Aerosol and Air Quality Research , 14: 1883–1896, 2014
Copyright © Taiwan Association for Aerosol Research
ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2013.12.0357
1
1,2 *
1,3
1
2
Institute of environmental sciences and engineering, National University of Sciences and Technology Islamabad, Pakistan
3
Visiting scientist, Satellite Group Max-Planck Institute for Chemistry Mainz, Germany
R2V GIS consultancy Rawalpindi, Pakistan
ABSTRACT
This paper presents the results of atmospheric Sulfur dioxide (SO
2
) column densities obtained from satellite observation over Pakistan during the time period of 2004–2012. The level-2 data product of the Scanning Imaging Absorption
Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument on-board ENVISAT-1 is used. SO
2
column densities are retrieved by applying differential optical absorption spectroscopy (DOAS) technique. The spatio-temporal distribution of SO
2
column densities along with the seasonal variation over main cities and regions of Pakistan are discussed. The Nabro volcano eruption in the year 2011 caused high SO
2
levels over East Africa, the Middle East and
South and Southeast Asia. Satellite images exhibited several episodes of volcanic SO
2
column densities transported to
Pakistan. The temporal trend in SO
2
column densities was calculated, and the significance of the data set was tested with statistical analysis. An overall increase of about 70% (8.7% per year) in SO densities over Pakistan by using satellite observation.
2
column densities over Pakistan during the time period of 2004–2012 has been calculated. This study presents the first spatial and temporal analyses of SO
2
column
Keywords: Satellite data; DOAS; Column densities; SO
2
; Pakistan; ArcGIS.
INTRODUCTION
Atmospheric Sulfur dioxide (SO
2
) results from both natural and anthropogenic sources (Krueger et al.
, 1995;
Krotkov et al.
, 1997; Khokhar et al.
, 2006; Lee et al.
,
2008) including the oxidation processes occurring in the soil and over oceans, volcanic eruptions, biomass burning metal smelters and combustion of fossil fuels (Eisinger and
Burrows, 1998; Finlayson-Pitts and Pitts, 2000). Sulfur (S) is found mostly in oil and coal and when burnt, sulfur combines with oxygen (O
SO
2
2
) and SO
2
is produced (Eisinger and Burrows, 1998; Finlayson-Pitts and Pitts, 2000; Mirza et al.
, 2013).
is very reactive and rapidly converted into other compounds such as sulfuric acid (H
4
–2
2
SO
4
), sulfurous acid
(H
2
SO
3
) and sulfate particles (SO ). These compounds are very harmful to the environment as well as to the human health (Finlayson-Pitts and Pitts, 2000; Ejaz et al.
,
2009a). SO
2
is also one of the key precursors of acid rain; hazardous for forests and many fresh water ecosystems
* Corresponding author.
Tel.: +92-51-90854308
E-mail address: fahim.khokhar@iese.nust.edu.pk around the world (Ferrari and Salisbury, 1999; Finlayson -
Pitts and Pitts, 2000; Platt and Stutz, 2008).
SO
2
life time varies from few days to several weeks (e.g.,
Finlayson-Pitts and Pitts, 2000; Platt and Stutz, 2008). In the troposphere due to its reaction with hydroxyl (OH) mainly and other oxidizing agents, it has a limited lifetime of just a few days (Finlayson-Pitts and Pitts, 2000; Platt and
Stutz, 2004). However, in the lower stratosphere its lifetime increases from several weeks to year (Eisinger and Burrows,
1998; Platt and Stutz, 2008). Lee et al.
(2009) calculated the global SO
2
life time during different seasons. They performed atmospheric chemistry transport model - GEOS-
Chem (Bey et al.
, 2001) simulations by taking into account gas-phase oxidation, dry deposition, and aqueous-phase oxidation (in clouds) of atmospheric SO
2
. Pakistan is situated between 24°–37°N and 62°–78°E and SO
2
lifetime over these latitude ranges from 17 h in summer to 40 h in winter. The longer SO
2
lifetime in winter reflects reduced oxidation by both aqueous (i.e., H
2
O
2
) and gas-phase (i.e.,
OH) processes as compare to summer time with relatively larger OH production and monsoon season.
SO
Traditionally, monitoring of atmospheric gases including
2
has been carried out via air quality monitoring stations located mainly in the cities. These stations along with tropospheric O
3
also provide SO
2
measurements and other criteria pollutants (e.g., Zerefos et al.
, 1986; Fioletov et al.
,

Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014 1884
1998). Such monitoring stations are located sparsely and monitoring on a global scale through these stations is not possible. Since last few decades the availability of satellite instruments, the atmospheric SO
2
monitoring on global scales has become possible (Eisinger and Burrows, 1998;
Khokhar et al.
, 2005; 2006; Carn et al.
, 2008). Space borne instruments such as Global Ozone Monitoring Experiment
(GOME - since 1996, Khokhar et al.
, 2005; Thomas et al.
,
2005), Ozone Monitoring Instrument (OMI - since 2004;
Krotkov et al.
, 2006), the Global Ozone Monitoring
Experiment-2 (GOME - 2 since 2008) in addition to Scanning
Imaging Absorption Spectrometer for Atmospheric
Chartography (SCIAMACHY - since 2002, Bovensmann et al.
, 1999; Lee et al.
, 2008) have opened new corridors by providing continuous monitoring of various trace and greenhouse gases globally.
Pakistan has no continuous air quality monitoring facility.
Pakistan Environmental Protection Agency (Pak-EPA) and
Pakistan Council of Scientific and Industrial Research
(PCSIR) have the facility of mobile laboratories. According to report from economic survey of Pakistan 2012–2013, all of the air quality monitoring stations are non-functional since year 2010 (ESoP, 2013). Thus, satellite instruments play a pivotal role in bridging such gaps in countries like
Pakistan.
This study has first time emphasized on the long-term record of SO
2
present in the atmosphere of Pakistan by using satellite observations. Focus is mainly on spatial and temporal variations in atmospheric SO
2
column densities over Pakistan during the time period of 2004–2012. For this purpose differential optical absorption spectroscopy
(DOAS) technique (Platt and Perner, 1979; van Roozendael,
1999; Khokhar et al.
, 2005) was applied to retrieve SO
2008; Rix et al.
, 2012) instrument.
2 column densities from nadir observations of SCIAMACHY
(Bovensmann et al.
, 1999; Khokhar et al.
, 2006; Lee et al.
,
METHODOLOGY
SCIAMACHY (Bovensmann et al.
, 1999) is a multichannel grating spectrometer on board Environmental Satellite
(ENVISAT-1) by European Space Agency (ESA) and was launched on March 1, 2002. It has ground pixel size of 60
 30 km 2 in standard scan mode. Several studies have been conducted on the degradation of SCIAMACHY instrument and its impact on level 1 and derived level 2 products (e.g.,
Noël et al.
, 2007; Bramstedt et al.
, 2009; Tilstra et al.
,
2012). An official correction for instrument degradation, called m-factor correction, was available to the users of
SCIAMACHY data since 2007 (Bramstedt et al.
, 2009). By implementation of these correction factors, the trend/artifact caused by instrument degradation in SCIAMACHY data was successfully removed (Tilstra et al.
, 2012). SCIAMACHY level-2 data for year 2004–2012 from the Tropospheric
Emission Monitoring Internet Service (TEMIS) European
Research Project (http://temis.nl/) was used to get a quantitative analysis of SO
2
column densities over Pakistan.
SO
2
Retrieval Analysis
The SO
2
slant column densities are retrieved in the spectral window of 315–326 nm by using DOAS technique
(Platt and Perner, 1979; Khokhar et al.
, 2005; Richter et al.
,
2006; Platt and Stutz, 2008). After the radiometric correction and application of m-factor to levle1 SCIAMACHY data, the retrieval of SO
2
columns involve three main steps: (1) determining total SO
2
slant column densities (SCD) by spectral retrieval (Khokhar et al.
, 2005; Richter et al.
,
2006; Krotkov et al.
, 2006, 2008; Lee et al.
, 2008), (2) by eliminating the latitude-dependent offset by normalizing
SO
2
SCD over pacific ocean region with relatively no/negligible atmospheric SO
2
(for details see, Khokhar et al.
, 2005, 2006; Lee et al.
, 2009) and (3) by applying an appropriate air mass factor (AMF) to convert slant columns into vertical columns (e.g., Khokhar et al.
, 2005; Thomas et al.
, 2005; Khokhar et al.
, 2008; Lee et al.
, 2009).
The retrieval of the SO
2
slant column- integrated along the light path, is affected largely by stronger ozone (O
3 absorption, therefore, both SO
2
and O
3
)
should be taken into account in the retrieval process to avoid/minimize spectral interference. This "interference" when concentrations of
SO
SO
2
2
are low, may produce negative SO the absence of emissions of SO
2
2
slant column values, with an error of the same magnitude. This then represents the
background level, i.e., the apparent SO
2
absorption in
. This is further dependent on solar zenith angle (SZA) and more prominent for higher
SZA due to increased slant light path and larger horizontal ozone concentrations. For low SZA, i.e., at low and mid latitudes, the interference between the SO
2
and O
3
absorption results in less negative SCD (low back ground level) and errors. Therefore, emissions of SO
2
(by pollution or volcano eruptions) will be detected against this background. This effect is corrected by applying background correction method used by various studies (e.g., Khokhar et al.
, 2005;
Lee et al.
, 2009; Theys et al.
, 2013).
Retrieval of data used in this study involved the absorption cross-sections of SO
2
and ozone at two temperatures (223°K and 243°K) along with two Ring spectra to correct for the Raman scattering (one to account for the general Ring effect of the Fraunhofer and one for the SZA dependent contribution of ozone) were fitted in the wavelength range 315–326 nm. In addition to correction for polarization effects, a wavelength calibration using a high-resolution solar spectrum is performed. Further details about retrieval algorithms can be obtained from various studies (e.g., Afe et al.
, 2004; Khokhar, 2006; Richter et al.
, 2006; Lee et al.
, 2008, 2009, 2011).
An air mass factor (AMF) is mandatory for the conversion of the retrieved slant columns into vertical column densities (VCD). The SO
2
AMF depends strongly on surface albedo, clouds, aerosols optical properties, the vertical distribution of SO
2
, and the ozone column (Khokhar et al.
, 2005; Thomas et al.
, 2005; Khokhar et al.
, 2008;
Krotkov et al.
, 2008; Lee et al.
, 2009). For data used in this study, if no cloud cover information is available for a given ground pixel, the AMF and VCD of that ground pixel were not computed and the data files will get the "no data" value/flag. Only the AMF for the clear-sky part were given a value that can be computed always.

Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014 1885
In case of volcanic eruption, SO
2
AMF calculation is strongly dependent on the SO
2
plume height. In general, there is no information available on the altitude of the SO
2 plume, so an assumption must be made. Therefore, VCDs were computed for three different assumed plume heights 2 km for Low AMF, 6 km for Middle AMF and 14 km for
High AMF, with an assumed plume thickness of 1 km (For further details see documents available at TEMIS website http://sacs.aeronomie.be/info/vcdamf.php). HYSPLIT trajectory model was used to have first guess of SO
2
cloud height and then VCD were computed by using the respective AMF
(middle or High) during days of volcanic activity. Finally, monthly mean average VCD were extracted over Pakistan.
The data files contain the value of the SCD and VCD in
Dobson Units and an estimate for the retrieval error on the
SCD for each individual pixel. Possible errors in the AMF calculation are therefore not taken into account. Several studies have reported AMF error of 10–15% by Thomas et al. (2005), about 10% error by Khokhar et al. (2008) and
35–70% in polluted regions (e.g., East Asia and the eastern
US) and less than 10% over clear ocean regions by Lee et al . (2009). The total error in the SO
2
retrieval from satellite data is below 50% (Khokhar et al.
, 2005; Thomas et al.
,
2005; Khokhar, 2006; Lee et al.
, 2011; Theys et al.
, 2013)
SCIAMACHY observations with back ground correction, cloud fraction less than 10% , SZA less than 88 degree and fit error (Chi sq < 5 × 10 –5 ) are used only in this study.
Further details about the data products, error analysis, cloud correction and AMF calculations are available through a document titled “Sulphur Dioxide Monitoring within
TEMIS” available at webpage (http://sacs.aeronomie.be/ info/docs.php, last accessed on February 21, 2014).
Monthly mean SO
2
data, gridded to a latitude-longitude grid of 0.25 by 0.25 degrees is used. For each grid cell the average of all measurement crossing that grid cell (using forward scans only) is computed from all orbit files for the given month. Monthly mean spatial maps of SO
2
were generated over Pakistan and time series analysis was carried out. This helped in identification of regions with major SO
2 column amounts over Pakistan during the selected time period.
RESULTS AND DISCUSSION
Spatial Distribution
Pakistan’s climate is mainly arid to semi-arid with exception of slopes of Hindukush and Karakorum mountain ranges, where rain fall ranges from 700 to 2000 millimetre
(mm) per year (Chaudhary et al.
, 2009; Ahmed et al.
, 2012).
Pakistan receives summer rainfall due to monsoon system while winter precipitation is due to western systems (e.g.,
Chaudhary et al.
, 2009; Wang et al.
, 2011 and references therein).
Spatial distribution of SO
2
DU = 2.69 × 10 16
column densities in DU (1
molecules/cm 2 ) over Pakistan is presented in “Fig. 1” during the time period of 2004–2011. Black circles are indicating the location of main cities of Pakistan.
Grey lines in each map are representing the district boundaries. Yearly mean over each grid point was calculated from monthly mean values and negative SO
2
VCDs caused by spectral interference, instrument degradation and/or imperfect removal of ring effect were not considered.
As SCIAMACHY takes six days for global coverage due to its dual mode of limb and Nadir viewing geometry, therefore, it limits the quantification of background SO
2 level in addition to violent dust storms and monsoon season (e.g., Chaudhary et al.
, 2009; Ahmed et al.
, 2012).
However, anthropogenic SO
2
analyses performed for various cities of Pakistan indicate background SO
± 0.02 Dobson units (3.6 ± 0.5 × 10 15
2
levels of 0.135
molecules/cm 2 ) during the time period of 2004–2011.
SO
2
columns are mostly higher over major cities of
Northern Areas, Punjab, Sindh, Khyber Pakhtunkhwa and in the western part of Baluchistan provinces. It is comparatively low in interior Sindh and Southern Baluchistan because of less populated and industrial activities in these regions. It’s worth mentioning that few small places like Nok Kundi
(western Baluchistan) Dadu (Sindh) and Okara (Cenral
Punjab) exhibited relatively large SO
2
columns. Nok Kundi is situated on N40-highway of Pakistan which connects
Quetta, Pakistan to the border cities of Iran. Nok Kundi is
221 km from Iranian city of Zahedan (the capital of Sistan and Baluchestan Province, Iran with population of 552, 706).
It has a hot desert climate with extremely hot summers, mild winters and gets just 35 millimeters (1.4 in) of rainfall over the year. There is no larger industry in this region and is also less populated. The observed SO
2
over Nok Kundi region is mainly due to traffic on N40-Highway and trans- boundary air pollution either anthropogenic SO
2
coming from Zahedan, Iran or due to regional/global volcanic eruptions.
This is further confirmed by mean wind fields (2004–2011) at 1000 hPa levels over Pakistan and neighboring countries presented in Fig. 2, prepared by using the NCEP Reanalysis
Derived data provided by the NOAA/OAR/ESRL PSD,
Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. Wind vectors are indicating the mean flow of air masses over Pakistan and neighboring countries at 1000 hPa during the study period. In general, the Nok Kundi is impacted by the air masses coming from
Zahedan, Iran. Furthermore, wind fields also indicated that many cities (Sialkot, Lahore, Okara, Faisalabad, Sargodha,
Multan and Bahawalpur) of Pakistan are also influenced by the air masses coming from Indian region which is densely populated and famous for extensive industrial activities
(western Gangetic plain). Therefore, the enhanced level of
SO
2
over various cities of Pakistan is also contributed by trans-boundary anthropogenic SO
2
pollution coming from
India, Iran and other neighboring countries.
In case of Dadu, Sindh; it is middle level urbanized district. However, the observed level of SO
2
are mainly due to oil and gas exploration/drilling activities at Zamzama and
Bhit Badra oil and gas fields in the region. Similarly,
Okara (central Punjab) is not highly urbanized region but still there exist several textile mills, thermal power generation and cottage industries that may cause such high level of SO
2
over this region in addition to trans-boundary air pollution.

1886 Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014
Fig. 1.
Spatial distribution of annual mean SO
2
column densities in Dobson units (DU) over Pakistan during time period of
2004–2011. Temporal and spatial variation from 2004–2011 in SO
2
columns densities over Pakistan can be identified.
Circles are indicating the location of major cities. Annual maps are prepared by taking average of monthly mean SO
2
VCDs over each grid point. SO
2
VCDs less than zero (mainly caused by spectral interference etc. - see the text) were not included while calculating the annual mean.

Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014 1887
Fig. 2. Wind fields over Pakistan and neighbouring region averaged over 2004 to 2011. Wind fields are generated from
NCEP Reanalysis Derived data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/ . Arrows (wind vectors) are indicating the wind direction and magnitude of each arrow is subject to respective wind speed (larger arrows for higher wind speed and smaller arrows for calm winds).
In general, “Fig. 1” exhibited a temporal increase in SO
2 column densities from year 2004 to 2011. Annual maps clearly indicate that higher SO
2
column densities are observed over the areas of KPK, Punjab, Sindh and
Baluchistan with larger population and traffic densities, agricultural practices, and industrial activities. There are some exceptions; (1) enhanced SO
2
levels are observed over larger regions due to tans-boundary volcanic SO
2 caused by Jabel at Tair, Dalaffilla, and Nabro volcanoes from African region during the years 2007, 2008 and 2011, respectively. (2) Over northern areas of Pakistan enhanced levels of SO
2
are partially caused by the albedo effect caused by ground snow/ice during winter months (Khokhar et al.
, 2005, 2006). These discrepancies are further discussed in the following sections of this study.
Atmospheric SO
2
over Major Cities of Pakistan
It is interesting to note that Northern Areas of Pakistan
(Skardu, Gligit and Chitral) exhibited larger SO
2
levels during the selected time period (2004–2012) in spite of that it is considered as less polluted region with less population and industrial activities. However, spatial maps of Pakistan exhibited larger SO
2
column densities especially during winter months over this region.
This region of the country has cold winters (temperature is well below zero, Chaudhary and Rasul, 2004), and snow covered areas from December to April. Therefore, one of the reasons for such high SO of ring effect.
2
columns could be due to the albedo effect of underlying snow during winter months (for details see Khokhar et al.
, 2006), which enhances the instrument sensitivity and also causes imperfect correction
Additionally, in these areas about 70–80% of household uses biomass for the purpose of cooking and heating
(NASSD, 2003). This consumption increases in winter for space heating and has slightly enhanced the SO densities. Moreover the non-availability of compressed natural gas (CNG) and mainly consumption of conventional fuel (diesel and other petroleum fuel) in motor vehicles might have resulted in increased SO reason is the trans-boundary SO
2
2
2
column
emissions. Another
pollution resulting from global volcanic eruptions such as: Dalaffilla (13.79°N,
40.55°E), Ethiopia and Nabro (13.37°N, 41.70°E), Eritrea volcanic activity in East African region has caused high
SO
2
columns over Pakistan during November 2008 and
June 2011, respectively. In order to avoid the influence of trans-boundary SO
2
resulting from global and/or regional volcanic activities two steps were taken as under: (1) for analyses of anthropogenic SO
2
over major cities presented in Fig. 3 and for identification of seasonal cycle presented in Fig. 4, SO
2
data for the months affected with transboundary volcanic SO
2
were not considered. (2) for the conversion of anthropogenic SO
2
SCD into VCD, AMF was calculated by considering SO
2
vertical distribution

1888 Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014
Fig. 3.
Annual variation of anthropogenic SO
2
column densities measured over major cities of Pakistan during the time period of 2004–2011. Months affected by trans-boundary volcanic SO
2
were not include in this time series.
Fig. 4.
SO
2 volcanic SO
2 seasonal cycle observed over different regions of Pakistan (provinces-solid lines) and over Pakistan (dashed line) devised from the SCIAMACHY observations over the period of 2004–2011. Months affected by trans-boundary
were not include in this time series. within first 2 km from the earth surface.
Frequent high SO
2
columns are observed over many cities of Pakistan such as Quetta, Dera Ismail Khan, Peshawar,
Bahawalpur, Faisalabad, Lahore, Sargodha, Skardu, Chitral,
Karachi and Gilgit. Table 1 presents the database of annually averaged SO
2
column densities over these cities during the time period of 2004–2011. South Asian region (India,
Bangladesh and Pakistan) is experiencing worst air quality due to rapid urbanization and industrialization. Decent growth in their GDPs has resulted in more cars, factories, and more people to consume coal and other petroleum products. According to (Ramanathan et al.
, 2001), brown haze is a severe problem over the tropics. It is mainly due to increased emissions of aerosols and precursor gases like
SO
2
that have increased over South Asia by a factor of 3 to
4 since 1970. Furthermore, these aerosols may affect largely the SO
2
slant columns retrieval and radiative transfer modelling for AMF calculation by inducing both shielding and albedo effects (Khokhar et al.
, 2008; Wagner et al.
, 2008) as mentioned in the previous sections.
“Fig. 3” shows a consistent behaviour of steadily increasing SO
2
for all cities till the year 2010 (with one exception of Skardu with maximum SO
2
column densities during the year 2011). The highlighted values (bold) in
Table 1 are the highest SO
2
columns observed over the selected cities during the time period of 2004–2011.
Anomalous behaviour of low SO
2
columns over Karachi
(the largest city with population of 23.5 million and industrial hub of Pakistan) was observed during the year
2004 to 2008 as compared to other cities. From year 2009 onward, Karachi exhibited highest SO
2
column densities.
According to reports from Pak-EPA, EURO II emission

Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014 1889
Table 1. Annually averaged SO
2
2004–2011.
columns in DU (1DU = 2.68 × 10 16 molecules/cm 2 ) over major cities of Pakistan during
Cities 2004 2005 2006 2007 2008 2009 2010 2011
Bahawalpur 0.104 0.173 0.187 0.166 0.298 0.062 0.367 0.282
Chitral 0.129 0.146 0.177 0.186 0.185 0.238 0.426 0.151
Khan 0.149 0.147 0.16 0.203 0.298 0.193 0.371 0.261
Lahore 0.147 0.131 0.153 0.148 0.242 0.226 0.355 0.291
Faisalabad 0.161 0.171 0.18 0.228 0.241 0.233 0.399 0.151
Gilgit 0.186 0.191 0.285 0.203 0.212 0.244 0.434 0.405
Peshawar 0.111 0.149 0.116 0.195 0.171 0.215 0.479 0.145
Quetta 0.132 0.124 0.107 0.105 0.065 0.124 0.318 0.191
Sargodha 0.156 0.163 0.197 0.163 0.254 0.172 0.436 0.419
Skardu 0.16 0.239 0.295 0.205 0.168 0.23 0.393 0.452
Karachi 0.039 0.106 0.155 0.108 0.129 0.288 0.541 0.386 standards for motor vehicles were implemented in January
2012 and hardly met by most of the vehicle in Pakistan.
This and increased emissions due to rapid urbanization, trans-boundary fraction of SO
2
and excessive use of furnace oil for thermal power generation could be the main reasons of observed temporal increase of SO densities over main cities of Pakistan. cities. An analysis about seasonal variation in SO
2
column
“Fig. 3” exhibits the average annual behaviour of SO pollution and does not reflect the seasonal cycle over these
2
2
column densities over major regions of Pakistan is presented in next section.
Seasonal Variation
The hydroxyl radical (OH) is very important especially in the tropospheric chemistry of sulfur species (Finlayson-
Pitts and Pitts, 2000). The oxidation process of almost all sulfur compounds (including reduced sulphur species) are initiated by OH (Finlayson-Pitts and Pitts, 2000; Hassan et al.
, 2013). In general low SO
2
amounts are observed in summer (due to high OH productivity) compared to high amounts in winter (less OH productivity). SO into sulfate particles and sulfuric acid (H
2
SO
4
2
conversion
O
) involves its reaction with O
3
SO
, hydrogen peroxide (H
2 hydroperoxyl (HO
2
2
), OH and
). Therefore, OH is the key parameter which determines the life time and seasonal cycle in the
2
concentrations in addition to some other minor and local phenomena (such as monsoon rains etc.). The SO
2 seasonal cycle may also be slightly dependent on both anthropogenic (coal, petroleum products and biomass burning) and natural (monsoon, snow) activities on regional basis. For instance during winter season, Northern
Areas of Pakistan are covered with snow and people burn biomass fuel for heating and cooking purposes which causes relatively high SO
2
concentrations (NASSD, 2003).
The underlying snow causes the high surface albedo and enhances the instrument sensitivity (albedo effect, for details see Khokhar, 2006). While anthropogenic activities in other regions of Pakistan may not reflect such variation in SO
2
emissions.
The SO
2
distribution varied for each year throughout the time period of 2004–2011 with large SO
2
column densities during winter and lower during the rest of the year. “Fig. 4” exhibits the seasonal cycle in atmospheric SO
SO
2 indicated by the very high SO order to avoid such anomalous high SO
Over Pakistan SO
2 the washout effect that lowers the SO
2
2
over the four provinces, Northern Areas and all Pakistan. Climatological-
column densities for each month of the year were calculated by taking average over whole time period of
2004–2012 for respective month. As mentioned in the previous section, Pakistan was affected by the various regional volcanic activities such as Jebal at Tair in October
2007 and Nabro volcano eruption in June 2011 etc. and was very well captured by SCIAMACHY observations as
2
column (see “Fig. 5”). In
column densities, data for the months affected by observed regional volcanic events for all regions was excluded from this analysis.
2
seasonal cycle is largely affected by the monsoon season (high rainy season generally prevails from July to September, Jhajharia et al.
, 2009). During these months the SO
2
amount is low compared to the post monsoon months (September to October). This is due to
amount in the atmosphere during rainy season by wet deposition and via its aqueous phase conversion into sulfate particles. In general, “Fig. 4” exhibits similar seasonal cycles of SO
2 column densities over all provinces of Pakistan, although, they differ in various aspects like having different traffic density, population density and industrial activities. Probably, the washout during the monsoon season and high OH
(Finlayson-Pitts and Pitts, 2000; Platt and Stutz, 2004) concentrations during summer months are the dominant factors in determining the seasonality in the SO
2
column densities over Pakistan.
Temporal Analysis
The temporal record of SO
2
over Pakistan was analysed by plotting the monthly averaged SO
2
column from 2004 till 2012, as presented in “Fig. 5”. The mean SO
2 were calculated by taking the mean of SO
2
columns
column densities over all cities of Pakistan. The bars show the spatial variation (standard deviation) for each month over all cities. In general, high SO
2
column densities are observed over cities with high population/traffic density and industrial activities (see Fig. 1). In June, 2011 unexpected high SO
2
columns were observed, caused by the Nabro volcanic eruption. It was very explosive and sulfur dioxide

1890 Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014
Fig. 5.
Temporal record of SO
2
column densities observed by SCIAMACHY instrument over Pakistan during the time period of 2004–2012. The arrows indicating the month influenced by trans-boundary SO volcanic eruptions. Dashed lines are the linear fits implemented to calculate the temporal increase in SO
(Blue) and without (red) trans-boundary volcanic eruptions.
2
pollution caused by respective
2
VCDs with rich volcanic eruption (Fee et al.
, 2011; Carn et al.
, 2012).
For instance Clarisse et al.
(2012) reported total SO
2
mass of 1.5 Tg retrieved from daily IASI observations and Carn et al.
(2012) estimated 1 to 2 Tg SO
2
based on OMI and
Atmospheric Infrared Sounder (AIRS) data. It has caused not only the stratospheric aerosol load (e.g., Bourassa et al.
, 2012) but also large amounts of SO
2
pollution was detected over surrounding regions. It has influenced
Pakistan’s atmosphere with maximum SO
2
of 7 DU detected on June 17, 2011 from daily observations of SCIAMACHY instrument. Other peaks in the time series were also investigated and it was found that they were also caused by various volcanic activities in neighbouring regions such as in October, 2007 (Jabal al-Tair, Yemen), November 2008
(Dalaffilla, Ethiopia) and January, 2010 (Tor Zawar,
Pakistan). The monthly averages affected by these volcanoes are indicated by the red data points in “Fig. 4”. Additionally, some other peaks in SO
2
columns (especially, Jan–May
2010) of amplitude comparable to the SO
2
peaks caused by trans-boundary volcanic eruptions can be identified. The reasons behind such anomalous high SO
2
are not clear yet, however, it might be due to various factors such as observed seasonal cycle (high during winter months), instrumental degradation (as discussed in the methodology and data section) and regional/global volcanic activities. According to reports from global volcanisms program (GVP, 2010), Tor
Zawar (30.48°N, 67.49°E) from Pakistan, Erta Ale volcano
(13.60°N, 40.67°E) from Ethiopia and Eyjafjallajökull
(63.63°N, 19.62°W) volcano from Iceland and Niyragongo
(1.52°S, 29.25°E) from Congo have been active during this time of the year 2010. However, satellite instrument and
Hybrid Single Particle Lagrangian Integrated Trajectory
Model-4 (HYSPLIT4 - Draxler et al.
, 2013) analyses could not track SO
2
plume over Pakistan caused by aforementioned volcanic activities. Therefore, it is difficult to speculate about the origin of such enhanced SO correlate it with trans-boundary SO
2
2
over Pakistan and to
without any scientific evidence. In case of Tor Zawar volcano, SCIAMACHY did not have overpass over the region on January 27, 2011 because of switching between limb and nadir viewing modes (Bovensmann et al.
, 1999). Therefore, SO
2
emitted from Tor Zawar volcanic eruption was not recorded by the
SCIAMACHY observations.
Pakistan is currently experiencing a severe energy/power crisis and consequent 10–12 hours of electricity blackout in addition to natural gas outages. Pakistan’s coal, oil and natural gas reserves are running out leaving the economy to rely on imported fossil fuel. The situation has gradually worsened overtime forcing industrialists to use poor quality fossil fuels (especially furnace oil etc.) to meet their industrial needs of electricity. Consequently, it is deteriorating the environment of Pakistan. According to a study Qudrat-
Ullah and Karakul (2007), has indicated a recent increasing trend in oil- and gas-based power generation while a decreasing trend in the hydro-based power generation in
Pakistan. It also mentioned that as a result the environment is exposed to the additional 167 million tons of CO
2
in addition to other air pollutants such as SO over Pakistan and power generation in Pakistan by using different types of fuel was calculated. Resulting correlation coefficients (r 2
Maximum r 2
2
and NO
2
etc. A temporal increase is observed in the thermal power generation data of Pakistan presented in the Table 2. For instance the thermal power generation was increased during year 2008 to 2011 with maximum during the year 2010. Among thermal power generation, the use of coal and furnace oil was larger as compared to use of gas as fuel. Similar behavior of increasing SO
2
columns was reflected by satellite observations over Pakistan with maximum during
2010 followed by a small decrease in 2011. This is further confirmed by statistics analysis presented in Table 2. The correlation between annul mean SO
2
columns densities
) and slope are also given in Table 2.
= 0.79 is observed for thermal power generation
(+ve slope) followed by use of gas (r 2 slope), furnace oil (r 2
= 0.77 with –ve
= 0.68 with +ve slope) and coal (r 2 =
0.18 with –ve slope) used for power generation. It is worth

Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014 1891
Table 2.
Share of coal, oil and gas consumption by power sector of Pakistan together with total Hydro and Thermal power generation.
Year
Oil
(GWh)
Power Generation in Pakistan by using fuels
Gas
(GWh) a,b
Coal
(000 metric tonne)
Power generation in Pakistan
Hydro
(GWh) by sector c
Thermal
(GWh)
Annual SO
2
Column
Densities
Over Pakistan
(DU)
2008 39.18 33.7 162.2 28667 57602 0.172
2009 42.27 31.8 112.5 27763 56614 0.161
2010 46.07 28.7 125.5 28492 60746 0.335
2011 43.09 27.2 96.5 32259 58316 0.298 r 2 0.68 0.24 0.79 slope 0.026 0.021 0.44
1
1 r
GWh: Giga watt hours
2 & slope are calculated by different fuel type verses annual SO a Source: Ministry of Petroleum and Natural Resources Pakistan
2
columns densities b Source: Hydrocarbon Development Institute of Pakistan c Source: Pakistan Electric Power Company (Pvt) Limited (PEPCO), National Transmission & Distribution Company
Limited (NTDC) to note that annual SO
2
columns and gas used for power generation are anti-correlated. Such anti-correlation was expected and has been stated in various studies (e.g.,
Goyal, 2003; Chelani and Devotta, 2007). Therefore, the observed temporal increase in SO
2
columns during the time period of 2004–2011 is mainly contributed by trans-boundary
SO
2
pollution caused by both regional volcanic eruptions
(Khattak et al.
, 2014) and anthropogenic activities (“Fig. 2”), and use of furnace oil and coal for thermal power generation in Pakistan to meet the industrial needs (especially, onward from year 2008) and to overcome the power outages.
In order to estimate the temporal increase in SO
2
columns over Pakistan, regression analysis was performed by applying linear fit to both data sets. The red dashed line represents the trend in SO
2
columns over Pakistan including the volcanic eruptions, whereas the blue dashed line represents the trend excluding the volcanic events. Analysis showed 120% increase in SO
2
columns with volcanic eruptions and 70% increase at the rate of 8.7% per year in SO
2
columns without volcanic eruptions over the time period of 2004 to
2012. As Pakistan is a developing country, the cause of this increase in SO
2
columns can be associated with various factors include increase in motor vehicles, new industries
(Ramanathan et al ., 2001; Ejaz et al.
, 2009a; MoF, 2012), use of low quality and sulfur containing fuel, burning of low grade coal and biomass for domestic and industrial purposes (especially thermal power generation), and open burning of solid waste. Additionally, the emission control technologies, strategies, environmental legislation and air quality standards are hardly adopted and met in the country. For example, even the ‘Euro II’ Emission Standards for motor vehicles are not met.
Source Apportionment
Four major air pollution sources that should be immediately addressed by Pakistan are: vehicular emissions, industrial emissions, emissions from solid waste burning and Natural dust or trans-boundary pollution (PCAP, 2008). The analysis presented in “Fig. 6” indicates the consumption of compressed natural gas (CNG) in Pakistan along with the number of motor vehicles and SO
2
column densities during
2004–2011. According to the Pakistan Economic Survey
2011–2012 (MoF, 2012), the number of motor vehicle on road has increased from 4.5 million in year 2000 to 11 million in the year 2011. These statistics indicate the huge growth in the transport sector of Pakistan, which is directly related to air quality degradation (Ejaz et al.
, 2009a) and health effects especially process of wound healing (Ejaz et al.
, 2009b). The growth in motor vehicles on roads of
Pakistan and annual SO
2
column densities averaged over main cities of Pakistan did not reflect the similar trend. As there is gradual increase in SO
2
emissions until year 2010 followed by a decrease in 2011 (contrary to trends in motor vehicles). Similar discrepancy was exhibited by correlation between total number of motor vehicles and observed SO columns with r 2
The correlation factor is not very high and r 2
2
= 0.50 presented in “Fig. 7” (green triangles). indicates that the increase in the number of vehicles cannot fully explain the observed trend in the SO
2
columns. It was further investigated by taking into account the type of fuel consumed by the transport sector of Pakistan during the selected time period. Table 3 presents the statistics of share of fuel used by the transport sector of Pakistan, growth in compressed natural gas (CNG) as fuel and number of vehicles converted from conventional fuel to CNG fuel and SO
2
column densities during 2004–2011. According to the International
Association of Natural Gas Vehicles (IANGV) there are approximately 4 million Natural Gas Vehicles (NGVs) in use worldwide, of which 1.6 million are in Argentina and
1.5 million in Pakistan. Additionally, due to its affordability
(CNG is cheaper than gasoline and diesel in Pakistan) during the last few years, a large number of motor vehicles has been shifted from conventional fuel (petroleum and diesel) to CNG. Therefore, the use of CNG as fuel has resulted in fewer emissions of SO
2
and other pollutant from motor vehicles (Goyal, 2003; Chelani and Devotta,

1892 Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014
Fig. 6. Compressed natural gas (CNG) consumption in billion cubic feet (y-axis, source: Ministry of Transport and
Communication (NTRC), yearly mean SO
2
column densities in DU (on secondary y-axis) over Pakistan and total no of motor vehicles (y-axis source: Ministry of Transport and Communication (NTRC) in Pakistan during the time period of
2004–2011).
Fig. 7.
Correlation plot among SO
2
column densities, total number of motor vehicles in Pakistan during the year 2004-
2011 (green triangles), number of vehicles converted to CNG from conventional fuel (blue diamond) and amount of CNG fuel consumed by transport sector in Pakistan. Dashed lines are showing the linear fits for respective datasets.
2007). The change in fuel type is probably the main cause behind the poor correlation between temporal behaviour of
SO
2
columns and total number of motor vehicles in Pakistan.
It is further supported by the correlations calculated among
SO
2
columns and CNG consumption by transport sector in
Pakistan (r 2 = 0.7, dark brown circles in “Fig. 7”) and number of motor vehicles converted to CNG fuel from conventional fuel (r 2 = 0.7, blue diamonds in “Fig. 7”).
This explains the fact that SO
2
emissions decreased in year
2011. Although, the number of motor vehicles in Pakistan were still increasing in year 2011 but on the other side the number of vehicles converted to CNG from conventional fuel was maximum during year 2011. Similar trend can be seen in Table 3 that share of petroleum products consumed by transport sector was also decreased during year 2011.
Regression Analysis
Partial Autocorrelation Function (PACF) was applied on the SO
2
data for the time period 2004–2012, it suggests an autoregressive nature of the time series. The descriptive analysis shows that data is right skewed with high peak, indicating non normality (see Table 4A - presents the KPSS test (Kwiatkowski et al.
, 1992) statistics). The Autoregressive model is employed to capture the time series structure. As the series is stationary; ordinary least square method provide consistent estimator. A trend variable ‘T’ is included in regressors to capture the temporal trend in the series. The intercept in the model represent monthly mean column

Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014 1893
Table 3. Share of Oil and CNG consumption by the transport sector of Pakistan.
Year
Share of Oil/petroleum products Consumption by Transport sector
(%) a
2004 63.07
2005 61.50
2006 55.77
2007 47.38
Share of CNG
Consumption by
Transport sector
(million cubic feet) b
No of vehicle converted to CNG
1.5
2.1
3.2
4.6
(million) c
Total No of vehicle
(million)
0.45
1.05
1.30 d
Yearly mean SO
Over Pakistan
(DU)
3.1464
2
Column Densities
0.114
0.138
3.8688
0.145
2008 51.90
2009 49.34
2010 46.32
2011 47.08
5.6
7.0
1.40
1.70
9.3 2.74
0.172
7.8 2.00
0.161
5.5012
0.298 a Source: Hydrocarbon Development Institute of Pakistan (CNG and oil consumption by Transport sector)
0.335
5.5586 b Source: Hydrocarbon Development Institute of Pakistan (CNG and oil consumption by Transport sector) c Source: OGRA, Ministry of Petroleum & Natural Resources (CNG fitted vehicles) d Source: National Transport Research Centre (total number of vehicles)
Table 4A. KPSS test and descriptive statistics of SO
2
data. retrieved from SCIAMACHY observations were used to
Mean 0.1797
SD 0.1321
Skewness 2.4903
Excess Kurtosis
KPSS with trend
Asymptotic critical values of KPSS
9.1523
0.0714
2
columns vary throughout the country. The annual average distribution of SO
2
showed that SO higher in Northern part of the country, mostly because of trans-boundary volcanic SO
2
2
is typically
, albedo effect, increased use of bio-fuel (for cooking and space heating) and usage of low quality fuel in motor vehicles. It is also high in various
Table 4B. Regression Analysis Parameters. t-value t-prob
SO
2
–1
Trend 0.001075
0.0004876
2.21
0.0011
Constant 0.06852 0.0267 2.57 0.0117
0.0299
densities. The estimated model is given below in Eq. (1).
SO
2t
= 0.0685 + 0.001
T + 0.3384
SO
2t-1
(1)   where SO
2t
and SO
2t-1
are mean SO
2
column densities for the current and previous month, respectively.
Regression analysis presented in Table 4B indicates that all of three regressors are significant at 5% significant level.
The current concentration depends upon the last month’s concentration about 34% with 0.1% per mensem increasing trend in SO
2
column densities. Especially, t-values greater than 2 (see Table 4B) confirm that calculated trend in SO
2 column densities is statistically significant.
CONCLUSION
This study has emphasised on the amount of SO
2
present in the atmosphere of Pakistan by using satellite data during the time period of 2004–2012. SO
2
column densities comparatively low in less populated areas of interior Sindh and Southern Baluchistan.
Meteorological analysis indicated that many cities of the
Punjab and Baluchistan Provinces are influenced by polluted air masses coming from India and Iran, respectively.
Temporal analysis showed that SO
2
has increased since
2004 and this increase is statistically significant.
Anthropogenic SO
2
increased 70% during the time period of 2004 to 2011 at rate of 8.7 percent per year (120% by including the trans-boundary SO volcanic eruptions).
2
pollution due to major
This increase in SO
2
columns over Pakistan is mainly due to rapid urbanization, use of poor quality petroleum products in vehicles and for thermal power generation (r 2 =
0.8), use of bio fuel (char coal) for cooking and space heating, open solid waste burnings and no implementation of strict emission standards for industry and motor vehicles
(EURO II emission standards are not implemented yet in its true spirit). Although, there is no strong correlation observed between the increase in number of motor vehicles in Pakistan and SO
2
column densities but still it may be partially attributed to extensive increase in vehicular emission and related activities in Pakistan during the time period of
2004–2011. The observed poor correlation is mainly due to extensive use of CNG as a fuel rather than a conventional fuel during last few years.
The Seasonal variation showed high SO
2
in winter and comparatively low values in summer months which is mainly driven by OH concentration and monsoon season

Khattak et al.
, Aerosol and Air Quality Research , 14: 1883–1896, 2014 1894
(local/regional effect).
The study also revealed that some major volcanic eruptions in the African region have affected SO
2
column densities over Pakistan through trans-boundary SO
2
pollution
(Khattak et al.
, 2014). Among those eruptions, the Nabro volcano event during June 2011 was explosive; sulfur dioxide enriched and has major effects on atmospheric composition of Pakistan by contributing substantial amount of SO
2
column densities.
ACKNOWLEDGMENT
Authors gratefully acknowledge the TEMIS project for providing level-2 satellite data of SO
2
measurements and details about the retrieval algorithm and error analyses. We are thank full to Mr. Zeeshan Shahid from Institute of
Atmospheric Physics (IAP) Chinese Academy of Sciences
(CAS) for meteorological analysis and to NOAA/OAR/
ESRL PSD, Boulder, Colorado, USA, for winds data from their Web site at http://www.esrl.noaa.gov/psd/NUST, Engr.
M. Zaheer and Mr. Obaid from IGIS-NUST, for providing guidance about ArcGIS software. Our special gratitude goes to NUST for providing financial support to conduct this study.
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Received for review, December 10, 2013
Accepted, May 27, 2014