Air Pollution In Pakistan Research Paper

1 Introduction

Pakistan is an urbanized country in South Asia with a population of 180.71 million [Government of Pakistan, 2012]. Air pollution in Pakistan is on the rise as a result of rapid economic growth due to industrialization. Motor vehicles, industrial activities, and coal-fired power plants are the major sources of air pollution in the country [Barber, 2008]. An average Pakistani vehicle, in general, emits about 25 times higher carbon dioxide (CO2) and carbon monoxide (CO), 20 times more nonmethane hydrocarbons (NMHCs), and 3.5 times more sulfur dioxide (SO2) than vehicles in the United States [Barber, 2008]. Usage of diesel-fueled electric generators on a large scale, due to extensive power outage in the country, is a significant factor adding to the urban air pollution burden [International Monetary Fund, 2010]. Reliance on diesel fuel by transport sector is another factor for particulate matter (PM2.5) pollution [Shyamsundar et al., 2001]. High sulfur content in the fuel, i.e., 0.5%–1% in diesel and 1%–3.5% in furnace oil, leads to higher emissions of SO2 [Asian Development Bank, CAI-Asia & Pakistan Clean Air Network, 2006]. These environmental damages cost the country an annual loss of about Rs. 365 billion, of which the urban air pollution loss is approximately Rs. 65 billion [World Bank, 2006].

Among 18 mega cities of the world, the highest average mass concentration of PM2.5 has been reported in Karachi as 668 µg m−3 [Gurjar et al., 2008]. Biswas et al. [2008] reported the average PM2.5 mass concentration to be manyfold higher in Lahore than New York City, Hong Kong, and Seoul. In a study conducted by Pak-EPA and JICA [2000] in three cities of Pakistan (Rawalpindi, Islamabad, and Lahore), the concentration of NOx and PM10 was found to be higher than WHO guideline. Hourly maximum concentration of CO has been reported as 3.3 ppm in Islamabad. The pollutants created a layer of smog reducing visibility, thus making Margalla Hills (a suburb) of Islamabad mostly invisible showing the severity of pollution within the city [Barber, 2008].

The permissible limit of ozone (O3) under Pakistan's National Environmental Quality Standards (NEQS) for Ambient Air is 180 µg m−3 for 1 h average. However, a revised standard value of 130 µg m−3 is now effective from January 2013 [Pak-EPA, 2010]. World Health Organization has set the guideline value for O3 levels at 100 µg m−3 for an 8 h daily average. The annual average standard value for SO2 is 80 µg m−3; however, 24 h average value is 120 µg m−3. The annual and 24 h average standard value for nitric oxide (NO) is 40 µg m−3; however, the annual average standard value for nitrogen dioxide (NO2) is 40 µg m−3 and 24 h average value is 80 µg m−3. The 8 h standard limit for CO is 5 mg m−3; however, 1 h limit is 10 mg m−3 [Pak-EPA, 2010]. The 24 h limit for PM2.5 is 40 µg m−3, and the annual average has been set at 25 µg m−3. A revised 24 h limit of 35 µg m−3 and annual and hourly average of 15 µg m−3 will be effective from 1 January 2013 [Pak-EPA, 2010].

The main objective of this study is to characterize the air pollutants in ambient air of Islamabad, Pakistan; examine their relationship to meteorology, and origin of air masses, i.e., back-trajectory analysis; perform a ratio analysis of the measured pollutants ([CO] to [NO], and [SO2] to [NO]) in Islamabad to gain insight in emission sources; and compare these results with available emission inventories for these pollutants. This study would be significant for regulatory agencies to conduct monitoring and plan mitigation measures in order to improve the air quality of the city. Moreover, this data set would be of immense value to the urban, regional, and global air quality modeling community.

1.1 Description of Sampling Site

Islamabad, Federal Capital of Pakistan, is located at 33°26′N 73°02′E at the foot of the Margalla Hills with 1.21 million inhabitants. It covers an area of 906 km2 with an extension of 2717 km2 of Margalla Hills in the north and northeast. Variation of elevation ranges from 507 m in the plains of city to 1604 m in the hill areas of Islamabad.

Islamabad falls in semiarid zone with hot humid summers followed by monsoon and cold winter. During summer, polluted air masses are advected predominantly into Islamabad from Industrial Estate and Rawalpindi (in the southeast). Vehicular emission, energy production, and industrial processes are other major source of air pollution.

The air monitoring station (AMS) located at Pakistan Environmental Protection Agency (Pak-EPA), Islamabad, was used for data collection.

1.2 Methodology

Hourly air quality monitoring data for 5 years (2007–2011) were collected by the Federal and Provincial Environmental Protection Agencies using automated fixed and mobile AMSs for ambient concentration of six major pollutants, and meteorology. The AMSs are equipped with Combined Wind Vane, Anemometer (Koshin Denki Kogyo Co, Ltd.; Model KVS 501), Thermohygrometer (Koshin Denki Kogyo Co, Ltd.; Model HT-010), Solar Radiation Meter (Koshin Denki Kogyo Co, Ltd.; Model SR-010), and Data Logging System (Horiba, Ltd.; Model Special).

Data Logging System at Federal and each Provincial EPA retrieves the air quality data from AMSs through data processing software. The ambient air quality parameters such as CO, oxides of nitrogen (NOx, i.e., NO and NO2), SO2, O3, fine PM2.5, and hydrocarbons (total hydrocarbons, NMHCs, and methane, CH4) were determined using specific and prescribed analyzers in AMSs.

CO Monitor (Horiba Ltd.; Model APMA-370), using nondispersive infrared ray method (ISO4224) with detection limit of 0.1 ppm and measuring range of 0–50 ppm, was used to measure CO ambient concentration.

Chemiluminescence (ISO7996) method was used to determine NOx, NO, and NO2 concentrations using NOx monitor (Horiba Ltd.; Model APNA-370) with detection limit of 0.5 ppb and range of 0–1 ppm. Thermal converter in NOx monitor is known to introduce error in accurate concentration determination of ambient NO2 and NOx. As all other reactive oxidized nitrogen compounds also get converted to NO during thermal conversion, we propose using NOy′ to denote NOx. NOy′ may be used as a surrogate for ambient NOy (=NO + NO2, HONO + HNO3 + PAN + NO3 + ···) concentration.

SO2 was measured by SO2 monitor (Horiba Ltd.; Model APSA-370) with detection limit of 1 ppb and range of 0–0.5 ppm through U.V. fluorescence method (ISO10498).

Ozone Monitor (Horiba Ltd.; Model APOA-370) with detection limit of 0.5 ppb, range of 0–1 ppm, and working on the principle of UV photometry method was used to determine O3 concentration in ambient air.

PM2.5 is measured by Dust Analyzer (Horiba Ltd.; Model APDA-370) with 0–5 mg m−3 range through β-ray absorption method (ISO6349).

Converter oven method applied in Hydrocarbon Monitor (Horiba Ltd.; Model APHA-370) with detection limit of 0.1 ppmC and range of 0–50 ppmC was used for hydrocarbon monitoring.

Backward air trajectories (48 h) were determined by using the Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which has been developed by the US National Oceanic and Atmospheric Administration's (NOAA) Air Resources Laboratory (ARL). Archived three-dimensional meteorological data are used by HYSPLIT model to compute the trajectories. Gridded Meteorological Data Archives from Global Data Assimilation System (GDAS) of National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) was used to calculate the back trajectories. The trajectories were computed for a height of 500, 1000, and 1500 m AGL (above ground level) for different time durations. Label interval was set to be 6 h to track the path of trajectory. The back trajectories were calculated for Pakistan with a buffer zone including some part of China, India, Afghanistan, Iran, and Arabian Sea.

2 Results and Discussion

Ambient air quality data of Islamabad for 5 years (2007–2011) were analyzed for determination of average concentration of representative six air pollutants. The hourly data for each pollutant collected were analyzed for average annual, seasonal, and diurnal variation. Analysis of various pollutants has also been conducted in order to find out the role of precursors, possible emission sources, meteorology, origin of air masses (based on back-trajectory analysis), and background concentrations.

2.1 Meteorology

The climate of Islamabad has a semiarid climate with warm to hot humid summers followed by monsoon season and a cold winter. In general, May and June are the hottest months with average high temperature of ∼38°C (100.4 F) observed in June. In winter season, the average low temperature of ∼2°C (35.6 F) may be observed in January. Fog occurs in Islamabad during the winter season. Monsoon season brings heavy rainfall and thunderstorm during July to September. In Islamabad, temperatures vary from cold to mild, routinely dropping below zero. In the hills (Margalla Hills) there is sparse snowfall. The highest temperature recorded was 46.5°C (115.7 F) in June, whereas the lowest temperature was −4°C (24.8 F) in January. On 23 July 2001, Islamabad received a record breaking 620 mm (24 inch) of rain fell in just 10 h. It was the heaviest rainfall in 24 h in Islamabad and at any locality in Pakistan during the past 100 years [Hameed, 2007].

2.2 Average Concentration of Pollutants

The average annual mean concentration of pollutants in Islamabad computed for PM2.5, NO, CO, and O3 concentrations is presented in Figures 1a–1d. As the CO standard is either 1 h or an 8 h standard, and the O3 is 1 h average, Figures 1e and 1f provide the numbers of exceedances of the ambient concentrations for CO and O3 during 2007–2011. The annual average mass concentration of PM2.5 exceeds the Pakistan's NEQS of 25 µg m−3 in each year (2007–2011). In Islamabad, the annual average PM2.5 mass concentration is 81.1 ± 48.4, 93.0 ± 49.9, 47.8 ± 33.2, 79.0 ± 49.2, and 66.1 ± 52.1 µg m−3 during 2007–2011, respectively, and the highest hourly values observed were 303 µg m−3 during December 2007, 495.0 µg m−3 during November 2008, 259.8 µg m−3 during September 2009, 456.0 µg m−3 during October 2010, and 379.0 µg m−3 during January 2011. Such high mass concentrations of PM2.5 may be attributed to primary sources such as black carbon aerosols [Viidanoja et al., 2002; Husain et al., 2007], and secondary formation (i.e., gas-to-particle conversion) also contributes to PM2.5 [Raja et al., 2010]. High PM2.5 is associated with adverse human health effects [Petrovic et al., 2000].

Annual mean concentration of NO is also higher than the NEQS of 40 µg m−3 during 2007–2010, indicating the contribution of vehicular NO emissions. The hourly average concentration of CO for all the years is below the NEQS of 10 mg m−3. On some occasions, the hourly average O3 concentration exceeds the NEQS primarily during the day during summer months (e.g., numbers of exceedances of O3 concentrations during 2007–2011 were 121, 277, 324, 107, and 462, respectively).

2.3 Correlation of Air Pollutants

Figure 2 shows the correlation of CO with PM2.5 during 2007–2011. As diesel combustion (from heavy duty vehicles and electric generators) is considered to be a major source of both CO and PM2.5, the correlation between PM2.5 and CO was used to determine the possibility of similar source for these two pollutants. Figure 2 also shows that PM2.5 is significantly correlated (r = 0.61; p-value ≤ 0.01) with CO. From this plot, it may be inferred that the sources other than automobiles (i.e., electric generators) also contribute toward primary and secondary PM2.5 in the troposphere (as CO is primarily emitted from the automobiles).

Both CO and the nitrogen oxides have many anthropogenic sources in common including mobile sources (i.e., automobiles) and point sources (i.e., energy production). It is therefore interesting to examine the relationships of these species in ambient air, especially in an urban environment where the photochemical transformations, including removal mechanisms, may be negligible, and then check these relationships against emission inventories. Mobile sources often have the characteristic of high CO/NO ratios and low SO2/NO ratios, whereas higher SO2/NO ratios and lower CO/NO ratios are associated with point sources (energy production). Based on ambient data, Figure 3 provides the relationship between CO and NO, and between CO and reactive nitrogen species, NOy′, in Islamabad during 2007–2011. A linear regression of hourly average CO and NO, and CO and NOy′ was performed, which shows a significant (p-value ≤ 0.01) correlation between CO and NO concentrations ([CO] = 10.13[NO] + 511.3; r2 = 0.76) and a significant (p-value ≤ 0.01) correlation between CO and NOy′ concentrations ([CO] = 9.84[NOy′] + 256.8; r2 = 0.78). From this ratio analysis, relative background concentrations may be determined by examining the intercept of the regression lines. The regression curves reveal a background CO concentration of ∼300 to ∼600 ppbv in the Islamabad urban area. This is similar to Raleigh, NC, USA, urban site value of 470 ± 52 ppbv [Aneja et al., 1997]; however, CO background concentration in New Delhi, India, has been observed as approximately 1693 ppbv [Aneja et al., 2001]. Moreover, relative source strengths like mobile sources versus point sources may also be suggested by examining the slope of the regression lines, and compared with emissions inventory. Klimont et al. [2013] and ECCAD (Emissions of atmospheric Compounds & Compilation of Ancillary Data, 2014,, hereinafter referred to as ECCAD, online report, 2014) have provided an emissions inventory (developed for the year 2010) for CO, SO2, and NOx. Table 1 compares and contrasts the emissions from this inventory by examining the relationship between ambient CO and NOx, and between ambient SO2 and NOx for 2007–2011 in Islamabad, Pakistan. It also compares and contrasts with CO and NOx relationship observed in Denver, CO, USA [Parrish et al., 1991]; Boulder, CO, USA [Goldan et al., 1995]; Raleigh, NC, USA [Aneja et al., 1997]; and New Delhi, India [Aneja et al., 2001]. Based on ratio analysis of CO and NOx, Parrish et al. [1991] reported values of 8.4, 7.8, and 10.2 for mobile sources in the Eastern United States, Pennsylvania area, and Western United States, respectively. Given the average ratio of about 10 (i.e., the slope of the regression line) in Islamabad, it appears that mobile sources contribute more to the concentrations of CO and NOx than point sources.

Eastern United Statesa,b4.30.94
Point sources0.951.8
Pennsylvania areaa,c2.61.7
Point sources0.82.3
Western United Statesa,d6.70.41
Point sources1.21.1
Denver metropolitan areaa,e7.30.19
Point sources0.180.44
Raleigh, NCf16.30.73
New Delhi, Indiag500.58
This study  
Based on 2010 Emission Inventoryh,i  
Point sources0.637.0
Based on ambient data100.01

Monthly averages of SO2 concentration (1 µg m−3 SO2 = 0.38 ppbv) are plotted in Figure 4. Sulfur dioxide concentrations are below Pakistan's 24 h average NEQS value of 120 µg m−3 during the measurement period. A linear regression of hourly average SO2 and NO concentrations (Figure 5) was performed ([SO2] = 0.01[NO] + 1.73; r = 0.4). The ratio analysis of SO2/NO for Islamabad (slope ∼0.01) (Table 1) indicates that point sources are contributing to SO2 in the city, also corroborated by the emissions inventory for Islamabad [Klimont et al., 2013; ECCAD, online report, 2014].

Figure 6 provides the correlation between PM2.5 and NO in Islamabad. The association between PM2.5 and NO is significantly positive (p-value ≤ 0.01; r = 0.5), suggesting that there is a contribution of NO in secondary production of PM2.5. Other precursors (e.g., SO2) and primary sources (e.g., diesel generators) also lead to the PM2.5 burden in the city as well.

From the correlation among pollutants such as CO, NO, SO2, and PM2.5 (Figure 7), it may be inferred that the pollution measured in Islamabad is primary in nature having more association among species with direct emissions. The correlation between PM2.5 and CO concentrations is an indication of direct emissions, most likely from transport sector and fresh emissions from the industrial areas within the city (Figure 7a). The CO concentrations are also owing to the chemical conversion of volatile organic compounds (VOCs) via photochemistry, and some fraction of the PM2.5 also originates from gas-to-particle conversion of SO2 and NOx. For the NOx emissions from the transport sector, the NO is >90% of the emissions [Vallero, 2008] and readily converts to NO2 in the presence of sunlight. The strong correlation between NO and SO2

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