Volume 04 Issue 07-2024
46
American Journal Of Applied Science And Technology
(ISSN
–
2771-2745)
VOLUME
04
ISSUE
07
Pages:
46-55
OCLC
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1121105677
Publisher:
Oscar Publishing Services
Servi
ABSTRACT
The environmental problem of the Aral Sea has had a significant impact on Central Asia in the last fifty years, as a
result, the most important factor of the emergence, spread of dust and sand storms and the spread of polluting
particles in the regions leads to negative changes in climate indicators. The formation of dust and sand storms is
directly related to various climatic elements such as heat, rain, wind formation conditions, pressure, air humidity and
solar radiation. As a result of the occurrence of the above conditions, the level of drought in the regions will increase,
causing a change in temperature, a change in wind speed, and an increase in the direction of the wind, which will lead
to an increase in dust and sand storms. It is observed that the formation of dust and sand storms in the conditions of
climate change on the earth, changes in the distribution conditions increase the range of negative effects and lead to
other unexpected consequences.
The construction of mathematical models of the processes of formation and diffusion of pollutants into the
atmosphere is carried out under certain conditions, restrictions and assumptions that do not contradict the physical
nature of these processes and the basic laws of conservation of energy, momentum and mass. it also meets the
required accuracy of solving specific theoretical or practical problems.
Therefore, as in many other areas, the optimal way to solve this problem is to use mathematical modeling methods.
KEYWORDS
Insular region, Atmospheric shell, hydrometeorological data, mathematical modeling of salt-dust aerosol dispersion
process, Hybrid Single-Particle Lagrangian Integrated trajectory model of hybrid single-particle Lagrangian.
Research Article
MODEL OF SPATIAL DISTRIBUTION OF DUST AND SAND EMISSION
Submission Date:
July 21, 2024,
Accepted Date:
July 26, 2024,
Published Date:
July 31, 2024
Crossref doi:
https://doi.org/10.37547/ajast/Volume04Issue07-07
G‘iyosiddin Rustamov
Research Institute of Environment and Nature Conservation Technologies, Bunyodkor Avenue 7-A, 100043,
Tashkent, Uzbekistan
Journal
Website:
https://theusajournals.
com/index.php/ajast
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Volume 04 Issue 07-2024
47
American Journal Of Applied Science And Technology
(ISSN
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VOLUME
04
ISSUE
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Pages:
46-55
OCLC
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1121105677
Publisher:
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INTRODUCTION
When solving various tasks related to the analysis and
control of the ecological situation, experts need
accurate information about the distribution of the
concentration of particles of pollutants (pollutants) in
the environment. To obtain such data, it is necessary to
have a fairly dense network of observation points,
regular and frequent sampling, as well as to know the
results of the measurement history. In addition, the
data of automatic atmospheric air monitoring stations
reflect its actual condition only at specific
measurement locations. That is, they determine the
consequences of pollution, while its causes and the
general picture of the ecological state of the
atmosphere in the neighboring area remain hidden.
Therefore, as in many other areas, the optimal way to
solve this problem is to use mathematical modeling
methods. Construction of mathematical models of
processes of emission, transmission and diffusion of
pollutants into atmospheric air is carried out under
certain conditions, restrictions and assumptions that
do not contradict the physical nature of these
processes and the basic laws of conservation of
energy, momentum and mass. it also meets the
required accuracy of solving specific theoretical or
practical problems.
The development of the methodology of mathematical
modeling of the processes of the transfer and spread
of harmful substances into the atmosphere is a topic of
interest to many researchers in far and near foreign
countries and Uzbekistan. To date, they have achieved
significant results of a theoretical and practical nature.
The existence of many approaches to modeling the
process of spreading pollution in the atmosphere is
due to the lack of some general physico-mathematical
model that takes into account all possible factors and
disturbances affecting the studied process. The choice
of one or another approach is determined by the exact
statement of the problem, the accuracy of the
modeling and the requirements for the quality of the
model in general [21].
The classification of existing models can be based on
various features: the dispersion mechanism, the
coordinate system used, the consideration of physical
processes, the mathematical apparatus used, etc.
Many researchers tend to separate the existing set of
atmospheric dispersion (Fig. 1.7). mixtures are divided
into three main types [22-25]:
statistics,
deterministic;
physicist.
Often there is a hybridization of different types of
models and modeling approaches. The most common
are: Gaussian models of impurity dispersion; Eulerian
models of turbulent diffusion are based, in particular,
on K-theory; Lagrangian models; computational fluid
dynamics models based on full or Reynolds-averaged
Navier-Stokes equations [26-29].
Today, many studies are aimed at the study of the
spatial distribution of dust and sand storms, but they
cannot offer clear solutions due to the fact that the
results are tied to empirical expressions and it is
impossible to compare them with experimental
results.
Research object
. The Aral Sea was a large saltwater
lake located in the Central Asian lowlands [1]. In
addition to supporting large fisheries in the region, the
sea served as one of the most important routes for
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regional transportation [2]. The Aral Sea was the fourth
largest inland lake on Earth until 1960, with an
estimated surface area of 68,000 km2. Also, image
analysis in 1990 showed an increase of 29,000 km of
dry bottom of the Aral Sea. As a result, new sources of
dust have been identified on the southeast coast of the
Big Island and on the east coast of the Small Island
based on research.
The width of the dry belt of the southeast coast
of the Aral Sea reached 30-50 km. In 1975, according to
the first estimates of the volume of dust transported
during the research, it was 45 million tons per year on
average, and in 1990, this figure doubled to 90 million
tons per year. Large-scale irrigation projects in many
parts of the transboundary watershed, mainly the sea,
have caused catastrophic drying of the Aral Sea and
ecological crises since the second half of the 20th
century [47]. This is mainly the result of the
unsustainable expansion of irrigation, which has dried
up two tributaries of Amudarya and Syrdarya and
seriously damaged their deltas [3].
The studied area is located in the western part
of Central Asia, until Arol - the lower reaches of
Amudarya, the main agricultural products are cotton,
wheat and rice. About 10.4 thousand km2 of land can
be irrigated in the region. The only source of water is
the Amudarya and its hierarchical network channels, as
well as a number of small rivers (Figure 3.1).
The Aral Sea was a large saltwater lake located in the
Central Asian lowlands [1]. In addition to supporting
the region's large fisheries, the sea served as one of the
most important routes for regional transportation.
Since 1960, the Aral Sea has rapidly dried up and
become salinized [2].
This is mainly the result of the unsustainable expansion
of irrigation, which has dried up the two tributaries of
the Amudarya and Syrdarya and seriously damaged
their deltas [3]. The processes of salinization and
desertification in the Aral Sea basin have intensified. At
the same time, the problem of water resources in the
regions exacerbates the ecological crisis in the sea
basin and poses a threat to the environment and
human health [6]. Therefore, revealing the trends and
causes of ecological changes in this region is an
important task and importance [7,8].
Figure 1. Geographical location of the study area
(Source: https://earthexplorer.usgs.gov/)
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The rich natural process ecosystems of the Aral Sea
region have been greatly damaged. In this case, as a
result of the decrease in the level of underground
water, the banks of the rivers flowing into the sea
suffered a lot of damage, which led to the expansion of
the desertification of the sea area [9]. Salts
accumulated on the surface of the Aral Sea form layers
where almost nothing grows [10]. The situation in the
area Since the 1990s, the whole world has become
aware of the environmental problem of the Aral Sea,
and scientific research on this research has begun [11].
That is why the stage of rapid drying of the Aral Sea
began from this period. As a result of the acceleration
of desertification processes in the region, a new desert
- Orolqum desert appeared [12]. Over the past few
decades, the Orolqum desert has become a new
"hotspot" of dust and salt storms in the region [9].
Data analysis and research on dust storms and their
origins have begun [3].
Special attention was paid to the analysis of land cover
changes in the New Orolqum desert [13]. The impact of
dust storms rising from the Orolkum desert became
more and more intense. The main change in land
surface cover is directly related to the significant
reduction of vegetation and small water bodies, the
significant increase in the area of salt marshes and
sandy massifs [14].
Figure 2. Satellite images showing the shrinking of the Aral Sea from 1990 to 2020 (Manba:
One of the unique features of the atmosphere is the
dry continental climate circulation in the dry basin of
the Aral Sea and the flat landscapes contribute to the
formation of dust storms. The territory is mostly
occupied by sandy, sandy-gypsum, loess-clay and direct
deserts.
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More than 70% of the study area serves as potential
sources of dust in the lower layers of the atmosphere.
A long dry season, unstable atmospheric stratification,
and frequent strong winds create large amounts of
dust in the surface air, and by transporting dust and
these pollutants over significant distances from the
emission regions, dust and sandstorms here pose a
threat to human health. salts and pose significant risks
to animals and the environment in general [11].
METHOD
A relatively high accuracy model based on aerological
and aerophysical studies is HYSPLIT, Hybrid Single-
Particle Lagrangian Integrated trajectory model.
The model calculation method is a hybrid between the
Lagrangian approach, which uses a moving reference
system for calculations of advection and diffusion of
trajectories or air carriers as they move from an initial
location, and is based on the Euler methodology, which
uses a fixed three-dimensional grid. This model has
evolved over more than 30 years, from a simplified
estimation of single trajectories based on radiosonde
observations to a system accounting for multiple
interacting pollutants that are transported, dispersed,
and deposited locally and globally.
3. Picture. History of the creation of the HYSPLIT model
The scientific basis for HYSPLIT's trajectory capabilities
dates back to 1949, when the US Weather Bureau's
Special Projects Division (SPS) (predecessor to ARL),
now NOAA's National Weather Service (NWS),
identified a source of radioactive debris from the first
Soviet nuclear test. used in an attempt to find and
identified by him. Reconnaissance aircraft near the
Kamchatka Peninsula. For this purpose, back
trajectories were manually calculated based on wind
data obtained from twice daily radiosonde balloon
measurements. These trajectories were at 500 hPa,
assuming a geostrophic wind flow. Although these
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back trajectories were calculated more than 60 years
ago, the percentage error between the calculated and
actual source location relative to the distance traveled
by the trajectories was very low (about 5%). Since then,
trajectory calculations have been a cornerstone of ARL
research.
In the mid-1960s, Pasquill (1961) and Gifford (1961)
described the estimation of the horizontal and vertical
standard deviation of a continuous concentration
distribution, which provided the basis for Gaussian
variance models. One such model was developed
based on ARL data. Using this Gaussian approach and
assuming a steady state with homogeneous and
stationary turbulence, air concentrations were
estimated from wind data collected at a single location.
Extending this work to handle more realistic (variable)
weather conditions in the late 1960s and early 1970s,
ARL scientists developed the Mesoscale Diffusion
(MESODIFF) model in response to health and safety
concerns in Idaho.
By the late 1990s, many new features were added to
HYSPLIT version 4, which is the basis for the current
model versions. Innovations include an automated
method of sequentially using several meteorological
networks and calculating the dispersion rate from the
vertical diffusivity profile, wind shear and horizontal
deformation of the wind field. HYSPLIT allows the use
of different types of Lagrangian representations of
transported air masses: three-dimensional (3D)
particles, aerosols, or hybrids of both.
Over the past 15 years, many updates have been made
to HYSPLIT, reflecting the latest advances in dispersion
and transport calculations.
Features of using HYSPLIT software. When using the
Hybrid Single-Particle Lagrangian Integrated program
as HYSPLIT, hydrometeorological data, wind speeds,
climate parameters, and terrain relief data are entered.
The possibilities of implementing meteorological data
through the HYSPLIT model:
HYSPLIT uses specially formatted meteorological data
to estimate the transport and dispersion of
atmospheric constituents.
Forecast and archived meeting data are also available
NOAA's
Air
Resources
Laboratory
converts
meteorological model outputs (eg from NOAA
Weather Models) to this HYSPLIT format and makes
them freely available over the Internet.
Many North American and Global datasets (primarily
NOAA-based) are available to run HYSPLIT on. RAMS,
etc.) includes conversion programs for conversion.
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Figure 4. HSPLIT model desktop
The trajectory shows the path of the air parcel
following the wind field provided by the numerical
weather prediction (NWP) model. Based on this, it will
be possible to determine the direction of the wind, and
to know the direction of the trajectory of dust and sand
particles.
The model notices that the SETUP.CFG file is now
present, indicating that one or more advanced settings
have been selected.
HYSPLIT can use a variety of meteorological model
data from mesoscale to global scale in its calculations.
Thus, the HYSPLIT model was used to study the
distribution of particles and the scale of sandstorms
during dust and sandstorms on the coast of Arol.
RESULTS
Modeling was done in three stages. At the first stage,
a model of atmospheric dispersion during sandstorms
observed so far was built. The NOAA model based on
the integral Gaussian equation was used in the
modeling.
The WRF (Weather Research and Forecasting with
Chemistry) module was used to study dust
concentration in the atmosphere during dust and sand
storms.
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Figure 5
. Trajectory of transported sand particles in the atmosphere during a dust and sand
storm in the region of the island (27.04.2017).
Figure 6.
The concentration of transported dust and sand particles in the atmosphere
during a dust and sand storm in the region of the island (June 27, 2017).
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CONCLUSION
On April 27, 2017, an unprecedented sandstorm
occurred in the recent history of Uzbekistan. At around
4:30 am, a strong wind formed in the Volga region and
entered Central Asia and a warm front joined the cold
front.
After 2 hours, he entered Khorezm region. The scope
of the sandstorm covers the northern regions of
Turkmenistan, Karakalpakstan, Khorezm and Navoi,
and the western parts of Bukhara regions. The
concentration of particles in the atmosphere reached
150-170 g/m3 in the center of the storm. Visibility drops
to 3-5 meters in some places. After a storm, dust
particles remain in the atmosphere for about 1 day.
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