=Paper=
{{Paper
|id=Vol-3006/45_short_paper
|storemode=property
|title=Ground and satellite monitoring of atmospheric pollution processes in urban areas
|pdfUrl=https://ceur-ws.org/Vol-3006/45_short_paper.pdf
|volume=Vol-3006
|authors=Ruslana A. Amikishieva,Vladimir F. Raputa,Anatoly A. Lezhenin
}}
==Ground and satellite monitoring of atmospheric pollution processes in urban areas==
https://ceur-ws.org/Vol-3006/45_short_paper.pdf
Ground and satellite monitoring of atmospheric
pollution processes in urban areas
Ruslana A. Amikishieva1,2 , Vladimir F. Raputa1 and Anatoly A. Lezhenin1
1
Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia
2
Siberian Center FGBU “SRC “Planeta”, Novosibirsk, Russia
Abstract
The results of the analysis of atmospheric pollution processes in the vicinity of the Chernorechensky
cement plant and the Iskitim city were presented. Snow cover samples and high-resolution satellite
images were used as research materials. The reconstruction of the fields of impurity concentration
was carried out on the basis of low-parameter models. Statistical relationships were identified between
ground-based and satellite observations.
Keywords
Atmosphere, pollution, monitoring, snow cover, NDSI, reconstruction model.
1. Introduction
The use of modern observation tools and mathematical modeling makes it possible to obtain a
picture of the processes of pollutants transport in the atmosphere, areas of pollution, to assess
the total aerosol fallout [1, 2, 3, 4]. Snow cover is the budgetary and informative material
for conducting ground and satellite observations in Siberia [3, 5, 6, 7, 8]. It accumulates the
multicomponent composition of pollutants and reflects the spatial dynamics of the spread of
atmospheric emissions from sources. Due to the snow cover, winter satellite images provide
a picture of aerosol deposition of impurities across the territories [5, 6, 9, 10, 11, 12]. Low-
parameter models of mono- and polydisperse, in the case of low sources — light impurities can
be used to reconstruct contamination fields. The features of the models (namely, the presence of
aggregated parameters) make it possible to numerically reconstruct the concentration field with
a limited set of ground-based observations [3, 13, 14, 15]. The use of multispectral satellite images
makes it possible to calculate the Normalized Difference Snow Index (NDSI), which indirectly
characterizes the presence of impurities in the snow [16, 17]. The methods of statistical analysis
reveal the presence of functional relationships between the data of ground measurements and
the NDSI values. Joint use of terrestrial and satellite information allows better validation of
modeling results and expands possible areas of research.
The aim of the work is a numerical analysis of the atmospheric pollution processes of urban
and industrial territories of the Iskitim region on the data of ground and satellite monitoring of
snow cover pollution.
SDM-2021: All-Russian conference, August 24–27, 2021, Novosibirsk, Russia
" ruslana215w@mail.ru (R. A. Amikishieva); raputa@sscc.ru (V. F. Raputa); lezhenin@ommfao.sscc.ru
(A. A. Lezhenin)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
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Ruslana A. Amikishieva et al. CEUR Workshop Proceedings 392–398
2. Research objects
Environmental monitoring is a very important task for the majority of large cities [18, 19]. Air
emissions from industrial enterprises have a negative impact on the health of the population
and the ecosystems of the surrounding areas. Control over the state of the atmosphere of cities
is carried out using both observation posts and natural tablets [5, 19].
The object of this study was the territory of Iskitim, one of the industrial centers of the
Novosibirsk region. Iskitim is located 50 km south of Novosibirsk along the Berd River. The main
part of the city is located in the valley of the Berd river. In this area, southern, southwestern
winds prevail. The river valley has a significant impact on the processes of the spread of
impurities from industrial enterprises, vehicles. Industrial enterprises such as a cement plant, a
thermal power plant, a plant of reinforced concrete products, etc. are located directly within
the city.
Particular attention is paid to the study of pollution in the vicinity of the Chernorechensk
cement plant (CCP), which is located in the northern part of Iskitim. The structure of the plant’s
emissions is dominated by suspended solids, oxides of nitrogen and sulfur, benz(a)pyrene. The
dispersed composition of dust emissions is characterized by significant heterogeneity [3].
3. Models and methods
The studies used ground-based monitoring data for the winter period of 2019-20, as well
as high-resolution satellite images from the Sentinel-2A, -2B and Landsat-8 spacecraft. The
numerical reconstruction of the fields of atmospheric pollution was carried out according to
low-parameter models: mono-, polydisperse. Linear correlation analysis methods were used to
search for functional relationships between the amount of foreign impurities in snow samples
and the value of the snow index (NDSI) calculated from satellite data. The capabilities of the
developed geographic information system (GIS) were used to graphically represent the results
of numerical modeling [15].
Mono- (1) and polydisperse (2) models for the numerical reconstruction of the fields of
impurity concentration over a long period of time from point sources are presented in the form
of formulas [13, 20, 21]:
(︂ )︂
(︁ )︁ −2𝑟𝑚
⃗ 𝜃2
𝑄𝑚 𝑟, 𝜑, 𝜃 = 𝜃1 𝑟 exp 𝑃 (𝜑 + 180∘ ), (1)
𝑟
(︂ )︂
(︁ )︁ 𝜃3
𝑄𝑝 𝑟, 𝜑, ⃗𝜃 = 𝜃1 𝑟 exp
𝜃2
𝑃 (𝜑 + 180∘ ). (2)
𝑟
Here 𝑟 — the current distance from the emission source to the concentration measurement
point, 𝑟𝑚 — the distance from the source where the maximum concentration of the impurity is
observed, ⃗𝜃 — the vector of approximated parameters depending on the characteristics of the
source and standard meteorological conditions, 𝑃 (𝜑) — the wind rose.
The NDSI was used to assess the state of pollution of the snow cover. It is used for detecting
snow cover and is the normalized difference between the visible green channel (0.5–0.6 𝜇m)
and the shortwave infrared channel (1.5–1.8 𝜇m) [16, 17].
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Numerical data processing was carried out using GIS functions. They allow processing
multispectral satellite images, interpolating the contamination field based on NDSI values at
reference points, and reconstructing the field of contamination from a point source based on
ground-based observations.
4. Results
A numerical analysis of the studies of pollution of the snow cover processes was carried out
separately for urban areas and the industrial zone of a cement plant. The city has a large number
of sources of pollutant emissions, which does not allow directly applying the reconstruction
models (1), (2). In the case of a cement plant, these models are quite appropriate.
4.1. Territory of the Iskitim city
In March 2020, snow sampling was carried out taking into account the dominant wind directions,
relief and location of sources in the city. The selection scheme is shown in Figure 1 (leftmost
fragment). Determination of the content of solid sediment in snow samples was carried out at
the Institute of Inorganic Chemistry SB RAS.
a
b
Figure 1: Sampling scheme in the territory of Iskitim, March 2020 correlation of NDSI values and
measured impurity concentration (a); correlation of NDSI and pH values (b).
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Ruslana A. Amikishieva et al. CEUR Workshop Proceedings 392–398
The image from the Sentinel-2A spacecraft dated March 11, 2020, suitable in terms of date
and quality, was selected to calculate the snow index.
Graphs of correlations between the values of the snow index and the physicochemical
characteristics of the snow samples composition are shown in Figure 1. Calculations were
made for the total concentration of impurities in the snow and for pH values (Figure 1, a, and b,
respectively). Taking into account the measurement error and the relatively small resolution of
the satellite image (20 m/pixel), we can observe consistency in the dynamics of changes in the
reflectivity of the snow cover and the qualitative composition of impurities in it.
4.2. The Chernorechensk cement plant
In the vicinity of the plant in early March 2020, ground monitoring was carried out along several
routes, taking into account the direction of the main removal of the admixture and the winter
wind rose [22]. The sampling scheme for 2020 is shown in Figure 2, the rhombus marks the
main sources of the plant — two closely spaced 80 meter pipes.
Northwestward from the source, sediment concentrations were calculated using a monodis-
perse reconstruction model (MMR) using two reference points at a distance of 0.72 and 1.64 km
from CCP. The remaining nine points of ground measurements were used to control the quality
of the numerical reconstruction. The result is shown in the graph (Figure 3, a and b) shows the
correlation of NDSI values with the concentration of impurities in the samples.
The conditions allowed for a more detailed chemical analysis of snow samples in 2019. In
particular, the concentrations of dissolved calcium (Ca2+ ) and potassium (K+ ), the compounds
of which constitute the main removal of impurities from the cement plant, were calculated
separately. The dependences of the dynamics of changes in the concentrations of Ca2+ , K+ and
the characteristics of the degree of reflection of the snow cover are shown in the Figure 4, a
and b. In both cases, a fairly high correlation coefficient is observed. To calculate the NDSI, an
image from the Landsat-8 spacecraft from February 25, 2019 was selected.
Figure 2: Snow sampling scheme in the vicinity Chernorechensk cement plant, 2020.
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Ruslana A. Amikishieva et al. CEUR Workshop Proceedings 392–398
a b
Figure 3: Graph of measured and calculated by MMR data impurity concentration (a); correlation of
NDSI values and measured sediment concentration (b).
a b
Figure 4: Correlation of snow index values and dissolved calcium (a), potassium (b).
Figure 5: Reconstructed contamination field according to the polydisperse model.
Also, based on the values of the snow index, using GIS-systems, the fields of atmospheric
pollution in the vicinity of the dry cement production workshop were reconstructed using MMR
and PMR (Figure 5). This workshop is a relatively small source located 3 km east of CCP. The
removal of suspended solids from the enterprise takes place on an open, flat area and is well
fixed on the snow cover.
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Analysis of Figure 5 indicates that the main precipitation of suspended solids occurred in
the northeast direction. The maximum concentration is located a few hundred meters from the
industrial site of the dry cement production workshop.
5. Conclusion
Based on the data of ground and satellite monitoring of the snow cover, a numerical analysis of
the pollution of the territory of Iskitim city and the environs of the Chernorechensk cement
plant was carried out. Linear correlations between the solid sediment of snow samples, pH and
the NDSI index at the sampling points were established for the territories of the city.
Reconstruction of the fields of precipitation of suspended solids in the zone of influence of
atmospheric emissions from the Chernorechensk cement plant was carried out on the basis of
low-parameter models. The functional relationships between the dissolved calcium, potassium
and the NDSI index were established along the sampling route northwest of the cement plant
pipes.
Joint use of ground and satellite information can significantly reduce the cost of monitoring
studies of pollution of territories. The involvement of model descriptions of the processes of
transfer of impurities from sources makes it possible to carry out the numerical reconstruction
of pollution fields from a small number of observation points.
Acknowledgments
The work was carried out within the framework of the State Assignment for the ICM&MG SB
RAS (project 0215-2021-0003), with the financial support of the Russian Foundation for Basic
Research and the Government of the Novosibirsk Region (project No. 19-47-540008).
References
[1] Berlyand M.E. Modern problems of atmospheric diffusion and atmospheric pollution.
Leningrad: Gidrometeoizdat, 1975.
[2] Sedunov Yu.S., Borzilov V.A., Klepikova N.V. Physical and mathematical modeling of
regional transport of radioactive substances in the atmosphere as a result of the accident
at the Chernobyl nuclear power plant // Meteorology and Hydrology. 1989. No. 9. P. 5–10.
[3] Shcherbatov A.F., Raputa V.F., Turbinsky V.V., Yaroslavtseva T.V. Assessment of air pollution
with dust based on snow survey data based on reconstruction of fallout fields // Health
Risk Analysis. 2014. No. 2. P. 42–47.
[4] Balter B.M., Balter D.B., Egorov V.V., Stalnaya M.V. Using Landsat satellites data to deter-
mine the concentration of pollutants in plumes from blowing gas wells based on a source
model // Earth Research from Space. 2014. No. 2. P. 55–66.
[5] Vasilenko V.N., Nazarov I.M., Fridman Sh.D. Monitoring of snow cover pollution. Leningrad:
Gidrometioizdat, 1985.
[6] Prokacheva V.G., Usachev V.F. Snow cover as an indicator of cumulative pollution in
397
Ruslana A. Amikishieva et al. CEUR Workshop Proceedings 392–398
the sphere of influence of cities and roads // Meteorology and Hydrology. 2013. No. 3.
P. 94–106.
[7] Zhuravleva N.V., Potokina R.R., Ismagilov Z.R., Khabibulina E.R. Pollution of the snow cover
with polycyclic aromatic hydrocarbons and toxic elements on the example of Novokuznetsk
city // Chemistry for sustainable development. 2014. Vol. 22. No. 5. P. 445–454.
[8] Koroleva G.P., Gorshkov A.G., Vinogradova T.P. Investigation of snow cover pollution as a
depositing environment (Southern Baikal region) // Chemistry for Sustainable Develop-
ment. 1998. Vol. 6. P. 327–337.
[9] Dmitriev A.V., Dmitriev V.V. Tracking the correlation of NDVI dynamics in comparison
with the aerosol fallout zone // Modern problems of remote sensing of the Earth from
space. 2008. Vol. 1. No. 5. P. 92–95.
[10] Dmitriev A.V., Dmitriev V.V. Theoretical and experimental study of the melting process of
snow cover in the region of Omsk based on materials from 2007–2009 // Modern problems
of remote sensing of the Earth from space. 2011. Vol. 8. No. 2. P. 34–41.
[11] Vasilevich M.I., Shchanov V.M., Vasilevich R.S. Application of satellite research methods
in assessing snow pollution around industrial enterprises in the tundra zone // Modern
problems of remote sensing of the Earth from space. 2015. Vol. 12. No. 2. P. 50–60.
[12] Krutskikh N.V., Kravchenko I.Yu. Using Landsat satellite images for geoecological monitor-
ing of urbanized territories // Modern problems of remote sensing of the Earth from space.
2018. Vol. 15. No. 2. P. 159–168.
[13] Bortnikova S.B., Raputa V.F., Devyatova A.Yu., Yudakhin F.N. Methods for analyzing data
on snow cover pollution in the zones of influence of industrial enterprises // Geoecology.
Engineering geology. Hydrogeology. Geocryology. 2009. No. 5. P. 447–457.
[14] Izrael Yu.A., Tsaturov Yu.S., Nazarov I.M. Reconstruction of the actual picture of radioactive
contamination of the area as a result of accidents and nuclear tests // Meteorology and
Hydrology. 1994. No. 8. P. 5–18.
[15] Amikishieva R.A., Raputa V.F., Yaroslavtseva T.V. Technologies for analyzing atmospheric
pollution processes based on ground-based and satellite observations // Interexpo GEO-
Siberia. 2020. Vol. 4. No. 1. P. 36–41.
[16] Riggs G., Hall D., Salomonson V. A snow index for the Landsat thematic mapper and
moderate resolution imaging spectrometer // Geoscience and Remote Sensing Symposium,
IGARSS’94. Vol. 4: Surface and Atmospheric Remote Sensing: Technologies, Data Analysis,
and Interpretation. 1994. P. 1942–1944.
[17] Hall D.K., Riggs G.A., Salomonson V.V., DiGirolamo N.E., Bayr K.J., Jin J.M. MODIS snow-
cover products // Remote Sensing of Environment. 2002. Vol. 83. No. 1. P. 181–194.
[18] Bezuglaya E.Yu., Smirnova I.V. Air of cities and its changes. St. Petersburg: Asterion, 2008.
[19] Bezuglaya E.Yu. Monitoring of the state of air pollution in cities. Leningrad: Gidrometeoiz-
dat, 1986.
[20] Berlyand M.E., Genikhovich E.L., Kanchan Ya.S., Onikul R.I., Chicherin S.S. On the calcula-
tion of the average annual concentrations of impurities in the atmosphere from industrial
sources // Proceedings of the GGO. 1979. No. 17. P. 3–18.
[21] Byzova N.L., Garger E.K., Ivanov V.N. Experimental studies of atmospheric diffusion and
calculations of impurity scattering. Leningrad: Gidrometeoizdat, 1991.
[22] The climate of Novosibirsk. Leningrad: Gidrometeoizdat, 1979.
398