings Compo Cumulat
nent
Cumulat
% of Total
% of ive
Total
Variance
ive Variance
% 1
3.766
94.157
94.157
% 3.76 6
94.157
94.157
2
0.221
5.524
99.682
/
/
/
3
0.012
0.305
99.986
/
/
/
4
0.001
0.014
100.000
/
/
/
(b) Component matrix and component coefficient. Variable
Loading Coefficient
RSES III
0.959
Coefficient
TN
0.994
0.494
RYLD
0.928
RD
-0.998
Component
Equation (7)
0.512 0.478 -0.512
Highlights： 1. Various factors affecting the NOx emissions from motor vehicles were considered; 2. The Pearson Correlation Coefficient (PCC) and Spearman Correlation Coefficient (SCC) were utilized to identify key sub-factors affecting the NOx emissions of motor vehicles; A reliable parameter is proposed to represent/estimate the variation in the NOx emissions of motor vehicles by Principal Component Analysis.
S0048-9697(19)34979-4 https://doi.org/10.1016/j.scitotenv.2019.134987 STOTEN 134987
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
5 July 2019 25 September 2019 13 October 2019
Please cite this article as: D. Liu, Q. Deng, Z. Ren, Z. Zhou, Z. Song, J. Huang, R. Hu, Variation Trends and Principal Component Analysis of Nitrogen Oxide Emissions from Motor Vehicles in Wuhan City from 2012 to 2017, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134987
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Variation Trends and Principal Component Analysis of Nitrogen Oxide Emissions from Motor Vehicles in Wuhan City from 2012 to 2017
Daoru Liu1, Qinli Deng1,*, Zhigang Ren1,*, Zeng Zhou2,*, Zhe Song1, Jiahui Huang3, Ruibo Hu1
1
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan
430070, China; 2
School of Urban Planning, Wuhan University, Wuhan 430070, China;
3
School of Civil Engineering, Technical University of Denmark, Copenhagen 2800 Kgs,
Denmark; *
Correspondence: [email protected] (Q.D.); [email protected] (Z.R.);
[email protected] (Z.Z.)
Variation Trends and Principal Component Analysis of Nitrogen Oxide Emissions from Motor Vehicles in Wuhan City from 2012 to 2017 Daoru Liu1, Qinli Deng1,*, Zhigang Ren1,*, Zeng Zhou2,*, Zhe Song1, Jiahui Huang3, Ruibo Hu1 1.
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China;
2.
School of Urban Planning, Wuhan University, Wuhan 430070, China;
3.
School of Civil Engineering, Technical University of Denmark, Copenhagen 2800 Kgs, Denmark;
* Correspondence: [email protected] (Q.D.); [email protected] (Z.R.); [email protected] (Z.Z.)
Abstract: In addition to fine particulate matter and oxysulfides, nitrogen oxides (NOx) emitted by motor vehicles are among the most important pollutants affecting air quality and public health in those urban areas where centralized heating and chemical industry absent. We utilized correlation analysis (pearson
correlation coefficient and spearman correlation coefficient) and principal component analysis (PCA) to identify the variation trends and main causes of NOx emissions from motor vehicles in Wuhan City. We considered the total number of motor vehicles (TN), ratios of motor vehicles of different emission standards (RE), rations of labeled motor vehicles (RL), and rations of motor vehicles’ fuel types (RF). The results show that: 1) with an increase in the total amount of motor vehicles, the NOx emissions of motor vehicles have been decreasing since 2015; 2) three sub-categories (the ratio of the State III emission standard, the ration of yellow label diesel vehicles, and the ration of diesel vehicles) were recognized as key indexes of PE, PL and PF, respectively, in the PCA; 3) a new parameter, the ESindex is proposed as an index to represent the variation trend of the NOx emissions of motor vehicles in Wuhan City.
Keywords: Public health; Emission standards; Correlation; Sub-factor
1. Introduction In the past three decades, China had made considerable economic progress, at the cost of immeasurable environmental damage [1, 2]. Environmental degradation endangers public health, and air pollution is one of the most serious and widely discussed issues of the recent 10 years. Fine particulate matter, sulfur oxides (SOx) and nitrogen oxides (NOx) are harmful to both the environment and public health. Fine particulate matter emissions have been effectively controlled in China since the implementation of the national policy on smog governance, and this improving trend will continue in the coming years [3-5]. Especially in areas where centralized heating is generally not provided, like central and southern areas of China, fine particulate matter is no longer the primary object of air pollution control. Wuhan
is a typical representative of these areas, and it has recorded a decrease in PM2.5 concentration for 5 consecutive years since 2013 [6]. Besides, SOx have not been the main component of air pollutants in the Wuhan area, because of the absence of chemical industry and coal combustion [6, 7]. In addition to PM2.5, NOx and ozone (O3) are the main components of air pollutants in Wuhan city [6, 8-10], and NOx is the main source of ozone generation at room temperature in air [11-13]. Research on NOx may therefore have a considerable impact on both NOx and O3 pollution control. Generally, NOx refers to NO and NO2. In non-industrial urban areas like Wuhan, most NOx comes from motor vehicles, and is produced when vehicles are driven by an internal combustion engine at high temperatures under oxygen-rich conditions [14-16]. Wang et al. [14] conducted studies in Beijing and Chongqing, utilizing a "chase" vehicle sampling strategy, in which a mobile laboratory follows target vehicles, repeatedly sampling their exhaust, to assess NOx and black carbon emission factors of 440 diesel trucks. The results show that effective multi-pollutant control strategies and in-use compliance programs are imperative to reduce the overall emissions from the transportation sector. Yamada et al. [15] utilized different techniques to study the emission properties of NO, NO2, N2O and other pollutants from diesel engines. Their results show that NO2 and N2O were strongly correlated with the equivalence proportions of the engines, and higher equivalence proportions lead to higher pollutant emissions. Wallington et al. [16] made an overview of vehicle emissions, ambient concentrations, and atmospheric chemistry of NOx. They also discussed in depth the formation of NOx in vehicle engines, technologies used to control emissions of NOx, and trends in vehicle emissions. The conclusion was that the emission of NOx is closely related to engine sizes and numbers of vehicles in urban areas. Denis et al. [17] thought NOx emissions of road vehicles were the major contributor to poor air quality in urban areas. High NOx concentrations, especially of NO2, are typically the most
problematic pollution in cities. Lin et al. [18] field-tested 185 diesel vehicles at an engine dynamometer station, for their black smoke reflectivity and NOx concentrations, to explore the correlation between these two pollutants. The results showed that the service life and engine size were the main factors affecting the emission of NOx from vehicles. Kadijk et al. [19] criticized the status quo of NOx emissions from vehicles in Europe, and expressed concern about future NOx emissions in Europe. Shorter et al. [20] utilized a chase vehicle sampling strategy to sample NOx from approximately 170 in-use New York City mass transit buses. The results showed that emissions of NOx from diesel and compressed natural gas (CNG) buses were comparable, but hybrid electric buses had approximately one-half the NOx emissions. In areas where fine particulate matter is effectively controlled, NOx are the dominant pollutants that seriously endanger the environment and public health [21]. It is therefore of great importance to study the variation trends and main causes of NOx emissions, in order to reduce NOx pollution.
2. Materials and Methods 2.1. Case Design and Factor Selection This study was carried out in Wuhan City, which is located at 29°58'-31°22' N, 113°41'-115°05' E, in the central southern part of China, at the junction of the Yangtze River and Hanjiang River, as shown in Figure.1. It is the inland transportation center of China, and a typical city where heating is not generally provided, and without chemical and petroleum industries.
Figure 1. Location of the research area. As a mega-city with a population of more than 10 million, Wuhan needs a developed transportation system and a large number of motor vehicles to maintain its normal operation. The extensive use of motor vehicles has resulted in a large amount of NOx emissions, and the intensity of NOx emissions has been increasing for more than two decades [22, 23]. We utilized data on air pollutants and motor vehicles collected and processed by the Wuhan Ecology and Environment Bureau and the Statistics Bureau of Wuhan Municipality, to study the statistical relationships between these two factors. The main factors affecting NOx emissions (NE, 104t) of motor vehicles in Wuhan City can be divided into four categories: (1) the total number of motor vehicles (TN, 104); (2) the ratios of motor vehicles of different emission standards (RE); (3) the ratios of labeled motor vehicles (RL, %); and (4) the ratios of fuel types of motor vehicles (RF).
The number of motor vehicles is the factor most directly impacting the NOx emissions of motor vehicles. Besides this, emission standards are also of great importance to NOx emissions. As mentioned in Section 2.2, the Ministry of Ecology and Environment of China proposed six grades, State 01 to State V, to evaluate the emission levels of motor vehicles. Each motor vehicle was given a label (green or yellow) according to its actual emission level. Gasoline motor vehicles whose emission level is higher than the State I emission standard, and diesel motor vehicles whose emission level is higher than the State III emission standard, are collectively called “yellow label” vehicles. According to emission limits, the emissions of a yellow label vehicle are equivalent to 5 State I vehicles, 7 State II vehicles, 14 State III vehicles, or more than 20 State IV vehicles consuming gasoline. Due to their high emissions, yellow label vehicles are especially subject to control and elimination. Fuels consumed by motor vehicles are divided into three categories: C1: gasoline, C2: diesel, C3: LNG and others. The “others” included in C3 are electric vehicles, gas-electric vehicles, and LNG-electric vehicles. As the traditional fuels for motor vehicles, gasoline and diesel dominate their fuel consumption. Table 3. Fuel types of motor vehicles from 2012 to 2017. Year 2012 2013 2014 2015 2016 2017
1
Gasoline 86.06 87.17 88.78 90.46 91.42 91.89
State 0: A motor vehicle whose emissions are higher than State I.
Proportion (%) Diesel 13.76 12.62 10.91 9.21 8.15 7.62
LNG and others 0.16 0.21 0.27 0.33 0.43 0.49
2.2. Model of Pollutant Emissions Calculation By integrating various foreign traffic emission models, and utilizing localized basic data to calculate the pollutant emissions of motor vehicles, determined by vehicle type and pollutant emission process, the Vehicle Emission Control Center of the Ministry of Ecology and Environment of China established the China Vehicle Emission Model (CVEM) [24-27]. In the CVEM, the calculation of motor vehicles’ emissions is divided into exhaust emissions and evaporation emissions, and the exhaust emissions include the cold starting process and hot running process. With consideration of the types and emission processes of motor vehicles, the CVEM gives equations to calculate their exhaust emissions as shown below.
EMEi , j CEEi , j Di
(1)
Di N i ADi
(2)
where, EMEi,j is the total amount of pollutant j emitted by i-type vehicles (g); CEEi,j is the emission factor when the vehicle type is i and the pollutant is j, and this factor is processed by the correction of speed, temperature, altitude, etc. (g/km); Di is the annual mileage of i-type vehicles (km); Ni is the registered number of i-type vehicles; and ADi is the average annual mileage of i-type vehicles (km). The emission factor CEEi,j is mainly determined by ambient conditions, driving conditions and technical properties of the vehicles, as shown in Table 1. Table 1. Key factors affecting exhaust emissions of motor vehicles in the CVEM Ambient conditions
Driving conditions
Technical properties
1. Temperature 2. Altitude 3. Fuel properties
1. Speeds on different roads 2. Proportions of vehicles on different roads 3. Annual mileage 4. Number of cold starts per day 5. Average annual mileage
1. Vehicle types 2. Emission level 3. Registered numbers 4. Gauge mileage
Based on the state regulation of the Ministry of Public Security of China, the vehicles in the CVEM are divided into 34 categories according to vehicle type, vehicle specifications, fuel types and emission standards, as shown in Table 2. Table 2. Categories of motor vehicles in the CVEM. Vehicle type
Vehicle specification
Fuel type
Emission standard2
Passenger vehicle
Mini, Small, Mid, Large
State 0 to State V
Cargo vehicle Low-speed CV4 Motorbike
Mini, Light, Mid, Heavy Tri-wheel, Low-speed General, Light
Gasoline, Diesel, LNG3 and others Gasoline, Diesel Diesel Gasoline
State 0 to State V State 0 to State V State 0 to State V
2.3. Pearson Correlation Coefficient and Spearman Correlation Coefficient The Pearson correlation coefficient (PCC) and Spearman correlation coefficient (SCC) are two indexes to evaluate the linear correlation between two random variables X and Y. If the correlation coefficient is positive, then most likely the variables are directly related and if it is negative, then they are inversely related (1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation). In this article, the PCC and SCC were used to calculate the correlation between the NOx emissions and sub-categories of RE, RL and RF, and to investigate the impact of each sub-category of RE, RL and RF on the NOx emissions. However, applicability of the PCC and KCC is different, depending on variable types [28, 29]. Generally, variables in statistical analysis are divided into continuous variables, disorderly classification variables, and orderly classification variables. The PCC is suitable for two normally-distributed continuous variables, and the KCC can be used to evaluate the linear correlation between any two kinds of variables. 2
Referring to European motor vehicle emission standards, China proposed six grades (State 0, State I, State II, State III, State IV
and State V) to evaluate the emission levels of motor vehicles. 3
LNG = Liquefied natural gas.
4
Low-speed CV = Low-speed cargo vehicle.
The PCC is defined as the quotient of covariance and standard deviations between two variables, and its mathematical expression is as follows [28]:
X
Y Y X X Y Y n
rXY
i 1
X 2
n
i 1
i
i
n
i
i 1
2
(3)
i
where, rXY is the PCC between variables X and Y, n is the sample size, Xi and Yi are individual values —
—
of dataset X and Y, X is the mean value of X, and analogously for Y. The SCC is defined as the PCC between hierarchical variables. For a sample of size n, the original data are converted into hierarchical data, and the mathematical expression for the SCC is as follows [29]:
=1-
6 di2
n n 2 1
(4)
where, ρ is the SCC, di is order differences, and n is the sample size. 2.4. Principal Component Analysis In this study, in order to comprehensively analyze the NOx emissions, many variables/factors are proposed, with each variable reflecting some information on the subject, though to varying degrees, and with correlations between the variables. When there is a certain correlation between two variables, it indicates that the two variables reflect overlapping information. Principal component analysis (PCA) is utilized to remove redundant variables (closely related variables) and establish as few new variables (principal components, PCs) as possible, so that these new variables are not related to each other, and these new variables keep the original information as far as is possible. In consideration of the unit differences of variables, variables were standardized using Z-score Standardization, as shown in Equation (5) as follows [30]:
X
X X SD X
(5) —
where X* is the standardized variables, X is the original variables, X is the mean value of X, and SD(X) is the standard deviation of X. After standardization, the original variables were transformed into standardized variables, which were utilized as calculated data in the PCA process. Based on PCA theory, PCs can be expressed as follows [31]:
PC1 C11 C12 PC C C22 2 21 PCi Ci1 Ci 2
C1 j X 1 C2 j X 2 Cij X j
(6)
where, PCi is the ith PC of the variables, Cij is the loading coefficient indicating the contribution of the jth variable to define PCi, X*j is the standardized variable. PC1 is generally required to contain the maximum possible variance and the most information of the original variables, while other PCs cover the remaining information and variability, in decreasing order of information content. Generally, the cumulative value of variation covered by the selected PCs is over 85%. For j dimensional variables, the relationship among the variable covariance matrix, i principal axes (i.e., PC1, PC2, …, PCi), and eigenvectors is given as: cPCi i PCi
where, c is the variable covariance matrix, and λi is the ith largest eigenvalue of the matrix c.
(7)
3. Results and Discussion 3.1. Variation Characteristics of NE, TN, RE, RL and RF As shown in Figure 2, the emissions of NOx had been increasing, with a growth rate of 4.30% in 2012 and 2.75% in 2014. Since 2015, the emissions of NOx have continued to decline. The growth rate in 2015 was the lowest in nearly six years, at -13.62%. Despite a substantial increase in the number of motor vehicles, the emissions of NOx from motor vehicles was decreasing. Among the reasons for this, emission standards and policy factors are extremely important. The number of motor vehicles increased by more than 1.5 million since 2012. From 2012 to 2013, the growth rate increased from 14.8% to 17.4%. After 2012, the growth rate in motor vehicles had been around 17% for the 5 years to 2016, but it then dropped to 10% in 2017.
(a)
(b) Figure 2. (a) NOx emissions of motor vehicles and growth rate, and (b) numbers and growth of motor vehicles from 2012 to 2017. Figure 3 shows the proportions of different emission standards of motor vehicles. It is obvious that proportions of motor vehicles of State 0, State I and State II emission standards had fallen for six years in a row, while those of motor vehicles of State IV and State V emission standards had risen. The proportion of motor vehicles of State III had its peak at 53.57% in 2013, and then fell continuously to 30.47% in 2017. Combining Figure 2(b) and Figure 3, it is found that the year of 2014 is a key time point. At the beginning of 2014, the government of Wuhan City began to implement the State V emission standards for motor vehicles. At the same time, the government was vigorously promoting the elimination of high-pollution and high-emission vehicles of State I, State II and State III, which greatly increased the proportion of vehicles of State IV [32]. The growth trend in NOx emissions from motor vehicles was then terminated, falling to -13.62% from 2014 to 2015.
As well as the European emission standards, each new emission standard will reduce NOx emissions by more than 30%, compared with the previous version of the emission standards [33-36]. Government-led mandatory emission standards therefore have a considerable impact on reducing emissions of NOx from motor vehicles. With more strict emission limits, the control of NOx emissions will be better [36, 37].
Figure 3. Proportions of emission standards of motor vehicles from 2012 to 2017. Since 2010, preliminary measures and mechanisms for eliminating yellow label vehicles have been formulated [38]. Under the same operating conditions, the pollutant emission level of a yellow label vehicle is 5 to 15 times that of normal vehicle, including NOx emissions [39]. As shown in Figure 4, the number of yellow label vehicles had decreased by about 90% from 2012 to 2017, which contributed a lot to the decline of NOx emissions. The proportion of green label vehicles increased every year from 2012 to 2017, and had reached 99.5% in 2017. At the same time, proportions of yellow label gasoline vehicles and yellow label diesel vehicles decreased to 0.27% and 0.23%,
respectively, in 2017. This was due to the government's compulsory measures and financial subsidies aimed at eliminating yellow label vehicles.
(a)
(b)
Figure 4. (a) Number of yellow label vehicles, and (b) proportions of labeled vehicles. As shown in Figure 5, the proportion of gasoline motor vehicles increased year by year, while the proportion of diesel motor vehicles decreased. The proportion of new-energy vehicles had an increment every year, but the variation range was limited. Fuel type is also an important factor affecting NOx emissions; with the same engine size, diesel vehicles emit three to seven times more NOx than gasoline vehicles [40, 41]. The decline of gasoline vehicles also contributed a lot to the reduction in NOx emissions.
Figure 5. Proportions of motor vehicles using different fuels (others are new-energy vehicles). 3.2. Correlation Analysis of RE, RL and RF Factors such as RE, RL and RF all affect the NOx emissions of motor vehicles to different degrees. These factors can be divided into several sub-categories, and each sub-category has its own variation characteristics. PCC and SCC were utilized to identify the sub-categories of each factor that have the
greatest correlation with the NOx emissions of motor vehicles, considering the variable types shown in Figure 6.
NOx Emissions （NE)
Variables
Variable Types
Correlation Model
Ratios of Emission Standards (RE)
Orderly Classification
SCC
Ratios of Labeled Vehicles (RL)
Continuous
PCC
Ratios of Fuel Type of Vehicles (RF)
Continuous
PCC
Figure 6. Variables and Correlation Models used in the Correlation Analysis. As shown in Table 3, a strong correlation was identified between the proportion of the State III emission standard and the NOx emissions of motor vehicles, with a correlation coefficient 0.829 (red background). Meanwhile, absolute values of correlation coefficients between the proportion of State III motor vehicles and proportions of motor vehicles of other emission standards are both over 0.75 (green background), which means there are strong correlations between them. Based on the analysis above, the proportion of the State III emission standard was determined to be the most significant subcategory of RE, and utilized for the subsequent PCA of the NOx emissions of motor vehicles.
Table 3. Correlation coefficients between NOx emissions and proportions of motor vehicles of different emission standards. Variables
Emission Standards
NOx Emission
0
I
II
III
IV
V
-0.600
-0.812*
NOx Emission
1.000
0.771
0.771
0.771
0.829*
0
0.771
1.000
1.000**
1.000**
0.943**
-0.829*
-0.986**
I
0.771
1.000**
1.000
1.000**
0.943**
-0.829*
-0.986**
II
0.771
1.000**
1.000**
1.000
0.943**
-0.829*
-0.986**
III
0.829*
0.943**
0.943**
0.943**
1.000
-0.771
-0.986**
IV
-0.600
-0.829*
-0.829*
-0.829*
-0.771
1.000
0.812*
V
-0.812*
-0.986**
-0.986**
-0.986**
-0.986**
0.812*
1.000
*Correlation is significant at the 0.05 level (two-tailed). **Correlation is significant at the 0.01 level (two-tailed).
As shown in Table 4, a strong correlation was identified between the proportion of yellow label diesel vehicles and the NOx emissions of motor vehicles, with a correlation coefficient 0.799 (red background). Meanwhile, the absolute values of the correlation coefficients between the proportion of yellow label diesel vehicles and proportions of other label vehicles are both over 0.90 (green background), which means there are strong correlations between them. Based on the analysis above, the proportion of yellow label diesel vehicles was determined to be the most significant sub-category of RL, and it was utilized for the PCA. Table 4. Correlation coefficients between NOx emissions and proportions of labeled vehicles. Variables
NOx Emission
NOx Emission
1.000
Yellow Label - Gasoline Yellow Label - Diesel Green Label
Labeled Vehicles Yellow Label - Gasoline
Yellow Label - Diesel
Green Label
0.743
0.799
-0.786
0.743
1.000
0.992**
-0.997**
0.799
0.992**
1.000
-0.999**
-0.786
-0.997**
-0.999**
1.000
**Correlation is significant at the 0.01 level (two-tailed).
As shown in Table 5, a strong correlation was identified between the proportion of diesel vehicles and the NOx emissions of motor vehicles, with a correlation coefficient 0.849 (red background). The absolute values of the correlation coefficients between the proportion of diesel vehicles and proportions of other vehicles are both over 0.90 (green background), which means there are strong
correlations between them. Based on the analysis above, the proportion of yellow label diesel vehicles was determined as the most significant sub-category of RF, and it was utilized for the PCA. Table 5. Correlation coefficients between NOx emissions and proportions of motor vehicles’ fuel types. Variables
NOx Emission
Fuels Gasoline
Diesel
Other
NOx Emission
1
-0.848*
0.849*
-0.856*
Gasoline
-0.848*
1.000
-1.000**
0.975**
Diesel
0.849*
-1.000**
1.000
-0.978**
Other
-0.856*
0.975**
-0.978**
1.000
*Correlation is significant at the 0.05 level (two-tailed). **Correlation is significant at the 0.01 level (two-tailed).
3.3. PCA of NOx Emissions of Motor Vehicles In Section 3.2, the proportion of the State III emission standard (RSES III), the proportion of yellow label diesel vehicles (RYLD), and the proportion of diesel vehicles (RD), were determined as key subcategories of RE, RL and RF, respectively. These three sub-categories, together with TN, are used as variables for the PCA. Table 6(a) shows that there are correlations between these variables. The Kaiser-Meyer-Olkin (KMO) test and Bartlett test were used to identify the correlation. The value of the KMO test was 0.635, and the significance value was below 0.001, as shown in Table 6(b). This proves the correlations between the variables, and the PCA can therefore be conducted. Table 6. Correlation test of RSES III, RYLD, RD and TN. (a) Correlation Matrix of RSES III, RYLD, RD and TN. RSES III TN RYLD RD
RSES III
TN
RYLD
RD
1.000 0.969 0.787 -0.962
0.969 1.000 0.897 -0.988
0.787 0.897 1.000 -0.923
-0.962 -0.988 -0.923 1.000
(b) KMO test and Bartlett test of RSES III, RYLD, RD and TN. KMO measure of sampling adequacy Bartlett test
0.635
Approximate Chi-square
34.826
Sig.
0.000
One principal component (PC1) was obtained in the PCA, as shown in Table 7, with an initial eigenvalue of 3.766, while the initial eigenvalues of the other components were 0.221, 0.012 and 0.001, respectively. The extraction sum of squared loadings of PC1 was 94.157%, which means that PC1 contributes more than 94% of the overall effect of RSES III, RYLD, RD and TN. Therefore, PC1 can be reliably treated as a unique index to represent the variation of the NOx emissions of motor vehicles in Wuhan City. Table 7. Component analysis of RSES III, RYLD, RD and TN. (a) Total variance explanation. Initial eigenvalues Component
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
3.766
94.157
94.157
3.766
94.157
94.157
2
0.221
5.524
99.682
/
/
/
3
0.012
0.305
99.986
/
/
/
4
0.001
0.014
100.000
/
/
/
(b) Component matrix and component coefficient. Variable
Loading Coefficient
Component Coefficient
RSES III
0.959
0.494
TN
0.994
0.512
RYLD
0.928
RD
-0.998
Equation (7)
0.478 -0.512
According to Table 7 and Equation (6), the mathematical expression PC1 can be expressed as follows:
RSESIII RYLD PC1 0.494 0.512 0.478 0.512 RD TN
(8)
ESindex PC1 0.494 RSESIII 0.512 RYLD 0.478 RD 0.512TN
(9)
4. Conclusions In this paper, data on nitrogen oxides emissions from motor vehicles in Wuhan City between 2012 and 2017 were utilized to identify their trends and principal components, using correlation analyses (KCC and SCC) and principal component analysis. Three principal conclusions were drawn, as follows. 1） The NOx emissions of motor vehicles increased from 2012 to 2014, and then declined continuously from 2015, while the total number of motor vehicles almost doubled between 2012 and 2017. 2） The ration of the State III emission standard (RSES III), the ration of yellow label diesel vehicles (RYLD), and the ration of diesel vehicles (RD), were found to be the key sub-categories of RE, RL and RF, respectively. 3） A new parameter/equation ESindex/PC1 was proposed as a unique index to represent the variation of the NOx emissions of motor vehicles in Wuhan City.
Acknowledgments: This project was funded by the National Natural Science Foundation of China (51608405, 51808410). We thank the staff at the various monitoring sites of the Wuhan Ecology and Environment Bureau for their work and their support for this study.
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Highlights： 1. Various factors affecting the NOx emissions from motor vehicles were considered; 2. The Pearson Correlation Coefficient (PCC) and Spearman Correlation Coefficient (SCC) were utilized to identify key sub-factors affecting the NOx emissions of motor vehicles;
A reliable parameter is proposed to represent/estimate the variation in the NOx emissions of motor vehicles by Principal Component Analysis.
Figure 1. Location of the research area.
(a)
(b) Figure 2. (a) NOx emissions of motor vehicles and growth rate, and (b) numbers and growth of motor vehicles from 2012 to 2017.
Figure 3. Proportions of emission standards of motor vehicles from 2012 to 2017.
(a)
(b) Figure 4. (a) Number of yellow label vehicles, and (b) proportions of labeled vehicles.
Figure 5. Proportions of motor vehicles using different fuels (others are new-energy vehicles).
NOx Emissions （NE)
Variables
Variable Types
Correlation Model
Ratios of Emission Standards (RE)
Orderly Classification
SCC
Ratios of Labeled Vehicles (RL)
Continuous
PCC
Ratios of Fuel Type of Vehicles (RF)
Continuous
PCC
Figure 6. Variables and Correlation Models used in the Correlation Analysis.
Table 1. Key factors affecting exhaust emissions of motor vehicles in the CVEM Ambient
Driving conditions
Technical properties
conditions 1. Speeds on different roads 1. Temperature
2. Proportions of vehicles on different roads
1. Vehicle types
2. Altitude
3. Annual mileage
2. Emission level
3. Fuel properties
4. Number of cold starts per day
3. Registered numbers
5. Average annual mileage
4. Gauge mileage
Table 2. Categories of motor vehicles in the CVEM. Vehicle type
5
Vehicle specification
Fuel type
Emission standard5
Referring to European motor vehicle emission standards, China proposed six grades (State 0, State I, State II, State III, State IV
and State V) to evaluate the emission levels of motor vehicles.
Passenger vehicle
Mini, Small, Mid,
Gasoline, Diesel, LNG6 and others
Large Cargo vehicle
State 0 to State V
Mini, Light, Mid,
Gasoline, Diesel
State 0 to State V
Heavy Low-speed CV7
Tri-wheel, Low-speed
Diesel
State 0 to State V
Motorbike
General, Light
Gasoline
State 0 to State V
Table 3. Correlation coefficients between NOx emissions and proportions of motor vehicles of different emission standards. Variables
Emission NOx
Emission Standards
NOx
1.000
0
I
II
III
IV
V
0.771
0.771
0.771
0.829*
-
-
0.600
0.812*
-
-
0.829*
0.986**
-
-
0.829*
0.986**
-
-
0.829*
0.986**
-
-
0.771
0.986**
Emission 0
I
II
III
IV
V
0.771
0.771
0.771
0.829*
-0.600
-0.812*
1.000
1.000**
1.000**
0.943**
1.000**
1.000
1.000**
0.943**
1.000**
1.000**
1.000
0.943**
-
-
-
0.829*
0.829*
0.829*
-
-
0.986**
0.986**
*Correlation is significant at the 0.05 level (two-tailed).
6
LNG = Liquefied natural gas.
7
Low-speed CV = Low-speed cargo vehicle.
0.943**
0.943**
0.943**
1.000
-0.771
1.000
0.812*
-
-
0.812*
1.000
0.986**
0.986**
**Correlation is significant at the 0.01 level (two-tailed).
Table 4. Correlation coefficients between NOx emissions and proportions of labeled vehicles. Labeled Vehicles Variables
NOx Yellow Label -
Green
Emission Gasoline
Yellow Label - Diesel
Label
NOx Emission
1.000
0.743
0.799
-0.786
Yellow Label -
0.743
1.000
0.992**
-0.997**
Yellow Label - Diesel
0.799
0.992**
1.000
-0.999**
Green Label
-0.786
-0.997**
-0.999**
1.000
Gasoline
**Correlation is significant at the 0.01 level (two-tailed).
Table 5. Correlation coefficients between NOx emissions and proportions of motor vehicles’ fuel types. Variables
Fuels
NOx Emission
Gasoline
Diesel
Other
1
-0.848*
0.849*
-0.856*
Gasoline
-0.848*
1.000
-1.000**
0.975**
Diesel
0.849*
-1.000**
1.000
-0.978**
Other
-0.856*
0.975**
-0.978**
1.000
NOx Emission
*Correlation is significant at the 0.05 level (two-tailed).
**Correlation is significant at the 0.01 level (two-tailed).
Table 6. Correlation test of RSES III, RYLD, RD and TN. (a) Correlation Matrix of RSES III, RYLD, RD and TN. RSES III
TN
RYLD
RD
RSES III
1.000
0.969
0.787
-0.962
TN
0.969
1.000
0.897
-0.988
RYLD
0.787
0.897
1.000
-0.923
RD
-0.962
-0.988
-0.923
1.000
(b) KMO test and Bartlett test of RSES III, RYLD, RD and TN. KMO measure of sampling adequacy
0.635
Approximate Chi-square
34.826
Sig.
0.000
Bartlett test
Table 7. Component analysis of RSES III, RYLD, RD and TN. (a) Total variance explanation. Extraction Sums of Squared Initial eigenvalues Loadings Compo Cumulat
nent
Cumulat
% of Total
% of ive
Total
Variance
ive Variance
% 1
3.766
94.157
94.157
% 3.76 6
94.157
94.157
2
0.221
5.524
99.682
/
/
/
3
0.012
0.305
99.986
/
/
/
4
0.001
0.014
100.000
/
/
/
(b) Component matrix and component coefficient. Variable
Loading Coefficient
RSES III
0.959
Coefficient
TN
0.994
0.494
RYLD
0.928
RD
-0.998
Component
Equation (7)
0.512 0.478 -0.512
Highlights： 1. Various factors affecting the NOx emissions from motor vehicles were considered; 2. The Pearson Correlation Coefficient (PCC) and Spearman Correlation Coefficient (SCC) were utilized to identify key sub-factors affecting the NOx emissions of motor vehicles; A reliable parameter is proposed to represent/estimate the variation in the NOx emissions of motor vehicles by Principal Component Analysis.