a preliminary assessment of air quality index (aqi) … · air pollution has long been recognized...

INTERNATIONAL JOURNAL OF ENVIRONEMNTAL SCIENCES

Volume 5, No 3, 2014

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4399

Received on September 2014 Published on November 2014 623

A preliminary assessment of air quality index (AQI) along a busy road in

Faisalabad metropolitan, Pakistan Wasim Javed1, 2, Ghulam Murtaza1, Hamaad Raza Ahmad1, M. Mazhar Iqbal1

1- Institute of Soil & Environmental Sciences, University of Agriculture, Faisalabad, Pakistan

2- Mechanical Engineering Program, Texas A&M University at Qatar, Doha, Qatar [email protected]

doi: 10.6088/ijes.2014050100055

ABSTRACT

Ambient air quality status along a busy road in the residential area of Faisalabad metropolitan was determined and air quality index (AQI) values were calculated regarding the selected air pollutants during a month (May, 2012). Overall deteriorated ambient air quality in terms of airborne particulate matter (PM), ambient smoke and noise levels was observed along the selected road as most of the sites have very poor to hazardous AQI categories. Air quality is worsen at traffic congested locations and sites proximal to industries where pollutant levels highly exceeding the standard values proposed by US-EPA. Heavy duty diesel vehicles i.e. trucks and buses along with two stroke auto rickshaws were found most polluting vehicles. It can be concluded that the atmospheric environment along the road under study is considered as highly polluted that can imply potential health threat to residents and immediate measures should be taken to reduce the concentrations of pollutants and thus improving the air quality. Keywords: AQI, pollutants, particulate matter, smoke, sound levels.

1. Introduction

Air pollution has long been recognized as a potentially lethal type of environmental pollution. In developing countries like Pakistan, a ‘no care’ attitude and total neglect along with ever-growing demands over the years have made air pollution as a most alarming and hazardous issue. The continuous and rapid increase in industrialization, urbanization and motorization along with concomitant growth of energy use have resulted this menace (Colbeck et al, 2010; Shah et al, 2012). Airborne particulate matter (PM), a major indicator of air quality in a given area is a most concerning air pollutant in all urban areas of Pakistan. PM is regulated globally under the permissible standards based on size fractions ranging from PM2.5 (respirable fine particles, aerodynamic diameter ≤ 2.5 µm) to PM10 (inhalable coarse particles, aerodynamic diameter ≤ 10 µm) to TSP (total suspended particles, aerodynamic diameter ≤ 100 µm). PM4

(aerodynamic diameter ≤ 4 µm) is also a known respirable size fraction (Mar et al, 2004). Continuous vehicular emission is the major cause of PM pollution in urban environment, which is a matter of grave concern due to its detrimental impact on ambient air quality as well as on the human health. Uncontrolled and rapid vehicular growth on inadequate and badly cared roads, old and poorly maintained vehicles, undisciplined drivers along with a bad traffic management have exacerbated impacts on urban air pollution. Smoke is by product of incomplete combustion and consists of a mixture of gases, liquid and solid tiny particles. On road vehicle and industries are two major contributors of smoke pollution in urban areas of the world.

A preliminary assessment of air quality index (AQI) along a busy road in Faisalabad metropolitan, Pakistan

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International Journal of Environmental Sciences Volume 5 No.3, 2014 624

Smoke, whether emitted from automobiles or from industries is leisurely hazardous for human health acting in a hushed mode so it is called silent killer (Ilyas, 2006; Sami et al, 2006). Presently, noise pollution has also become an urban environmental problem and noise nuisance is a serious health hazard in urban areas due to rapid increase in road traffic (Garg et al, 2007; Farid et al, 2013). The air quality index (AQI) is a grading scale for reporting the ambient air pollution status monitored at particular location during a certain monitoring period (e.g., one, 8 or 24 h). The main purposes of AQI are to report and caution the public about the risk of exposure to daily pollution levels and to implement mandatory regulatory measures (Gurjar et al, 2008). The greater the AQI value, higher the level of air pollution and the larger human health risk. The AQI reflects some aspect(s) of air quality related to a particular pollutant and it is generally related with pollutant ranges, quality category descriptors (e.g., good, moderate, poor or hazardous) and several associated messages so that meaning is easily understood by the public. So it can be used to describe the impact of the pollutants on human health and the environment (Gurjar et al, 2008; US-EPA, 2012).

2. Material and methods

2.1 Study area and sampling locations

The study area where the measurements took place is on the North-East suburbs of Faisalabad city, that is the third largest city in Pakistan and a major industrial hub that’s why popularly known as the Manchester of Pakistan. It is situated between longitude 73˚ to 74˚ east, latitude 30˚ to 31.15˚ north at an elevation of 184 m above sea level covering an area of 1230 km2 supporting a population of over 4 million. For the present survey study in Faisalabad, a representative area along a road from Jaranwala to Chiniot via Khurrianwala and Chak Jhumra towns was selected. The selection of this road is made considering the geography and importance of the road in the District and also has a range of anthropogenic activities such as the varying density of road traffic, industries, commercial, urban and agricultural activities taking place along the road. Among the various main roads in the district, Jaranwala - Chiniot road has the maximum concentration of these activities along it. For assessing the air quality status along this road, the sampling sites at every 3 km distance along the road and a total of 26 sites were selected as presented in Figure 1. Figure 1 shows the major industries in the proximity of this heavily trafficked road include textile mills, brick kilns, a power plant, and a refinery.

2.2 Monitoring procedure For the assessment of the air quality status along the road at selected sampling points the following air quality parameters were monitored during a month (May, 2012). Ambient PM of different size fractions (TSP, PM10, PM4, PM2.5) were monitored with a MicroDust Pro Real Time Particulate Monitor (model HB3275-07, Casella CEL, UK) on 1-h average basis for four hours at each sampling point. The instrument has detection range of 0.001-2500 mg m-3 with a resolution 0.001 mg m-3. This instrument measures PM concentrations using a near forward angle (12- 20°) infrared light (880 nm) scattering technique. A polyurethane foam (PUF) filter adapter with a flow rate of 3.5 L min-1 was used for size-selective monitoring of airborne PM. The PUF filter loaded into the adapter determined the size of PM being monitored. Filters are available for PM10, PM4, PM2.5 size fractions.


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Figure 1: Map showing the location of sampling sites

The smoke parameters; smoke opacity (%) and soot density (µg m-3) were measured by using Portable Smoke/Opacity analyzer (Model Smart 1500, Galio, Canada). Noise levels were recorded with the help of a portable digital sound meter (model Model-CEL-231, England). Sampling monitors/analyzers were deployed 10 m from the center of the adjacent road and 5 m above the ground level. Traffic count on road at three sampling point was recorded continuously for four hours by using video camera and per hour average was calculated after manually counting by tally method. Smoke emissions from different types of vehicles plying on road were also measured in term of smoke opacity and density, different size fractions of PM in smoke at mouth of exhaust pipe by using the respective instruments as mentioned above. Within the constraints of available facilities, space and manpower, monitoring of selected pollutants was performed on 1-h basis for consecutive four hours during a whole month (May, 2012).

2.3 Air quality index (AQI)

In this study, the AQI related to PM pollutants proposed by US-EPA (US-EPA, 2012) was used to describe the air quality status. The AQI was calculated by relating PM mass concentrations to the relevant standards (US-EPA). The following calculation was used to drive the AQI of the sites: AQI= ¼ x (CTSP/STSP + CPM10/SPM10 + CPM4/SPM4 + CPM2.5/SPM2.5) x 100 Where, CTSP, CPM10, CPM4 and CPM2.5 and STSP, SPM10, SPM4 and SPM2.5 are the measured concentrations and US-EPA standards of TSP, PM10, PM4 and PM2.5, respectively. In general, an AQI of 100 is considered to be equivalent to the standard limit value. The intermediate value of 50 is considered equivalent to one-half the value of standard, which is the upper limit of the ‘‘good’’ category (see Table-1; adapted from US-EPA (2012). The pollutant with the highest AQI at a selected site becomes the AQI reading for that area. It can be used to describe the impact of the pollutants on human health and the environment. The pollutant with the highest AQI number becomes the ‘‘overall’’ AQI for a particular location (Gurjar et al, 2008).


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Table 1: AQI criteria associated with PM concentration ranges and quality category descriptors

AQI Concentration Breakpoints of PM (µg m-3)

AQI Category

PM2.5 PM4 PM10 TSP

0–50 0.0–12 0.0–15.4 0–54 0-104 Good

51–1 00 12.1–35.4 15.5–40.4 55–1 54 105-264 Marginal (moderate)

101–1 50 35.5–55.4 40.5–65.4 155–254 265-364 Unhealthy for sensitive groups 151–200 55.5–150.4 65.5–150.4 255–354 365-464 Poor (unhealthy)

201–300 150.5–250.4 150.5–250.4 355–424 465-524 Very poor (very unhealthy)

301–400 250.5–350.4 250.5–350.4 425–504 525-604 Hazardous

401–500 350.5–500 350.5–500 505–604 605-704 Very hazardous

> 500 > 500 > 500 > 604 > 704 Very Critical

US-EPA

standards 35 - 150 260

Source: US-EPA (2012) and Gurjar et al. (2008)

3 Results and discussion

3.1 Aerosol particulate matter (PM)

The average mass concentrations of airborne PM at all selected sampling points along the road were measured and presented in Table-2. The spatial variation of PM along the entire length of the road was highly significant (p<0.01) tested by one way Analysis of Variance (ANOVA) -- all of the sampling points were significantly different from each other. In general, the average PM concentrations were higher in urban and industrial areas with high traffic density than the rural sites. Most of the sites have PM concentrations exceeded the permissible limits stipulated by US-EPA (US-EPA, 2012). The highest mean concentrations of PM of all sizes (TSP, PM10, PM4 and PM2.5) were observed at S9 followed by S8. These two sites are industrial areas having industrial clusters on both sides of the road. These sites also exhibited high traffic density mainly consist of heavy duty diesel vehicles especially trucks, tractors, trailers, vans and local buses. Roads are poorly maintained, unpaved and dusty with limited vegetation along the sides. The industrial processes especially combustion boilers fueled by coal and wood, and heavy electric generators fueled by diesel are the main PM pollution contributor. High PM concentrations were also recorded at S11, S26 and S1, the major road intersections that are heavily crowded by traffic round the clock and busy commercial centers. Traffic is congested due to high density of heavy vehicles and encroachment on both sides of the road. Vehicles are typically old and poorly maintained overloaded, smoky employing inefficient engines that use poor quality fuel (Colbeck, et al, 2010; Shah et al, 2012; Ashraf et al, 2013). Contribution to PM at these sites was mainly due to re-suspended road dust and emissions from public and commercial transportation (i.e. buses, trucks, vans, auto-rickshaws and two-wheelers) (Ilyas, 2006; Sami et al, 2006; Colbeck et al, 2011). Similarly, elevated levels of PM at S5 and S22 (rural sites) were likely due to emissions from brick kilns located upwind and proximal to these sampling points. Interestingly, site S18 (M3


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motorway intersection), which is also a high traffic density area, observed the comparatively medium levels of PM. This site is located on the motorway bridge which is an open space, so strong winds caused the dispersion and dilution of the PM generated in the area. While, at typical rural sites (S19, S21, S17, S23 and S2), the PM concentrations were recorded reasonably low because these areas have relatively less vehicular and industrial activities in the surroundings. The categorization of ambient air quality with respect to the AQI is presented in Table-2. The overall air quality along the road was found to be very poor (very unhealthy) having the range from poor (unhealthy) to very hazardous at all selected road sites with the respective AQI values of 158-909. The AQI analysis (Table-2) showed that 34.6% of the sites were very unhealthy for inhabitants having very poor AQI category, 53.8% were unhealthy (poor AQI category), while 11.6% were considered as hazardous or very hazardous, which is a cause of concern for the inhabitants of these areas (Gurjar et al, 2008). It can be seen that the maximum deteriorated air quality was recorded at S9 and S8; sites proximal to industrial complexes, having the highest AQI values of 909 and 715, respectively. In general, the average indices of air quality of urban/industrial areas were found higher than the rural areas. The industrial sites viz. S9, S8, S10 and S13 were heavily polluted (AQI 377-909) whereas the urban/traffic sites viz. S11, S1, S26 and S10 (AQI 356-568) were moderately polluted as compare to other rural sites (AQI 158-293). The higher AQI values in the study area indicate the very high levels of air pollution and the greater the danger to human health. It is evident that the area along the road under study is significantly considered as polluted and action should be taken to reduce the concentrations of pollutants and thus improving the air quality.

Table 2: Mass concentrations of PM (µg m-3) in the study area along with AQI values and categories

No Sampling

Location

Location

Name

PM (µg m-3) AQI AQI Category

TSP PM10 PM4 PM2.5

1 S1 Jaranwala

Town 690 302 205 192 356

Very poor (very unhealthy)

2 S2 Rafan Maize

Gate 245 175 120 105 188

Poor (unhealthy)

3 S3 Attock

Petroleum 280 210 180 133 247

Poor (unhealthy)

4 S4 Ali Filling

Station 266 190 145 125 219

Poor (unhealthy)

5 S5 Brick klin, Ch#102RB

650 320 280 245 431 Very poor (very

unhealthy)

6 S6 Ali Pur Bangla,

430 260 188 160 293 Very poor (very

unhealthy)

7 S7 Awan Petrol

Pump 496 309 241 196 360


8 S8 Fatima, Kamran,

MSC Textiles 1490 820 410 322 715 Hazardous

9 S9 Ali, Kamal, Q. Rasheed

1665 995 530 445 909 Very hazardous


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3.2 Ambient smoke and noise levels The spatial variation of ambient smoke opacity, soot density and noise levels along the road was highly significant (p<0.01) tested by one way ANOVA (Table-3). The maximum smoke opacity and soot density levels were recorded at S5 and S22 rural sites, which have brick kilns to upwind direction in the close vicinity. So, main contribution to smoke pollution at these sites is emissions from local brick kilns. In these brick kilns low grade coal, wood, used

Textiles

10 S10 Amtex,Faisal,

Inter Loop, Textiles

820 462 257 205 431 Very poor (very

unhealthy)

11 S11 khurrianwala Intersection

1065 580 395 240 568 Hazardous

12 S12 Outskirt of

Khurrianwala 595 260 185 160 307


13 S13 Bismillah

Textile 450 332 256 210 377


14 S14 Bypass

Intersection, Sikandar pur

350 219 160 145 254 Very poor (very

unhealthy)

15 S15 Chak# 188RB 563 325 243 184 361 Very poor (very

unhealthy)

16 S16 Chak Jhumra

Intrsection 493 316 262 145 335

Poor (unhealthy)

17 S17 Mubarak

Flour Mill 210 182 130 90 180 Very poor

18 S18 M3

Motorway Intersection

366 281 233 195 338 Very poor (very

unhealthy)

19 S19 Remote rural area (control)

145 125 110 96 158 Poor

(unhealthy)

20 S20 Khichian- Millat road Intersection

493 226 198 152 293 Very poor (very

unhealthy)

21 S21 Barnalla 220 140 113 95 169 Poor

(unhealthy)

22 S22 Brick kiln at Jhang Branch

662 310 288 232 425 Very poor (very

unhealthy)

23 S23 Chak Jappay 250 150 132 101 187 Poor

(unhealthy)

24 S24 Kot Khudayar 306 201 184 125 244 Poor

(unhealthy)

25 S25 Moza Talib 296 212 195 105 236 Poor

(unhealthy)

26 S26 Chinoit Town 865 450 295 201 449 Very poor (very

unhealthy)

Range 145-1665

125-995

110-530

90-445

158-909

Overall mean 552 321 228 177 347 Very poor (very unhealthy) CV % 68 63 43 45


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rubber tires and waste oil are mostly used for fuel. This low quality fuel, combined with inefficient combustion process produces a huge quantity of hazardous smoke into local atmosphere (Ilyas, 2006). Whereas, high smoke concentrations at S9, S8 and S10 are likely related to the emissions from the local industries (plastic, rubber and chemical industries, boilers and electric generators in textile factories) and also from high vehicular traffic. The industrial practices along the road especially coal/wood burning boilers and diesel fueled electric generators are the main contributor to smoke pollution. The on road vehicles are typically old, poorly maintained, overloaded and smoky employing inefficient engines that use poor quality fuel and high volume of vehicles having obsolete two-stroke engines, thus resulting the vehicular traffic as a major source of smoke at these sites (Sami et al, 2006; Colbeck et al, 2011; Ashraf et al, 2013). After these sites, high smoke concentration was recorded at S11, S26 and S1, the major road intersections that are heavily crowded by road traffic and commercial activities. On the other hand, at the typical rural sites (S2, S3, S15, S17, S19, S23 and S24), that were recorded reasonably low because these sites have relatively less vehicular and industrial activities in its surrounding. The minimum and maximum noise levels recorded along the road are 40 dB and 104 dB, respectively. The highest noise levels were recorded at S11 followed by S16, S10, S9 and S26, which are busy commercial and trafficked sites. The acceptable level for road traffic noise is 70 dB prescribed by US-EPA. At all these urban sites along the road, the noise levels have exceedingly surpassed the standard limit. The higher recorded noise levels at these sites are predominantly associated to high motor vehicular traffic, excessively used vehicle horns, poorly maintained vehicles and badly cared roads (Garg et al, 2007). This exceedance from standard limit is considerable and with this level on chronic extent, can cause definitive health problems to the exposed population (Farid et al, 2013). Lowest noise levels were found at remote rural sites having very less vehicular and commercial activities. The AQI was also calculated with respect to smoke opacity, soot density and noise levels at all selected sites along the road, as given in Table-3. The categorization of ambient air quality with respect to the AQI is given in Table-4. The overall air quality along the road was found to be unhealthy for local inhabitants with the range of categories from safe to very hazardous at all selected road sites. The AQI analysis indicated that 11.5% site were hazardous or very hazardous, 26.9% of the sites fall under unhealthy category of AQI, similarly 30.8% sites were marginal categorized, while only 23.1% were considered as safe regarding these pollutants concentrations. It can be seen that the industrial sites were heavily polluted whereas the urban/traffic sites were moderately polluted as compare to other rural sites, which is a cause of concern for the inhabitants of these areas.

Table 3: Smoke opacity, soot density and noise levels in the study area along with AQI categories

No Sampling

Locations

Smoke

Opacity (%)

Soot

Density

(µg m-3)

Noise

levels

(dB)

AQI Category

1 S1 1.85 2.75 86 Unhealthy

2 S2 0.03 0.5 60 Safe

3 S3 0.05 0.62 73 Marginal


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Table 4: Concentration ranges for ambient smoke opacity, soot density and sound levels for AQI categories

AQI

Category

Smoke

Opacity

Limits

Soot Density

Limits

Noise

Limits

Very hazardous > 5 > 15 > 90

Hazardous 4.1-5 12.1-15 > 85

Very poor 3.1-4 8.1-12 > 70

Unhealthy 2.1-3 4.1-8 > 65

Marginal 1-2 1-4 > 50

Safe < 1 < 1 < 50

3.3 Grouping of sampling sites

In this section, selected sampling sites are combined into different groups by taking into account the nature of the local human activities. Within each group, all sites have almost similar type of anthropogenic activities. Five groups are characterized as (i) vehicular-commercial areas having high traffic density, (ii) vehicular-rural areas having low traffic intensity, (iii) industrial areas having industrial enterprises along with heavy traffic, (iv) rural areas having typical rural sites and (v) a remote rural background site. A significant variation

4 S4 0.12 0.8 70 Marginal

5 S5 4.82 16.2 65 Very hazardous

6 S6 0.15 0.9 67 Safe

7 S7 0.9 2.8 72 Marginal

8 S8 3.25 8.75 86 Very poor

9 S9 3.73 12.3 90 Hazardous

10 S10 2.7 6.25 94 Unhealthy

11 S11 2.9 5.9 104 Unhealthy

12 S12 0.25 1.1 82 Marginal

13 S13 1.2 5.3 73 Unhealthy

14 S14 0.17 1.2 85 Unhealthy

15 S15 0.02 0.6 92 Unhealthy

16 S16 0.92 5.6 96 Unhealthy

17 S17 0.05 0.7 65 Safe

18 S18 0.9 2.2 68 Marginal

19 S19 0.01 0.07 40 Safe

20 S20 0.09 0.9 71 Marginal

21 S21 0.58 1.2 56 Marginal

22 S22 3.5 19.1 50 Very hazardous

23 S23 0.01 0.5 52 Safe

24 S24 0.02 0.8 54 Safe

25 S25 0.03 1.1 53 Marginal

26 S26 1.9 9.8 90 Very poor

Range 0.01-4.82 0.07-19.1 40-104

Overall mean 1.16 4.15 73 Unhealthy

CV % 125 124 23


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of all measured pollutants was observed between all the groups considered, tested by one way ANOVA (Figure-2). It is observed that alarmingly the highest concentration of all measured pollutants was found at the industrial areas followed by vehicular-commercial areas. Interestingly higher levels of PM and smoke concentration were also recorded at typical rural areas as compare to vehicular-rural areas, which is likely due to the presence of brick kilns in the surrounding of the rural sites as discussed earlier. On the contrary, remote rural background site had the lowest concentrations of all these pollutants, so serves as a reference site for comparison of the other polluted sites.

Figure 2: Distribution of pollutants among groups of the sampling sites

3.4 Assessment of vehicular exhaust

The emissions behavior of the on-road vehicle fleet was assessed by determining average levels of pollutants released by different vehicles plying on the road along with average (1h) traffic density at three sampling points. The vehicular exhaust smoke levels are measured in terms of smoke opacity, soot density and smoke PM of different size fractions. The on-road vehicle fleet mostly consisted of petrol fueled public transport vehicles, i.e. motor bikes (38%) and auto rickshaws (18%). The road was also observed to have diesel powered heavy duty vehicles (trucks and buses) in a significant proportion (28%) of the road traffic. The heavy duty diesel trucks represent 21% of the vehicle fleet but contribute the most of the smoke emissions, of which soot is a major component (Sami et al, 2006). All of the trucks, buses and auto rickshaws were found to be unfit as emitting the smoke of higher opacity than the Pak-NEQS smoke opacity standard (40%) for vehicular emissions. Diesel trucks also contribute significantly to emissions of smoke particles (PM) into roadside atmosphere. Auto rickshaws were found to be the highest contributors to the emissions of smoke PM of different size fractions, because of their two stroke engines. Numerically vehicle exhaust is dominated by fine particles, i.e. particles with a diameter < 2.5 µm. Cars which were mostly CNG fueled, were the least contributor to smoke emissions. Besides the


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pollutants from the exhaust emissions of the vehicles, there are also non-exhaust emissions such as dust re-suspension and particles from tires and brakes wear.

4. Conclusion

Overall deteriorated ambient air quality in terms of aerosol PM, ambient smoke and noise levels was observed along the selected road as most of the sites have very poor to hazardous AQI categories. Air quality is worsen at traffic congested locations and sites proximal to industries where pollutant levels highly exceeding the standard values proposed by US-EPA. Heavy duty diesel vehicles, i.e. trucks and buses along with two stroke auto rickshaws were found most polluting vehicles. It is recognized that combustion processes particularly vehicular emissions, contribute significantly to gaseous and PM pollutants in urban atmosphere. It can be concluded that the area along the road under study is considered as highly polluted that may imply potential health threat to residents and immediate measures should be taken to reduce the concentrations of pollutants and thus improving the air quality.

Acknowledgements

The work presented in this paper was supported by Higher Education Commission, Government of Pakistan. The study is a part of a Ph.D. research work of corresponding author. One of the authors (W. Javed) would like to thank Prof. (Ret.) Dr. Abdul Ghafoor for his guidance regarding research plan and personnel from the Air Quality Research Center, UC Davis, California, USA for writing assistance. Table 5: Average levels of pollutants released by different vehicles along with average traffic

density hour-1 at the road

Vehicle

type

No of

Sample

Vehicle

Smoke

opacity (%)

Soot

density

(µg m-3)

Smoke PM (mg m-3)

Sound

levels

(dB)***

Traffic

count

hour-1

** TSP PM10 PM4 PM2.5

Standard*

40 - 0.300 - - - 85

Truck n=6 72.6

±15.2 172 ±32

4.12 ±2.2

3.69 ±1.8

2.96 ±1.1

2.62 ±0.6

109 ±15

340

Bus n=5 46.8 ±8.7

144 ±20 3.37 ±1.8

2.66 ±1.2

1.45 ±1.0

1.32 ±0.5

112 ±12

120

Auto Rickshaw

n=6 57.8 ±9.6

135 ±15 5.03 ±2.1

4.43 ±1.6

3.41 ±1.2

3.17 ±0.9

105 ±9 290

Motor Bike

n=10 7.13 ±2.1

21.7 ±3 0.785 ±0.3

0.596 ±0.3

0.284 ±0.2

0.225 ±0.1

96 ±12 620

Car n=5 2.72 ±1.5

6.6 ±2 0.395 ±0.2

0.225 ±0.2

0.205 ±0.2

0.190 ±0.1

82 ±8 250

Average n=32 37.41 95.66 3.24 2.32 1.46 1.35 101 1620

*Pak-NEQS, (2012); **only sampled type of vehicles counted for four hours; ***Sound measured at 7.5 m from vehicle


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3. Colbeck, I., Nasir, Z.A., Ahmad, S. et al., (2011), Exposure to PM10, PM2.5, PM1

and Carbon Monoxide on Roads in Lahore, Pakistan, Aerosol Air Quality and Research, 11, pp 689-695.

4. Pak-EPA, (2012), The National Environmental Quality Standards (NEQS) for Air.

Pakistan Environmental Protection Council, Ministry of Environment, Government of Pakistan.

5. Shah M.H., Shaheen N., Nazir R., (2012), Assessment of the trace elements level in

urban atmospheric particulate matter and source apportionment in Islamabad, Pakistan,

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6. Mar T.F., Larson T.V., Stier R.A. et al., (2004), An analysis of the association between respiratory symptoms in subjects with asthma and daily air pollution in Spokane, Washington, Inhalation Toxicology, 16, pp 809-815.

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Siryad Road, Quetta, Pakistan, World Applied Science Journal, 1, pp 22-126.

8. Sami M., Amir W., Sher A., (2006), Quantitative estimation of dust fall and smoke particles in Quetta Valley, Journal of Zhejiang University Science and Biology, 7, pp 542-547.

9. Garg N.K., Gupta V.K., Vyas R.K., (2007), Noise pollution and its impact on urban

life, Journal of Environmental Research and Development, 2, pp 290-296.

10. Farid M., Ali S., Shakoor M.B. et al., (2013), Comparative study of noise levels in various areas of Faisalabad, Pakistan. Greener Journal of Environmental Management and Public Safety, 2, pp 166-171.

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a preliminary assessment of air quality index (aqi) … · air pollution has long been recognized...

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