A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments

ARTICLE IN PRESS

Atmospheric Environment 39 (2005) 4025–4040 www.elsevier.com/locate/atmosenv

A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments Sharad Gokhale1, Mukesh Khare Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110 016, India Received 6 October 2004; accepted 21 March 2005

Abstract Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the ‘extreme’ concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the ‘extreme’ concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only ‘extreme’ ranges but also the ‘middle’ ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the ‘entire range’ of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models (Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO)2 concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous3 in nature and meteorology is ‘tropical’. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM–LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5–99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well (d ¼ 0:91) in 10–95 percentiles range. The regulatory compliance is also developed to estimate the probability of exceedance of hourly CO concentration beyond the National Ambient Air Quality Standards (NAAQS) of India. r 2005 Elsevier Ltd. All rights reserved. Keywords: Vehicular pollution modelling; Hybrid model; Extreme pollutant concentrations; Heterogeneous traffic conditions; Statistical distribution models; Log logistic distribution

Corresponding author. Tel./fax: +91 11 2658 1117.

E-mail addresses: [email protected] (S. Gokhale), [email protected] (M. Khare). 1 Present address: Indian Institute of Technology Guwahati, Department of Civil Engineering, North Guwahati, Guwahati 781 039, India. 2 CO has been used as an indicator in the vehicular exhaust emission modelling studies (Hickman and Colwill, 1982). 3 It consists of light vehicles, heavy vehicles, three- wheelers (auto rickshaws) and two-wheelers (scooters, motorcycles, etc).

1. Introduction Rapid economic growth through urbanization is causing serious air pollution related problems in several areas around the world. The World Health Organization (WHO) has recently estimated that 1.4 billion urban residents in the developing countries breathe air exceeding the WHO air quality guidelines. In mega cities, such as Bombay, Calcutta, Delhi, Dhaka, and Karachi in

1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.04.010

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South Asia, and Bangkok, Beijing, Shanghai, Jakarta and Manila in East Asia, the pollution levels often exceed the WHO air quality guidelines by a factor of 3 or 4 (Faiz and Sturm, 2000; World Resources Institute WRI, 1992, 1998). As a result, the mortality due to outdoor air pollution is ranging between 0.4–1.1% of total annual deaths (World Resource Institute (WRI), 2000). The most affected region is the developing world, where, the steadily increasing vehicle fleets, with poor emission and maintenance standards, has become a significant contributor of transport-related pollutants in the urban environment (Sturm, 2000). India is one of the largest developing countries in South Asia and the second most populated in the world (WHO, 1992). The 1991–2001, censuses reveals an increase in population from 843 million to 1027 million and this growth is invariably seen in the urban conurbations (http://health.nic.in/Yb01.pdf; Tata Energy Research Institute (TERI) (1996, 1999). The Hindustan Times (2004) recently reports 8–10% annual growth of motorized vehicles in five mega cities of India, leading to severe traffic congestions and concomitant air pollution problems. Delhi, being one of the metros and the capital city of India, is rapidly expanding centre of government, trade, commerce and industry and is the fourth most polluted city in the world (WHO, 1992). One of the major sources of air pollution in Delhi is the road traffic. It contributes 72% of total pollution load (based on the criteria pollutants mass) as shown in Fig. 1 (Tata Energy Research Institute (TERI), 2000; CPCB, 2001). An estimated 3000 tons of pollutants are emitted into the atmosphere every day (MoEF, 1997). Among the primary pollutants, the carbon monoxide (CO) levels remain higher than the prescribed national ambient air quality standards (NAAQS) of India (Fig. 2). It contributes 62% of the entire pollution load (CPCB, 2000). Besides, the background concentration of CO is 1939 mg m3 (Aneja et al., 2001) with a mixing ratio of about 1.6.

2000-01

Years

1999-00 1990-91 1980-81

Vehicular Domestic

1970-71

PM 1%

CO 62%

SO2 1% NOx 12%

HC 24%

Fig. 2. Percentage contribution of pollutants in the Delhi city.

Air pollution ‘episodes’ are associated with sudden occurrences of higher concentrations of pollutants. Such ‘episodes’ are governed by the local meteorology and dispersion phenomena. These ‘episodic’ incidences take place mainly at urban ‘hot spots’.4 This urges a need of an episodic urban air quality management plan (eUAQMP), targeting specifically to predict such ‘exceedances’ and thus prevent catastrophic damages to the environment. However, an efficient e-UAQMP includes not only ‘extreme ranges’ of prediction but also the ‘middle ranges’ (i.e. average concentrations). It can be achieved effectively, if emission standards are taken up in relation with other issues, such as, analyzing the relative impact on human health, integrating land use with traffic planning in environmental management and showing the usefulness of decision support or regulatory models (Gokhale and Khare, 2004). It is, therefore, clear that, there is a need to predict the probability of exceedance of concentrations beyond NAAQS or some other predetermined levels over a shorter time scales. It can be accomplished by using the air quality models (AQM) that may help in developing emission reduction strategies (AMS report, 1981). Several deterministic based models exist to evaluate and predict the pollutant dispersion in urban areas but the majority of them are ‘causal’ in nature and so fail to predict the ‘extreme’ concentrations (Khare and Sharma, 2002; Jakeman et al., 1988). As a result, these models are not suitable for emergency response regulatory planning. The statistical distribution models that are ‘non-causal’ and based on the historical data, capture the past statistical trend of the pollutants and thus back-cast the pollutant concentrations what is not likely by the deterministic models. Thus, they predict the ‘extreme’ concentrations with reasonable accuracy (Zanetti, 1990. However, an efficient e-UAQMP needs both ranges, i.e. extreme and middle. Hybrid modelling is one of the techniques that estimate the ‘entire range’

Industrial

0

20

40 60 Percentage pollution

Fig. 1. Sector-wise pollution in the Delhi city.

80

4 The traffic junctions, intersections and signalized roadways of any urban center that receive maximum input of traffic exhaust pollutants converting them into the localized highpollution zones.

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of the distribution of air pollutant concentration by combining the best features of the deterministic and statistical distribution models. These features include the ‘causality’, the ‘stochastic variability’ and the ‘uncertainty’, involved in the data. Jakeman et al. (1988) first proposed this deterministic-statistical distribution approach to predict the entire distribution of air pollutant concentrations. A few studies have been carried out in the past on the applications of hybrid technique to predict the pollutant concentrations from vehicular exhausts. Taylor et al. (1985) applied this technique to predict the entire range of pollutant concentrations for vehicular exhaust emissions. Recently, Gokhale et al. (2003) attempted to develop a hybrid model for one of the traffic intersections in Delhi, India, to predict the entire concentration distribution of the CO. The detailed literature review on deterministic, stochastic and hybrid vehicular exhausts models has been presented elsewhere (Gokhale and Khare, 2004a). The present paper aims at developing a hybrid model for predicting the entire distribution (lower–middle–upper percentiles) of CO concentrations, emitted from vehicular exhausts at a traffic intersection and a roadway in the urban environment of the Delhi city.

2. Experimental methods

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The AQCR2, which is known as Sirifort, is about 7 km away from the AQCR1. It is in the south of the city. It comprises a straight roadway, i.e. Khelgaon Marg (KGM). It is one of the dense links in the south of the city. The daily average traffic flow is between 50,000 and 70,000 with about 8–9% vehicles, during peak hours. The roadway constitutes two lanes of 7.5 m, each with a separator of 1 m wide (Fig. 4a). The AQCR2 is comprised of mixed residential and commercial settlements. The vehicular composition and characteristics are almost similar as in AQCR1 (Fig. 4b).

2.2. Data acquisition 2.2.1. The CO concentration data One-hourly average CO concentration data are obtained from CPCB for the period of three years (i.e. from 1997 to 1999), for both the AQCRs, which have single receptor locations (i.e. monitoring station). The height of the receptor is 3 m above the ground level. The data sets at AQCR1 and AQCR2 are nomenclatured as CO197 and CO297 (for the data set corresponding to 1 January 1997 to 31 December 1997); CO198 and CO298 (for the data set corresponding to 1 January 1998 to 31 December 1998); and CO199 and CO299 (for the data set corresponding to 1 January 1999 to 31 December 1999), respectively.

2.1. Selection of air quality control regions (AQCRs) Two AQCRs were selected for the development and validation of the hybrid model based on the differences in vehicle count and street configuration. AQCR1, which is in the ‘north’ of the city, includes an intersection, i.e. the Income Tax Office (ITO). It is one of the most congested intersections in the Delhi city. It has a number of administrative and commercial buildings, which attract large volume of traffic. The residents of this region and people using the roads, are exposed to the ‘worst’ air quality. Four major roads cross each other, with a separator in the middle of each road, constituting 2 lanes in each direction. The width of each lane is 7.5 m and that of the separator is 1 m. Of the four roads, Bahadur Shah Zafar Marg (BZM) is highly trafficked road, crossing this intersection (Fig. 3a). The Central Pollution Control Board (CPCB) maintains the monitoring station at this intersection. Approximately 113,000–176,000 vehicles pass through the AQCR1 daily, with about 8–12% of them, during peak hours (9:00–11:00 and 17:00–19:00) (Sharma, 1998). About, 91% are light vehicles, (i.e. cars, jeeps, vans), twowheelers, (i.e. scooters and motorcycles) and threewheelers, (i.e. auto rickshaws); about 9%, are the heavy-duty vehicles, (i.e. buses, minibuses and trucks) (CRRI, 1998) (Fig. 3b).

2.2.2. The traffic data Traffic data are obtained from the various agencies, viz. the Central Road Research Institute (CRRI) and the CPCB. The vehicles are classified according to the criteria for which the emission factors are estimated for the Indian vehicles. The classifications are, light duty gasoline powered, (i.e. cars, jeeps, vans, and taxis); light duty diesel powered, (i.e. buses, minibuses, jeeps, vans); two wheelers, (i.e. scooters, motorcycles); and three wheelers, (i.e. auto rickshaws).

2.2.3. The meteorological data The meteorological data on wind speed (WS), wind direction (WD), stability, temperature, humidity, and solar radiation are obtained from Indian Metrological Department (IMD). One hourly stability categories (SC) are estimated from Pasquill–Gifford (PG) stability scheme (Schnelle and Dey, 2000). The data sets are nomenclatured, as WS97, WS98, WS99; WD97, WD98, WD99 and SC97, SC98, SC99 for wind speed, wind direction and stability classes for the periods 1 January 1997 to 31 December 1997, 1 January 1998 to 31 December 1998, and 1 January 1999 to 31 December 1999, respectively.

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Din

doy

ol U

pad

Times of India Gulab Bhawan Pratap monitoring site I.T.O

I.N.Science Academy

hy

Commissioner Police g

aya Gandhi Ma rg Peace Foundation

Indraprast

Inst.of engineers

ha Mar

Custom House

RING ROAD

UGC

ZAFAR MARG BAHADUR SHAH

NH 2

Maulana Azad Medical College

IAMR

ITP SPA

Vikas Marg

Vikas Minan

Vikas Bhawan

ma

hat

Ma

MATHURA ROAD

M AR G TI LA K

oad

(a)

iR

ndh

Ga

Supreme Court

Railway line

The map of AQCR1 9%

40% 30%

2-wheelers 3-wheelers cars buses

21% (b)

Daily traffic composition at AQCR1 Fig. 3. (a) The map of AQCR1. (b) Daily traffic composition at AQCR1.

2.2.4. The site data The site-specific details such as road bearings, road lane widths, and road lengths are measured at both the AQCRs.

3. Model formulation, testing and validation Four basic steps of model formulation are (Jakeman et al. 1988):

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District Park

4029

SIRIFORT SPORT COMPLEX

HAUZ KHAS SIRI INSTITUTIONAL AREA DCP Police Qrts

Monitoring Station

SIRI FORT

ASIAN GAMES VILLAGE COMPLEX

Shahpur Jat

MAY PAIR GARDEN PANCHSHILA PARK NORTH

The map of AQCR2

(a) 6%

29%

2-wheelers 3-wheelers cars buses 54%

(b)

11%

Daily traffic composition at AQCR2

Fig. 4. (a) The map of AQCR2. (b) Daily traffic composition at AQCR2.

(i) Selection of the deterministic model to predict a reliable range of air pollutant concentrations from emission, meteorological and site data. (ii) Identification of a statistical distributional form to represent the historical air pollutant concentration data. (iii) Hybrid model generation by estimating parameters of the statistical distributional form (identified in step ii) from the reliable part of the deterministic model’s output (estimated in step (i)). (iv) Inference of the percentile concentration values and other distributional properties from the hybrid model.

3.1. Selection of the deterministic model Table 1 shows the performance statistics of CALINE-4 (Benson, 1992), ROADAIR (NILU, 1989), GFLSM (Luhar and Patil, 1989), and DFLSM (Khare and Sharma, 1999) models, which are evaluated, validated and tested for Indian traffic and meteorological conditions (Fukan, 2002). The index of agreement (d) statistic assesses the GFLSM as the most reliable when compared with other models. The hybrid model formulation, therefore, uses the GFLSM as its deterministic component, for predicting the CO concentration

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Table 1 Performance evaluation of the deterministic models Index of agreement

GFLSM

DFLSM

ROADAIR

CALINE-4

d

0.58

0.40

0.35

0.55

BSZ 15m

receptor location

1m

15m

foot over bridge 10m

separator

Predicted CO in ppm

40

30

20

10

0 0

5

10

15 20 25 30 Observed CO in ppm

35

40

Fig. 6. The scatter between observed and predicted 1-hr CO concentrations by GFLSM at AQCR1.

Dindayal Upadhyaya Indraprastha North direction Tilak Marg

Fig. 5. The characteristic details of ITO traffic intersection at AQCR1.

within a reliable range of concentrations distribution. Further, the descriptive statistics of all the data sets revealed that the data sets corresponding to the year 1999, exhibit relatively less noise and variability. The model is therefore applied at AQCR1 to estimate the hourly time series of CO concentration for the year 1999 using the data sets, WS99, WD99 and SC99. Fig. 5 shows the characteristic details of AQCR1, (ITO intersection), describing the traffic flow, receptor location relative to the roadways, and other intrinsic features. Fig. 6 shows the scatter between the observed and predicted 1-hr CO concentrations with a line of best fit. The model exhibits overall satisfactory correlation with observed CO concentrations. However, towards the higher concentrations, model seems to predict with lesser accuracy than in the middle ranges. Fig. 7 shows the diurnal variation of the predicted and the observed CO concentrations for typical days in a year. Similar trend is observed for the entire study period. Fig. 8 shows the annual hourly variation of the predicted and observed CO concentrations. The statistical descriptors, i.e. ‘the index of agreement’ (d), ‘the fractional bias’ (FB) and ‘the normalized mean square error’ (NMSE) (Fox, 1981; Willmot, 1982; Tangirala, et al., 1992) evaluate the performance of the model. The annual means of observed and predicted CO concentrations are 3.69 and 3.72 ppm, respectively. The value of ‘d’ is 0.6, which

explains that 60% of the model predictions are error free. Further, the FB and NMSE statistics also indicate the satisfactory model performance (Table 2), though the negative value of FB is due to vast number of over prediction at low concentrations by the model. 3.1.1. Censoring the GFLSM output Taylor et al. (1985) report satisfactory performance of the deterministic models within the range of 30–70 percentile. As a result, the GFLSM predictions are censored outside the above range using type-II censoring technique. 3.2. The statistical distribution model The historical data sets, CO197, CO198, CO199 and CO297, CO298, CO299, at both the AQCRs, are used to develop the statistical distribution model. The first step is the identification of the statistical distribution model, for which the goodness-of-fit tests are used. These tests include the Kolmogorov–Smirnov statistics (KS), Anderson–Darling statistics (AD), Pearson correlation coefficient (PCC) and the probability plot method. The KS and AD statistics choose the appropriate model corresponding to their minimum values, whereas, the PCC chooses the model, corresponding to the maximum value. The probability plots are also used to evaluate the data. The results show that the majority of the data sets fit the log logistic distribution (LLD) among the parametric range of models. The detailed analysis of goodness-of-fit tests is explained elsewhere (Gokhale and Khare, 2004b). The LLD model fits the ‘larger range’ of percentiles (i.e. 5 to 99%) of the CO concentration data (Fig. 9(a)–(c)) for all the study periods, at AQCR1 and relatively lesser range (Fig. 9(d)–(f)) at AQCR2 which is attributed to the more missing observations, and hence, is more versatile

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6

CO in ppm

5

Observed Predicted

4 3 2 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0 Time in hours 20 CO in ppm

Observed

15

Predicted

10 5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0 Time in hours

CO in ppm

20 15

Observed Predicted

10 5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0 Time in hours Fig. 7. Diurnal variations of observed and predicted CO concentrations by GFLSM for typical days in a year at AQCR1.

in fitting the data accurately, not only in the ‘middle’ but also in the ‘upper’ percentile ranges. Therefore, the LLD seems to be the most appropriate distribution model and so is used in further development of the hybrid model. Eq. (1) represents the LLD model (Gajjar and Khatri, 1969) exp½ðlnðxi Þ  aÞ=b fxðxi : a; bÞ ¼ , b½1 þ exp½ðlnðxi Þ  aÞ=b2  1oxo1; 1oao1; b40,

ð1Þ

where, xi , the ith pollutant concentration; a, the location parameter; b, the scale parameter; and f ðxi Þ, the pdf for the random variable xi . The measure of central tendency, such as ‘mean’ or ‘median’ is used frequently as the location parameters of the distribution and the scale parameter which determines the location relative to

some specified point often uses ‘standard deviation’ or ‘range’.

3.3. The hybrid model: generation The 1999-CO data set at AQCR1 is used in generating the hybrid model. The censored output of the GFLSM outside the percentile range of 30–70, estimates the location (a) and scale (b) parameters (Table 3). The LLD (Eq. (1)) uses estimated a and b in calculating the CO concentrations, and generates the hybrid model (GFLSM–LLD) output. Figs. 10 and 11 show the histogram and cumulative distribution frequency with LLD curve fit by the MLE parameters. The hybrid model uses parameters, a ¼ 1:037 and b ¼ 0:3288.

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4032 35 Observed CO in ppm

30 25 20 15 10 5 0

Predicted CO in ppm

20 15 10 5 0 1

360 719 1078 1437 1796 2155 2514 2873 3232 3591 3950 4309 4668 5027 5386 5745 6104 6463 6822 7181 7540 7899 8258 8617 Time in hours

Fig. 8. Annual hourly variations of observed and predicted CO concentrations by GFLSM at AQCR1.

Table 2 Performance evaluation of GFLSM at AQCR1 Statistical parameters Mean SD Observed Predicted

3.69 3.72

N

FB

NMSE d

2.75 2.98 8412 0.008 0.81

0.6

3.3.1. The hybrid model: application Fig. 12 shows the CO concentrations as predicted by hybrid model and the corresponding observed CO concentrations. The 5–99 percentiles range of the predicted CO concentrations distribution is in agreement with that of the observed data. Table 4 shows the performance evaluation of the hybrid model. The value of ‘d’ is 0.98, which implies that the predictions of hybrid model are 98 percent error free. The FB and NMSE values indicate small scatter in the predicted and observed CO concentrations data. The approximate values of ‘upper’ and ‘lower’ confidence limits for every percentile values are estimated using ‘bootstrap percentile confidence interval’ method at 95% confidence level. Table 5 describes the percentile values for the hybrid model and their approximate confidence limits at 5% significance level. The confidence levels are closely matching with the percentile values, indicating, that beyond 95%, the percentiles violates the confidence limits. Further, Fig. 13 shows the pdf from the hybrid model. It shows

the high probability of occurrence of the ‘most probable’ CO concentration values (i.e. the average) and the low probability of occurrence of the ‘worst’ CO concentration values (i.e. the extremes). It is also observed that the ‘peakedness’ for the predicted CO concentrations is more than that for the observed and the ‘spread’ is more or less matching. It is due to the fact that the mean of the predicted CO values is closely matching with the observed mean. The variation in observed and predicted pdf results into a slight ‘underestimation’ of the estimated probability. However, the high ‘peakedness’ overestimates the most frequently occurring values of the CO concentrations.

4. Regulatory compliance The determination and the evaluation of pdf are useful in regulatory applications of air quality management programs. The pdf allows the calculation of exact number of violations or expected violations of a specified NAAQS or any specific predetermined value. The integral of the pdf gives the ‘probability of exceedance’, which is used in developing the ‘regulatory compliance’. The NAAQS are preferably expressed in a probabilistic manner. The objective of developing a regulatory compliance is therefore framed in terms of the number of occasions in a calendar year on which the objective concentration (i.e. NAAQS) does not exceed. The regulatory compliance from the hybrid model is developed for the NAAQS of 1-hr average CO

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Fig. 9. (a) CO197 on LLD probability scale. (b) CO198 on LLD probability scale. (c) CO199 on LLD probability scale. (d) CO297 on LLD probability scale. (e) CO298 on LLD probability scale. (f) CO299 on LLD probability scale.

concentrations, i.e. 4 mg m3 (3.5 ppm) for the residential and commercial land zone (CPCB, 2000b) (Fig. 14). The probability of exceedance of the CO concentration beyond NAAQS is 0.34 for 120 days in a year (Table 6).

5. Verification of the hybrid model The hybrid model is tested and verified for the years 1997 and 1998 at AQCR1 (Table 7). The emission

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> 3.103

(2.819,3.103]

(2.25,2.534]

(2.534,2.819]

(1.965,2.25]

(1.681,1.965]

(1.396,1.681]

(1.112,1.396]

(.258,.543]

(-.311,-.026]

(-.88,-.595]

(-.595,-.311]

1680 1568 1456 1344 1232 1120 1008 896 784 672 560 448 336 224 112 0

(-1.165,-.88]

0.3288 0.3199

(-1.449,-1.165]

1.037 1.062

(-1.734,-1.449]

MLE LSE

(-2.018,-1.734]

b

<= -2.018

a

Number of observations

Methods

(-.026,.258]

Table 3 Parameters of the hybrid model

(.543,.827]

resulting estimates of the percentiles of the CO concentration distributions are in excellent agreement with that of the observed distributions, in both the test cases. However, there are a few deviations in the predicted and observed concentrations in small portions of the middle percentile range. It is attributed to the more annual fluctuations in meteorology during these years compared to that of the year 1999. Table 8 shows ‘d’ values of the hybrid model predictions for the years 1997 and 1998, which are 0.76 and 0.75, respectively. It explains that the model predictions are 76 and 75 percents error free for the corresponding study periods. The FB and NMSE values also show fairly good

scenario and meteorology are different, whereas the site geometry is same. Figs. 15 and 16 show the CO concentrations as predicted by the hybrid model and the corresponding observed CO concentration values for the verification test case-I and -II, respectively. The

(.827,1.112]

4034

CO classes

> 3.103

(2.819,3.103]

(2.25,2.534]

(2.534,2.819]

(1.965,2.25]

(1.681,1.965]

(1.396,1.681]

(1.112,1.396]

(.543,.827]

(.827,1.112]

(.258,.543]

(-.026,.258]

(-.311,-.026]

(-.595,-.311]

(-.88,-.595]

(-1.165,-.88]

(-1.449,-1.165]

(-1.734,-1.449]

<= -2.018

8976 8415 7854 7293 6732 6171 5610 5049 4488 3927 3366 2805 2244 1683 1122 561 0 (-2.018,-1.734]

Number of observations

Fig. 10. Histogram of the CO concentrations with the hybrid model fit by MLE parameters at AQCR1.

CO classes

Fig. 11. Cumulative frequency distribution of the CO concentrations with the hybrid model fit by MLE parameters at AQCR1.

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Observed Predicted 99

Percent

95 90 80 70 30 20 10 5 1

0.1

1.0

10.0

100.0

CO in ppm

Fig. 12. The observed and predicted CO concentrations by hybrid model at AQCR1.

Table 4 Performance evaluation of the hybrid model

0.8

Observed Predicted (MLE) Predicted (LSE)

0.7

d

FB

NMSE

Hybrid model (GFLSM–LLD)

0.98

0.105

0.04

0.6 0.5 pdf

Description

0.4 0.3 0.2

Table 5 Percentile values of the predicted CO concentrations with confidence limits at AQCR1

0.1 1 5 10 20 30 40 50 60 70 80 90 95 98 99

0.0 0

Hybrid model Percentile

7CL

0.8 0.93 1.07 1.37 1.77 2.14 1.47 2.82 3.23 3.76 4.46 5.83 9.43 10.14 13.25

0.021 0.034 0.044 0.050 0.060 0.060 0.070 0.080 0.090 0.120 0.160 0.260 0.400 0.680 1.020

1.0

20 CO in ppm

30

Probability of exceedance >= 3.5 ppm

0.9 0.34 probability i.e. NAAQS 3.5 ppm will exceed

0.8 0.7

124 days in a year that CO will exceed 3.5ppm

0.6 0.5 0.4

0.34

0.3 0.2 0.1 CO = 3.5 ppm

0.0 0

performance of the model but indicating a large scatter between the observed and predicted CO concentrations. Further, the comparison with ‘d’ value of the hybrid

10

Fig. 13. The pdfs for observed and predicted CO concentrations by the hybrid model at AQCR1.

inverse cdf

Percents

0.1

2

4

6

8

10

12

14

16

18

CO in ppm Fig. 14. The regulatory compliance for the predicted CO concentrations at AQCR1.

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Table 6 Probability of exceedance at AQCR1 Probability of exceedance

44 mg m3 (3.5 ppm)

No. of days4NAAQS for CO

Observed MLE LSE

0.37 0.34 0.35

131 120 123

Table 7 Parameters considered in the hybrid model and the verification test case-I and -II Description

Hybrid model

Verification test case-I

Verification test case-II

Site geometry Meteorology Emissions

ITO intersection (AQCR1) WS99, WD99, SC99 Year 1999

ITO intersection (AQCR1) WS97, WD97, SC97 Year 1997

ITO intersection (AQCR1) WS98, WD98, SC98 Year 1998

Observed Predicted

Percents

99 95 90 80 70 30 20 10 5 1

0.1

1.0

10.0

100.0

CO in ppm

Fig. 15. The observed and predicted CO concentrations by hybrid model for the verification test case-I.

model prediction developed for the year 1999 (Table 4) shows the variations are well within the 30% (TG3, 2003).

6. Validation of the hybrid model The hybrid model is validated for the year 1999 at AQCR2 (i.e. Sirifort roadway), which is 7 km away from the AQCR1. Fig. 17 shows the Sirifort roadway with the traffic flow, receptor location relative to the roadway and other intrinsic features. Table 9 shows the para-

meters that are considered in validating the hybrid model. The meteorology is same, whereas the site geometry and emissions are different. The observed and predicted CO concentrations are reasonably matching within the percentile range of 10–95 (Fig. 18). The ‘d’ value, i.e. 0.91, also indicates that the model predictions are 91 percents error free. The FB and NMSE values of 0.19 and 0.34, respectively, show a good model performance and also small scatter (Table 10). Further, the comparison with ‘d’ value of the hybrid model prediction developed for the year 1999 (Table 4) shows the variations well within the 30%. However, the

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Observed Predicted

99

Percents

95 90 80 70 30 20 10 5 1

0.1

1.0

10.0

100.0

CO in ppm

Fig. 16. The observed and predicted CO concentrations by hybrid model for the verification test cfase-II.

Table 8 Performance evaluation of the hybrid model for the verification tests case-I and –II Test cases

d

FB

NMSE

% variation with respect to the hybrid model predictions for the year 1999

Verification test case -I Verification test case-II

0.76 0.75

0.495 0.489

1.38 1.14

22 23

North direction 15m 1m 15m dwarf compound wall 10m receptor location

Khelgaon Marg

points sensitivity’ of the concentration data, resulting into either ‘over prediction’ or ‘under prediction’ by the model (Sortino and Forsey, 1996). Table 11 describes the percentile values for the verification and validation test cases and their approximate confidence limits at 5% significance level. The confidence levels are matching with the percentile values. Figs. 19 and 20 show the pdfs at AQCR1 for both the verification test cases by the hybrid model. Fig. 21 shows the pdf at AQCR2 for the validation test case.

7. Conclusions Fig. 17. The characteristic details of Sirifort roadway at AQCR2.

predicted and observed concentration values, in the ranges, less than 10 percentiles and more than 95 percentiles, do not match. It may be due to the ‘end

The hybrid modelling approach predicts the concentration distributions at street scale with information on extreme events, where widely used deterministic models may perform unsatisfactory. It predicts the entire range (middle, lower and upper extremes) of concentration distribution. The GFLSM shows satisfactory performance in predicting the hourly CO concentration for

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Table 9 Parameters considered in the hybrid model and the validation test case Description

Hybrid model

Validation test case

Site geometry Meteorology Emissions

ITO intersection (AQCR1) WS99, WD99, SC99 Year 1999

Sirifort roadway (AQCR2) WS99, WD99, SC99 Year 1999

Observed Predicted

Percents

99 95 90 80 70 30 20 10 5 1

0.1

1.0

10.0

100.0

CO in ppm

Fig. 18. The observed and predicted CO concentrations by hybrid model for the validation test case at AQCR2.

Table 10 Performance evaluation of the hybrid model for the validation test case Test case

d

FB

NMSE

% variation with respect to the hybrid model predictions for the year 1999

Validation test case

0.91

0.19

0.34

7

‘heterogeneous’ traffic and ‘tropical’ meteorology. Hence, it is used as the deterministic component of the hybrid model. The two-parameter LLD model is the most appropriate distribution model for majority of the CO concentration data sets. Moreover, the LLD model predicts the ‘most frequently occurring’ values as well as ‘rare’ events (i.e. extreme percentiles) with reasonable accuracy. The parameter estimation of the model is best done by the MLE method with minimum bias. The hybrid model predicts almost the entire range of the CO concentrations, i.e. 5–99 percentiles of the distribution, at the 95% confidence level. The ‘d’ value, for the year 1999 (i.e. 0.98), reveals that the model predictions at the

ITO intersection (AQCR1), are 98 percent error free. Further, the verification of the model is carried out using the data sets corresponding to the years, 1997 and 1998. The ‘d’ statistics reveals that the model predicts with 76 and 74 percents accuracy for the respective years. The validation of the hybrid model shows that the predictions are 91% error free (i.e. d ¼ 0:91), within the percentile range of 10–95. The hybrid model, thus, demonstrates the inherent flexibility in the modeling application, and its reliable prediction accuracy. However, it is tested on a limited number of data sets and need to be explored further for other climatic conditions and traffic compositions.

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Table 11 Percentile values of the predicted CO concentrations with confidence limits for the verification test case-I, -II, and validation Percents

Verification test case-I

1 5 10 20 30 40 50 60 70 80 90 95

Verification test case-II

Percentile

7CL

Percentile

7CL

Percentile

7CL

0.8 1.37 1.79 2.14 2.47 2.82 3.23 3.73 4.45 5.82 9.47 10.3

0.04 0.04 0.04 0.05 0.06 0.06 0.08 0.10 0.15 0.24 0.37 0.65

— 1.37 1.79 2.14 2.47 2.82 3.22 3.73 4.45 5.81 9.46 15.8

— 0.054 0.062 0.068 0.077 0.088 0.106 0.133 0.182 0.298 0.468 0.812

0.91 1.07 1.8 2.15 2.48 2.83 3.24 3.7 4.45 5.82 7.28 9.67

0.03 0.04 0.04 0.05 0.05 0.06 0.07 0.09 0.13 0.21 0.34 0.57

0.8

0.8 Observed Predicted (MLE) Predicted (LSE)

0.7 0.6

0.7

Observed Predicted MLE

0.6

Predicted LSE

0.5

0.4

pdf

pdf

0.5

0.3

0.4 0.3

0.2

0.2

0.1

0.1

0.0 0

10

20

30

40

CO in ppm

0.8

0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

10

20

30

10

20

30

40

CO in ppm Fig. 21. The pdfs for observed and predicted CO concentrations by hybrid model for the validation test case at AQCR2.

References

Observed Predicted (MLE) Predicted (LSE)

0.7

0.0 0

Fig. 19. The pdfs for observed and predicted CO concentrations by hybrid model for the verification test case-I at AQCR1.

pdf

Validation test

40

CO in ppm Fig. 20. The pdfs for observed and predicted CO concentrations by hybrid model for the verification test case-II at AQCR1.

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