Abstract
Urban vegetation is an effective urban feature to protect citizens from urban warming. The aim of this study is to assess the temperature in the urban ‘cooling islands’ in Tashkent, Uzbekistan to keep them cool in summer. The area of the tree canopy, planting density, spatial structures and microclimatic data were observed in 30 green zones over three summer months to determine their thermal overheating. The observed data was then compared with the daily weather forecasts. The results show that while large tree canopies are crucial for maintaining a cool microclimate through shading, small shrubs, and lawns, which are popular in local landscaping, are not effective. Planting density is weakly related to a cool microclimate in green spaces. The best cooling results are achieved when the tree canopy shades more than 75% of the area. These results provide new insights for the development of more sustainable strategies and standards for the design and maintenance of cool green spaces in Tashkent and other Central Asian cities with similar climates.
Introduction
Urbanization remains one of the most important global trends for cities in the 20th century, with its downsides, such as Urban Heat Island (UHI) effects, where the air temperature of cities is increased compared to surrounding rural areas (Grimmond 2007). The causes of this phenomenon are physical characteristics of the built urban environment, such as the large number of hard surfaces, anthropogenic heat emissions, and little vegetation (
Mukhamedjanov, Tetsuo, et al., 2021; Oke, 1987; O’Malley, Piroozfar, et al. 2015). Greater challenges for urban dwellers include labor productivity, thermal discomfort, and increased electricity consumption, especially in summer (Hanaki, 2008; Uchiyama, 2011). As most cities grow, UHI is expected to exacerbate urban problems in the long term (Gu 2019; Kleerekoper, van Esch, et al. 2012).
Urban greening is an effective solution for creating a cool and pleasant climate in cities (
Oke, 1987; Zhou, Yu, et al., 2022). Summer temperatures in parks are generally lower than in dense urban environments (Yang, Xiao, et al., 2016). Strategically located parks or so-called “urban cooling islands” can cool down cities (
Yokohari, Brown, et al., 2001). The natural environment has a positive impact on the health of residents (Cao, Yang, et al. 2024). Informal green spaces, such as wilderness, also contribute to the well-being of people in Ichikawa, Japan (Kim, Rupprecht, et al. 2020). However, further research is needed as climatic differences, urban morphology, and topography in different parts of the world can influence the outcomes of green design strategies.
Greenery is also one of the most important elements that make urban areas more attractive to city dwellers (Lehmann, Mathey, et al. 2014). Using GIS, the relationship between planting density and cooling effect has been successfully observed (
Sharipjonova, Karimov, et al., 2020). However, less developed countries face difficulties in planning and managing national resources due to increasing pressure from natural resource depletion, financial instability, lack of qualified decision makers and computer specialists (Mennecke and West Jr., 2001). Therefore, it is unclear which method is more efficient and cost-effective to assess and maintain cool thermal conditions in urban green spaces in low and lower-middle income countries.
The government of Uzbekistan is implementing reforms to improve the industrial sector and accelerate urbanization. The city of Tashkent, the capital of Uzbekistan, faces dramatic challenges in terms of land use strategies. The lack of an approved comprehensive masterplan and sustainable spatial planning policy leads to disastrous consequences such as the deterioration of urban infrastructure and the reduction of green spaces due to new construction in green recreational areas. For example, to create more entertainment opportunities for children and make parks financially self-sustainable, the government launched the Park Development Program between 2011 and 2015; one of the main requirements was to preserve at least 50% of green spaces (
Lexuz, 2010). This strategy has led to a discrepancy between urban planning standards and government policies described below.
According to the local urban planning standard, namely "Urban planning: planning for the development of the territories of urban and rural settlements" (
MCRUz, 2009), green areas are understood as part of the natural complex on which are located: artificially created garden and park complexes and objects - parks, gardens, squares, boulevards; the territory of residential, public, commercial and other territorial zones, of which less than 70 percent of the area is occupied by green areas and other vegetation. Consistent with Section 83, a specific greening norm (m² per person) should be maintained for calculating and allocating public green space in cities. However, Section 75.6 states that the area of recreation zones in cities may be reduced in cases of reconstruction (
MCRUz, 2009). These loopholes have opened the possibility to reduce the greenery in cities. As a result, new developments appeared in parks, many trees were cut down and the temperature in the public “cool islands” increased.
The consequences of this policy were partially reversed after the government issued a moratorium on tree cutting in December 2021. Even though Park Development Program does not violate current urban planning norms, Tashkent's parks remain between the hammer and the anvil (Lee 2021). The trend of urban greening deterioration in Tashkent is evident from a recent study showing that the built-up area of the city increased by almost 60% between 1990 and 2019, while the proportion of urban greening decreased by the same amount (Aslanov, Mukhtorov, et al. 2021). The researchers concluded that the decline of vegetation will negatively affect the sustainable development of Tashkent. However, less is known about what maintains the cool microclimate in Tashkent's green spaces and alleviates the thermal discomfort of citizens.
The aim of this study is to find a cost-effective way to estimate how much greenery should be preserved in Tashkent's green spaces to keep them cool. The hypothesis is that a certain planting density and landscaping methods can be evaluated to maintain the cool microclimate in existing green spaces in summer.
To the best of our knowledge, this is the first study from the Central Asian region to analyze the characteristics of the cool microclimate of the remaining green areas in Tashkent. It is expected that results will help fill a gap in urban planning research and policy that is an emerging field of research in this local geographic context.
This paper is organized as follows. The methodology and the study area are presented together with the data compilation. This is followed by the results of the correlation analysis between the tree species, their planting density, and the climate data. The relevance of the results for landscape research is then discussed, solutions for local landscape policy are proposed and conclusions are drawn.
Material and Methods
Study area
Uzbekistan is an urbanized country with 50.49% of the total population living in cities (
MEDPRUz, 2020). The city of Tashkent (41°18' N 69°16' E) is the capital of Uzbekistan. In 2023, the population of Tashkent is 2,956,400 people and the population density is 6,599 inhabitants per square kilometer. The local climate is hot and arid with long summers that last from May to September. Summers can be extremely hot, especially in July and August. In July 1997, a record high temperature of up to 44.6 °C was recorded (
Pogodaiklimat, 2019). According to the Köppen climate classification scheme (Figure 9), Tashkent is in the Mediterranean warm climate zone (Csa). The noon altitude of the sun in Tashkent in the summer months is almost 70 degrees. To ensure the reliability of the study, we measure the cooling effect in public green spaces that are located away from large bodies of water and densely built-up areas. Data was collected between 12:00 and 15:00, as temperatures are highest at this time. This strategy helps to measure the cooling effect of tree shade more accurately in the selected green spaces with a tolerance of ±1 °C.
To determine the temperature contrast of different green spaces, we conducted a field study at selected sites in June, July, and August 2022. Planting density and tree canopy area that could potentially influence thermal conditions through shading were measured. Due to the lack of green spaces in the city suitable for this experiment (i.e., aside from large bodies of water and densely built environment), we were only able to select thirty sites. These sites were small parks, public squares, and alleys with different areas, proportions of green space, and functions (Figure 1). The field study included measurements of air temperature, humidity, and wind speed at a height of 1.5 m above the ground surface on shaded pedestrian paths using a thermometer and an anemometer. This method of data collection is less expensive and more reproducible than stationary measurements (
Oke, 1987). The following loggers were used to measure temperature, humidity, and wind speed: a GM 1362 thermometer (Benetech) with an accuracy of ±1 °C and ±3 % (range of 30-95 %), a GM816 anemometer (Otraki) with a wind speed accuracy of ±5 % (precision of ± 0.1 dgt) and a temperature accuracy of ±2 °C, and a FLIR ONE Pro thermal imaging camera for Android Gen 3 used in conjunction with a POCO 4X 5G cell phone. To visually illustrate local landscaping issues, such as planting shrubs instead of shade trees in public green spaces, e.g. site No. 3 (Figure 5), we also provide some examples of thermal images using the free software "FLIR ONE" for mobile devices and computers.
Field data
The green spaces were visited between June and August 2022. Several types of data were collected that could potentially affect the thermal comfort of green spaces: tree species, height, plant density, and canopy area; climate data (i.e., temperature, humidity, and wind speed); and area of green zones. Most of these areas were circular and rectangular in shape with varying vegetation characteristics: some had mixed vegetation types with low, medium, and tall trees; others had only a few planted junipers with a conical shape.
Figure 1 shows the map of Tashkent city with the data collection points of thirty selected green spaces that have not yet been redeveloped. In most locations, the tree species were mixed. However, there were areas where juniper was predominantly planted. To determine planting density per hectare, we manually counted the number of trees planted during field visits. Later, we marked these areas on the map using Google Earth and exported them to ArcMap to estimate the total area of these sites (ST). Next, we divided the number of tree species by the measured area of the site. To estimate the percentage of tree canopy cover (Canopy cover %), we divided the area of tree canopy (S canopy cover) by the total area of public green space (Formula 1). The area of the tree crowns was measured visually using Google map images by outlining them in polygons with ArcMap. Similarly, we estimated the area of hard pavements (SH), the area of green space (SG), and their ratios, namely (SG %) and (SH %).
Formula for accessing the percentage of canopy cover:
Canopy cover %=(S canopy cover)/ST
(1)
A total of 7089 trees were counted in all areas, of which 5221 (73.7%) were tall trees (7 m and taller), 1080 (15.3%) were medium trees (between 3 and 7 m), and 778 (11%) were small trees (less than 3 m). Most of the tall trees, usually oaks (Quercus), sophoras, and chinars (platanus orientalis) had a dense, broad canopy, while only a few trees were poplars and spruces. Medium and small trees were dominated by only Sophora and Juniper. Sites 2,3, 10, 14, 17, 23, 24, 25 were landscaped with lawns and conical juniper trees, which provide little shade and serve only decorative purposes. Table 1 shows the data collected during the field trips in the selected areas. Missing data refers to the absence of a particular tree category at the study sites.
Table 1. Collected data from 30 selected areas.
| No.
|
Tree > 7m
|
Tree 3-7 m
|
Tree < 3m
|
Total tree №
|
Planting Density
|
SG (ha)
|
SH (ha)
|
ST (ha)
|
SG %
|
SH %
|
S canopy cover (ha)
|
Canopy cover %
|
| 1 |
155 |
300 |
235 |
690 |
775.3 |
0.58 |
0.31 |
0.89 |
65.17 |
34.83 |
0.73 |
82.02% |
| 2 |
15 |
57 |
310 |
382 |
300.8 |
1.08 |
0.19 |
1.27 |
85.0 |
15.0 |
0.41 |
32.28% |
| 3 |
7 |
|
|
7 |
19.4 |
0.31 |
0.05 |
0.36 |
86.1 |
13.9 |
0.054 |
15.00% |
| 4 |
20 |
525 |
155 |
700 |
114.0 |
4.14 |
2 |
6.14 |
67.43 |
32.57 |
2.505 |
40.80% |
| 5 |
275 |
20 |
|
295 |
590.0 |
0.42 |
0.08 |
0.5 |
84.00 |
16.00 |
0.218 |
43.60% |
| 6 |
55 |
|
|
55 |
114.6 |
0.4 |
0.08 |
0.48 |
83.33 |
16.67 |
0.24 |
50.00% |
| 7 |
50 |
|
15 |
65 |
361.1 |
0.15 |
0.03 |
0.18 |
83.33 |
16.67 |
0.072 |
40.00% |
| 8 |
10 |
80 |
|
90 |
321.4 |
0.26 |
0.02 |
0.28 |
92.86 |
7.14 |
0.061 |
21.79% |
| 9 |
116 |
26 |
5 |
147 |
111.4 |
1.03 |
0.29 |
1.32 |
78.03 |
21.97 |
1.02 |
77.27% |
| 10 |
24 |
|
11 |
35 |
175.0 |
0.19 |
0.01 |
0.2 |
95.00 |
5.00 |
0.024 |
12.00% |
| 11 |
111 |
32 |
6 |
149 |
85.1 |
1.15 |
0.6 |
1.75 |
65.7 |
34.3 |
0.273 |
15.60% |
| 12 |
100 |
|
45 |
145 |
88.4 |
1.07 |
0.57 |
1.64 |
65.2 |
34.8 |
0.881 |
53.72% |
| 13 |
31 |
2 |
|
33 |
126.9 |
0.2 |
0.06 |
0.26 |
76.9 |
23.1 |
0.195 |
75.00% |
| 14 |
0 |
18 |
|
18 |
200.0 |
0.09 |
0 |
0.09 |
100.0 |
0.0 |
0.006 |
6.67% |
| 15 |
2140 |
|
|
2140 |
279.0 |
6.52 |
1.15 |
7.67 |
85.0 |
15.0 |
5.878 |
76.64% |
| 16 |
28 |
|
|
28 |
58.3 |
0.23 |
0.25 |
0.48 |
47.92 |
52.08 |
0.168 |
35.00% |
| 17 |
0 |
5 |
6 |
11 |
61.1 |
0.15 |
0.03 |
0.18 |
83.33 |
16.67 |
0.005 |
2.78% |
| 18 |
128 |
15 |
|
143 |
357.5 |
0.34 |
0.06 |
0.4 |
85.00 |
15.00 |
0.26 |
65.00% |
| 19 |
500 |
|
|
500 |
108.7 |
3.78 |
0.82 |
4.6 |
82.17 |
17.83 |
1.419 |
30.85% |
| 20 |
270 |
|
|
270 |
139.9 |
1.7 |
0.23 |
1.93 |
88.08 |
11.92 |
0.736 |
38.13% |
| 21 |
215 |
|
|
215 |
111.4 |
1.93 |
0 |
1.93 |
100.00 |
0.00 |
0.636 |
32.95% |
| 22 |
155 |
|
|
155 |
140.9 |
1.063 |
0.037 |
1.1 |
96,64 |
3.36 |
0.281 |
25.55% |
| 23 |
38 |
|
|
38 |
131.0 |
0.29 |
0 |
0.29 |
100.00 |
0.00 |
0.088 |
30.34% |
| 24 |
33 |
|
|
33 |
53.2 |
0.62 |
0 |
0.62 |
100.00 |
0.00 |
0.065 |
10.48% |
| 25 |
40 |
|
|
40 |
62.5 |
0.64 |
0 |
0.64 |
100.00 |
0.00 |
0.116 |
18.13% |
| 26 |
115 |
|
|
115 |
60.5 |
1.48 |
0.42 |
1.9 |
77.89 |
22.11 |
0.402 |
21.16% |
| 27 |
355 |
|
|
355 |
144.3 |
1.96 |
0.5 |
2.46 |
79.67 |
20.33 |
0.776 |
31.54% |
| 28 |
155 |
|
|
155 |
174.2 |
0.69 |
0.2 |
0.89 |
77.53 |
22.47 |
0.306 |
34.38% |
| 29 |
40 |
|
|
40 |
32.8 |
0.94 |
0.28 |
1.22 |
77.05 |
22.95 |
0.351 |
28.77% |
| 30 |
40 |
|
|
40 |
41.7 |
0.77 |
0.19 |
0.96 |
80.21 |
19.79 |
0.177 |
18.44% |
SG = Green area; SH – paved area; ST = total area of site; S canopy cover (ha) = area of tree canopy cover in hectares; Canopy cover % = percentage of canopy cover area in relation to total area of green space.
By knowing the tree species, their height and number, it is possible to determine their relationship to the microclimate in the public green spaces. While some areas, namely No. 15, consisted only of tall tree species, others had a combination of medium and small trees (for example, No. 17).
Table 2. Climatic data from selected sites in July 2022
| №
|
Date
|
July
|
W/S (Con)
|
| t° (F)
|
t° (M)
|
t° (Con)
|
RH% (F)
|
RH% (M)
|
RH% (Con)
|
W/S (F)
|
W/S(M)
|
| 1 |
July 19 |
34 |
31.8 |
-2.2 |
27 |
36.5 |
9.5 |
3 |
0.5 |
-2.5 |
| 2 |
July 20 |
37 |
37.7 |
0.7 |
18 |
31.4 |
13.4 |
3.9 |
0.4 |
-3.5 |
| 3 |
July 20 |
37 |
39.7 |
2.7 |
18 |
31.4 |
13.4 |
3.9 |
0.7 |
-3.2 |
| 4 |
July 25 |
41 |
40.5 |
-0.5 |
13 |
37.9 |
24.9 |
3.9 |
0.8 |
-3.1 |
| 5 |
July 20 |
37 |
36.3 |
-0.7 |
18 |
34 |
16 |
3.9 |
0.8 |
-3.1 |
| 6 |
July 20 |
37 |
35.6 |
-1.4 |
18 |
39.1 |
21.1 |
3.9 |
1 |
-2.9 |
| 7 |
July 19 |
34 |
35 |
1 |
27 |
35 |
8 |
3 |
1 |
-2 |
| 8 |
July 26 |
42 |
42.5 |
0.5 |
15 |
29 |
14 |
3 |
1 |
-2 |
| 9 |
July 25 |
41 |
38.7 |
-2.3 |
13 |
37.4 |
24.4 |
3.9 |
0.5 |
-3.4 |
| 10 |
July 26 |
41 |
43.2 |
2.2 |
18 |
29.5 |
11.5 |
3 |
1 |
-2 |
| 11 |
July 26 |
41 |
42.5 |
1.5 |
15 |
30.1 |
15.1 |
2.2 |
0.8 |
-1.4 |
| 12 |
July 26 |
41 |
40.4 |
-0.6 |
15 |
29 |
14 |
2.2 |
0.5 |
-1.7 |
| 13 |
July 22 |
41 |
39 |
-2 |
14 |
29.4 |
15.4 |
2.2 |
1 |
-1.2 |
| 14 |
July 20 |
37 |
39.9 |
2.9 |
18 |
32 |
14 |
3.9 |
1.8 |
-2.1 |
| 15 |
July 31 |
33 |
31.2 |
-1.8 |
22 |
40.7 |
18.7 |
3.9 |
1,1 |
-2,8 |
| 16 |
July 25 |
41 |
41.4 |
0.4 |
13 |
30.2 |
17.2 |
3 |
1.5 |
-1.5 |
| 17 |
July 31 |
33 |
36 |
3 |
22 |
36.6 |
14.6 |
3.9 |
0.6 |
-3.3 |
| 18 |
July 26 |
42 |
39.8 |
-2.2 |
15 |
38.2 |
23.2 |
3 |
0.5 |
-2.5 |
| 19 |
July 29 |
34 |
34.5 |
0.5 |
23 |
37.3 |
14.3 |
5 |
1.2 |
-3.8 |
| 20 |
July 29 |
34 |
34.1 |
0.1 |
23 |
37.4 |
14.4 |
5 |
1 |
-4 |
| 21 |
July 29 |
34 |
35 |
1 |
23 |
34.5 |
11.5 |
5 |
1 |
-4 |
| 22 |
July 29 |
34 |
36 |
2 |
23 |
35.9 |
12.9 |
5 |
0.5 |
-4.5 |
| 23 |
July 29 |
34 |
35.4 |
1.4 |
23 |
35.4 |
12.4 |
5 |
1.5 |
-3.5 |
| 24 |
July 29 |
34 |
36.9 |
2.9 |
23 |
35.5 |
12.5 |
5 |
1.5 |
-3.5 |
| 25 |
July 29 |
34 |
35.4 |
1.4 |
23 |
34.5 |
11.5 |
5 |
1.2 |
-3.8 |
| 26 |
July 29 |
34 |
35.5 |
1.5 |
23 |
36.9 |
13.9 |
5 |
1 |
-4 |
| 27 |
July 29 |
34 |
34.9 |
0.9 |
23 |
34.1 |
11.1 |
5 |
1 |
-4 |
| 28 |
July 29 |
34 |
35.4 |
1.4 |
23 |
34.1 |
11.1 |
5 |
1.5 |
-3.5 |
| 29 |
July 29 |
34 |
35 |
1 |
23 |
35.9 |
12.9 |
5 |
1 |
-4 |
| 30 |
July 29 |
34 |
35.8 |
1.8 |
23 |
36.2 |
13.2 |
5 |
1 |
-4 |
t° (F) = temperature forecast, t° (M) = temperature measured, t° (Con) = temperature contrast between t° (F) and t° (M), RH% (F) = relative humidity forecast, RH% (M) = relative humidity measured, RH% (Con) = RH contrast between RH% (F) and RH% (M), W/S (F) = wind speed forecast, W/S (M) = wind speed measured, W/S (Con) = wind speed contrast between W/S (F) and W/S (M).
To illustrate the measured temperatures, Table 2 shows the comparison between the daily weather forecast using "AccuWeather" data and the actual climate data for July 2022. The data for June and August can be found in the supplementary materials. The measured climate conditions at most of the locations visited were generally warmer than the daily weather forecast data. The measured temperature at sites No. 2,3, 10, 14, 17, 23, 24, 25, for example, was high and the air was low in humidity. Thus, comparing differences in temperature, humidity, wind speed, and greening methods can provide insight into how many trees are needed to maintain a cool microclimate in public green spaces. The next section deals with the analysis and the results.
Results
Tables 1 and 2 summarize all the data collected. For the main data analysis, we chose linear regression because it can illustrate the clear correlation between variables. First, statistical data were compared between tree planting density and temperature (Figure 2). The coefficients of determination for June, July and August were relatively low, albeit positive (i.e. a cooler temperature is not necessarily guaranteed by more planted trees). However, the correlation is somewhat stronger in July, which is most likely due to a stronger temperature contrast during data collection, as July is the hottest month in Uzbekistan.
Next, we analyzed the contrasts between the measured and daily weather forecast data on temperature, and relative humidity with the tree canopy area. The higher the r² value, the stronger the correlation between two types of data.
Linear regression between the area of tree canopy and temperature contrast shows that the larger the area covered by tree canopy, the lower the temperature in the green space (Figure 3). Surprisingly, the highest coefficient of determination was observed in July with r²=0.9, while it was lower in June and August (r²=0.76 in June and r²=0.8 in August). The more precise correlation can be explained by the higher temperature contrast in July, when the temperature perception between unshaded and shaded areas (i.e. where the temperature was measured) was stronger. This suggests that under local climate conditions, trees with a broad canopy should play a critical role in creating a cool microclimate; junipers and grassy ground covers are not successful in lowering temperature (Figure 5).
The next figure shows a positive correlation between the relative humidity and the area of the canopy, i.e. the larger the area of the canopy, the more humid the air (Figure 4). However, when calculating the regression, the coefficients were low, with r²=0.2 for June, r²=0.16 for July and r²=0.39 for August.
These values can be explained by the following reasons. Firstly, although some sites had a high planting density, e.g. green area No. 3 with 300.8 units/ha and 85% green ground cover, the correlation between planting density and temperature values was low (Figure 2). This is because the junipers do not provide enough shade to lower the temperature (Figure 5). The tree canopy of small trees barely covered 33% of the green area No. 2. This is confirmed by previous research: smaller trees cool less and provide less shade in summer (McPherson, 1993). The wind speed was also insufficient to lower the temperature.
Several public green spaces with good microclimates were discovered at the selected sites. The measured temperatures were consistent with the daily weather forecast and sometimes even cooler. These sites had diverse vegetation with many trees that were at least seven meters tall (usually around 20 m). An important aspect of these sites is the intensive planting of large trees with a broad canopy (Figure 6). However, previous studies have shown that green spaces with dense vegetation are cooler during the day, but at night the dense canopy of trees can trap heat in the green space and lead to long-wave radiation losses (Jaganmohan, Knapp, et al. 2016).
Most of the trees with large crowns are species with relatively small leaves, such as sophoras, which could be one of several reasons why temperatures are cooler in these locations during the day. As previous studies have confirmed, the smaller the leaves, the lower the temperature in the crowns (Leuzinger, Vogt, et al., 2010). In addition, trees that provide more shade are important in hot and dry climates with high solar radiation (McPherson, 1993). For example, the crowns of sophoras at site No. 15 cover more than 75% of the area (Figure 6) and the temperature is lower there. Wu, Man, et al., (2022) come to a similar conclusion, the extent of tree canopy cover has a significant influence on the cooling of green spaces. At site No. 15, the way the trees are pruned and maintained creates an umbrella-like structure: the shape of the tree crowns helps to protect people from the sun, while the underside of the crowns at pedestrian height is ventilated by the wind (Figure 7). However, to release the heat that collects under the treetops at night, it is important to use a combination of planted trees with a broad canopy and open areas (Jaganmohan, Knapp, et al. 2016).
The results show that trees with a broad canopy successfully reduce the air temperature in green spaces. The r² values were low in green areas with a high planting density, which can be attributed to the planted tree species (e.g. Sophora) that do not provide shade. In addition, the temperature was higher in hotter places due to the hard surfaces used in landscaping. The next section discusses landscaping methods that promote cool microclimatic conditions in the green areas of Tashkent city.
Discussion and Conclusions
This study investigated the relationship between vegetation density and its cooling effect in the urban green spaces of Tashkent, which is new in the local geographical context. The field study helped to categorize tree species, green space structures and microclimatic data from selected sites and to understand their relationship to hot summer temperature. Seven specific green spaces: 1, 6, 9, 12, 13, 15, and 18 showed that the degree of cooling was comparable to, and sometimes cooler than, daily weather forecast temperatures. Results showed a high correlation between tree canopy area and lower temperatures, suggesting that a high number of shade trees can mitigate urban heat. However, planting density alone is not a panacea for alleviating the summer heat in Tashkent's urban green spaces. Factors such as a sufficient number of trees with a broad canopy, the strategic positioning of tall trees and the combination of shaded and open areas should be considered to dissipate the warm temperatures at night. Similar conclusions were found in a recent study (Wu, Man, et al., 2022). The results also show that the degree of cooling is weakly correlated with increasing planting density (Figure 2).
In addition, the results outline seven potentially comfortable green areas with similar or lower temperature values than the daily weather forecast in the summer season. These sites were selected as good examples with normal climatic conditions to evaluate the required number of planting densities and landscaping strategies for Tashkent.
To support part of the hypothesis we set two conditions: First, the proportion of tall trees with a planting density of at least 111 units/ha should cover more than 75 percent of the green area, which is sufficient to provide a comfortable temperature in summer through shading. At the same time, the shortest side of the green area should be about 100 meters. Second, if there are "shady" trees that are less than 7 meters high, with a planting density of about 775 units/ha, the tree canopy area should cover about 82% of the green area.
However, under both conditions, the tree canopy should cover more than 75% of the selected green area. These two proposals can be combined to create a cool green space development policy in Tashkent. The following diagram explains how certain policies can contribute to green space development (Figure 8).
This figure is divided into four steps. First, the required number of "shading" trees in each area should be calculated. Second, the area should be planned, zoned, and prepared for planting, with about 775 units/ha potentially providing about 82% of the shade for the entire area. If the trees have grown taller than 7 meters, a cool thermal microclimate will be created in the summer. If the trees have grown up to 20 meters tall, a large canopy will shade a larger area. Later, some of the trees could be removed to free up about 25% of the green space for commercial use. This is one of many ways to make this area financially self-sustaining. However, other options for the development of self-sustaining green spaces should also be considered. For example, it would be possible to increase the property tax for shops and businesses near cool parks and use this money to maintain the vegetation in green spaces. Similar to the previous study, the integration of the Green Open Space (GOS) indicator can also be useful for the preservation of green spaces in Tashkent (
Subadyo, Tutuko, et al., 2019).
As many cities suffer from a decreasing number of green spaces, these findings could be helpful in developing strategies to preserve urban greenery in Central Asian cities with similar climatic characteristics. According to Köppen climate zones, Tashkent is in the Mediterranean warm climate (Csa), where the average temperature in the warmest month is above 22 °C. However, there are many cities in the Csa zone with similar climatic conditions to Tashkent, such as Olmaliq, Samarkand and Jizzakh in Uzbekistan, Saryagash and Arys in Kazakhstan, and Orzu and Kulob in Tajikistan (Figure 9). Our findings could therefore also be transferable to the neighboring countries of Uzbekistan.
This study has several limitations. Although we properly maintained the instrument used to measure air temperature, heating sources near green spaces, such as moving cars on the roads, may affect the values of our data sets. We also did not use more complex methods to obtain more precise results. For example, it is possible to analyze urban greening using the leaf area index (LAI). LAI represents the ratio of total unilateral leaf area to ground area and is divided into direct and indirect types of methods (Bréda 2003). However, these two methods are still too limited, and LAI is a dimensionless quantity (Parker, 2020). Nevertheless, a recent study successfully estimated the LAI of peanut plants (Arachis hypogaea L.) using remote sensing (Sarkar, Cazenave, et al., 2021). This means that there is a potential to provide more accurate data on urban cooling islands in Central Asian cities. Future studies should include a larger proportion of green spaces in cities with similar climates and use more complex methods. The promotion of GIS software in combination with microclimatic simulations is highly recommended for the planning and design of cool green spaces for cities in Central Asia.
To the best of our knowledge, this is the first study to address the maintenance of a cool temperature in urban green spaces in the Central Asian region. Despite limitations, the study helps fill the gap in the existing literature on sustainable urban development through a relatively simple and inexpensive methodology. The sample size of thirty selected green spaces was sufficient to establish the strong correlation between temperature and tree canopy area. The objective of this study was to analyze the relationship between vegetation density and microclimate using a low-cost approach and to understand how to maintain the cool microclimate in public green spaces of Tashkent. At the pedestrian level, air temperature, relative humidity, and wind speed were measured during field surveys. The results extend the findings of most previous research that has looked at the cooling effect of green spaces in a city, both on a large and small scale.
The findings will help landscape planners and policy makers improve Tashkent's green structure and adjust urban design standards to manage urbanization in a sustainable way. Researchers who consider GIS and computer simulations a complicated method for assessing microclimatic conditions in urban green spaces may find this method valuable for an initial analysis. However, it is strongly recommended to use remote sensing techniques, microclimate modelling and LAI estimates for further investigations. Our observations suggest that vegetation with a broad canopy has a better chance of maintaining a cool microclimate in public green spaces. The possibility of implementing the proposed greening strategy in Central Asian regions was also discussed. These results can be useful to inform policy makers and urban planners on how green spaces in Tashkent should be designed to maintain a cool microclimate.
Author Contributions
Conceptualization, A. M. and D. I.; methodology, A. M; investigation, A. M.; data curation A.M and D.I.; writing - original draft preparation, A. M; writing – review & editing: A. M., D. I., and B.T; supervision D.I and B.T. All authors have read and agreed to the published version of the manuscript.
Ethics Declaration
The authors declare that they have no conflicts of interest regarding the publication of the paper.
Acknowledgments
The authors would like to thank their families, colleagues, and anonymous reviewers for their support, valuable comments, and suggestions. We believe that our research will inspire urban planners and policy makers to make cities in Uzbekistan more sustainable, vibrant, and resilient.
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Appendices
APPENDIX
Table 2. Climatic data from selected sites during June, July and August, 2022.
| №
|
Date
|
June
|
W/S (Con)
|
| t° (F)
|
t° (M)
|
t° (Con)
|
RH% (F)
|
RH% (M)
|
RH% (Con)
|
W/S (F)
|
W/S(M)
|
| 1
|
June 14 |
34 |
32.1 |
-1.9 |
20 |
32.4 |
12.4 |
2.2 |
0.5 |
-1.7 |
| 2
|
June 14 |
34 |
36.3 |
2.3 |
20 |
27.3 |
7.3 |
2.2 |
0.1 |
-2.1 |
| 3
|
June 14 |
34 |
37 |
3 |
20 |
28.3 |
8.3 |
2.2 |
0.4 |
-1.8 |
| 4
|
June 14 |
36 |
37.7 |
1.7 |
18 |
29.5 |
11.5 |
3.9 |
1.2 |
-2.7 |
| 5
|
June 14 |
36 |
37 |
1 |
17 |
27.4 |
10.4 |
3.9 |
1.6 |
-2.3 |
| 6
|
June 14 |
35 |
35 |
0 |
17 |
29.5 |
12.5 |
3.9 |
1.4 |
-2.5 |
| 7
|
June 14 |
34 |
35 |
1 |
20 |
30.1 |
10.1 |
2.2 |
1.1 |
-1.1 |
| 8
|
June 14 |
35 |
36.3 |
1.3 |
17 |
29.6 |
12.6 |
3.9 |
1.2 |
-2.7 |
| 9
|
June 16 |
35 |
33.1 |
-1.9 |
19 |
38.9 |
19.9 |
3.9 |
0.5 |
-3.4 |
| 10
|
June 16 |
36 |
38.3 |
2.3 |
21 |
34 |
13 |
3 |
0.3 |
-2.7 |
| 11
|
June 16 |
35 |
36.9 |
1.9 |
19 |
34.4 |
15.4 |
3.9 |
0.4 |
-3.5 |
| 12
|
June 16 |
35 |
34.6 |
-0.4 |
19 |
35.5 |
16.5 |
3.9 |
1.5 |
-2.4 |
| 13
|
June 16 |
35 |
34.1 |
-0.9 |
19 |
34.6 |
15.6 |
3.9 |
1.8 |
-2.1 |
| 14
|
June 16 |
35 |
39.1 |
4.1 |
19 |
29.2 |
10.2 |
3.9 |
1 |
-2.9 |
| 15
|
June 18 |
35 |
32.2 |
-2.8 |
14 |
35.9 |
21.9 |
2.2 |
0.8 |
-1.4 |
| 16
|
June 18 |
35 |
35 |
0 |
15 |
27.1 |
12.1 |
3 |
1.2 |
-1.8 |
| 17
|
June 18 |
35 |
37.7 |
2.7 |
15 |
25.7 |
10.7 |
3 |
0.7 |
-2.3 |
| 18
|
June 18 |
35 |
34.2 |
-0.8 |
14 |
35 |
21 |
3 |
1.1 |
-1.9 |
| 19
|
June 24 |
36 |
36.6 |
0.6 |
23 |
40.7 |
17.7 |
3 |
0.8 |
-2.2 |
| 20
|
June 24 |
36 |
37.3 |
1.3 |
23 |
45.2 |
22.2 |
3 |
0.8 |
-2.2 |
| 21
|
June 24 |
35 |
34.1 |
-0.9 |
26 |
35.9 |
9.9 |
3 |
0.8 |
-2.2 |
| 22
|
June 24 |
35 |
36.3 |
1.3 |
26 |
41.1 |
15.1 |
3 |
1 |
-2 |
| 23
|
June 24 |
35 |
36.4 |
1.4 |
26 |
41 |
15 |
3 |
1 |
-2 |
| 24
|
June 24 |
35 |
39.9 |
4.9 |
26 |
28.1 |
2.1 |
3 |
0.6 |
-2.4 |
| 25
|
June 24 |
35 |
37.5 |
2.5 |
26 |
41.6 |
15.6 |
3 |
0.5 |
-2.5 |
| 26
|
June 24 |
35 |
36.3 |
1.3 |
26 |
41.6 |
15.6 |
3 |
1 |
-2 |
| 27
|
June 24 |
35 |
36.5 |
1.5 |
26 |
32.1 |
6.1 |
3 |
0.6 |
-2.4 |
| 28
|
June 24 |
35 |
35.6 |
0.6 |
26 |
44.4 |
18.4 |
3 |
0.8 |
-2.2 |
| 29
|
June 24 |
35 |
36.3 |
1.3 |
26 |
36.6 |
10.6 |
3 |
0.6 |
-2.4 |
| 30
|
June 24 |
35 |
36.3 |
1.3 |
26 |
37.4 |
11.4 |
3 |
0.6 |
-2.4 |
| №
|
Date
|
July
|
W/S (Con)
|
| t° (F)
|
t° (M)
|
t° (Con)
|
RH% (F)
|
RH% (M)
|
RH% (Con)
|
W/S (F)
|
W/S(M)
|
| 1 |
July 19 |
34 |
31.8 |
-2.2 |
27 |
36.5 |
9.5 |
3 |
0.5 |
-2.5 |
| 2 |
July 20 |
37 |
37.7 |
0.7 |
18 |
31.4 |
13.4 |
3.9 |
0.4 |
-3.5 |
| 3 |
July 20 |
37 |
39.7 |
2.7 |
18 |
31.4 |
13.4 |
3.9 |
0.7 |
-3.2 |
| 4 |
July 25 |
41 |
40.5 |
-0.5 |
13 |
37.9 |
24.9 |
3.9 |
0.8 |
-3.1 |
| 5 |
July 20 |
37 |
36.3 |
-0.7 |
18 |
34 |
16 |
3.9 |
0.8 |
-3.1 |
| 6 |
July 20 |
37 |
35.6 |
-1.4 |
18 |
39.1 |
21.1 |
3.9 |
1 |
-2.9 |
| 7 |
July 19 |
34 |
35 |
1 |
27 |
35 |
8 |
3 |
1 |
-2 |
| 8 |
July 26 |
42 |
42.5 |
0.5 |
15 |
29 |
14 |
3 |
1 |
-2 |
| 9 |
July 25 |
41 |
38.7 |
-2.3 |
13 |
37.4 |
24.4 |
3.9 |
0.5 |
-3.4 |
| 10 |
July 26 |
41 |
43.2 |
2.2 |
18 |
29.5 |
11.5 |
3 |
1 |
-2 |
| 11 |
July 26 |
41 |
42.5 |
1.5 |
15 |
30.1 |
15.1 |
2.2 |
0.8 |
-1.4 |
| 12 |
July 26 |
41 |
40.4 |
-0.6 |
15 |
29 |
14 |
2.2 |
0.5 |
-1.7 |
| 13 |
July 22 |
41 |
39 |
-2 |
14 |
29.4 |
15.4 |
2.2 |
1 |
-1.2 |
| 14 |
July 20 |
37 |
39.9 |
2.9 |
18 |
32 |
14 |
3.9 |
1.8 |
-2.1 |
| 15 |
July 31 |
33 |
31.2 |
-1.8 |
22 |
40.7 |
18.7 |
3.9 |
1.1 |
-2.8 |
| 16 |
July 25 |
41 |
41.4 |
0.4 |
13 |
30.2 |
17.2 |
3 |
1.5 |
-1.5 |
| 17 |
July 31 |
33 |
36 |
3 |
22 |
36.6 |
14.6 |
3.9 |
0.6 |
-3.3 |
| 18 |
July 26 |
42 |
39.8 |
-2.2 |
15 |
38.2 |
23.2 |
3 |
0.5 |
-2.5 |
| 19 |
July 29 |
34 |
34.5 |
0.5 |
23 |
37.3 |
14.3 |
5 |
1.2 |
-3.8 |
| 20 |
July 29 |
34 |
34.1 |
0.1 |
23 |
37.4 |
14.4 |
5 |
1 |
-4 |
| 21 |
July 29 |
34 |
35 |
1 |
23 |
34.5 |
11.5 |
5 |
1 |
-4 |
| 22 |
July 29 |
34 |
36 |
2 |
23 |
35.9 |
12.9 |
5 |
0.5 |
-4.5 |
| 23 |
July 29 |
34 |
35.4 |
1.4 |
23 |
35.4 |
12.4 |
5 |
1.5 |
-3.5 |
| 24 |
July 29 |
34 |
36.9 |
2.9 |
23 |
35.5 |
12.5 |
5 |
1.5 |
-3.5 |
| 25 |
July 29 |
34 |
35.4 |
1.4 |
23 |
34.5 |
11.5 |
5 |
1.2 |
-3.8 |
| 26 |
July 29 |
34 |
35.5 |
1.5 |
23 |
36.9 |
13.9 |
5 |
1 |
-4 |
| 27 |
July 29 |
34 |
34.9 |
0.9 |
23 |
34.1 |
11.1 |
5 |
1 |
-4 |
| 28 |
July 29 |
34 |
35.4 |
1.4 |
23 |
34.1 |
11.1 |
5 |
1.5 |
-3.5 |
| 29 |
July 29 |
34 |
35 |
1 |
23 |
35.9 |
12.9 |
5 |
1 |
-4 |
| 30 |
July 29 |
34 |
35.8 |
1.8 |
23 |
36.2 |
13.2 |
5 |
1 |
-4 |
| №
|
Date
|
August
|
W/S (Con)
|
| t° (F)
|
t° (M)
|
t° (Con)
|
RH% (F)
|
RH% (M)
|
RH% (Con)
|
W/S (F)
|
W/S(M)
|
| 1
|
August 09 |
34 |
31.4 |
-2.6 |
19 |
40.4 |
21.4 |
3.9 |
1.1 |
-2.8 |
| 2
|
August 04 |
34 |
34.1 |
0.1 |
19 |
31 |
12 |
3.6 |
0.6 |
-3 |
| 3
|
August 04 |
34 |
34.8 |
0.8 |
19 |
32.5 |
13.5 |
3.6 |
0.6 |
-3 |
| 4
|
August 04 |
34 |
33.3 |
-0.7 |
23 |
44 |
21 |
2.2 |
0.8 |
-1.4 |
| 5
|
August 05 |
35 |
34.1 |
-0.9 |
17 |
35.7 |
18.7 |
3.9 |
0.9 |
-3 |
| 6
|
August 05 |
35 |
33.4 |
-1.6 |
17 |
34.3 |
17.3 |
3.9 |
0.9 |
-3 |
| 7
|
August 09 |
34 |
34.8 |
0.8 |
19 |
31.5 |
12.5 |
3.9 |
0.8 |
-3.1 |
| 8
|
August 04 |
34 |
34.1 |
0.1 |
23 |
32.3 |
9.3 |
2.2 |
0.3 |
-1.9 |
| 9
|
August 04 |
34 |
31 |
-3 |
23 |
43.7 |
20.7 |
2.2 |
1.2 |
-1 |
| 10
|
August 09 |
34 |
35.5 |
1.5 |
17 |
30.1 |
13.1 |
5 |
0.5 |
-4.5 |
| 11
|
August 09 |
34 |
34 |
0 |
17 |
32.7 |
15.7 |
5 |
1.5 |
-3.5 |
| 12
|
August 09 |
34 |
32.7 |
-1.3 |
17 |
34.3 |
17.3 |
5 |
1.2 |
-3.8 |
| 13
|
August 09 |
34 |
32.8 |
-1.2 |
19 |
31.5 |
12.5 |
3.9 |
1.2 |
-2.7 |
| 14
|
August 09 |
34 |
36.8 |
2.8 |
19 |
29 |
10 |
3.9 |
0.3 |
-3.6 |
| 15
|
August 09 |
34 |
31.3 |
-2.7 |
19 |
39.3 |
20.3 |
3.9 |
0.5 |
-3.4 |
| 16
|
August 04 |
34 |
34 |
0 |
20 |
34 |
14 |
3.6 |
1.5 |
-2.1 |
| 17
|
August 09 |
34 |
35.5 |
1.5 |
17 |
32 |
15 |
5 |
1 |
-4 |
| 18
|
August 14 |
37 |
35.4 |
-1.6 |
19 |
34 |
15 |
3 |
0.5 |
-2.5 |
| 19
|
August 14 |
37 |
36.8 |
-0.2 |
18 |
35.4 |
17.4 |
3 |
1 |
-2 |
| 20
|
August 14 |
37 |
35.8 |
-1.2 |
18 |
37.4 |
19.4 |
3 |
1 |
-2 |
| 21
|
August 14 |
36 |
36.1 |
0.1 |
21 |
34 |
13 |
3 |
0.3 |
-2.7 |
| 22
|
August 14 |
36 |
36.4 |
0.4 |
21 |
32.5 |
11.5 |
3 |
0.5 |
-2.5 |
| 23
|
August 14 |
36 |
37.5 |
1.5 |
21 |
29.3 |
8.3 |
3 |
0.5 |
-2.5 |
| 24
|
August 14 |
36 |
37.5 |
1.5 |
21 |
29 |
8 |
3 |
0.5 |
-2.5 |
| 25
|
August 14 |
37 |
37.5 |
0.5 |
20 |
30 |
10 |
3 |
0.3 |
-2.7 |
| 26
|
August 14 |
37 |
37.1 |
0.1 |
20 |
32.6 |
12.6 |
3 |
0.5 |
-2.5 |
| 27
|
August 14 |
37 |
37 |
0 |
20 |
32.4 |
12.4 |
3 |
0.5 |
-2.5 |
| 28
|
August 14 |
37 |
37 |
0 |
20 |
32.5 |
12.5 |
3 |
1 |
-2 |
| 29
|
August 14 |
37 |
38 |
1 |
20 |
30 |
10 |
3 |
1 |
-2 |
| 30
|
August 14 |
37 |
38 |
1 |
20 |
30 |
10 |
3 |
1 |
-2 |
