







































Georgian Geographical Journal 

 

Holiday Climate Index in Kvemo Kartli 

(Georgia) 
Avtandil Amiranashvili1,* , Nana Bolashvili2 , Liana 

Kartvelishvili3 , Guliko Liparteliani2 , Gvantsa Tsirgvava2  
1 Mikheil Nodia Institute of Geophysics, TSU, Tbilisi, Georgia 
2 

Vakhushti Bagrationi Institute of Geography, TSU, Tbilisi, Georgia
 

3 
3National Environmental Agency of Georgia, Tbilisi, Georgia

 

* Corresponding author: avtandilamiranashvili@gmail.com 

 

 

 

 

 

 

 

 

 

 

 

 

Introduction 

The organization and development of the resort and tourism industry in the region directly depend on 

its geographical location, topography, vegetation, presence of natural disasters, weather and climate, 

etc. Weather and climate are two main factors that determine the bioclimatic resources of an area. Thus, 

the study of these resources, which are necessary for the organization and development of the resort 

and tourism industry, plays a major role and requires significant effort. 

The study of the resort and recreational resources of Georgia was founded in the 20 s of the last 

century, when the “Research Institute of Balneology and Physiotherapy” was created. As a result of 

many years of field, stationary and practical studies of the institute, a trilingual atlas about the resorts 

and resort resources of Georgia was created and published (Vadachkoria et al., 1987). This atlas was 

awarded the State Prize of Georgia. It should be noted that this work (Vadachkoria et al., 1987) provided 

an impetus for the further development of multilateral research into the resort and tourism potential of 

Georgia. 

Past studies have used many climate indices for tourism (Matzarakis, 2006; Matzarakis et al., 2021a, 

b; Amiranshvili et al., 2011, 2015a, 2018, 2022, 2019; Bolashvili et al., 2016; Amiranashvili & 

Kartvelishvili, 2008; Lanchava et al., 2021; Rutty et al., 2021; Kartvelishvili et al., 2023). The most 

Georgian Geographical Journal, 2024, 4(1) 35-46 

© The Author(s) 2024 

 
This article is an open access article distributed under 

the terms and conditions of the Creative Commons 

Attribution (CC BY) licence 

(https://creativecommons.org/licences/by/4.0/). 

DOI: 

https://journals.4science.ge/index.php/GGJ 

Abstract 

Weather and climate are two main factors that determine the bioclimatic resources 

of a territory and, accordingly, the degree of its suitability for the organization and 

development of the resort and tourism industry. Early studies used a variety of 

climate indices for tourism. In recent years, the so-called Holiday Climate Index 

(HCI), which is a combination of five climate elements (air temperature maximum, 

relative humidity, cloud cover, precipitation and wind), has been gaining 

popularity. Determination of HCI values for various locations in Georgia began in 

2020 (Tbilisi, Kakheti region, 13 high-mountain points, etc.). In this work, an 

analysis of data on the long-term average values of the Holiday Climate Index 

(HCI) for 8 settlements in the Kvemo Kartli region of Georgia (Bolnisi, Gardabani, 

Dmanisi, Tetri Tskaro, Marneuli, Tsalka, Manglisi, Rustavi) is presented. The 

intra-annual distribution of HCI values was studied; correlations between 

individual stations were determined based on average monthly and seasonal HCI 

values; it was found that the regression equations for the intra-annual variation of 

average monthly HCI values for all points of Kvemo Kartli have the form of a 

ninth-order polynomial; categories of average monthly and seasonal HCI values in 

the specified settlements of Kvemo Kartli were determined; a comparison was 

made of the statistical characteristics of average monthly HCI values in 8 points of 

Kvemo Kartli with the indicated characteristics in Bolnisi, Gardabani, Marneuli, 

Rustavi (height of stations above sea level H < 1 km) and in Dmanisi, Tetri Tskaro, 

Tsalka, Manglisi (H ˃ 1 km), and a corresponding analysis of the repeatability of 

HCI categories was conducted. It is shown that the bioclimatic conditions in 

Kvemo Kartli are favourable for the development of the resort and tourism industry 

for all months of the year. A visual map of the distribution of mean monthly HCI 

categories on the territory of Kvemo Kartli has been constructed. 

Keywords: climate, bioclimate, resort and tourism industry, cartography  

Citation: Amiranashvili, A.; Bolashvili, N.; 

Kartvelishvili, L.; Liparteliani, G.; Tsirgvava, 
G. Holiday Climate Index in Kvemo Kartli 
(Georgia). Georgian Geographical 
Journal 2024, 4(1), 35-46. 

https://doi.org/10.52340/ggj.2024.04.01.05 

 

Received: 1 October 2023 

Revised: 25 January 2024 
Accepted: 5 May 2024 

Published: 1 June 2024 



Amiranashvili et al. 2024 4(1) 

36 
 

widely known index used both in the past and in the present is the Tourist Climate Index (TCI), proposed 

by Mieczkowski (1985). 

In southern Caucasus countries, the monthly TCI was first calculated in Georgia for Tbilisi 

(Airanashvili et al., 2008) and then for many other locations in the Caucasus (Armenia, Azerbaijan, 

North Caucasus, etc.) (Amiranashvili et al., 2014, 2015b, 2017, 2018a, 2018b; Rybak & Rybak, 2016; 

Kartvelishvili et al., 2019). 

The study by Mushawemhuka et al. (2020) presents the first TCI calculations for Zimbabwe. Tanana 

et al. (2021) evaluated the climate comfort of Argentina as an intangible resource for tourism. 

Despite the wide application of the TCI, it has been subject to substantial critiques (Scott et al., 2016). 

The four key deficiencies of the TCI include the following: (1) the subjective rating and weighting 

system of climatic variables; (2) it neglects the possibility of the overriding influence of physical 

climatic parameters (e.g., rain, wind); (3) the low temporal resolution of climatic data (i.e., monthly 

data) has limited relevance for tourist decision-making; and (4) it neglects the varying climatic 

requirements of major tourism segments and destination types (i.e., beach, urban, winter sports 

tourism). 

To overcome the above limitations of the TCI, the Holiday Climate Index (HCI) was developed to 

more precisely assess the climatic suitability of tourism destinations. The word “holiday” was chosen 

to better reflect what the index was designed for (i.e., leisure tourism), as tourism is much broader by 

definition (“Tourism is a social, cultural and economic phenomenon which entails the movement of 

people to countries or places outside their usual environment for personal or business/professional 

purposes”) (Javan, 2017; Rutty et al., 220; Hejazizadeh et al., 2019). In the same works, comparisons 

between the HCI and TCI were made. 

A comparison of the Holiday Climate Index and Tourism Climate Index at several locations in Georgia 

and the North Caucasus (Amiranashvili et al., 2020; Amiranashvili & Kartvelishvili, 2021; 

Amiranashvili et al., 2021) is presented. The article by Amiranashvili et al. (2018b) compares the values 

and categories of the TCI and HCI in Tbilisi. The long-term average HCIs for 12 Kakheti locations 

(Akhmeta, Dedoplistskaro, Gombori, Gurjaani, Kvareli, Lagodekhi, Omalo, Sagarejo, Shiraki, Telavi, 

Tsnori and Udabno) are presented in Amiranashvili & Kartvelishvili (2021). For 6 stations in this region 

(Dedoplistskaro, Gurjaani, Kvareli, Lagodekhi, Sagarejo and Telavi), detailed analyses of the monthly, 

seasonal and annual HCIs over the 60-year period (1956-2015) were carried out. Comparisons of 

monthly HCIs and tourism climate indices (TCIs) for four points in the Kakheti region (Dedoplistskaro, 

Kvareli, Sagarejo and Telavi) based on data from 1961 to 2010 were carried out. The results of the 

comparative analysis of the Tourism Climate Index and the Holiday Climate Index, as well as the ratings 

of the components of these indices for six points in the North Caucasus (Kislovodsk, Pyatigorsk, 

Essentuki, Zheleznovodsk, Teberda and Nalchik), are presented in (Amiranashvili et al. 2021). 

It was found that there is a high degree of correlation between the HCI and TCI. However, considering 

that the TCI is calculated for the so-called “average tourist” (regardless of gender, age, physical 

condition), the value and category of this index are lower than the HCI values and categories. In general, 

based on our estimation, the HCI more adequately determines the bioclimatic state of the environment 

for the development of various types of tourism than does the TCI (Amiranashvili et al. 2020, 2021; 

Amiranashvili & Kartvelishvili, 2021). 

Using the Holiday Climate Index (HCI: Urban), this research (Williams, 2021) examines long-term 

tourism climate records in Tokyo between 1964 and 2019. The findings suggest greater climatic 

variability and a decrease in the favourability of Tokyo’s tourism climatic resources in all three summer 

months. According to these findings, adaptation and mitigation strategies are recommended, and a 

Japanocentric tourism climate index is proposed. 

Carrillo et al. (2021) noted that the TCI and HCI are good indicators of the environmental conditions 

for leisure activities in the Canary Islands. Using the Regional Climate Model, it is shown that by 2030-

2059 and 2070-2099, tourism performance is expected to improve significantly in the winter and off-

season but deteriorate in the summer months, including October, in the southeast, which is where hotels 

are currently located. 

The aim of this study (Araci et al., 2021) is to assess the future HCI performances of urban and beach 

destinations in the greater Mediterranean region. For this purpose, HCI scores for the reference (1971-

2000) and future (2021-2050, 2070-2099) periods were computed. HCI: The urban results showed that 

the Canary Islands have suitable conditions for tourism during almost all four seasons and all periods, 

which will have certain implications when other core Mediterranean competitors lose their relative 

climatic attractiveness. The HCI:Beach results for the summer season showed that Las Canteras, 



Amiranashvili et al. 2024 4(1) 

37 
 

Alicate, Pampelonne, Myrtos, Golden Sands and Edremit all pose very good to excellent conditions 

without any Humidex risks for the extreme future scenario (2070-2099). 

Detailed information on the variability of the monthly values of the Holiday Climate Index in Tbilisi 

in 1956-2015 is presented in Amiranashvili et al. (2020). It also presents data on the interval forecasts 

of HCI variability in Tbilisi for the next few decades. 

Amiranashvili et al. (2021) performed a detailed analysis of monthly, seasonal and annual HCI values 

during a 60-year period (1956-2015) for 13 mountainous locations in Georgia (Bakhmaro, Bakuriani, 

Borjomi, Goderdzi, Gudauri, Khaishi, Khulo, Lentekhi, Mestia, Pasanauri, Shovi, Stepantsminda, and 

Tianeti) and compared HCIs and TCIs of monthly values for three points in Georgia (Goderdzi, Khulo 

and Mestia) based on data from 1961 to 2010. The variability data of the HCI in 1986-2015 compared 

to those in 1956-1985 and the trends of the HCI in 1956-2015 are also presented. Using Mestia as an 

example, the expected changes in the monthly, seasonal and annual HCIs of 2041-2070 and 2071-2100 

were assessed. Some results of this work were used in (Kartvelishvili et al., 2023; Fourth National 

Communication of Georgia, 2021). 

It should be noted that the scale of various bioclimatic indices (including TCI and HCI) is quite 

consistent with data on public health in various regions of Georgia (Amiranashvili et al., 2012, 2018, 

2021), as well as on the spread of the COVID-19 virus in Tbilisi (Amiranashvili et al 2022). 

This work is a continuation of previous studies. This study develops a long-term average HCI for 8 

stations in the Kvemo Kartli region of Georgia (Bolnisi, Gardabani, Dmanisi, Tetri Tskaro, Marneuli, 

Tsalka, Manglisi, Rustavi), which is known for its historical attractions and resort and tourism 

resources. 

Methods and Materials 

Study Area 

Kvemo Kartli region of Georgia (below - Kvemo Kartli). Kvemo Kartli is in the southeastern part of 

Georgia. The area is 6 436.2 km2, the population is 442.8 thousand. pers., (including of urban - 197.5 

thous. pers.), the capital of the region, Rustavi (population - 132.3 thous. pers.). 

The natural-geographic conditions of Kvemo Kartli, as well as natural, cultural and historical 

monuments, create an opportunity for the development of tourism in the region. The prospective 

directions of tourism are horse-riding, hunting tourism, eco-tourism, cognitive tourism, family tourism, 

ethnographic tourism, agro-tourism, medical-rehabilitation tourism, etc. In Kvemo Kartli, tourists can 

see settlements dating back to the first millennium BC. The discovery of a prehistoric settlement and 

human remains in Dmanisi is considered a major archaeological discovery. According to experts, a 

hominid lived in Dmanisi 1.8 million years ago. Therefore, Dmanisi can be considered the earliest 

settlement in Europe and Asia. Kvemo Kartli has more than 650 historical monuments, 300 of which 

are included in various tourist routes. 

Figure 1. Locations of the 8 meteorological stations in Kvemo Kartli 



Amiranashvili et al. 2024 4(1) 

38 
 

Methodology 

Studies of 8 locations in Kvemo Kartli (Bolnisi, Gardabani, Dmanisi, Tetri Tskaro, Marneuli, Tsalka, 

Manglisi, and Rustavi) were carried out. Fig. 1 shows a map of the arrangement of the indicated 

meteorological stations. 

Table 1 presents information about the coordinates and heights of these 8 meteorological stations, 

whose data were used in this work. These stations are located 300 to 1458 meters above sea level. 

Table 1. Coordinates and heights of the 8 meteorological stations in Kvemo Kartli 

Location (Abbreviation) Latitude, N° Longitude, E° 
Elevation (H), m, 

a.s.l. 
Period of observation 

Bolnisi (Boln) 41.45 44.55 534 1956-2015 

Gardabani (Gard) 41.45 45.10 300 1956-2015 

Dmanisi (Dman) 41.33 44.20 1309 1961-1990 

Tetri Tskaro (T-Tsk) 41.55 44.47 1151 1961-1990 

Marneuli (Marn) 41.48 44.80 432 1938-1960 

Tsalka (Tsal) 41.60 44.08 1458 1956-2015 

Manglisi (Mang) 41.70 44.38 1194 1961-1990 

Rustavi (Rust) 41.55 45.02 332 1949-1960 

In this work, the Holiday Climate Index (HCI) is used. The following five climatic variables are used 

for HCI identification: air temperature maximum, relative humidity, cloud cover, precipitation and wind 

(Scott et al., 2016; Amiranashvili et al., 2020a; Amiranashvili & Kartvelishvili, 2021; Amiranashvili et 

al., 2021a). 

The rating scheme and HCI categories (Scott et al., 2016; Amiranashvili et al., 2020a) are presented 

in Table 2. 

Table 2. HCI’s Category 

HCI Score Category (Abbreviation) HCI Score Category (Abbreviation) 

90÷100 Ideal 40÷49 Marginal (Marg.) 

80÷89 Excellent (Excellent) 30÷39 Unfavourable (Unf.) 

70÷79 Very Good (Very Good) 20÷29 Very Unfavourable (V_Unf.) 

60÷69 Good 10÷19 Extremely Unfavourable (Ext_Unf.) 

50÷59 Acceptable (Acceptable) 9÷-9; -10÷-20 Impossible (Impos.) 

In this work, the monthly mean data of the indicated meteorological parameters from the Georgian 

National Environmental Agency (Bolnisi, Gardabani, Tsalka), famous reference books on the climate 

of the USSR (issue 14; Marneuli, Rustavi) and the Scientific and Applied of Georgia Climate Reference 

(2020) (Dmanisi, Tetri Tskaro, Manglisi) were used. Based on these data, the HCI monthly average 

values were calculated. Analysis of the HCI data using standard statistical analysis methods was carried 

out (Kobisheva & Narovlianski, 1978). The following designations are used: Mean – average values; 

Min – minimal values; Max – maximal values; St Dev – standard deviation; Cv – coefficient of variation, 

% (Cv = 100· St Dev/Mean); R² – coefficient of determination; R – coefficient of linear correlation; α 

– level of significance; H – altitude of the weather station at sea level, meter or km. 

Figure 2. Mean HCI values at 8 locations of Kvemo Kartli 



Amiranashvili et al. 2024 4(1) 

39 
 

Results 

The results are presented in figures 2-3 and tables 3-8. The long-term mean HCI real values at 8 

locations in Kvemo Kartli are presented in Fig. 2. 

As shown in Fig. 2, the mean monthly HCI changed from 58 (Tetri Tskaro, Rustavi, January, 

Acceptable) to 91 (Manglisi, July, Ideal). The variability of HCI values for individual items is as 

follows: 

Bolnisi (62, February–84, May, September), Gardabani (63, December–85, October), Dmanisi (59, 

February–89, July, August), Tetri Tskaro (58, January–89, August), Marneuli (59, January–87, May), 

Tsalka (59, February–85, July), Manglisi (59, January–91, July), and Rustavi (58, January–86, May). 

Table 3. Linear correlation coefficients between the monthly means and seasonal values of the HCI at the separate stations 

(R min = 0.41, α = 0.15; R max = 0.98, α = ˂0.001) 

Location Boln Gard Dman T-Tsk Marn Tsal Mang Rust 

Boln 1 0.94 0.68 0.73 0.97 0.65 0.58 0.96 

Gard 0.94 1 0.49 0.53 0.93 0.44 0.41 0.95 

Dman 0.68 0.49 1 0.96 0.62 0.97 0.84 0.58 

T-Tsk 0.73 0.53 0.96 1 0.70 0.98 0.82 0.66 

Marn 0.97 0.93 0.62 0.70 1 0.62 0.60 0.98 

Tsal 0.65 0.44 0.97 0.98 0.62 1 0.83 0.57 

Mang 0.58 0.41 0.84 0.82 0.60 0.83 1 0.53 

Rust 0.96 0.95 0.58 0.66 0.98 0.57 0.53 1 

The data analysis in Fig. 2 (Table at the bottom of the figure) shows that the linear correlation 

coefficients between the mean monthly and seasonal HCI values at the separate stations change as 

follows (Table 3). Bolnisi: 0.58 (Manglisi) - 0.97 (Marneuli); Gardabani: 0.41 (Manglisi) – 0.95 

(Rustavi); Dmanisi: 0.49 (Gardabani) – 0.97 (Tsalka); Tetri Tskaro: 0.53 (Gardabani) – 0.98 (Tsalka); 

Marneuli: 0.60 (Manglisi) – 0.98 (Rustavi); Tsalka: 0.44 (Gardabani) – 0.98 (Tetri Tskaro); Manglisi: 

0.41 (Gardabani) – 0.84 (Dmanisi); Rustavi: 0.53 (Manglisi) – 0.98 (Marneuli). 

Table 4. Coefficients of regression equation of the intra-annual motion of HCI monthly mean values for 8 points of Kvemo 

Kartli 

Equation of 
regress., 

coefficients 

HCI = a·X9+b·X8+c·X7+d·X6+e·X5+f·X4+g·X3+h·X2+i·X+j, (X-Month) 

a b c d e f g h i j R² 

Boln 

-

1.50
E-04 

8.56E-

03 

-2.06E-

01 

2.72E+

00 

-

2.15E+
01 

1.05E+

02 

-

3.12E+
02 

5.48E+

02 

-

5.12E+
02 

2.53E+

02 
0.993 

Gard 
3.85

E-05 

-2.32E-

03 

6.18E-

02 

-9.41E-

01 

8.91E+

00 

-

5.31E+
01 

1.94E+

02 

-

4.05E+
02 

4.31E+

02 

-

1.10E+
02 

0.999 

Dman 

-

5.14

E-05 

2.89E-
03 

-6.97E-
02 

9.45E-
01 

-

7.91E+

00 

4.21E+
01 

-

1.41E+

02 

2.84E+
02 

-

3.03E+

02 

1.87E+
02 

0.996 

T-Tsk 

-

1.39

E-04 

8.16E-
03 

-2.04E-
01 

2.83E+
00 

-

2.39E+

01 

1.26E+
02 

-

4.06E+

02 

7.71E+
02 

-

7.65E+

02 

3.54E+
02 

0.989 

Marn 

-

2.23

E-06 

1.48E-
04 

-1.67E-
03 

-4.61E-
02 

1.36E+
00 

-

1.43E+

01 

7.49E+
01 

-

2.01E+

02 

2.57E+
02 

-

5.93E+

01 

0.990 

Tsal 
1.36

E-05 

-8.02E-

04 

1.97E-

02 

-2.62E-

01 

2.05E+

00 

-
9.78E+

00 

2.82E+

01 

-
4.60E+

01 

3.68E+

01 

4.90E+

01 
0.996 

Mang 
-

4.59

E-06 

2.98E-

04 

-8.73E-

03 

1.49E-

01 

-
1.59E+

00 

1.07E+

01 

-
4.35E+

01 

1.02E+

02 

-
1.22E+

02 

1.13E+

02 
0.999 

Rust 

-

6.21
E-05 

3.65E-

03 

-8.88E-

02 

1.15E+

00 

-

8.69E+
00 

3.82E+

01 

-

9.50E+
01 

1.24E+

02 

-

7.08E+
01 

6.88E+

01 
0.993 

The distributions of the mean monthly values of the TCI for 8 locations in Kvemo Kartli according to 

the ninth power of the polynomial (R² ≥ 0.989) are described. The coefficients of the equation of the 

regression of the intra-annual motion of the mean monthly HCIs for these points are presented in Table 

4. 

Table 5 shows the distribution types of the mean monthly HCIs at 8 locations in Kvemo Kartli. 

Table 5. Intra-annual distribution types of HCI monthly mean values at 8 locations in Kvemo Kartli 

Location Distribution type First extremum (Max) Second extremum 



Amiranashvili et al. 2024 4(1) 

40 
 

Bolnisi Bimodal May Sep 

Gardabani Bimodal May Oct 

Dmanisi Unimodal, flat Jul, Aug  

Tetri Tskaro Bimodal Jun Aug 

Marneuli Bimodal May Oct 

Tsalka Unimodal Jul  

Manglisi Unimodal Aug  

Rustavi Bimodal May Oct 

According to this table, a generally bimodal distribution of HCIs is observed (5 locations from 8 

locations). For the Gardabani, Marneuli, and Rustavi stations, the first and second extrema of the HCI 

distribution occur in May and October, respectively; for the Bolnisi station, they occur in May and 

September; and for the Tetri Tskaro station, they occur in June and August. 
Table 6. Categories of HCI monthly means and seasonal values at 8 locations in Kvemo Kartli during the cold period 

Location Jan Feb Mar Oct Nov Dec Cold Year 

Bolnisi 

Good 

 

Good 

Good 

Excellent 

Good 

Good 

Good 

Very 

Good 

Gardabani 
Very 

Good 

Dmanisi 

Acceptable 

Good 

Good 

Tetri Tskaro 
Acceptable 

Very 

Good 
Acceptable 

Marneuli Good Excellent 

Good 

Tsalka Good 

Acceptable 

Good 

Manglisi 

Acceptable 

Very 

Good 
Good 

Rustavi Good Excellent 
Very 

Good 

For Dmanisi, a unimodal distribution of HCIs with plateaus from July–August was observed; for 

Tsalka and Manglisi, unimodal distributions with maxima occurred in July and August, respectively. 
Table 7. Categories of HCI monthly means and seasonal values at 8 locations in Kvemo Kartli during the warm period 

Location Apr May Jun Jul Aug Sep Warm Year 

Bolnisi 
Very 

Good Excellent 
Very 

Good 

Very 

Good 

Very 

Good 

Excellent 

Very 

Good 

Very 

Good 

Gardabani Excellent 
Excellent 

Dmanisi 

Good 
Very 

Good 
Excellent 

Excellent Excellent Tetri 

Tskaro 

Very 

Good 

Marneuli 
Very 

Good 
Excellent 

Very 

Good 

Very 

Good 

Tsalka 

Good 

Good 
Very 

Good 
Excellent 

Excellent 
Very 

Good 

Manglisi 
Very 

Good 
Excellent 

Ideal 

Excellent 

Good 

Rustavi 
Very 

Good 
Excellent 

Very 

Good 

Very 

Good 
Excellent 

Very 

Good 

In Tables 6 and 7, the mean monthly and seasonal HCI values at 8 locations in Kvemo Kartli during 

cold and warm periods are presented. 

As shown in these tables, the categories of the mean monthly and seasonal HCIs at 8 locations in 

Kvemo Kartli change from acceptable to ideal. 

Table 8 shows the statistical characteristics of the monthly mean HCIs at 8 locations in Kvemo Kartli 

(all stations); at Bolnisi, Gardabani, Marneuli, and Rustavi (H < 1 km); and at Dmanisi, Tetri Tskaro, 

Tsalka, and Manglisi (H ˃ 1 km). 
Table 8. Statistical characteristics of the monthly mean HCIs at 8 locations in Kvemo Kartli (all stations); at Bolnisi, 

Gardabani, Marneuli, and Rustavi (H < 1 km); and at Dmanisi, Tetri Tskaro, Tsalka, and Manglisi (H ˃ 1 km). 

Location All station H < 1 km H ˃ 1 km 

Variable HCI Category HCI Category HCI Category 

Min 58 Acceptable 58 Acceptable 58 Acceptable 

Max 91 Ideal 87 Excellent 91 Ideal 



Amiranashvili et al. 2024 4(1) 

41 
 

Mean 72 Very Good 73 Very Good 71 Very Good 

St Dev 9.8  8.5  11.0  

Cv,% 13.6  11.7  15.5  

As follows from this table, the HCIs for stations with H < 1 km change from 58 (Acceptable) to 87 

(Excellent), and for stations with H ˃ 1 km from 58 (Acceptable) to 91 (Ideal). For both groups of 

stations, the average HCIs are in the “Very Good” category (73 and 71, respectively). 

Fig. 3 shows the repetition of the monthly mean HCI category at 8 locations in Kvemo Kartli (all 

stations); at Bolnisi, Gardabani, Marneuli, and Rustavi (H < 1 km); and at Dmanisi, Tetri Tskaro, 

Tsalka, and Manglisi (H ˃ 1 km). 

Therefore, as shown in Tables 6 and 7 and Fig. 3, in Kvemo Kartli, there are favourable conditions 

for the development of tourism and resorts throughout the year. 

Notably, the research results of this work, in addition to scientific interest, also have practical 

applications for planning the development of the resort and tourism industry in the Kvemo Kartli region. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finally, in Fig. 4, a map of the distribution of mean monthly HCI categories in the territory of Kvemo 

Kartli (in Georgian) is presented. 

This map was constructed in accordance with previously reported methods (Rekacewicz, 2005; 

Rekacewixz &Stienne, 2013). Such maps are very visual and are intended for a wide range of people 

who want to receive information about various data, phenomena, events, etc., presented in an easy-to-

understand form. The bioclimatic conditions of Kvemo Karli are related to the resort and tourism 

potential of this region. 

Note that a similar map has been prepared for the Atlas of the Kakheti region (ready for publication) 

from the series Geographical Atlases of Georgia. 

The indicated map (Fig. 4) will be included in the Atlas of the Kvemo Kartli region (forthcoming) 

from the same series of geographical atlases of Georgia. 

In both cases, the methodology for constructing maps (Rekacewicz, 2005; Rekacewicz & Stienne, 

2013) under Georgian conditions was used for the first time. 

Discussions 

In recent decades, due to the unprecedented rate of increase in air temperature, climate change on our 

planet has become a particularly urgent problem. At the same time, changes in air temperature and other 

climate elements have significant spatial and temporal heterogeneity on both global and regional (even 

the territory of small countries with complex terrain) scales. 

This problem of climate change is also very relevant in Georgia due to the diversity of climatic regions 

in its territory. Moreover, changes in the thermal regime of the atmosphere increase people's 

vulnerability to external factors. 

The negative impact of the environment on human health can be mitigated by the development of 

resorts and the tourism industry, which allows people to undergo treatment, health and rehabilitation 

activities and to actively relax. Therefore, in recent years, special attention has been given to the 

development of this sector of the economy and, accordingly, to the identification of new bioclimatic 

resources in existing and promising resort and tourist areas. 

Figure 3. Repetition of monthly mean HCI category at 8 locations of Kvemo Kartli (All station) and at Bolnisi, 

Gardabani, Marneuli, Rustavi (H < 1 km) and at Dmanisi, Tetri Tskaro, Tsalka, Manglisi (H ˃ 1 km) 



Amiranashvili et al. 2024 4(1) 

42 
 

Therefore, studying the impact of climate change on the variability of various thermal indices, 

including the TCI and HCI, is important. 

The conducted studies once again confirmed the presence of a variety of climatic and bioclimatic 

conditions in Georgia, as well as the characteristics of their temporal variability. It is concluded that it 

is necessary to conduct a detailed study of climate change (as well as bioclimate) not only on a regional 

but also on a local scale. 

Conclusion 

In recent decades, due to the unprecedented rate of increase in air temperature, climate change on our 

planet has become a particularly urgent problem. At the same time, changes in air temperature and other 

climate elements have significant spatial and temporal heterogeneity on both global and regional (even 

the territory of small countries with complex terrain) scales. 

This problem of climate change is also very relevant in Georgia due to the diversity of climatic regions 

in its territory. Moreover, changes in the thermal regime of the atmosphere increase people's 

vulnerability to external factors. 

The negative impact of the environment on human health can be mitigated by the development of 

resorts and the tourism industry, which allows people to undergo treatment, health and rehabilitation 

activities and to actively relax. Therefore, in recent years, special attention has been given to the 

development of this sector of the economy and, accordingly, to the identification of new bioclimatic 

resources in existing and promising resort and tourist areas. 

Therefore, studying the impact of climate change on the variability of various thermal indices, 

including the TCI and HCI, is important. 

The conducted studies once again confirmed the presence of a variety of climatic and bioclimatic 

conditions in Georgia, as well as the characteristics of their temporal variability. It is concluded that it 

is necessary to conduct a detailed study of climate change (as well as bioclimate) not only on a regional 

but also on a local scale. 

Competing interests 

The authors declare that they have no competing interests. 

Authors’ contributions 

Figure 4. Map of the distribution of mean monthly HCI categories on the territory of Kvemo Kartli. Designations on the 

map. ბიოკლიმატური პირობების შეფასების პუნქტი - Point of assessment of bioclimatic conditions. Name of 

points: (წალკა - Tsalka, მანგლისი - Manglisi, დმანისი-Dmanisi, თეთრი წყარო - Tetri Tskaro, ბოლნისი-Bolnisi, 

მარნეული - Marneuli, რუსთავი - Rustavi, გარდაბანი-Gardabani).  წელიადის დროები თვეების მიხედვით 

(I-XII) - Times of the year by month (I-XII): გაზაფხული (III-V), ზაფხული (VI-VIII), შემოდგომა (I-XI), ზამთაფი 

(XII-II) - Spring (III V), Summer (VI-VIII), Autumn (I-XI), Winter (XII-II). დასვენების კლიმატური ინდექსი (დკი): 

იდეალური, შესანიშნავი, ძალიან კარგი, კარგი, სასიამოვნო - Holiday Climate Index (HCI): Ideal, Excellent, 

Very Good, Good, Acceptable. 



Amiranashvili et al. 2024 4(1) 

43 
 

A.A. and N.B. conceived of the presented idea. L.K. and G.L. performed the analytic calculations. 

G.T. constructed the map and edited the manuscript. All authors provided critical feedback and helped 

shape the research, analysis and manuscript. 

Acknowledgements 

The work was carried out in accordance with program financing (state budget). 

ORCID iD 

Avtandil Amiranashvili https://orcid.org/ 0000-0001-6152-2214 

Nana Bolashvili https://orcid.org/ 0000-0001-9854-2614 

Liana Kartvelishvili https://orcid.org/ 0009-0007-6836-9313 

Guliko Liparteliani https://orcid.org/0009-0009-5915-0488 

Gvantsa Tsirgvava https://orcid.org/0009-0004-7595-0657 

Reference 

Amiranashvili A., Bliadze T., Chikhladze V. (2012). Photochemical smog in Tbilisi.//Monograph:  Trans. of 

Mikheil Nodia institute of Geophysics, ISSN 1512-1135, vol. 63, 160 p., (in Georgian). 

http://www.dspace.gela.org.ge/bitstream/123456789/636/3/ფოტოქიმიური%20სმოგი%20თბილისში_

წიგნი_2012_Ge.pdf 

Amiranashvili A.G, Bolashvili N.R., Chikhladze V.A., Japaridze N.D., Khazaradze K.R., Khazaradze R.R., 

Lezhava Z.L., Tsikarishvili K.D. (2015a). Some New Data about the Bioclimatic Characteristics of the 

Village of Mukhuri (Western Georgia). Journal of the Georgian Geophysical Society, Issue B. Physics of 

Atmosphere, Ocean and Space Plasma, v.18B, Tbilisi, pp. 107-115. 

Amiranashvili, A., Chargazia, Kh., Matzarakis, A. (2014). Comparative Characteristics of the Tourism Climate 

Index in the South Caucasus Countries Capitals (Baku, Tbilisi, Yerevan). Journal of the Georgian 

Geophysical Society, ISSN: 1512-1127, Issue (B). Physics of Atmosphere, Ocean, and Space Plasma, 

vol.17B, pp. 14-25. 

Amiranashvili A., Chargazia Kh., Matzarakis A., Kartvelishvili L. (2015b). Tourism Climate Index in the 

Coastal and Mountain Locality of Adjara, Georgia.//In the book: Int. Sc. Conf. “Sustainable Mountain 

Regions: Make Them Work”. Proceedings, Borovets, Bulgaria, ISBN 978-954-411-220-2, 14-16 

May,2015, pp. 238-244, http://geography.bg/MountainRegions_Sofia2015 

Amiranashvili A.G., Chikhladze V.A., Saakashvili N.M., Tabidze M.Sh., Tarkhan-Mouravi I.D.  (2011). 

Bioclimatic Characteristics of Recreational Zones – Important Component of the Passport of the Health 

Resort – Tourist Potential of Georgia. Trans. of the Institute of Hydrometeorology at the Georgian 

Technical University, vol. 117, ISSN 1512-0902, pp. 89-92. 

Amiranashvili A., Chikhladze V., Tsikarishvili K., Tsiklauri Kh. (2019). On the Restoration of the Ionization 

Properties of “Tetra” Cave (Tskaltubo, Georgia).//In the book: Proc. of Sc. Conf. “Actual Problems of 

Geography” Dedicated to Prof. Davit Ukleba’s 100th Anniversary, ISBN 978–9941–13-885-0, 5-6 

November, 2019, Tbilisi, Georgia, pp. 33-36, 

http://dspace.gela.org.ge/bitstream/123456789/8596/1/Amiranashvili%20etc._Act_Probl_Geography_201

9.pdf 

Amiranashvili A.G., Japaridze N.D., Khazaradze K.R. (2018a). On the Connection of Monthly Mean of Some 

Simple Thermal Indices and Tourism Climate Index with the Mortality of the Population of Tbilisi City 

Apropos of Cardiovascular Diseases. Journal of the Georgian Geophysical Society, ISSN: 1512-1127, 

Physics of Solid Earth, Atmosphere, Ocean and Space Plasma, v. 21(1), Tbilisi, pp.48-62. 

http://www.jl.tsu.ge/index.php/GGS/article/view/2489 

Amiranashvili A.G., Japaridze N.D., Kartvelishvili L.G., Khazaradze K.R., Matzarakis A., Povolotskaya N.P., 

Senik I.A. (2017). Tourism Climate Index of in the Some Regions of Georgia and North Caucasus. 

Journal of the Georgian Geophysical Society, ISSN: 1512-1127, Issue (B). Physics of Atmosphere, Ocean, 

and Space Plasma, vol.20B, pp. 43-64. 

Amiranashvili A.G., Japaridze N.D., Kartvelishvili L.G., Khazaradze K.R., Kurdashvili L.R. (2018b). Tourism 

Climate Index in Kutaisi (Georgia).//In the book: Int. Sc. Conf. "Modern Problems of Ecology“, 

Proceedings, ISSN 1512-1976, v. 6, Kutaisi, Georgia, 21-22 September, 2018, pp. 227-230. 

Amiranashvili A., Japaridze N., Kartvelishvili L., Megrelidze L., Khazaradze K. (2018d). Statistical 

Characteristics of the Monthly Mean Values of Air Effective Temperature on Missenard in the 



Amiranashvili et al. 2024 4(1) 

44 
 

Autonomous Republic of Adjara and Kakheti (Georgia). Transactions of Mikheil Nodia Institute of 

Geophysics, ISSN 1512-1135, vol. LXIX, pp. 118-138, (in Russian). 

 Amiranashvili A., Japaridze N., Kartvelishvili L., Khazaradze K., Revishvili A. (2022a). Changeability the 

Monthly Mean Values of Air Effective Temperature on Missenard in Batumi in 1956-2015. Journal of the 

Georgian Geophysical Society, e-ISSN: 2667-9973, p-ISSN: 1512-1127, Physics of Solid Earth, 

Atmosphere, Ocean and Space Plasma, v. 25(2), pp. 49–58. DOI: https://doi.org/10.48614/ggs2520225960 

Amiranashvili A.G., Kartvelishvili L.G., Matzarakis A., Megrelidze L.D. (2018c). The Statistical Characteristics 

of Tourism Climate Index in Kakheti (Georgia). Journal of the Georgian Geophysical Society, ISSN: 

1512-1127, Physics of Solid Earth, Atmosphere, Ocean and Space Plasma, v. 21(2), Tbilisi, pp. 95-112. 

Amiranashvili A., Kartvelishvili L., Matzarakis A. (2020a). Comparison of the Holiday Climate Index (HCI) 

and the Tourism Climate Index (TCI) in Tbilisi.//In the book: Int. Sc. Conf. "Modern Problems of 

Ecology“, Proc., ISSN 1512-1976, v. 7, Tbilisi-Telavi, Georgia, 26-28 September, 2020, pp. 424-427. 

Amiranashvili A.G., Kartvelishvili L.G., Kutaladze N.B., Megrelidze L.D., Tatishvili M.R. (2021a). Holiday 

Climate Index in Some Mountainous Regions of Georgia. Journal of the Georgian Geophysical Society, e-

ISSN: 2667-9973, p-ISSN: 1512-1127, Physics of Solid Earth, Atmosphere, Ocean and Space Plasma, v. 

24(2), pp. 92 – 117. DOI: https://doi.org/10.48614/ggs2420213327 

Amiranashvili A., Matzarakis A., Kartvelishvili L. (2008). Tourism Climate Index in Tbilisi. Trans. of the 

Institute of Hydrometeorology, ISSN 1512-0902, Tbilisi, vol. 115, pp. 27 - 30. 

Amiranashvili A., Povolotskaya N., Senik I. (2021b). Comparative Analysis of the Tourism Climate Index and 

the Holiday Climate Index in the North Caucasus.Transactions of Mikheil Nodia Institute of Geophysics, 

ISSN 1512-1135, vol. LXXIII, pp. 96-113, (in Russian). 

Amiranashvili A.G., Revishvili A.A., Khazaradze K.R., Japaridze N.D. (2021c). Connection of Holiday Climate 

Index with Public Health (on Example of Tbilisi and Kakheti Region, Georgia). Journal of the Georgian 

Geophysical Society, e-ISSN: 2667-9973, p-ISSN: 1512-1127, Physics of Solid Earth, Atmosphere, 

Ocean and Space Plasma, v. 24 (1), 2021, pp.  63-76.  DOI: https://doi.org/10.48614/ggs2420212884 

Amiranashvili   A., Japaridze N., Kartvelishvili L., Khazaradze K., Revishvili A. (2022b). Preliminary Results 

of a Study on the Impact of Some Simple Thermal Indices on the Spread of COVID-19 in Tbilisi. Journal 

of the Georgian Geophysical Society, e-ISSN: 2667-9973, p-ISSN: 1512-1127, Physics of Solid Earth, 

Atmosphere, Ocean and Space Plasma, v. 25(2), pp. 59–68. DOI: https://doi.org/10.48614/ggs2520225961 

Amiranashvili A., Kartvelishvili L., Matzarakis A. (2020b). Changeability of the Holiday Climate Index (HCI) 

in Tbilisi. Transactions of Mikheil Nodia Institute of Geophysics, ISSN 1512-1135, vol. LXXII, 2020, pp. 

131-139. 

Amiranashvili A.G., Kartvelishvili L. G. (2008). Long-Term Variations of Air Effective Temperature in Tbilisi. 

Trans. of the Institute of Hydrometeorology, vol. 115, ISSN 1512-0902, Tb., pp. 214–219, (in Russian). 

Amiranashvili A., Kartvelishvili L. (2019). Statistical Characteristics of the Monthly Mean Values of Tourism 

Climate Index in Mestia (Georgia) in 1961-2010. Journal of the Georgian Geophysical Society, ISSN: 

1512-1127, Physics of Solid Earth, Atmosphere, Ocean and Space Plasma, v. 22(2), pp.  68–79. 

Amiranashvili A.G., Kartvelishvili L.G. (2021). Holiday Climate Index in Kakheti (Georgia). Journal of the 

Georgian Geophysical Society, e-ISSN: 2667-9973, p-ISSN: 1512-1127, Physics of Solid Earth, 

Atmosphere, Ocean and Space Plasma, v. 24(1), pp.  44-62. 

Araci S.F. S., Demiroglu O. C., Pacal A., Hall C. M., Kurnaz, M. L. (2021). Future Holiday Climate Index 

(HCI) Performances of Urban and Beach Destinations in the Mediterranean.//In the book: EGU General 

Assembly 2021, online, 19–30 Apr 2021, EGU21-13217, https://doi.org/10.5194/egusphere-egu21-13217, 

2021. 

Bolashvili N.R., Chikhladze V.A., Khazaradze K.R., Lezhava Z.I., Tsikarishvili K.D. (2016). Some Bioclimatic 

Characteristics of Martvili Canyon (Western Georgia).//In the book: The Questions of Health Resort 

Managing, Physiotheraphy and Rehabilitation, International Collection of Scientific Articles, Vol. I, ISSN 

2449-271X, Tbilisi, ppp. 81-87. 

http://109.205.44.60/bitstream/123456789/6244/1/Bolashvili%2CChikhladze..._2016_Article.pdf 

Carrillo J., González A., Pérez J. C., Expósito F. J., Díaz, J. P. (2021). Impact of Climate Change on the Future 

of Tourism Areas in the Canary Islands. //In the book: EGU General Assembly 2021, online, 19–30 Apr 

2021, EGU21-11981, https://doi.org/10.5194/egusphere-egu21-11981, 2021. 

Fourth National Communication of Georgia. Under the United Nations Framework Convention on Climate 

Change. (2021)//Book: Tbilisi. pp. 333-339. 



Amiranashvili et al. 2024 4(1) 

45 
 

https://unfccc.int/sites/default/files/resource/4%20Final%20Report%20-

%20English%202020%2030.03_0.pdf 

Hejazizadeh Z., Karbalaee A., Hosseini S.A., Tabatabaei S.A. (2019). Comparison of the Holiday Climate Index 

(HCI) and the Tourism Climate Index (TCI) in Desert Regions and Makran Coasts of Iran. Arab. J. 

Geosci. 12, 803, https://doi.org/10.1007/s12517-019-4997-5 

Javan K. (2017). Comparison of Holiday Climate Index (HCI) and Tourism Climate Index (TCI) in Urmia. 

Physical Geography Research Quarteli. vol. 49, iss. 3, pp. 423-439. 

Kartvelishvili L., Tatishvili M., Amiranashvili A., Megrelidze L., Kutaladze N. (2023). Weather, Climate and 

their Change Regularities for the Conditions of Georgia. //Monograph: Publishing House “UNIVERSAL”, 

Tbilisi, 406 p., https://doi.org/10.52340/mng.9789941334658 

Kartvelishvili L., Matzarakis A., Amiranashvili A., Kutaladze N. (2011). Assessment of Touristical-Recreation 

Potential of Georgia on Background Regional Climate Change.//In the book: Proc. of IIst Int. Scientific-

Practical Conference “Tourism: Economics and Business”, June 4-5, Batumi, Georgia, pp. 250-252. 

Kartvelishvili L., Amiranashvili A., Megrelidze L., Kurdashvili L. (2019). Turistul Rekreaciuli Resursebis 

Shefaseba Klimatis Cvlilebebis Fonze.//Book: Publish House "Mtsignobari”, ISBN 978-9941-485-01-5, 

Tbilisi, 161 p., (in Georgian). http://217.147.235.82/bitstream/1234/293074/1/turistulRekreaciuli 

ResursebisShefasebaKlimatisCvlilebebisFonze.pdf. 

Khazaradze K.R. (2017). Comparative Analysis of Mean-Daily Value of Air Equivalent Effective Temperature 

in Tbilisi and Kojori.  Journal of the Georgian Geophysical Society, Issue B. Physics of Atmosphere, 

Ocean and Space Plasma, v. 20B, pp. 65–72. 

http://www.dspace.gela.org.ge/bitstream/123456789/7105/1/JGGS_20B_2017_5.pdf 

Kobisheva N., Narovlianski G. (1978). Climatological Processing of the Meteorological Information.//Book:  

Leningrad, Gidrometeoizdat, 294 p., (in Russian). 

Lanchava O. A., Iliashi N., Radu S., Tsikarishvili K., Lezhava Z., Amiranashvili A., Chikhladze V., Asanidze L. 

(2021). წყალტუბოს (დასავლეთი საქართველო) "თეთრა მღვიმის“ პრაქტიკული 

სარგებლიანობის პოტენციალი. GEORGIAN SCIENTISTS, E-ISSN: 2667-9760, Vol. 3, N 1, 15 p. 

Retrieved from https://journals.4 science.ge/index.php/GS/article/view/285 

Matzarakis A. (2006). Weather - and Climate-Related Information for Tourism. Tourism and Hospitality 

Planning & Development, August, vol. 3, No. 2, pp. 99–115. 

Matzarakis A., Cheval S., Lin T.-P., Potchter, O. (2021). Challenges in Applied Human Biometeorology. 

Atmosphere, 12, 296. https://doi.org/10.3390/atmos12030296 

Matzarakis A., Cheval S., Lin T.-P., Potchter O. (2021). Challenges in Applied Human Biometeorology. 

Atmosphere, 12, 296. https://doi.org/10.3390/atmos12030296 

Mieczkowski Z. (1985). The Tourism Climate Index: A Method for Evaluating World Climates for Tourism.  

The Canadian Geographer, N 29, pp. 220-233. 

Mushawemhuka W.J., Fitchett J.M., Hoogendoorn G. (2020). Towards Quantifying Climate Suitability for 

Zimbabwean Nature-Based Tourism. South African Geographical Journal. DOI: 

10.1080/03736245.2020.1835703 

Rekacewicz P. (2005). River Fragmentation, Flow Regulation and Dams Under Construction. UNEP/GRID-

Arenda. Image.//Retrieved from https://www.geo.fu-

berlin.de/en/v/iwrm/Implementation/water_and_the_physical_environment/bilder/River-fragmentation-

and-flow-regulation.jpg?html=1&locale=en&ref=65912901 

Rekacewicz P., Stienne A. (2013). An Over-Nuclear Europe.//Retrieved from 

https://mondediplo.com/maps/overnuclear 

Rutty M., Steiger R., O. Demiroglu O.C., Perkins D.R. (2021). Tourism Climatology: Past, Present, and Future. 

Int. Journ. of. Biometeorology. Published online: 08 January 2021. https://doi.org/10.1007/s00484-020-

02070-0 

Rutty M., Scott D., Matthews L., Burrowes R., Trotman A., Mahon R., Charles A. (2020). An Inter-Comparison 

of the Holiday Climate Index (HCI: Beach) and the Tourism Climate Index (TCI) to Explain Canadian 

Tourism Arrivals to the Caribbean. Atmosphere, 11, 412. 

Rybak, O. O., & Rybak, E. A. (2016). APPLICATION OF CLIMATIC INDICES FOR EVALUATION OF 

REGIONAL DIFFERENCES IN TOURIST ATTRACTIVENESS. Naučnyj Žurnal Kubanskogo 

Gosudarstvennogo Agrarnogo Universiteta. https://doi.org/10.21515/1990-4665-121-016  



Amiranashvili et al. 2024 4(1) 

46 
 

Sakartvelos sametsniero-gamoq’enebiti k’limat’uri tsnobari. (2020).//Book: SSIP Garemos erovnuli saagento, 

gamomcemloba  “Universali”, ISBN 978-9941-26-798-7, 307 p. 

Scott, D., Rutty, M., Amelung, B., & Tang, M. (2016). An Inter-Comparison of the Holiday Climate Index 

(HCI) and the Tourism Climate Index (TCI) in Europe. Atmosphere, 7(6), 80. 

https://doi.org/10.3390/atmos7060080 

Tanana, A. B. et al. (2021). Confort climático en la Argentina: un recurso intangible para el turismo. Cuadernos 

Geográficos 60(3), pp. 52-72. 

Vadachkoria M.K., Ushveridze G.A., Jaliashvili V.G. (1987). Health Resorts of the Georgian SSR.//Book: 

"Sabchota Sakartvelo" Publishers, Tbilisi, 383 p., (in Georgia, English and Russian). 

Williams D. (2021). An Examination of the Tourism Holiday Climate Index (HCI: Urban) in Tokyo 1964-2019. 

Josai International University Bulletin, Vol. 29, No. 6, March 2021, p. 1-31.  

Yu D. D., Rutty M., Scott D., Li S. (2020). Comparison of the Holiday Climate Index: Beach and the Tourism 

Climate Index Across Coastal Destinations in China. International Journal of Biometeorology, 

https://doi.org/10.1007/s00484-020-01979-w, 8 p. 

 

 

 

 

 

 

 

 

 

 

 

 


