1. health and fitness
2. disorders

Aerosol and Air Quality Research, 15: 657–672, 2015
Copyright © Taiwan Association for Aerosol Research
ISSN: 1680-8584 print / 2071-1409 online
doi: 10.4209/aaqr.2014.09.0200
Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using
MODIS Satellite Data and a HYSPLIT Model
Fozia Sharif1, Khan Alam2,3*, Sheeba Afsar1
1
Department of Geography, University of Karachi, Karachi 75270, Pakistan
Institute of Space and Planetary Astrophysics, University of Karachi, Karachi 75270, Pakistan
3
Department of Physics, University of Peshawar, Peshawar 25120, Pakistan
2
ABSTRACT
In this study, aerosols spatial, seasonal and temporal variations over Sindh, Pakistan were analyzed which can lead to
variations in the microphysics of clouds as well. All cloud optical properties were analyzed using Moderate Resolution
Imaging Spectroradiometer (MODIS) data for 12 years from 2001 to 2013. We also monitored origin and movements of
air masses that bring aerosol particles and may be considered as the natural source of aerosol particles in the region. For
this purpose, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to make trajectories
of these air masses from their sources. Aerosol optical depth (AOD) high values were observed in summer during the
monsoon period (June–August). The highest AOD values in July were recorded ranges from 0.41 to 1.46. In addition, low
AOD values were found in winter season (December–February) particularly in December, ranges from 0.16 to 0.69. We
then analyzed the relationship between AOD and Ångström exponent that is a good indicator of the size of an aerosol
particle. We further described the relationships of AOD and four cloud parameters, namely water vapor (WV), cloud
fraction (CF), cloud top temperature (CTT) and cloud top pressure (CTP) by producing regional correlation maps of their
data values. The analyses showed negative correlation between AOD and Ångström exponent especially in central and
western Sindh. The correlation between AOD and WV was throughout positive with high correlation values > 0.74 in
whole Sindh except eastern most arid strip of the Thar Desert in the region. The correlation between AOD and CF was
positive in southern Sindh and goes to negative in northern Sindh. AOD showed a positive correlation with CTP and CTT
in northern Sindh and a negative correlation in southern Sindh. All these correlations were observed to be dependent on the
meteorological conditions for all of the ten sites investigated.
Keywords: AOD; CTT; CTP; CF; Ångström exponent.
INTRODUCTION
Aerosols are minute solid and liquid particles suspended
in the atmosphere. These may occur in the form of natural
dust particles, volcanic dust, mist, clouds, fog, sea salts,
anthropogenic aerosols, oceanic sulphates, industrial
sulphates, soot (Black carbon), organic particles and some
toxic pollutants after various physical and chemical processes
that have several effects on our atmosphere ( Gupta et al.,
2013).
Interactions between aerosols and clouds have become a
key topic of scientific research due to the importance of
clouds in controlling climate (Mahowald and Kiehl, 2003;
Alam et al., 2010, 2014). Aerosols and clouds play a very
*
Corresponding author.
Tel.: +92-91-9216727
E-mail address: [email protected]
significant role in the climatic system (Balakrishnaiah et
al., 2012; Alam et al., 2014). Aerosols demonstrate both
spatial and temporal variations, which may lead to variations
in the cloud optical properties (Alam et al., 2010). Their
ability to absorb or scatter solar radiations have direct impact
to increase or decrease the temperature of the atmosphere
(Ichuku et al., 2004; IPCC, 2013). In addition, their indirect
affect modifies the cloud lifetime, albedo, and the
precipitation by changing the size and density of cloud
droplets in the atmosphere (Lohmann., 2002; Alam et al.,
2010; Balakrishnaiah et al., 2012). In addition, as smaller
cloud droplets are less efficient to produce precipitation, an
increased aerosol population will cause to a longer cloud life.
Cloud formation requires super-saturation with aerosol that
also explains the importance of aerosols in our atmosphere
(Alam et al., 2010). These particles also emitted and reflected
radiation from the earth in different directions depending
on their physical and chemical properties (Ichuku et al.,
2004). Therefore, the description of atmospheric aerosols
is important to understand global solar heat budgets, water
658
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
cycle and various dynamics of climate change (Ichuku et al.,
2004). Increasing anthropogenic aerosol concentrations lead
to form cloud condensation nuclei (CCN) that ranges from
smaller effective radii to longer-lived clouds, affecting the
hydrological cycle (Ramanathan et al., 2001; Balakrishnaiah
et al., 2012). A wide range of studies has shown aerosol and
cloud interactions with large uncertainties in the magnitude
of radiative forcing (e.g., Twomey et al., 1984; Coakley et
al., 1987; Kaufman and Nakajima, 1993; Kaufman and
Fraser, 1997a, b; Ackerman et al., 2000; Rosenfeld, 2000;
Ramanathan et al., 2001; Rosenfeld et al., 2002; Schwartz
et al., 2002; Kim et al., 2003; Andreae et al., 2004; Koren
et al., 2004; Penner et al., 2004; Kaufman et al., 2005; Koren
et al., 2005; Alam et al., 2010; Balakrishnaiah, 2012; Alam
et al., 2014).
Alam et al. (2010) and Balakrishnaiah et al. (2012) have
examined the spatial and temporal variations in aerosol
particles and the impact of these variations over different
locations in Pakistan and India. They found highest AOD
values during the monsoon period (June–August). Their
analysis also showed a strong positive correlation for AOD
and cloud parameters in some regions and a negative
correlation in other regions. Ramachandran and Jayaraman
(2003) investigated AOD values for February–March 2001
over the Bay of Bengal. They found higher AODs on 21
February influenced by the air masses originating either
from India or Bangladesh at different heights, indicating
the anthropogenic sources. They concluded the difference
in AOD values over Chennai that is a coastal urban station
of India with that of clean continental land. Reasons may
be due to the difference of anthropogenic activities and
meteorological conditions, wind patterns and subsequently
loading of aerosol particles. Kaufman et al. (2002) observed
strong link of anthropogenic aerosols with the climate
system and the hydrological cycle. A region with a clean
atmosphere (with no pollutant) has relatively large cloud
droplets to form clouds for precipitation, although natural
aerosols are required to condense upon; a cloud does not
form otherwise. In contrast, urban smoke and pollutants
affect cloud radiative properties and reduce precipitation at
the same time. Thus, anthropogenic aerosols impact the
hydrological cycle with a change in cloud cover, cloud
property and rainfall patterns in a region. Gupta et al. (2013)
examined the quantity and quality of MODIS data over
urban areas of Pakistan. They found a lack of availability
of MODIS data over Karachi due to the presence of bright
surfaces that increases surface reflection. They further
added in their findings that high AOD values are shown
near the city center and decrease as we move out of that
city. The population is more concentrated inside the city
with more anthropogenic activities impacts atmospheric
aerosols. Biomass burning, industrial activities and transport
might also have significant impact on this analysis. Ashraf
et al. (2014a) found high AOD values in two seasons of
Pakistan i.e., monsoon and pre-monsoon. They associated
these higher values of coarse particles with a high frequency
of a dust storm in the region as well as fine particles from
burning activities in the agricultural fields of Pakistan and
India (Indian Gigantic Plain). They found a highly polluted
year i.e., 2008 with highest AOD concentrations in the
atmosphere. Alam et al. (2014) examined AOD and cloud
parameters such as water vapor, cloud top temperature, cloud
top pressure and cloud fraction over five cities in Pakistan
during the period (2001–2011). The AOD and cloud optical
properties correlations changes with changing seasons in
Pakistan e.g., AOD showed a positive correlation with CTP
and CTT for the spring season and a negative correlation
was observed in summer in all investigated regions.
This study focused on three objectives. The first objective
was to understand the spatio-temporal distribution of AOD
over Sindh, Pakistan. The second objective was to investigate
the relationship between AOD and cloud parameters (i.e.,
Cloud fraction, cloud-top pressure, cloud-top temperature
and water vapor). The third objective was to analyze the
influence of air mass trajectory on AOD concentrations.
Aerosols spatio-temporal distributions, seasonal variations
and physical properties may be determined within a short
span of time from land, aircraft, or satellite remote sensing
(Ichoku et al., 2004). The analysis of air mass trajectories
helps to determine natural source of aerosols in southern
Pakistan. The months of January (winter), July (summer),
April (spring) and October (autumn) were chosen as these
are considered as representatives of four seasons in the
study region. In particular, AOD-cloud parameter correlation
study acquired for thirteen years (2001–2013) suggests new
findings in a region.
MATERIALS AND METHODS
Site Description
Sindh (23°42′–28º29′N; 67°24′–71°05′E) is the third
largest province of Pakistan with a population of
approximately 42 million. It is bounded by Balochistan to
the west, Punjab to the north, Indian states (Gujarat and
Rajasthan) to the east and the Arabian Sea to the south of its
location (Fig. 1(b)). The landscape of Sindh is diversified,
having Kirthar Mountains to the west, Thar Desert to the
east, and Piedmont Plains to the north of Sindh. The
climate of Sindh is hot and semi-arid. It has a variation in
temperature as inland extreme temperatures to maritime
moderate temperatures. For an inland Sindh, the temperature
rises above 46°C in May and falls up to 2°C in January. In
addition, the coastal region has temperature not more than
40°C and less than 10°C for summer and winter months,
respectively. Mainly rainfalls during monsoon season (July–
September) that is about 7 inches in a year. Dust Storms hit
frequently, mainly during April–May and October–November
to the region.
In Sindh, there are ten industrial estates, commonly named
as SITE (Sindh Industrial and Trading Estates). Karachi,
Hyderabad, Kotri, Sukkur, Tando Adam, Nooriabad, Larkana,
Nawabshah and Shikarpur are common stations of Sindh
industries. The main industries include cement industries,
oil refineries, chemical industries, heavy machinery and
pharmaceutical industries, automobiles, shipbuilding, fish
factories, sugar and flour mills, fertilizer industries and a
steel mill. Their industrial emissions contribute to increase
anthropogenic aerosols in the region. Similarly, vehicular
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
emissions from the roads of Sindh, like Super Highway
and National Highway and sea salt emissions from Arabian
Sea are the major local sources of aerosol in the study area.
All the ten sites selected for the study are covering almost
whole Sindh with some geographical characteristics. For
example, most of the stations chosen for study are cities
and industrial centers like Karachi, Hyderabad, Rohri,
Larkana, Nawabshah, Jacobabad and Sukkur. Other sites
are small cities with some topographical characteristics
either mountain or desert like Dadu, Ghotki, Umerkot and
Tharparkar.
The Moderate Resolution Imaging Spectroradiometer
(MODIS) Instrument
MODIS (Moderate Resolution Imaging Spectroradiometer)
acquires data at 36 spectral bands to give atmospheric,
oceanic and terrestrial information (King et al., 2003; Ichoku
et al., 2004). These bands range from visible to the thermal
infrared wavelengths, which are very useful to collect and
summarize large statistical and visual impacts of aerosols
on climatic conditions (Ichoku et al., 2004). MODIS provides
observations at moderate spatial resolution of 250 m (Band
1 & Band 2), 500 m (Bands 3 to Band 7) and 1 km (Band 8
to Band 36) or temporal resolution (1–2 days) over different
portions of the electromagnetic spectrum (King et al., 2003;
Ichoku et al., 2004; Alam et al., 2010). MODIS data include
daily, 8-day or 16-day composite images and products with
different time scales. Several climatic parameters also
retrieved at a spatial resolution of 5 km and 10 km from
MODIS (Alam et al., 2010; Balakrishnaiah et al., 2010; Alam
et al., 2011c; Alam et al., 2014). MODIS provides global
coverage every 1 to 2 days. This source is also beneficial to
represent detailed local, regional and global distributions of
several climatic parameters over land as well as over the
ocean. These MODIS instruments are equally beneficial to
look at atmospheric, terrestrial and ocean phenomenology to
monitor various changes and collect statistics. These statistics
are especially helpful to analyze aerosol concentrations and
the impacts on cloud formation (Alam et al., 2011c, 2014).
MODIS aerosol retrievals are expected to be more
accurate over the ocean than land (Kaufman et al., 1997;
Tanre et al., 1997). Therefore, data accuracy is found to be
more over the oceans than it is over land (Remer et al.,
2005). All bright surfaces such as deserts and snow covered
areas cause problems for the MODIS instrument, as do
complex terrains, leading to a large bias in models and needs
to have ground-based observations (De Meij et al., 2006;
Alam et al., 2010;). There is a difference in the usage of
the visible and the thermal bands of MODIS datasets, as
the visible bands are used during the day and the thermal
bands are used during the day and night both to measure
bright surfaces, in particular. MODIS determine the physical
properties of cloud as the cloud top pressure and cloud top
temperature using infrared bands; optical and microphysical
cloud properties using visible and near-infrared bands (Jin
and Shepherd, 2008; Alam et al., 2010).
MODIS calculates AOD with an estimated error of ± 0.03
± 0.05AOD over the ocean and ± 0.05 ± 0.02AOD over the
land (it has been learned that 0.03AOD is an offset due to
659
uncertainty in the ocean state and 0.05AOD is another due
to uncertainty in aerosol characteristics). Over the land,
MODIS measures AOD from the reflection of residual at
the top of the atmosphere and uses the 2.1-μm channel for
it. Over the ocean, MODIS distinguishes fine particles of
pollutants to coarse particles of dust and sea-salt with
aerosol spectral signatures of 0.47–2.1 μm range (Kaufman
et al., 2002). The MODIS aerosol retrieval algorithm has
recently been improved in order to correct systematic biases
in the MODIS algorithm used previously (Remer et al.
2005; Levy et al. 2007).
MODIS Level 3 collection 5.1 deep blue (DB) data are
available from 2002 to present from Aqua, and 2000 to 2007
from Terra due to the known calibration issues. Therefore,
in the present study MODIS DB Terra and Aqua data were
used from 2001–2002 and 2003–2013, respectively. This
DB algorithm of the MODIS aerosol product provides more
reliable and accurate data over bright surfaces such as
deserts and snow or ice covered regions. While, the
standard MODIS AOD relies on finding dark targets. Deep
blue AODs data are more accurate over Sindh due to the
advantage of taking dark-surface properties at blue channels
(0.412, 0.47 μm) and less absorption of dust at the red
channel (0.65 μm).
The monthly MODIS Level 3 data with a 1° × 1° degree
spatial resolution was obtained from the NASA DAAC
website http://disc.sci.gsfc.nasa.gov/giovanni. AOD was used
as a surrogate for aerosol concentration and calculated the
correlation coefficient for each set of parameters on a monthly
basis, averaged annually, for the period from 2001 to 2013.
All data sets were interpolated using software ArcGIS
(version 10.1). Spline was found as best technique used to
do interpolation of MODIS data. MODIS Level 3 atmosphere
products contain no runtime QA flags. Therefore, Level 2
runtime QA flags are used in Level 3 to calculate (aggregate
and QA weight) statistics in Level 3. To identify cloud freepixel, the cloud mask algorithm is used. This algorithm
classifies each pixel as either confident clear, probably clear,
probably cloudy, or cloudy (Hsu et al., 2004). In order to
minimize the cloud contamination problem, DB uses AOD
spatial variance method to the radiance at 412 nm at every 3
× 3 pixel spatial domain (Hsu et al., 2004). The uncertainty
of DB AOD retrievals is expected as ± 0.05 ± 20% ×
AODAERONET (Hsu et al., 2006, Huang et al., 2011, Shi et
al., 2013). MODIS detailed information on algorithms for
the retrieval of aerosol data is available at http://modisatmos.gsfc.nasa.gov.
The Hybrid Single Particle Lagrangian Integrated
Trajectory (HYSPLIT) Model
In this study, we used the HYSPLIT model to archive
data of an air parcel at several localities of Sindh named as
‘backward trajectories’. This model computes air mass
trajectories with several meteorological parameters like
rainfall, relative humidity, ambient temperature and solar
radiation flux (Alam et al., 2011c). This meteorological
input for the trajectory model was processed as the FNL
dataset and reprocessed from NOAA (National Oceanic
and Atmospheric Administration) by ARL (Air Resource
660
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
Laboratory) to understand air masses (Alam et al., 2010).
Furthermore, several different pre-processor programs
convert NOAA, ECMWF (European Centre for Mediumrange Weather Forecasts), MM5 (The fifth generation
NCAR/Penn State Mesoscale Model), NCAR (National
Centre for Atmospheric Research) re-analysis, and WRF
(weather research and forecasting) model output fields in a
format compatible for direct input into the model (Alam et
al., 2010).
HYSPLIT has the ability to determine almost 40 forward
or backward trajectories at different altitudes of 500, 1000,
1500, 2000, and 2500 (Alam et al., 2010, 2011c). All
backward trajectories in this study were calculated at 500,
1500 and 2500 m heights above ground level by the model
archived from the website (http://ready.arl.noaa.gov/HYSP
LIT.php). We analyze the origin of air masses with aerosol
such as dust, sea salt, pollutants, etc. at various sites of
Sindh. With some limitations of web version all registered
PC has no computational restrictions with the user to obtain
the necessary meteorological data files in advance. In
addition, all unregistered version is similar to the registered
version except not work with forecasted meteorological
data files (Draxler and Rolph, 2003; Alam et al., 2010).
RESULTS AND DISCUSSIONS
The spatial distribution of annual mean AOD at 550 nm
wavelength has been plotted for the period from 2001 to
2013. We also used mean spatial seasonal distribution of
AOD for the whole period during 2001–2013. The correlation
maps of aerosol and cloud parameters named water vapor
(WV), cloud fraction (CF), cloud top temperature (CTT), and
cloud top pressure (CTP) have been plotted for the period
from 2001 to 2013. The air mass HYSPLIT trajectories
demonstrate a source of dust aerosols in the mid-months of
all four seasons in Sindh.
Spatial Distribution of Annual Mean Aerosol Optical
Depth
The spatial distribution of annual mean AOD at a
wavelength of 550 nm has been plotted for the period 2001–
2013 (see Fig. 1(a)). The results from Fig. 1 demonstrates that
aerosols had a marked impact on ten stations in Sindh,
namely Karachi, Hyderabad, Nawabshah, Larkana, Sukkur,
Tharparkar, Umerkot, Dadu, Jacobabad and Ghotki. This
study includes most of the cities due to their geo-strategic
locations, heavy industrial emissions, vehicular emissions
and other anthropogenic activities. The spatial gradient of
AOD for thirteen years shows high AOD values in central
and northern Sindh. This region has Indus River with dense
urban populations, residences, commercial and industrial
estates that are well connected by the highways of Sindh
i.e., Indus Highway and National Highway. High AOD
values (> 0.74) are observed over the areas with intense
anthropogenic activities. This includes an industrial region of
Jacobabad, Rohri (Sukkur), Larkana, Nawabshah, Hyderabad
and Karachi. Some other areas with spatial geomorphologic
locations such as Dadu and Ghotki has high AOD which may
consider a result of wind masses and weather conditions that
affects the aerosol loads in each locality. This study also
examined a very different case of Karachi. Karachi shows
AOD values ranges 0.30 to 0.65 instead to show highest
AOD values due to continuous sea breezes from Arabian
Sea that keeps AOD in motion. Balakrishnaiah et al. (2012).
determined aerosols marked impact on six southern India
sites, namely TVM, BLR, ATP, HYD, PUNE and VSK
Fig. 1(a). Spatial distributions of annual mean AOD at 550 nm for the period 2001–2013.
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
661
Fig. 1(b). Geographical location of Sindh.
They observed increased aerosol particles from southern
India to northern India up to the Himalayas. Prasad et al.
(2004) also examined the spatial gradient of AOD with
high AOD values over areas with intense anthropogenic
activities. Alam et al. (2010) analyzed AOD distribution
for eight cities of Pakistan named Peshawar, Rawalpindi,
Zhob, Lahore, Multan, D.G. Khan, Rohri and Karachi. It
was found that during summers AOD values were very
high in all selected cities, particularly in southern Pakistan,
these values were recorded more than 0.9. They also observed
an increase in AOD from north to south of Pakistan.
Similarly, high AOD in the summer months was reported
by Munir and Zareen (2006) between 24°–25°N and 66°–
71°E location of Pakistan. Alam et al. (2014) found high
AOD values specifically over Karachi, Lahore and D.G.
Khan. They attributed these variations to the reason that
these are urban, industrial and coastal areas. The dust from
Cholistan and Arabian Sea also contributed persistence of
dust aerosols along with local vehicular emission.
Seasonal Variations in Aerosol Optical Depth (AOD)
Seasonal variations in AOD were demonstrated and
calculated for the period 2001–2013 (Fig. 2). Very high
AOD values were found in almost whole Sindh during the
summer season, particularly in the northern and central
parts of Sindh where AODs greater than 1.27 were calculated.
In the central parts of Sindh AOD ranges from 1.17 to
1.27. Likewise, in the southern Sindh AODs values greater
than 0.98 were reported. Similar increases in AOD during
the summer season have previously been reported in other
cities of Pakistan (Alam et al., 2010, 2011a, b, c, 2012,
2014, 2015). In contrast, very low AOD values were observed
in almost whole region during the winter season. During
the winter season low AODs values were observed. During
the winters, very low AOD values (0.29–0.47) were found
in the southeastern and the southwestern parts of Sindh. The
reasons for these differences are the arrival of air masses
with dust in the region from the southern coast of the
Middle East and the monsoon winds during the summer
season. The monsoon winds blow from the Indian Ocean
after crossing the Indian peninsula, these winds with a very
large amount of moisture enters in the northeastern Pakistan.
The tail end of these winds enters in the regions of
southeastern Pakistan that is specifically Sindh Province.
All the northern and the central areas of Sindh are
considered as the hotspots of dust aerosols in the summer
months. Karachi, which is the coastal city, has a relatively
high AOD value (1.17) in the summer and low AOD value
(0.38) in the winter season. During the autumn season,
Dadu had specifically the highest AOD value (0.72) of the
season while other areas of Sindh had AOD values range
from 0.54 to 0.66. In addition, the spring months are the
period of dust storms in the region that’s why after winters
again AOD had shown increased values (0.71–0.86). The
southeastern and southwestern Sindh had low AOD values,
which are greater than 0.49 during the autumn season.
662
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
Fig. 2. Monthly mean AOD retrieved from MODIS for the period 2001–2013 over ten sites in Sindh, Pakistan.
Pre-monsoon period prevails in the month of June,
monsoon in the months of July and August and postmonsoon in the month of September (Alam et al., 2010;
Balakrishnaiah et al., 2012). Pre-monsoon shows high
AOD in northern Sindh including sites, like Jacobabad,
Sukkur, Ghotki, and Larkana which experience air masses
from the Punjab that impacts on aerosol concentration.
Monsoon shows high AOD value in northern, northwestern
Sindh due to air masses through Punjab and southern
Sindh, which have maximum water droplets and sea sprays
by tropical cyclones of coast. The AOD seasonal variations
are maximum in Sindh in all four seasons, i.e., summer,
winter, autumn and spring (Fig. 3). During the winter and
summer seasons AOD distribution shifts from low AOD
values (winter) to high AOD values (summer). Autumn
marks a spot of high AOD values and spring shows a similar
spatial distribution with little difference in extent. The
similar seasonal variations were studied by Balakrishnaiah
et al. (2012) in India and Alam et al. (2010, 2012, 2014,
2015) in Pakistan. They found low AOD values in summer
and high AOD values in winter season. Alam et al. (2014)
found a high value of 2.3 in summer and low values of 0.2
in winter for coastal areas of Pakistan.
The spatial distribution of aerosol is much higher in
extreme east and west of Sindh that are remote arid areas
experiencing dust plumes from Middle-east, Iran and Arabian
Sea, or north to south central Sindh because of increases in
urbanization and industrialization, dust storms, and salt
particles in Sindh. Similar studies have been conducted over
northern China by Qi et al. (2012), who concluded that the
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
663
Fig. 3. Spatial correlation of AOD vs. Alpha for the period 2001–2013.
AOD values have significant seasonal variation, specifically
small and high in summer. Tiwari and Singh (2013) studies
shown that coarse-mode aerosol particles are dominant in the
pre-monsoon, while fine-mode aerosol particles are dominant
in the winter season. Other studies have been conducted by
Kuniyal et al. (2009) and Gupta et al. (2013), concluded
AOD variations were highly dependent on both the travel
pathways and the sources (e.g., sea salt, desert dust, smoke
from burning biomass, and industrial pollution) of the air
masses.
Relationship between AOD and Ångström Exponent
Ångström exponent (Alpha) is the exponent that
describes the spectral dependency of aerosol optical depth
on wavelength. This spectral dependence of the measured
optical depth is determined as <Alpha> that is considered
as a good indicator of the size of aerosol particles. High
Alpha values indicate a dominance of very fine particles,
whereas, low values indicate a dominance of coarse particles.
Therefore, <Alpha> is a useful exponent to estimate the size
of the aerosol particle. Alpha is determined from the spectral
dependence of the measured optical depth as suggested by
Angstrom (1963). The spatial correlation plot of AOD at
550 nm versus <Alpha> shows that the <Alpha> increases
mostly with AOD over Sindh shown in Fig. 3. Many factors
are responsible for high AOD in the region. Sometimes
AOD concentrations present near clouds influence weather
of the surface and increases size of humidified aerosols in
the atmosphere (Balakrishnaiah et al., 2012).
This study analyzed an inverse relationship between AOD
and Alpha. Smaller the exponent defines larger the particle
size over ten sites of Sindh. Correlations for Alpha vs. AOD
ranges from –1.00 to –0.32 over Sindh. This decrease in
correlation may be associated with dust loading for most of
the Sindh sites except the coastal area, where sea salt aerosols
are dominant. In order to investigate the origins of the air
masses arriving at the studied sites we performed back
trajectory analyses based on the NOAA HYSPLIT (National
Oceanic and Atmosphere Administration Hybrid Single
Particle Particle Langrangian Integrated Trajectory) model
shown in Fig. 4. Most of the air masses reached Sindh from
the Dasht (Iran), Hamoun Wetlands (Afghanistan), Cholistan
(Pakistan), Thar (India) deserts, and also from the Arabian
Sea.
Alam et al. (2012) found high <Alpha> values in January
and low values in June that indicates the dominance of fine
particles in January and coarse particles in June over urban
areas of Pakistan. June is one of the months of dust storms
in the region. They also found higher AOD values over
Lahore than Karachi due to the fact that Karachi is a
coastal city of Pakistan. Aerosols keep moving because of
constant sea breezes over Karachi.
Analysis of Influence of Air Mass Trajectory on AOD
Concentration
In Figs. 4(a)–(h) back trajectories are representatives of
four seasons, i.e., summer, winter, autumn and spring.
These were drawn for the 500, 1500, and 2500 m altitude
for the measurements to study complex behavior of aerosol
near the earth’s surface. These were conducted on typical
days of four seasons, i.e., 15th January (winter), 15th April
(spring), 15th July (summer) and 15th October (autumn).
These trajectories are the part of GDAS (Global Data
Assimilation) dataset reprocessed by National Centers for
664
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
Environmental Prediction (NCEP) of the Air Resource
Laboratory. The NOAA HYSPLIT model is helpful to
understand the origins of the air masses arriving in the
studied region (Draxler and Rolph, 2003). We found similar
trajectories for other time periods, but this study includes only
selective back trajectories as these are the representatives
of the entire season.
The cruise locations on these days of months were
25°00′N, 67°01′E (Karachi), 26°14′N, 68°24′E (Nawabshah),
27°40′N, 68°53′E (Rohri), 24°74′N, 69°80′E (Tharparkar),
25°22′N, 68°22′E (Hyderabad), 28°16′N, 68°27′E (Jacobabad).
All the back trajectories are calculated at 0000 h UTC. The
trajectories originate from different sources, mostly from
Hamoun Wetlands of Afghanistan, Dasht Desert of Iran,
Cholistan Desert of Pakistan and Thar Desert of Pakistan and
India and Arabian Sea that increase in aerosol concentrations
Fig. 4. Back trajectories of different heights in meters ending at 2300 UTC for six stations in Sindh, Pakistan dated
January 15, July 15, April 15, and May 15, 2013.
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
665
Fig. 4. (continued).
could be observed over this region. Sindh is considered as
lower Indus Plain that is influenced by four seasons: summer
with monsoon time period (June–August), winter under the
influence of western depressions (December–February),
spring (March–May) and autumn (September–November).
Dust storms and thunderstorms are characteristics of April,
May, October and November (Prasad et al., 2004, 2005,
2007). In addition, local pollution in cities and industrial
estates, like Karachi, Hyderabad, Nawabshah and Rohri
also seen to have contributed to an increase in the aerosol
concentrations in the studied region (Gupta et al., 2006;
Alam et al., 2011c).
On 15 January the air parcels at 2500 m originated from
UAE and Oman arid regions and before reaching Karachi,
Hyderabad, Nawabshah and Tharparkar traveled through
the Arabian Sea. For Rohri and Jacobabad 2500 m trajectory
originated from the Dasht Desert of Iran. A short distance
has been traveled from Thar to Nawabshah, Makran for
666
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
Rohri, Coast of Iran for Hyderabad, and Persian Gulf for
Karachi and Tharparkar at 1500 m. The 500 m air trajectory
originates from the Arabian Sea and Thar Desert of Pakistan
and India. On 15 July the 2500 m air trajectories had north,
northeast, and east origin of Pakistan. The 500 m air
trajectories originated from the Arabian Sea and spent about
12 hours in the sea before reaching Karachi, Hyderabad and
Tharparkar. The 1500 m air parcels originating within
Sindh had little journeyed with zigzag air mass pattern. It
is therefore analyzed that air masses in January traveled
long distances mostly from south-west before reaching
Sindh whereas, in June traveled shorter distances mostly
from the northeast of Sindh. The air trajectories spent more
time over sea in January; in contrast, in June these trajectories
spent more time over land that is the reason why AOD
values are recorded highest in summer months and lowest
in winter season. Most of the locations from where air
masses originated are arid or semi-arid in all seasons thus
there is a great chance of dust particles blown with air masses
to increase aerosols concentrations in Sindh. A similar source
was found by Ramachandran and Jayaraman (2003) that
most of the air masses with the altitude of 500, 1000 and 5000
m originated from North Atlantic Ocean passes through
Africa having world's largest Sahara Desert and the Arabian
peninsula with the states like Yemen, Oman and Saudi
Arabia traveled through Afghanistan and southern Pakistan
before reaching India. The possibility of the air masses
getting mixed with the anthropogenic particles during travel
cannot be eliminated. From air mass trajectories it has been
clear that aerosol concentrations are influenced by arid and
semi-arid regions of middle-east, the Arabian Sea, the Indian
Ocean and industrial estates in Sindh (Ramachandran and
Jayaraman, 2003; Alam et al., 2011c). Most of the air masses
reached to various cities of Pakistan from Dasht (Iran), Thar
(India), Arabian Sea, Afghanistan, and Sahara desert (Alam
et al., 2010, 2011c, 2015). Therefore, both local sources
and long-range transported aerosol lead to high AOD in
the region.
Relationship between Aerosol Optical Depth and Cloud
Parameters
The MODIS provides an enormous valuable data to
understand how aerosols relate cloud parameters (Myhre et
al., 2007; Alam et al., 2010; Alam et al., 2014). This section
deals with the relationships between AOD and cloud
parameters for the ten sites of Sindh through the spatial
correlation map. We used the correlation maps between
AOD and cloud parameters for the time period from 2001
to 2013. For the entire study area correlation maps used for
a real comparison between AOD and WV, AOD and CF,
AOD and CTT, and AOD and CTP. It has been noticed
that higher aerosol loading favors the distributions of higher
clouds and larger cloud fractions, either the MODIS AOD
data is calculated from all-data or the stringently less-cloudy
data sets. Most of the data are noisier when recorded high
clouds datasets with a significant deficiency in the sample
size (Balakrishnaiah et al., 2012). These relationships are
discussed in the following sub-sections.
Relationship between Aerosol Optical Depth and Water
Vapor
In order to understand the aerosol impact and the process
of the hydrological cycle, we investigated the change in
column water vapor in relation to aerosols (Platnick et al.,
2003; Myhre et al., 2007; Balakrishnaiah et al., 2012). The
spatial correlation between AOD and WV (Fig. 5) reveals,
that AOD and WV have a stronger positive correlation over
western and central Sindh than on eastern Sindh. The highest
Fig. 5. Spatial correlation for AOD vs. Water Vapor for the period 2001–2013.
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
positive correlations (correlation coefficient greater than 0.67)
were found for most of the eastern Sindh sites including
Karachi, Hyderabad, Nawabshah, Larkana, Sukkur, Ghotki
and Dadu. In addition relatively lower correlations (greater
than 0.62) was found for other site, such as Umerkot. The
lowest levels of correlation coefficient (greater than 0.22)
were found on eastern most strip of Sindh that’s the part of
Tharparkar. Similar results were found by Alam et al. (2010,
2014) that shows a strong positive correlation between
AOD and WV at higher latitudes than at lower latitudes of
Pakistan. They analyzed highest positive correlation greater
than 0.8 at Peshawar and Rawalpindi and lower levels of
correlations greater than 0.7 over other studied sites of
relatively lower latitudes of Pakistan. In addition, Alam et
al. (2014) found no significant relationship between AOD
and WV during the winter season due to absence of dust
aerosols consequently lowers the WV in several cities of
Pakistan. They related these results with the fact that aerosols
might be removed by monsoon rains of summer and the AOD
values reduced by colder ground conditions. An elevated WV
values was founded along the coast like Karachi where
industrial and vehicular emissions is also very high.
The water absorbing ability of aerosols may vary for
different types of aerosol. Shi et al. (2008) and Alam et al.
(2011) found during their studies that coarse fractions of
aerosols i.e., dust particles are mostly insoluble but may
become hygroscopic when coated with sulphate or other
soluble inorganic aerosols during transport. In addition, Li
and Shao (2009) found that when these dust particles well
coated with a nitrate may become hygrophobic. Indeed, the
concentration of aerosols with their types and nature changes
the climate with meteorological parameters such as
precipitation, temperature, wind speed and wind direction
(Aloysius et al., 2009; Alam et al., 2011). During the summer
667
season, most of the air masses come from Arabian Sea shown
in Fig. 4, that is the source of water vapor and higher
values of AOD in the region. IPCC (2013) describes the
role of water vapor on the scattering behavior of aerosols.
All hydrophilic aerosols are important to form clouds with
the availability of water vapors to change in both direct
and indirect radiative forcing on climate. Therefore, the
correlation of aerosol and water vapor is an important topic
for modeling of aerosol and for remote sensing to understand.
Relationship between Aerosol Optical Depth and Cloud
Fraction
The spatial-temporal correlation between AOD and CF
has been plotted for various locations over Sindh during the
period 2001–2013 as shown in Fig. 6. Overall the correlation
for AOD and CF was found higher at lower Sindh and
lower/negative at upper Sindh. For example, the correlation
between AOD and CF was greater than 0.23 in Karachi
whereas, in Dadu, Larkana, and Sukkur it was less than
Karachi and greater than 0.08 and in some cases less than
zero or negative. Such areas are biomass and dust prone
areas that affect the correlation between two parameters.
Kaufman et al. (2005a, b) examined the influence of AOD
on meteorological parameters such as cloud cover and air
pressure and temperature differences. They found that various
cloud properties and aerosol concentrations are being affected
by changing in atmospheric circulations for instance, a low
pressure region may tend to accumulate aerosol particles
and water vapors that lead generating favorable atmospheric
conditions to increase cloud cover.
The increased aerosol exposure to solar radiation will result
in stronger aerosol absorption effect on clouds. Physically,
aerosol absorption of solar radiation can evaporate or thin
cloud optically (Hoeve et al., 2011), and that the decrease of
Fig. 6. Spatial correlation for AOD vs. Cloud fraction for the period 2001–2013.
668
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
CF with AOD. Thus absorption effects suppress the formation
and growth of clouds. Some desert clouds are dust formed
clouds made with dust and tiny droplets in a case of presence
of humidity in surroundings as studied by Rosenfeld et al.
(2001). Similar results were found by Balakrishnaiah et al.
(2012) that the correlation between AOD and CF was found
to be higher than that of coastal sites compared to continental.
Nakajima et al. (2001) found that most of the global regions
adjacent to the coasts of the continents, have shown positive
correlation between AOD and CF. In addition, most of the
tropical continental regions have shown a negative correlation
with small CF and large AOD value due to the total absence
or a very few low clouds interacting with aerosols or may
be due to the presence of dust particles that are not very
effective as CCN.
For most of the southern Sindh sites AOD and cloud
fraction are positively correlated such as in Karachi,
Hyderabad, Nawabshah, Thatta, Badin, Tharparkar, and
Umerkot. As we move towards the north of Sindh correlation
starts to reduce for the rest of the Sindh sites like for Dadu,
Larkana, Khairpur, Jacobabad and Ghotki especially the
negative correlation has been noticed in eastern and western
Sindh region. In these areas negative correlation was found
in the range from –0.56 to –0.60. Similarly, Alam et al.
(2010) found the correlation between CF and AOD higher
at lower latitudes and lower at higher latitudes of Pakistan.
They mentioned all those regions of Pakistan dominated
by biomass and dust aerosols have the marked increase in
the correlation. All meteorological factors influenced that
relationship of AOD and CF as well. Alam et al. (2014)
analyzed the correlation between AOD and CF, and found
lower in urban areas and higher in arid and coastal stations
of Pakistan. During the summer months, it was found that
AOD and CF were positively correlated as both values
were in increasing trend. In contrast, the correlation was
found negative during winter months.
Relationship between Aerosol Optical Depth and Cloud
Top Temperature
In order to retrieve many properties of the atmosphere
closest to surface that is important to know the atmospheric
temperature at the level of the cloud top (Balakrishnaiah et
al., 2012). In Fig. 7, the spatial correlation of AOD and CTT
has shown over the period 2001 to 2013. This demonstrates
that CTT increased as AOD increased in almost all northern
Sindh sites, whereas, started to decrease and shown a negative
correlation towards southern Sindh. Similarly, Alam et al.
(2010) found a positive correlation between AOD and CTT
at higher latitudes like Peshawar, Zhob, and Rawalpindi and
a negative correlation at lower latitudes like Karachi. This
is the opposite case as we discussed earlier for AOD and
CF correlation map in which CF was observed positively
correlated with AOD over southern Sindh, whereas, negatively
correlated over northern Sindh. There was a positive
correlation between AOD and CTT at Ghotki, Jacobabad,
Dadu, Sukkur, Larkana and Nawabshah indicate a consistent
positive correlation at higher latitudes of Sindh (High values
are up to 0.17). In addition, Karachi, Hyderabad, Umerkot
and Tharparkar following a negative correlation at lower
latitudes of Sindh (Low values are not less than –0.58).
Therefore, mainly a positive correlation between AOD and
CTT has been observed for most of the stations of Sindh may
be due to large-scale meteorological situations in MODIS
data. This correlation might be involved in a very complex
interplay among convection, layer of boundary and largescale cloud parameterizations in the region (Quass et al.,
Fig. 7. Spatial correlation for AOD vs. Cloud Top Temperature for the period 2001–2013.
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
669
Fig. 8. Spatial correlation for AOD vs. Cloud Top Pressure for the period 2001–2013.
2009; Alam et al., 2010). Several atmospheric and surface
properties may retrieve by accurate information on cloud
top temperature. It plays an essential role in the net earth
radiation budget studies (Balakrishnaiah et al., 2012).
Furthermore, Alam et al. (2014) found a negative correlation
between AOD and CTT in the winter season, except for
Lahore and D G Khan. On the other hand, a consistent
negative correlation was observed at coastal sites of lower
latitudes like Karachi due to the presence of fine mode
aerosol particles that may couple within the water column
to show decreased CTT. In addition, anthropogenic aerosols
may change the cloud-top temperature by their induced
secondary circulations in the atmosphere (Balakrishnaiah
et al., 2012).
It has been found that the aerosol concentration influence
CCN for most of Sindh areas as aerosol acts on clouds to
change the size and composition of cloud particles with a
change in nearby temperature conditions in the atmosphere.
Some southern most sites are insensitive to changes in CTT
with changing aerosol concentrations. The anthropogenic
aerosols increase CTT by the induced secondary circulation,
and thus change the cloud top temperature (Santer et al.,
1995; Sekiguchi et al., 2009; Balakrishnaiah et al., 2012).
Relationship between Aerosol Optical Depth and Cloud
Top Pressure
The spatial correlation between AOD and CTP over the
period 2001 to 2013 indicates overall a negative correlation
in Sindh (see Fig. 8). It was observed that as CTP decreased
with an increased in AOD especially at lower latitudes,
whereas in relatively mid-latitude this decrease has been
observed moderate. Overall, at coastal sites such as Karachi
there was a significant decrease in CTP with AOD whereas
at continental sites such as Hyderabad, Nawabshah, Larkana,
Sukkur, Dadu, Jacobabad and Ghotki this decrease was
only moderate. The correlation was found lower in Karachi
(–0.45), Tharparkar (–0.35), Hyderabad (–0.25), Nawabshah
(–0.16) and more than –0.16 at Ghotki. In addition, Alam et
al. (2014) found a consistent positive correlation between
AOD and CTP at Peshawar (higher latitudes) and a
negative correlation at Karachi (lower latitudes). Furthermore,
they have investigated a positive correlation of CTP with
AOD for the entire region of Pakistan in the spring season.
These correlation facts may be caused by the large-scale
meteorological variations.
A similar study has been conducted over different cities
of Pakistan by Alam (2010) in which they showed a
correlation of AOD and CTT or CTP. Overall, they found
a negative correlation of CTP with AOD in the southern
region of Pakistan as there was a significant decrease in
CTP relative to AOD, moderately decreases at mid latitudes
and increase at high-latitudes. Kaufman (2005) reported
that CTP decreased with an increased of AOD due to the
suppression by increasing the cloud lifetime thus affecting
the cloud albedo and CTP. Balakrishnaiah (2012) and
Myhre (2007) investigated the same correlation in order to
understand atmospheric characteristics that changes with
time.
Tripathi et al. (2007) analyzed reduced updrafts of vertical
winds in the winter seasons and very strong drafts in the
monsoon seasons of Indo- Gangetic Plain. They suggest
that all these vertical winds of the region are strongly
correlated with changing CTP and CF. Koren et al. (2005)
also found the vertical wind velocity as a very important
meteorological parameter to influence cloud properties. They
investigated the Atlantic convection clouds are restructured
670
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
and invigorated by vertical winds. These vertical updrafts
at 500 mb are correlated to the CTP and CF larger than 0.9 for
almost the entire studied region, 0.6 for some regions and
–0.7 for other regions. All these variations suggest associations
between meteorology and aerosol in these regions.
CONCLUSION
All aerosol characteristics based on satellite data were
not deeply studied over Sindh. To the best of our knowledge
this is the first attempt to analyze and assist aerosol
characteristics over this region. Thus, this paper fills the
scientific gap in understanding of aerosols and its
distributions, concentrations and variations over the region.
AOD spatial and temporal distributions were analyzed
through the use of the MODIS satellite data. The various
sources of aerosols arriving to Sindh were identified
through the HYSPLIT model. The Angstrom exponent was
determined through the MODIS dataset that may change if
the concentration of small and large aerosol particles changes.
A correlation of AOD products with Cloud parameters such
as CF, WV, CTT, and CTP is necessary for climatological
studies and to improve the accuracy and the coverage
achievable with MODIS sensor. Cloud parameters were
investigated in order to represent MODIS information that
was rarely studied for the understanding of atmospheric
conditions over this region of Pakistan.
In this study of aerosol spatio-temporal distribution,
maximum AOD was found in the summer season and
minimum AOD was recorded in the winter season. The
highest AOD values were observed in cities of Sindh where
industrial and vehicular emissions with various anthropogenic
activities increases the number of concentration and
distribution over Sindh. Similarly, AOD concentration is
also influenced by water cycle of the southern humid
regions that converts particles in hygroscopic aerosols. The
desert of Sindh has high seasonal concentrations mostly
from the Arabian Sea during the monsoon windy season. It
has also been found that the water vapor increased as AOD
increased over most of Sindh except the desert, CF
increased with AOD, where as CTP and CTT have shown
a positive correlation in the northern Sindh, a negative in
the central Sindh and a highly negative in the southern
Sindh. The observed relationship between AOD and CF,
the co-variation of AOD with CTP, CTT and WV may be
attributed to the large-scale meteorological variation.
ACKNOWLEDGEMENTS
We are highly grateful to Meritorious Prof. Dr. Syed
Jamil Hasan Kazmi, Department of Geography, University
of Karachi for his support and assistance. We are
extremely grateful to Dean, Faculty of Science, University
of Karachi for the financial support extended which make
this research study possible.
REFERENCES
Ackerman, A.S., Toon, O.B., Stevens, D.E., Heymsfield,
A.J., Ramanathan, V. and Welton, E.J. (2000). Reduction
of Tropical Cloudiness by Soot. Science 288: 1042–1047.
Andreae, M.O., Rosenfeld, D., Artaxo, P., Costa, A.A. and
Frank, G.P. (2004). Smoking Rain Clouds over the
Amazon. Science 303: 1337–1342.
Alam, K., Iqbal, M.J., Blaschke, T., Qureshi, S. and Khan,
G. (2010). Monitoring Spatiotemporal Variations in
Aerosols and Aerosol-cloud Interactions over Pakistan
Using MODIS Data. Adv. Space Res. 46: 1162–1176.
Alam, K., Trautmann, T. and Blaschke, T. (2011a). Aerosol
Optical Properties and Radiative Forcing over Mega
City Karachi. Atmos. Res. 101: 773–782.
Alam, K., Blaschke, T., Madl, P., Mukhtar, A., Hussain,
M., Trautmann, T. and Rahman, S. (2011b). Aerosol Size
Distribution and Mass Concentration Measurements in
Various Cities of Pakistan. J. Environ. Monit. 13: 1944–
1952.
Alam, K., Qureshi, S. and Blaschke, T. (2011c). Monitoring
Spatio-temporal Aerosol Patterns over Pakistan Based on
MODIS, TOMS and MISR Satellite Data and a HYSPLIT
Model. Atmos. Environ. 45: 4641–4651.
Alam, K., Trautmann, T., Blaschke, T. and Majid, H. (2012).
Aerosol Optical and Radiative Properties during Summer
and Winter Seasons over Lahore and Karachi. Atmos.
Environ. 50: 234–245.
Alam, K., Khan, R., Blaschke, T. and Mukhtiar, A. (2014).
Variability of Aerosol Optical Depth and Their Impact
on Cloud Properties in Pakistan. J. Atmos. Sol. Terr.
Phys. 107: 104–112.
Alam, K., Khan, R., Ali, S., Ajmal, M., Khan, G., Wazir,
M. and Azmat, M. (2015). Variability of Aerosol
Optical Depth over Swat in Northern Pakistan Based on
Satellite Data. Arabian J. Geosci. 8: 547–555.
Aloysius, M., Mohan, M., Babu, S.S., Parameswaran, K.
and Moorthy, K.K. (2009). Validation of MODIS
Derived Aerosol Optical Depth and an Investigation on
Aerosol Transport over the South East Arabian Sea
during ARMEX-II. Ann. Geophys. 27: 2285–2296.
Ångström, A. (1961). Techniques of Determining the
Turbidity of the Atmosphere. Tellus 8: 214–223.
Ashraf, A., Aziz, N. and Ahmed, S.S. (2013). Spatio
Temporal Behavior of AOD over Pakistan Using MODIS
Data. IEEE Inter. Conf. Aerospace Science & Engineering
(ICASE).
Balakrishnaiah, G., Raghavendra Kumar, K., Kumar, Suresh,
Reddy, B., Rama Gopal, K., Reddy, R.R., Reddy, L.S.S.,
Swamulu, C., Nazeer Ahammed, Y., Narasimhulu, K.,
Krishna Moorthy, K. and Suresh Babu, S. (2012). Spatiotemporal Variations in Aerosol Optical and Cloud
Parameters over Southern India Retrieved from MODIS
Satellite Data. Atmos. Environ. 47: 435–445.
Coakley, J.A., Bernstein, R.L. and Durkee, P.A. (1987).
Effects of Ship Stack Effluence on Cloud Reflectance.
Science 237: 953–1084.
Chin, M., Chu, A., Levy, R., Remer, L., Kaufman, Y.,
Holben, B., Eck, T., Ginoux, P. and Gao, Q. (2004).
Aerosol Distribution in the Northern Hemisphere during
ACE-Asia: Results from Global Model, Satellite
Observations, and Sun Photometer Measurements. J.
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
Geophys. Res. 109, doi: 10.1029/2004JD004829.
De Meij, A., Krol, M., Denterner, F., Vignati, E., Cuvelier,
C. and Thunis, P. (2006). The Sensitivity of Aerosol in
Europe to Two Different Emission Inventories and
Temporal Distribution of Emission. Atmos. Chem. Phys.
6: 4287–4309.
Draxler, R.R. and Rolph, G.D. (2003). HYSPLIT (Hybrid
Single-Particle Lagrangian Integrated Trajectory) Model
Access via the NOAA ARL READY Website
(http://www.arl.noaa.gov/ready/hysplit4.html). NOAA
Air Resources Laboratory, Silver Spring, MD.
Gupta, P., Christopher, S.A., Wang, J., Gehrig, R., Lee,
Y.C. and Kumar, N. (2006). Satellite Remote Sensing of
Particulate Matter and Air Quality Assessment over
Global Cities. Atmos. Environ. 40: 5880–5892.
Gupta, P., Khan, M.N., Silva, A.D. and Patadia, F. (2013).
MODIS Aerosol Optical Depth Observations over Urban
Areas in Pakistan: Quantity and Quality of the Data for
Air Quality Monitoring. Atmos. Pollut. Res. 4: 43–52.
Hoeve, J.E.T., Remer, L.A. and Jacobson, M.Z. (2011).
Microphysical and Radioactive Effect of Aerosols on
Warm Clouds during the Amazon Biomass Burning
Season as Observed by MODIS: Impact of Water Vapor
and Land Cover. Atmos. Chem. Phys. 11:3021–3036.
Hsu, N.C., Tsay, SC., King, M.D. and Herman, J.R.
(2004). Aerosol Properties over Bright-Reflecting Source
Regions. IEEE Trans. Geosci. Remote Sens. 42: 557–569.
Hsu, N.C., Tsay, S., King, M.D. and Herman, J.R. (2006).
Deep Blue Retrievals of Asian Aerosol Properties during
ACE-Asia. IEEE Trans. Geosci. Remote Sens. 44: 3180–
3195.
Huang, J., Hsu, N.C., Tsay, S.C., Hansell, R.A., Jeong, M.,
Bettenhausen, C., Warner, J., Gautam, R., Sayer, A.M.,
Holben, B.N., Petrenko, M. and Ichoku, C.M. (2011).
Evaluation of the Collection 5.1 MODIS Deep Blue
Aerosol Products Using Aerosol Robotic Network,
MODIS Science Team meeting, Adelphi, MD.
Ichoku, C., Kaufman, Y.J., Remer, L.A. and Levy, R. (2004).
Global Aerosol Remote Sensing from MODIS. Adv.
Space Res. 34: 820–827.
IPCC (2013). Climate Change: The Physical Science Basis.
Chapter-7, p. 03–118.
Jin, M. and Shepherd, J.M. (2008). Aerosol Relationships
to Warm Season Clouds and Rainfall at Monthly Scales
over East China: Urban Land versus Ocean. J. Geophys.
Res. 113, doi: 10.1029/2008JD010276.
Kaufman, Y.J. and Fraser, R.S. (1997a). Confirmation of
the Smoke Particles Effect on Clouds and Climate.
Science 277: 1636–1639.
Kaufman, Y.J. and Fraser, R.S. (1997b). The Effect of
Smoke Particles on Clouds and Climate Forcing. Science
277: 1636–1639.
Kaufman, Y.J., Koren, I., Remer, L.A., Rosenfeld, D. and
Rudich, Y. (2005a). The Effect of Smoke, Dust, and
Pollution Aerosol on Shallow Cloud Development over
the Atlantic Ocean. Proc. Nat. Acad. Sci. U.S.A. 102:
11207–11212.
Kaufman, Y.J., Remer, L.A., Tanré, D., Li, R.R., Kleidman,
R., Mattoo, S., Levy, R., Eck, T., Holben, B.N., Ichoku, C.,
671
Martins, V. and Koren, I. (2005b). A Critical Examination
of the Residual Cloud Contamination and Diurnal
Sampling Effects on MODIS Estimates of Aerosol over
Ocean. IEEE Trans. Geosci. Remote Sens. 43: 2886–
2897.
Kaufman, Y.J., Nakajima, T. (1993). Effect of Amazon
Smoke on Cloud Microphysics and Albedo – Analysis
from Satellite Image. J. App. Meteorol. 32: 729–744.
Kaufman, Y.J., Tanre, T. and Boucher, O. (2002). A Satellite
View of Aerosols in the Climate System. Nature 419:
215–223.
Kim, B.G., Schwartz, S.E., Miller, M.A. and Min, Q.L.
(2003). Effective Radius of Cloud Droplets by Groundbased Remote Sensing: Relationship to Aerosol. J.
Geophys. Res. 108, doi: 10.1029/2003JD003721.
King, M.D., Kaufman, Y.J., Menzel, W. and Tanre, D.
(1992). Remote Sensing of Cloud, Aerosol, and Water
Vapor Properties from the Moderate Resolution Imaging
Spectrometer (MODIS). IEEE Trans. Geosci. Remote
Sens. 30: 2–27.
Koren, I., Kaufman, Y.J., Remer, L.A. and Martins, J.V.
(2004). Measurement of the Effect of Amazon Smoke
on Inhibition of Cloud Formation. Science 303: 1342–
1345.
Koren, I., Kaufman, Y.J., Rosenfeld, D., Remer, L.A. and
Rudich, Y. (2005). Aerosol Invigoration and Restructuring
of Atlantic Convective Clouds. J. Geophys. Res. 32, doi:
10.1029/2005GL023187.
Kuniyal, J.C., Thakur, A., Thakur, H.K., Sharma, S., Pant,
P., Rawat, P.S. and Moorthy, K.K. (2009). Aerosol
Optical Depths at Mohal-Kullu in the Northwestern
Indian Himalayan High Altitude Station during ICARB.
J. Earth Syst. Sci. 118: 41–48.
Li, W.J. and Shao, L.Y. (2009). Observation of Nitrate
Coating on Atmospheric Mineral Dust Particles. Atmos.
Chem. Phys. 9: 1863–1871.
Lohmann, U. (2002). Possible Aerosol Effects on Ice
Clouds via Contact Nucleation. J. Atmos. Sci. 59: 647–
656.
Mahowald, N.M. and Kiehl, L.M. (2003). Mineral Aerosol
and Cloud Interactions. J. Geophys. Res. 30: 1475, doi:
10.1029/2002GL016762.
Munir, S. and Zareen, R. (2006). Study of MODIS-derived
Cloud Top Temperature/Pressure and Aerosol Optical
Thickness, 36th COSPAR Scientific Assembly Held in
Beijing, China.
Myhre, G., Stordal, F., Johnsrud, M., Kaufman, Y.J.,
Rosenfeld, D., Storelvmo, T., Kristjansson, J.E., Berntsen,
T.K., Myhre, A. and Isaksen, I.S.A. (2007). Aerosolcloud Interaction Inferred from MODIS Satellite Data and
Global Aerosol Models. Atmos. Chem. Phys. 7: 3081–
3101.
Nakajima, T., Higurashi, A., Kawamoto, K. and Penner,
J.E. (2001). A Possible Correlation between Satellitederived Cloud and Aerosol Microphysical Parameters. J.
Geophys. Res. 28: 1171–1174.
Penner, J.E., Dong, X.Q. and Chen, Y. (2004). Observational
Evidence of a Change in Radiative Forcing Due to the
Indirect Aerosol Effect. Nature 427: 231–234.
672
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
Platnick, S., King, M.D., Ackerman, S.A., Menzel, W.P.,
Baum, B.A., Riedi, J.C. and Frey, R.A. (2003). The
MODIS Cloud Products: Algorithms and Examples from
Terra. IEEE Trans. Geosci. Remote Sens. 41: 459–473.
Prasad, A.K., Singh, R.P. and Singh, A. (2004). Variability
of Aerosol Optical Depth over Indian Subcontinent Using
Modis Data. J. Indian Soc. Remote Sens. 32: 313–316.
Prasad, A.K. and Singh, R.P. (2005). Extreme Rainfall
Event of July 25–27, 2005 over Mumbai, West Coast,
India. J. Indian Soc. Remote Sens. 33: 365–370.
Prasad, A.K. and Singh, R.P. (2007). Changes in Aerosol
Paramters during Major Dust Storm Events (2001-2005)
over the Indo-Gangetic Plains Using AERONET and
MODIS Data. J. Geophys. Res. 112, doi: 10.1029/2006
JD007778.
Prasad, A.K., Singh, S., Chauhan, S.S., Srivastava, M.K.,
Singh, R.P. and Singh, R. (2007). Aerosol Radiative
Forcing over the Indo-Gangetic Plains during Major
Dust Storms. Atmos. Environ. 41: 6289–6301.
Qi, Y.L., Ge, J.M. and Huang, J.P. (2012). Spatial and
Temporal Distribution of MODIS and MISR Aerosol
Optical Depth over Northern China and Comparison
with AERONET. Atmos. Sci. 58: 2497–2506, doi:
10.1007/s11434-013-5678-5.
Quass, J., Ming, Y., Menon, S., Takemura, T., Wang, M.,
Penner, J.E., Gettelman, A., Lohmann, U., Bellouin, N.,
Boucher, O., Sayer, A.M., Thomas, G.E., McComiskey,
A., Freingold, G., Hoose, C., Kristjánsson, J.E., Liu, X.,
Balkanski, Y., Donner, L.J., Ginoux, P.A., Stier, P.,
Feichter, J., Sednev, I., Bauer, S.E., Koch, D., Grainger,
R.G., Kirkevåg, A., Iversen, T., Seland, Ø., Easter, R.,
Ghan, S.J., Rasch, P.J., Morrison, H., Lamarque, J.F.,
Iacono, M.J., Kinne, S. and Schulz, M. (2009). Aerosol
Indirect
EffectsGeneral
Circulation
Model
Intercomparison and Evaluation with Satellite Data.
Atmos. Chem. Phys. 9: 12731–12779.
Ramachandran, S. and Jayaraman, A. (2003). Spectral
Aerosol Optical Depths over Bay of Bengal and Chennai:
II—Sources, Anthropogenic Influence and Model
Estimates. Atmos. Environ. 37: 1951–1962.
Ramanathan, V., Crutzen, P.J., Kiehl, J.T. and Rosenfeld,
D. (2001). Aerosol, Climate, and the Hydrological Cycle.
Science 294: 2119–2124.
Remer, L.A., Kaufman, Y.J., Tanré, D., Matto, S., Chu,
D.A., Martins, J.V., Li, R.R., Ichoku, C., levy, R.C.,
Kleidman, R.G., Eck, T.F., Vermote, E. and Holben,
B.N. (2005). The MODIS Aerosol Algorithm, Products,
and Validation. J. Atmos. Sci. 62: 947–973.
Rosenfeld, D. (2000). Suppression of Rain and Snow by
Urban and Industrial Air Pollution. Science 287: 1793–
1796.
Rosenfeld, D., Rudich, Y. and Lahav, R. (2001). Desert
Dust Suppressing Precipitation: A Possible Desertification
Feedback Loop. Proc. Nat. Acad. Sci. U.S.A. 98: 5975–
5980.
Rosenfeld, D., Lahav, R., Khain, A. and Pinsky, M. (2002).
The Role of Sea Spray in Cleansing Air Pollution over
Ocean via cloud processes. Science 297: 1667–1670.
Santer, B.D., Taylor, K.E. and Penner, J.E. (1995). A
Search for Human Influences on the Thermal Structure
of the Atmosphere (No. UCRL-ID--121956). Lawrence
Livermore National Lab., CA (United States).
Schwartz, S.E., Harshvardhan, Benkovitz, C.M. (2002).
Influence of Anthropogenic Aerosol on Cloud Optical
Depth and Albedo Shown by Satellite Measurements
and chemical Transport Modeling. Proc. Nat. Acad. Sci.
U.S.A. 99: 1784–1789.
Sekiguchi, M., Nakajima, T., Suzuki, K., Kawamoto, K.,
Higurashi, A., Rosenfeld, D., Sano, I. and Mukai, S.
(2009). A Study of The direct and Indirect Effects of
Aerosols Using Global Satellite Data Sets of Aerosol
and Cloud Parameters. J. Geophys. Res. 108: 4699, doi:
10.1029/2002 JD003359.
Shi, Y., Zhang, J., Reid, J.S., Hyer, E.J. and Hsu, N.C.
(2013). Critical Evaluation of the MODIS Deep Blue
Aerosol Optical Depth Product for Data Assimilation
over North Africa. Atmos. Meas. Tech. 6: 949–969.
Shi, Z., Zhang, D., Hayashi, M., Ogata, H., Ji, H. and
Fujiie, W. (2008). Influences of Sulfate and Nitrate on
the Hygroscopic Behaviour of Coarse Dust Particles.
Atmos. Environ. 42: 822–827.
Tanre, D., Kaufman, Y.J., Herman, M. and Mattoo, S. (1997).
Remote Sensing of Aerosol Properties over Oceans Using
the MODIS/EOS Spectral Radiance. J. Geophys. Res.
102: 16971–16986.
Tiwari, S. and Singh, A.K. (2013).Variability of Aerosol
Parameters Derived from Ground and Satellite
Measurements over Varanasi Located in the IndoGangetic Basin. Aerosol Air Qual. Res. 13: 627–638.
Tripathi, S.N., Pattnaik, A. and Dey, S. (2007). Aerosol
Indirect Effect over Indo-Gangetic Plain. Atmos. Environ.
41: 7037–7047.
Twomey, S.A., Piepgrass, M. and Wolfe, T.L. (1984). An
Assessment of the Impact of Pollution on the Global
Albedo. Tellus Ser. B 36: 356–366.
Received for review, September 30, 2014
Revised, January 7, 2015
Accepted, January 9, 2015