Download - Development of baseline (air quality) data in Pakistan

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Environ Monit Assess (2007) 127:237–252

DOI 10.1007/s10661-006-9276-8

O R I G I N A L A R T I C L E

Development of baseline (air quality) data in PakistanBadar Ghauri · Arifa Lodhi · M. Mansha

Received: 20 October 2004 / Accepted: 8 May 2006 / Published online: 21 October 2006C© Springer Science + Business Media B.V. 2006

Abstract During 2003–2004, SUPARCO, the Pak-

istan Space and Upper Atmosphere Research Comm-

ission has conducted a year long baseline air quality

study in country’s major urban areas (Karachi, Lahore,

Quetta, Rawalpindi, Islamabad and Peshawar). The

objective of this study was to establish baseline

levels and behavior of airborne pollutants in urban

centers with temporal and spatial parameters. This

study reveals that the highest concentrations of

CO were observed at Quetta (14 ppm) while other

pollutants like SO2 (52.5 ppb), NOx (60.75 ppb) and

O3 (50 ppb) were higher at Lahore compared to

other urban centers like Karachi, Peshawar etc.

The maximum particulate (TSP) and PM10 levels

were observed at Lahore (996 ug/m3 and 368 ug/m3

respectively), Quetta (778 ug/m3, 298 ug/m3) and in

Karachi (410 ug/m3, 302 ug/m3). In all major cities the

highest levels were recorded at major intersections and

variations were directly correlated with traffic density.

These pollutants showed highest levels in summer

and spring while lowest were observed in winter and

monsoon. A data bank has been generated for future

planning and air pollution impact studies.

B. Ghauri (�) · A. Lodhi · M. ManshaPakistan Space & Upper Atmosphere ResearchCommission (SUPARCO) P.O. Box 8402, Karachi-75270,Pakistane-mail: [email protected]

Keywords Air quality . Mega cities . Particulates .

Clean air

Introduction

Pakistan Space & Upper Atmosphere Research Com-

mission (SUPARCO) has kept a very close watch on

some atmospheric pollutants and trace metals for last

many years. In its earlier study a chemical mass balance

and multivariate analysis technique had been applied to

delineate pollution sources in major cities of Pakistan.

It was also reported that daily concentrations of partic-

ulate matter in Karachi city were found exceeding the

ambient air quality standard during most of time of the

year except in monsoons. A non-automotive source of

Pb aerosols was also identified (Parekh et al., 1987).

Later (Ghauri et al., 1994) demonstrated that most of

the air pollution in major cities came from the com-

bustion of fossil fuels in vehicles, power plants, ce-

ment and textiles mills, etc. Exposure to elevated pol-

lutants concentrations or long continued exposure to

low levels of ambient air pollutants has received in-

creasing attention due to wide range of adverse effects

of air pollutants on ecological system and human health

(Dockery and Pope, 1994; Koenig, 2000; Pope et al.,2002).

SUPARCO has undertaken air quality study dur-

ing 2003–2004 in response to the initiative taken by

FERTS (Fuel Efficiency in Road Transport Sector) of

ENERCON/UNDP. This has generated the most

Springer

238 Environ Monit Assess (2007) 127:237–252

needed information on environmental conditions of

major cities of the country. One of the important ob-

jectives of the study was to establish the baseline (air

quality) data in Pakistan with temporal and spatial pa-

rameters, to identify pollution sources and to determine

their relative contribution towards prevailing ambient

air quality of urban areas. The measurements of the fol-

lowing pollutants were carried out: SO2, CO, CO2, O3,

NOx, Hydrocarbons (Methane and Non-Methane), Par-

ticulates (TSP & PM10) and noise as well as meteoro-

logical parameters. TSP (total suspended particulates)

are aerosol particles (sometimes even above 100 μm

sizes) suspended in the air. PM10 means particulate

mass of particles smaller than 10 μm in diameter. The

study was carried out in six major cities of Pakistan i.e.

Karachi, Lahore, Peshawar, Quetta, Rawalpindi and Is-

lamabad and continued during the whole year. The state

of air quality in urban areas of the country and various

factors are being presented in the paper which are dete-

riorating air quality. A brief description for these cities

is given below:

Karachi (Lat: 24◦48′N, Long: 66◦59′E) with a

population of over 10 millions, is the biggest industrial

and commercial center in Pakistan. It offers immense

employment and business opportunities. It borders on

the Arabian Sea, and is not under the influence of any

other immediate industrial center. Therefore, we expect

the pollutants present are entirely derived from local

sources. With a population of more than seven million,

Lahore (Lat: 31◦35′N, Long: 74◦20′E) is country’s

second largest city after Karachi. It occupies a choice

site in the midst of fertile alluvial plains. It is country’s

second commercial and banking center. Although little

industry is located in the city itself, Lahore serves

as a distribution center for the heavily industrialized

surrounding area. The city of Quetta (Lat: 30◦12′N,

Long: 67◦E) is located in the mountainous region and

lies at the mouth of the Bolan Pass. Here population

is about 0.7 million. The district lies outside the range

of the monsoon currents and the rainfall is scanty and

irregular. Rawalpindi (Lat: 33◦36′N, Long: 73◦04′E) is

an industrial and military center with a petroleum refin-

ery, engineering workshops, steel-rolling mills, etc. Its

population is over 1.4 million. Lying at altitudes rang-

ing from 457 to 610 meters, Islamabad (Lat: 33◦42′N,

Long: 73◦10′E) the capital city is an expanse of natural

terraces drained by the Kurang River with the Margalla

Hills in the north east. Population is 524,500 (National

Census, 1998). Peshawar (Lat: 34◦1′N, Long: 71◦35′E)

is situated near the entrance to the Khyber Pass. Local

industries produce handicrafts and processed food.

Population is 988,055 (National Census, 1998).

Methodology

Measurements of major pollutants were carried out us-

ing two Mobile Pollution Monitoring Labs. The fixed-

point observations do not explain the whole situation

because pollutants concentration may be dominated by

local sources, making the results to some degree un-

representative of the area as a whole. Air pollution

in general, is much too complex and diverse because

of the wide variability in the type, intensity, density

and spatial distribution of emission sources. Interval of

measurement was taken as 15 minutes and monitoring

was carried out continuously for 48 h at a site and was

repeated over the year for all the four seasons. One-

hour mean were calculated from 15 min data. The peak

value in hourly mean was quoted as maximum value.

Any single extraordinary high peak was omitted (e.g.

at some places a single CO peak was omitted and then

2nd highest value was quoted as maximum level). First

time measurements at a site were marked as cycle 1,

repeated measurements at the same site were marked as

cycle 2, cycle 3 & cycle 4. There were total 33 sites in

six cities and each site was monitored four times. Fig-

ure 1 shows the location of six cities on country map.

Measurement techniques

Suspended particulate matter was collected on 20 ×25 cm Whatman 41 filters using high-volume sam-

plers. Air samples were collected for 24 h starting from

0800 h at a flow rate of 1.13 m3/min. The flow was

held at a constant volume by a mass flow controller

and was corrected for local temperature and barometric

pressure. A Sierra Andersen PM10 size-selective stage

(Model 321-A) was used to eliminate coarse particles

(>10 um). Weight of the filter paper was taken before

and after the loading at constant humidity (maintained

by keeping the filter paper in a desiccator for at least

24 h).

In accordance with 40 CFR part 53 of USEPA

air quality assessment in term of NOx , O3, CO &

SO2 was carried out using the Mobile lab, which

had on-board all analyzers. Ambient NOx Monitor

Springer

Environ Monit Assess (2007) 127:237–252 239

Fig. 1 Map of Pakistan showing locations of the cities

(Thermo Environmental Instrument Inc, USA, Model

42C) based on chemiluminescent technology was used

to monitor NOx levels. Ambient SO2 was monitored

using Environmental SA, France, Model AF 22M.

The instrument is based on ultraviolet (UV) fluores-

cent measurement techniques. The instrument manu-

factured by M/S. Thermo Environmental Instruments

Inc USA, (Model 48C and 48 H) were used for CO &

CO2 based on Gas Filter Correlation (GFC) technique.

The Ozone Analyzer of Monitor Lab. Inc., USA (Model

8810) based upon UV photometry was employed for

surface ozone measurements.

Results and discussion

The need to investigate ambient air quality appears

paramount especially when bearing in mind that

Pakistan is shifting its economic base from agriculture

to industry. The environmental impacts of air pollu-

tants include a number of effects on earth’s atmosphere

and on atmospheric processes. Generally, the sources

of gaseous pollutants are considered in three cate-

gories, combustion sources, industrial manufacturing

processes and natural emission mechanism. Industrial

sources of particulates, like steel, cement factories, in-

discriminate burning of solid wastes and heavy traffic

loads are the largest sources of particulate matter be-

sides noise and gaseous pollutants. Sulfur dioxide, car-

bon monoxide, hydrocarbons and nitrogen oxides are

common emissions from auxiliary systems such as in-

cinerators, steam boilers including processing residue

waste fuel fire systems, glass and can manufacturing.

The aerosol contents of the atmosphere are sensitive

to the location of local sources and meteorological

conditions. The growing air pollution is resulting in

increased health cost, losses to crops and properties.

The Economic Survey of Pakistan 2004 pinpoints ve-

hicular and industrial emissions as the main causes

of poor air quality (www.irinnews.org/report.asp). The

Annual Economic Survey reports that the average com-

pounded growth of vehicles in Pakistan is about 12

percent a year, and over the last two decades the total

number of motor vehicles on the road has jumped

from 0.8 million to almost 5 million (Lahore, 11 Oct

2004, IRIN, www.irinnews.org/report.asp). This recent

Springer

240 Environ Monit Assess (2007) 127:237–252

Table 1 Mean (48 h) & maximum (hourly) number of vehicles counted per day at monitoring sites (2003–2004)

Bick-up Loader Rickshaws

City Buses Jeeps Pick-ups Tractors Trucks M/Cycles (three wheelers) Cars

Karachi (10 sites) Mean 6512 2182 1928 18 1112 3400 1161 6330

Maximum 16052 4335 6032 217 4259 7588 2586 10593

Quetta (3 sites) Mean 3066 1222 1462 15 212 2228 1927 3376

Maximum 5091 1733 2412 313 637 2740 2001 5153

Lahore (7 sites) Mean 5256 1493 1559 30 515 3146 2213 4912

Maximum 14928 3926 3818 277 1645 5496 3744 7921

Rawalpindi (3 sites) Mean 4914 1276 1342 17 477 2865 1578 4268

Maximum 10829 2409 4399 140 1163 4431 1972 6700

Islamabad (3 sites) Mean 2764 1067 1054 13 220 1149 338 3008

Maximum 5091 1711 2412 39 733 2001 1019 4679

Peshawar (5 sites) Mean 3364 1226 920 42 327 1445 1035 3095

Maximum 6531 3391 3259 229 840 3450 3125 4940

Fig. 2 Total suspended particles observed in six major cities of Pakistan

increase in the population of vehicles in urban areas

not only leads to frequent traffic jams at intersections

but also ultimately result in rise in pollutants levels

as so far no vehicular pollution control device is em-

ployed in Pakistan. During this study, traffic density

at each location was determined using digital photog-

raphy. Table 1 gives daily mean and maximum num-

ber of vehicles counted at monitoring sites in various

cities.

Particulate matter (TSP & PM10)

Airborne particulate matter is ubiquitous in the atmo-

sphere and varies widely (spatially and temporally)

in size, concentration and chemical composition.

Particulate emissions to air are the main environmental

challenges for industry and transport sectors. Also of

concern are the sulfates and nitrate particles that are

formed as a byproduct of SO2 and NO2 emissions,

primarily from fossil fuel-burning in power plants and

vehicular exhausts. According to the report of World

Health Organization (WHO), South Asia has become

one of the most polluted areas in the world due to

its rapid industrialization and increasing population.

Exposure to particulate matter leads to more visits to

the doctor or emergency room. Health effects include

coughing, wheezing, shortness of breath, aggravated

asthma, lung damage (including decreased lung

Springer

Environ Monit Assess (2007) 127:237–252 241

function and lifelong respiratory disease), and prema-

ture death in individuals with existing heart or lung

diseases. In major urban areas of Pakistan, peoples of

all ages suffer from throat infections especially when

season is cold and dry. In Pakistan, like the other low-

income countries the vehicular emissions have been

dominated by emissions from old and poorly main-

tained vehicles that contribute to enhanced ambient

concentrations of fine particulates & carbon monoxide.

It has been observed that levels of most of air pollutants

were higher in summer than in winter and monsoons,

which can easily be interpreted in terms of time depen-

dent changes primarily in meteorological conditions.

In urban areas the higher concentrations of PM10 are

indicative of higher traffic density, whereas higher TSP

values are indicative of size reduction process such as

iron, cement and ceramic industrial activities or natural

dust entrainment. The particulate levels as monitored in

different cities contain a significant amount of crustal

dust, which is a peculiar characteristics of local soil (es-

pecially in north & eastern Pakistan), lack of vegetation

and paved areas. It was observed that maximum TSP

load was recorded during summer season and minimum

in monsoon. Further daytime particulate load is higher

than nighttime one, indicating more urban activities

during day time. Lahore is facing alarming level of TSP

followed by Quetta, Peshawar, Rawalpindi, Islamabad,

and Karachi. The high TSP maximum (1 hr) TSP

levels in these cities are 2 to 4 times higher than the

standard limit of USEPA of 260 ug/m3 (Fig. 2). Even

the mean of 48 h exceeded the recommended limits

(Tables 2–7)

The maximum (1 hr) TSP peaks were found at

Chowk Yateem Khana, Lahore (996 ug/m3), Satel-

lite Town, Quetta (778 ug/m3), Attock Oil Refin-

ery, Rawalpindi (500 ug/m3), Kohat Ada, Peshawar

(530 ug/m3) and I-9, Islamabad (490 ug/m3), Civic-

Center, Karachi (410 ug/m3) as shown in Fig. 2. In

Quetta trans-boundary dust storm (seasonal), fugitive

emissions from lime stone quarries are major contribu-

tors. The dry weather, soil erosion, and lack of veg-

etation/trees, also contribute to higher level of TSP

in addition to low dispersion in the valley. The dust

and vehicular emissions are the major part in TSP load

observed in five cities, whereas low TSP levels were

observed in Karachi, which borders Arabian sea hav-

ing different meteorological conditions of higher wind

speed and humidity. There is a clear difference of TSP

levels measured in monsoons (1st Cycle) and those Tabl

e2

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77

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Springer

242 Environ Monit Assess (2007) 127:237–252

Tabl

e3

Mea

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48

hours

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ial

and

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pora

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riat

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of

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(dB

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measured in summer months (4th Cycle). The trend

remained same for mean TSP levels, with maxima of

means at Lahore and Quetta. Other cities, though ex-

ceeding prescribed limits of USEPA, had mean TSP

levels around 350 ug/m3 (Fig. 3).

The PM10 (particles measuring 10 microns or less)

are particles likely to be inhaled by humans. These

microscopic inhalable particles PM10 affect breath-

ing and respiration and cause lung damage. Chil-

dren, the elderly, and people suffering from heart

or lung disease are especially at risk. Higher lev-

els of particulates especially PM10 are attributed to

large number of 2 stroke vehicles, diesel driven ve-

hicles and adulteration of petrol/diesel. The max-

imum (I hr) PM10 levels were again observed at

Lahore (368 ug/m3), Quetta (331 ug/m3), Rawalpindi

(276 ug/m3), Peshawar (350 ug/m3) and Islamabad

(280 ug/m3), Karachi (302 ug/0m3) (Fig. 4). These lev-

els were clearly higher in summer months (4th cycle)

as compared to other seasons (Tables 2–7). At most

of the sites, the mean (48 h) PM10 level exceeded the

USEPA standard limit of 150 ug/m3 (Fig. 5). However

comparatively lowest PM10 concentrations has been

recorded at Baloch Colony, Karachi (99 ug/m3) and F-

10, Islamabad (126 ug/m3), which is a clear indication

that these sites have lesser activities and were visited

by comparatively fewer vehicles.

Gaseous pollutants

The ever increasing vehicular emissions are the main

factor leading to bad air quality in urban areas. Motor

vehicles account for about 90 percent of total emissions

of hydrocarbons, NOx , particles and CO. Oxides of ni-

trogen produced under high temperature combustion

are major criteria pollutants that are precursors to pho-

tochemical smog, ozone and acid formation. Oxides

of nitrogen are found within limit but their concentra-

tions in the ambient air has been found increasing since

the compressed natural gas (CNG) was introduced in

vehicles without any catalytic converters.

In all the six cities, NOx levels fall within speci-

fied U.S. ambient air quality standards (50 ppb as an-

nual mean, 100–170 ppb as one hourly mean). Lahore

city has shown highest levels of nitrogen oxides, which

was caused by the heavy traffic around the sampling

sites, however stationary sources such as power plants

around the city are also the contributers. The overall

NOx concentrations ranged 5.4–60.7 ppb during 24 h at

Springer

Environ Monit Assess (2007) 127:237–252 243

Tabl

e4

Th

em

ean

con

cen

trat

ion

(48

h)

show

ing

spat

ial

and

tem

po

ral

vari

atio

no

fd

iffe

ren

tgas

es,p

arti

cles

,le

adan

dn

ois

ele

vel

atL

aho

re

SO

2N

Ox

CO

O3

HC

Met

han

eN

on

-met

han

eC

O2

TS

PP

M1

0L

ead

No

ise

Sit

eC

ycl

e/se

aso

n(p

pb

)(p

pb

)(p

pm

)(p

pb

)(p

pm

)(p

pm

)(p

pm

)(S

PM

)u

g/m

3u

g/m

3u

g/m

3le

vel

(dB

)

Ch

owk

Yat

eem

Kh

ana

Cy

cle

I/M

on

soo

n9

Jul

to1

1Ju

l,0

32

1.4

25

.64

.21

6.8

1.0

0.8

33

7.6

51

02

63

5.0

18

5.5

Cy

cle

II/W

inte

r2

3S

epto

25

Sep

,0

32

5.6

30

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7.9

0.8

0.9

37

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69

02

69

4.7

88

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Cy

cle

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rin

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arto

26

Mar

,0

43

0.6

36

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1.6

38

47

85

28

04

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Cy

cle

IV/S

um

mer

7Ju

nto

9Ju

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04

31

53

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di

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cle

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soo

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14

Jul,

03

23

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20

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50

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20

15

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85

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Cy

cle

II/W

inte

r2

5S

epto

27

Sep

,0

32

2.9

27

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0.6

1.3

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35

6.1

56

12

31

4.5

67

6.9

Cy

cle

III/

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rin

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28

Mar

,0

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4.5

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62

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80

26

84

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79

Cy

cle

IV/S

um

mer

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11

Jun

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42

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29

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Sh

alim

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Jul

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24

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81

68

3.4

57

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Cy

cle

II/W

inte

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25

Sep

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20

52

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Cy

cle

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31

Mar

,0

42

4.3

30

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33

7.3

37

22

55

2.8

18

7.0

Cy

cle

IV/S

um

mer

12

Jun

to1

49

Jun

,0

42

5.7

31

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34

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41

82

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Qu

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14

Jul

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31

62

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18

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33

.63

27

11

92

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71

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Cy

cle

II/W

inte

r3

0S

epto

1O

ct,0

31

4.7

22

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2.1

1.1

1.3

33

9.9

32

71

19

2.9

88

0.3

Cy

cle

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rin

g3

1M

arto

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pr,

04

24

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0.1

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22

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47

.63

31

12

53

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84

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Cy

cle

IV/S

um

mer

14

Jun

to1

6Ju

n,

04

26

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53

.92

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1.2

1.5

35

4.5

81

82

60

3.5

78

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Ban

kS

qu

are

Cy

cle

I/M

on

soo

n1

7Ju

lto

19

Jul,

03

16

.92

3.0

2.0

13

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.71

.13

24

.12

20

13

02

.76

70

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Cy

cle

II/W

inte

r3

Oct

to5

Oct

,0

31

9.1

24

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1.0

1.3

33

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21

11

48

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57

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Cy

cle

III/

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rin

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Ap

rto

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23

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41

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69

14

92

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81

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Cy

cle

IV/S

um

mer

17

Jun

to1

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n,

04

23

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27

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53

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46

26

52

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Ich

raC

ycl

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6Ju

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7Ju

l,0

31

8.4

24

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6.2

0.8

1.3

34

1.5

40

09

54

.14

73

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Cy

cle

II/W

inte

r3

Oct

to5

Oct

,0

31

9.1

24

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1.1

1.0

1.3

34

4.9

60

81

64

3.8

97

7.3

Cy

cle

III/

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rin

g5

Ap

rto

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pr,

04

21

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8.8

4.3

27

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64

75

02

00

3.9

18

0.4

Cy

cle

IV/S

um

mer

19

7Ju

nto

21

Jun

,0

42

4.9

50

4.5

30

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40

80

82

65

4.4

80

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Lh

rA

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ort

Cy

cle

I/M

on

soo

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2Ju

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24

Jul,

03

15

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1.8

16

.20

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26

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93

10

52

.37

63

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Cy

cle

II/W

inte

r8

Oct

to1

0O

ct,0

31

7.9

19

.82

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7.7

0.9

1.3

33

2.2

15

01

12

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16

4.3

Cy

cle

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rin

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Ap

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Ap

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34

01

61

11

72

.05

80

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Cy

cle

IV/S

um

mer

22

Jun

to2

4Ju

n,

04

26

38

2.9

24

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38

51

82

24

2.6

82

Springer

244 Environ Monit Assess (2007) 127:237–252

Tabl

e5

Th

em

ean

con

cen

trat

ion

(48

h)

show

ing

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ial

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ral

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7S

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9S

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35

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20

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37

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Cy

cle

II/W

inte

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Nov

to7

Nov,

03

20

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18

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26

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32

14

64

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83

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Cy

cle

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Sp

rin

g5

Mar

to7

Mar

,0

42

3.8

28

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1.2

34

1.9

33

92

31

4.8

87

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Cy

cle

IV/S

um

mer

23

May

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5M

ay,

04

24

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9.9

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28

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45

.84

10

30

25

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4.7

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den

Cy

cle

I/M

on

soo

n1

0S

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12

Sep

,0

31

7.1

26

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8.3

2.0

0.9

31

6.5

34

12

20

4.8

37

6.3

Cy

cle

II/W

inte

r7

Nov

to9

Nov,

03

25

.42

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5.5

16

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32

.22

45

16

43

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84

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Cy

cle

III/

Sp

rin

g7

Mar

to9

Mar

,0

42

92

76

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21

.51

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39

32

11

89

4.3

19

1

Cy

cle

IV/S

um

mer

25

May

to2

7M

ay,

04

27

.03

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26

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31

20

34

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7.5

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un

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on

13

Sep

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ep,0

31

7.9

20

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.92

7.0

0.6

0.9

32

7.2

33

31

98

3.9

78

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Cy

cle

II/W

inte

r1

0N

ov

to1

2N

ov,

03

19

.22

3.6

5.7

24

.60

.81

.23

33

.02

89

20

13

.28

2.5

Cy

cle

III/

Sp

rin

g1

0M

arto

12

Mar

,0

42

52

86

.22

71

.11

.63

44

28

82

10

3.1

89

Cy

cle

IV/S

um

mer

28

May

to3

0M

ay,

04

26

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7.6

7.6

30

.31

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54

.53

00

22

23

.58

6.2

Ko

ran

gi

Cy

cle

I/M

on

soo

n1

6S

epto

18

Sep

,0

31

6.5

22

.34

.62

4.4

0.9

0.9

33

9.9

36

72

39

3.8

18

2.7

Cy

cle

II/W

inte

r1

4N

ov

to1

6N

ov,

03

20

.32

2.2

6.5

18

.20

.81

.23

41

.03

29

20

83

.25

82

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Cy

cle

III/

Sp

rin

g1

3M

arto

15

Mar

,0

42

62

77

.02

21

.11

.43

49

33

72

17

3.2

87

Cy

cle

IV/S

um

mer

30

May

to1

Jun

,0

42

7.5

33

.08

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4.5

1.4

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34

8.9

34

62

28

3.2

85

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Giz

riC

ycl

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on

17

Nov

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9N

ov,

03

18

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0.6

2.6

25

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.40

.73

24

.22

75

21

93

.35

84

.9

Cy

cle

II/W

inte

r1

9N

ov

to2

1N

ov,

03

21

.82

3.4

3.2

20

.30

.71

.03

33

.02

58

19

93

.03

82

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Cy

cle

III/

Sp

rin

g1

6M

arto

18

Mar

,0

42

72

94

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40

.81

.33

45

26

22

04

3.1

85

Cy

cle

IV/S

um

mer

2Ju

nto

4Ju

n,0

42

7.8

32

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0.9

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34

9.5

28

72

16

3.5

84

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Sit

eC

ycl

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Mo

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on

22

Sep

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4S

ep,0

31

8.1

21

.54

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6.7

0.5

1.0

33

0.8

36

02

15

5.8

78

3.1

Cy

cle

II/W

inte

r2

2N

ov

to2

4N

ov,

03

22

.62

5.4

4.5

27

.61

.41

.03

36

.02

87

19

84

.36

76

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Cy

cle

III/

Sp

rin

g1

8M

arto

20

Mar

,0

42

72

95

.03

11

.21

.73

44

28

21

89

4.2

38

3

Cy

cle

IV/S

um

mer

4Ju

nto

6Ju

n,0

42

7.0

32

.67

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8.1

1.5

2.0

35

0.1

29

62

01

4.4

81

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Naz

imab

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ycl

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on

25

Sep

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7S

ep,0

31

2.6

23

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5.0

0.8

0.9

33

4.1

28

51

85

3.2

57

8.6

Cy

cle

II/W

inte

r2

5N

ov

to2

7N

ov,

03

17

.41

8.5

4.4

18

.41

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.03

38

.82

54

15

13

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75

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Cy

cle

III/

Sp

rin

g2

1M

arto

23

Mar

,0

42

42

45

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20

.91

.63

54

20

81

63

3.1

28

6

Cy

cle

IV/S

um

mer

7Ju

nto

9Ju

n,0

42

3.0

29

.67

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1.1

1.0

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35

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28

01

84

3.7

85

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F.B

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aC

ycl

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on

28

Sep

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5.8

25

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5.2

0.7

1.1

33

8.2

26

81

72

4.2

18

1.4

Cy

cle

II/W

inte

r6

Nov

to8

Nov,

03

16

.82

5.9

4.6

25

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.73

25

.72

86

18

83

.67

78

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Cy

cle

III/

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rin

g2

4M

arto

26

Mar

,0

42

2.8

30

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8.8

0.7

1.1

33

3.7

26

81

72

3.7

83

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Cy

cle

IV/S

um

mer

9Ju

nto

11

Jun

,0

41

8.3

35

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1.0

1.2

34

5.8

28

61

88

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Bal

oo

chC

ycl

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on

1O

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3O

ct,0

31

5.9

20

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6.2

0.7

0.8

32

2.5

28

91

60

4.1

38

2.2

Cy

cle

II/W

inte

r2

8N

ov

to3

0N

ov,

03

20

.72

2.4

3.4

22

.80

.80

.73

25

.72

45

14

63

.97

78

.8

Cy

cle

III/

Sp

rin

g2

7M

arto

29

Mar

,0

42

5.7

28

.44

.22

5.8

0.6

1.1

33

7.7

25

51

39

3.9

80

Cy

cle

IV/S

um

mer

12

Jun

to1

4Ju

n,0

43

0.7

37

.25

.82

6.2

0.6

1.0

35

1.4

27

91

56

3.2

82

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Jau

har

Cy

cle

I/M

on

soo

n4

Oct

to6

Oct

,0

31

9.8

21

.73

.52

4.1

0.6

0.7

32

4.7

22

81

28

4.9

88

3.8

Cy

cle

II/W

inte

r3

0N

ov

to2

Nov,

03

17

.72

2.1

3.2

23

.81

.00

.93

27

.32

10

17

84

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80

.4

Cy

cle

III/

Sp

rin

g2

9M

arto

31

Mar

,0

42

2.6

26

.64

.02

7.8

1.2

0.8

34

0.3

22

01

86

4.1

68

7.4

Cy

cle

IV/S

um

mer

14

Jun

to1

6Ju

n,0

42

4.9

27

.35

.62

5.5

1.2

1.4

34

3.9

24

72

02

4.4

68

2.2

Springer

Environ Monit Assess (2007) 127:237–252 245

Tabl

e6

Th

em

ean

con

cen

trat

ion

(48

h)

show

ing

spat

ial

and

tem

po

ral

vari

atio

no

fd

iffe

ren

tgas

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cles

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ead

No

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vel

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28

Jul

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0Ju

l,0

31

4.0

16

3.0

12

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.93

05

.72

44

13

12

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4.8

Cy

cle

II/W

inte

r1

5O

ctto

17

Oct

,0

31

51

93

.61

80

.60

.93

09

28

21

68

2.1

74

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Cy

cle

III/

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rin

g1

7F

ebto

19

Feb

,0

41

6.1

20

5.0

22

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.60

.93

14

.33

08

17

91

.75

75

.9

Cy

cle

IV/S

um

mer

9M

ayto

11

May

,0

41

6.8

23

5.5

26

.81

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23

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92

21

52

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on

30

Jul

to1

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g,0

31

3.4

22

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13

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95

.22

73

17

93

.46

70

.2

Cy

cle

II/W

inte

r1

7O

ctto

19

Oct

,0

31

5.8

23

4.2

16

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08

31

31

68

3.0

27

2

Cy

cle

III/

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rin

g1

9F

ebto

21

Feb

,0

41

6.8

23

4.8

20

.30

.81

.13

08

32

71

31

2.9

76

.8

Cy

cle

IV/S

um

mer

11

May

to1

3M

ay,

04

18

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65

21

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31

24

16

21

53

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Rad

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Au

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31

52

11

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20

.51

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11

27

31

31

3.5

71

.9

Cy

cle

II/W

inte

r2

0O

ctto

22

Oct

,0

31

5.8

22

.02

.01

50

.61

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15

29

01

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Springer

246 Environ Monit Assess (2007) 127:237–252

Tabl

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Springer

Environ Monit Assess (2007) 127:237–252 247

Fig. 3 Mean concentration (48 h) of TSP in six major cities of Pakistan

Fig. 4 Max. concentration of PM10 in six major cities of Pakistan

all 33 sampling sites. Consequent upon the conversion

of gasoline driven engines to CNG and LPG (Liquid

Petroleum Gas) usage, NOx emissions has increased

and presently it is second to CO as the most prevalent

pollutant.

SO2 is the principal pollutant supposed to be caus-

ing acid deposition after being oxidized to sulfuric

acid. Coal burning is the single largest source of at-

mospheric SO2, accounting for about 50% of an-

nual global emissions in recent years. Man-made sul-

phur emission in the form of SO2 arises mostly from

combustion of fuel containing trace amounts of inor-

ganic and organic sulphur. The maximum daily SO2

concentration recorded in Lahore was 53 ppb and at

Quetta around 46 ppb compared to other cities. How-

ever ambient mean SO2 levels in urban areas are

within USEPA permissible limit of 140 parts per billion

(Fig. 7).

Springer

248 Environ Monit Assess (2007) 127:237–252

Fig. 5 Mean conc. of PM10 in six major cities of Pakistan

Fig. 6 Mean conc. of NOx in six major cities of Pakistan

Fig. 7 Mean (48 h) conc. of SO2 in six cities of Pakistan

Springer

Environ Monit Assess (2007) 127:237–252 249

Fig. 8 Mean average concentration of ozone in six major cities of Pakistan

Fig. 9 Mean conc. of CO in six major cities of Pakistan

Since 1980 the maximum growth has been noted in

2-stroke vehicles i.e delivery vans which are 1190%,

followed by Motor cycles 634% and Rickshaws 192%.

Diesel trucks and buses have also increased at an alarm-

ing rate of 200–300% since 1980. Diesel vehicles due

to overloading, faulty injection nozzles and weak en-

gines emit excessive graphitic carbon (visible smoke).

Available diesel in local market contains 1 ∼ 1.5% sul-

phur (NTRC, 1995).

Comparatively low sulphur dioxide was recorded

at Karachi as compared to that at Lahore and Quetta.

Although vehicle emission estimates show that a con-

siderable amount of SO2 is being emitted every-day

at Karachi due to larger number of diesel vehicles be-

ing operated in the city, but due to well established sea

breeze pattern of the city even the road side levels of

SO2 were observed well within specified limits of US

EPA of 140 ppb as 24 hr. In addition to diesel fueled

Springer

250 Environ Monit Assess (2007) 127:237–252

Fig. 10 Mean conc. of methane in six major cities of Pakistan

Fig. 11 Mean on non methane in six major cities of Pakistan

vehicles, a number of brick kilns are being operated

around Lahore, Peshawar and Islamabad (which burn

high sulphur coal) also contribute to ambient SO2 con-

centration. It has also been reported (Hameed et al.,2001) that coal based power plants across the border

contribute towards increasing SO2 levels in northeast-

ern Pakistan.

In recent years attention has also been drawn to-

wards the concentration of surface ozone. Eye ir-

ritation and increased number of asthmatic attacks

are attributed to photochemical oxidant levels around

200 ug/m3 (0.1 PPM). Most of the ozone in the tro-

posphere (lower sphere) is formed indirectly by the

action of sunlight on nitrogen dioxide. In addition to

ozone (O3), photo chemical reactions produce a num-

ber of oxidants including peroxyacetyl nitrates (PAN),

nitric acid and hydrogen per oxide. Figure 8 shows

the surface mean ozone levels in the six cities, which

are well within prescribed USEPA limit of 120 ppb.

Surface ozone has shown marked afternoon maximum

at all sampling sites between 1200–1500 h irrespec-

tive of climatic conditions and site location. The sur-

face ozone ranged from 6–41 ppb at Karachi, 8.5–

45 ppb at Lahore, 6.2–32 ppb at Islamabad, 11–25 ppb

at Quetta, 3.3–33.5 ppb at Rawalpindi, and 4–46 ppb at

Peshawar.

Springer

Environ Monit Assess (2007) 127:237–252 251

Peak ozone levels were seen dependent on seasonal

temperatures and observed numbers of sunny days in

a particular city besides availability of its precursors.

There has been 50–60 % rise in ozone levels in summer

compared to winter and monsoon months when solar

insolation is week. In Karachi the carbon monoxide lev-

els in the ambient air on the average were found vary-

ing from 3 ppm to 14 ppm along the busy urban streets.

The max. (1 h) carbon monoxide exceeded the recom-

mended limit about 10% to 30 % at Lahore, Karachi and

Quetta. Though CO demonstrated a diurnal cycle at all

the monitoring sites, but a minimum CO level always

prevailed with or without traffic indicating contribution

from indiscriminate burning of solid waste in urban ar-

eas. The maximum CO concentration (14 ppm) was

observed at Quetta. However mean CO concentration

was found within the permissible limit (10 ppm) in all

the six major cities (Fig. 9). CO varied as 1.3–12 ppm

at Lahore, 1.5–5.2 ppm at Islamabad, 1.6–13 ppm at

Karachi, 1.6–8 ppm at Rawalpindi and 2–10 ppm at

Peshawar.

Hydrocarbon (Methane & Non Methane) levels

were also measured at all sampling sites in six

cities. The 48 h average concentration of hydrocarbon

(methane & non-methane) in six cities are presented in

Figs. 10 and 11 respectively.

The maximum HC level for methane was observed

exceeding the prescribed US EPA limit of 0.24 ppm

at most of study sites. The observed range of HC

(Methane) was 0.25–2.8 ppm at Karachi, 0.45–2.2 ppm

at Lahore, 0.3–1.7 ppm at Islamabad, 0.20–1.3 ppm at

Quetta, 0.1–1.3 ppm at Rawalpindi, and 0.2–2.8 ppm

at Peshawar, while HC (Nonmethane) ranged from

0.1–3.2 ppm at Karachi, 0.6–2.5 ppm at Lahore, 0.4–

2.2 ppm at Islamabad, 0.4–1.6 ppm at Quetta, 0.6–

1.8 ppm at Rawalpindi, 0.1–1.9 ppm at Peshawar. The

main source of H-Cs (non-methane) in air has been

evaporative losses and leakages from ill-maintained

vehicles and storage facilities. Natural gas (mostly

methane) is main fuel being used in industry, as CNG

in vehicles and in day to day cooking. Frequent leakage

is mainly responsible for higher methane levels. Bio-

genic methane could also be contributing since signif-

icant agricultural cultivation is being practiced around

urban areas. Swampy area exists along the coastal

belt.

The exceptionally high HC (non methane) upto

6 ppm was recorded at Karachi just after an oil spill

from an accident of oil tanker Tasman Spirit at coastal

belt of Karachi on 13th August 2003.

Although noise is a significant environmental prob-

lem, but it is often difficult to quantify its associated

costs. The social costs of noise identified as productiv-

ity loss due to poor concentration, communication dif-

ficulties or fatigue due to insufficient rest. The biggest

source of noise pollution that affects most people is

from transport sector. According to NEQS (Pakistan’s

National Environmental Quality Standards) the level of

vehicular noise should not exceed beyond 85 decibels

(dB) at a distance of 7.5 m from source. Maximum

noise level was seen approaching 100 dB at road sides

especially in Karachi. In Lahore the max. sound level

was recorded as 90.75 dB, at Rawalpindi and Peshawar

the maximum level was 85 dB, the comparatively low

level was found at Quetta (83.2 dB) and Islamabad

(82 dB).

Conclusion

Concentrations and seasonal variations of all the crite-

ria air pollutants i.e. particulate matter (TSP, PM10),

CO, NOx , SO2, O3, as well as traffic count were de-

termined in major urban centers of the country. The

study established the air quality baseline. The lev-

els of TSP and other parameters were higher in sum-

mer (May–June) than in winter and monsoons, which

can easily be interpreted in terms of time dependent

changes primarily in meteorological conditions. The

hourly and daily mean concentrations were compared

with USEPA and NEQS prescribed limits. The high

concentrations of CO, NOx and HCs are related to

excessive generation of the gases due to high vol-

ume of traffic congestions at intersections. The par-

ticulates levels as monitored in different cities con-

tain a significant amount of vehicular soot besides

crustal dust, especially in Lahore and Quetta. High-

est particulate load was found at Lahore. Quetta city

has been 2nd polluted city in term of TSP load.

The TSP level in these cities are 2–3.6 times higher

than the standard limits of USEPA (260 ug/m3). Sim-

ilarly PM10 were maximum in Lahore and Quetta,

which is not only due to poorly maintained vehi-

cles, traffic jams, poor engine condition and fuel qual-

ity, but also due to inadequate vertical mixing and

calm winds. The microscopic inhalable particles can

Springer

252 Environ Monit Assess (2007) 127:237–252

affect breathing and respiration, cause lung damage

and possibly cause premature death in children and

elder people. High levels of particulates especially

PM10 and gases were also attributed to large num-

ber of 2 stroke vehicles (60 to 75% gasoline vehi-

cles were motorcycles) and adulteration of petrol/diesel

and lube oil. Maximum NOx varied from 37 ppb to

58 ppb recorded at major intersections of the cities.

Sulpur dioxide and Ozone levels at all sites were with in

USEPA limits. Carbon monoxide constituted the great-

est mass of any air pollutant, followed by sulphur diox-

ide, hydrocarbons, nitrogen oxides and particulates.

Its maximum levels have been recorded alarmingly

high, ranging from 3 ppm to 14 ppm along the busy

urban streets. Maximum methane and non-methane

levels were observed exceeding the prescribe limit

of 0.24 ppm at most of study sites. Maximum street

noise level (95 dB) has been observed at Karachi and

Lahore.

Acknowledgements The authors thank National Energy Con-servation Center (ENERCON) and UNDP, Pakistan for spon-soring this study. We also thank SUPARCO staff for providingyearlong hourly data of particulate matter (TSP, PM10),CO, NOx , SO2, O3, as well as traffic count at monitor-ing locations and assistance in the operation of variousequipment.

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