American Journal of Remote Sensing

American Journal of Remote Sensing
2014; 2(4): 20-29
Published online October 20, 2014 (http://www.sciencepublishinggroup.com/j/ajrs)
doi: 10.11648/j.ajrs.20140204.11
ISSN: 2328-5788 (Print); ISSN: 2328-580X (Online)
The seasonal variability of aerosol optical depth over
Bangladesh based on satellite data and HYSPLIT model
Mainul Islam Mamun*, Monirul Islam, Pallab Kumar Mondol
Department of Applied Physics and Electronic Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
Email address:
[email protected] (M. I. Mamun), [email protected] (M. Islam), [email protected] (P. K. Mondol)
To cite this article:
Mainul Islam Mamun, Monirul Islam, Pallab Kumar Mondol. The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on
Satellite Data and HYSPLIT Model. American Journal of Remote Sensing. Vol. 2, No. 4, 2014, pp. 20-29. doi: 10.11648/j.ajrs.20140204.11
Abstract: Atmospheric aerosols have constituted a crucial environmental and climate issue. There is lack of studies dealing
with monitoring of aerosol patterns over Bangladesh. This review attempts to analyze the seasonal variations in AOD over
Bangladesh during the period 2002-2011, using MODerate resolution Imaging Spectroradiometer (MODIS) Level 3 remote
sensing data. A Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to generate a backward
trajectory in order to identify the origins of air masses, with the aim of understanding the spatio-temporal variability in aerosol
concentration. Seasonal variations during the last decade show maximum AOD values during pre-monsoon season; while
minimum AOD during post-monsoon. An evidence of decadal decreasing trend in AOD is found during monsoon season while
all other seasons show increasing trends. High spatio-temporal variations of AOD are observed during different seasons in
2010. Back trajectory analysis indicates that Bangladesh is mainly affected by the pollutants and desert dust of India
combining with sea salt particles blown from the Arabian Sea. The air masses are arriving at lower altitudes (500m, 1500m)
mainly from western India and Indian subcontinent but at higher altitude (2500m) especially in winter season it comes from far
western regions, such as Europe and various sub-Saharan regions of Africa. Different flow patterns of air masses during postmonsoon are observed that the air masses are arriving from southeast in the direction; in case of Sylhet division the sources of
air masses are in the coastal regions of Thailand, border regions of Myanmar and China. These studies become helpful to
understand the nature and influence of aerosols on seasonal dynamics over Bangladesh.
Keywords: Aerosol Optical Depth, MODIS, Seasonal Variations, HYSPLIT, Bangladesh
1. Introduction
Atmospheric aerosols have many impacts on global
climatic system. Aerosols attenuate the solar radiation to
reach in earth surface by scattering and absorbing the
radiation, altering the solar spectrum, affecting the earth’s
energy budget, influencing the cloud properties, hydrological
cycle and also human health [9]. Earth’s atmosphere is
changing due to the variations of atmospheric aerosol load,
greenhouse gases, solar radiation, and land surface properties
[18]. Aerosols are suspended liquid or solid particles in air.
They exhibit a wide range of compositions and shapes
depending on the origins and associated atmospheric
processes. Aerosol processes are very complex and highly
inhomogeneous in their distributions. As a result accurately
modeling of anthropogenic aerosols and their effects remains
a challenge till to present. Mass concentration quantification
by an optical measure known aerosol optical depth (AOD) is
usually used to estimate the aerosol loading or amount in the
atmosphere [27]. Various scientific researchers have been
recognized that atmospheric aerosol is a key parameter in
climate change studies especially over South Asia.
Atmospheric temperature fluctuations [15], [8] radiative
forcing (e.g. [22]), rainfall pattern changes (e.g. [19], [25]),
intensive tropical cyclone (e.g. [4]) and desertification (e.g.
[20]) are examined by aerosol studies. Aerosols exhibit high
spatio-temporal variations which influence the cloud
properties and precipitation processes. These indirect effects
are recognized by several literatures on regional and global
scale [16], [17]. Satellite data is important and useful for the
study about air quality research [6], [12]. Satellite retrievals
of column aerosol optical depth (AOD) are a cost effective
way to monitor and study aerosols distribution and effects on
climate [3]. [15] reported that for mapping the distribution
and properties of aerosols satellite data has tremendous
potential. Due to sparse of ground measurements in many
regions of the world satellite data is the only way to
monitoring aerosol loads and aerosol properties in a regional
21
Mainul Islam et al.: The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on Satellite Data and
HYSPLIT Model
and global scale. The USA’s National Oceanic and
Atmospheric Administration (NOAA) HYSPLIT model can
indirectly describe and map the spatio-temporal
temporal variations of
aerosols. Aerosol concentration in Asia is increasing over the
industrial and urban both areas due to growing populations,
increasing urbanization and industrialization, demands for
energy, changes in land use, increasing motorized traffic,
increasing temperature and increasing other kinds of
pollution. Different studies in India show increasing trend of
aerosol optical depth which reflects its polluted environment
as increasing anthropogenic aerosols. [14] reported that
spatial and temporal variations of aerosol load, optical
properties and their climate implications due to large
diversity in population density, regional emissions, land use
and seasonally-changed air masses. [23] analyzed the
seasonal AOD variability over Rajkot, in India, found lower
values in winter and higher values in summer. [19] also
found the maximum AOD in the summer season than in the
winter season over Indo-Gangetic plain. Similarly, [1] found
higher AOD values in summer and lower values in winter for
various cities in Pakistan. But a detailed analysis of
distribution of AOD over South Asia basically on monthly
and seasonally basis are rather sparer. To understand the
properties and impacts of aerosols monthly and diurnal
studies are needed.
The objective of this study is to investigate the seasonal
variability of AOD over Bangladesh during the period 20022011 using MODIS level-3 data. A detailed observation in
each season is done in the year of 2010. HYSPLIT model is
also used to understand the spatial and temporal variability
in aerosol load for seven divisions in Bangladesh of different
seasons.
2. Methodology
2.1. Study Area
Bangladesh is a low-lying, riverine country located in
South Asia with 710 km coastline largely marshy jungle on
the northern littoral of the Bay of Bengal. Geographically
Bangladesh is formed by the Ganges delta which is
constructed by the confluence of the Ganges (Padma),
Meghna and Brahmaputra (Jamuna) rivers and their
tributaries. The west, north, and east of Bangladesh are
bordered with India by a 4,095-kilometer land frontier and
the southeast is bordered with Myanmar by a short land and
water frontier (193 km) (Fig. 1). Four distinct seasons are
considered in the study area from climatic point of view.
Firstly the dry winter season from December to February,
secondly the pre monsoon hot summer from March to April,
thirdly the monsoon or rainy season from June to September
and fourthly the post monsoon season which lasts from
October to November [24]. Wind velocity is higher in
summer than in winter. The average temperature everywhere
is about 25°C. Mean monthly temperatures range between
18°C in January and 30°C in April-May. Maximum summer
temperature ranges between 38 °C and 41 °C. April is the
hottest month in most parts of the country and January is the
coolest month. Significant differences in seasonal
temperatures occur across the country. Heavy rainfall is
characteristic of Bangladesh as a result flood occurred every
year. The rainy season is characterized by southerly or southwesterly winds, very high humidity, and heavy rainfall and
long consecutive days of rainfall. About 80 % of total
rainfall occurs during the monsoon season.
Fig 1. Geographical location of Bangladesh.
2.2. Dataset and Analyses
Aerosol optical depth (AOD) can be measured by both in
situ and remote sensing measurement systems. Here the
satellite remote sensing measurement is used. To study the
large spatial and temporal heterogeneities of aerosol
distributions satellite remote sensing is essential. The
MODerate resolution Imaging Spectroradiometer (MODIS)
AOD datasets are used. The MODIS instruments onboard
NASA’s Terra and Aqua satellites which launched in
December 1999 and in May 2002 respectively. It has 36
spectral channels to provide information about atmosphere,
land and oceanic conditions. MODIS provides a continuous
data collection which is important for understanding both
long- and short-term change in the global environment.
Aerosol retrieval is different over land [11] from over oceans
[26]. The MODIS provides observations at moderate spatial
(250m to1 km) and temporal (1 to 2 days) resolutions using
different portions of the electromagnetic spectrum. For this
investigation MODIS Terra Level-3
Level data is used.
A 7-day air-mass back trajectory is made using the Hybrid
Single Particle Lagrangian Intregrated Trajectory (HYSPLIT)
model to reveal the sources of aerosols over Bangladesh of
seven different divisions with four representative seasons.
The FNL dataset which is reprocessed from the National
Oceanic and Atmospheric Administration (NOAA) is used as
the meteorological input for the trajectory model. The user
can calculate up to 40 forward or backward trajectories at
different altitudes with a horizontal grid of 1.5° × 1.5° and a
resolution of 500 × 500m using the HYSPLIT model. In this
study, the backward trajectories are calculated at 500m,
1500m, and 2500m heights above ground level to investigate
the origin of air masses bringing particular matters (such as
desert dust, sea salt) into the various divisions of Bangladesh
during different seasons.
American Journal of Remote Sensing 2014; 2(4): 20-29
3. Result and Discussions
3.1. Seasonal variations in AOD
Fig. 2 shows the spatial distribution of seasonal mean
AOD over Bangladesh in 2010, for four representative
seasons namely pre-monsoon (March-May), monsoon (JuneSeptember), post-monsoon (October-November) and winter
(December-February) which reveals high spatial variability.
The seasonality of AOD exhibits the patterns of higher
values during pre-monsoon & monsoon seasons whereas
lower values during post-monsoon & winter seasons.
Fig. 3 shows the temporal variations of seasonal AOD by
time series plots over Bangladesh during 2010. The results
show that at the beginning of pre-monsoon season the AOD
is increasing then from July AOD is rapidly decreasing and
this decreasing trend remains constant throughout the whole
monsoon season. Again from starting of post-monsoon
season the AOD is increasing rapidly and this increasing
trend follows the winter season.
3.1.1. Variations in AOD During Pre–Monsoon Season
In pre-monsoon (March-May) the spatial distribution of
AOD is presenting deviations from region to region as
shown in Fig. 2a. The northern, southern and eastern parts
show higher AOD distributions during pre-monsoon season.
It is found that the AOD starts to increase rapidly from the
beginning of pre-monsoon (March), reaching its maximum
in June as shown in Fig. 3a. The summer increase could be
due to high temperature and high wind velocities producing
larger quantities of wind-driven dust particles [1]. During
pre-monsoon season also the water soluble aerosols grow
hygroscopically in the presence of water vapor causes higher
AOD values [1]. A higher concentration of water vapor in
pre-monsoon season leads to a higher AOD because water
vapor and AOD are directly related to each other’s [1], [23].
The increasing AOD also indicate to increased emissions of
aerosols from biomass burning in Bangladesh, as the other
source of aerosols which must be examined [10]. Dust
activity is maximum during May-July. Dust frequency and
intensity during pre-monsoon depend on various
meteorological phenomenons such as rainfall amount, soil
moisture, wind speed and wind direction etc. [8].
3.1.2. Variations in AOD During Monsoon Season
The strong gradient in AOD distribution is shown during
monsoon season over Bangladesh in 2010 (Fig. 2b). The
AOD concentration is increasing from very low to very high
from northeastern part to southwestern part of the country.
Khulna, Barisal and the coastal areas are shown highest
AOD distribution in monsoon period; this may be due to a
high concentration of water vapors and sea salt spray. From
monsoonal time series plot it is shown that the high AOD
value is decreased rapidly from July and reached its
minimum value (~0.1) in October. This decrease just before
the winter season could be due to cloud scavenging and rain
washout process [23]. Bangladesh and northeast India is
affected by monsoon at the end of June, and as a
consequence, the MODIS retrievals in July and August are
limited due to extensive cloudiness. So, there are
possibilities of lack of data over some times over specific
22
pixels in this area leading to the observed complicated
pattern as regards the AOD variations [10].
3.1.3. Variations in AOD During Post- Monsoon Season
In post-monsoon, spatial distribution shows comparatively
lower values of AOD distribution than other seasons over
Bangladesh while the south-western region (Khulna and
Barisal) shows higher AOD distribution than other regions.
As this area are affected by sea salt and marine aerosols. The
eastern regions especially Chittagong and Sylhet divisions
show very lower AOD distribution. From the AOD time
series graph (Fig. 3c) it is shown that AOD is rapidly
increasing from September, reaching a maximum AOD value
of ~1.00 in November. This increasing manner may be due
to local activities which are started from October [10].
3.1.4. Variations of AOD During Winter Season
Winter season shows high aerosol loading over
Bangladesh in 2010 except the Chittagong and Sylhet
divisions, as shown in Fig. 2d. The spatial distribution shows
a strong gradient in AOD from very high to low from west to
east. From Fig. 3d the winter period clearly reveals the
lowest variability of AOD in 2010 over Bangladesh among
all seasons. Note that some area in northwestern Rajshahi
division shows extremely high AOD distributions in winter.
Dense fog is a common activity in Bangladesh during winter
which may be the reason for this peak AOD. Dense fog
causes enormous economic loss and also the daily life
become stand still. [10] also found a pronounced eastwards
increasing AOD over Eastern IGP during winter season. [5]
reported that “the Bihar pollution pool” strongly influencing
the aerosol load & properties over the northern Bay of
Bengal. Again dryer weather and lower humidity causes a
lower AOD values in winter season than summer season [1].
3.1.5. Seasonal Mean Variations of AOD
Seasonal mean variations and trends of AOD are also
analyzed over Bangladesh for a period of ten years (20022011) as shown in Fig. 4. The highest AOD level is observed
in pre-monsoon and the lowest AOD level in post-monsoon.
The annual mean AOD in summer is 0.61 and in post
monsoon it is 0.33. Where winter season and monsoon
season show medium to high AOD levels. It is also evident
that pre-monsoon AODs are higher than those for all other
seasons in the year, followed in order by winter, monsoon
and post-monsoon. Our analysis shows that the AOD trends
since 2002 are increasing in pre-monsoon, post-monsoon and
winter seasons but decreasing in monsoon season. It is also
shows that the AOD trend is rapidly increasing in winter than
all other seasons, ordered by post-monsoon and pre-monsoon.
The rapid increase of AOD in winter may be due to increase
in fog and local activities with conjunction of anthropogenic
aerosols. The decreasing trend of AOD in monsoon during
the last decade indicates that the rainfall amounts and
patterns may be changing as the rainy season shifts
backward. It is also found that AOD values varied between
low values in post-monsoon and high values in pre-monsoon,
where the pattern values in winter and monsoon are quite
similar to each other. During pre-monsoon high temperature
plays a vital role in heating lifting loose material from soil
and also due to higher wind speed consequently higher AOD
23
Mainul Islam et al.: The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on Satellite Data and
HYSPLIT Model
values observed. Rain washout processes reduce the AOD
values in post-monsoon season [1]. Anthropogenic aerosols
are the major type over south Asia as they contribute about
70 – 80% to the total AOD [7]. They also the dominant type
both over urban areas and downwind Oceanic areas [21]. In
rural and agricultural areas the fine mode aerosols mainly
originate from burning of bio-fuels, while in urban areas they
are originating from fossil-fuel combustions [10].
(d) Winter
Fig 2. AOD distribution images of Terra-MODIS over Bangladesh during (a)
pre-monsoon, (b) monsoon (c) post-monsoon and (d) winter seasons in 2010.
(a) Pre-monsoon
(b) Monsoon
(c) Post-monsoon
American Journal of Remote Sensing 2014; 2(4): 20-29
24
4. Influence of Air Masses on AOD,
Using Back Trajectory Analysis
Fig 3. Seasonal time series plots of AOD during (a) pre-monsoon, (b)
monsoon (c) post-monsoon and (d) winter seasons over Bangladesh for the
year 2010.
Seven day back trajectory analyses based on the NOAA
HYSPLIT model are studied in order to understand the
sources of the air masses in the study regions. The study
regions are the seven divisions of Bangladesh, for winter,
pre-monsoon, monsoon and post-monsoon
post
seasons. These
trajectories are computed at several altitudes (2500m, 1500m,
and 500m). The trajectories for 08 December 2009, 29 May
2010, 22 July 2010 and 15 October 2010 are shown in Fig. 5.
These trajectories are only included here as a representative
because similar trajectories found for other time period. Fig.
5 reveals that most of the back trajectories identified air
masses flow originating either from Arabian Sea (extremely),
Bay of Bengal, the great Thar Desert of India, Pakistani
Desert (Cholistan), the Sahara and sub-Saharan regions,
Libya, various western regions of India towards north-south,
Africa & Europe also from Atlantic Ocean and sometimes
from south-eastern regions such as China, Myanmar, Luzon
Sea and that consequently a significant increase in AOD over
this areas could be observed. In general the air masses from
the western land mass and from the Arabian Sea are
responsible for the increase in AOD values over Bangladesh.
Fig 4. Annual-mean seasonal variations and trends of AOD over
Bangladesh for the period 2002-2011.
Barisal
25
Mainul Islam et al.: The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on Satellite Data and
HYSPLIT Model
During winter (08 December 2009) back trajectories at
several areas reveal that the trajectories are from the west
and north-west in the direction. For 500m and 1500m
altitudes the sources of air mass loading mainly are west
Indian and east Pakistani deserts and the middle & western
regions of India, all over the seven divisions. But in Khulna
and Sylhet divisions at 1500m altitude a different air mass
loading are observed. The sources are Red Sea for Khulna
and Bay of Bengal for Sylhet. Where in 2500m altitude the
air masses are loading from far western regions, such as
Europe and various sub-Saharan region of Africa such as
Poland, Estonia, Libya, Sahara desert etc. In Sylhet this
source is Saudi Arab and in Rangpur this source is Persian
Gulf.
Fig. 5 shows that, in pre-monsoon and monsoon seasons
the highest AOD values in Bangladesh occurred due to Sea
salt bringing from the Arabian Sea via Bay of Bengal
through southwest direction. This highest AOD value is
observed in Bangladesh in most of the divisions. Only in
northern divisions Rajshahi and Rangpur at 2500m altitude
in summer other type of air masses are loading with
conjunction of Arabian Sea salt. Such as north Atlantic
Ocean salt and air masses from Nepal (close to Indian border)
are loading in Rajshahi and Rangpur respectively. Again in
Chittagong division at 1500m altitude the air masses are
loading from Goa of India (coastal region).
During post-monsoon (15 October 2010) verities are
observed on air masses loading. Fig. 5 reveals that air masses
loading are observed from both southeast and southwest
direction in Bangladesh. Such as Arabian Sea salt,
anthropogenic air masses of different regions in India, China,
Myanmar, Thailand, Luzon Sea etc. Dhaka and Sylhet
divisions are shown highest differences of air mass loading
among all of the divisions. The sources in Dhaka are
Myanmar, coastal regions of Thailand and Arabian Sea and
the sources in Sylhet are southwestern border region of
China and Myanmar, costal region of Thailand, Bay of
Bengal and Luzon Sea.
Therefore the air mass back trajectories indicate that in
pre-monsoon, monsoon, post-monsoon
post
and winter the
sources of air masses loading are different. For example,
winters air masses are reaching in Bangladesh have traveled
long distances from northwest, while the other seasonal air
masses have traveled shorter distances. Therefore it seems
likely that the air masses spent more time over land during
the summer than during the winter. During the summer
months it would explain because in this time the higher
levels of AOD are observed. This is also due to the uneven
distribution of solar flax reaching the earth’s surface. The
higher latitudes air is warmer than at lower latitudes. The
tropical air which rises vertically moves towards the poles at
high altitudes while the cooler polar air moves towards the
equator at lower levels. The air presser consequently
increases at equatorial latitudes due to the outflow of air
mass and most equatorial air moves towards the poles under
this pressure gradient. Since over the region of Bangladesh
the air moves away from the equator in northerly direction at
high altitudes and the coriolis force has a significant effect,
south and south-westerly winds dominate [1]. These
variations of sources, directions and distances of air mass
loading may be due to various metrological parameters such
as wind speed, wind direction, temperature, pressure etc. It is
clear from the back trajectories that the air masses have been
influenced by the combine effects
effect of land, industries and the
Ocean and that this has resulted in increased AOD values
over Bangladesh.
Chittagong
American Journal of Remote Sensing 2014; 2(4): 20-29
Dhaka
Khulna
26
27
Mainul Islam et al.: The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on Satellite Data and
HYSPLIT Model
Rajshahi
Rangpur
American Journal of Remote Sensing 2014; 2(4): 20-29
28
Sylhet
Fig 5. Seven-day back trajectories for 7 divisions in Bangladesh for 8 Dec 2009, 29 May 2010, 22 Jul 2010 and 15 Oct 2010.
5. Conclusion
The aim of this study is to analysis the seasonal AOD
variations over Bangladesh based on Terra-MODIS
Terra
remote
sensing data during the last decade (2002-2011)
(2002
and to
develop an understanding of the impact of aerosols on
climate system. Aerosol air masses loading are identified by
a backward trajectory with the help of a Hybrid Single
Particle Lagrangian Integrated Trajectory (HYSPLIT) model.
The seasonal mean AOD variations and trends are estimated
for a period of 10 years (2002-2011), also the seasonal AOD
variations and trends for one specific year of 2010 are
examined over Bangladesh. Seasonal analyses report that the
highest AODs are found in pre-monsoon
monsoon season and
minimum AODs are found in post-monsoon.
monsoon. These high
AODs in pre-monsoon are interpreted as being a result of
higher temperature, higher humidity and lots of sea salt
particles blown from Arabian Sea and Bay of Bengal, where
low AODs in post-monsoon due to a result of clean and cool
environment after rainy season by rain washout processes.
Seasonal variations in 2010 present high spatial variations in
each season over Bangladesh. Examination of these
variations using back trajectory analysis reveal that air
masses from Indian deserts and sub-Sahara
Saharan regions, sea
particles from Arabian Sea along with Indian pollutants
(industrial emissions, locally derived dust and biomass
burning) are the main sources of aerosol loading in
Bangladesh. These air masses cause different AOD loading
in various divisions, and overall influence in Bangladesh.
This study has some limitations as there is no ground-based
data and it is a newer study in Bangladesh. The comments of
this research over Bangladesh may be less accurate as this
satellite AOD retrievals don’t compare with ground based
data also with other research projects. It is therefore
recommends that further much more researches are needed
about aerosols to achieve a better understanding of spatiotemporal variations in aerosols and their various atmospheric
impacts. On the whole, this study in Bangladesh will prove
useful in the assessments of spatio-temporal variations in
aerosols on regional and global climate impact due to
aerosols.
Acknowledgements
MODIS data used in this study were developed and
maintained by the GES-DISC
DISC of NASA is gratefully
acknowledged with thanks. NOAA Air Resources Laboratory
(ARL) is also gratefully acknowledged for providing the
HYSPLIT model to analyze the Back Trajectories.
Research Highlights
Seasonal variations of AOD are examined using
MODIS data and the HYSPLIT model.
Highest AOD level is reported in pre-monsoon season
while lowest in post-monsoon.
monsoon.
The assessment of AOD shows high spatio-temporal
variations in seasonal pattern.
29
Mainul Islam et al.: The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on Satellite Data and
HYSPLIT Model
Back trajectory analysis uses to identify the origin of
air masses.
The aerosol sources of high AOD over Bangladesh are
mainly external.
References
[1]
Alam, K., Iqbal, M.J., Blaschke, T., Qureshi, S., Khan, G.,
2010. Monitoring the spatiotemporal variations in aerosols
and aerosol-cloud interactions over Pakistan using MODIS
data. Advances in Space Research 46, 1162e1176.
[2]
Alam, K., Qureshi, S., Blaschke, T., 2011. Monitoring spatiotemporal aerosol patterns over Pakistan based on MODIS,
TOMS and MISR satellite data and a HYSPLIT model,
Atmospheric environment 45: 4641-4651.
[3]
[4]
[5]
Anup K Prasad, Ramesh P Singh and Ashbindu Singh., 2004.
Variability of Aerosol Optical Depth over Indian subcontinent
Using MODIS Data. Journal of Indian Society of Remote
Sensing. V. 32, No. 4 (December, 2004 Issue).
Badarinath, K. V. S., Kharol, S. K., 5 Sharma, A. R.,
Ramaswamy, V., Kaskaoutis, D. G., and Kambezidis, H. D.,
2009. Investigations of an intense aerosol loading during
2007 cyclone SIDR – A study using satellite data and ground
measurements over Indian region, Atmos. Environ., 43, 3708–
3716.
Di Girolamo, L., Bond, T. C., Bramer, D., Diner, D. J.,
Fettinger, F., Khan, R. A., Martonchik, J. A., Ramana, M. V.,
Ramanathan, V., and Rasch, P.J., 2004. Analysis of multi –
angle imaging SpectroRadiometer (MISR) aerosol optical
depths over greater India during winter 2001-2004, Geophys.
Res. Lett., 31,L23115, doi:1029/2004GL021273.
[6]
Chu, D.A., Kaufman, Y.J., Zibordi, G., Chern, J.D., Mao, J.,
Li, C., Holben, B.N., 2003. Global monitoring air pollution
over land from the earth observing systemterra moderate
resolution imaging spectroradiometer (MODIS). Journal of
Geophysical Research 108, D21. doi:10.1029/2002JD003179.
[7]
Chung, C.E., Ramanathan, V., Kim, D. and Podgorny, I.A.,
2005. Global Anthropogenic Aerosol Direct Forcing Derived
from Satellite and Ground-based Observations, J. Geophys.
Res. 110, doi: 10.1029/2005JD006356.
[8]
Gautam, R., Hsu, N. C., Lau, K.-M., and Kafatos, M., 2009a.
Enhanced pre-monsoon warming over the HimalayanGangetic region from 1979 to 2007, Geophys. Res. Lett., 36,
L07704, doi:10.1029/2009GL037641.
[9]
IPCC (Intergovernmental Panel on Climate Change), 2007.
Changes in Atmospheric Constituents and in Radiative
forcing, in Climate Change 2007, Cambridge University Press,
New York, Cambridge University Press, 2007.
[10] Kaskaoutis D. G, S. K. Kharol, P. R. Sinha, R. P. Singh, K. V.
S. Badrinath, W. Medhi, and M. Sarma, 2011. Contrasting
aerosol trends over South Asia during the last decade based
on MODIS observations, Atmos. Meas. Tech. Discuss., 4,
5275-5323.
[11] Kaufman, Y., D. Tanré, L. Remer, E. Vermote, A. Chu, and B.
Holben, 1997. Operational remote sensing of tropospheric
aerosol over land from EOS moderate resolution imaging
spectroradiometer. Journal of Geophysical Research, 102,
17051-17067.
[12] King, M., S. Platnick, C. Moeller, Revercomb, and D. Chu,
2003. Remote sensing of smoke, land, and clouds from the
NASA ER-2 during SAFARI 2000. Journal of Geophysical
Research, 108, 8502, doi:10.1029/2002JD003207.
[13] Lau, K. M., Kim, M. K., and Kim, K. M., 2006. Asian
summer monsoon anomalies induced by aerosol direct forcing:
the role of the Tibetan Plateau, Clim. Dynam., 26, 855–864.
[14] Lawrence, M. G. and Lelieveld, J., 2010. Atmospheric
pollutant outflow from southern Asia: a review, Atmos. Chem.
Phys., 10, 11017–11096, doi:10.5194/acp-10-11017-2010.
[15] Liu, Y., Franklin, M., Kahn, R., Koutrakis, P., 2007. Using
aerosol optical thickness to predict ground-level PM
concentrations in the St. Louis area: a comparison between
MISR and MODIS. Remote Sensing of Environment 107,
33e44.
[16] Matheson, M.A., Coakley, J.A., Tahnk, W.R., 2005. Aerosol
and cloud property relationships for summertime stratiform
clouds in the northern Atlantic from advanced very high
resolution radiometer observations. Journal of Geophysical
Research 110, D24204. doi:10.1029/2005JD006165.
[17] Nakajima, T., Higurashi, A., Kawamoto, K., Penner, J.E.,
2001. A possible correlation between satellite-derived cloud
and aerosol microphysical parameters. Journal of Geophysical
Research Letters 28, 1171e1174.
[18] Papadimas, C.D., Hatzianastassiou, N., Mihaloppoulos, N.,
Kanakidou, M., Katsoulis, B.D., Vardavas, I., 2008.
Assessment of the MODIS collections C005 and C004
aerosol optical depth products over the Mediterranean basin.
Atmospheric Chemistry and Physics 9, 2987e2999.
[19] Prasad, A.K., Singh, S., Chauhan, S.S., Srivastava, M.K.,
Singh, R.P., Singh, R., 2007. Aerosol radiative forcing over
the Indo-Gangetic plains during major dust storms.
Atmospheric Environment 41, 6289e6301.
[20] Prospero, J. M., Ginoux, P., Torres, O., Nicholson, S. E., and
Gill, T., 2002. Environmental characterization of global
sources of atmospheric soil dust identified with Nimbus 7 Total
Ozone Mapping Spectrometer (TOMS) absorbing aerosol
product, Rev. Geophys., 40, 1002,doi:10.1029/2000RG000095.
[21] Ramachandran, S., 2004. Spectral aerosol optical
characteristics during the northeast monsoon over the Arabian
Sea and the tropical Indian Ocean: 2. Angstrome and
anthropogenic influence. J. Geophys. Res., 109, D19208,
doi:10.1029/2003JD 004483.
[22] Ramachandran, S. and Kedia, S., 2010. Black carbon aerosols
over an urban region: Radiative forcing and climate impact, J.
Geophys. Res., 115, D10202, doi:10.1029/2009JD013560.
[23] Ranjan, R.R., Joshi, H.P., Iyer, K.N., 2007. Spectral variation
of total column aerosol optical depth over Rajkot: a tropical
semi-arid Indian station. Aerosol and Air Quality Research 7,
33e45.
[24] Rashid, H., 1991. Geography of Bangladesh. The University
Press Limited, Dhaka, Bangladesh.
[25] Sarkar, S., Chiu, L., Kafatos, M., and Singh, R., 2007.
Sensitivity of rainfall on land cover change over South East
Asia: Some observational results, Adv. Space Res., 39, 73–78.
[26] Tanre, D., Y. Kaufman, M. Herman, and S. Mattoo, 1997.
Remote sensing of aerosol properties over oceans using the
MODIS/ EOS spectral radiances. Journal of Geophysical
Research, 102, 16971-16988.
[27] U.S. Climate Change Science Program, 2009. Atmospheric
Aerosol Properties and Climate Impacts. Synthesis and
Assessment Product 2.3.