CC BY
9
RESEARCH PAPER / НАУЧНАЯ СТАТЬЯ UDC 692.4
DOI: 10.22227/1997-0935.2023.6.889-900
Assessment of PM25 particulate air pollution near highways
Elena V. Sysoeva1, Margarita O. Gelmanova2
1 Moscow State University of Civil Engineering (National Research University) (MGSU);
Moscow, Russian Federation; 2 ARCHIATELIER; Moscow, Russian Federation
ABSTRACT
Introduction. The purpose of this study is to investigate air pollution with respect to PM25 particulate matter hazardous to the health of the urban population, generated by the movement of motor vehicles. The development of existing and construction of new transport networks in large cities leads to the fact that the problem of air pollution by PM25 particles becomes extremely urgent.
Materials and methods. The ENVI-met calculations were based on the data provided by the meteorological station. The following methods were applied: system analysis, numerical modelling method — finite difference method, processing of numerical results.
Results. A calculation model of Moscow district territory was developed in ENVI-met programme in order to determine PM25 dispersion patterns along the road network of the selected district. The calculation was carried out for a 24-hour time period. The schemes of fine particles dispersion on the territory of the building at a height of 1.5 and 10 m are obtained. Their evaluation shows that the highest concentration of PM25 is observed along the most frequent wind direction near roads. The width of the roadway and, accordingly, the number of cars passing per hour plays a paramount role in the formation of PM25. Conclusions. The greatest negative impact of fine dust occurs in residential buildings located along highways. It is most rational to increase the density of landscaping in areas with a high concentration of PM25 along the main roads and on the roofs <S" ST of existing low-rise buildings and medium-rise buildings. & 5
i x
KEYWORDS: ENVI-met, numerical modelling, air pollution, vehicle emissions, particulate matter, PM25, urban area green- ^ *
вблизи автомагистралей
Елена Владимировна Сысоева1, Маргарита Олеговна Гельманова2
1 Национальный исследовательский Московский государственный строительный университет
< П
G) M
ing, green roofs
FOR CITATION: Sysoeva E.V., Gelmanova M.O. Assessment of PM25 particulate air pollution near highways. Vest-nik MGSU [Monthly Journal on Construction and Architecture]. 2023; 18(6):889-900. DOI: 10.22227/1997-0935.2023.6. *
889-900 (rus.). о S
K ' n CO
l z
Corresponding author: Elena V. Sysoeva, [email protected]. y 1
n 0
Оценка загрязнения воздуха твердыми частицами PM2. Z 33
о Z
О о
со со
n M
(НИУ МГСУ); г. Москва, Россия; n 0
2АРХИ АТЕЛЬЕ; г. Москва, Россия
> 6 О О
АННОТАЦИЯ (
с О
Введение. Целью исследования является изучение загрязнения воздуха опасными для здоровья городского на- и = селения твердыми частицами РМ25, образующимися в результате движения автотранспортных средств. Развитие Ф е существующих и строительство новых транспортных сетей в крупных городах приводит к тому, что проблема загряз- V ФФ нения воздуха частицами РМ25 становится актуальной. ° О
Материалы и методы. Для расчетов в ENVI-met использовались данные, предоставленные метеостанцией. При- с ^
менялись следующие методы: системный анализ, метод численного моделирования — метод конечных разностей, обработка численных результатов.
з :
(D
00
Результаты. В программе ENVI-met разработана расчетная модель территории района Москвы для определения . И
закономерностей рассеивания РМ25 вдоль дорожной сети выбранного района. Расчет проводился для 24-часового ^ §
периода времени. Получены схемы рассеивания мелких частиц на территории на высоте 1,5 и 10 м. Их оценка пока- (л у
зывает, что наибольшая концентрация РМ25 наблюдается вдоль наиболее частого направления ветра вблизи дорог. ф §
Ширина проезжей части и, соответственно, количество проезжающих в час автомобилей играют первостепенную о» о
роль в образовании РМ25. ° °
Выводы. Наибольшее негативное воздействие мелкодисперсной пыли приходится на жилые дома, расположенные О О
вдоль автомобильных дорог. Наиболее рационально увеличивать плотность озеленения в районах с высокой кон- и и центрацией РМ вдоль основных дорог и на крышах существующих малоэтажных и среднеэтажных зданий.
© Е.В. Сысоева, М.О. Гельманова, 2023 889
Распространяется на основании Creative Commons Attribution Non-Commercial (CC BY-NC)
КЛЮЧЕВЫЕ СЛОВА: ENVI-met, численное моделирование, загрязнение воздуха, выбросы автотранспорта, мелкодисперсные частицы, PM25, озеленение городских территорий, «зеленые» крыши
ДЛЯ ЦИТИРОВАНИЯ: Сысоева Е.В., Гельманова М.О. Assessment of PM25 particulate air pollution near highways // Вестник МГСУ. 2023. Т. 18. Вып. 6. С. 889-900. DOI: 10.22227/1997-0935.20223.6.889-900
Автор, ответственный за переписку: Елена Владимировна Сысоева, [email protected]
(О (О
N N
О О
N N
«В «В
¡г <и
U 3
> (Л
С (Л
ОТ "
от Е
Е о с
ю °
S 1
о Е
СП ^ т- ^
от от
£
il
О И
INTRODUCTION
In September 2015, the United Nations1 adopted the Resolution "Transforming our World: the 2030 Agenda for Sustainable Development". The adopted document covers all aspects of life and activities of people on the planet, especially inside cities, in order to "protect the planet from degradation including through the introduction of rational consumption and production models, rational use of its natural resources and taking urgent measures regarding climate change so that the planet can meet the needs of present and future generations".
Cities, being the centers of industry and a transport system development, have become the main source of pollution of the air basin. According to the Russian Federal State Statistics Service 75 % of the country's population is concentrated in cities, and every year the share of the urban population increases (Fig. 1).
human activity, achieving the harmonization of human-nature relations. Man cannot and should not interfere in the natural processes of the Biosphere evolution".
Currently the problem of air pollution by particu-late matter PM25 and PM10 is becoming significant in large megacities, which becomes a priority for maintaining the health and well-being of the urban population in the context of urban growth3 and technological development [3]. One of the most dangerous pollutants in the city's air environment, along with O3, NO2 and SO2, are particulate pollutants PM2 5, which pose a serious threat to public health [4]. PM25 are an air pollutant consisting of solid and liquid particles suspended in the air with a diameter equal or less than 2.5 microns.
Fig. 2 shows that PM2 5 is a part of PM10, but PM2 5 are more toxic and contain more heavy metals in comparison with PM10 [5], and, as a result, contribute more to the deterioration of the urban population health [6], leading to an increase in cases of respiratory diseases such as asthma, an increase in the frequency of cardiovascular and oncological diseases [7, 8]. Due to the fact that particulate pollutants PM2 5 pose a great danger to human health, only PM2 5 are considered in this study.
Fig. 1. The share of the urban population of the Russian Federation in the total population2
Cities are destroying nature in an effort to improve the quality of life. Academician VA. Ilyichev [1] developed the principles of transforming a city into a biosphere-compatible and developing people. Principle No. 1 is awareness of the man and nature unity and the need for a symbiosis between the city and the Biosphere.
The task of symbiotically embedding the city into the natural environment is extremely important. Academician VI. Osipov [2] notes that it is necessary to preserve the biosphere, "... and it is necessary to manage
Fig. 2. Comparison of the particle diameter of some pollutants4
Particulate matter PM25 are divided into primary and secondary by origin. Primary PM25 enter the airspace ready-made. Secondary PM25 appear during the interaction of various substances in the atmosphere and are more dangerous in comparison with primary. The ability of dust particles PM25 to absorb highly toxic organic compounds and to release these compounds into the human body via penetrating into
1 United Nations. Transforming our world: The 2030 agenda for sustainable development. New York, 2015.
2 The share of the urban population in the total population as
of January 1. URL: https://showdata.gks.ru/report/278932/
3 United Nations. World urbanization prospects: The 2014 revision. New York, 2014.
4 World Health Organization. URL: https://www.euro.who. int/_data/assets/pdf_file/0005/78638/E90038.pdf
the upper respiratory tract and alveoli of the lungs makes them secondary damaging factors that enhance the negative impact of the urban environment on the human body.
Fine dust has natural and man-made sources (stationary and mobile). In cities particulate matter PM2 5 are mainly formed during the combustion of transport fuel and when vehicle tires rub against the road surface, which leads to the appearance of road dust, usually containing heavy metals and metalloids. Thus, the intake of Sb, Zn, Cu, Pb, Mo is associated with the emission of engine oil particles and emissions occurring during the combustion of automobile fuel, Zn, W come from the abrasion of the road surface and markings, Sb, Cd, Zn, Pb, Cu — with the abrasion of vehicle tires, Sb, Zn, Cu, Pb, W — with the wear of brake pads and alloy surfaces [9]. Industrial emissions are also a source of PM2 5 formation, however, due to the fact that industrial facilities are mainly located outside a city, their importance is small [10-12].
The existing regulatory documentation of different countries has significant differences in the value of maximum allowable concentration for particulate pollutants (Table 1).
Table 1. One-time, average daily and average year limits for PM25: RF, EU, USA, China, WHO
Indicators* RF EU USA China WHO
PM 5 one-time limit, ^g/m3 160 - - - -
PM 5 average daily, ^g/m3 35 25 35 35 25
PM 5 average year, ^g/m3 25 12 12** 15*** 15 10
timates of the long-term chronic impact of PM2 5 on human health in European cities, life expectancy may increase by up to 22 months solely due to the fact that the average annual concentrations of particles in the air will be set equal to 10 micrograms/m3. Moreover, results have been obtained showing that an increase in daily concentrations of fine dust leads to an increase in subsequent days of hospitalizations associated with respiratory and cardiovascular diseases [13], and chronic exposure to PM2 5 leads to increased mortality [14-17]. The WHO Guidelines on Air Quality establish interim targets (IT) of average annual concentrations of PM25 (IT-1 — 0.035 mg/m3, IT-2 — 0.025 mg/m3, IT-3 — 0.015 mg/m3) and average daily concentrations of PM25 (IT-1 — 0.075 mg/m3, IT-2 — 0.050 mg/m3, IT-3 — 0.0375 mg/m3). Levels from IT-3 (the lowest) to IT-1 (the highest) show an increase in mortality. Thus, with respect to the average annual values IT-1 is characterized by a significant risk of mortality in developed countries (the risk of mortality is 15 % higher than the risk of mortality at the recommended value of 0.010 mg/m3), at the level of IT-2 the risk of mortality decreases by about 6 % compared to IT-1, at the level IT-3, the risk of mortality also decreases by about 6 % compared to IT-2. According to average daily values the risk of mortality is 5 % higher for IT-1, 2.5 % higher for IT-2, 1.2 % higher for IT-3 compared with the recommended value of 0.025 mg/m3 (Fig. 3).
Notes: * — PM 5 one-time limit — the maximum one-time permissible concentration of particulate matter in the air when averaged over 20 min; PM 5 average daily limit — maximum permissible average daily concentration of particulate matter in the air; PM2 5 average year limit — the maximum permissible average annual concentration of particulate matter in the air; ** — a value corresponding to the primary US standards that protect the health of the most vulnerable groups of the population; *** — a value corresponding to the secondary US standards that protect the health of the rest of the population.
In accordance with the international documentation "WHO Guidelines on Air Quality"5 concerning the content of particulate matter, ozone, nitrogen dioxide and sulfur dioxide in the air, the recommended daily average value of PM25 concentration should be 0.025 mg/m3; the annual average is 0.010 mg/m3. According to es-
< n
i H
5 Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide and sulfur dioxide // World Health Organization (WHO). 2006. URL: http://apps.who.int/iris/ handle/10665/107823
Fig. 3. Recommended value and IT of average annual and average daily concentrations of PM^ (mg/m3) in accordance with the WHO Guidelines on Air Quality (MR — mortality risk)
Thus, in accordance with the existing evidence base according to the maximum permissible values of PM2 5, based on a large number of foreign studies, the long-term impact of PM2 5 exceeding the values of 25 mg/m3 for daily averaged value and 10 mg/m3 for year averaged value is dangerous and significantly increases the risk of mortality of the population. This suggests the need to revise the existing regulatory values in a number of countries. However, even with PM25 values not exceeding the WHO recommended values (0.025 mg/m3 daily average, 0.010 mg/m3 annual average), adverse effects on human health cannot be completely excluded, but the risk to public health can be significantly reduced.
n M n 0
D 6
r ra t ( an
CD )
[Ï
® »
» n
■ T
s □
s y c o <D K
WW
M 2 O O 10 10 u w
W (O N N
o o
N N
«b <o ¡É ai
u 3 > m c M
2 "i ta oo
. r
« gi j
<D <U
O ¡g —■ "t^ o o
z ■ i
w 13
OT iE
E o
CLU c
Ln °
s ¡¡
o E
CD ^
M M
El
o iñ №
The following factors can affect the decrease in the concentration of fine particles in the air:
1) the number of sources of PM25 formation (the number of vehicles powered by gasoline and diesel fuel, the number of industrial facilities) and their features;
2) climatic conditions (average annual precipitation, air humidity, wind speed and direction);
3) urban planning factors (geometry of the building area, number of storeys of buildings, built-up-ness — for example, fine dust particles accumulate at intersections and buildings located on turning sections of a road) in combination with the wind rose;
4) the area of green areas at various horizontal levels, including green roofs, as well as green facades (surfaces that retain fine dust particles).
Various management solutions are used in cities to reduce air pollution by particulate pollutants. As a rule, the main measures to combat air pollution are aimed to reducing the number of pollutant sources or controlling their emissions. This strategy leads to an effective reduction in the emissions rate, but does not affect the pollutants already exists in the air. It is important to note that the green spaces currently aren't used to regulate the concentration of PM2 5.
To improve air quality, it is necessary to modernize traditional approaches for reduction the concentration of existing pollutants in the air to an acceptable level, especially in the summer and at positive temperatures. One of the ways to achieve this goal is to use green spaces in a city that can reduce air pollution by depositing dust particles on the surface of leaves [18-25]. Subsequently, during precipitation particulate matter enter the ground, the upper part of which must be changed every 2-3 years, as approved in the current Order of the State Construction Committee of the Russian Federation No. 153 dated December 15, 1999. It is important to note that the dust-retaining properties of various vegetation are not the same and are characterized by the dust-filtering ability of plants, depending on their morphological characteristics, biomass, the amount and fraction of dust. Moreover, trees and shrubs are more effective in removing pollutants than lawn grass due to the larger leaf surface area, which is characterized by Leaf Area Index (LAI). Thus, by [26] a numerical study was carried out, which showed that trees planted along highways contribute to reducing the concentration of exhaust gases in nearby pedestrian zones by 7 %. Scientists have also revealed that trees throughout the United States annually remove 711,000 tons of pollutants from the air [27]. Based on the results of these studies, in 2004, the US Environmental Protection Agency (EPA) decided to plant trees in urbanized areas to reduce the concentration of pollutants in the air in cities6. Thus, it is extremely important to recover landscaping in urban areas.
6 United States Environmental Protection Agency (US EPA). Incorporating Emerging and Voluntary Measures in a State Implementation Plan (SIP). US Environmental Protection Agency, Research Triangle Park, NC, 2004. URL: https://www. epa.gov/sites/default/files/2016-05/documents/voluntarycontro lmeasurespolicyepa.pdf
In conditions of high building density, where it is impossible to allocate free space, landscaping the roofs of existing buildings is an exceptional solution. Few studies on the ability of green roofs to remove pollutants from the air do not provide sufficient information to judge the effectiveness of greening the roofs of buildings in order to reduce the concentration of PM25. Currently, the greening of roofs cannot be used as an independent measure to combat air pollution due to the high cost, but it can be an additional measure in combination with other ones, in addition green roofs can provide a solution to a number of environmental problems.
The purpose of the current research is to obtain and analyze PM2 5 dispersion schemes in urban area on the example of a residential neighborhood in the southern administrative district of Moscow. In this study, the following tasks were set:
1. Development of the residential area 3D numerical model in the ENVI-met software, which makes it possible to estimate and predict the concentrations of PM2 5 in the air.
2. Dynamic calculation of an air environment state of the selected site with the use of computational hydrodynamics methods for a 24-hour period, analyze and assess of the obtained PM2 5 dispersion schemes.
MATERIALS AND METHODS ENVI-met description
The solution of the problem of urban landscaping rational planning became possible due to the intensive development of computational fluid dynamics (CFD), which allows to analyze the effectiveness of the proposed measures to improve the quality of urban air [28]. For the first time CFD was used to calculate pollutants dispersion in the atmosphere in 1986 [29]. The models and methods of calculating heat and mass transfer in the air, which have been improving since then, have made it possible to investigate the influence of many parameters on the dispersion of particulate matter in the urban atmosphere: turbulence [30], humidity and temperature, the diameter of polluting particles, the location of emission sources in space, the geometry of buildings and the type of building surface materials. The introduction of CFD modeling into the research process is an important step in determining the patterns of the particulate matter desperation in urban development and identifying effective measures to reduce dust pollution in urbanized areas.
ENVI-met is a programme that uses computational fluid and dynamics methods, designed to build a three-dimensional predictive model of microclimate and modeling processes occurring in the boundary layer of the earth, taking into account the properties of urban surfaces, location and types of buildings and green spaces. The CFD calculation in ENVI-met is based on the numerical solution of the air flows equations in the mesh region. ENVI-met uses the Reyn-
olds averaged Navier-Stokes equations (RANS) and the standard k-e turbulence model, first proposed in the study [31]. The k-e turbulence model is based on several assumptions, the most important of which is that the Reynolds number is sufficiently large. In addition, it is important that turbulence is in equilibrium in the boundary layers (i.e., near the walls of solid objects), which means that the amount of turbulent energy generated is equal to the amount of turbulent energy dissipated. These assumptions limit the accuracy of the model, however, lead to savings in computing resources compared to using more complex turbulence models.
In this study, the calculation of PM2 5 dispersion was performed in the ENVI-met software version 4.4.5.
Fig. 4. Three-dimensional model of the research area in AutoCAD: ■ — residential buildings with up to 15 floors; ■ — residential and public buildings up to 9 floors; ■ — pedestrian zones; ■ — highways;^ — green areas; ■ — deciduous trees
RESULTS Research area
For this study, the territory of a residential neighbourhood which is remote from industrial zones and located in the southern administrative district of Moscow (with an area of 12,856 hectares) was selected (Fig. 4). Public and residential buildings up to 15 floors are located on this territory. The study area is bounded on four sides by highways: in the east - Warsaw Highway, in the west - Simferopol Boulevard, in the north - Nakhi-movsky Avenue, in the south - Bolotnikovskaya Street, among which Warsaw Highway and Nakhimovsky Avenue have the densest traffic.
Meteological data for the study was taken from a weather station located near the study area at the address: Moscow, Kashirskoe highway, 10 (Fig. 5, 6).
Meteorological data was used to set boundary conditions in the model. The hourly averaged data from the weather station was used as input parameters. Data on relative humidity, %, air temperature, °C and PM2 5 concentration are performed in the Table 2.
Thus, sufficiently high concentration of PM25 were detected. The maximum one-time concentration on August 26, 2020 was equal to 136.9 ^g/m3 (Fig. 7) in the period between 20:00 and 22:00, which is lower than the value of one-time concentration limit (160 ^g/m3) established by Russian Sanitary Regulations and Standards 1.2.3685-21. The average daily concentration on August 26, 2020 was equal to 24.92 ^g/m3, which is also lower than the average daily concentration limit (35 ^g/m3). Despite the fact that the data obtained fall within the limits of the Standard, their maximum values
Table 2. Weather station data for August 26, 2020
Fig. 5. Satellite image of the research area
Fig. 6. The scheme of the research area: a — the 70th quarter of Volkhonki-ZIL; b — the 72nd quarter of Volkhonki-ZIL; c — the 71st quarter of Volkhonki-ZIL; d — the location of the weather station
Time, h 0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16 16-18 18-20 20-22 22-24
PM2 5 averadge, ^g/m3 30 29 32 39 33 35 33 34 10 4 5 15
PM2 5 maximum, ^g /m3 54.80 29.50 40.60 38.50 43.70 34.90 46.80 34.00 12.60 13.20 136.90 15.20
21 19 18 18 18 20 20 20 17 17 17 16
Relative humidity, % 84 100 100 100 100 93 96 99 100 95 91 100
CD )
[Ï
® »
» n
■ T
s □
s y c o D K
fi"
M 2 O O 10 10 W W
W (0
N N
o o
N N
to to
x ai
U 3
> in
C M
ta oo
. r
« gi j
<D <U
O ig
---' "t^
o
o u
are dangerous to human health according to WHO and as a result can cause respiratory, cardiovascular and oncological diseases.
AutoCad
Rhinoceros 3D
EN VI-met
1 > — — — — — — — — '
/
✓ ✓ ✓
✓
S /V
243 m
Fig. 7. Hourly graph of the average PM^5 concentration measured by the weather station at August 26, 2020
ENVI-met model
The AutoCAD programme was used to build the research area. Rhinoceros 3D software based on Grasshopper visual programming was used to import the model from the AutoCAD programme to ENVI-met (Fig. 8).
CO "
co E
Fig. 8. The stages of importing the model into ENVI-met
In this case, the finite element ENVI-met model (Fig. 9) includes the research area with the selected model parameters and ENVI-met objects which are assigned different types of materials (asphaltic-cement concrete road surfaces, concrete and brick surfaces of buildings, landscaped surfaces).
The calculation was carried out for a twenty-four-hour period on August 26, 2020. Table 3 shows the parameters of the ENVI-met model.
Adding nesting grids can improve the convergence of the numerical solution, but it is not necessary
1,230 m
Fig. 9. Computational domain of the ENVI-met model
if there is sufficient free space between the boundary of the computational domain and the outermost objects of development. To improve the convergence of the solution, 8 nesting grids were added in this calculation.
The ENVI-met programme can construct 2 types of a vertical grid: equidistant grid A, where all cells, except the five lower ones with a height of 0.2Az, have the same vertical dimensions Az, and a telescopic grid B1, B2, C, where with increasing distance from the earth's surface, the cells height increases taking into account the expansion coefficient 5. Fig. 10 shows an equidistant grid (A) and a telescopic grid in three variants (B1, B2, C), where variant C, which is a special case of variants B1 and B2, has an expansion coefficient 5 equal to zero, as a result the heights of each cell of all layers of the computational domain are equal. In this research, the equidistant method of generating a vertical grid (A) was applied. Near the earth's surface, a smaller vertical size of five grids was set equal to 0.6 m in order to obtain more accurate calculation results since at ground level the exchange processes between the atmosphere and the earth's surface have a significant impact on the microclimate.
The size of a grid cell in ENVI-met computational domain ranges from 0.5 to 10 m, while the time step is 1-5 s [32]. In this study, the cell size is 3 x 3 x 3 m, the computational domain of the model has dimensions of 1,230 x 1,140 m and a height of 243 m. The comparison of the model in AutoCAD and ENVI-met is shown in Fig. 11 and 12.
E o
CLU c
Ln O
si
o E
fee
CD ^
CO CO
£
S!
O M
Table 3. The main parameters of the ENVI-met model
Simulation date August 26, 2020 Computational domain of the ENVI-met model, m 1230 x 1140 x243
Simulation time 24 hours Grid cell dimensions 3 x 3 X 3
Wind speed, measured at a height of 10 m, m/s 4.05 dx, dy, dz, m
Wind direction Northeast Nesting grids 8
The initial temperature of atmospheric air at 00:00 on August 26, 2020 t, °C 21
Minimum temperature over 24 hours on August 26, 2020 t . , °C min' 16 Vertical grid generation method Equidistant
The maximum temperature over 24 hours on August 26, 2020 t , °C ' max' 21
Az
Az
Az
Az
Az /
.0.2 Az 0.2Az 0.2Az 0.2 Az 0.2Az
B1
Az(4)
Az(3)
Az(2)
Az(l)
= Az(3) ■ + s -Az(3)
= Az(2) -+ ,v -Az(2)
= Az(l) i-+ 5 ■ Az(l)
B2 C
Az( 5) - Az(4) + ■■ s-Az(4) Az
Az
Az(4) - Az + ■■ s -Az Az
Az
Az Az
Az Az
Az Az
5 = 0
Fig. 10. Types of vertical grids in ENVI-met
Fig. 11. Three-dimensional model of the studied area in AutoCAD, top view: ■ — residential buildings with up to 15 floors; ■ — residential and public buildings up to 9 floors; ■ — pedestrian zones; ■ — highways; ■ — landscaped areas; ■ — deciduous trees
After the calculation, the distribution schemes of fine particles in the air of urban development were drawn up, which are shown in Fig. 13-16.
The calculation results demonstrated that the width of the roadway, and, consequently, the number of vehicles passing per hour, plays a primary role in the formation of fine dust in the studied area (Fig. 13). Moreover, the speed and direction of air movement determine the location of areas of PM25 accumulation (Fig. 14). Wind speed (and, consequently, PM25 concentration) have the greatest values on the streets oriented along the wind direction. Since the air flows in the computational domain have a north-easterly direction (in the southern part of the studied area wind has northwesterly direction due to the vortex movement of air), the greatest negative impact of PM2 5 falls on residential buildings located along two highways: Varshavskoe Highway and Simferopol Avenue. The wind speed inside the area (in courtyards between houses away from wide highways) is significantly lower due to the shielding effect of residential buildings, as a result, fine particles PM2 5 will stagnate in these areas.
Fig. 13. The distribution scheme of PM^ 5 in the studied area at 21:00 at the height of 1.5 m from the ground surface
Fig. 12. Three-dimensional model of the studied area in ENVI-met, top view: ■ — residential and public buildings;
■ — pedestrian zones; ■ — green areas; □ — highways;
■ — deciduous trees
The traffic intensity was determined by averaged traffic calculations with dividing transport into different classes (truck transport > 7.5 tons; buses; passenger cars). Traffic data was averaged over 1 hour. Emissions from road traffic are presented in the form of linear sources. The sources were placed across the entire width of the traffic lanes for a more realistic spatial distribution.
Fig. 14. Wind speed in the studied area at 21:00 at at the height of 1.5 m from the ground surface
W (O
N N
o o
N N
<0 <0
¡É ai
u 3
> m
c M
2 "i
ta oo
. r
« gi j
<D <U
O ¡g
o o
™. I
w 13 ot E
E o clu
c
LO °
s ¡¡
o E
CD ^
Fig. 15. The distribution scheme of PM^ 5 in the studied area at 21:00 at the height of 10 m from the ground surface
Fig. 16. Wind speed in the studied area at 21:00 at at the height of 10 m from the ground surface
Fig. 13 and 15 show that dust pollution remains highly concentrated along the roads. At the altitude of 1.5 m from the earth's surface, the maximum concen-££ tration of PM2 5 was 77.02 ug/m3 in the central part of -o the Warsaw Highway, which is lower than the short-term exposure limit = 160 ug/m3, which is nevertheless dangerous for human health. At a distance of 10 m from the border of the nine-lane road carriageway, PM2 5 concentrations decrease by approximately 5-10 % compared to the central part of Warsaw highway.
In further studies, roofs landscaping in the development area at various altitude relative to the earth's surface will be proposed to reduce PM2 5 concentrations in order to study the effectiveness of this factor.
CONCLUSIONS
Thus, according to the results of the study, we can come to the following conclusions.
Air pollution by particulate matter in the context of urban development and expansion is becoming an urgent problem that requires researches and development of effective and environmentally friendly management solutions. PM2 5 are part of PM10, but it is PM2 5 that are more toxic and contain more heavy metals, and, as a result, contribute more to the deterioration of the health of the urban population, leading to respiratory, cardiovascular and oncological diseases.
The analysis of domestic and foreign regulatory documentation has shown the need to revise the existing regulatory values on the territory of the Russian Federation. Reducing the average daily and average annual concentration limits of PM2 5 will reduce the risks of mortality and improve the well-being and health of the urban population. So, with a decrease in average annual concentration limit for PM2 5 from 25 ug/m3 to the recommended value of 10 ug/m3, the risk of mortality of the population will decrease by more than 6 %. At that, the regulation of the average annual concentration limit is a more important step because this indicator causes more harmful long-term effects of fine dust on human health.
Urban landscaping is a little-studied factor that has a potential to decrease in the concentration of PM25. Reduction of air pollution by fine particles occurs due to their deposition on the surface of leaves, and if it is impossible to allocate free space in conditions of high building density, greening roofs of existing buildings may become a solution to reduce air pollution.
The results of the calculation carried out in this study demonstrated that the width of a roadway and, consequently, the number of vehicles passing it per hour plays a primary role in the formation of fine dust in the studied area. Moreover, the speed and direction of air movement are of the greatest importance on the streets oriented along the wind direction and determine the location of areas of PN2 5 accumulation. Dust pollution remains highly concentrated along the roads. The greatest negative impact of fine dust falls on residential buildings located along highways.
It is most rational to increase the density of landscaping in areas with a high concentration of PM25 along the main roads and on the roofs of existing low-rise buildings and medium-rise buildings.
M
cn
* i 5 (9
x S 1. Il'ichev V.A. Principles of transformation of
I £ a city in biosphere compatible and developing the person.
¡3 -g Industrial and Civil Engineering. 2010; 6:3-13. (rus.).
u £ 2. Osipov V.I. Assessment and management of natural risks (state of the problem). Geoekologiya.
REFERENCES
Inzheneraya geologiya, gidrogeologiya, geokriologiya. 2007; 3:201-211. (rus.).
3. Grafkina M.V., Azarov A.V., Dobrinsky D.R., Nikolenko D.A. On the issue of control and normalization of fine dust emissions into the atmospheric air
during the road transport movement. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2017; 12(4):373-380. DOI: 10.22227/19970935.2017.4.373-380 (rus.).
4. Polichetti G., Cocco S., Spinali A., Trimarco V., Nunziata A. Effects of particulate matter (PM10, PM2 5 and PMj) on the cardiovascular system. Toxicology. 2009; 261(1-2):1-8. DOI: 10.1016/j.tox.2009.04.035
5. Arden Pope III C. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA. 2002; 287(9):1132. DOI: 10.1001/jama.287.9.1132
6. Mayer H. Air pollution in cities. Atmospheric Environment. 1999; 33(24-25):4029-4037. DOI: 10.1016/S1352-2310(99)00144-2
7. Kholodov A.S., Kirichenko K.Y., Zador-nov K.S., Golokhvast K.S. Effect of particulate matter in the air of residential areas on human health. Bulletin of Kamchatka State Technical University. 2019; 49: 81-88. DOI: 10.17217/2079-0333-2019-49-81-88 (rus.).
8. Tabakaev M.V., Artamonova G.V. Particulate matter air pollution effects on the incidence of heart diseases among the urban population. Annals of the Russian academy of medical sciences. 2014; 69(3-4):55-60. DOI: 10.15690/vramn.v69i3-4.996 (rus.).
9. Kosheleva N.E., Vlasov D.V., Samsonov T.E., Kasimov N.S. Chemical composition of road dust particles and its variation as influenced by geometry of urban canyons in Moscow. Reports of the Russian Scientific and Technical Society of Radio Engineering, Electronics and Communications named after A.S. Popova : Proceedings of the International Symposium. Ser. "Scientific International Symposia. Engineering Ecology". 2019; 61-65. (rus.).
10. Morawska L., Thomas S., Gilbert D., Green-away C., Rijnders E. A study of the horizontal and vertical profile of submicrometer particles in relation to a busy road. Atmospheric Environment. 1999; 33(8):1261-1274. DOI: 10.1016/S1352-2310(98)00266-0
11. Ulanova T.S., Antipieva M.V., Volkova M.V., Gileva M.I. Investigations of fine particles concentrations in the atmospheric air near highways. Health Risk Analysis. 2016; 4:38-46. DOI: 10.21668/health. risk/2016.4.05 (rus.).
12. Bespalov V.I., Gurova O.S., Samarskaya N.S. Main principles of the atmospheric air ecological monitoring organization for urban environment mobile pollution sources. Procedia Engineering. 2016; 150:20192024. DOI: 10.1016/j.proeng.2016.07.286
13. Faustini A., Stafoggia M., Berti G., Bisanti L., Chiusolo M., Cernigliaro A. et al. The relationship between ambient particulate matter and respiratory mortality: a multi-city study in Italy. European Respiratory Journal. 2011; 38(3):538-547. DOI: 10.1183/09031936.00093710
14. Chow J.C., Watson J.G., Mauderly J.L., Costa D.L., Wyzga R.E., Vedal S. et al. Health effects of fine particulate air pollution: lines that
connect. Journal of the Air & Waste Management Association. 2006; 56(10):1368-1380. DOI: 10.1080/10473289.2006.10464545
15. Anderson H.R., Atkinson R., Peacock J.L., Marston L., Konstantinou K. Meta-analysis of time-series studies of particulate matter (PM) and ozone (OJ: report of a WHO task group. Copenhagen, WHO Regional Office for Europe, 2004. URL: https://apps.who. int/iris/handle/10665/107557
16. Garrett P., Casimiro E. Short-term effect of fine particulate matter (PM2 5) and ozone on daily mortality in Lisbon, Portugal. Environmental Science and Pollution Research. 2011; 18(9):1585-1592. DOI: 10.1007/ s11356-011-0519-z
17. Jerrett M., Burnett R.T., Ma R., Pope C.A., Krewski D., Newbold K.B. et al. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology. 2005; 16(6):727-736. DOI: 10.1097/01. ede.0000181630.15826.7d
18. Abhijith K.V., Kumar P., Gallagher J., McNab-ola A., Baldauf R., Pilla F. et al. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments : a review. Atmospheric Environment. 2017; 162:71-86. DOI: 10.1016/j. atmosenv.2017.05.014 S e
19. Morakinyo T.E., Lam Y.F., Hao S. Evaluating n H the role of green infrastructures on near-road pollutant k s
dispersion and removal: Modelling and measurement. S —
O
Journal of Environmental Management. 2016; 182: S c 595-605. DOI: 10.1016/j.jenvman.2016.07.077 » y
20. Pugh T.A.M., MacKenzie A.R., Whyatt J.D., O S Hewitt C.N. Effectiveness of green infrastructure for h N improvement of air quality in urban street canyons. En- J« 9 vironmental Science & Technology. 2012; 46(14):7692- U — 7699. DOI: 10.1021/es300826w
— CO
21. Wu J., Luo K., Wang Y., Wang Z. Urban o ( road greenbelt configuration: The perspective of PM2 5 o i
O o
removal and air quality regulation. Environment In- ^ c
ternational. 2021; 157:106786. DOI: 10.1016/j.en- E S
vint.2021.106786 o 2
22. Luo H., Wang N., Chen J., Ye X., Sun Y. O O Study on the thermal effects and air quality improve- cc 6 ment of green roof. Sustainability. 2015; 7(3):2804- E o 2817. DOI: 10.3390/su7032804 e O
23. Viecco M., Vera S., Jorquera H., Bustaman- U i te W., Gironás J., Dobbs C. et al. Potential of particle C ) matter dry deposition on green roofs and living walls < H vegetation for mitigating urban atmospheric pollution ¡ | in semiarid climates. Sustainability. 2018; 10(7):2431. 3 8 DOI: 10.3390/su10072431 8 »
24. Tong Z., Whitlow T.H., Landers A., Flan- " ? ner B. A case study of air quality above an urban roof S y top vegetable farm. Environmental Pollution. 2016; 3 £ 208:256-260. DOI: 10.1016/j.envpol.2015.07.006 ®®
25. Viecco M., Jorquera H., Sharma A., Bustaman- g g te W., Fernando H.J.S., Vera S. Green roofs and green 3 3 walls layouts for improved urban air quality by mitigat-
ing particulate matter. Building and Environment. 2021; 204:108120. DOI: 10.1016/j.buildenv.2021.108120
26. Jeanjean A.P.R., Hinchliffe G., McMul-lan W.A., Monks P.S., Leigh R.J. A CFD study on the effectiveness of trees to disperse road traffic emissions at a city scale. Atmospheric Environment. 2015; 120:1-14. DOI: 10.1016/j.atmosenv.2015.08.003
27. Nowak D.J., Crane D.E., Stevens J.C. Air pollution removal by urban trees and shrubs in the United States. Urban Forestry & Urban Greening. 2006; 4(3-4):115-123. DOI: 10.1016/j.ufug.2006.01.007
28. Croitoru C., Nastase I. A state of the art regarding urban air quality prediction models. E3S Web of Conferences. 2018; 32:01010. DOI: 10.1051/e3s-conf/20183201010
29. Svensson U. PHOENICS in environmental flows. A review of applications at SMHI. Lecture Notes
Received February 27, 2023.
Adopted in revised form on April 12, 2023.
Approved for publication on May 18, 2023.
in Engineering. 1986; 87-96. DOI: 10.1007/978-3-642-82781-5_6
30. Neofytou P., Haakana M., Venetsanos A., Kousa A., Bartzis J., Kukkonen J. Computational fluid dynamics modelling of the pollution dispersion and comparison with measurements in a street canyon in Helsinki. Environmental Modeling & Assessment. 2008; 13(3):439-448. DOI: 10.1007/s10666-007-9110-x
31. Launder B.E., Spalding D.B. The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering. 1974; 3(2): 269-289. DOI: 10.1016/0045-7825(74)90029-2
32. Paas B., Schneider C. A comparison of model performance between ENVI-met and Austal2000 for particulate matter. Atmospheric Environment. 2016; 145:392-404. DOI: 10.1016/j.atmosenv.2016.09.031
W (O
N N
О О
N N <0<0
к ai
u з
> (Л
с и
ta «о
« (U j
<u aj
Bionotes: Elena V. Sysoeva — Candidate of Technical Sciences, Associate Professor, Associate Professor of the Department of Architectural and Structural Design and Environmental Physics; Moscow State University of Civil Engineering (National Research University) (MGSU); 26 Yaroslavskoe shosse, Moscow, 129337, Russian Federation; Scopus: 57192373360, ORCID: 0000-0001-7250-3190; [email protected];
Margarita O. Gelmanova — researcher, lecturer-researcher, architect-visualizer; ARCH ATELIER; 29-116 Baryshikha st., Moscow, 125368, Russian Federation; SPIN-code: 8462-8299, ORCID: 0000-0003-2232-5239; margo. [email protected].
Contribution of the authors:
Elena V. Sysoeva — provided scientific supervision, edited the text of the publication and the conclusions. Margarita O. Gelmanova — collected and processed the materials for the article, formed the results, and made changes according to the planned adjustments.
The authors declare no conflict of interest with regard to the materials discussed in this publication.
О ig —'
о
О и
СПИСОК ИСТОЧНИКОВ
s с
z ■ i
ОТ I
от iE
Е О
^ с ю °
S 1
о Е
СП ^ т- ^
от от
£
S!
О И
1. Ильичев В.А. Принципы преобразования города в биосферосовместимый и развивающий человека // Промышленное и гражданское строительство. 2010. № 6. С. 3-13.
2. Осипов В.И. Оценка и управление природными рисками (состояние проблемы) // Геоэкология. Инженерная геология, гидрогеология, геокриология. 2007. № 3. С. 201-211.
3. Графкина М.В., Азаров А.В., Добрин-ский Д.Р., Николенко Д.А. К вопросу контроля и нормирования выбросов мелкодисперсной пыли в атмосферный воздух при движении автомобильного транспорта // Вестник МГСУ. 2017. Т. 12. № 4 (103). С. 373-380. DOI: 10.22227/1997-0935.2017.4.373-380
4. Polichetti G., Cocco S., Spinali A., Trimarco V., Nunziata A. Effects of particulate matter (PM10, PM25 and PMj) on the cardiovascular system // Toxicology.
2009. Vol. 261. Issue 1-2. Pp. 1-8. DOI: 10.1016/j. tox.2009.04.035
5. Arden Pope IIIC. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution // JAMA. 2002. Vol. 287. Issue 9. P. 1132. DOI: 10.1001/jama.287.9.1132
6. Mayer H. Air pollution in cities // Atmospheric Environment. 1999. Vol. 33. Issue 24-25. Pp. 40294037. DOI: 10.1016/S1352-2310(99)00144-2
7. Холодов А.С., Кириченко К.Ю., Задорнов К.С., Голохваст К.С. Влияние твердых взвешенных частиц атмосферного воздуха населенных пунктов на здоровье человека // Вестник Камчатского государственного технического университета. 2019. № 49. С. 81-88. DOI: 10.17217/2079-0333-2019-4981-88
8. Табакаев М.В., Артамонова Г.В. Влияние загрязнения атмосферного воздуха взвешенными
веществами на распространенность сердечно-сосудистых заболеваний среди городского населения // Вестник Российской академии медицинских наук. 2014. Т. 69. № 3-4. С. 55-60. DOI: 10.15690/vramn. v69i3-4.996
9. Кошелева Н.Е., Власов Д.В., Самсонов Т.Е., Касимов Н. С. Химический состав частиц дорожной пыли и его варьирование в зависимости от геометрии городских каньонов в г. Москве // Докл. Российского научно-технического общества радиотехники, электроники и связи имени А.С. Попова : мат. Междунар. симпозиума. Сер. «Научные Международные симпозиумы. Инженерная экология». 2019. С. 61-65.
10. Morawska L., Thomas S., Gilbert D., Green-away C., Rijnders E. A study of the horizontal and vertical profile of submicrometer particles in relation to a busy road // Atmospheric Environment. 1999. Vol. 33. Issue 8. Pp. 1261-1274. DOI: 10.1016/S1352-2310(98)00266-0
11. Уланова Т.С., Антипьева М.В., Волкова М.В., Гилёва М.И. Исследование содержания мелкодисперсных частиц в атмосферном воздухе вблизи автомобильных дорог // Анализ риска здоровью. 2016. Т. 4. С. 38-46. DOI: 10.21668/health. risk/2016.4.05
12. Bespalov V.I., Gurova O.S., Samarskaya N.S. Main principles of the atmospheric air ecological monitoring organization for urban environment mobile pollution sources // Procedia Engineering. 2016. Vol. 150. Pp. 2019-2024. DOI: 10.1016/j.proeng.2016.07.286
13. Faustini A., StafoggiaM., Berti G., Bisanti L., Chiusolo M., Cernigliaro A. et al. The relationship between ambient particulate matter and respiratory mortality: a multi-city study in Italy // European Respiratory Journal. 2011. Vol. 38. Issue 3. Pp. 538-547. DOI: 10.1183/09031936.00093710
14. Chow J.C., Watson J.G., Mauderly J.L., Costa D.L., Wyzga R.E., Vedal S. et al. Health effects of fine particulate air pollution: lines that connect // Journal of the Air & Waste Management Association. 2006. Vol. 56. Issue 10. Pp. 1368-1380. DOI: 10.1080/10473289.2006.10464545
15. Anderson H.R., Atkinson R., Peacock J.L., Marston L., Konstantinou K. Meta-analysis of time-series studies of particulate matter (PM) and ozone (O3): report of a WHO task group. Copenhagen : WHO Regional Office for Europe, 2004. URL: https://apps.who. int/iris/handle/10665/107557
16. Garrett P., Casimiro E. Short-term effect of fine particulate matter (PM2 5) and ozone on daily mortality in Lisbon, Portugal // Environmental Science and Pollution Research. 2011. Vol. 18. Issue 9. Pp. 15851592. DOI: 10.1007/s11356-011-0519-z
17. Jerrett M., Burnett R.T., Ma R., Pope C.A., Krewski D., NewboldK.B. et al. Spatial analysis of air pollution and mortality in Los Angeles // Epidemiology.
2005. Vol. 16. Issue 6. Pp. 727-736. DOI: 10.1097/01. ede.0000181630.15826.7d
18. Abhijith K.V., Kumar P., Gallagher J., McNab-ola A., Baldauf R., Pilla F. et al. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments : a review // Atmospheric Environment. 2017. Vol. 162. Pp. 71-86. DOI: 10.1016/j.atmosenv.2017.05.014
19. Morakinyo T.E., Lam Y.F., Hao S. Evaluating the role of green infrastructures on near-road pollutant dispersion and removal: Modelling and measurement // Journal of Environmental Management. 2016. Vol. 182. Pp. 595-605. DOI: 10.1016/j.jenvman.2016.07.077
20. Pugh T.A.M., MacKenzie A.R., Whyatt J.D., Hewitt C.N. Effectiveness of green infrastructure for improvement of air quality in urban street canyons // Environmental Science & Technology. 2012. Vol. 46. Issue 14. Pp. 7692-7699. DOI: 10.1021/es300826w
21. Wu J., Luo K., Wang Y., Wang Z. Urban road greenbelt configuration: The perspective of PM2 5 removal and air quality regulation // Environment International. 2021. Vol. 157. P. 106786. DOI: 10.1016/j. envint.2021.106786
22. Luo H., Wang N., Chen J., Ye X., Sun Y. Study
on the thermal effects and air quality improvement of e e green roof // Sustainability. 2015. Vol. 7. Issue 3. n H Pp. 2804-2817. DOI: 10.3390/su7032804 k K
23. Viecco M., Vera S., Jorquera H., Bustaman- 3 ^
te W., Gironás J., Dobbs C. et al. Potential of particle S 2
c
matter dry deposition on green roofs and living walls . •
vegetation for mitigating urban atmospheric pollution o S
in semiarid climates // Sustainability. 2018. Vol. 10. Is- h N
sue 7. P. 2431. DOI: 10.3390/su10072431 o 9
24. Tong Z., Whitlow T.H., Landers A., Flanner B. E — A case study of air quality above an urban roof top veg- g 3 etable farm // Environmental Pollution. 2016. Vol. 208. o (( Pp. 256-260. DOI: 10.1016/j.envpol.2015.07.006 o O
25. Viecco M., Jorquera H., Sharma A., Busta- ^o
mante W., Fernando H.J.S., Vera S. Green roofs and E ^
0 w
green walls layouts for improved urban air quality by o o
o
mitigating particulate matter // Building and Environ- m 0 ment. 2021. Vol. 204. P. 108120. DOI: 10.1016/j.build- o 6
env.2021.108120 c 0
1 o
26. Jeanjean A.P.R., Hinchliffe G., McMul- eg lan W.A., Monks P.S., Leigh R.J. A CFD study on the ef- r O fectiveness of trees to disperse road traffic emissions at o ) a city scale // Atmospheric Environment. 2015. Vol. 120. < ^ Pp. 1-14. DOI: 10.1016/j.atmosenv.2015.08.003 | |
27. NowakD.J., Crane D.E., Stevens J.C. Air pol- 3 j,
O m
lution removal by urban trees and shrubs in the Unit- 1 ■
ed States // Urban Forestry & Urban Greening. 2006. ™ ?
Vol. 4. Issue 3-4. Pp. 115-123. DOI: 10.1016/j. £ J
ufug.2006.01.007 ■ g
28. Croitoru C., Nastase I. A state of the art re- ■ ■ garding urban air quality prediction models // E3S Web g g of Conferences. 2018. Vol. 32. P. 01010. DOI: 10.1051/ g g e3sconf/20183201010
29. Svensson U. PHOENICS in environmental flows. A review of applications at SMHI // Lecture Notes in Engineering. 1986. Pp. 87-96. DOI: 10.1007/978-3-642-82781-5_6
30. Neofytou P., Haakana M., Venetsanos A., Kousa A., Bartzis J., Kukkonen J. Computational fluid dynamics modelling of the pollution dispersion and comparison with measurements in a street canyon in Helsinki // Environmental Modeling & Assessment. 2008. Vol. 13. Issue 3. Pp. 439-448. DOI: 10.1007/ s10666-007-9110-x
31. Launder B.E., Spalding D.B. The numerical computation of turbulent flows // Computer Methods in Applied Mechanics and Engineering. 1974. Vol. 3. Issue 2. Pp. 269-289. DOI: 10.1016/0045-7825(74)90029-2
32. Paas B., Schneider C. A comparison of model performance between ENVI-met and Austal2000 for particulate matter // Atmospheric Environment. 2016. Vol. 145. Pp. 392-404. DOI: 10.1016/j.at-mosenv.2016.09.031
Поступила в редакцию 27 февраля 2023 г. Принята в доработанном виде 12 апреля 2023 г. Одобрена для публикации 18 мая 2023 г.
Об авторах: Елена Владимировна Сысоева — кандидат технических наук, доцент, доцент кафедры архитектурно-строительного проектирования и физики среды; Национальный исследовательский Московский государственный строительный университет (НИУ МГСУ); 129337, г. Москва, Ярославское шоссе, д. 26; РИНЦ ID: 755880, Scopus: 57192373360, ORCID: 0000-0001-7250-3190; [email protected];
Маргарита Олеговна Гельманова — научный сотрудник, преподаватель-исследователь, архитектор-ви-зуализатор; АРХИ АТЕЛЬЕ; 125368, г Москва, ул. Барышиха, д. 29-116; SPIN-код: 8462-8299, ORCID: 00000003-2232-5239; [email protected].
Р) м Вклад авторов:
Сысоева Е.В. — научное руководство, редактирование текста и выводов.
'Ч 'Ч Гельманова М.О. — сбор и обработка материалов для статьи, формирование результатов, внесение измене-
но (О
¡^ ф нии, соответствующих намеченным корректировкам.
£ Авторы заявляют об отсутствии конфликта интересов.
с
Ш 00 . г
« <и
<и <и —■ ^
о
0 у
СО <т
от | от Е
— ч^
1 §
^ с ю °
8 Ц
о Е
СП ^ т- ^
от от
£ w
Е!
О (Я №
