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Prediction of NO2Concentration from Roadside NOx Measurement
(Case Study Jakarta and Bandung City)
Prediksi Konsentrasi NO2dari Pengukuran NOx Tepi Jalan
(Studi Kasus Kota Jakarta dan Bandung)
Nadya Leviana1and Driejana
2
Environmental Engineering Department, Faculty of Civil and Environmental EngineeringBandung Institute of Technology
Jl.Ganesha 10 Bandung [email protected],
Abstract: Statistical analysis is commonly used to convert monitoring data into meaningful data that can be used in
making decision on designing and managing the environment. In air quality assessment there are tools that related to
each other, consist of monitoring, modeling and evaluation. The statistical analysis has to be done because the
monitoring results not always existed, so the modeling can be an effective alternative to assess the air quality. But
the modeling usually cannot obtain completed monitoring data as the monitoring results,so the statistical analysis
needed. The empirical function will be used to predict NO2 concentration from NOx that obtained later by
modeling. In this research the data source are the roadside NO2 and NOx from direct measurement at Jakarta
(Busway Line,2005) and Bandung (UNPAD Dipati Ukur, BAPPEDA dan SMA 1 Dago,2006). The statistical
analysis has done in a some trials by combining the data from both cities and dividing the data based on the places
and time (morning, noon and afternoon). The statistical analysis result shows that the best empirical function for
combined time is the result of trial 3 with Bandung City data, and for the splitted time the best empirical function is
the result of noon time at Bandung City. All of the empirical function can be used, depends on the traffic pattern on
the cities that will be analyzed. Jakarta has an unique traffic pattern that causing the NO2 and NOx correlation
coefficient smaller than Bandung that has a common traffic pattern.
Key words: statistical analysis, NOx, NO2, empirical function
Abstrak: Analisa statistik dalam bidang Teknik Lingkungan biasa digunakan untuk mengolah data hasil
pemantauan menjadi data yang berarti untuk pengambilan keputusan dalam desain dan manajemen lingkungan.
Dalam penilaian kualitas udara terdapat perangkat penting yang saling berhubungan yakni pemantauan,
permodelan dan evaluasi. Analisa statistik ini dilakukan sebab tidak selamanya data pemantauan dapat diperoleh
sehingga permodelan menjadi cara lanjutan yang efektif untuk menilai kualitas udara.Sedangkan permodelan
tentunya tidak selalu dapat menghasilkan data selengkap data pemantauan, oleh karena itulah dibutuhkan analisa
statistik. Persamaan empiris yang diperoleh nantinya akan digunakan untuk memprediksi konsentrasi NO2dari NOx
yang dihasilkan dari permodelan. Pada penelitian ini sumber data berasal dari hasil pemantauan tepi jalan
parameter NO2dan NO di Jakarta (Jalur Busway,2005) dan Bandung (UNPAD Dipati Ukur, BAPPEDA dan SMA 1
Dago,2006). Analisa statistik dilakukan dengan beberapa percobaan yaitu dengan menggabungkan data kedua
kota dan memisahkan data kedua kota berdasarkan tempat dan waktu (pagi, siang dan sore hari). Hasil analisa
statistik menunjukkan persamaan empiris terbaik untuk waktu gabung ialah hasil dari percobaan 3(data KotaBandung), sedangkan untuk waktu pisah hasil terbaik ialah dari percobaan 3-siang hari Kota Bandung. Semua
persamaan empiris yang dihasilkan dapat digunakan, berdasarkan pola lalu lintas kota yang akan dianalisa.
Jakarta memiliki pola lalu lintas yang unik yang mengakibatkan koefisien korelasi NO2and NOx Jakarta lebih kecil
dari Bandung yang memiliki pola lalu lintas yang lebih standar.
Kata Kunci: analisa statistik, NOx, NO2, persamaan empiris
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INTRODUCTIONNitrogen dioxide (NO2) is ubiquitous in the urban atmospheric environment and because of
its toxicity to humans, is one of six criteria pollutants by the US Environmental Protection
Agency (USEPA). Nitrogen oxides (NO+NO2 = NOx) play a major role in ozone (O3)
production, aerosol formation, and acid deposition. According to the USEPA estimates, nearly50% of NOx emissions come from motor vehicles and in Indonesia it is estimated about 60%
(KLH, 2003). As a result, there is continuing interest in developing models for the prediction of
NOx concentrations near roadways (Kenty et al., 2007). Of the six or seven oxides of nitrogen,nitric oxide (NO) and nitrogen dioxide (NO2) are important air pollutants. Nitrogen dioxide acts
as an acute irritant and in equal concentrations is more injurious than NO. However, at
concentrations found in the atmosphere NO2 is only potentially irritating and potentially related
to chronic pulmonary fibrosis. In combination with unburned hydrocarbons, the oxides of
nitrogen react in the present of sunlight to form photochemical smog (Wark and Warner, 1981).
Nitric oxide is produced from the reaction of N2 with O2 in air during high temperature
combustion processes: N2(g)+ O2(g) 2NO(g)
as well as from oxidation of nitrogen in the fuel. Smaller amounts of NO 2are produced by thefurther oxidation of NO; trace amounts of nitrogenous species are also formed (Pitts and Pitts,
1986). The nitrogen photolytic cycle is summarized in the following three reactions :
NO2+ hv NO + O (1)
O + O2 O3 (2)O3+ NO NO2+ O2 (3)
The atomic oxygen produced by the photolysis of NO2 is very reactive and rapidly combines
with O2 in the air to produce O3. However, in the presence of NO, the O3 will immediately
decompose regenerating the nitrogen dioxide (Cooper and Alley, 1986).
The assessment of air quality impacts due to emissions from road traffic relies upon theapplication of air quality models. These models predict the dispersion and dilution of primary
pollutants. Complications arise in the case of pollutants that undergo chemical transformations in
the atmosphere. This applies in the case of nitrogen oxides (NOx) (the sum of nitric oxide (NO)
and nitrogen dioxide (NO2)). The emissions occur primarily as NO, but this is transformed in the
atmosphere to NO2, principally by reaction with ozone. The reaction with ozone changes the
proportion of NO2and this has to be allowed for in the modelling. There is the added complexity
of background NO and NO2 mixing with freshly emitted NO and NO2. Prediction of NO2concentrations is thus not straightforward. ( Laxen and Wilson, 2002).
The principal interest when assessing NOx emissions from road traffic is the concentrations of
NO2at the roadside, as it is the NO2that is associated with adverse health effects, not the NOx. Itis thus necessary to predict the transformation of NO to NO2. There are various approaches to
this, ranging from the application of complex chemical models, through the use of simple
chemical models, to empirically based models ( Laxen and Wilson, 2002).
In this research we will try to find an empirical function for the ratio NO2/NOx using
statistical analysis with SPSS (Statistical Package for Social Science) program. That ratio will be
useful for predicting the oxides of nitrogen pollutants in an air quality model. The research willtake place at Jakarta and Bandung as Indonesias big cities, using the roadside air quality
monitoring data of those locations.
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METHODOLOGYThis research consists of statistical method on predicting NO2 concentration from roadside
NOx monitoring results using the SPSS 15 software. The first step is collecting the source data
that will be used for the statistical method. The required data are the roadside monitoring data as
the concentration of NO2, NO,O3 and meteorological data. Data are taken from the roadsidemonitoring result Jakarta (Trans Jakarta Busway Line : Thamrin, Fatmawati, ASMI, Jakarta
Utara and Petojo Utara ) from BPLHD DKI Jakarta,2005 and Bandung (UNPAD Dipati Ukur,
BAPEDA and SMA 1 Dago) from TL ITB, 2006. Before undertaking statistical analysis, thenext step is data pre treatment. Data pre treatment includes converting the NO2 and NOx
concentration data from g/m3to ppb, sorting the data into three terms of time (Morning: 06.00-
09.00, Noon: 11.00-14.00 and Afternoon: 16.00-19.00) and labeling the data based on dates,
location and the monitoring time. After pre treatment, data were treated and analysed by SPSS
15 software. There are 3 trials, trial 1 is for pooled city data (Jakarta and Bandung), trial 2 for
Jakarta data and trial 3 for Bandung data. Each trials were analysed as pooled-data and split into
3 groups based on morning (06.00-09.00), noon (11.00-14.00) and afternoon (16.00-19.00)
measurement time.The the normality test was done to NO2and NOx concentration in SPSS. If the data has not
distributed normally (indicated by positive/negative skewness), the data were transformed by the
chosen transformation. In some occasion, the outliers were excluded from the data analysis. The
empirical function is derived by linear regression (simple regression). The dependent variable(DV) in the regression is NO2, and the independent variable (IV) is NOx. Statistical parameters
such as R, R2, F and Sig are analyzed to determined the best function that could be used for
deriving NO2from NOx . Finally, sensitivity of the empirical function is.
The step in empirical model building is described on Figure 1 below:
No No
Yes Yes
Figure 1.Diagram of Model Building
Source : Driejana, 2004
NOx and NO2concentration
KS test
Histogram
Stem and Leavesplot
Normal Q-Q plot
Regress
Transform (Distribution
formula (Tabachnick,2000)
Outliers Excluded
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RESULTS AND ANALYSISNormality Test
Results of the first trial : pooled city data from Jakarta and BandungAt this trial, the roadside air quality monitoring data for oxides nitrogen (NOx) and nitrogendioxides (NO2) parameter is pooled by places, Jakarta and Bandung. This trial also divided
into terms of time (pooled time, morning split, noon split and afternoon split). The normality
result for this trial could be seen on Figure 2.
NO2gbg
200.00150.00100.0050.000.00
Frequency
300
200
100
0
Histogram
.
Std. . .
NOxgbg
600.00500.00400.00300.00200.00100.000.00
Frequenc
y
300
200
100
0
Histogram
M .Std. . .
(a) (b)
Figure 2. Normality for NO2and NOx at the first trial
Those results shows that for both parameter, at the first trial it has a positive skewness. In
order to normally distribute the data, the data were transformed. The most proper
transformation form were chosen based on the new normality graphs resulted by bothparameters ( the NOx and NO2concentration). The new normality graphs result from the
data transformation illustrated on Figure3 and Figure 4.
akarNO2gbg
12.5010.007.505.002.500.00
Frequency
200
150
100
50
0
Histogram
.Std. . .
logNO2gbg
2.001.000.00-1.00
Frequency
400
300
200
100
0
Histogram
.Std. . .
(c) (d)
Figure 3.New normality graph for sqrt NO2 and log10 NO2
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akarNOxgbg
25.0020.0015.0010.005.00
Frequency
120
100
80
60
40
20
0
Histogram
M
Std
logNOxgbg
2.502.001.501.00
Frequency
150
100
50
0
Histogram
Std.
(e) (f)
Figure 4.New normality graph for sqrt NOx andlog10 NOx
From those new normality graphs, the transformation data chosen for this trial are Sqrt NO2
and log10NOx because those results is closer to the normality than the other one. To make
the data closer to the normality, the outliers (extreme value data) is being checked and
excluded if necessary. The outliers of the transformed data can be seen in the form of boxplot
graphs on Figure 5.
akarNO2gbg
14
12
10
8
6
4
2
0
411
814813
4128151,220
34934
81635
757413
427
828827
833105838
834
logNOxgbg
3.0
2.5
2.0
1.5
1.0
0.5
9141,436
926
(g) (h)
Figure 5.Boxplot graph of Sqrt NO2 and log10 NOx
From the boxplot graphs above, the bulleted marks are the case number of the outliers data.
After the outliers being checked, it is excluded by unselecting those outliers on data analysis.
Excluding outliers made the data distributed more normally and reduced the outliers. It could
be seen in the normality test results after excluding the outliers on Figure 6 and Figure 7.
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akarNO2gbg
12.5010.007.505.002.500.00
Frequency
200
150
100
50
0
Histogram
.
Std. . .
akarNO2gbg
12.5
10.0
7.5
5.0
2.5
0.0
815
410801
1,122350
1,219 1,2481,121
111
104835
110
837
(i) (j)
Figure 6.Normality and boxplot graph after excluding outliers for Sqrt NO2
logNOxgbg
2.502.001.501.00
Frequen
cy
150
100
50
0
Histogram
.Std. . .
logNOxgbg
3.0
2.5
2.0
1.5
1.0
(k) (l)
Figure 7.Normality and boxplot graph after excluding outliers for log10 NOx
From those results, trial 1 for combined data on Jakarta and Bandung distributed closer tonormality after data transformation and excluding of outliers. After data distributed closer to
the normality, the data being regressed to know how much the roadside NOx concentrationconverted into NO2. So, the NOx concentration is used as the independent value (IV), andNO2concentration used as dependent value (DV). The regression analysis result for this trial
explained on Table 1 and 2.
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Table 1. Regression Result for Trial 1
Model UnstandardizedCoefficients StandardizedCoefficients t Sig. Correlations Collinearity Statistics
B Std. Error Beta Zero-order Partial Part Tolerance VIF B Std. Error
1 (Constant)-.544 .191 -2.847 .004
logNOxgbg
3.365 .096 .612 35.087 .000 .612 .612 .612 1.000 1.000
Table 2. ANOVA result for Trial 1
Model Sum ofSquares df Mean Square F Sig.
1 Regression 2458.513 1 2458.513 1231.123 .000(a)
Residual 4109.759 2058 1.997
Total 6568.272 2059
a Predictors: (Constant), logNOxgbgb Dependent Variable: akarNO2gbg
Those table above means that the empirical function for this trial is :
Sqrt NO2 = 3.365 log NOx + (-0.544)With the R value = 0.612, the R2value = 0.374, the F value is 1231.123 and the Sig value
0.00
Based on the literature (Tabachnick,2000 ; Damanhuri,1994), determination coefficient (R2)is the value of total variation of dependent value coupled with independent variable that can
be explained by the regression function. If the R2= 0.374, it means that about 37.4% of the
variation in the dependent value can be explained by the independent value.
The correlation coefficient (R) is the square root of determination coefficient that shows how
close are the correlation between dependent and independent variable. If the R = 0.612, itproves that there is a correlation between those variables for about 61.2%. The bigger R
value shows more correlation between the variables (R= 0 : no correlation ; R~1 : perfectcorrelation). The F value (weighing coefficient) is better when it has bigger value than 0. The
Sig value (Significance) has to be 0.00.
All of the trials has to passed the normality test steps before the regression analysis. The
normality test includes analysis of distribution curve, and if the data has not distributednormally it has to be treated by transformation of the data based on the skewness type,
analyse the outliers and exclude it if necessary. After the data being treated it will pass the
normality test again before regression analysis.
The data is called distributed normally if it has a bell shaped curve when the data served byhistogram graphs, stem and leaf plot or normal Q-Q or P-P plot. It defined that the data isnormally distributed if the distance between the means and the deviation is the same (same
sided bell). On this research we use the most common normality graph, it is the histogram
with normal curve that can be seen on Point 1 of the results and analysis. Besides from the
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curve, the normality of data can also be analyse by the value of skewness and kurtosis. The
perfect normally distributed data has the zero value of skewness and kurtosis. But for the
sample that consist of more than 100 case, there is a rule of thumb for it, that the data still
distributed normally if :
-2*standard error of skewness < sample skewness < +2*standard error of skewness-2*standard error of the kurtosis < sample kurtosis < 2*standard error of kurtosis
On this research we only analyse the normality test with the curve and range of skewness.
Here are the results of the normality tests from the 3 trials (Table 3 and 4):
Table 3.Skewness Data
Trial Parameters stderror -2stderror sample 2stderror skewness type
1 NO2 0.054 -0.108 1.537 0.108 positive
Jakarta and Bandung NOx 0.054 -0.108 1.333 0.108 positive
2 NO2 0.059 -0.118 1.417 0.118 positive
Jakarta NOx 0.059 -0.118 1.238 0.118 positive
3 NO2 0.124 -0.248 0.703 0.248 positive
Bandung NOx 0.124 -0.248 2.071 0.248 positive
From skewness data on Table 3, it is obvious that the data has to be transformed to normalize
it. The skewness data after transformation could be seen on Table 4.
Table 4.Skewness Data After Transformation
Trial Parameters stderror -2stderror sample 2stderror skewness type
1 SqrtNO2 0.054 -0.108 0.421 0.108 positive
Jakarta and Bandung
logNO2 0.054 -0.108 -2.017 0.108 negative
SqrtNOx 0.054 -0.108 0.465 0.108 positive
logNOx 0.054 -0.108 -0.332 0.108 negative
2 SqrtNO2 0.059 -0.118 0.312 0.118 positive
Jakarta
logNO2 0.059 -0.118 -2.201 0.118 negative
SqrtNOx 0.059 -0.118 0.397 0.118 positive
logNOx 0.059 -0.118 -0.378 0.118 negative
3 SqrtNO2 0.124 -0.248 0.246 0.248 positive
Bandung
logNO2 0.124 -0.248 -0.209 0.248 negative
SqrtNOx 0.124 -0.248 0.868 0.248 positive
logNOx 0.124 -0.248 -0.054 0.248 negative
The data transformation type will be choose based on the best skewness result after
transformed. If the skewness value is close to the normally skewness range, the data also
closer to the normality. For example, in trial 1 the transformation type chosen is Sqrt NO 2
and log10 NOx because it obtained the closest skewness value to the normal skewness range.
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It can be seen that the skewness value after transformation mostly become closer to the
normally skewness range. So, the proper data transformation makes the data closer to the
normality.
From the normality test results, the data from all trials become more distribute normally afterthe outliers being excluded. It assumed that after passed the transformation and excluding
outliers, the monitoring data has close to the normal distribution so it can be regress to find
an empirical function.
Linear Regression
On this research, the best empirical function being analysed to represents the empiricalfunction on deriving NO2 from NOx measurement in Indonesia. So, the results from all the
trials will be compared based on those statistical parameters. The regression analysis will becompared using this results table below (Table 5).
Table 5.Regression Analysis Results of 3 Trials
Trial Time Empirical function R R2 F Sig
1 combined time Sqrt NO2= 3.365 log10 Nox + (-0.544) 0.612 0.374 1231.123 0.00
(Jakarta
and
Bandung) splitted time
morning Sqrt NO2= 2.207 log10 Nox + 1.031 0.447 0.2 166.088 0.00
noon Sqrt NO2= 5.013 log10 Nox + (-3.046) 0.792 0.628 1171.351 0.00
afternoon Sqrt NO2= 3.304 log10 Nox + (-0.367) 0.745 0.555 864.686 0.00
2 combined time Sqrt NO2jkt= 3.406 log 10 Noxjkt+ (-0.614) 0.572 0.327 812.701 0.00
(Jakarta) splitted time
morning Sqrt NO2jkt= 2.331 log 10 Noxjkt+ 0.767 0.423 0.179 120.115 0.00
noon Sqrt NO2jkt= 5.052 log 10 Noxjkt+ (-3.160) 0.74 0.548 698.274 0.00
afternoon Sqrt NO2jkt= 3.406 log 10 Noxjkt+ (-0.614) 0.735 0.541 638.686 0.00
3 combined time Log10 NO2bdg= 0.513 log10 Noxbdg+ 0.525 0.822 0.676 802.832 0.00
(Bandung) splitted time
morning Log10 NO2bdg= 0.375 log10 Noxbdg+ 0.734 0.689 0.475 106.574 0.00
noon Log10 NO2bdg= 0.645 log10 Noxbdg+ 0.316 0.937 0.879 963.157 0.00
afternoon Log10 NO2bdg= 0.541 log10 Noxbdg+ 0.5 0.838 0.702 306.388 0.00
Based on the regression analysis above, the best empirical function for the combined time isthe trial for Bandung City. And for the splitted time, the best empirical function is at
noontime, Bandung City. Both of the best function is from Bandung data, it is predicted that
it is because the monitoring data from Bandung is more reliable because the monitoring
activity is more controlled, and the pattern of the data is more normal. It could also be
explained by the results of diurnal fluctuation of traffic and NOx at Jakarta and Bandung.The diurnal fluctuation of traffic and NOx in Jakarta shown on Figure 8 (Driejana, 2006) and
for Bandung shown on Figure 9 (Driejana, 2005).
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(m) (n)
Figure 8.Diurnal fluctuation of traffic and NOx at ASMI and Thamrin, JakartaSource: Driejana, 2006
(o) (p)
Figure 9.Diurnal fluctuation of NO and NO2at BandungSource : Driejana, 2005
From those diurnal fluctuation graphs, it could be seen that at Jakarta the concentration of
NOx started to increased at the morning when the traffic activities started. At noon there aredecreasing on NOx concentration, it predicted that at noon the reaction between NOx and
ozone in forming NO2is occurred. But at noon there also a little increasing of NOx that could
be happened because of the high peak of traffic volume at noon. At the afternoon, the NOx
concentration started to increased again because the peak traffic volume also occurred, and
started to decreased at late night (1-3 am). This is an unique case, because Jakarta has a very
high traffic volume at almost all the time in a day.
From the Bandung diurnal fluctuation graphs we could see that the pattern of NO and NO2 iscloser to the ideal theory of NO and NO2 formation. The concentration of NO started to
increase in the morning when the traffic activities started. The NO2 concentration is alsohigh, but still smaller than NO. At noon when the sunlight came out, the NO concentration is
decreased into very low level, but the NO2concentration is started to decrease. It shows that
the NO2formation from the reaction of NO and ozone with UV radiation is occurred. At the
afternoon, the NO concentration started to increase again because of the increasing of traffic
volume at the afternoon. But, the NO2concentration is still high because the transformation
still occurred, and will be decreased at night.
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Those diurnal fluctuation of NOx may caused the lower correlation coefficient (R and R2)
value on Jakarta empirical function results compared from Bandung results. It also could
explain why the correlation coefficient of the pooled city (Jakarta and Bandung, Trial 1) is
also low, it is caused by the Jakarta data that breaks the pattern.
From all of the regression trials, the empirical function at noon is always the best. It ispredicted that at noon there is a formation of nitrogen dioxide (NO2) in the air because thereaction between NO and ozone (the photolytic cycle). So, at noon the correlation between
NOx and NO2is closer and causing bigger R and R2values. On the other side, the empirical
function at the morning always has the lowest value of R and R2 values because at the
morning there are less formation of NO2 from NOx. It makes the correlation between NOx
and NO2is low. At the evening, the correlation coefficient is also high, but not as high at the
noon time. It happens because at the afternoon there are still a little formation of NO2added
with the residuals of NO2formation from the noon time.
This point could be analyze more by seeing the NO2/NO ratio graphs below on Figure 10from the roadside monitoring data.
Figure 10.NO2/NO ratio from roadside monitoring data (pooled city data)
From the NO2/NO ratio, it could be seen that the highest transformation of NO2from NO is
at the noon time and the smallest is at the morning. It could explained the biggest R and R2
value is at noontime and smallest R and R2 is at the morning.
The linear regression results can be used to predict NO2 concentration from NOx
measurement based on the citys diurnal fluctuation of traffic and NOx. Before using theempirical function, the diurnal fluctuation has to be analysed first so the empirical function
chosen is proper for that city. The diurnal fluctuation of traffic and NOx in Bandung ispredicted will be more commonly use because it has more common traffic volume than
Jakarta that has a very high traffic volume at all time. If the diurnal fluctuation of the city
doesnt fit any diurnal fluctuation of Bandung nor Jakarta, that city could use the empirical
function of the combined data on Trial 1 ( pooled city/ Jakarta and Bandung data) that
assumed could represent the general empirical function for Indonesia this time.
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CONCLUSIONSAll of the NOx and NO2 monitoring data needed to be normalized first by data
transformation and excluding outliers before do the regression analysis.
The best empirical function result for combined data is from Trial 3 ( Log10 NO2bdg= 0.513
log10 Noxbdg+ 0.525), it has the biggest R, R2and F value among other results of combined time
and also has zero Sig number. This result is caused because Bandung City has more common
traffic volume than Jakarta. It could also explained by the diurnal fluctuation of traffic and NOx
that affects the transformation of NO2at the roadside.The noon time regression results always has a bigger correlation values of NO2 and NOx
caused by the photolytic cycle of NO2 transformation mainly occurred at noon. It could be
explained by the NO2/NO ratio of the roadside NOx measurement data that has a high value at
noon, middle value at the afternoon and low value at the morning.
All of the empirical function results can be used to predict NO2concentration from roadside
NOx measurement in Indonesia by considering the diurnal fluctuation of traffic and NOx of the
city before using the function. If the diurnal fluctuation of the city does not fit any diurnal
fluctuation of Bandung nor Jakarta, that city could use the empirical function of the pooled citydata on Trial 1 ( Jakarta and Bandung data) that assumed could represent the general empirical
function for Indonesia this time.
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