Due to the high population density of more than twenty-one million, rapid urbanization and environmental degradation are some of the big problems that Dhaka faces today ( Dewan et al., 2012 ). Green areas in the urban area represent less than 20% of the total land area and, as a result, promote the UHI that affects the thermal comfort of the city ( Zhou et al., 2023 ). Of all the zones in Dhaka, Dhanmondi has shown a drastic change in land use over the past few years alone ( Hasan & Reza, 2022 ). Dhanmondi, the 49-no. Ward under the Dhaka South City Corporation (DSCC), is the densely populated residential neighbourhood in the Dhaka Metropolitan Area ( Nancy & Hafiz, 2023 ). A unique characteristic of this area is the Dhanmondi Lake, which strictly occupies 16% of Dhanmondi’s entire land, still is a primary concern for inhabitants’ essential needs and for changing local microclimate ( Sharmeen, 2014 ). Nowadays, Dhanmondi Lake also has an important role in controlling temperature and humidity levels in the area ( Sharmeen, 2014 ).

Dhanmondi has features typical for an urban environment, such as paved areas and some features of the natural environment, especially near the barrier of the Dhanmondi Lake; therefore, the area seems to be suitable for investigating how urban green infrastructures affect the regulation of microclimates.

The study focused on a 1-kilometer neighboring area of Dhanmondi Lake where both greenspace and concrete landscapes are present. The two roads, across the lake and along the lake, were selected to observe the details of microclimatic change and highlight contrasts in greenspace and concrete coverage, as shown in Figure 1 . The monitoring points, detailed in Table 1 , between 20 – 618 meters across the lake and within 72 meters along the lake, were selected to capture the varying effects of proximity to the lake on temperature and humidity regulation. The spatial arrangement of these points is illustrated in Figure 2 , providing a clear

depiction of the study layout. Points along the lake, dominated by greenspaces, and points across the lake, reflecting a mix of urban settlements and sparse greenery, allowed for a clear differentiation of microclimatic effects. The zoomed-out and zoomed-in views of the study area, shown in Figure 3 , further highlight the distinction between these two areas. The points across the lake, such as Point 7 near the lake and Point 1 toward settlement, as well as points along the lake, were crucial in this study for understanding whether there is any impact that greenspace creates in the urban environment or not.

This study used a spatial-diurnal approach to measure the temperature and humidity at 14 monitoring points by GPS tracking at different distances from Dhanmondi Lake. Data were collected twice daily for two months (March and April 2024) using the Humidity & Temperature Meter AR827. The AR827 is a hand-held relative humidity and temperature meter that can measure temperature from −20˚C to 60˚C and relative humidity (RH) from 0% to 100% at the same time, which is suitable for indoor-outdoor uses and also very high-low humid environments. Distances from the lake were calculated using Google My Maps.

Temperature and humidity were selected as microclimate variables due to their sensitivity to land cover changes and relevance in assessing urban thermal and moisture dynamics ( Imran et al., 2021 ). Data collection times were consistent across all days to ensure comparability, with 9:00 AM and 9:00 PM chosen to capture typical diurnal microclimatic variations in Dhaka’s urban environment ( Lam et al., 2023 ). March and April months were selected as the pre-monsoon period in Dhaka, marked by high temperatures and low rainfall, providing ideal conditions to observe urban heat and microclimatic impacts of greenspaces and concrete landscapes ( Tabassum et al., 2024 ).

Data analysis was carried out through descriptive statistics such as Mean, Standard Deviation, and Median to synthesize microclimate variability amongst different locations using IBM SPSS Statistics 25 and Microsoft Excel. Various types of charts were prepared based on those data. An inferential statistical analysis, Pearson´s Correlation Coefficient, was performed to test the association between distance from the lake and microclimate variables regarding temperature and humidity, as illustrated in the methodological framework of Figure 4 . The spatial analysis was implemented in ArcMap 10.5 by using the interpolation technique named Inverse Distance Weighting (IDW) to map temperature and humidity fluctuations in the study area and present a realistic view of how lake proximity operates on local climate conditions in detail. In the end, a policy and recommendation framework is given based on the findings from the results to improve the thermal comfort condition in urban environments in a sustainable manner.

Field measurements were conducted at 14 locations (7 across the lake and 7 along the lake) during both daytime (9:00 AM) and nighttime (9:00 PM) over two months (March and April). The variables of interest were temperature (˚C) and relative humidity (%). The values were averaged to reduce noise and account for temporary fluctuations. The results are summarized in Table 2(a)-(d) , which presents the spatial (across vs. along the lake) and diurnal (day vs. night) microclimate variations.

Descriptive statistics were computed for each dataset: Maximum and Minimum values to identify the range of microclimatic conditions, Mean values to represent the average temperature and humidity among the locations, Median values to understand the central tendency of the data distribution, and Standard Deviation to quantify the variability in microclimate conditions across the study area.

Microclimate Variation Across the Lake

In the daytime, temperature values across the lake ranged from 25.61˚C to 27.13˚C, with a mean temperature of 26.60˚C and a standard deviation of 0.49˚C. Humidity levels ranged from 69.69% to 74.70%, with a mean of 71.96% and a standard deviation of 1.70%. During nighttime, the temperature varied between 28.22˚C and 28.83˚C, where the mean was 28.46˚C, and humidity levels ranged from 65.21% to 65.76%, where the mean was 65.44%.

Microclimate Variation Along the Lake

The daytime temperature ranged from 26.43˚C to 26.60˚C, with a mean of 26.50˚C and a low standard deviation of 0.07˚C, indicating minimal temperature fluctuation along the lakeside. Humidity levels showed a similar narrow range, with values between 71.92% and 72.21%, and a mean of 72.05%. During nighttime, the temperature varied between 28.50˚C and 28.64˚C, where the mean was 28.60˚C, and humidity levels ranged from 65.42% to 65.57%, where the mean was 65.52%.

The following charts, shown in Figure 5(A)-(D) , illustrate key insights into how temperature and humidity vary across and along Dhanmondi Lake.

Daytime Temperature Variation Across the Lake: A clear cooling gradient is observed from 27.13˚C at Point 1 to 25.61˚C at Point 7. Temperatures near the lake edge decrease significantly throughout each day.

Nighttime Temperature Stability Across the Lake: Nighttime temperatures are stable across all points, with mean values varying narrowly between 28.22˚C and 28.83˚C and less cooling effect during the night.

Temperature Stability Along the Lake (both Day and Night): There is only minimal fluctuation in temperature during both day and night. Daytime mean temperatures fluctuate between 26.43˚C and 26.60˚C, coupled with almost constant nighttime temperatures ranging from 28.50˚C to 28.64˚C.

Humidity Increase Across the Lake During Daytime: The daytime humidity rises slightly from 69.69% at Point 1 to 74.70% at Point 7, paralleling the lake’s cooling effect.

Nighttime Humidity Stability Across the Lake: Nighttime humidity levels are similar, varying from 65.21% at Point 1 to a maximum of 65.76% at Point 7. Despite the lake’s proximity, humidity fluctuates less than during the day.

Consistent Humidity Along the Lake (both Day and Night): Daytime and nighttime humidity levels remain stable throughout the lake. Daytime humidity varies from 71.92% to 72.21%, while nighttime values vary narrowly between 65.42% and 65.57%.

The spatial variation of temperature and humidity in the study area, as depicted in Figure 6 (A) & (B), is more pronounced during the day compared to nighttime, particularly across the lake. The daytime temperature across the lake shows the highest variability, with a standard deviation of 0.49˚C, as illustrated in Figure 6(A) , while humidity variability is also significantly higher during the day, with a standard deviation of 1.70%, as shown in Figure 6(B) . Conversely, nighttime variations in both parameters are generally lower across the lake, suggesting a more consistent environment after sunset. However, temperature and humidity variations are minimal along the lake during both day and night, with standard deviations remaining below 0.08˚C for temperature and 0.12% for humidity, indicating a stable microclimate near the lake.

During the day, the median temperature is higher across the lake, at 26.60˚C, compared to areas along the lake, where it is 26.48˚C, as depicted in Figure 7(A) , while the opposite pattern is observed at night, with higher median temperatures along the lake, at 28.60˚C, than across the lake, where it is 28.46˚C, as shown in Figure 7(B) . In terms of humidity, daytime median humidity is slightly higher along the lake, reaching 72.02%, compared to 71.91% across the lake, as illustrated in Figure 7(C) , and the same pattern is observed at night, where the median humidity along the lake at 65.52%, exceeding the 65.44% recorded across the lake, as shown in Figure 7(D) . These variations, visualized in Figure 7 (A)-(D) , suggest that the lake has a more stabilizing influence on the temperature at night and contributes to higher humidity levels along its immediate vicinity throughout the day and night ( Zhao et al., 2023 ).

Pearson’s Correlation Coefficient was utilized to examine the significance of the data on the association between distance from Dhanmondi Lake and microclimate factors in terms of temperature and humidity, as summarized in Table 3 (A) & (B), Table 4 (A) & (B), Table 5 (A) & (B), and Table 6 (A) & (B).

Daytime Microclimate Regulation Across the Lake

Temperature Regulation: Pearson correlation coefficient: 0.933 (p = 0.002), a marked positive correlation, as shown in Table 3(A) . The temperature increases with distance, allowing the lake to have a cooling effect ( Zhou et al., 2021 ).

Humidity Regulation: Pearson correlation coefficient: −0.993 (p = 0.000), strong negative association outlined in Table 3(B) . The further away from the lake, the lower humidity values are noticed, emphasizing how much the lake supports high humidity in neighboring areas ( Zhou et al., 2021 ).

Nighttime Microclimate Regulation Across the Lake

**. Correlation is significant at the 0.01 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Temperature Regulation: Pearson correlation: −0.983 (p = 0.000), high negative relationship, as shown in Table 4(A) . Nighttime temperatures decrease with distance from the lake, which suggests that heat is retained near the surface ( Cosgrove & Berkelhammer, 2018 ).

Humidity Regulation: Pearson correlation: −0.977 (p = 0.000), substantial negative link outlined in Table 4(B) . Further inland humidity is less as the distance from the lake increases, revealing how the lake boosts humidity at night in its vicinity ( Chakraborty et al., 2023 ).

Daytime Microclimate Regulation Along the Lake

Temperature Regulation: Pearson correlation: 0.957 (p = 0.001), demonstrating a strong positive correlation outlined in Table 5(A) . As one moves farther along the lake, daytime high temperatures increase slightly, keeping it more relaxed near the water ( Piccolroaz et al., 2024 ).

**. Correlation is significant at the 0.01 level (2-tailed).

Humidity Regulation: Pearson correlation: −0.895 (p = 0.007), a strong and negative relationship, as shown in Table 5(B) . Lowering humidity during the day with increasing distance from the lake is one key aspect in increasing daytime humidity around its periphery ( Gunawardena et al., 2017 ).

Nighttime Microclimate Regulation Along the Lake

**. Correlation is significant at the 0.01 level (2-tailed).

Temperature Regulation: Pearson correlation: −0.957 (p = 0.001), strong negative correlation outlined in Table 6(A) . Further inland, it is colder at night because the lake gives off heat and warms its vicinity ( Jacobs et al., 2020 ).

Humidity Regulation: Pearson correlation: −0.953 (p = 0.001), indicating an inverse strong relationship, as shown in Table 6(B) . Nighttime humidity decreases with increasing distance from the lake, reflecting that the lake’s moisture levels rise only at night ( Ayanlade, 2016 ).

Dhanmondi Lake helps regulate temperatures and humidity in its vicinity, creating a balanced microclimate in the urban area. The visualized data, presented in Figure 8 (A) & (B) and Figure 9 (A) & (B), demonstrates how the distance from the lake affects local climate conditions.

Daytime Temperature and Humidity

Being close to the lake means it is colder most of the day, with a more humid environment. Areas near the waterbody experience a strong cooling effect, which cools the local atmosphere and raises humidity, contrasting with hotter temperatures and lower humidity farther inland. Figure 8(A) depicts these daytime temperature variations, while Figure 9(A) illustrates the daytime humidity variations.

Nighttime Temperature and Humidity

The lake stabilizes temperature and humidity overnight. Due to the water’s heat storage capacity, areas around the lake stay warmer, preventing sudden temperature drops. Lakeside humidity is also somewhat greater, indicating the lake’s nighttime impact on moisture levels. Figure 8(B) represents these nighttime temperature variations, and Figure 9(B) shows the nighttime humidity variations.