Chang’an University Chang’an University The Statistical Distributions of SO 2 , NO 2 and PM 10 Concentrations in Xi’an, China Jiang Xue 1 , Shunxi Deng 1 , Ning Liu 1 , Binggang Shen 2 1 Chang’an University, Xi’ an, China 2 Shaanxi Institute of Env ironmental Sciences and Tech nology Xi’an, China Chang’an University
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The Statistical Distributions of SO 2 , NO 2 and PM 10 Concentrations in Xi’an, China
The Statistical Distributions of SO 2 , NO 2 and PM 10 Concentrations in Xi’an, China. Jiang Xue 1 , Shunxi Deng 1 , Ning Liu 1 , Binggang Shen 2. 1 Chang’an University, Xi’an, China 2 Shaanxi Institute of Environmental Sciences and Technology Xi’an, China. Chang’an University. - PowerPoint PPT Presentation
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Chang’an UniversityChang’an University
The Statistical Distributions of SO2, NO2 and PM10 Concentrations in Xi’an,
China
Jiang Xue 1, Shunxi Deng 1, Ning Liu 1, Binggang Shen 2
1 Chang’an University, Xi’an, China2 Shaanxi Institute of Environmental Sciences and Technology Xi’an, China
Chang’an University
Chang’an UniversityChang’an University
Xi’an, is one of four world-famous ancient cities
Chang’an UniversityChang’an University
IntroductionIn this work, the time series data of three conventional
air pollutants concentrations in recent years were taken and analyzed.
The purpose is to determine the best distribution models for SO2, NO2 and PM10 concentrations and to estimate the required emission reduction to meet the ambient air quality standard (AAQS), through fitting the daily average concentration data to the several used commonly distribution functions.
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The data were taken over a three-year period from 1 January 2006 to 31 December 2008, the time series data of three air pollutants were measured at seven ambient monitoring stations in Xi’an.
The detailed locations of these stations are shown in Fig.1
5
1
2
3
5 67
4
Cao Tan
Switch Factory
West Gaoxin Zone
Municipal Stadium
Xingqing District
Xiao Zhai
Textile City
N
5公里
Fig1. The locations of the monitoring
sites in Xi’an
Data sources
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(a) SO2
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 300 600 900 1200days
SO
2 d
aily
ave
rage
con
cent
rati
on,
mg/
m3
AAQS (secondarystandard), 24-hour
0.15 mg/m3
Fig.2. The variability of daily average concentration for each air pollutant with time. (a) SO2 (b) NO2 (c) PM10,
from 1 January 2006 to 31 December 2008.
The variability of daily average concentration of air pollutants with time
Basic statistics SO2 NO2 PM10
N (number of observations)
1096 1096 1095
Missing 0 0 1
Zero values 0 0 0
Maximum 0.2406 0.1052 0.3728
Minimum 0.0114 0.0116 0.0346
Mean 0.0507 0.0416 0.1260
Median 0.0404 0.0413 0.1188
SD 0.0309 0.0137 0.0535
Variance 0.0010 0.0002 0.0029
Skewness 1.9311 0.4700 1.4609
Percentiles
25 0.0288 0.0319 0.0912
50 0.0404 0.0413 0.1188
75 0.0649 0.0498 0.1443
Table 1 Summary of the basic statistics
Note: the unit are mg/m3.
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(c) PM10
0.00
0.10
0.20
0.30
0.40
0 300 600 900 1200
days
PM1
0 da
ily
aver
age
conc
entr
atio
n, m
g/m3
AAQS (secondarystandard), 24-hour
0.15 mg/m3
(b) NO2
0.00
0.04
0.08
0.12
0 300 600 900 1200days
NO
2 d
aily
ave
rage
con
cent
rati
on, m
g/m3
AAQS (secondarystandard), 24-hour
0.08 mg/m3
The daily average concentrations of three pollutants have strongly seasonal variability from these figures.
Fig.2 also shows the exceedance of three air pollutants, and the probabilities of exceeding the secondary standard of AAQS are 1.09% for SO2, 0.82% for NO2 and 20.73% for PM10. This means that the number of days exceeding the AAQS for three air pollutants in a year are 4, 3 and 76, respectively.
The probability of exceedance for PM10 is significantly higher than SO2 and NO2.
So, PM10 has become a major air pollutant in Xi’an.
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Distribution models used in representing air pollutant concentrations
In this study, the following distributions are chosen to fit the concentration data, they are Lognormal, Gamma, Inverse Gaussian, Log-logistic, Beta, Pearson 5, Pearson 6, Weibull and Extreme value distributions.
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Goodness-of-fit tests The goodness-of-fit tests are used to determine the
most appropriate statistical distribution model of air pollutant concentrations, including KS test, AD test , PCC test and Chi-squared test.
KS test:
AD test: test:
|)()(|max 0 xFxFD nn
k
i i
ii
np
npn
1
22 )(
nn
xFxFiA
n
i
inoio
1
12 )]}(1ln[)(){ln12(
2
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The identification of the best distribution model
TypesSO2 NO2 PM10
KS AD χ2 KS AD χ2 KS AD χ2
Lognormal 0.042(1) 3.66(3) 53.4(3)0.029(4
)1.03(3) 17.0(6) 0.059(4) 3.80(4) 70.8(4)
Pearson 6 0.046(2) 3.04(2) 43.0(1)0.029(6
)1.03(4) 17.0(7) 0.069(6) 4.95(6) 73.2(6)
Pearson 5 0.047(3) 2.99(1) 44.3(2)0.029(3
)1.01(2) 15.3(4) 0.056(3) 3.44(3) 59.8(2)
Extreme Value 0.049(4) 5.48(5) 67.0(4)0.027(1
)1.00(1) 15.2(3) 0.052(2) 3.35(2) 61.4(3)
Log-Logistic 0.053(5) 4.83(4) 74.1(5)0.032(7
)1.48(8) 18.4(8) 0.041(1) 2.15(1) 44.9(1)
Inv. Gaussian 0.069(6) 8.89(6) 95.7(6)0.060(9
)8.46(9) 54.9(9) 0.062(5) 4.91(5) 80.7(8)
Gamma 0.082(7)11.52(7
)98.9(7)
0.029(5)
1.09(5) 15.0(2) 0.069(7) 4.97(7) 72.7(5)
Beta 0.082(8)11.54(8
)98.9(8)
0.028(2)
1.15(6) 16.6(5) 0.070(8) 5.17(8) 76.4(7)
Weibull 0.085(9)15.80(9
)168.6(9
)0.033(8
)1.38(7) 14.7(1) 0.088(9) 9.77(9) 104.1(9)
Table 2 The results of goodness-of-fit tests
Note: The number in parentheses is the results of goodness-of-fit tests; red font corresponding distribution is the best distribution model under the different goodness-of-fit tests.
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The most appropriate statistical distribution models for the daily average concentration of SO2, NO2 and
PM10 were Pearson 6, Extreme Value and Log-Logistic
Fig.3. The best distribution models of three air pollutant concentrations: (a) SO2 (b) NO2 (c) PM10.
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Parameter estimation The commonly methods of parameter estimation are th
e maximum likelihood estimator (MLE), the least square estimator (LSE), the method of quantiles (MoQ) and the method of moments (MoM). MoM is more widely used and MLE provides the best estimate of the parameters (Lynn, D.A., 1974).
In the study, MLE was used, it is defined as:
n
iix xfL
1
)|()(
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The estimated values of parameters for the best distribution model of air pollutants are shown in Table 3.
After determining the most appropriate distribution model for air pollutant concentrations, the emission source reduction R (%) required to meet the AAQS can be predicted from a rollback equation:
where E{c}s is the expected concentration of distribution when the extreme value equals cs (i.e. the values of the AAQS), E{c} is the mean concentration of the actual distribution and cb is the background concentration.
{ } { }
{ }S
b
E c E cR
E c c
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Table 4 The emission reduction
Air pollutants
The best distribution models
E{c}s ( mg/m3)
E{c}( mg/m3)
R(%)
PM10 Log-Logistic 0.100 0.1268 21.1
NO2 Extreme Value 0.040 0.0416 3.8
SO2 Pearson 6 0.060 0.0514 -16.7Note: when estimating the emission reduction in this study, cb is neglected in the rollback equati
on.
Therefore, the emission source reductions of SO2, NO2 and PM10
concentrations to meet the AAQS are -16.7%, 3.8% and 21.1%, respectively.
It means that the annual average SO2 concentration meets to the
AAQS without requiring further mitigation and with an environmental capacity of 16.7% in future, while control of PM10
and NO2 emission sources in Xi’an should be increased in order to