Modeling the dispersion of vehicular carbon-monoxide (CO) pollution

in Kathmandu valley, Nepal:

A CALINE4 approach combined with GIS Techniques

Sarbajit Gurung, Department of MGIS, University of Calgary

Abstract

Kathmandu valley is more vulnerable to air pollution than other rapidly growing Asian

cities because of the bowl like structure of the valley and poor wind speed inside the

valley. The main objective of this study is to model the dispersion pattern of vehicular

carbon monoxide in Kathmandu valley by using CALINE4 software combined with GIS

techniques. CALINE4 uses vehicular count, pollution and meteorological data to predict

the carbon monoxide (CO) concentration. A typical day (15th

of February 2007) is chosen

to calculate 1-hour average CO concentration at the receptor points during peak hour

(8:00 – 9:00 am). A road network extending from Maitighar to Koteshwor, which is

approximately 4 km in length, is considered as the main road network. Ninety receptor

points are created within the 500 meters buffer area of the main road network and

CALINE4 is used to predict CO concentration at these points. The predicted CO

concentration at the receptor points are then interpolated using K-Bessel universal

kriging. The resulting map is reclassified to create ‘hot-spots’ where the areas are

classified based on the predicted CO concentration. Root mean square error (RMSE)

method is carried out to evaluate the model performance by comparing the predicted and

observed CO concentration within 10 meters buffer from the study site. The RMSE value

is found to be 0.77 and the accuracy of the model performance as 74%.

1. Introduction

Urban transportation is one of the major sources of energy consumption and

environmental emissions (Dhakal 2003). Recent evidence indicates that road traffic

emissions are a major source of air pollution in urban areas with subsequent adverse

human health effects (Faiz, 1993; Colvile et al. 2001). Although improvements in vehicle

technology play a significant role in reducing traffic emissions at the source, air pollution

abatement will remain challenge because of increasing demand for transportation

(WBCSD 2001).

Traffic-generated air pollution is one of the primary environmental concerns of the

general public. Motor vehicles are responsible for emitting a variety of pollutants

including nitrogen oxides (NOx), carbon monoxide (CO), volatile organic compounds

(VOC), which consist primarily of hydrocarbons, and particulate matter (Ganguly et al.

2008). Carbon monoxide is the result of incomplete fuel combustion that characterizes

mobile as opposed to stationary pollution sources and therefore it can be used as a marker

for the contribution of traffic to air pollution (Fenger 1999). CO gas is a good indicator of

dispersion and dilution of the vehicular exhausts in the street since its chemical response

time is rather long compared with other vehicle exhausted pollutants (APPETISE 2001).

Many cities, particularly those in developing countries of Asia, suffer from high

concentration of air pollutants (Bose and Srinivasachary 1997). Kathmandu valley, that

comprises three districts including Capital city Kathmandu, suffers from serious air

pollution (Shrestha and Malla 1996). Kathmandu is a valley surrounded by mountains

and this topographic condition are favorable for worsening pollutant concentrations and

for accelerating photochemical reaction rate responsible for smog formation and visibility

loss (Shrestha 1995).

The main aim of this study is to model the dispersion pattern of vehicular Carbon

Monoxide (CO) emission in Kathmandu valley. This paper shows that the application of

a Gaussian dispersion model (CALINE4) in conjunction with spatial data analysis can

help to illustrate the dispersion pattern and identify areas that are highly affected by

vehicular CO pollution. Additionally, several development and transportation scenario

can be developed and ‘hot-spots’ of traffic-originated air pollution can be identified and

visualized within a GIS framework. This study also aims in evaluating the efficiency of

the CALINE4 model to predict CO concentration in Kathmandu valley.

The study of air pollution modeling is not a new concept in case of Kathmandu valley.

There are several research publications on this subject using GIS and non-GIS

techniques. However, this study combines the CALINE-4 approach with GIS techniques

to model the CO pollution and this proposed framework is innovative and makes

contribution to understanding the CO air pollution in Kathmandu valley caused by traffic.

2. Modeling air pollution

According to Levitin et al. (2005), there are various versions of the Gaussian line source

model that have been used for dispersion evaluation from a road such as GM (Chock

1977), HIWAY-2 (Petersen 1980), California line source dispersion model, version 4

(CALINE4) (Benson 1992), FGLSM (Luhar and Patil 1989) and CAR-FMI (Harkonen

2002). CALINE4, designed by California Transport for the analysis of carbon monoxide

pollution on the basis of knowledge of gaseous emission factors from stationary and

moving vehicles, is one of the most well developed software packages for the analysis of

busy road pollution (Gramotnev et al. 2003).

Recent developments in Geographic Information System (GIS) software have enhanced

our ability to evaluate spatial dispersion of air pollution from motor vehicles

(Chakraborty et al. 1999). Hallmark and O'Neill (1996) conducted a series of case studies

integrating GIS software (TransCAD) and CAL3QHCR and documented the

incompatibilities between the coordinate systems used by current dispersion models and

most GIS software. Collins (1996) combined dispersion models with GIS to map air

pollution. Similarly, Souleyrette et al (1991) found that GIS enhanced EPA air quality

models by integrating spatial and temporal aspects of transportation and environmental

conditions that influence air quality. Therefore the use of GIS software for air pollution

dispersion modeling has been successfully carried out in several studies and this paper

explicitly presents the CALINE4 approach coupled with GIS techniques to predict CO

concentration in Kathmandu valley.

3. Methodology

3.1 Description of study area

Kathmandu valley lies between 27°37’30”N and 27°45’0”N latitude, and 85°15’0”E and

85°22’30”E longitude (Sapkota and Dhaubhadel 2002). The valley occupies about 351

square kilometer area and is situated at an altitude of 1300m to 1350 m above the sea

level (Pokhrel 2002). Kathmandu valley is more susceptible to air pollution due to its

bowl-like topography, haphazard urban growth and possible occurrence of temperature

inversions during winter months (Sapkota and Dhaubhadel 2002). According to MOEST

(2006) air pollutants emitted at the valley floor ground level are poorly dispersed due to

the high hills surrounding Kathmandu valley, and to the general occurrence of low wind

speeds. The average annual temperature of Kathmandu valley is 18° C and average

annual precipitation is 1400mm (Pokhrel 2002).

The CALINE4 approach is applied to a sample road extending from Maitighar to

Koteshwor. The sampling road network lies on the eastern part of Kathmandu. It connects

the centre of Kathmandu to its neighbouring districts Bhaktapur and Lalitpur. It is a part

of Araniko Highway which joins the capital city with China. This road segment is very

close to the Tribhuwan International Airport. The sampling road segment is a four lane,

black topped road and is 4 Km long. This is one of the busiest roads in the capital and

runs through different places like governmental organizations premises (Babar Mahal),

semi urban business centers (Koteshwor, Baneshwor), hospital (Everest nursing home),

five star hotel (Everest hotel) and international conference centre (Birendra International

Convention Centre, BICC) and also through different residential areas. The detail of the

study area is shown in the map below:

Figure 1: Location of the study area

3.2 Data collection

The data used for CALINE4 analysis which includes traffic flow and pollution data was

obtained from Paudel (2007) for his research on ‘pollution dispersion modeling of CO

and PM10 in Kathmandu valley’. The meteorological data was obtained from The

Department of Meteorology and Hydrology, Kathmandu. The arcview shape files were

obtained from ICIMOD, Kathmandu, Nepal. The shape files were originally projected

using wrong ‘False Easting’ and ‘False Northing’. This information was not included in

the metadata. Therefore, transformation in the projection was carried out with the help of

‘google earth’ so as to align the shape files with correct projection. The shape files were

finally projected to WGS 1984 UTM Zone 45N Modified with modification on the False

Easting (308388) and False Northing (-6760).

3.3 Framework for assessing CO concentration in the study area

The operational framework for assessing CO concentration is shown in figure 2. The

study is broadly divided into two parts viz. CALINE4 analysis and Geographic

Information System (GIS) analysis. As a first step, the traffic loads on all the links of the

transportation network for the morning peak period (8:00 – 9:00 am) of a typical day

(15th

February 2007) is obtained. This traffic flow data is combined with pollution and

meteorological data of that particular day by using a generalized form of the existing road

network to estimate one-hour average CO concentration at point locations (receptors)

using CALINE4 dispersion model. The second part of the study involves the use of

ArcGis 9.2 GIS software where the predicted CO concentration from CALINE4 software

is interpolated to estimate CO concentration over the entire study area.

Figure 2: Operational framework for assessing CO concentration in Kathmandu valley

(Adapted from Potoglou and Kanaroglou 2005)

CALINE-4

Dispersion of pollutants – Estimation of CO at point locations over 500m buffer

Pollution data

Meteorological data

GIS Data (Road network)

Introduction of values from CALINE-4 output

GIS Analysis such as pollution mapping, creating hotspots, model evaluation etc.

Create Buffer (Upto 500m)

Traffic flow data

3.4 Using CALINE4 to predict CO concentration

CALINE4 is a dispersion model that predicts carbon monoxide (CO) impacts near

roadways. CALINE4 is a simple line source Gaussian plume dispersion model which

allows users to define the proposed roadway geometry, worst-case meteorological

parameters, anticipated traffic volumes, and receptor positions to predict CO

concentration at the receptors within 500m of the roadway (Benson 1989). CALINE4 is

free software created by California Department of Transportation (Caltrans) and can be

downloaded from the internet.

According to Benson (1992), CALINE4 divides individual highway links into a series of

elements from which incremental concentrations are computed and summed. Incremental

downwind concentrations are computed using the crosswind Gaussian formulation for a

line source of finite length:

(1)

where, q is the lineal source length, u is the wind speed, σ y and σ z are the horizontal and

vertical Gaussian dispersion parameters, and y 1 and y 2 are the finite line sources (FLS)

endpoint y-coordinates.

CALINE 4 contains five data entry screens viz. i) Job Parameters, ii) Link Geometry, iii)

Link Activity, iv) Run conditions, and v) Receptor conditions. The Job Parameters screen

requires the user to enter the ‘Run Type’ to determine averaging times for CO

concentrations and how the hourly average wind angle will be determined, the

‘Aerodynamic roughness coefficient’ to determine the amount of local air turbulence that

affects plume spreading and the ‘model information’ to set the units (feet or meters) that

will be used to input data on the Link Geometry and Receptor Positions Screens (Coe et

al. 1998). The Link Geometry screen allows user to fill in the road network parameters

such as link type, endpoint coordinates, link height, mixing zone width etcetera. The Link

Activity screen allows user to input traffic volume and auto emission rate observed at

each link. The Run Conditions screen contains the meteorological parameters needed to

run CALINE4. Finally, the Receptor Positions screen allows the user to input the receptor

positions where CO concentration can be predicted.

3.5 GIS Analysis

3.5.1 Buffer Creation to mark the receptor positions

The first step in GIS involves the creation of buffer around the study area to mark the

receptor positions. Different buffer lines at 10m, 50m, 100m, 200m, 300m, 400m and

500m are created on the either sides of the sampling road. 10 points are created randomly

on each buffer lines, which are later represented as receptor points. The receptors points

are chosen in such a way that 5 receptor points fall on each buffer line to evenly represent

the buffer lines. The receptor points are created in all the 7 buffer lines adding to a total

of 70 receptor points. To evenly represent the study area, 10 more receptor points are

added on both the sides of the road segments. Therefore, a total of 90 receptor points are

created. The CO concentrations predicted in all the 90 receptor points are later merged to

a single shape file.

3.5.2 Interpolating surface using Inverse Distance weighting (IDW) and Global

Polynomial Interpolator in ArcGis

In the mathematical subfield of numerical analysis, interpolation is a method of

constructing new data points within the range of a discrete set of known data points

(ESRI 2006). The predicted CO from CALINE4 in the 90 receptor points along the study

road is interpolated to predict the CO concentration over the study area.

IDW can be a good way to take a first look at an interpolated surface. IDW is a method of

interpolation that estimates cell values by averaging the values of sample data points in

the neighborhood of each processing cell (ESRI, 2006). According to Erol and Celik

(2004), IDW can be calculated with the following equation:

(2) where,

N’ = geoid undulation value of point k, Ni = geoid undulation value of ith reference point

Pi = weight of ith reference point

Global Polynomial interpolation fits a smooth surface (defined by a mathematical

function - a polynomial) to the input sample points by taking the entire data points into

account and measures errors with the method of ‘least squares’ fit (ESRI 2006).

Therefore the output of the Global Polynomial interpolation is capable of performing

‘trend surface analysis’ and shows the presence or absence of trend in the predicted CO

concentration data in the study area. According to Unwin (1975), a trend surface analysis

assumes that each mapped value can be decomposed into two components that arise from

two scales of process:

Observed value of surface = Trend component at that point + Residual at that point

i.e. Zobsi = f(xi,yi,) + ui (3)

where,

Zobsi = the observed value of the surface at the ith

point,

xi = the co-ordinate on the x-axis (northings) of the ith

data point,

yi = the co-ordinate on the y-axis (eastings) of the ith

data point, and

ui = the residual at the ith

data point.

Figure 3: Framework of IDW, Global Polynomial interpolation and Kriging of Predicted CO concentration

3.5.3 Ordinary and Universal Kriging

Kriging is an interpolation technique based on semivariogram developed to control more

adequately the points used in the estimation process. Kriging methods explicitly take into

account the range over which the degree of spatial autocorrelation or dependence

between points extend (Dutton-Marion 1988). According to Davis (1989), the

semivariance is a measure of the degree of spatial dependence between samples along a

specific support. If the spacing between samples along a line is some distance ∆, the

semivariance can be estimated for distances that are multiples of ∆ as:

Predicted CO concentration (X, Y, Predicted CO from CALINE4)

Inverse Distance Weighting Input data: Predicted CO (X,Y) Attribute: Predicted CO Power: 2

Global Polynomial Interpolation: Input data: Predicted CO (X,Y) Attribute: Predicted CO Power: 2

IDW Surface Output

GP Surface Output

Geostatistical wizard in ArcMap

Presence of Trend in data

Absence of Trend in data

Universal Kriging of data

Ordinary Kriging of data

(4)

where,

Xi is a measurement of a regionalized variable taken at location i,

Xi+h is another measurement taken h intervals away,

n is the number of points

The general formula used by Kriging for interpolation is formed as a weighted sum of the

data (ESRI 2006):

(5)

where:

Z(si) = the measured value at the ith

location.

λi = an unknown weight for the measured value at the ith

location.

s0 = the prediction location.

N = the number of measured values.

One of the basic assumptions of Ordinary Kriging is that the data must be stationary i.e.

show no trends. Universal Kriging was developed to ease the stationarity assumption

which is rarely satisfied (Dutton-Marion, 1988). ‘Universal Kriging is used to find a

linear estimator that is not unbiased in the presence of trend. It is a stepwise procedure

which i) estimate and remove drift from a regionalized variable, ii) Krige stationary

residuals and iii) combine estimated residuals with drift to obtain estimates of the actual

( )

n

xxi

hii

h2

2

∑ +−

=γ

surface’ (Davis, 1989). The Global Polynomial interpolation, which shows the presence

or absence of trend in the predicted CO data, can be used to decide in using ordinary

kriging or universal for interpolation.

3.6. Evaluation of the model - calculation of Root Mean Square Error (RMSE)

In order to determine the accuracy of the model, an accuracy assessment is carried out by

using the following equation:

∑=

−=n

i

PedictedObserved COCOn

RMSE1

2)(1

(6)

Where,

RMSE is the root mean square error of the model,

n is the number of accuracy assessment points,

CO Predicted is the concentration of CO predicted by CALINE4

CO Observed is the concentration of CO observed

4. Results and Discussion

4.1. Estimation of CO concentration from CALINE4

CALINE4 is capable of processing a maximum of twenty links and twenty receptor-

points on each run (Potoglou et al. 2005). The road network is divided into ten links and

10 receptor points are created in the 10m, 50m, 100m, 200m, 300m, 400m and 500m

buffer areas. The road extending from Maitighar to Koteshwor is divided into ten

different links as shown in the table below:

Table 1: Links with their extension and approximate length

Links Extension Approximate Length (m)

Link A Maitihar – Babar Mahal 437

Link B Babar Mahal – Bijuli Bajar 463

Link C Bijuli Bajar – New Baneshwor 413

Link D New Baneshwor – BICC 542

Link E BICC – Min Bhawan 320

Link F Min Bhawan – Santi Nagar Gate 430

Link G Santi Nagar Gate – Tinkune 1 247

Link H Tinkune 1- Tinkune 2 340

Link I Tinkune 2 – Koteshwor 297

Link J Koteshwor 1 – Koteshwor 2 402

In the ‘Job Parameters’ screen; the run type is chosen as ‘Standard’ to calculate 1-hour

average CO concentration at the receptor points. The Aerodynamic Roughness

Coefficient is set to 200 cm as suggested by the CALINE 4 manual for centers of large

towns or cities. The altitude of the study area is set to 1310 m above the sea level.

In the ‘Link Geometry’ screen; the link type is set to ‘At-Grade’ to prevent the plume

from mixing below the ground level. In order to enter the ‘endpoint coordinates’, it is

necessary to define the link geometry in a Cartesian coordinate system which is

consistent with the ‘Receptor Positions’. Most GIS software packages use a latitude-

longitude coordinate system to reference map features (Chakraborty et al. 1999).

However this can be displayed in meters as well which can be used to locate the (x, y)

positions of any given point. The consistency in defining the coordinates by using this

coordinate system seem to work with CALINE4 model and this is verified by looking at

the diagram that displays the link geometry and receptor points in the ‘receptor positions’

screen. The (x, y) values are noted for all the links (ten) and entered in the ‘endpoint

coordinate’. The Link height is set to 0 as the road network does not contain any major

bridges or tunnels. According to Coe et al. (1998), mixing width is the total width of the

road network plus 3 meters on either sides of the road. The width of the road network is

28 feet. So, the mixing width for the sampling road is taken as 14 meters. As the road

does not contain any major canyons or bluffs, the parameters for canyons and bluffs are

not taken into consideration.

The ‘Link Activity’ screen needs traffic volume and auto emission rate observed at each

link. The vehicular count per hour at the study area on the 15th

day of February 2007

from 8 to 9 am is shown in Appendix I. According to Dhakal (1998), the emission factor

(gm/km) for heavy 4 wheelers, light 4 wheelers and two wheelers are 12 gm/km, 62

gm/km and 24 gm/km respectively. The weighted average emission rate of the local

vehicle fleet is calculated by multiplying the number of vehicular types multiplied by the

corresponding emission factor and divided by the total number of vehicles plying on that

link. For example, the total number of vehicles on Link A is 2892 out of which 336 are

large four wheelers, 948 are light four wheelers and 1608 are two wheelers. Therefore the

emission factor (weighted average emission) for Link A is:

migmkmgmALinkinfactorEmissionofmeanWeighted /10.56/06.351608948336

1608*24948*62336*12==

++

++=

Table 2: Emission factor for all the links

Links Traffic volume

(vph) Hour 1

Emission Factor

(gm/mi) Hour 1

Link A 2892 56.1

Link B 3336 51.3

Link C 3300 52.9

Link D 4392 52.6

Link E 2172 51.5

Link F 3780 50.2

Link G 3324 50.3

Link H 3216 48.5

Link I 2940 48.4

Link J 7212 47.7

The ‘Run Conditions’ screen contains all the important meteorological input needed to

run CALINE4. The meteorological condition of the 15th

day of February 2007 on the

study area is used as an input. Some of the parameters are taken from the CALINE4

Manual as recommended for the analysis.

Table 3: CALINE4 meteorological and run conditions

Parameter Value Remarks

Wind speed (m/s)

Wind direction (degrees)

Wind direction Std. Dev. (degrees)

Atmospheric Stability Class (1-7)

Mixing Height (m)

Ambient Temperature (degrees C)

Ambient Pollutant Concentration (ppm)

0.5

0

20

6

100

19

2.3

Value appropriate for the project location

Wind blowing from the north (0 = north)

The central valley geographic location (like Kathmandu

valley) can take a value of 20 degrees (Coe et al. 1998)

The meteorological condition was almost stable, so the

atmospheric stability is taken as 6 (range 1 to 7, where 7

is the most stable condition)

The mixing height is the altitude to which thermal

turbulence occurs due to solar heating of the ground.

Reasonable values for the worst-case mixing height

rarely have a significant impact on CALINE4 model

results (Coe et al. 1998)

Average temperature in February 2007

Off peak hour average CO concentration on the study

area

In the ‘Receptor Positions’ screen; the (x, y) coordinates of the 10 receptor points are

entered. In total, 90 receptor points are created along the study area. To evenly represent

the receptor points, 10 receptors are marked along the sideways of all the seven buffer

areas. 20 receptor points are added across the sideways of the study area to uniformly

represent the study area. Therefore, the CALINE4 model is run nine times and the

receptor positions are the only input changed every time the model is run. The

screenshots of the input together with the CALINE4 output for receptors located in the

300 meters buffer is shown in Appendix II.

4.2 Surface map obtained from IDW and Global Polynomial Interpolation

The IDW interpolation explicitly implements the assumption that things that are close to

one another are more alike than those that are farther apart (ESRI 2006). IDW predicts

the CO concentration of the surrounding area based on the neighbouring control points by

using equation 2. In case of pollution mapping, the prediction based on the neighbouring

points may not be effective. Although IDW can be used to interpolate surface, there are

several drawbacks of this method. According to Dutton-Marion (1988) the clusters of

points may distort the resulting values especially if the numbers of control points to be

used in the interpolation is clustered in one area. IDW is based on the distance weighting

methodology which assumes that the spatial autocorrelation exhibited by a surface is

isotropic and unrelated to varying distances and directions of surrounding control points

(Dutton-Marion, 1988). Due to the limitations of IDW, it cannot be used as an

interpolator to predict the CO concentration in the study area.

Figure 4: Interpolated surface using IDW of predicted CO concentration from the receptor points

The output of the Global Polynomial interpolation shows the trend of the predicted CO

concentration over the study area. With the increase in polynomial power, it is noticed

that the trend component increases and this ultimately reduces the residuals. According to

ESRI (2006), the first order polynomial is suitable for a flat terrain, second order for a

valley and the higher order polynomial for complex terrain. As Kathmandu is a valley,

second order polynomial is suitable for Global Polynomial interpolation.

Figure 5: Global Polynomial Surface Interpolation of predicted CO concentration from the receptor points

It can be clearly noted from figure 5 that the trend of CO concentration increases from the

north-west to the south-east direction. The benefit of performing Global Polynomial

interpolation of a surface is to know if the data is stationary and shows no trends. This

ultimately helps the user to choose between ordinary kriging and universal kriging to

interpolate the surface. The presence of trend in the predicted CO data in this study

requires the use of Universal kriging for interpolation.

4.3 Surface mapping of CO concentration using Universal Kriging

There are several models available in ArcGis to perform Universal Kriging such as

Circular, Spherical, Exponential, Gaussian, K-Bessel etcetera. The Universal Kriging in

ArcGis is performed by using equation 5. According to ESRI (2006); when comparing

models, we should look for one with the standardized mean nearest to zero, the smallest

root-mean-squared prediction error, the average prediction standard error nearest the

root-mean-squared prediction error, and the standardized root-mean-squared prediction

error nearest to one. All the statistics of the available models are noted and compared. It

is found that the K-Bessel model meets the criteria outlined by the ArcGis while selecting

the model for interpolation. The prediction error of the K-Bessel model is shown in the

table below:

Table 4: Prediction errors of K-Bessel universal Kriging

Prediction error Value

Mean

Root mean square (RMS)

Average standard error

Mean Standardized

Root mean square standardized

0.175

2.918

3.074

0.05395

0.9576

As shown in table 4, the value of mean prediction error of 0.175 being close to zero,

indicates that the predicted values are unbiased. Similar information is provided by the

mean standardized prediction error of 0.05395. Also, the average standard error value of

3.074 is slightly higher than the root-mean-square of prediction errors value of 2.918.

This shows that the K-Bessel model slightly over-estimates the variability of CO

concentration. The root-mean square prediction error is a measure of the error that occurs

when predicting data from point observations and provides the means for deriving

confidence intervals for the predictions. Finally, the root-mean-square standardized with

a value of 0.9576 prediction error is very close to one, and thus corresponds to a very

good fit between the point estimates of CALINE4 and the geostatistical model using

universal kriging.

Figure 6: Predicted Carbon monoxide concentration (ppm) using K-Bessel Universal Kriging

4.4 Creation of hot spots

According to the US Ambient Air Quality Criteria for Carbon monoxide set out by the

US Environmental Protection Agency (1991), carbon monoxide concentration less than 3

parts per million (ppm) in hemoglobin is not harmful for human health. The

concentration of 3 to 5 ppm can cause aggravation of cardiovascular disease and

decrement in vigilance. The concentration above 5 ppm is harmful to human health. The

concentration of 80 ppm can cause death. This criterion was used to reclassify the

predicted surface from K-Bessel kriging to create the CO pollution hot spots over the

study area.

Figure 7: Creation of Carbon monoxide hot-spots from the interpolated surface over the study area

It is to be noted that CALINE4 is able to predict carbon monoxide concentration around

500 meters from the road. Therefore the predicted surface holds true within the 500

meters buffer area only. Most of the area has moderate level of carbon monoxide

concentration i.e., within 3 to 5 ppm. The areas where the prediction is higher than 5 ppm

are around New Baneshwor and Koteshwor. This makes sense because as shown in table

2, there are about 4392 and 7212 vehicles plying in New Baneshwor and Koteshwor

respectively, which is higher than vehicles running in any other links. New Baneshwor is

the area where most of the facilities such as office premises, shopping malls, restaurants,

hospital etcetera are located. It is not surprising to see high concentration of carbon

monoxide around that area. Similarly, Koteshwor is the main outlet of the Kathmandu

valley which joins Bhaktapur district and is the main outlet to Araniko highway leading

to the border of China. The south end of Koteshwor connects to Kalanki which is the

other outlet from the Kathmandu valley joining Kathmandu with other part of the cities

and is the only outlet to the border of India. Therefore the vehicles in Koteshwor are at

unprecedented level and it is logical to predict Koteshwor with high level of carbon

monoxide.

4.4 Evaluation of the model

Root mean square error (RMSE) method is carried out (using equation 6) to evaluate the

model performance by comparing the predicted and observed CO concentration within 10

meters buffer from the study site. The assessment could not be carried out for the other

areas due to unavailability of data. The data for observed CO concentration within 10

meters buffer area from the study area network is collected as a secondary data. The data

was primarily collected by Paudel (2007) by using a GASTEC 100 sampler on the 15th

day of February 2007 from 8:00 to 9:00 am (peak hour) during stable and clear climatic

condition. The decimal value of the predicted CO concentration is not taken into account

as the observed CO concentration is in whole number. The observed versus the predicted

CO concentration and the calculation of RMSE is shown in the table below:

Table 5: Calculation of RMSE for the evaluation of the model

Locations Observed CO (ppm)

Predicted CO from CALINE4 (ppm) Obs - Pred

Squared (Obs - Pred)

Maitighar 3 2 1 1

Babar Mahal 4 3 1 1

Bijuli Bajar 5 5 0 0

New Baneshwor 7 6 1 1

BICC 4 4 0 0

Min Bhawan 3 3 0 0

Santi Nagar Gate 3 3 0 0

Tinkune 1 3 4 -1 1

Tinkune 2 4 5 -1 1

Koteshwor 7 6 1 1

Sum of Squared (Obs - Pred) 6

Divided by n (10) 0.6

RMSE 0.77

Observed versus Predicted CO concentration

(within 10m buffer only) y = 0.7557x + 0.8507

R2 = 0.7467

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7 8

Observed CO concentration (ppm)

Pre

dic

ted

CO

co

ncen

trati

on

(p

pm

)

Figure 8: Observed versus Predicted CO concentration (within 10m buffer only)

The observed versus predicted CO concentration within the 10 meters buffer is plotted as

a graph in excel. The value of R-square is found to be 0.7467 which suggests that the

CALINE4 model is able to explain 74 % of the variation in the model within the 10

meters buffer area from the main road network. This shows that CALINE4 has

satisfactorily predicted CO concentration in the study area. Additionally, the low RMSE

value of 0.77 also supports that CALINE4 model has predicted the CO concentration

well.

5. Conclusion

Many cities, particularly those in developing countries of Asia, suffer from high

concentration of air pollutants. Vehicular emission is a major contributor to air pollution

in Kathmandu valley, which is comparatively more vulnerable to air pollution than other

rapidly growing Asian cities because of the bowl like structure of the valley and poor

wind speed inside the valley. The main objective of this study is to model the dispersion

pattern of vehicular carbon monoxide in Kathmandu valley by using CALINE4 software

combined with GIS techniques. The CALINE4 software requires vehicular count data,

pollution data and meteorological data as inputs. These data are entered into the software

and CO concentration is predicted on the ninety receptor points that are created within the

500 meters buffer area around the study road network. These receptor points are merged

together in ArcGis as a single shape file.

In ArcGis, IDW and Global Polynomial interpolation are carried out at first. IDW is just

a good way to take a first look at an interpolated surface but is not used for interpolation

due to its drawbacks. The second order Global polynomial interpolation shows a trend in

predicted carbon monoxide concentration from north-west to south-east direction.

Therefore Universal Kriging is used for interpolation instead of Ordinary Kriging.

Among the different models available in Universal Kriging, K-Bessel is found to be the

one with least prediction error. Therefore, K-Bessel model from the Universal Kriging is

used to interpolate the surface. It is found that most of the areas within the 500 meters of

the study road network have values between 3 to 5 ppm. New Baneshwor and Koteshwor

are the two areas where the predicted CO concentration exceeds 5 ppm.

In order to evaluate the performance of the model, the RMSE method is carried out by

comparing the predicted and observed CO concentration within 10 meters buffer from the

study site. The low RMSE value of 0.77 and the R-square value of 0.74 suggested that

CALINE4 model predicted the CO concentration satisfactorily.

Acknowledgement

The author wishes to thank Paudel R. (University of Aberdeen, UK) for making the

vehicular count and pollution data accessible for this study. Sincere thanks to The

Department of Meteorology and Hydrology, Kathmandu for providing the meteorological

data. The arcview shape files of the study area from International Centre for Integrated

Mountain Development (ICIMOD, Jawlakhel, Kathmandu) is gratefully acknowledged.

The author finally wishes to thank Dr. Jacobson (University of Calgary) for his help and

support.

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Appendix

Appendix I

Table: Vehicular Count per hour at different locations (Source: Paudel R, 2007)

Location Large Vehicle Smaller Vehicle Two-Wheeler

Maitighar 336 948 1608

Babar Mahal 420 840 2076

Bijuli Bazaar 312 888 2100

New Baneshwor 444 1164 2784

BICC 36 480 1656

Min Bhawan 492 888 2400

Shanti Nagar 492 804 2028

Tinkune 1 444 672 2100

Tinkune 2 828 744 1368

Koteshwor 1128 1464 4620

Appendix II: CALINE4 Screenshots

Figure: Job Parameters Screen

Figure: Link Geometry screen

Figure: Link Activity Screen

Figure: Run conditions screen

Figure: Receptor positions screen

Figure: CALINE4 output for 10 receptor points 300 meters away from the study area