Summer Thermal Comfort in Russian Big Cities (1966-2015)

Abstract

The main goal of the study is the assessment of modern bioclimatic conditions (1966-2015) for determining the level of comfort in large Russian cities based on the observations at the meteorological stations, including Physiological Equivalent Temperature (PET) for the main extent of thermal comfort. According to the distribution of thermal stress events (calculated for meteorological fix hours, 8 times per day) the authors created the comfort diagram for each city during daytime heat wave period and evaluated their comfort conditions. In the current research we are operating with WMO climatic data for eleven biggest cities of the Russian Federation: from the European part (Moscow, Saint-Peters-burg, Ekaterinburg, Voronezh, Volgograd, Kazan, Nizhny Novgorod, Perm, Ufa) and from Siberia (Omsk and Krasnoyarsk). The most interesting result of the comparison of the long-period (50 years) urban trends (PET-index and Air Temperature) in different parts of Russia is its extraordinary cross-shaped form in Moscow (in other cities the trends lines are practically parallel to each other). It means that at the level of the average annual values, only in Moscow the PET index (and, hence, potentially the thermal stress) grows faster than the regional climate warms. In other cities this tendency is much weaker (N.Novgorod) or not significant. This interesting tendency is caused by both Moscow related urban planning dynamics in post-USSR period and by regional climate dynamics.

Full text

35Geographica Pannonica • Volume 25, Issue 1, 35–41 (March 2021)
Pavel KonstantinovA*, Diana TattimbetovaA, Mikhail VarentsovA, Natalia ShartovaA
Received: November ,  | Revised: March ,  | Accepted: March , 
doi: 10.5937/gp25-29440
A Lomonosov Moscow State University, Faculty of Geography, Leninskie Gory  ;
kostadini@mail.ru; dianoka_..@mail.ru, mvar@gmail.com, shartova@yandex.ru
* Corresponding author: Pavel Konstantinov; e-mail: kostadini@mail.ru
ISSN 0354-8724 (hard copy) | ISSN 1820-7138 (online)
Abstract
The main goal of the study is the assessment of modern bioclimatic conditions (-) for de-
termining the level of comfort in large Russian cities based on the observations at the meteorologi-
cal stations, including Physiological Equivalent Temperature (PET) for the main extent of thermal com-
fort. According to the distribution of thermal stress events (calculated for meteorological fix hours, 
times per day) the authors created the comfort diagram for each city during daytime heat wave peri-
od and evaluated their comfort conditions. In the current research we are operating with WMO climatic
data for eleven biggest cities of the Russian Federation: from the European part (Moscow, Saint-Peters-
burg, Ekaterinburg, Voronezh, Volgograd, Kazan, Nizhny Novgorod, Perm, Ufa) and from Siberia (Omsk
and Krasnoyarsk). The most interesting result of the comparison of the long-period ( years) urban
trends (PET-index and Air Temperature) in different parts of Russia is its extraordinary cross-shaped
form in Moscow (in other cities the trends lines are practically parallel to each other). It means that at
the level of the average annual values, only in Moscow the PET index (and, hence, potentially the ther-
mal stress) grows faster than the regional climate warms. In other cities this tendency is much weak-
er (N.Novgorod) or not significant. This interesting tendency is caused by both Moscow related urban
planning dynamics in post-USSR period and by regional climate dynamics.
Keywords: Physiological Equivalent Temperature (PET); regional urban climate; urban thermal comfort
Summer Thermal Comfort in Russian Big Cities
(-)
Introduction
In recent times, the urban climate studies have inev-
itably shied the emphasis towards the problems of
sustainable development of (mega)cities. Such con-
cept is closely connected with the studies of the hu-
man comfort in large cities in Europe and Asia. It is a
rational approach to the resettlement and peaceful co-
existence of a large number of people within the con-
fines of a small territory (villages, cities, metropolises,
etc.). From this point of view, the cities of the Russian
Federation are an ideal monitoring platform, since the
concept process of their development has just started.
At the same time, it should be taken into account that
the Russian Federation is a highly urbanized coun-
try (Kolosov & Nefedova, 2014), and this process has
been connected to internal migration of the popula-
tion since the 1970s.
Rapid urbanization in Russian Federation led to
cities growth and its economic advance. Alongside
this population of big cities (>1 000 000 inhabitants) is
quite vulnerable to heat wave events due to intensive
urban heat island event (Kislov & Konstantinov, 2011).
In July and August 2010 in the biggest city in Rus-
sia – Moscow, where more than 11 million people live,
the longest and the strongest heat wave as well as the
warmest day (29th of July 2010) were recorded since
the meteorological observations in Russia (Konstan-

Summer Thermal Comfort
in Russian Big Cities (1966-2015)
36 Geographica Pannonica • Volume 25, Issue 1, 35–41 (March 2021)
tinov et al., 2014). ere were close to 11 000 excess
deaths from non-accidental causes (predominant-
ly temperature and air pollution) during this period,
mainly among people older than 65 years. Increased
risks (Zemtsov et al., 2020) also occurred in younger
age groups (Shaposhnikov et al., 2014). us, the main
goal of the study is the assessment of modern biocli-
matic conditions (1966-2015) for determining the lev-
el of comfort in large Russian cities based on the ob-
servations at meteorological stations.
Also, the variety of natural and climatic conditions
in the Russian Federation allows to study the biocli-
matic characteristics of the large cities (there are 15
cities with more than 1 million inhabitants in Rus-
sia) in various subtypes of the temperate zone (from
marine to ultracontinental). e similar conditions of
planning efforts in these big cities create a uniform
background of administrative influence on the part of
the government. is allows to identify in which geo-
graphical regions the traditional type of city manage-
ment is more successful from a bioclimatic point of
view. In other words, in which regions conditions of
thermal comfort in cities is a successor of air temper-
ature trends and in which not.
Of course, within the framework of this study, we
accept the hypothesis that changes in the trends
of thermal comfort in Russian cities are associat-
ed with both the change in the regional climate and
with the change of land-use properties in the urban
environment. However, since the natural factor acts
with approximately the same strength, noticeable dif-
ferences in trends (if they are detected) can be generat-
ed by the influence of the urban microclimate, which
is indirectly related to urban development strategies.
Materials and Methods
is study is based on the characterization of the cli-
matic trends of human thermal comfort and its as-
sessment during heat wave periods. From the stand-
point of human health heat wave is a period of time in
which an excessive stress of thermoregulation of the
body is accessed, as well as an increased risk of mor-
bidity and mortality, especially from respiratory and
cardiovascular diseases (Robinson, 2001; Arsenović et
al., 2019; Urban et al 2019).
ere are many approaches to the definition of heat
waves. For this study the most convenient criteria de-
veloped by the World Meteorological Organization
(WMO) was chosen. According to it, the heat wave is
the excess of the maximum temperature for five con-
secutive days or more at 5°C from the average max-
imum value for the base period from 1961 to 1990
(Frich et al., 2002)
In this study we choose Physiological Equivalent
Temperature (PET) for the main extent of thermal
comfort. “Equivalent-physiological temperature for
a given place” is the air temperature at which for or-
dinary room conditions the heat balance of the hu-
man body remains unchanged with the internal body
temperature and skin temperature for a given situa-
tion (Hoppe, 1999) is, however, does not mean that
this index can not be applied to the open spaces. On
the contrary, it helps a person to compare sensations
he/she has in the open air with the room conditions
that are familiar to him – some evaluations for Rus-
sian Federation were described in paper (Shartova et
al., 2018).
e basis for the study was the analysis of standar-
tized data from the regular Roshydromet (Russian
WMO meteorological network) over a 50-year period
from 1966 to 2015. is was due to the fact that the
best quality data of instrumental observations were
available during this period. Russia’s cities with pop-
ulation of 1 million were chosen as the objects of the
research.
In current research we are operating with WMO
climatic data for 11 biggest cities of the Russian Fed-
eration: from the European part (Moscow, Saint-Pe-
tersburg, Ekaterinburg, Voronezh, Volgograd, Ka-
zan, Nizhny Novgorod, Perm, Ufa) and from Siberia
(Omsk and Krasnoyarsk). A brief description of each
city is given in Table 1.
For calculating PET index we used RayMan model
which is widely used in the European practice. For ex-
ample, with the help of this model the PET index was
calculated in Freiburg, Germany, or, more precisely,
in the center of this city. e frequency of observed
certain gradations of the comfort level by the PET in-
dex was calculated with Rayman, as well as the local
maps of the bioclimatic comfort of this area (Frohlich
& Matzarakis, 2010). e same index was applied in
2010 for the detailed analysis of the bioclimatic condi-
tions of Freiburg for the conditions of the modern cli-
mate (period 1961-1990) and the forecast period (2071-
2100) based on IPCC scenarios (Matzarakis & Endler,
2010). In general, the results show that the number of
days with heat stress conditions has increased.
Another example of using this index with RayMan
is the study based on the data analysis of 33,212 hos-
pitalizations among people over 60 years old in São
Paulo, Brazil between 2003 and 2007. (Silva & Ribeiro,
2012). e results of the study showed the increase in
the probability of hospitalization among the group of
people in unsatisfactory socio-economic conditions

Pavel Konstantinov, Diana Tattimbetova,
Mikhail Varentsov, Natalia Shartova
37Geographica Pannonica • Volume 25, Issue 1, 35–41 (March 2021)
by 12% with the increase in the value of the bioclimat-
ic index by 10°C.
In this study, we use the Rayman model on a one-
dimensional scale, without taking into account envi-
ronmental obstacles and SVF in WMO-station stand-
ard environment.
Since there is no other long-term data for a simi-
lar period on the territory of the studied cities, it is as-
sumed in the study that the measurement data char-
acterize the city climate quite reliably. According
to LCZ climate zones classification (Stewart & Oke,
2012) , WMO station areas in cities, selected for long-
term trend investigation (Moscow, Saint-Petersburg,
Nizhny Novgorod, Perm, Ekaterinburg and Krasno-
yarsk) are situated in Type 6 (Open low-rise) and Type
9 (Sparsely built)– see Fig.1
Table 1. Main big cities of Russian Federation: geographical overview (Bolshaya…, 2007; Census, 2010)
City Population Coordinates WMO station
ID
Köppen
climate zone Basic climatic info
Moscow 11 503 501 55°45' N
37°37'E 27612 Dfb
Coldest month: January (-9.4°C)
Warmest month: July (+18.3°C)
Average annual rainfall: 684 mm
Saint-Petersburg 4 879 566 59°57'N
30°18'E 26063 Dfb
Coldest month: January (-5°C)
Warmest month : July (+18°C)
Average annual rainfall: 661 mm
Ekaterinburg 1 349 772 56°50'N
60°35'E 28440 Dfb
Coldest month: January(-12.6°C)
Warmest month: July (+19°C)
Average annual rainfall: 537 mm
Voronezh 889 680 51°40' N
39°12'E 34123 Dfb
Coldest month: January(-6.1°C)
Warmest month: July (+20°C)
Average annual rainfall: 587 mm
Volgograd 1 021 215 48°4 2'N
44°31'E 34560 Dfa
Coldest month: January (-6.3°C)
Warmest month: July (+23.6°C)
Average annual rainfall: 347 mm
Kazan 1 141 535 55°47 'N
49°06'3E 27595 Dfb
Coldest month: January (-10.4°C)
Warmest month : July (+20.2°C)
Average annual rainfall: 558 mm
Krasnoyarsk 973 826 56°01'N
93°04'E 29572 Dfc
Coldest month: January (-15.5°C)
Warmest month : July (+15.7°C)
Average annual rainfall: 465 mm
Nizhny Novgorod 1 250 619 56°19'N
44°00'E 27459 Dfb
Coldest month: January (-12°C)
Warmest month: July (+18.1°C)
Average annual rainfall: 648 mm
Omsk 1 154 116 54°59'N
73°22'E 28698 Dfb
Coldest month: January (-16.3°C)
Warmest month : July (+19.6°C)
Average annual rainfall: 415mm
Perm 991 162 58°00'N
56°19'E 28224 Dfc
Coldest month: January (-12.6°C)
Warmest month : July (+18.6°C)
Average annual rainfall: 638 mm
Ufa 1 062 319 54°44'N
56°00'E 28722 Dfb
Coldest month: January (-12.4°C)
Warmest month : July (+19.76°C)
Average annual rainfall: 590 mm

Summer Thermal Comfort
in Russian Big Cities (1966-2015)
38 Geographica Pannonica • Volume 25, Issue 1, 35–41 (March 2021)
Results
According to the distribution of thermal stress events
it is possible to create comfort diagram for each city
during daytime heat wave period (for Moscow and
Saint-Petersburg see Fig.2).
is plot shows that in both capitals the greatest fre-
quency during daytime is in strong heat stress area
(33.3% and 39.6%). Frequency of extreme heat stress in
Moscow is 13.8% and in Saint-Petersburg 5.3%. e cases
of comfortable sensations in the period of heat waves for
the whole warm period in Moscow constitute only 6.9%.
e lowest frequency is graded as “a slightly cold stress”
which corresponds to a slight cold exposure (0.2%).
In general, we can say that in Moscow during the
period of heat waves people in 47.5% of cases is vul-
Figure 1. Satellite images of the cities, considered in the study (taken from Google maps) with locations of the used
weather stations indicated by asterisk symbols (right panels). Right panees shows satellite images of the nearest
surrounding of the weather stations (area within yellows squares in the left panels)
Figure 2. Frequency of РЕТ grades in Moscow and Saint-Petersburg in the day time during heat waves
(1966 - 2015 period)

Pavel Konstantinov, Diana Tattimbetova,
Mikhail Varentsov, Natalia Shartova
39Geographica Pannonica • Volume 25, Issue 1, 35–41 (March 2021)
nerable to heat stress. In Saint-Petersburg this value
is 44.9%
Based on the results obtained in the process of the
PET index calculation we plotted a map-diagram
which shows the distribution of various degrees of
heat stress in 11 large cities of Russia. (Fig.3)
Proceeding from this, we can conclude that the
most inclined to heat stress city in the heat waves in
the daytime is Volgograd, as Volgograd is one of the
hottest cities in Russia. Main PET and air tempera-
ture trend results can be briefly summarized in Ta-
ble 2.
Discussion
So, what if we decide to take a look at long period
trends of PET and air temperature in cities of differ-
ent parts of Russia? We know that climate is chang-
ing, air temperature rises in most parts of the Russia
(Federal Service for Hydrometeorology and Environ-
mental Monitoring, Roshydromet, 2014). But the ther-
mal comfort is complex characteristic and its trend
can clarify the real tendencies of human comfort sen-
sation against the background of regional climate
change (see Fig.4)
It is well known that the cities’ growth leads to the
increase in trends of warming of the local urban cli-
mate, which is due to the joint impact of the global
climate trends and the impact of the urban heat is-
land (Kataoka et al., 2009). However, practically noth-
ing is known about the relationship between temper-
ature growth and changes in comfort parameters on
the territory of Russia.
e most interesting result of the comparison of the
urban trends (PET-index and Air Temperature) in dif-
ferent parts of Russia is its extraordinary cross-shaped
form in Moscow (in other cities the trends lines are
practically parallel to each other): in further research
we plan to investigate such phenomenon by using dif-
ferent thermal comfort indices (UTCI etc) It can be
caused just as by Moscow related urban planning dy-
namics in post-USSR period so by regional climate dy-
namics. e more detailed analysis of the dynamics of
PET-predictors (direct solar radiation, wind speed), in-
dicates that its growth is due to the presence of signifi-
cant negative trends for wind speed and a score of low-
er clouds. e obvious, at first glance, the explanation
of such well-pronounced wind speed trends - an in-
crease in the roughness in the vicinity of the stations
against the background of local land use change. How-
ever, the obtained trends for the Moscow region are in
good agreement with the estimates from (Federal Ser-
vice for Hydrometeorology and Environmental Moni-
toring, Roshydromet, 2014) and (Meshcherskaya, 2004;
2006), according to which the decrease in wind speed
with speeds of 0.1-0.5 m/s/10 years over the last decades
is typical for the European territory of Russia.
e trend of the decrease in the lower cloudiness
in Moscow is making the greatest contribution to the
Table 2. Mean-Decade Trends for Air Temperature and for PET in different parts of Russian Federation in 1966-2015
Cities, Russian
Federation
Moscow Saint-
Petersburg
Nizhny
Novgorod
Perm Ekaterinburg Krasnoyarsk
Air temperature
linear trend
0.36˚C/10yr 0.40˚C/10yr 0.38˚C/10yr 0.34˚C/10yr 0.36˚C/10yr 0.29˚C/10yr
PET linear trend 0.93˚C/10yr 0.25˚C/10yr 0.83˚C/10yr 0.47˚C/10yr 0.54˚C/10yr 0.24˚C/10yr
Figure 3. Level of thermal stress in big Russian cities in the
day time during heat waves (1966 - 2015 period)
Figure 4. Linear trends of PET-index
(annual mean for warm period) and air temperature
(annual mean for warm period) in different parts of
Russian Federation in 1966-2015

Summer Thermal Comfort
in Russian Big Cities (1966-2015)
40 Geographica Pannonica • Volume 25, Issue 1, 35–41 (March 2021)
relative discomfort in summer is manifested more ev-
idently. e similar changes are consistent with the
trend of increasing duration sunshine against the
background of the increase in the total cloud score for
Moscow and Kazan (Gorbarenko et al., 2017; Sidoren-
ko et al., 2012) and the trend of erythema ultraviolet
radiation (Chubarova et al., 2018).
We used a small number of cities in our research
and can not say about the global trends in the coun-
try, nevertheless it is possible to make some valuable
conclusions.
e high probability of the heat stress is more evi-
dent for large and fast-evolving cities of Central Rus-
sia. e faster growth of PET temperatures is observed
as compared to air temperatures. is is confirmed by
the examples of Moscow and Nizhny Novgorod (PET-
warming trend is two times more intensive than ther-
mal one). We can suggest that the cross-shaped form of
T and PET can be noticed in Nizhny Novgorod soon.
e maritime climate of Saint Petersburg (the only
city with this type of climate among the observed) has
the impact on the conditions of thermal comfort. De-
spite the increase of Т the significant changes in PET
(feeling of heat for human) did not occur. e proba-
bility of heat stress in this city is inconsiderable.
Perm and Yekaterinburg are both located in the
area of the Ural Mountains and have similar trends
in PET values. We can observe the increase in PET
greatly correlated with temperature changes without
any unusual effects.
Krasnoyarsk has the most continental climate type
among the cities reviewed. We can observe small dif-
ference between T and PET in addition to the growth
of both parameters. Krasnoyarsk can be considered as
the city with the lowest probability of heat stress.
An argumentative issue, of course, is the choice
of this particular parameter (the frequencies of РЕТ
grades) for determining the relative risk of thermal
discomfort phenomenon. However, taking into ac-
count the absolute temperature values of thermal
waves is also not ideal - because of both the adapta-
tion of the population of more southern regions to hot
weather, and the vulnerability of the criterion (Frich
et al., 2002) for determining heat waves in the north-
ern regions.
e obtained results can be considered in the fur-
ther analysis with larger number of weather stations
and can used for categorization of cities according to
the level and the dynamics of thermal comfort con-
ditions.
Conclusions
Within the frames of this study, the PET equivalent-phys-
iological temperature index was calculated for each day
of the warm period for 11 biggest cities of Russia. Based
on the results of the calculations, we have plotted the dia-
grams with the frequency of occurrence of extreme ther-
mal events during the heat waves for each town.
Also showed that at the level of the average annu-
al values, only in Moscow-city the PET index (and,
hence, potentially the thermal stress) grows faster
than the regional climate warms (0.93˚C/10yr for PET
and 0.36˚C/10yr for air temperature). In other cities
this tendency is much more weak (N.Novgorod) or
not significant. e most inclined to risk city during
the heat waves in the daytime is Volgograd, while St.
Petersburg can be considered the safest, since the fre-
quency of thermal stress even in this dangerous peri-
od does not exceed 5.3% of all the cases.
e main result achieved during the study is the
creation of Russia’s first comparative climatology of
comfort in biggest cities and the determination of the
relative danger of heat waves for each of them based
on the analysis of 50 year time series, as well as the de-
termination of the dynamics of heat comfort indices
for the last 50 years (1966-2015).
Acknowledgments
Research was supported by the grant program of Russian Science Foundation (project no. 17-77-20070 “An initial
assessment and projection of the bioclimatic comfort in Russian cities in XXI century against the context of cli-
mate change”).
Author Contributions
Pavel Konstantinov and Natalia Shartova conceived and designed the experiments; Diana Tattimbetova per-
formed the experiments with Rayman model for warm season periods; Pavel Konstantinov and Mikhail Varentsov
analyzed the data; Pavel Konstantinov finally wrote the paper.

Pavel Konstantinov, Diana Tattimbetova,
Mikhail Varentsov, Natalia Shartova
41Geographica Pannonica • Volume 25, Issue 1, 35–41 (March 2021)
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