Journal of Atmospheric Science Research | Vol.2, Iss.3

Editor-in-Chief
Dr. José Francisco Oliveira Júnior
ICAT/ UFAL, Brazil
Editorial Board Members
Fan Ping, China
Marko Ekmedzic, Germany
Xuezhi Tan, China
Hirdan Katarina de Medeiros Costa, Brazil
Chuanfeng Zhao, China
Suleiman Alsweiss, United States
Aditi Singh, India
Boris Denisovich Belan, Russian Federation
Perihan Kurt-Karakus, Turkey
Hongqian Chu, China
Isidro A. Pérez, Spain
Mahboubeh Molavi-Arabshahi, Iran
Tolga Elbir, Turkey
Junyan Zhang, United States
Thi Hien To, Vietnam
Jian Peng, United Kingdom
Zhen Li, United Kingdom
Anjani Kumar, India
Bedir Bedir Yousif, Egypt
assan Hashemi Hassan Hashemi, Iran
Mengqian Lu, Hong Kong
Lichuan Wu, Sweden
Raj Kamal Singh, United States
Zhiyong Ding, China
Elijah Olusayo Olurotimi, South Africa
Jialei Zhu, United States
Xiying Liu, China
Naveen Shahi, South Africa
Netrananda Sahu, India
Luca Aluigi, Università di Modena e Reggio Emilia
Daniel Andrade Schuch, Brazil
Vladislav Vladimirovich Demyanov, Russian Federation
Jingsong Li, China
Priya Murugasen, India
Nathaniel Emeka Urama, Nigeria
Barbara Małgorzata Sensuła, Poland
Service Opare, Canada
Che Abd Rahim Bin Mohamed, Malaysia
Maheswaran Rathinasamy, India
Masoud Rostami, Germany
Osvaldo Luiz Leal De Moraes, Brazil
Ranis Nail Ibragimov, United States
Masoud Masoudi, Iran
Pallav Purohit, Austria
B. Yashwansingh Surnam, Mauritius
Alexander Kokhanovsky, Germany
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Nastaran Parsafard, Iran
Sarvan Kumar, India
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B.T. Venkatesh Murthy, India
Olusegun Folarin Jonah, United States
Amos Apraku, South Africa
Foad Brakhasi, Iran
Debashis Nath, India
Chian-Yi Liu, Taiwan
Mohammad Moghimi Ardekani, South Africa
Yuzhu Wang, China
Zixian Jia, France
Md. Mosarraf Hossain, India
Prabodha Kumar Pradhan, India
Tianxing Wang, China
Bhaskar Rao Venkata Dodla, India
Lingling Xie, China
Kazi Sabiruddin, India
Nicolay Nikolayevich Zavalishin, Russian Federation
Xizheng Ke, China
Alexander Ruzmaikin, United States
Peng Si, China
Zhaowu Yu, Denmark
Manish Kumar Joshi, United Kingdom
Aisulu Tursunova, Kazakhstan
Enio Bueno Pereira, Brazil
Samia Tabassum, Bangladesh
Donglian Sun, United States
Zhengqiang Li, China
Haider Abbas Khwaja, United States
Haikun Zhao, China
Wen Zhou, Hong Kong
Suman Paul, India
Katta Vijaya Kumar, Sri Venkateswara University
Mohammed Adnane Douar, Algeria
Chunju Huang, China
Habibah Lateh, Malaysia
Meng Gao, China
Bo Hu, China
Akhilesh Kumar Yadav, India
Archana Rai, India
Pardeep Pall, Norway
Upaka Sanjeewa Rathnayake, Sri Lanka
Yang Yang, New Zealand
Somenath Dutta, India
Kuang Yu Chang, United States
Sen Chiao, United States
Mohamed El-Amine Slimani, Algeria

Dr. José Francisco Oliveira Júnior
Editor-in-Chief
Journal of
Atmospheric Science
Research
Volume 2 Issue 3· July 2019 · ISSN 2630-5119 (Online)

Comparative Study of The ALADIN and AROME Wind Effect on Waves Characteristics:
Application On The International Port Of Algiers
Sara Chikhi Mohamed El Amine Slimani
Identification of Black Dragon forest fire in Amur River Basin Using Satellite Borne NDVI
Data and Its Impact on Long Range Transport of Pollutants: A Case Study
Ankita Nath Reshmita Nath
Role of Atmospheric Boundary Layer (ABL) Height and Ventilation Coefficient on Urban
Air Quality- A study based on Observations and NWP Model
Aditi Singh
Perception and Knowledge on Climate Change: A Case Study of University Students in
Bangladesh
Bezon Kumar Arif Ibne Asad Borun Chandraaroy Purnima Banik
Volume 2 | Issue 3 | July 2019 | Page 1-22
Journal of Atmospheric Science Research
Article
Contents
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Journal of Atmospheric Science Research | Volume 02 | Issue 03 | July 2019
Distributed under creative commons license 4.0 DOI: https://doi.org/10.30564/jasr.v2i3.708
https://ojs.bilpublishing.com/index.php/jasr
ARTICLE
Comparative Study of The ALADIN and AROME Wind Effect on
Waves Characteristics: Application On The International Port Of
Algiers
Sara Chikhi1,2*
Mohamed El Amine Slimani1
1. Department of Energetic and Fluid Mechanics, faculty of physics, University of Science and Technology Houari Bou-
medienne (USTHB), 16111 Algiers, Algeria
2. Numerical Weather Forecast, National Center for Meteorology, Avenue Mohamed Khemisti BP 153 Dar el Beida,
16011 Algiers, Algeria
ARTICLE INFO ABSTRACT
Article history
Received: 1 April 2019
Accepted: 20 September 2019
Published Online: 30 November 2019
Numerical modeling of sea states has been developed for years, and used
for varied fields such as coastal work sizing, navigation safety, beaches
and water leisure stability study. The third-generation ocean wind-wave
spectral model WAVEWATCH III (WW3) software was adopted and de-
veloped to simulate wave propagation in the Mediterranean basin. In this
work, a more detailed study was carried out on the port of Algiers. Two
different atmospheric models have been used to get the wind forcing:
ALADIN (Area Limited Dynamic Adaptation Inter National Develop-
ment) with an 8 km resolution. And AROME (Application to Operational
Research at Meso-scale) with a 3 km resolution. The results obtained
using both of the atmospheric models have been compared and analyzed.
Keywords:
Wind-Waves
Wave propagation
Mediterranean basin
WAVEWATCH
Wave-characteristics
*Corresponding Author:
Sara Chikhi,
Department of Energetic and Fluid Mechanics, faculty of physics, University of Science and Technology Houari Boumedienne
(USTHB), 16111 Algiers, Algeria; Numerical Weather Forecast, National Center for Meteorology, Avenue Mohamed Khemisti BP
153 Dar el Beida, 16011 Algiers, Algeria;
Email: scusthb@gmail.com
1. Introduction
T
he sea states Forecasts is historically linked
to the military and commercial navigation se-
curity. For these applications, we first look
at the wave’s significant height and the mean period.
Any marine activity is utilizing sea state forecasts in
various forms. Thus the towing of large installations
(barges, drilling platforms) and their use may require
detailed information on the height of ridges, the energy
of long waves that can arouse resonances in anchors.
Sea states play an important role in the ocean surface
mixing and coastal circulation, with effects that are begin-
ning to be well understood [1,2,8]
, but the subject is far from
exhausted. Thus the amplitude of long waves forced by
waves, responsible for the generation of cuttlefish in small
ports [7]
, and coastal circulation can be linked empirically
to sea state parameters, but their detailed and quantitative
explanation is not yet resolved. The vertical structure
of the coastal currents and their role in the exchanges
between the coast and the offshore are still very poorly
known: it is nevertheless the vehicle of the mineral salts,

2
Distributed under creative commons license 4.0
nutrients, plankton and pollutants, which makes coastal
zone management very sensitive.
Several works dealt with wind waves in numerical
modeling. Bouws et al. [4]
presented a self-similar spectral
form (the TMA spectrum) to describe the finite deep wind
waves. In order to show the general validity of the form,
this self-similar spectral selected about 2800 spectra from
three data sets (TEXEL storm, MARSEN, ARSLOE).
Chen et al. [5]
used an unstable curvilinear spectral wave
model, which offers the flexibility to solve the large
bathymetric and geometric gradients and enable to take
into account the unstable forcing and currents allowing to
predict the wind waves in Mobile Bay, Alabama. To test
the wave’s curvilinear model, Chen et al. [5]
have chosen a
set of laboratory data on wave transformation for a high
circular background. Where they founded an excellent
agreement between numerical results and laboratory mea-
surements; and this for a directional wavelength input and
a fine spatial resolution. In order to predict the variation of
water levels and the current field that serve as the basis for
the wave model, he used a three-dimensional circulation
model and compared the results to existing field measure-
ments of wind waves in Mobile Bay. Numerical simula-
tions are conducted to examine the effects of grid resolu-
tion and estuarine circulation on model results. The study
shows that the technique of linking a spectral wave model
with a hydrodynamic model on curvilinear grids is an ef-
fective tool for predicting waves in estuaries. Adapting the
Third Generation of Spectrum Sea Wind Model, WAVE-
WATCH III (WW3), operational since January 2005 at
the Department of Applied Sciences of the University
of Parthenope (Italy). Benassai & Ascione [3]
simulated
the spread of the waves in the Naples Gulf. The model
has been coupled to the PSU/NCAR meso-scale model
(MM5), which gives the forcing of the wind at one-hour
intervals. The model is implemented using a configuration
of four nested networks covering the Mediterranean Sea
to the Naples Gulf. The internal mesh is having a higher
resolution of 1 km*1 km. Simulated directional spectral
waves were compared to storm surge data recorded in
the winter of 2000 off the Naples Gulf and to wind-wave
data collected by Idrografico and Mareografico off the
mouth of the Sele in the Salerno Gulf. It showed that by
the implementation of the wave model with reference to
December 2004 storm on the Naples Gulf coast the need
for a regional model of wind waves for this complex area
from the orographic point of view. Ardhuin, et al. [1,2]
used
four different meteorological models and three different
wave models to compare the characteristics of wind and
waves measured in the Mediterranean basin, with satellite
observations. Or he found that near high-resolution coasts,
DOI: https://doi.org/10.30564/jasr.v2i3.708
nested wave patterns are needed for sufficient reliability.
An analysis of the wave threshold suggests sufficient re-
liability only off the coast, with a substantial decrease for
low-level waves. Zijlema et al. [9]
proposed to use a low
value of quadratic friction law empirical coefficient for
both cases: waves in a storm and swell. Examining a large
number of more recent observations gives a new config-
uration of the wind drag with lower values he deduced
from the same storm the lower value of the coefficient of
friction lower. Zijlema also proved that using this lower
value also improves estimates of wave growth in shallow
waters and the decay of low-frequency waves in a tidal
entrance, regardless of the wind drag. Zodiatis et al. [10]
presented the main characteristics of the wave’s energy
potential in the Levant basin, eastern Mediterranean. This
zone plays a significant role in exploration/exploitation of
energy resources. The numerical results are analyzed us-
ing various statistical measures. He found that the regions
where the wave’s energy potential is increasing are main-
ly the western and southern coasts of the island Cyprus,
the maritime areas of Lebanon, as well as the Egyptian
coastline, especially around Alexandria. In these areas,
the wave potential energy is relatively low but also stable
and therefore exploitable. However, the non-negligible
impact of infrequent values is also recorded. Mentaschi
et al. [6]
analyzed the Wavewatch III wave model perfor-
mance forced by a limited-surface atmospheric model
for the Mediterranean Sea and compared the simulation
results to buoy measurements using single-point statistical
indicators, such as standardized bias and symmetrically
standardized mean square error. It has realized a perfor-
mance evaluation of the terms source growth-dissipation
and their reference characterizations on 17 cases studies
corresponding to storms in the off the Spanish Mediterra-
nean coast and northern Tyrrhenian Sea. Comparing these
simulations with measures using single-point statistical
indicators, he showed that high-resolution results are af-
fected by the so-called double sanction effect, although in
some cases they offer a better qualitative description of
the event. Using a performance analysis of the configura-
tion calibrated on the post-prediction dataset, he showed
that it is more efficient than the reference configuration
over a wide range of wave heights, for calm to moderate
seas, while it increases the tendency to underestimate the
significant wave’s height under severe weather conditions.
This work aims to compare the sea characteristics
forced by the wind obtained by the atmospheric model
ALADIN (Area Limited Dynamic Adaptation Inter Na-
tional Development) 8 km resolution, with those obtained
using AROME wind (Application to Operational Research
at Mesoéchelle) 3km resolution, which propagate in the

3
Algiers port region of using the Wave Watch III numerical
model.
2. Simulation
The wave model solves the random spectral phase action
density equilibrium equation for the wavelength direction
spectrum. Indeed, thanks to this spectrum it is possible to
carry out a sea state modeling since this spectrum contains
implicitly or explicitly wave data, sea current, wind, etc.
The implicit assumption of this equilibrium equation is
that the characteristics and the properties of the medium
such as water depth and current, as well as the field of
wave, vary with scales of time and space that are much
larger than the single wave variation scales. The govern-
ing equations modeling the spatial and temporal variations
in the growth and decay of waves produced by surface
wind, dissipation, and the bottom friction effects.
(1)
represents the limited spatial divergence operator
on the ocean surface, is the group speed,
U is the advection speed (current function), the intrinsic
frequency and S represents the source term for wave for-
mation and dissipation. The net source term is frequently
given by summing up the nonlinear term of wave-wave
interactions (Snl), the term of wind-wave interaction (Sin),
and the term of dissipation (Sds). In shallow water, addi-
tional processes have to be taken account, most notably
wave-bottom interactions S bot.
S = S in + S nl + S ds + Sbot (2)
As force, we used the zonal and meridian wind ALA-
DIN atmospheric model output (Area Limited Dynamic
Adaptation Inter National Development) with an 8 km
resolution (Figure 2), and AROME (Application to Oper-
ational Research at Mesoéchelle) with a 3km resolution
(Figure 3), for the 01/01/2019 on international port of Al-
giers (Figure 1). While the other simulation hypothesis is
shown in Table 1.
Figure 1. Study Zone: international port of Algiers
Table 1. Simulation characteristics
Simulation Model WaveWatch III
Study period 24h for 01/01/2019
Temporal resolution Criterion CFL (Courant-Friedrichs-Levy)
Initial Conditions Fetch-lim.JONSWAP
bathymetry ETOPO1
Parameterization shallow water
Time step 900s
(a)
(b)
Figure 2. Zonal (a) and meridian (b) wind predicted by
ALADIN model

4
(a)
(b)
Figure 3. Zonal (a) and meridian (b) wind predicted by
AROME model
3. Results and Discussions
The different waves characteristics propagating on Al-
giers port evolution has been traced, for both fcases; that
forced by the atmospheric model ALADIN, and the other
with AROME:
0 3 6 9 12 15 18 21 24
0,10
0,11
0,12
0,13
0,14
0,15
0,16
0,17
0,18
0,19
0,20
0,21
0,22
0,23
0,24
0,25
0,26
0,27
0,28
Significant
Wave
Height
(m)
Time (h)
Hs (ALADIN)
Hs (AROME)
Figure 4. Significant wave height at the Algiers port for
the 01/01/2019
0 3 6 9 12 15 18 21 24
0
2
4
6
8
10
12
14
16
18
20
22
24
wave
lenght
(m)
Time (h)
L(ALADIN)
L (AROME)
Figure 5. Wavelengths at the Algiers port for the
01/01/2019
0 3 6 9 12 15 18 21 24
1,00
1,25
1,50
1,75
2,00
2,25
2,50
2,75
3,00
3,25
3,50
3,75
4,00
Periode(s)
Time (h)
Tr (ALADIN)
Tr (AROME)
Figure 6. wave periods at the Algiers port for the
01/01/2019
0 3 6 9 12 15 18 21 24
0,20
0,25
0,30
0,35
0,40
0,45
0,50
0,55
0,60
0,65
0,70
frequency
(Hz)
Time (h)
fp (ALADIN)
fp (AROME)
Figure 7. Wave frequencies at the Algiers port for the
01/01/2019

5
The significant height (Figure 4), is a very important
statistical parameter used to characterize the sea state, it
represents the average of the heights (measured between
peak and trough) of the one-third of the highest waves.To
calculate it from a surface elevation record, the waves are
classified in order of height, and the average of the heights
of the upper third gives the short of time, their evolution
is random due to its direct discordance to the wind. The
comparison between the two significant height obtained
by ALADIN and AROME forcing wind shows a wide
range of variability. The amplitude degrades from 0.14 m
around the 3h AM to 0.02m at 24h PM (Figure 5) Also
present a difference between the two results obtained for
wavelengths. It noted L and defined by the distance be-
tween two successive ridges. This difference varies from
1m to 3m around 24h PM. The periods corresponding to
the maximum spectral density was influenced to (Figure
6), and a difference of 0.25 s has been registered. The
peak periods of the spectrum are empirically related to
the periods significant by the relation: Tp=1.05 Ts. Peak
frequencies (Figure 7) representing the number of wave
trains passing at a fixed point in one second (in Hertz),
marks a gap from 0.25Hz to 0.5Hz. These differences are
mainly due to the high resolution (3km) of the AROME
model, compared to that of ALADIN (8km).
4. Conclusion
The numerical modeling of sea states is a fundamental
field in the coastal work sizing, the navigation safety, and
the beaches stability study . A sea state numerical simu-
lation in the Algiers port for 01/01/2019 using the Wave-
Watch III software was carried out. We used as forcing the
zonal and meridian wind of the two atmospheric models;
ALADIN (Area Limited Dynamic Adaptation Inter Na-
tional Development) with an 8 km resolution and AROME
(Application to Operational Research at Meso-scale) with
a 3km resolution. We also used ETOPO1 bathymetry, a
900s time steps, a Time Resolution and an initial Criterion
CFL (Courant-Friedrichs-Levy), and Fetch-Lim. JON-
SWAP respectively. The results represented by wave char-
acteristics such as significant height, wavelength, the peak
frequency and period show a gap between those obtained
using the wind of the ALADIN model and AROME.
These gaps are mainly due to the high resolution (3km) of
the AROME model, compared to that of ALADIN (8km).
References
[1] Ardhuin, F., Jenkins, A. D., Belibassakis, K. A..
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land, A.. Observation and estimation of Lagrangian,
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[3] Benassai, G., Ascione, I.. Implementation and Val-
idation of Wave Watch III Model Offshore the Coast-
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[4] Bouws, E., Günther, H., Rosenthal, W., Vincent,
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H.. Bottom friction and wind drag for wave models.
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[10] Zodiatis, G., Galanis, G., Nikolaidis, A., Kalogeri,
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https://doi.org/10.1016/j.renene.2014.03.051

6
ARTICLE
Identification of Black Dragon forest fire in Amur River Basin Using
Satellite Borne NDVI Data and Its Impact on Long Range Transport
of Pollutants: A Case Study
Ankita Nath1
Reshmita Nath2*
1. Vivekananda College, West Bengal State University, India
2. Department of Earth System Science/Institute for Global Change Studies, Tsinghua University, Beijing, 100084, Chi-
na
Article history
Received: 5 September 2019
Accepted: 23 September 2019
The Greater Hinggan Forest was the world’s largest stand of evergreens,
along the Black Dragon River (also known as Amur), which forms the
border between Chinese Manchuria and Soviet Siberia. Black Dragon
fire ranks as one of the worst environmental disasters of the 20th century
and it burned about 18 million acres of conifer forest. In the 2nd week of
May, 1987, we observe more than 10K rise in brightness temperature over
a wide region in the China-Russia border. The weekly mean NDVI data
shows the changes in greenness after the forest fire broke out. The NDVI
value is positive with persistent greenness and vegetation in the Amur
River valley, but from the 2nd week of May onwards the reddish patch
appears to spread over the entire region, indicates the burned areas. In ad-
dition, we observe the impact of Black Dragon forest fire on tropospheric
ozone concentration, aerosol index away from the location over North
Pacific Ocean. A clear increase in atmospheric pollutants can be noticed
after the forest fire event and the long range transports are confirmed with
72 hours NOAA HYSPLIT forward trajectory analysis.
Keywords:
Black Dragon forest fire
NDVI
Ozone
Aerosol
Transport
HYSPLIT model
Reshmita Nath,
Department of Earth System Science/Institute for Global Change Studies, Tsinghua University, Room S-807, Meng Minwei Science
Technology Building, Haidian, Beijing, China;
E-mail: reshmita@mail.tsinghua.edu.cn
1. Introduction
F
orests, being the crucial ecological functions, reg-
ulate the climate and the water resources and serv-
ing the habitats for numerous plants and animals.
Moreover, it provide a wide range of essential products for
the humanity such as wood, food, fodder, medicines, fossil
fuels etc. But in the recent decades, various anthropogenic
factors accelerate the frequency and the intensity of the
extreme natural disasters which also escalate the occur-
rences of the forest fires. Forest fires constitute a hazard
that causes large damages, especially in arid and semi-arid
regions. In many cases, this hazard contributes significantly
to changes in the local and even global climate, soil erosion
and leads to soil loss and desertification. The destruction of
vegetation by forest fires can affect the land surface and the
hydrologic cycle, by increasing the surface albedo, surface
runoff, and decreasing the evapotranspiration [5]
. Moreover,
the biomass burning can contribute, with gases, to the
greenhouse effect and cause destruction of the stratospheric

7
ozone layer or the production of tropospheric Ozone [4]
.
With the increasing number of satellite systems and on
board efficient sensors, forest fires can be identified with
extreme accuracy by implying various remote sensing
techniques. Among them the NOAA/AVHRR and MO-
DIS satellites are widely used by the scientific community.
The meteorological satellite NOAA/AVHRR contributed
to the operational and assessment of natural hazards [9]
.
Remotely sensed data and techniques have been used to
detect active fires and extract the extent of the burned area
during the fire [2]
. The methods usually applied are based
on the thermal signal generated by flaming and/or smoul-
dering combustion [5]
and the daily fire growth. The use of
contextual algorithms [3]
can improve the detection of ac-
tive fires. Domenikiotis et al. [1]
performed the case studies
of the forest fire on21–24 July 1995 in Penteli Mountain
near Athens (shown below), and the forest fire of 16 Sep-
tember 1994 in Pelion Mountain, Central Greece. He had
used the Normalized Difference Vegetation Index (NDVI)
and surface temperature (ST) derived from the National
Oceanic and Atmospheric Administration’s Advanced
Very High Resolution Radiometer (NOAA/AVHRR) sat-
ellite data. The availability of data from NASA’s Moderate
Resolution Imaging Spectro radiometers (MODIS), which
were launched in 2000 onboard the NASA Terra platform
and 2002 onboard the Aqua platform, collect high-quality,
continuous directional observations to support the long
term monitoring of key biophysical variables. Products
generated from MODIS data characterize global vegeta-
tion dynamics, the surface energy budget, land cover, fire
and so on (Justice et al., 2002). Koji Nakau et al. detected
the boreal forest fires in Alaska and Siberia using MODIS
satellite imagery and compare the results with NOAA
satellite imagery. Morisette et al. [6]
validate the MODIS
active fire detection products derived from two algorithms
and Csiszar et al. (2006) and Giglio et al. (2003) validate
the active fire detection by MODIS in Northern Eurasia.
In the present report we have focused on a case study
of Black Dragon Forest Fire, which broke out in May
1987 along the Amur River, the boundary between eastern
Siberia and Chinese Manchuria. Although, China is often
not considered as a country in which large forest fires
occur, Black Dragon fire rank as one of the worst environ-
mental disasters of the 20th or any other century [8]
. The
fires were more than 10 times the size of the 1986 fires in
Yellowstone National Park and it burned about 18 million
acres of conifer forest [7]
. The outline of the present report
include the (a) identification of the Black Dragon fire from
NOAA/AVHRR NDVI and Brightness temperature data,
(b) changes in the greenness before and after the event, (c)
enhancement in columnar Ozone and aerosol index after
the event and (d) trajectory of the particles released from
the event far away from the source location.
2. Data Used
Following are the data used for analysis:
(1) NOAA/AVHRR smoothed weekly means NDVI
and Brightness temperature data with 16 km resolution.
(2) TOMS-Nimbus 7 data for columnar Ozone and
aerosol index.
(3) NOAA HYSPLIT -Hybrid Single Particle Model
Trajectories.
3. Results and Discussions: A case study
3.1 Black Dragon Fire in Russia and China
The Greater Hinggan Forest was the world’s largest stand of
evergreens, stretching like a green velvet sea approximately
500 miles long and 300 miles wide. It is bisected by the
Heilongjiang, or Black Dragon River (known in the West
by its Russian name, the Amur), which forms the border
between Chinese Manchuria and Soviet Siberia. Before the
fires, the Manchurian part of the forest accounted for one-
third of China’s timber reserves. In 1987, there had been
a prolonged period of dry weather, and the danger of fire
was high on both sides of the river in the spring. The Black
Dragon Fire is perhaps an example that climate consider-
ations need to be fully integrated into fire management.
Courtesy: Qu et al.[7]
, Developments in Environmental Science.
We have identified the Black Dragon Fire from the
NOAA/AVHRR weekly mean brightness temperature
data. It reveals the hot spots caused by the fire with tem-
perature ranges from 300 to 335 K. A clear increase in
brightness can be seen in the 2nd
week of May, 1987, when
the Black Dragon fire broke out severely. Figure 1 shows
the weekly difference in brightness temperature before
and after the event. More than 10K rise in brightness tem-
perature has been recorded over a wide region in the Chi-
na-Russia border. We have also plotted the weekly mean
NDVI data to observe the changes in greenness after the

8
forest fire broke out (Figure 2). The NDVI value is posi-
tive with persistent greenness and vegetation in the Amur
River valley, but from the 2nd
week of May onwards the
reddish patch (negative NDVI) appears to spread over the
entire region, indicates the burned areas. A wider swath
of the region was affected by the Black Dragon fire with
intense loss greenness and vegetation.
Figure 1. Weekly mean difference in NOAA/AVHRR,
Brightness temperature, before and after the Black Dragon
Fire event
Figure 2. Weekly mean NOAA/AVHRR, NDVI data
during the fire event. The green color indicate the greenness
i.e. vegetation and the red indicates lack of vegetation
3.2 Impact of Black Dragon Fire on Atmospheric
Pollution
Tropospheric Ozone has negative impact on the human
health and ecosystems and the wildfires are one of the
sources which have significant impact on the climate.
Moreover, the forest fires emit pollutants and aerosols
particles (pm 2.5) which persist in the atmosphere for long
time and have significant impact on the radiation budget
of the atmosphere. In this case study, we have observed a
significant increase in the total columnar ozone over the
North Pacific Ocean and aerosol index soon after the fire
broke out. The top, middle and the lower panel shows the
changes in tropospheric ozone before, during and after the
Black Dragon forest fire broke out (Figure 3). In the sec-
ond week of May the total columnar ozone increases by
about 200 DU, which is possibly due to long range trans-
port of ozone from the forest fire location.
Figure 3. Pentad means difference in TOMS-Nimbus 7
columnar Ozone value before and after the fire event
In addition the aerosol index also increases by 8 units
(Figure 4) after the Forest fire broke out. The particles
species like PM 2.5 travel far away from the source and
can be seen over the North Pacific Ocean in the second
week of May within 72 hours of the massive forest fire
broke out. To visualize this long distance transport by
wind we have also plotted the trajectories of plumes and
the particles after 72 hours of the onset of the fire event
using NOAA HYSPLIT MODEL trajectories analysis.
Figure 4. Pentad means difference in TOMS-Nimbus 7
Aerosol Index value before and after the fire event

9
3.3 NOAA HYSPLIT Trajectories
The HYSPLIT model is a complete system for computing
simple air parcel trajectories, as well as complex transport,
dispersion, chemical transformation and deposition simu-
lations. HYSPLIT model is widely used to study the atmo-
spheric transport and dispersions by simulating back and
forward trajectories to determine the origin of air masses. It
is used in variety of simulations to describe the atmospheric
transport, dispersion, deposition of pollutants and hazard-
ous materials. In this case study we used the 72 hours for-
ward trajectories of the air masses from the location of the
Black Dragon forest fire and reported long range transport
of pollutants over North Pacific Ocean. The Figure 5 shows
the 72 hours HYSPLIT MODEL forward trajectories for
the plumes which contain minute aerosol particles like PM
2.5 from the Black Dragon fire location. After 72 hours,
the PM 2.5 particle trajectories appear to advect over North
Pacific Ocean and results are consistent with Figure 4. Sim-
ilarly, Figure 6 shows the three dimensional propagation of
particles from the ground level location of the forest fire to
~6 km over North Pacific Ocean.
Figure 5. NOAA HYSPLIT MODEL trajectories for the
plumes which contain minute aerosol particles like PM 2.5
after 72 hours of onset of the event
Figure 6. NOAA HYSPLIT MODEL, 3 dimensional
trajectories for the particles which after 72 hours of onset
of the event
4. Summary and Conclusions
The Greater Hinggan Forest was the world’s largest stand
of evergreens, along the Black Dragon River (also known
as Amur), which forms the border between Chinese Man-
churia and Soviet Siberia. In this study we have used the
NOAA/AVHRR weekly mean NDVI and Brightness tem-
perature data, TOMS-Nimbus 7 data for columnar Ozone
and aerosol index and NOAA HYSPLIT -Hybrid Single
Particle Model Trajectories for long range transport of the
pollutants from the source region.
Black Dragon fire is one of the biggest forest fire and
worst environmental disasters of the 20th
century and it
burned about 18 million acres of conifer forest. In the 2nd
week of May, 1987, the brightness temperature increases
more than 10K along the Amur River basin. The chang-
es in greenness can be seen in the weekly mean NDVI
data during and after the forest fire broke out. In the 2nd
week of May the NDVI shifted from positive value i.e.
greenness to negative and widespread burning can be seen
along the Amur River basin. We observe the impact of
Black Dragon forest fire on tropospheric ozone concen-
tration and aerosol index, which increases sharply during
and after the forest fire broke out, however, at locations
far away from the origin. A clear increase in atmospheric
pollutants can be noticed over the North Pacific Ocean,
which is due to long range transports and the results are
confirmed using 72 hours NOAA HYSPLIT forward tra-
jectory analysis.
Acknowledgments
The authors acknowledge NOAA Atmospheric Re-
search Laboratory for providing the HYSPLIT model
trajectories. The research work is supported by National
Natural Science Foundation of China International Coop-
eration and Exchange Program (4181101072).
References
[1] Domenikiotis, C et al.. The use of NOAA/AVHRR
satellite data for monitoring and assessment of forest
fires and floods, Natural Hazards and Earth System
Sciences, 2003, 3: 115–128.
[2] Domenikiotis, C., Dalezios, N. R., Loukas, A., Kar-
teris, M.. Agreement assessment of NOAA/AVHRR
NDVI with Landsat TM NDVI for mapping burned
forested areas, Int. J. Remote Sens. 2002, 23: 4235–
4246, .
[3] Eva, H. D. and Flasse, S.. Contextual and multi-
ple-threshold algorithms for regional active fire de-
tection with AVHRR data, Remote Sens. Rev., 1996,

10
14: 333–351.
[4] Jaffe, D. A., N. L.. Wigder. Ozone production from
wildfires: A critical review, Atmospheric Environ-
ment, 2012, 51: 1-10.
[5] Matson, M., Stephens, G., Robinson, J. M.. Fire de-
tection using data from NOAA-N satellites, Int. J.
Remote Sens, 1987, 8: 961–970.
[6] Morisette, J. T., et al.. Validation of MODIS Active
Fire Detection Products Derived from Two Algo-
rithms, Earth Interactions, 2005, 9(9): 1.
[7] Qu et al.. Remote Sensing Applications of Wildland
Fire and Air Quality in China, Developments in En-
vironmental Science, 2009, 8.
[8] Salisbury, H.E.. The great black dragon fire: A Chi-
nese inferno. Little, Brown Company, Boston,
1989.
[9] San Miguel-Ayanz, J., Vogt, J., De Roo, A.,
Schmuck, G.. Natural hazards monitoring: Forest
fires, droughts, and floods-The example of European
pilot projects, Surv. Geophys., 2000, 21: 291– 305.

11
ARTICLE
Role of Atmospheric Boundary Layer (ABL) Height and Ventilation
Coefficient on Urban Air Quality- A study based on Observations and
NWP Model
Aditi Singh*
Ministry of Earth Sciences, New Delhi, India
Article history
Received: 18 November 2019
Accepted: 25 November 2019
Air pollution is an issue of great concern in any urban region due to its
serious health implications. The capital of India, New Delhi continues
to be in the list of most polluted cities since 2014. The air quality of any
region depends on the ability of dispersion of air pollutants. The height or
depth of the atmospheric boundary layer (ABL) is one measure of disper-
sion of air pollutants. Ventilation coefficient is another crucial parameter
in determining the air quality of any region. Both of these parameters
are obtained over Delhi from the operational global numerical weather
prediction (NWP) model of National Centre for Medium Range Weather
forecasting (NCMRWF) known as NCMRWF Unified Model (NCUM).
The height of ABL over Delhi, is also obtained from radiosonde obser-
vations using the parcel method. A good agreement is found between
the observed and predicted values of ABL height. The maximum height
of ABL is obtained during summer season and minimum is obtained in
winter season. High values of air pollutants are found when the values of
ABL height and ventilation coefficient are low.
Keywords:
ABL
Ventilation Coefficient
Parcel Method
Air Quality Index
NWP model
Aditi Singh,
Ministry of Earth Sciences, New Delhi, India;
Email: aditi.singh76@gov.in
1. Introduction
A
ir Pollution has become one of the major envi-
ronmental issues in urban areas all over the world
due to its adverse effects on human health [5]
. The
air quality of any region decreases due to emission from
vehicular and industrial sources. In addition, the air qual-
ity also depends on the prevailing meteorological condi-
tions. For example, when the pollutants are trapped below
an inversion and there is no exchange between polluted
and clean air the air quality of that region gets affected se-
verely. The atmospheric boundary layer (ABL) is the low-
est part of troposphere and plays a vital role in dispersion
of air pollutants. The height of the atmospheric boundary
layer is the height at which the maximum vertical mixing
occurs and thus determines the ability of pollutants to
disperse. The height of the boundary layer varies both in
time and space ranging from hundreds of meters to few ki-
lometres. The ventilation coefficient, is another significant
parameter which gives the ability of atmosphere to dilute
and disperse the pollutants over a region. It is a function
of height of ABL and average wind speed within the ABL.
A number of studies conducted in recent past has related
ABL height and ventilation coefficient to air quality [8,12]
.
Delhi, the capital of India, is located at 28.5° N lati-
tude and 77° E longitude at 216 m above mean sea level.

12
It has Thar desert in the West, central hot plains in the
South and hills in the North and the East. The city has a
semi-arid climate with long summers from April to Octo-
ber with monsoon season in between and winters during
October to January with a large number of fog events
[1]
. There has been increase in air pollutant emissions of
particulate matter (PM), sulfur dioxide (SO2), nitrogen
oxides (NOx), carbon monoxides (CO) and hydrocar-
bons due to rapid population growth. The increased level
of pollutants in Delhi results in health and respiratory
impacts and the city is characterized as the “asthma cap-
ital” of India [4]
.
The studies conducted in past [2,3,6,11,13]
indicates that
not a single factor but a number of sources including in-
dustries, power plants, domestic combustion of coal and
biomass and transport are responsible for air pollution in
Delhi. The contributions from different sources is also
affected during summer and winter months. The pollu-
tion levels in Delhi are higher during winter season in the
months of November to February. The events of smog and
fog occur frequently over Delhi in winter season causing
frequent delays and cancellations of flights [1]
. A particu-
late matter source apportionment study for four seasons
was conducted on measured PM2.5 concentration at
various locations over Delhi by Chowdhary et al. [2]
. The
study indicated that average PM2.5 during winter months
is higher than summer months.
While the previous studies helped us in understanding
the sources of air pollution in Delhi, but the studies on
association of ABL height and ventilation coefficient with
pollution levels are limited. Keeping a view of this, the
present study addresses the variation of boundary layer
height and ventilation coefficient and their correlation
with air pollution over Delhi. The focus of this study is to
utilize the forecast of boundary layer height and ventila-
tion coefficient of global operational numerical weather
prediction model. The main objectives of the study is to
obtain boundary layer height over Delhi from model and
verify it against observation during 2017-2018 and to in-
vestigate the role of boundary layer height and ventilation
coefficient on the dispersion of air pollutants.
2. Determination of Height of ABL and Venti-
lation Coefficient from NCUM
The Unified Model (UM) is the operational model of NC-
MRWF and is known as NCUM. The horizontal resolu-
tion of the model used in the present study is 17 km and it
has 70 vertical levels spanning from ground up to around
80 km altitude. The hourly forecast of height of the ABL
is available from NCUM and is used in the present study.
The height of the ABL in NCUM is based on parcel
and bulk Richardson number method. Both of these meth-
ods are widely used to obtain the ABL height in convec-
tive conditions. The parcel method determines the height
of the ABL in convective conditions as the height of inter-
section of actual potential temperature profile with the dry
adiabatic lapse rate starting with the near surface tempera-
ture [7]
.
Another method used to determine ABL height is based
on bulk Richardson number (Rib) for boundary layer. This
method defines the top of the ABL as the level at which
Rib exceeds a critical value. The critical value of Rib is cho-
sen as 0.25 [14]
. The difference between ABL height ob-
tained from parcel and bulk Richardson number method is
negligible [7]
. The height of the boundary layer in NCUM
is computed by taking maximum height of the two meth-
ods- parcel and Rib number method.
The bulk Richardson number at any level (h) is defined
as:
R h
ib ( ) =
θ
gh
v1 U h V h
θ θ
(
v v
(
)
2 2
h
+
)−
(
1
)
(1)
Here θv1 is the virtual potential temperature at the low-
est vertical level and θv(h) is the same at height h. U and V
are mean flow components at height h and g is the gravity
of earth.
The ventilation coefficient (VC) in the model is com-
puted as the product of ABL height and wind speed within
the ABL. The wind speed within the ABL is the average
of wind speed at surface and at the top of the ABL. Eq. (2)
is used in the model to compute VC.
VC= (Height of the ABL x Wind speed within the ABL)
(2)
The ABL height obtained over Delhi from NCUM is
verified with the observed ABL height for a period of one
year.
3. Materials and Methods
An attempt has been made in the study to correlate the
air pollution over Delhi with ABL height and ventilation
coefficient. The analysis is carried out for a period of one
year and the values of Air Quality Index (AQI) are cor-
related with height of boundary layer. Air quality index is
a tool that monitors air quality of any location at real time.
It accurately reflects the extent of air pollution in region.
The values of AQI at different locations across Delhi and
National Capital Region (NCR) are available on website
of Central Pollution Control Board (CPCB)[3]
(https://

13
app.cpcbccr.com/AQI_India/). The AQI is computed
based on real time data of particulate matter (PM10 and
PM2.5), sulphur dioxide, nitrogen dioxide, carbon mon-
oxide, ozone, ammonia and benzene obtained from the
number of air quality monitoring stations installed in dif-
ferent parts of Delhi. The real time pollution figures from
these stations in the city are available on Delhi Pollution
Control Committee (DPCC), System of Air Quality and
Weather Forecasting and Research (SAFAR) and CPCB
websites. The data from all these websites is used to cal-
culate the overall AQI at different locations in Delhi and
is displayed on website of CPCB. Six different categories
of air pollution are identified depending on the values of
AQI (Table 1).
Table 1. Classification of Air Quality
AQI Category
0-50 Good
51-100 Satisfactory
101-200 Moderate
201-300 Poor
301-400 Very Poor
401-500 Severe
The height of the ABL from the model is obtained for
convective conditions, thus the ABL height at 1200 UTC
is correlated with air quality index at 1700 IST. The ABL
height from the model is obtained for Indira Gandhi In-
ternational (IGI) Airport (Latitude 28.57° N, Longitude
77.12° E) and thus AQI values of IGI are utilized in the
present study.
The AQI of IGI airport in the present study is the con-
centration in micrograms/m3 of the primary pollutant from
the five pollutants PM2.5, PM10, NO2, CO and Ozone.
The value at any hour is the average of previous 24 hours.
Figure 1. shows the primary pollutant at the site in different
months during December 2017-November 2018. It is clear
that PM2.5 is primary pollutant from October-February
whereas from March-September majority of days have
PM10 as primary pollutant at the selected site in the present
study. The concentrations of NO2 and Ozone are zero for
the entire study period and thus both of them are not includ-
ed in the figure. The air quality is in moderate and satisfac-
tory category for maximum number of days in pre-mon-
soon season (March, April and May) and monsoon season
(June, July, August and September) respectively. Out of 299
days, there are only three days of good air quality one in
the month of July and two days in the month of September.
Similarly, there are only three days with severe air quali-
ty two observed in the month of June and one during the
month of November. The air quality shifts from moderate
category to poor and very poor category from the month of
October. There are maximum number of days in poor and
very poor category in the months of November, December,
January and February (Figure 2).
Figure 1. Primary pollutant from December 2017-No-
vember 2018
Figure 2. Air Quality Index (AQI) from December
2017-November 2018
The height of ABL and the ventilation coefficient (VC)
is obtained from the operational global model NCUM at 12
UTC every day for a period of one year during 2017-2018.
The observed ABL height, computed from radiosonde ob-
servations using the parcel method is utilized to verify the
ABL height obtained from the model. The observed ABL
height over Delhi is computed using the high-resolution
radiosonde observations available from University of Wy-
oming site (http://weather.uwyo.edu/upperair). The radio-
sonde observations for Delhi (Station ID-42182, Latitude
28.580
N, Longitude 77.20
E) are available at 00 and 12
UTC, the present study utilizes the observations at 12 UTC

14
to compute the ABL height. The traditional parcel method
is utilized in present study to obtain ABL height over Delhi.
The altitude (z) where the dry adiabatic line (DLR) inter-
sects the temperature profile i.e. environmental lapse rate
(ELR) is defined as the height of the ABL (Figure 3).
Figure 3. ABL Height determination using Parcel Method
4. Results and Discussions
Figure 4. Observed and Predicted ABL Height during
2017-2018
The objective of the present study is to analyse the
relationship of ABL height and ventilation coefficient ob-
tained from NCUM with air pollution over Delhi. In view
of this, the 36-hour forecast of ABL height from NCUM is
verified against the observed ABL height over Delhi. The
observed ABL height at 12 UTC correlates well with 36
hours forecast of ABL height from NCUM (Figure 4). The
coefficient of determination (R2
) for ABL height is 0.55.
The monthly variations of ABL height are shown in Fig-
ure 5a, during the period from 2017-2018 at the study site.
A gradual rise is noticed in ABL height from December
to May and then sudden drop occurs in June. The higher
values in the month of May are due to thermal convection
processes during pre-monsoon season and the lowest values
are in the month of December (winter season).
The monthly variations in VC are shown in Figure 5b.
The highest value of VC is obtained in the month of May
(pre-monsoon season) due to high values of ABL height
and the lowest value is obtained in the month of Novem-
ber (post-monsoon season). The values of VC in Decem-
ber and January are higher in comparison to those ob-
tained in the month of October and November. Although
the height of ABL is higher (~1000 m) in the month of
October and November than those obtained in the month
of December and January (~500 m) Figure 5a., the higher
values of VC in winter months (December and January
may be due to higher wind speed within the boundary
layer during these months. Thus, not only convection but
mixing in the boundary layer also have significant role in
dispersion of air pollutants in the lower atmosphere.
Figure 5c shows the variations of AQI over Delhi from
2017-2018. It is obvious that high values of AQI during
winter and post monsoon season are due to low values of
ABL height and VC during these months. It is found that
AQI is in poor and very poor category in winter and post
monsoon season due to low values VC, promoting the lon-
ger residence time of pollutants in the atmosphere during
these seasons (Figure 5b). Figure 6 explains the correla-
tion between AQI and VC and both are inversely related
to each other in agreement with results reported earlier [9,10]
.

15
Figure 5. Variation of (a) Observed and Predicted ABL
height (b) VC and (c) Average AQI over Delhi during
2017-2018
Figure 6. Scatter Plot between VC and AQI
The most significant meteorological parameters for dis-
persion of air pollutants are wind speed and ABL height,
within which the pollutants are mixed. The results of the
present study indicate that the model predicted values of
ABL height and VC can be utilized to determine the dis-
persion of air pollutants as the higher values of AQI are
found for low values of ABL height and VC.
5. Summary and Conclusions
The present study examines the role of ABL height and
VC in dispersion of air pollutants over Delhi during 2017-
2018. The ABL height and VC are obtained from global
NWP model NCUM. The height of the ABL from NCUM
is validated with observed ABL height obtained using
radiosonde observations over Delhi. The main findings of
the study include the following:
(1) The average monthly observed and predicted ABL
height is maximum in pre-monsoon season due to strong
convective activity and minimum in winter season in
association with stable atmosphere. A good agreement is
found between observed and predicted ABL height.
(2) VC is maximum in the month of May and mini-
mum value is obtained during November. The value of
VC is dependent on ABL height and wind speed within
the boundary layer, thus despite of lower values of ABL
height in December and January in comparison to those
in October and November the values of VC are higher in
these two months than October and November .
(3) Monthly variation of AQI shows minimum values
in monsoon season and maximum values in winter and
post-monsoon season. Due to low values of ABL height
in winter and post monsoon season, the pollutants get
trapped in stable layer and act as a capping to the mixed
layer that leads to elevated ground level concentrations
and thus higher values of AQI. The values of AQI are
minimum in monsoon season although the values of VC
are highest in pre-monsoon season. This may be due to
the fact that in monsoon season the pollutant get washed
out due to precipitation events leading low ground level
concentrations. During pre-monsoon season Delhi and
most parts of north west India experiences a number of
dust storms which leads to high values of AQI.
References
[1] Ali, K., Momin, G. A., Tiwari, S., Safai, P. D., Chate,
D. M., Rao, P. S. P.. Fog and precipitation chemis-
try at Delhi, North India. Atmospheric Environment,
2004, 38: 4215–4222.
[2] Chowdhury, Z., Zheng, M., Schauer, J. J., Sheesley,
R. J., Salmon, L. G., Cass, G. R., et al.. Speciation

16
of ambient fine organic carbon particles and source
apportionment of PM2.5 in Indian cities. Journal of
Geophysical Research, 2007, 112: D15303
[3] CPCB. Central Pollution Control Board. New Delhi:
Government of India, 2010.
[4] Dubey, M.. Delhi is India’s Asthma capital. New
Delhi: Mail Today, 2009.
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and Pozzer, A. The contribution of outdoor air pollu-
tion sources to premature mortality on a global scale,
Nature, 2015, 525: 367–371.
[6] Gurjar, B. R., van Aardenne, J. A., Lelieveld, J.,
Mohan, M. Emission estimates and trends (1990–
2000) for megacity Delhi and implications. Atmo-
spheric Environment, 2004, 38: 5663–5681.
[7] Hennumth, B. and Lammert, A. Determination of At-
mospheric Boundary Layer height from Radiosonde
and Lidar Backscatter, Boundary Layer Meteorology,
2006, 120: 181-200.
[8] Iyer, U. S. and Ernest Raj P. Ventilation coefficients
trends in the recent decades over four major Indian
metropolitan cities, Journal of Earth System and Sci-
ence, 2013, 122: 537-549.
[9] Krishnan, P., Kunhikrishnan, P. K.. Temporal vari-
ations of ventilation coefficient at a tropical Indian
station using UHF wind profiler. Current Science,
2004, 86: 447–451.
[10] Mahalakshmi, D. V., Sujatha, P., Naidu, C. V.,
Chowdary, V. M.. Contribution of vehicular emission
on urban air quality: Results from public strike in
Hyderabad. Indian Journal of Radio Space Phys-
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[11] Mohan, M., Kandya, A.. An analysis of the annual
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1001–1027.

17
ARTICLE
Perception and Knowledge on Climate Change: A Case Study of
University Students in Bangladesh
Bezon Kumar1*
Arif Ibne Asad2
Borun Chandraaroy1
Purnima Banik3
1. Department of Economics, Rabindra University, Bangladesh
2. Department of Economics, Varendra University, Bangladesh
3. Department of Information Science and Library Management, University of Rajshahi, Bangladesh
Article history
Received: 28 July 2019
Accepted: 25 December 2019
Published Online: 30 December 2019
This paper mainly investigates the perception and knowledge on climate
change of the university students in Bangladesh. To carry out this study,
primary data collected from 370 students and several statistical methods
are used. Perception and knowledge on the causes, effects and mitigation
ways of climate change problems, and perceived duties to combat against
climate change are analyzed with descriptive statistics. This paper finds
that deforestation is the main cause of global warming and climate change
and, the effects of climate change is very serious on people’s health. Ma-
jority portion of the students think that it is difficult to combat against cli-
mate change problem because it has already been too late to take action.
Besides this study also finds that government is crucially responsible for
combating against climate change problem. The study calls for govern-
ment mainly besides industry and youths to aware people about the caus-
es, effects, mitigation ways of climate change so that they can contribute
to the sustainable development by mitigating climate change problem.
Keywords:
Climate Change
Sustainable Development
Bangladesh
Bezon Kumar,
Department of Economics, Rabindra University, Bangladesh, Shahjadpur, Sirajganj, Bangladesh;
Email: bezon.kumar3@gmail.com
1. Introduction
T
he earth is threatened due to the climate change and
environmental degradation. In addition, the world
is again induced by the rapid growth of economy,
urbanization and population. In the case of climatic change
concerns, Bangladesh is one of the most vulnerable coun-
tries in the world [1]
. There have been several reasons for
Bangladesh to remain standing such a susceptible situation
regarding climate change, for example, geographical loca-
tion, flat and low-lying landscape, high density of population,
poverty and malnutrition, unsafe agro-food production, lack
of proper education, poor institutional set up and so on [2]
.
These problems trigger serious consequences when the phys-
ical, socio-cultural and economic condition set in motion of
below average [3]
. As a result, it is the responsibility for all
walks of life to come forward to tackle the climate change
problem and it requires introducing a basic understanding of
public perception on vulnerabilities, risks, uncertainties and
adaptations in relation to climate change [1]
. Although the 13th
goal of Sustainable Development Goals (SDGs) has strongly
expressed about “climate actions”, it can only be successfully
achieved when community based strategies are designed and
implemented. To tackle environmental degradation as well
as implementation to SDGs, it is intuitively required to the
involvement of the youth. As soon as they are understood
about the differential features of the atmosphere, they can
employ their efforts not only to face immediate challenges

18
but even they partake preparing against the long term effects.
In the flowchart, adapted from Harding, et al. [4]
, stated
below, the youth’s (students) perception regarding the
climate change as well as sustainable environment is very
straight forward. It is the present youths will build to the
next generation where their choices and activities are real-
ly inevitable for the society. That means the students’ per-
formance of choosing either their career or consumption
level depends how much amiable would be the climate
for people in the near future. Firstly, whenever students
motive to welfare the community as well as surrounding,
there would not any outcome from them which further
degrades the environment. The outcome from economic
activities, such as agro-productions, investments, manu-
facturing products, services and others will accommodate
the market demand. While the youth try to consume bio
or organic products, it would much more closer to achieve
the way of environmental sustainability. On the other
hand, there is no commitment to a safe climate at all.
The climate will take revenges in terms of climatic di-
sasters and it will stimulate world’s suffering and vulner-
ability. Secondly, the students’ consumption behavior will
affect the atmosphere to a large context. Either they choose
the basket of goods is associated with high mass carbon
emissions or they can choose environment friendly bio or
organic products. Indeed, the choices are going to stimulate
activities which either damage the survivals or not. That
means rapid carbon emission to the environment triggers
the climatic vulnerabilities, like floods, river erosions,
thunder storms, droughts, typhoons and cyclical storms.
In addition, the number of pollutants will increase further
and it will be treated hardly a better place for human being.
Nevertheless, people particularly the young generation may
raise voices against the detrimental effects what humans al-
ready have done and endangered the upcoming generations.
To support the polluted and impaired world, they may come
forward with a unique slogan, “To help the environment is
to save the human being from extinction”. That is the moral
attitude, indeed individuals are far away from it. In such
way, perhaps people can be able to survive in the world.
If the effects are spread very rapidly, people will reach for
sustained environmental development.
Figure 1. Youth’s perception in sustainable environmental
development
Source: Adapted from Harding, et al. [4]
Moreover, according to Harding, et al. [4]
the involve-
ment of students as well as young generation to the envi-
ronmental accountabilities has tremendous effect in the
long-run, such as, accountability behaviors and attitudes
of youth may contribute to the low environmental degra-
dation; utilizing the technological devices, young people
can know how and where carbon pollution is eliminated
and can help to communicate the vulnerable peasant soci-
ety to the prosperous nation; gathering the technological
knowledge from school, youths can expand green technol-
ogies. Throughout the world, many researchers investigat-
ed the farmers’ and agricultural professionals’ perceptions,
attitudes and adaptation strategies on climate change [5-9]
.
In addition, studies on indigenous people’s perception [10]
and public’s perception about climate change [11-13]
are also
investigated.
However, very scant attention has been drawn to the
students’ perception on climate change. From the deliber-
ate review of literature, it is found that [14-18]
investigated
on the students’ perception. The focal point of these stud-
ies implies that climate change awareness creates major
influence on its adaptation and mitigation strategies. On
the other hand, very few studies investigated the connec-
tion between students’ perception and climatic issues. In
this regard, a study by Zhao [19]
suggests that the ongoing
curriculum among college students is insufficient regard-
ing students’ responsibility towards climate change cure.
On the other hand, Hoffman [20]
demonstrates that students
can adopt a better solution through the updated technolo-
gies in the world although there are very limited beneficia-
ry groups for thinking about the future environment. Both
the perception on climate change and the role of forests
played crucial contributions among students about the cli-
matic development [14]
. In this regard, urban students are
far better than rural students as urban students have better
understanding than rural students on global warming and
climate change [21]
.
Authors of this paper find some limitations in the
previous studies. Moreover, proper investigation has not
been carried out on this issue in the context of Bangladesh
which pushes authors to investigate deeply. Thus, this pa-
per specifically explores students’ perception and knowl-
edge on: (i) the causes of climate change, (ii) the effects
of climate change, (iii) the mitigation ways of climate
change and (iv) the duties to climate actions.
It is definitely undeniable that the importance of cli-
matic study among the youth is inevitable, particularly
the university students who are going to rule the society
very soon. They will understand about the relevance of
such discipline in the practical arena. In this study, the
researchers try to find out the aspect to possess a better

19
living place in the world.
2. Data and Methods
This paper is mainly based on primary data. To carry out
this paper, Rajshahi district among 64 districts of Bangla-
desh is selected randomly as the study area as it is known
as education city of the country. In this district, hundreds
of educational institutions are currently providing edu-
cation services to the students. As this study focuses on
the university level students, only the universities of this
district are considered here. Rajshahi district belongs four
universities of which two are public university and the
rest two are private. Among these four universities, one
university is randomly selected and Varendra University
was selected. The university is running with 5000 students
at 3 faculties such as Arts and Social Sciences, Business
and, Science and Engineering faculties. From the uni-
versity registrar office, the list of faculty wise students is
collected. These faculties are assigned as stratum. Using
the stratified sampling method, sample is selected and the
number of sample size is determined by the following for-
mula stated by Taro Yamane.
n
= = =
1 1 5000(0.05 )
+ +
N
Ne2 2
5000
370
where, n = sample size, N= population size and e = rate
of precision (0.05). Data are collected from 370 students
from the faculties randomly with a well-structured ques-
tionnaire during January to June 2019 through face to face
interview.
After sorting, coding and finalizing, data were analyzed
through SPSS 23 by descriptive statistics such as frequen-
cy distribution and presented in tabular form. More spe-
cifically, students’ perception about the causes of climate
change is measured with three points likert scale such as
true, false and don’t know while the seriousness of the ef-
fects of climate change is measured with five point likert
scale such as very unserious, unserious, moderate, serious
and very serious. The ways to mitigate the adverse effects
of climate change is measured with five point likert scale
such as strongly disagree, disagree, neutral, agree and
strongly agree besides duties to climate action is also mea-
sured with five point likert scale such as not at all, small
portion, half, major portion and almost.
3. Results and Discussion
3.1 Students’ Perception about the Causes of Cli-
mate Change
This paper intends to examine the students’ perception
about the causes of climate change. Perceptions on the
causes of climate change are divided into three categories:
true, false and don’t know. The perceptions of the stu-
dents’ perception about the causes of climate change are
presented in Table 1.
Table 1. Students’ perception about the causes of climate
change
Factors
Frequency
True False
Don’t
know
Carbon dioxide emission causes global warming and
climate change
352 7 11
Unplanned human settlements causes climate change 334 3 33
High consumption and production causes climate
change
314 7 48
Deforestation cause climate change 364 4 2
Methane is a greenhouse gas causes climate change 241 55 74
Unsustainable development causes climate change
problem
362 3 5
Burning fossil fuels causes climate change 358 3 9
Rising livestock farming causes climate change 222 73 75
Violation of the commitment of “Kyoto Protocol”
causes climate change
7 49 314
Source: Field survey, 2019
Table 1 represents that 364 students out of 370 students
stated that deforestation is the prime cause of climate
change. In addition, the second highest portion answered
the causes of climate change is true in case of unsus-
tainable development causes climate change. Besides,
352 students responded that the carbon dioxide emission
causes global warming potential and this gas is stronger
than all other greenhouse gases while 7 students perceived
wrong and the rest 11 is don’t know. Moreover, among the
370 students, 352 confirmed that unplanned human set-
tlement causes climate change. Contrarily, the large por-
tion of students answered don’t know about the violation
of the commitment of “Kyoto Protocol” causes climate
change. From this analysis, it is found that deforestation is
the main cause climate change.
3.2 Seriousness of the Effects of Climate Change
on Different Sectors
The effects of climate change is quite diversified and
multi-folds. The intensity of these effects in different
sectors is not same. In this study, the researchers try to
identify the intensity of different climate related effects in
different sectors. Based on the responses of students, the
effects of climate change in different sectors is shown in
Table 2.

20
Table 2. Seriousness of the effects of climate change in
different sectors
Sectors
Frequency
Not very
serious
Not
serious
Mod-
erate
Seri-
ous
Very
serious
Ecological environment
and wildlife
- - 11 26 333
Industrial and commercial
activities
- 30 155 167 18
Physical assets/infrastruc-
ture
3 26 26 56 259
Energy use and supply - 11 26 92 241
Food supply - 7 11 74 278
People’s health - - 3 8 359
Table 2 reveals that the highest portion of the students
(359 students) responded that the effects of climate change
are very serious on people’s health while the second
highest portion of the students perceived that the effects
is very serious in case of the ecological environment and
wildlife (333). In addition, the effect was very serious on
physical assets/infrastructure (259), energy use and sup-
ply (241) and food supply (278). On the other hand, the
majority portion of the students responded that the effects
of climate change are serious in case of the industrial and
commercial activities (167). This analysis reveals that the
effect of climate change is very serious on people’s health.
3.3 Students’ Perception about the Ways Mitigat-
ing the Climate Change Problem
To make different sectors of the countries like Bangladesh
free from the adverse effects of climate change, it is inev-
itable to find out the ways mitigating the problems of cli-
mate change. Table 3 shows the students’ perception about
the ways to mitigate the climate change problem.
Table 3. Students’ perception about ways to mitigate the
climate change problem
Ways to mitigate cli-
mate change problem
Frequency
Strongly
disagree
Disagree Neutral Agree
Strongly
agree
An individual’s actions
can help in mitigating
the climate change
problem
- 7 241 49 74
Influencing people
to adopt low-carbon
lifestyle can combat
against the climate
change problem
- - 59 34 277
Technology can help
to mitigate the climate
change problem
167 55 111 92 19
The governments and
businesses is more
influential to mitigate
the climate change
problem seriously
- 11 248 48 63
The awareness about
climate change can
help significantly to
decrease the effects of
climate change
- - 59 44 267
It is difficult to combat
against climate change
as it is too serious and
our actions are already
too late
- - 11 22 337
Table 3 represents that as the ways of mitigating climate
change problem, majority portion of the students are strong-
ly agree with ‘influencing others to adopt low-carbon life-
style can combat climate change (277)’ and ‘the awareness
about climate change can help significantly to decrease the
effects of climate change (267)’. More than this, the highest
portion of the students are strongly agree with ‘it is difficult
to combat climate change problem as it is too serious and
our actions are already too late (337)’. Besides, a significant
portion of the students are neutral for mitigating the climate
problem of the following ways such as ‘an individual stu-
dent actions can help mitigate the climate change problem
(241)’ and ‘the governments and businesses is more in-
fluential to mitigate the issue of climate change seriously
(248). On the other hand, the major portion of the students
are strongly disagree with ‘Technology can help to mitigate
the climate change problem (167)’ as a way to mitigate of
the climate change problem. Although there were some
ways mitigating the effects of climate change to a great
extent, it is difficult to combat climate change problem as it
is too serious and our actions are already too late has been
highly perceived by the highest portion of the students.
3.4 Perceived Duties to Climate Action
Although climate change is a global concerning issue, espe-
cially it is more pressing in the developing countries. Over
last few decades, not only natural environment but also all
the physical assets, wildlife and human being are badly af-
fected by the adverse effects of climate change. Therefore, it
stresses to take responsibilities for reducing climate change
problem. Table 4 represents the distribution of different
agents who can take responsibilities to work for mitigating
the climate change problem and sustainable environment.
Table 4. Perceived duties to climate action
Level of responsibility (Frequency)
Agents Not at all
Small por-
tion
Half
Major por-
tion
Almost
Government - 8 11 37 314
Producers - 18 30 26 296
Consumers 259 74 19 18 -
Individuals 333 17 20 - -

21
Table 4 indicates maximum portion of the students re-
sponded that the individuals (333) and consumers (259)
are not responsible for reducing climate change problem.
On the other hand, according to the perception of the stu-
dents, government (314) and producers (296) are almost
responsible for reducing climate change problem. From
the above table, it is found that besides others, govern-
ment has the crucial responsibility to combat against cli-
mate change problem.
4. Conclusion
This paper explores four distinct research questions on
students’ perception about climate change. First, what are
the causes of climate change? Second, what are the effects
of climate change? Third, what are the ways to mitigate
the climate change problem? Finally, who are responsible
for climate action? Using primary data collected from 370
students and several statistical methods, the study finds
four interesting findings. First, the paper finds that 364
students out of 370 students perceived “deforestation” as
the top most cause of climate change. Second, majority of
the students (359 students) think that very serious effect
of climate change falls on people’s health. Third, 337 stu-
dents out of 370 students reported “it is difficult to combat
against climate change problem because it is too serious
issue and it has already been too late to take actions” as a
response to the ways to mitigate climate change problem.
Four, this study also finds majority of the students (314
students) reported that government has the major duty to
combat against climate change problem.
The findings of the study justifies the need for aware-
ness and enlightenment of knowledge on climate change
of the students. Therefore, the study calls for government
mainly besides industry and youths to aware people about
the causes and effects of climate change along with the
ways to mitigate the effects of climate change. In doing
so, this study suggests to arrange seminar, symposium,
workshop, group discussion and enroll a course in the ac-
ademic curriculum for enhancing students’ knowledge on
climate change and ensuring the participation to the mit-
igation of climate change problem and achieving SDGs.
Since this study is carried out within short budget and
time, sample size is henceforth small. Thus, authors of
this paper recommend deep further investigation to bring
to light the real scenario on this issue.
Acknowledgement
This research has not received grant from any funding
agency in the public, commercial or non-profit sectors.
Authors are duly acknowledged to BK School of Research
for providing software and technical assistance.
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and social science. Reflecting the latest research from a broad sweep of subjects, our content is accessible world-
wide——both in print and online.
BPC aims to provide an analytics as well as platform for information exchange and discussion that help organizations
and professionals in advancing society for the betterment of mankind. BPC hopes to be indexed by well-known
databases in order to expand its reach to the science community, and eventually grow to be a reputable publisher recognized
by scholars and researchers around the world.
BPC adopts the Open Journal Systems, see on ojs.bilpublishing.com
Database Inclusion
China National Knowledge
Infrastructure
Asia Pacific Science
Citation Index
Creative Commons
Google Scholar Crossref MyScienceWork
About the Publisher

Journal of Atmospheric Science Research | Vol.2, Iss.3

Journal of Atmospheric Science Research | Vol.2, Iss.3

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