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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],

    [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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