Spatial and temporal analysis of ground level ozone and nitrogen dioxide concentration across the twin cities of Pakistan

Spatial and temporal analysis of ground level ozoneand nitrogen dioxide concentration across the twin citiesof Pakistan

Sheikh Saeed Ahmad & Neelam Aziz

Received: 24 May 2011 /Accepted: 5 July 2012# Springer Science+Business Media B.V. 2012

Abstract The analyses presented in this paper includethe concentration levels of NO2 and O3 measuredduring 2 successive years in twin cities (Rawalpindiand Islamabad) of Pakistan from November 2009 toMarch 2011. NO2 was determined using the passivesampling method, while ozone was determined byModel 400E ozone analyzer. The average NO2 andO3 concentration in twin cities of Pakistan was foundto be 44±6 and 18.2±1.24 ppb, respectively. Resultsindicate that the concentration of NO2 and O3 showseasonal variations. Results also depict that NO2 andO3 concentration levels are high in areas of intensetraffic flow and congestion. Rawalpindi has more ele-vated levels of NO2 and O3 as compared to the Islam-abad due to the narrow roads, enclosing architecture ofroad network and congestion. Climatic variables alsoinfluenced the NO2 and O3 concentration, i.e., temper-ature is positively related with O3, while negativelyrelated with NO2, relative humidity is directly relatedwith NO2 and inversely related with O3, whereasrainfall show negative association with both NO2 andO3 concentration. Comparing the results with WHOstandards reveals that NO2 concentration levels at allthe sampling points are above the permissible limit,while ozone concentration is still lower than the WHO

standards. Thus, there is a need to take appropriatesteps to control these continuously increasing levels ofNO2 and O3 before they become a serious hazard forthe environment and people living in those areas.

Keywords Nitrogen dioxide (NO2) . Ozone (O3) .

Passive sampling . Spatial interpolation . Climaticvariables . Twin Cities (Rawalpindi and Islamabad) .

Pakistan

Introduction

Air pollution is emerging as a key threat to humanhealth, the environment and the world’s climate. Ur-ban air pollution problems are frequently associatedwith the products of combustion in industries, vehiclesand for domestic purposes. Urban air pollution ismainly a mixture of carbon monoxide (CO), sulphurdioxide (SO2), nitrogen oxides (NOx), ozone (O3),suspended particulate matter (SPM) and volatile or-ganic matter. Excessive levels of these pollutants areexhibited by most of the metropolitan cities around theworld.

Photochemical oxidants are of predominant con-cern among the urban air pollutants. Most importantphotochemical oxidants include nitrogen dioxide(NO2) and ozone (O3), which have the capability toadversely affect the human health and environment(Lee et al. 1996; Mazzeo and Venegas 2002, 2004;Ali and Athar 2008; Martin et al. 2009; Ahmad et al.

Environ Monit AssessDOI 10.1007/s10661-012-2778-7

S. S. Ahmad (*) :N. AzizDepartment of Environmental Sciences,Fatima Jinnah Women University,Rawalpindi, Pakistane-mail: [email protected]

2011). Nitrogen dioxide plays a vital role in atmo-spheric chemistry, particularly in the formation ofsecondary air pollutants including tropospheric O3,peroxyacetyl nitrate (PAN) and nitrate aerosols andalso an important source of acid rain. It absorbs thevisible radiation and contributes to reduce atmosphericvisibility and due to this quality it has the potential tochange the global climate if its concentration increasein the atmosphere (Varshney and Singh 2003; Martinet al. 2009; Ahmad et al. 2011).

Road transport is the major contributor in environ-mental pollution particularly in urban areas (Chen etal. 2009). In Pakistan, road transport has grown at afaster pace in the last decade. According to the Na-tional Transport Research Center (NTRC), total num-ber of registered vehicle increased to 52 % from 1991–1992 to 2006–2007 as compared to road infrastructurein last 16 years (Sindhu 2008). Sudden expansion intraffic growth can be attributed to the introduction ofcar leasing schemes by different banks (Ahmad et al.2011). Continuous increase in road transport is leadingto severe traffic jams, slow speeds, extended travellengths and worsening of air pollution. In 2002 ap-proximately 40 % of the total urban population (i.e.,16.28 million people) were exposed to fog and suf-fered from health problems (Pak-EPA 2005).

Comprehensive assessment and visualization of NO2

and O3 is therefore critically important for developingeffective emission control policies and strategies to re-duce air pollution and improve air quality (Varshney andSingh 2003; Gujar et al. 2008; Lozano et al. 2009a,b;Martin et al. 2009; Ahmad et al. 2011). NO2 can bemeasured by using passive or active sampling techni-ques. Numerous studies all over the world have beenexecuted by measuring the NO2 concentration by usingpassive sampler technique (Spengler et al. 1983; Atkinset al. 1986; Campbell 1988; Campbell et al. 1994;Hargreaves et al. 2000; Lebret et al. 2000; Hansen etal. 2001; Stevenson et al. 2001; Lodhi 2006; Lozano etal. 2009a, b; Vardoulakis et al. 2009; Campos et al.2010; Esplugues et al. 2010; Ahmad et al. 2011).

Air pollution mapping is an effective tool for visu-alizing the concentration of pollutants and identifyingthe sources of pollutants emission (Wald et al. 1998).Geographic Information System (GIS) helps inmanaging the statistical and spatial data in orderto evaluate the relationship between poor air qual-ity and frequency of deficient human and environ-mental health. GIS modeling and statistical analysis also

enables to examine and predict air pollution con-centrations spatially and temporally that will helpin developing the strategies to reduce the pollu-tants emission (ESRI 2007). Different studies havebeen conducted on air pollution in conjunctionwith GIS (Gualtier and Tartaglia 1998; Jensen1998; Lin et al. 2001; Kim et al. 2004; Barneset al. 2005; Pummakarnchana et al. 2005; Afsharand Delavar 2007; Lozano et al. 2009a, b; Martin et al.2009; Vienneau et al. 2009; Banja et al. 2010; Veen et al.2010; Elbir et al. 2010; Ahmad et al. 2011).

The present study was aimed to monitor and visu-alize the NO2 and O3 concentration in twin cities ofPakistan and to examine the impact of climatic varia-bles on the measured concentrations. The monitoringof NO2 and O3 concentration level is very essential, todetermine the potential effects of NO2 and O3 onhuman and environmental health, in order to developthe effective strategies to minimize these elevatedlevels of pollutants and their effects.

Material and methods

Study area

Rawalpindi and Islamabad lie between 33°30′N and33°50 ′N (latitude) and 72°45 ′E and 73°30 ′E(longitude) in the Pothwar region (Fig. 1). The totalpopulation of both cities reached nearly 1.3 million.The Soan and Kurang Rivers are the main streamsdraining the area. Climatic condition of Rawalpindiand Islamabad is sub-humid to tropical. Rainfall ismore frequent in monsoon and due to western distur-bance. During monsoon season maximum rainfalloccurs during July to September. Annual average rain-fall is 1,044 mm, with 50 % occurring during mon-soon. Average maximum temperature of Rawalpindiranges from 25.6 °C to 39.4 °C in June and averageminimum temperature ranges from 3.2 °C to 16.7 °Cin January (ADB 2005).

For monitoring of NO2 and O3, the selected 135locations in the urban area of Rawalpindi and Islamabadwere divided into sub-divisions of main roads, sub-roads, small roads, educational institutes (EI), hospitals,commercial area, old residential area, new residentialarea, recreational sites and semi-rural areas. Sites selec-tion is done in such a way as to cover the different trafficintensity areas of the urban Rawalpindi and Islamabad.

Environ Monit Assess

Nitrogen dioxide determination

Passive sampling of NO2

Passive diffusion tube samplers were used to mon-itor the NO2 concentration levels as it is the mostcommonly used technique for the determination ofNO2 concentrations illustrated by Atkins et al.(1978). Passive samplers are simple and economicalmethod for broad spatial measurement of pollutantconcentration on a monthly or weekly basis. Pas-sive samplers have already been used by severalcountries like the UK, Italy, France, Turkey, China,Argentina, Brazil and the United States for mea-surement of NO2 concentration in an ambient envi-ronment (Varshney and Singh 2003).

The passive samplers are based on the principleof diffusion of air. For a tube of known length and

internal diameter with an efficient absorber at oneend, the NO2 sampling rate may be calculatedusing Fick’s law of the unidirectional flow of gasthrough a mixture of gases under conditions ofconstant temperature (Palmes et al. 1976). Thetemperature dependence of the diffusion coefficientis 0.2 % per 1 °C and the sampling of gases indiffusion tubes is independent of air pressure. Thesampling rate of the tubes used here was calculat-ed to be 62.3 ml of air per hour with a diffusiontube constant of 10,437.8 μg NO2 ppb−1 h−1. Thediffusion tubes (7.1×1.1 cm internal diameter)were made from acrylic tubing, having two stain-less steel wire mesh of 1 cm diameter. The methodused for diffusion tube preparation, transportationand analysis was adopted from Atkins et al.(1986), Palmes et al. (1976) and Ahmad et al.(2011).

Fig. 1 Base map (area codes are given in Appendix)


Calculations

The total weight of NO2− (μg) was derived from this

analysis. In the following, NO2 (ppb) was calculatedaccording to Eq. 1:

NO2 ppbð Þ¼ SampleNO2 μgð Þ � blankNO2 μgð Þ�10; 437:8 t= hð Þ

ð1Þ

where 10,437.8 is a diffusion tube constant (μg NO2

ppb−1 h−1).The data regarding the meteorological variables, i.e.,

temperature, rainfall and relative humidity of Rawal-pindi and Islamabad, was provided by the Pakistan

Meteorological Department, Islamabad on a monthlybasis, and the mean was calculated for each month.

Ozone determination

The Model 400E ozone analyzer (microprocessor-con-trolled analyzer that determines the concentration ofozone (O3) in a sample gas drawn through the instru-ment) was used to measure the ozone concentrations.It requires that sample and calibration gas be suppliedat ambient atmospheric pressure in order to establish astable gas flow through the absorption tube where thegas ability to absorb ultraviolet (UV) radiation of acertain wavelength (in this case, 254 nm) is measured.

Table 1 Seasonal mean values of NO2 with SD of all 135 sites in urban Rawalpindi and Islamabad during November 2009 to August2010

Sampling categories Mild winter(November)

Winter(December–January)

Early spring(February)

Spring(March)

Mildsummer(April)

Summer(pre-monsoon)(May–June)

Monsoon(July–August)

Nitrogen dioxide conc. (ppb) (ppb) (ppb) (ppb) (ppb) (ppb) (ppb)

Rawalpindi Dual carriage ways (5) 87±19.78 98±26.87 63±12.29 53±6.49 44±10.64 22±4.22 18±1.91

Major roads (10) 60±12.19 68±9.56 52±13.52 45±10.23 36±8.97 26±5.88 19±4.74

Sub-roads (6) 74±20.50 86±24.47 60±16.49 50±11.05 38±12.65 33±13.01 21±4.39

Small roads (3) 55±9.78 63±4.89 47±5.57 40±8.24 31±3.40 25±4.68 18±4.81

Public hospital (5) 48±18.71 63±18.40 37±0.74 29±2.29 22±2.24 18±0.79 14±0.96

Private hospitals (8) 61±14.47 75±14.19 38±1.16 32±2.0.3 25±2.29 20±5.57 14±3.98

Public EI (11) 85±30.58 95±32.94 75±23.75 63±17.94 47±17.37 31±10.14 20±1.94

Private EI (17) 55±9.71 66±9.54 45±4.56 43±9.65 38±10.89 26±4.54 18±3.18

Old residential areas (5) 83±15.24 95±16.09 55±13.32 51±6.66 37±6.44 26±2.54 19±1.05

Modern residential areas (5) 65±20.07 73±14.89 69±24.49 59±12.55 36±7.13 28±5.08 21±2.61

Commercial area (2) 75±0.83 82±17 61±6.69 51±7.11 36±4.29 21±6.20 18±4.78

Bus stops (9) 74±20.26 83±31.47 69±33.78 58±17 39±17.32 28±8.41 20±5.25

Recreational spots (9) 75±38.40 87±40.76 62±36.39 56±21.88 43±19.97 31±11.12 19±2.37

Semi-rural areas (5) 46±8.98 59±5.64 42±6.41 33±7.87 31±5.29 24±3.22 18±3.19

Islamabad Dual carriage ways (3) 84±28.73 95±33.64 66±23.78 57±12.31 45±16.69 24±5.98 19±4.16

Major roads (3) 50±3.72 60±2.04 40±0.81 32±2 26±4.42 21±2.97 15±2.16

Sub-roads (4) 54±6.06 67±6.39 49±6.49 43±7.24 38±12.79 25±1.38 18±1.26

Small roads (3) 59±12.65 64±6.33 51±9.60 44±8.93 35±4.53 26±3.66 20±3.08

Public hospitals (3) 44±0.58 57±0.29 39±0.29 32±0.58 23±1.47 19±0.51 15±1.71

Private hospitals (1) 42 56 38 30 24 19 14

Public EI (5) 53±13.34 64±9.32 46±9.30 39±10.76 34±14.19 25±6.01 18±1.28

Private EI (6) 58±11.23 63±7.18 49±7.72 39±9.93 31±5.85 24±2.66 17±1.77

Commercial area (1) 61 68 57 50 35 25 16

Bus stops (2) 72±14.25 78±16.23 65±7.51 55±5.23 34±6.22 25±3.21 19±2.56


Semi-rural Areas (2) 44±5.2 55±2.56 40±5.6 30±4.2 27±2.89 23±1.25 17±2.91


Calibration of the instrument is performed in softwareand does not require physical adjustments to the in-strument. The basic principle by which the Model400E Ozone Analyzer works is called Beer's law (alsoreferred to as the Beer–Lambert equation). It defineshow the light of a specific wavelength is absorbed by aparticular gas molecule over a certain distance at agiven temperature and pressure.

Results and discussion

The data used in this study were gathered from 135sites in Rawalpindi and Islamabad as mentioned in the

previous section. Tables 1, 2, 3 and 4 show the sea-sonal mean values of NO2 and O3 with standard devi-ation measured at these different sampling sites.

Tables 1 and 2 suggest that the maximum concentra-tion is recorded on dual carriage ways and bus stops. Thepossible cause of this increase in concentration was theextensive increase in number of vehicles, increase inpopulation, busy roads, fuel-inefficient vehicles, drivingways and traffic jams. Gilbert et al. (2005) reported intheir study that NO2 was significantly associated withboth the distance and the traffic count on the nearesthighway. Sampling sites in recreational areas categoryalso exhibited the high levels of NO2 concentration asmost of the locations were close to the dual carriage

Table 2 Seasonal mean values of NO2 with SD of all 135 sites in urban Rawalpindi and Islamabad during September 2010 to March2011

Sampling categories Mild summer(post summer)(September)

Autumn(October)

Mild winter(November)



Spring(March)

Nitrogen dioxide conc. (ppb) (ppb) (ppb) (ppb) (ppb) (ppb)

Rawalpindi Dual carriage ways (5) 30±4.00 51±7.52 88±22.28 100±26.42 63±18.52 50±20.32

Major roads (10) 27±6.57 49±9.72 61±10.33 68±9.34 48±3.76 37±3.79

Sub-roads (6) 32±7.31 53±12.97 74±23.42 87±26.60 58±13.69 39±6.36

Small roads (3) 28±3.65 37±2.53 53±6.30 62±2.58 54±12.39 43±11.13

Public hospital (5) 20±0.98 32±5.46 48±18.01 64±18.03 40±6.13 29±3.94

Private hospitals (8) 23±3.90 38±7.19 60±15.37 73±14.03 40±4.05 31±1.95

Public EI (11) 44±16.98 81±36.87 86±31.78 96±34.20 73±21.26 63±18.23

Private EI (17) 31±4.85 42±6.10 55±8.91 66±9.82 45±7.08 35±5.90

Old residential areas (5) 27±2.97 61±14.74 84±14.18 95±16.51 58±12.41 48±10.06

Modern residential areas (5) 32±7.86 49±11.70 66±20.07 75±16.16 60±19.16 48±16.53

Commercial area (2) 32±1.23 46±6.09 63±1.00 71±3.57 56±7.02 48±8.41

Bus stops (9) 32±9.11 53±20.30 76±20.07 87±32.40 69±31.34 54±19.54

Recreational spots (9) 37±18.55 52±25.23 71±37.63 84±39.83 57±29.71 46±24.78

Semi-rural areas (5) 31±9.47 41±7.44 53±6.51 62±6.21 44±7.50 36±6.99

Islamabad Commercial area (3) 32±1.23 46±6.09 63±1.00 71±3.57 56±7.02 48±8.41

Major roads (3) 22±0.80 37±1.93 53±4.33 65±0.30 44±2.30 33±2.00

Sub-roads (4) 26±2.48 39±4.60 54±5.74 65±4.08 46±3.02 35±9.17

Small roads (3) 30±5.94 41±4.12 54±4.24 63±6.67 47±2.60 38±2.79

Public hospitals (3) 22±2.14 34±2.66 45±0.80 60±1.41 45±0.22 34±0.82

Private hospitals (1) 22 31 40 55 38 30

Public EI (5) 31±7.41 40±3.60 52±7.94 64±8.39 46±9.34 37±9.62

Private EI (6) 29±11.58 41±8.65 54±10.14 63±7.03 47±7.93 36±9.17

Commercial area (1) 30 44 59 67 52 42

Bus stops (2) 30±5.4 49±5.6 72±10.2 84±8.4 64±5.9 51±4.86


Semi-rural areas (2) 27±1.56 37±5.42 48±2.85 57±1.91 42±2.22 35±2.51


ways. Higher traffic emission rate in old residential ascompare to modern residential areas is attributed to nar-row roads, enclosing architecture and congestion leadingto trapping and accumulation of NO2 and O3. Sub-roadsand small roads also show high NO2 concentration levelsdue to the road geometry especially in Rawalpindi. Spa-tial variations in concentration across an urban landscapeof Cambridge, UK, showed similar results (Kirby et al.1998). Educational institutions being present close to thedual carriage ways also experience elevated concentra-tion levels. A study conducted in San Francisco Bay Areain 2001 reported similar results (Kim et al. 2004). Tables 3and 4 indicate that almost all the sampling sections havenearly similar O3 concentration levels as most of thesampling sites were located near the dual carriage ways.

A study conducted in Bilbao, Spain (1993–1996),depicted the higher O3 concentrations levels in trafficcongested areas as compared to fluent traffic areas(Ibarrar-Berastegi and Madariaga 2003).

Most of the sampling points were close to the dualcarriage ways, i.e., Murree Road, Grand Trunk Road,Airport Road, Islamabad Express Highway, FaizAhmad Faiz Road and Jinnah Avenue. These roads aremost commonly used roads for traveling within the cityor among the twin cities. A buffer of 1.5 km along eachroad is applied by using ArcGIS in order to identify themost polluted route. Average NO2 and O3 concentrationof all the sampling points was calculated within the 1.5-km buffer along each road. Results showed that NO2

concentration was highest along Murree Road, while O3

Table 3 Seasonal mean values of O3 with SD of all 135 sites in urban Rawalpindi and Islamabad during November 2009 to August2010

Sampling categories Mild winter(November)



Spring(March)

Mildsummer(April)

Summer(pre-monsoon)(May–June)

Monsoon(July–August)

Ozone concentration (ppb) (ppb) (ppb) (ppb) (ppb) (ppb) (ppb)

Rawalpindi Dual carriage ways (5) 19±1.10 17±0.86 18±1.24 19±1.30 21±1.32 22±1.34 15±0.95

Major roads (10) 18±1.98 17±1.77 18±2.32 19±2.32 20±2.35 22±2.64 15±0.68

Sub-roads (6) 17±1.44 16±1.53 17±1.79 18±1.84 19±1.83 21±20.02 16±0.68

Small roads (3) 18±0.90 16±0.78 16±0.85 18±0.75 19±0.85 21±0.91 15±0.79

Public hospital (5) 16±1.61 14±1.46 15±1.29 16±1.32 17±1.12 19±1.25 14±1.70

Private hospitals (8) 16±0.96 13±0.94 14±1.08 15±0.96 17±0.96 19±0.99 14±1.13

Public EI (11) 19±1.18 17±0.99 18±1.07 19±1.30 21±1.24 23±1.11 15±0.57

Private EI (17) 18±0.79 16±0.89 17±0.80 18±0.70 19±0.74 21±0.99 15±0.47

Old residential areas (5) 17±1.50 16±1.59 16±1.46 18±1.50 19±1.70 21±2.22 15±1.29

Modern residential areas (5) 19±1.65 17±1.36 18±1.56 19±1.70 20±1.94 23±1.99 15±0.35

Commercial area (2) 19±0.70 17±0.64 18±1.73 19±1.56 21±1.32 23±1.60 14±0.07

Bus stops (9) 19±1.81 17±1.42 17±20.20 19±2.25 21±2.37 23±2.76 14±0.46


Semi-rural areas (5) 18±1.41 17±1.66 18±2.05 19±2.26 21±2.34 24±3.17 17±1.01

Islamabad Dual carriage ways (3) 18±1.22 16±0.90 17±1.06 18±1.27 19±0.96 22±1.22 16±0.46

Major roads (3) 17±1.28 16±0.95 17±1.21 18±1.45 19±1.00 21±1.00 15±0.54

Sub-roads (4) 17±2.75 15±2.66 16±3.01 17±2.75 19±30.1 21±3.02 15±1.50

Small roads (3) 17±2.7 14±2.41 16±2.75 17±2.71 18±2.63 21±2.64 15±0.80

Public hospitals (3) 18±0.63 16±1.01 17±1.15 19±1.09 19±1.63 25±1.41 15±0.61

Private hospitals (1) 17 15 16 17 18 20 14

Public EI (5) 19±0.44 17±0.39 18±0.30 19±0.48 20±0.58 22±0.59 15±0.56

Private EI (6) 18±1.33 16±1.42 17±1.51 18±1.28 19±1.38 22±1.59 15±0.65

Commercial area (1) 18 16 17 18 20 22 14

Bus stops (2) 19±1.26 17±159 17±2.56 19±2.59 21±1.68 23±1.58 14±0.25


Semi-rural areas (2) 18±1.78 17±2.54 18±2.48 19±2.15 21±2.45 24±2.17 17±2.56


concentration was highest along the Airport road.Results indicated that areas in the vicinity of MurreeRoad and Airport Road are considered hot spots, morevulnerable to the higher concentration levels of bothNO2 and O3 as this area is more commercial and is maintraveling route between the twin cities.

Seasonal trends in the level of NO2 and O3

Seasonal variation in NO2 and O3 concentration levelsis clearly depicted in Figs. 2 and 3.

It can be seen that NO2 and O3 concentration levelsfluctuate throughout the year in different seasons. Thepeak NO2 values were recorded in winters (December

and January), while minimum values of NO2 were mon-itored in the summer and monsoon season, i.e., in May,June, July and August. The low concentration values ofO3 were recorded in winters (December and January)and monsoon (July and August), while the high concen-tration values were recorded in summer (May and June).Vehicular emission is the predominant source responsi-ble for higher level of NO2 and O3 in twin cities, leadingto more or less constant NO2 and O3 concentrationvalues throughout the year. However, slightly higherO3 concentration levels in summer are attributed to thehigher intensity of solar radiation. Photolysis of NOx

into secondary air pollutant (O3) rises with the increasein solar radiation leading to the increased levels of O3 in

Table 4 Seasonal mean values of O3 with SD error of all 135 sites in urban Rawalpindi and Islamabad during September 2010 toMarch 2011

Sampling categories Mild summer(post Summer)(September)

Autumn(October)

Mild winter(November)



Spring(March)

Ozone conc. (No. of sites) (ppb) (ppb) (ppb) (ppb) (ppb) (ppb)

Rawalpindi Dual carriage ways (5) 21±1.01 17±1.03 16±1.32 15±1.12 19±0.91 20±1.33

Major roads (10) 20±2.05 19±1.92 17±1.73 16±1.78 19±2.01 20±2.01

Sub-roads (6) 20±1.54 18±1.49 17±1.25 16±1.46 18±1.47 19±1.58

Small roads (3) 20±1.53 19±0.93 18±1.05 16±0.79 17±1.19 18±1.18

Public hospital (5) 12±1.89 16±1.64 14±1.62 14±1.75 16±1.45 17±1.53

Private hospitals (8) 12±1.41 17±1.16 15±1.20 15±0.78 16±1.24 17±1.02

Public EI (11) 21±1.26 20±1.13 17±1.50 16±0.99 19±0.78 20±1.34

Private EI (17) 19±0.89 19±0.86 17±0.76 17±0.98 18±0.79 19±0.79

Old residential areas (5) 19±1.63 18±1.49 17±1.20 16±1.59 17±1.96 19±1.58

Modern residential areas (5) 20±1.34 19±1.68 18±2.19 17±1.27 19±1.50 20±1.39

Commercial area (2) 21±1.84 20±0.55 17±1.06 16±1.69 18±1.45 20±1.72

Bus stops (9) 21±2.74 19±1.77 18±2.68 17±1.63 19±1.37 20±2.17

Recreational spots (9) 20±1.66 18±1.22 17±2.23 16±1.18 18±.70 19±2.06

Semi-rural areas (5) 20±1.53 19±1.31 17±1.37 16±1.65 19±2.08 20±2.33

Islamabad Dual carriage ways (3) 20±1.01 18±0.96 17±1.11 16±1.05 18±1.13 19±1.22

Major roads (3) 20±1.15 18±1.20 16±0.77 15±0.86 18±1.49 19±1.35

Sub-roads (4) 19±2.21 18±2.65 16±2.21 15±2.86 17±3.03 18±2.58

Small roads (3) 19±3.05 17±2.99 15±3.11 15±2.76 16±2.97 18±2.65

Public hospitals (3) 16±1.73 19±0.58 17±0.71 16±1.23 18±.52 19±0.84

Private hospitals (1) 14 17 15 14 17 17

Public EI (5) 20±0.68 20±0.37 18±0.55 17±0.48 19±0.32 20±0.76

Private EI (6) 19±1.35 18±1.20 17±0.78 16±1.64 18±1.84 19±1.17

Commercial area (1) 21 20 17 16 18 20

Bus stops (2) 21±1.74 19±1.58 18±2.48 17±1.68 19±1.25 20±2.48


Semi-rural areas (2) 20±1.45 19±1.18 17±1.89 16±1.14 19±2.98 20±2.78


the atmosphere. Meanwhile, reduction in solar radiationslows down the photolysis of NOx and tends to accu-mulate. A number of studies have been conducted onNO2 and O3 concentration levels in different parts of theworld, either rural or urban, showing similar results(Lodhi 2006; Ghauri et al. 2007; Chen et al. 2009;Martin et al. 2009; Ahmad et al. 2011).

Relationship with climatologic variables

NO2 and O3 concentration in the atmosphere is greatlyaffected by climatic variables such as temperature,rainfall and humidity, as shown in Figs. 4 and 5.

Figure 4 indicates the positive association betweenNO2 concentration level and relative humidity (RH

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Fig. 2 Seasonal variation inNO2 concentrations level(November 2009–March2011)

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18

20

22

24

Mild

_Win

ter

2009

Col

d_W

inte

r_20

10

Ear

ly S

prin

g 20

10

Spri

ng_2

010

Mild

_Sum

mer

201

0

Pre

_Mon

soon

201

0

Mon

soon

201

0

Pos

t_M

onso

on 2

010

Aut

umn

2010

Mild

_Win

ter

2010

Col

d_W

inte

r 20

11

Ear

ly_S

prin

g 20

11

Spri

ng 2

011

Fig. 3 Seasonal variation inO3 concentrations level(November 2009–March2011)


in %) and negative association with the temperature(measured in degree Celsius), whenereas Fig. 5depicts that the O3 concentration level is negativelyrelated to relative humidity (RH in %) and positive-ly related to temperature (°Celsius). Periods withhigher temperature and lower humidity usually leadto higher levels of O3 and lowers level of NO2 inthe atmosphere and vice versa. Similar changes inO3 and NO2 concentration levels with respect to theclimatic variables have already been described inliterature (Lieu et al. 1987; Chan et al. 2001;

Martin et al. 2009; Ahmad et al. 2011). This, again,can be attributed to the intensity of solar radiationand temperature. Figures 4 and 5 also show anegative correlation of NO2 and O3 concentrationwith rainfall (measured in mm). Therefore, in mon-soon season, i.e., from July to August, as the rateof rainfall increased, the concentration of NO2 andO3 gradually decreased due to the increase in dis-persion and transportation of pollutants. Similarassociations of metrological variables with NO2

and O3 concentration have previously been reported

0

20

40

60

80

100

120

Mild Winter Cold Winter EarlySpring

Spring MildSummer

(Pre-Monsoon)

HotSummer

Monsson MildSummer

(PostSummer)

Autumn Mild Winter Cold Winter EarlySpring

Spring

Nit

rog

en d

ioxi

de

Co

nc.

(p

pb

)

0

50

100

150

200

250

300

Tem

per

atu

re (

C0 ),

Rai

nfa

ll (m

m)

& H

um

idit

y (%

)

NO2 Conc. of Dual Carriage Ways NO2 Conc. of Major Roads NO2 Conc. of Sub RoadsNO2 Conc. of Small Roads NO2 Conc. of Bus Stops NO2 Conc. of Hospitals

NO2 Conc. of Educational Institutions NO2 Conc. of Commercial Area NO2 Conc. of Old Residential AreaNO2 Conc. of Modern Residential Area NO2 Conc. of Recreational Spots NO2 Conc. of Semi-Rural AreaTemperature Rainfall Humidity

Fig. 4 Relationship between NO2 concentration and temperature, rainfall and relative humidity (November 2009–March 2011)

0

5

10

15

20

25

Mild Winter ColdWinter

EarlySpring

Spring MildSummer

(Pre-Monsoon)

HotSummer

Monsson MildSummer

(PostSummer)

October Mild Winter ColdWinter

EarlySpring

Spring

Ozo

ne

Co

nc.

(p

pb

)

0

50

100

150

200

250

300

Tem

per

atu

re (

C0 ),

Rai

nfa

ll (m

m)

& H

um

idit

y (%

)

O3 Conc. of Dual Carriage Ways O3 Conc. of Major Roads O3 Conc. of Sub RoadsO3 Conc. of Small Roads O3 Conc. of Bus Stops O3 Conc. of HospitalsO3 Conc. of Educational Institutions O3 Conc. of Commercial Area O3 Conc. of Old Residential AreaO3 Conc. of Modern Residential Area O3 Conc. of Recreational Spots O3 Conc. of Semi-Rural AreaTemperature Rainfall Humidity

Fig. 5 Relationship between O3 concentration and temperature, rainfall and relative humidity (November 2009–March 2011)


by Atkins and Lee (1995),Wahid et al. (1995a, 1995b),Markovic et al. (2008), and Shan et al. (2009). Similar

variations in NO2 and O3 concentration with climaticvariables were observed in Spain. Ghauri et al. (2007)

Table 5 Correlation matrix ofNO2 and O3 concentration withclimatic variables

*P<0.05

**P<0.01

S. no. Variables Coefficients

NO2 O3 Temperature Rainfall Humidity

1 NO2 1

0.000

2 O3 −0.704* 1

0.023 0.000

3 Temperature −0.839** 0.420* 1

0.002 0.047 0.000

4 Rainfall −0.928** −0.707* 0.859** 1

0.000 0.022 0.001 0.000

5 Humidity 0.957** −0.759* −0.814** −0.953** 1

0.000 0.011 0.004 0.000 0.000

Fig. 6 Spatial interpolation of nitrogen dioxide (ppb) in study area (November 2009–March 2011)


and Ahmad et al. (2011) also reported that in monsoon,concentration of O3 and NO2 in the atmospheredecreases with the increase in the rate of rainfall. Bivari-able correlation analysis of NO2 and O3 in relation toindependent meteorological variables including temper-ature, relative humidity and rainfall is shown in Table 5.

Bivariable correlation analysis indicate that NO2 issignificantly associated with O3 concentration(coefficient0−0.704, P00.023), temperature (coef-ficient0−0.839, P00.002), rainfall (coefficient0−0.928,P00.000) and humidity (coefficient00.957, P00.000).Correlation analysis of O3 indicate that the O3

concentration was significantly associated withNO2 concentration (coefficient0−0.704, P00.023),temperature (coefficient00.420, P00.047), rainfall(coefficient0−0.707, P00.022) and humidity (coef-ficient0−0.759, P00.011).

Spatial interpolation

Spatial interpolation maps for NO2 and O3 have beendeveloped by using ArcGIS 9.2 to visualize the higherpollution areas in Rawalpindi and Islamabad (Figs. 6and 7).

Maps clearly depict the areas of higher and lowerconcentration levels of NO2 and O3 in Rawalpindi andIslamabad. Higher concentration levels are repre-sented by darker shades while the lower concentrationlevels are shown with lighter shades. Dual carriageways, sub-roads, major roads, commercial areas, oldresidential areas and areas of educational institutionshave higher concentration levels of NO2, while higherO3 concentration levels were observed near dual car-riage ways and semi rural areas. Intense traffic flowand congestion are the major reasons of these elevated

Fig. 7 Spatial interpolation of O3 (ppb) in study area (November 2009–March 2011)


levels of NO2 and O3 concentration in those areas asvehicular emission is the predominant source of NO2.Vehicle growth rate in twin cities is extensively high.Traffic congestion is continuously increasing with thisgrowing vehicle population leading to the highestemission rates per vehicle. The higher emission rateof NO2 can also be attributed to the type of fuel andquality of fuel (Heywood 1988). Rawalpindi showshigher concentration levels than Islamabad. These el-evated levels of NO2 and O3 in Rawalpindi may beattributed to the road network. As Islamabad is aplanned city with wide and extensive road networklaid on a grid structure, while the road network ofRawalpindi is narrow and congested, representing aspider net-like structure (Ministry of Communicationof Pakistan and Scandia consult 1995).

Comparison of obtained results with WHO standards

Comparison of obtained average values of NO2 andO3 in Tables 1, 2, 3 and 4 with the WHO standards onNO2 concentration level (i.e., 40 μg/m3 [21 ppb];average timing 1 year) and O3 concentration levels(i.e., 0.075 ppm [75 ppb]; average timing 8 h) showsthat the average values of NO2 obtained during thecampaign are all above the WHO standards, while theaverage values of O3 are within the allowed limits setby WHO standards. NO2 concentration values deter-mined by Ahmad et al. (2011) in Rawalpindi were alsofound to be above the WHO standards.

Conclusion

This research article highlights the air pollutionscenario of twin cities of Pakistan. The resultsindicate that the variation in concentration ofNO2 and O3 is either due to possible increase inprecursor gases by anthropogenic activities or bythe influence of metrological variables. Graphicalvisualization of NO2 and O3 also show spatialvariation due to the difference in road networkarchitecture of Rawalpindi and Islamabad. More-over, NO2 concentration levels in all samplingpoints are above the WHO standards, while thoseof O3 are less then allowed limits of WHO. There-fore, it is necessary to take appropriate steps tocontrol these elevated levels of air pollution.

Acknowledgment This research project has been supportedby the Pakistan Science Foundation.

Appendix

Sampling sites Codes

Dull carriage ways

Rawalpindi Airport road 2

Peshawar Road 90

6th Road 0

Murree Road 86

Mall Road 119

Islamabad Islamabad Expressway Highway 63

Main Zero point 132

National Highway Authority Underpass 87

Major Roads

Rawalpindi Faiz Abad 48

Bank Road 16

Ammar Chowk 11

Missile Chowk 84

Main Khanna Road 73

Ali Nawaz Gol Chowk 8

Karal Chowk 72

Sir Syed Chowk Tipu Road 114

Chaklala Garrisons 26

Islamabad Faiz Ahmad Faiz Road 47

Rawal Dam Road 99

Islamabad Toll Plaza 65

Sub-roads

Rawalpindi Justice Ali Nawaz Chohan Chowk 136

Tipu Road 120

Rawal Road 100

Saidpur Road 106

College Road 35

Hummak Road 60

Islamabad Sufi Tabassam Road 116

Post Office Road G-6/2 94

Tarnol 117

Khokhar Road 76

Small Roads

Rawalpindi Transformer Chowk 122

Raja Ashraf Road 98

Chaklala Road 25

Islamabad Lehtrar link Road 79



KRL Road 77

Enercon G-5/2 42

Public Hospitals

Rawalpindi CMH Hospital 34

Benazir Bhutto Hospital 19

Cantonment General Hospital 39

Social Security Hospital I 12 115

Holy Family Hospital 58

Islamabad Poly Clinic Hospital 93

PIMS 91

NOORI 89

Private Hospitals

Rawalpindi Jinnah Memorial Hospital 69

Anwar Hospital

Al-Sadiq Hospital 4

Ibrar Surgery 61

Bilal Hospital 20

Marium Memorial Hospital 82

Anwar clinic Peshawar Road 12

Al-shifa Eye hospital 5

Valley Clinic 124

Islamabad Ali Medical Centre 6

Public Educational Institutions

Rawalpindi FJWU 52

Gorden College 54

F.G Sir Syed College 46

Government College for Women satelliteTown

57

F.G Postgraduate College for Girls 45

FWO Primary School Chaklala 53

Govt. Post Graduate College for Women 128

University of Arid Agriculture 13

RMC 103

Waqar-un-Nisa College 126

Government degree College for women 56

Rawalpindi College for Girls 6th Road 101

Islamabad F.G Margalla College for Women F-7/4 44

H-8/2 School for Boys and Girls 130

F.G Junior Model School for Boys 43

Allama Iqbal Open University 10

Quaid-e-Azam University 96

Islamabad College for Girls F-6/2 66

Private educational institutions

Rawalpindi Silver Oaks School 113

Al-Mehr Public School 7


The Lassanians 118

City School 31

Leaders College 78

Beacon House 17

Fazaia Inter College 127

Toddlers High School 121

Beacon House Peshawar Road 18

Rawalpindi College of Commerce 102

University of Lahore 123

Educators 40

City School Peshawar Road 32

Roots 105

House of Secondary Education 59

Educators Peshawar road campus 41

Islamic International Medical College 67

Cambridge School System 23

Govt. Denny’s Higher Secondary School 131

Islamabad CIIT 29

FAST University 50

Islamabad Medical and Dental College 64

International Islamic University 62

Preston University 95

Old residential area

Rawalpindi Khayaban Sir Syed 75

Jhanda Chichi 68

Shamsabad 112

Rehmanabad 104

Sadiq Abad 107

Modern residential area

Rawalpindi Satellite Town 108

Scheme-2 110

Civil Lines 33

Fazaia Colony 51

Babar Colony 15

Commercial area

Islamabad Blue Area 22

Rawalpindi City Saddar Road 30

Commercial Market 36

Bus stations

Rawalpindi Sowan 109

Pir Wadhi Moor 92

Kachehri Chowk 71

Ammar Chowk 134

Missile Chowk 135



Airport 1

Liaquat Bagh Chowk 81

Islamabad Golra Mor 55

Faizabad Bridge 49

Play/recreational grounds

Rawalpindi Community Park 38

Shah Baloot Park 111

Nawaz Sharif Park 88

Ayub Park 14

Liaquat Bagh 80

Jinnah Park 70

Race Course 97

Biodiversity Park 21

Islamabad Pir Sohawa 133

Virsa Café 125

Semi-rural area

Rawalpindi Morgah 85

Misrial 83

Gujar Khan 129

Ali Pur Farash 9

Khanna 74

Islamabad Choor Chowk 28

Chak shehzad 24

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Documents

Spatial and temporal analysis of ground level ozone and nitrogen dioxide concentration across the twin cities of Pakistan