[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Article in Journal
Dynamic Response of Vegetation Net Primary Productivity to Climate and Human Impacts in Mining-Dominated Basin in Inner Mongolia, China
Previous Article in Journal
Estimating the Effects of Climate Fluctuations on Precipitation and Temperature in East Africa
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 15
Issue 12
10.3390/atmos15121456
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analyzing the Cooling Effects of Water Facilities in Urban Park: The Case of Sangju Namsan Park, South Korea

by
Young-Shin Lim
1,
Hyunmin Daniel Zoh
2,
Tae Hyoung Kim
1 and
Tae Kyung Kwon
3,*
1
Korea Environment Institute, 370 Sicheong-daero, Sejong 30147, Republic of Korea
2
College of Urban Planning and Public Affairs, University of Illinois Chicago, Chicago, IL 60607, USA
3
AIRPLE Co., Ltd., Hwaseong-si 18479, Republic of Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(12), 1456; https://doi.org/10.3390/atmos15121456
Submission received: 4 November 2024 / Revised: 27 November 2024 / Accepted: 3 December 2024 / Published: 5 December 2024
(This article belongs to the Section Biometeorology and Bioclimatology)
Browse Figures
Figure 1
<p>Locations for measuring devices and aerial view.</p> "> Figure 2
<p>(<b>a</b>) View of smart park from north; (<b>b</b>) view of smart park from south side; (<b>c</b>) park central zone ④; (<b>d</b>) water path ③; and (<b>e</b>) pergola ⑤(with cooling fog). AWS Location: ③④⑤.</p> "> Figure 3
<p>(<b>a</b>) Park central zone; (<b>b</b>) small water path; (<b>c</b>) pergola; and (<b>d</b>) overlook of small water path.</p> "> Figure 4
<p>(<b>a</b>) Comparison of hourly air temperature measurements; and (<b>b</b>) comparison of hourly humidity measurements.</p> "> Figure 5
<p>Comparison of hourly UTCI values during 26–27 August.</p> "> Figure 6
<p>Comparison of hourly air temperature during 26–27 August.</p> "> Figure 7
<p>Comparison of hourly PET values during 26–27 August.</p> "> Figure 8
<p>(<b>a</b>) Sangju daily park usage (9 August–7 September); (<b>b</b>) Sangju hourly park usage (9 August–7 September).</p> "> Figure 9
<p>Correlation between the number of observed people and measured climate data: (<b>left</b>) temperature; (<b>middle</b>) humidity; and (<b>right</b>) solar and UVI.</p> ">
Versions Notes

Abstract

:
This study evaluates the cooling effects of small-scale water features and fog systems in Sangju Namsan Park, South Korea, focusing on their impact on thermal comfort. While previous studies have demonstrated the potential of urban parks in reducing temperatures, studies on small-scale interventions that examine their effects on thermal comfort and analyze microclimate data collected in specific areas are limited. This study collected and analyzed microclimate data using the Universal Thermal Climate Index (UTCI) and physiological equivalent temperature (PET) to assess the effectiveness of a small water path and a cooling fog system. The results indicate that surface temperature reductions reached up to 1.1 °C, with the pergola area showing the most significant cooling effect, lowering PET values to an average of 36.2 °C. In contrast, the small water path recorded the highest PET values, peaking at nearly 50.2 °C, likely due to radiant heat from the surrounding surfaces. While these interventions provided localized cooling, their overall effect on urban temperature reduction remained modest. This study suggests that small-scale water features are effective in enhancing thermal comfort in neighborhood parks but must be integrated into broader urban cooling strategies to maximize their impact.

1. Introduction

With the increasing focus on climate change, various local governments are implementing climate adaptation measures in urban parks and public spaces [1]. In particular, various water spaces and facilities, which can reduce temperatures on hot summer days, have been introduced [2,3]. Climate change has significant impacts on the health of urban residents; urban parks and green spaces are crucial for responding to these challenges [4]. Parks help to stabilize the local climate by purifying the air and provide essential shade that mitigates the urban heat island effect [5,6]. As the importance of addressing climate change grows, urban parks are increasingly seen not just as spaces for public recreation [7,8], but also as vital areas for improving urban thermal environments and effectively removing air pollutants such as fine particulate matter [9,10].
Previous studies have primarily focused on examining the effects of urban parks on temperature reduction. For example, research by Cohen et al. (2012) demonstrated how green urban spaces in Mediterranean climates reduce local temperatures, while similar studies in South Korea, such as those by Lee et al. (2009), emphasize the cooling impact of urban parks in densely populated areas [4,11]. What differentiates the present study is its focus on small-scale interventions in urban parks where space and resource constraints limit large-scale solutions. This study provides new insights into the effectiveness of water facilities in enhancing thermal comfort in neighborhood parks, offering practical solutions for cities with limited space or resources. Research on the temperature reduction effects of urban parks and changes in meteorological measurements has been conducted across various regions and scales, both in South Korea and internationally. In South Korea, studies, such as those by Kim et al. (2010) and Choi et al. (2012), have analyzed temperature changes before and after park construction, highlighting the cooling effects of urban green spaces on the surrounding environment [12,13]. The studies analyzed temperature changes before and after park construction, examined how park size and conditions affected temperature reduction, compared thermal comfort indices between forested areas and lawns, and assessed the impact of parks on fine dust reduction. Several studies in various countries have explored the temperature reduction effects of urban parks and green spaces during summer. For instance, Chang et al. (2007) examined the cooling effects of parks in Taipei, and Yan et al. (2018) studied the impact of large urban parks in China on local thermal environments [14,15]. These studies investigated the complex effects of parks on thermal comfort and the urban heat island effect from various perspectives, with most finding that parks reduce temperatures by between 0.8 °C and 3.8 °C. For instance, a 1.12 ha green space was found to reduce temperatures by 0.78 °C within a 90 m radius; some studies reported effects ranging from 5 °C to 7 °C, indicating that the size of the green space greatly influences its cooling effect [4,11,14,15]. As shown by the results of the above studies, the extent to which urban parks reduce surrounding temperatures varies depending on factors such as the park’s canopy cover, size, and shape. This highlights the need to consider these elements in the planning and design of urban parks to mitigate the heat island effect exacerbated by climate change. Most prior studies have focused on the macro scale, using simulation techniques to estimate the effects of thermal environment improvement rather than measuring the actual effects. For example, studies by Du et al. (2016) and George et al. (2006) employed simulations to assess cooling effects without directly measuring temperature reductions in real-world conditions [16,17]. Additionally, most studies have analyzed large parks, with few examining the effects of small parks. Although some studies have investigated the effects of water spaces and climate adaptation facilities within parks, few have examined the impact of water spaces or facilities on the surrounding thermal environment. In contrast to these larger-scale studies, our research focuses on small-scale interventions, such as water paths and fog systems in smaller parks, offering solutions for cities where space is limited.
The focus of this study is on small-scale interventions in neighborhood parks, where space and resource constraints often limit large-scale solutions. This study examines the effects of water facilities, such as a water path and a cooling fog system, on the thermal environment of Sangju Namsan Park. These interventions are part of a broader environmental adaptation project aimed at enhancing thermal comfort in compact urban spaces. Using microclimate data collected from sensors, this research explores how these water features influence air temperature and perceived thermal comfort, addressing a gap in the literature on small-scale climate adaptation measures.

2. Previous Studies

The role of urban parks in mitigating urban heat and improving thermal comfort has been the subject of many studies, with research demonstrating that green spaces can reduce local temperatures by 0.8 °C to 3.8 °C, and, in some cases, by as much as 5 °C to 7 °C in large parks [4,15,18]. The results and methods varied according to the context of the study. For example, in South Korea, studies have focused on the temperature reduction effects of urban parks, examining differences in thermal conditions before and after park construction [4,12]. Internationally, many studies have explored how park size, canopy cover, and proximity influence cooling effects, with findings consistently showing that larger parks, or those with dense vegetation, provide greater temperature reductions [4,16,18]. Some studies also highlight the role of green spaces in fine dust reduction and improving public health outcomes [19,20,21,22,23]. These studies consistently show that parks significantly reduce temperatures and fine dust; however, the extent and range of the temperature reductions vary depending on the park’s location, size, measurement methods, and duration.
Research on the impact of park size on urban temperature reduction has been conducted using various methodologies. For example, a study that focused on Ilsan New Town, and analyzed the effect of park size on temperature reduction, offered insights for park planning to mitigate the urban heat island effect [24]. This study explored the minimum size of parks and the spatial impact on temperature reduction, seeking to demonstrate the ways in which parks can positively influence the urban environment. Another research method is to use satellite images. One study analyzed satellite imagery to evaluate the impact of green spaces on reducing urban temperatures, extracting surface temperatures comparing them to air temperatures in order to observe how temperatures changed within 500 m of green spaces [25]. Several studies have recently been conducted on securing carbon sinks and carbon reduction strategies pertaining to park and green space management [26,27,28]. A study in northeast China investigated differences in outdoor thermal comfort in different spatial types [29].
Despite this robust body of research, most studies have concentrated on large-scale parks and green spaces, employing macro-scale simulations methods rather than using site-specific measured data. For example, one research held in Xiamen City applied simulation models to suggest the proper locations for choosing green roofs [30]. Despite the extensive amount of research on the role of parks in thermal regulation, there is limited knowledge on the effectiveness of small-scale interventions, such as water paths and cooling fog systems, in neighborhood parks with spatial and resource constraints. Most prior studies focus on large urban parks, rely on simulation methods, or use satellite images, overlooking the potential of small-scale features to enhance thermal comfort in localized settings and monitoring micro-scale climate data measured from the specific site.
In particular, it is important to understand the heat mitigation effects of water elements, applying micro-scale methods. Studies examining the effects of urban green and blue infrastructure, such as trees and water elements, have covered various regions. Many studies reveal that parks, mostly green infrastructure, could reduce temperatures by between 0.8 °C and 3.8 °C, with some reporting reductions as high as 5 °C to 7 °C. These cooling effects extend beyond park boundaries and improve surrounding urban environments during summer months [21,31]. In line with previous studies, one study demonstrated that a small green space of 1.12 ha had a cooling distance of 90 m and reduced temperatures by 0.78 °C, highlighting how park size significantly influences cooling effects [16]. However, water elements (e.g., fountains and ponds) can play critical roles in thermal reduction. For instance, one study noted that water bodies reduce temperatures primarily through evaporation, while tree canopy helps to mitigate heat by providing shade and enhancing evapotranspiration [12,16]. The cooling effect of green spaces varies according to the climate, urban form, and land cover types, making it essential to conduct measurements that are tailored to the specific conditions of each area. In order to understand the heat mitigation effects of water elements to nearby green spaces, it is important to measure the data on site and analyze long-term data measured at the micro scale. Therefore, this paper aims to address these gaps by directly measuring the cooling effects of small-scale water features and fog systems in a real-world setting, using comprehensive metrics such as the Universal Thermal Climate Index (UTCI) and the physiological equivalent temperature (PET).

3. Study Area and Research Method

3.1. Study Area and Data Collection

This study examines Sangju Namsan Park, which was selected for a climate change adaptation project led by the Ministry of the Environment that involved installing facilities to improve the thermal environment in urban public spaces and parks. Sangju is a small city located in the inland region of northwestern Gyeongsangbuk-do and is influenced by a continental climate, classified as a monsoon-influenced hot-summer continental climate (Dwa), according to the Köppen climate classification system. This classification indicates that the region experiences a wide range of temperatures throughout the year, with particularly hot summers. The average annual temperature is 13.4 °C. As of the end of July 2023, Sangju had a population of 94,386 (46,448 male, 47,938 female). The average age is 53.0 years, indicating an aging population. The proportion of the population vulnerable to high summer temperatures (those younger than 18 or aged 65 and older) is about 42.5%, with the elderly group (65+) accounting for approximately 34.4%, making it the second most aged region in South Korea. In addition to age (senior population and children), people with chronic diseases, such as cardiovascular and respiratory conditions, are particularly vulnerable to urban heat. These groups face heightened risks of heat-related illnesses, making it crucial to consider their needs in urban park planning.
The water path in Sangju Namsan Park is approximately 150 m long and 2 m wide, while the pergola spans 50 square meters and is equipped with a fog cooling system. In terms of monitoring, pedestrian flow was observed using AI cameras installed at key entry and exit points, along with IoT-based environmental sensors for measuring temperature, humidity, and air quality. These sensors were placed near the water path, pergola, and non-water areas to compare the cooling effects across different zones. The specific places to put sensors were discussed by the research team to measure the microclimate without disturbances from other external factors like shade. The microclimate data were collected using the Ecowitt WH45 air quality monitor, which measures PM2.5 and CO2 levels. The WS2320 weather station was used to measure temperature and humidity. The Ecowitt WH45 has a measurement accuracy of ±10% for PM2.5 and ±50 ppm for CO2, ±5% for humidity, and ±1 °C for temperature, making it suitable for urban environmental monitoring.
To ensure the reliability and accuracy of measurements, the WS 2320 sensor was used in its original state, as tested during factory calibration. Sensor readings were compared against a reference thermometer and hygrometer in a stable environment to verify alignment with known standards. No manual adjustments were made, as the factory calibration ensured sufficient accuracy for deployment. The sensors were pre-tested during production across a range of temperatures (−10 °C to 50 °C) and humidity levels (10% to 95%), ensuring accuracy under diverse conditions. Post deployment, the collected data were cross-referenced with publicly available datasets from the Korea Meteorological Administration to confirm consistency and reliability. Performance specifications for the WS 2320 sensor include:
  • Accuracy: ±1° C (Temperature), ±5% (Humidity);
  • Resolution: 0.1 °C (Temperature), 1% (Humidity);
  • Operating Temperature Range: −40 °C to 60 °C;
  • Rain Volume Accuracy: ±10%;
  • UV-Index Range: 0 to 15;
  • Light Range: 0 to 120 kLux;
  • Sensor Reporting Interval: 16 s.
The specific data collection process is as follows. This study collected microclimate data during summer months, using data loggers and weather stations capable of measuring temperature, humidity, fine dust, and CO2 at 5 min intervals. To accurately measure the effects of water facilities, preliminary measurements were conducted during afternoon hours over two days on 27 and 28 June 2023. Based on these measurements, the locations for equipment installation were determined, and microclimate data were collected from late July to August 2023, for about 28 days, at locations with and without water facilities. Weather stations were installed at the following three locations: near the water path facility; at the park entrance about 30 m from the water path rest area; and near a pergola featuring a cooling fog system. Additionally, data loggers were installed at six locations, as follows: two near the water path rest area; two in green spaces located away from the water path rest area; and two near the water pump facility to measure changes in surface temperature. The exact equipment locations where the equipment was installed are shown in Figure 1 and Figure 2.
Microclimate data were collected using these devices at 5 min intervals over a 28-day period from July to August. Supplementary data were also collected using thermal imaging cameras, drones, and pedestrian flow sensors. The specific measurement equipment and details are provided in Table 1 and Table 2. Data collected from the commercial measuring device were monitored and raw data files(.csv) were downloaded through the website (www.ecowitt.net). To ensure accuracy, the collected data were compared with readings from data loggers and trends observed in publicly available data from the Korea Meteorological Administration.

3.2. Research Methods

The collected data were analyzed using IBM SPSS statistical software (version 28.0.1.1). One-way analysis of variance (ANOVA) was used to determine the statistical significance of temperature differences across various locations within the study area. Post hoc tests, such as the Duncan test, were employed to further investigate the significance of measured differences between individual sites that we applied in our previous study to compare the difference for the measured microclimate temperature [32]. Additionally, the analysis incorporated the perceived temperature measured by the equipment, along with the actual meteorological values.
In addition, to measure the changes of thermal comfort, this study uses traditional temperature measurements and incorporates additional thermal comfort indices, such as PET (physiological equivalent temperature) and UTCI (Universal Thermal Climate Index) to assess the impact of the water facilities on perceived thermal comfort. These indices provide a more comprehensive understanding of how surface temperature reductions translate to improved thermal conditions for park users.

4. Results

4.1. Effect on Thermal Environment–Surface Temperature Changes

As shown in Figure 3, thermal imaging camera (FLIR E60, Wilsonville, Oregon) footage was used to measure temperatures at the small water path rest area and various measurement sites. The results are shown in Table 3. In the central area of the park, the measurement sensors recorded a spot point temperature of 39.7 °C and a temperature range of 38.6 °C–58.8 °C (minimum to maximum); 39.1 °C (range 35.0 °C–60.1 °C) at the small water path; and 36.8 °C (range 32.2 °C–53.3 °C) at the site with the cooling fog system. The thermal camera data showed that the lowest temperatures were recorded in the pergola, followed by the small water path rest area, and the park center.
The results presented in Figure 3 and Table 3 show the surface temperatures of each facility recorded on a hot day. This chapter will use the term ‘hot day’, defined as a day when the maximum air temperature exceeds 33 °C. This threshold is commonly used by the Korea Meteorological Administration to define heatwave conditions, as it represents a critical point at which outdoor activities become significantly affected by heat stress. The overall thermal image footage of the park, including the small water path rest area, shows that the water in the small water path had a spot point temperature of 33.5 °C, a minimum temperature of 32.2 °C, and a maximum surface temperature of 62.5 °C. Even in summer conditions, when surrounding surfaces showed significant temperature increases, it was observed that the temperature of the water in the path remained relatively low, between 30 °C and 32 °C. Specifically, the surface temperature of the water path rest area was recorded as 32.2 °C, compared to a maximum surrounding temperature of 43.6 °C, indicating that the water path rest area provided a cooling effect relative to its surroundings. The cooling effect provided by the water path benefits not only vulnerable populations, such as the elderly, children, and people with chronic illnesses, but also all users of these spaces. By improving thermal comfort, these facilities create a more enjoyable environment for the public, especially during the hottest hours of the day. Although rainwater retention and purification facilities may have a limited effect in reducing maximum temperature, the relatively low water temperature in the water path rest area shows potential for providing a comfortable environment on hot summer days.

4.2. Comparison Between Air Temperature and Surface Temperature

In this study, both surface temperature and air temperature were measured and analyzed separately to assess the distinct effects of the small water path and cooling fog system. This approach provides a comprehensive understanding of how these interventions affect thermal comfort at different scales. And, air temperature has been applied for the comparison work mainly. Surface temperature, measured with infrared thermometers, is more responsive to direct solar radiation and changes rapidly depending on surface material and sun exposure. The small water path and pergola both showed significant reductions in surface temperature, with up to 1.1 °C decrease observed around these features. This demonstrates the immediate cooling effect on ground and surface materials, particularly in areas exposed to direct sunlight.
Air temperature, measured at 1.5 m above ground, changes more gradually as it is less directly affected by solar radiation compared to surface temperature. The air temperature reductions were more modest compared to surface temperature, highlighting the slower cooling effects of these interventions on the ambient air. This was particularly evident in the Pergola area, where shading and the cooling fog system lowered air temperatures but to a lesser degree than surface temperatures.
These findings reveal how surface and air temperatures interact. As surface temperatures decrease, especially around the small water path and pergola, there is a corresponding localized reduction in air temperature, though to a smaller extent. The cooling of surfaces reduces the amount of radiant heat experienced by visitors, which indirectly lowers air temperature in those areas, contributing to an improved microclimate.
By distinguishing between these two metrics, the analysis demonstrates that surface temperature responds more rapidly and directly to the interventions, while air temperature reflects the longer-term, localized cooling effects. This distinction is critical for urban planners and landscape designers aiming to optimize cooling interventions in urban parks.

4.3. Effect on Thermal Environment–Air Temperature Changes

Over the course of the entire measurement period, the average temperature of the water path rest area was 25.73 °C, that of the park center was 25.86 °C, and the pergola had the lowest average temperature at 25.67 °C. Hourly averages and variations in temperature and humidity are shown in Figure 4.
The comparison of average temperatures shown in Figure 4 showed that the maximum air temperature difference between the water path rest area and the park center was 0.22 °C, with the highest temperature difference being 3.2 °C. The difference between the water path rest area and the pergola was measured at 0.35 °C, with the highest temperature difference being 3.3 °C. A comparison of the average humidity during the measurement period showed that the maximum humidity difference between the water path rest area and the park center was 2.4%, with the highest humidity difference being 21%. The difference between the water path rest area and the pergola was measured at 4.04%, with the highest humidity difference being 11%. While there are differences depending on the specific measurement time, overall, the pergola showed the lowest temperatures, likely due to periodic water spraying and some shading at the measurement site. While the accuracy of the sensor used for measurement is ±1 °C, this study analyzed the average of long-term data, which could offset the potential inaccuracy that occurred due to the sensor.
The ANOVA analysis (Table 4), based on the entire microclimate dataset, revealed that the perceived temperature showed greater differences between the sites than the actual temperature. As the analysis focused solely on the temperature reduction effects provided by the water facilities. While improvements in air quality (e.g., reductions in PM2.5 and CO2) were observed, these results are beyond the scope of this study and will not be discussed in detail here. Specifically, the park center (B) had the highest average temperature at 25.86 °C, followed by the water path rest area (A) at 25.73 °C, and the pergola (C) at 25.67 °C. This indicates that the water path rest area and pergola provided cooler environments compared to the central zone of the park. The same trend was observed for perceived temperature, which was highest at the park center (B) at 27.72 °C, followed by the water path rest area (A) at 27.63 °C, and the pergola (C) at 27.22 °C.
These results indicate the presence of cooling effects provided by the water path rest area and pergola. The subtle differences in temperature and perceived temperature indicate that these areas can offer some protection from the heat. Overall, the water features showed a cooling effect, with lower measured and perceived temperatures compared to those of the park center. In particular, the pergola, with its operational cooling fog system, provided an even more comfortable environment by reducing fine dust and CO2 levels.
To more specifically compare the effects of improved thermal environment, the following Table 5, Table 6, Table 7 and Table 8 compare temperatures during active periods, afternoon hours, and times when temperatures exceeded the heatwave threshold of 33 °C. The ‘heatwave threshold’ refers to the temperature level at which heat stress becomes significantly hazardous for human health. In South Korea, 33 °C is commonly used as the threshold for defining heatwave conditions, based on national meteorological standards. This threshold represents the temperature at which heat-related illnesses increase significantly, particularly during prolonged exposure. Specifically, for the study period 7 August to 7 September, hourly temperature data collected from 7 a.m. to 10 p.m. demonstrated the following trend: park center > water path rest area > pergola, with differences of 0.30 °C and 0.15 °C, respectively. Data from 10 a.m. to 4 p.m. demonstrated the following trend: park center, water path rest area > pergola, with differences of 1.08 °C and 0.36 °C, respectively. Data from 10 a.m. to 2 p.m. demonstrated the following trend: park center, water path rest area > pergola, with differences of 0.32 °C and 0.30 °C, respectively. A final comparison of hourly temperature data during periods exceeding the heatwave threshold of 33 °C shows the following trend: park center, water path rest area > pergola, with differences of 0.83 °C and 0.84 °C, respectively.

4.4. Effect on Thermal Environment–Universal Thermal Climate Index (UTCI)

The Universal Thermal Climate Index (UTCI) is a sophisticated index developed to assess outdoor thermal comfort. It is based on a complex heat balance model of the human body, which integrates several environmental parameters such as air temperature, wind speed, humidity, and radiation. The formula considers the heat transfer between the human body and the environment, allowing for a detailed analysis of perceived heat stress.
In this study, we utilized Python’s Pythermalcomfort library to calculate the UTCI, leveraging its robust framework to incorporate multiple environmental factors such as wind speed, temperature, humidity, and solar radiation. The use of this library is noted throughout sections discussing the UTCI results, underscoring the accuracy and consistency of the approach. The following equation was borrowed from the UTCI webpage (www.utic.org, accessed on 26 November 2024):
U T C I = T a + ∑ i = 1 n a i · Δ T i
where:
  • Ta is the ambient air temperature;
  • ai are the coefficients for various parameters such as wind speed, humidity, and radiation;
  • Ti represents the changes in perceived temperature due to these environmental factors.
This formula enables the UTCI to account for dynamic outdoor conditions, making it a reliable tool for assessing human thermal stress in various climates. The UTCI has been widely adopted for urban climate studies, providing insight into how different environmental interventions, such as shading and water features, affect thermal comfort (Bröde et al., 2012) [33].
The UTCI is widely recognized as a robust tool for assessing outdoor thermal comfort. Unlike temperature alone, which measures air heat, UTCI incorporates various meteorological factors, such as wind speed, humidity, and solar radiation, to gauge how the human body experiences heat stress. In the context of urban park environments, this index is essential for understanding how features like water paths and shaded pergolas improve thermal comfort more comprehensively than traditional temperature measurements alone.
Comparison of Sites Based on UTCI: An analysis of UTCI values at the three sites (park central zone, pergola, and small water path) between 26 August and 27 August 2023, reveals several key insights, as follows: the park central zone exhibited the highest UTCI values during peak sunlight hours, reaching a maximum of approximately 42.5 °C. These elevated values indicate reduced shading and limited cooling features, contributing to a greater heat stress burden in this zone (Figure 5 and Table 9).
  • Pergola: equipped with a cooling fog system, the pergola consistently recorded lower UTCI values. The maximum UTCI at this location was around 39 °C, while the average UTCI remained moderate compared to the park central zone. This suggests that while the pergola provides intermittent cooling, the effects are more localized and dependent on the usage periods of the fogging system. The discontinuities observed in the green curve (pergola UTCI and air temperature) are attributed to missing temperature data (NaN values). This issue likely occurred during the transmission of sensor data to the cloud server via radio frequency. Specifically, interruptions in the data transmission process resulted in gaps, leading to missing values. These missing values subsequently impacted the calculation of UTCI, which depends on complete input data such as ambient temperature, relative humidity, and wind speed;
  • Small Water Path: the small water path offered the most consistently comfortable conditions, with an average UTCI below 30 °C, illustrating the cooling impact of the water path. Despite its smaller size, the water feature effectively mitigated heat stress in its immediate vicinity.
The differences between these sites suggest that water features, such as the small water path, provide a more sustained cooling effect, while shading and fog systems deliver immediate, short-term relief. This analysis emphasizes the importance of diverse cooling interventions to enhance thermal comfort in urban park settings.
Temperature Analysis: A traditional temperature comparison further reinforces the cooling effects of these interventions: (Figure 6 and Table 10)
  • Park Central Zone: this zone recorded the highest air temperature, reaching approximately 32.5 °C. This suggests that in areas without specific cooling interventions, surface temperatures tend to remain elevated during the hottest parts of the day;
  • Pergola: the pergola, equipped with shading and a fog cooling system, recorded slightly lower maximum temperatures than the park central zone. The shading and fog system contributed to reducing the ambient temperature, providing localized cooling benefits;
  • Small Water Path: the path displayed the most consistent cooling effect, with temperatures generally lower than both the park central zone and pergola areas. Despite reaching similar peak values to the central zone, the overall cooling impact of the water feature is evident, contributing to a more comfortable environment.
In conclusion, this analysis demonstrates that while traditional temperature measurements provide insights into heat levels, they alone do not capture the full picture of human thermal comfort. The UTCI analysis reveals that water features, particularly the small water path, are highly effective in lowering perceived heat stress in public spaces. The cooling effects provided by pergolas with fog systems are also beneficial, though they may vary depending on usage periods and fog intensity.
Incorporating thermal comfort indices like UTCI and PET enhances our understanding of how microclimate interventions improve public comfort in urban parks. These findings provide valuable insights for urban planners and designers seeking to enhance thermal comfort in public spaces through targeted cooling interventions.

4.5. Effect on Thermal Environment–Physiological Equivalent Temperature (PET)

The physiological equivalent temperature (PET) is a thermal comfort index that calculates the heat load on the human body by combining factors such as air temperature, humidity, wind speed, and solar radiation. PET is expressed in degrees Celsius, making it directly comparable to standard temperature scales. PET values were calculated using Python-based custom formulas developed to integrate ambient temperature, humidity, wind speed, and solar radiation. By employing a streamlined computational model tailored to this study, PET values reflect the specific conditions of Sangju Namsan Park’s microclimate settings. PET results are presented with consistent methodology references, ensuring transparency in the experimental approach across all sections of the study.
The mean radiant temperature (Tr) is a key parameter in understanding human thermal comfort, representing the uniform temperature of a hypothetical surrounding surface that would emit the same amount of thermal radiation as the actual environment. The Tr formula used in this study is expressed as Formula (2) [34], as follows:
T r ¯ = ∑ j = 1 n T i F i j
where:
Ti: Surface temperature of the ith surrounding element (°C);
Fij: View factor, representing the proportion of radiation exchanged between the human body and the jth surface.
The summation accounts for all surrounding elements, including direct solar radiation, diffuse sky radiation, and reflected radiation from nearby surfaces.
Fij factors are determined based on the geometric relationship between the observer and each surface, as well as their reflective properties.
This formula (2,3) is derived from the RayMan model [34], which calculates radiation fluxes in simple and complex environments. By incorporating Tr into the PET calculation, the model accounts for the radiative heat exchange between the human body and its environment, making it a critical input for accurate thermal comfort assessments.
The PET formula, in particular, is rooted in the Munich Energy Balance Model for Individuals (MEMI), a framework designed to integrate environmental and physiological factors. This study adopts the following specific equation for the PET calculation, as follows:
P E T = ∑ i = 1 n ( T a + R + a d d i t i o n a l   t e r m s ) · F i j
where:
Ta: Ambient air temperature (°C);
R: Both direct and reflected radiation (W/m2);
v: Wind speed (m/s), which influences convective heat transfer from the human body to its surroundings. Higher wind speeds generally enhance heat dissipation, contributing to a cooling effect.
Fij: View factor, representing the proportion of radiation exchanged between the human body and the jth surface.
Additional terms include factors accounting for metabolic heat production, clothing insulation, and body heat storage, which are vital for human thermal comfort assessments.
Note: These terms represent physiological and individual parameters essential for assessing human thermal comfort, based on MEMI principles.
PET quantifies how the body exchanges heat with its environment under typical physiological conditions, taking into account factors like metabolic heat and clothing. It is particularly useful in urban climate studies, where it helps to assess how interventions like shading, water features, and ventilation influence human thermal comfort. PET is widely used to evaluate localized microclimates, making it an important tool for urban planners and researchers [34]. The paragraphs below show the detailed results of the PET values for each of the measurement areas (Figure 7, Table 11).
Small Water Path (highest PET values): Contrary to expectations, the small water path recorded the highest PET values, peaking at 50 °C. This can be explained by the strong solar radiation and potential heat from surrounding surfaces. Despite the cooling effect of the water itself, other factors—such as high radiant heat from nearby surfaces or inadequate shading—may have resulted in higher thermal stress. The water feature, while providing some evaporative cooling, was not sufficient to offset the overall heat load in this case;
Park Central Zone (moderate PET values): The park central zone recorded moderate PET values, typically staying close to 45 °C. This area, while more open and exposed to natural airflow, lacks the cooling effects of water bodies or shading interventions. As a result, it experienced high levels of solar radiation during peak sunlight hours, leading to a significant heat load on users. However, the lack of increased humidity (unlike the fog system in the pergola) kept the PET values moderate compared to those of the small water path;
Pergola (lowest PET values): The pergola consistently recorded the lowest PET values, with an average of around 38 °C. The combination of shading and the cooling fog system effectively reduced the thermal load experienced by users. Although increased humidity from the fog system could raise the heat load in some scenarios, the shading and periodic cooling outweighed any negative impact from humidity, leading to significantly lower PET values compared to those of other areas.
In conclusion, The PET analysis shows that the small water path, despite the presence of water, did not provide sufficient cooling to offset the radiant heat from its surroundings, resulting in the highest PET values. On the other hand, the pergola—with its shading and fog system—provided the lowest PET values, demonstrating the effectiveness of such interventions in reducing heat stress. The park central zone, while more open, fell in between due to the absence of active cooling systems and moderate exposure to sunlight. This revised analysis provides a clearer interpretation of how different design interventions (e.g., water paths, fog systems, and shading) impact thermal comfort in urban parks, addressing the reviewer’s comments about using diverse methods to draw conclusions.
This chapter shows how two different indexes show the measured results.
It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
The UTCI index is highly sensitive to wind speed and solar radiation, which makes it particularly useful for analyzing overall environmental heat stress in well-ventilated, open spaces. In contrast, PET is more affected by humidity and radiant heat from surrounding surfaces. This results in higher PET values in shaded areas with high humidity or near hot surfaces, even if UTCI values remain lower.
In this study, the small water path shows higher PET values, likely due to radiant heat from surrounding surfaces, while the pergola demonstrates lower PET values due to effective shading, despite higher humidity from the fog system. Conversely, UTCI values reflect different trends, being more influenced by wind and solar radiation.
Both UTCI and PET offer unique insights into thermal comfort. Together, they provide a comprehensive understanding of how urban interventions, such as water features, shading, and cooling systems, impact human thermal stress.

4.6. Other Data: Changes in the Number of Park Visitors

In addition to temperature data, this study aimed to assess whether the improved thermal environment created by water features and cooling systems influenced park usage. Motion capture sensors that detect pedestrian flow sensors were used to monitor daily and hourly changes in park activity from 9 August to 7 September. The graphs below Figure 8 display variations in visitor patterns based on time of day and weekday versus weekend usage.
The highest levels of park usage were recorded between 6 p.m. and 8 p.m., a period when temperatures typically began to decline, providing more comfortable conditions for visitors. Additionally, as temperatures gradually decreased from late summer to early autumn, a noticeable increase in pedestrian activity was observed. This suggests that, as the thermal comfort of the park improved, both due to seasonal cooling and the presence of water features, visitor engagement increased accordingly.
These findings indicate that water features and cooling systems not only enhance the thermal environment but may also contribute to greater park usage, particularly during the cooler hours of the day. Such insights are valuable for urban planners aiming to maximize the usability of public spaces during periods of high heat.
Figure 9 shows the relationship between measured microclimate data and park usage, demonstrating that park usage was highest at temperatures between 23 °C and 28 °C, at humidity levels of 75–85%, and when the UVI was low. This indicates that people used the park more often when weather conditions were good. However, the sample size used to determine park usage was not large, and there were likely significant variations based on the time of day. Nevertheless, long-term data collection to examine how actual microclimate metrics relate to park usage could yield significant insights for park management, policy-making, and academic research. While these results do not directly relate to the findings of our study, a consideration of the relationship between thermal comfort and the changes in visitor usage should be studied in greater depth to understand the real effects of thermal comfort changes on visitors.

5. Discussion

5.1. Colling Effects on Air Temperature and Thermal Comfort

The cooling effects observed in Sangju Namsan Park are consistent with findings from previous studies, such as those by Du et al. (2016) and Chang et al. (2007), which documented temperature reductions of up to 3.8 °C and extended cooling beyond park boundaries [14,27]. These results highlight the significant role that urban parks play in mitigating urban heat island effects. However, the cooling effects in this study, with a maximum surface temperature reduction of 1.1 °C, are more modest compared to larger parks with more extensive interventions.
This study highlights the distinct and synergistic contributions of water features and green spaces to cooling. The pergola area, equipped with a cooling fog system, demonstrated the most significant impact on both surface temperatures and thermal comfort. The fog system, combined with shading from the pergola, reduced average PET values to 36.2 °C and surface temperatures by up to 0.36 °C compared to the water path rest area. This suggests that the interaction between shading and evaporative cooling was critical in enhancing thermal comfort.
In contrast, the small water path exhibited a more limited cooling effect, with PET values peaking at nearly 60 °C due to high radiant heat from the surrounding surfaces and insufficient shading. Surface temperature reductions in this area were modest, highlighting the importance of integrating shading to maximize the effectiveness of water features.
This study highlights the distinct and synergistic contributions of water features and green spaces to cooling. The pergola area, equipped with a cooling fog system, demonstrated the most significant impact on both surface temperatures and thermal comfort. The fog system, combined with shading from the pergola, reduced average PET values to 36.2 °C and surface temperatures by up to 0.36 °C compared to the water path rest area. This suggests that the interaction between shading and evaporative cooling was critical in enhancing thermal comfort. In contrast, the small water path exhibited a more limited cooling effect, with PET values peaking at nearly 60 °C due to the high radiant heat from the surrounding surfaces and insufficient shading. Surface temperature reductions in this area were modest, highlighting the importance of integrating shading to maximize the effectiveness of water features.
The analysis also revealed that while water features independently provide localized cooling, their effectiveness is significantly enhanced when combined with green infrastructure. The ANOVA results showed that at elevated temperatures exceeding 33 °C, areas with both shading and water features (e.g., the pergola) recorded the lowest surface and perceived temperatures. For example, the pergola was, on average, 0.83 °C and 0.84 °C cooler than the park center and water path rest area, respectively, confirming the synergistic cooling effects of shading and water features. To isolate the contribution of water features, comparisons were made between shaded green spaces without water interventions and areas with water features. While surface temperatures near the water features were consistently lower, the absence of complementary shading diminished their overall cooling potential. These findings emphasize the localized nature of water feature cooling effects and the importance of strategic placement in shaded areas to optimize their impacts.

5.2. Recommendations for Policy and Planning

To ensure that small-scale water features and fog systems maximize their cooling potential, the following strategies are recommended for urban planners and landscape architects:
  • Optimal Placement: Water bodies and fog systems should be strategically placed in high-traffic areas that are exposed to the most heat such as open and sunny zones within the park. Placing these features in such locations can enhance cooling effects and improve user comfort;
  • Size and Circulation: The size of water features should be proportional to the park’s overall size. In smaller parks, shallow water features can be particularly effective in providing cooling without requiring excessive water resources. Proper water circulation is essential to prevent stagnation and maintain water quality;
  • Maintenance Requirements: Regular maintenance is key to ensuring the continued effectiveness of water and fogging systems. Maintenance should include routine water-quality checks to ensure safety and regular inspections of the fogging system, particularly during peak usage in the hottest months;
  • Cost and Safety Considerations: Installing water features involves higher costs and potential safety concerns, especially for children. It is essential to incorporate safety barriers or signage around water features to prevent accidents. These costs should be carefully considered in the design phase;
  • Integration with Broader Cooling Strategies: Water features should be part of a larger urban cooling strategy such as increasing tree canopy cover and creating shaded areas. Integrating these elements will enhance the overall cooling effects and provide a more comprehensive solution to urban heat.
It is also crucial to recognize that surface temperature reductions do not always directly translate into improved thermal comfort. While surface temperatures were lowered in this study, these reductions do not fully capture air temperature or perceived comfort as measured by PET and UTCI. Future research should focus on integrating both surface and air temperature data along with thermal comfort metrics to gain a more complete understanding of the cooling benefits.
In conclusion, small-scale water features are effective at mitigating localized heat stress but are not sufficient on their own to address broader urban heat island challenges. Future efforts should focus on combining these interventions with other green infrastructure and integrating them into larger cooling strategies to maximize their overall effectiveness.

6. Conclusions

This study investigated the cooling effects of small-scale water features—a water path and a cooling fog system—installed at Sangju Namsan Park, a neighborhood park in Republic of Korea. An analysis of the temperature distribution from 7 August to 7 September 2023, during a period of hot summer weather, showed that the water path rest area recorded the lowest average temperature of 24.73 °C, maintaining an average temperature that was 0.13 °C lower than surrounding spaces, indicating a cooling effect. Specifically, the pergola, with an average temperature of 25.67 °C, recorded the second-lowest temperature after the water path rest area, with a temperature difference of 0.19 °C, confirming the additional cooling effect of the pergola’s fog system.
This study contributes to the growing body of research on urban cooling strategies by focusing on small-scale interventions in resource-constrained environments, where the feasibility of large-scale green infrastructure is limited. Unlike previous studies that predominantly examine large parks or rely on simulations, this research emphasizes direct measurements of microclimate changes and perceived thermal comfort using advanced indices like the Universal Thermal Climate Index (UTCI) and PET.
The cooling effect provided by water features resulted in a maximum surface temperature reduction of 1.1 °C, observed during the hottest hours of the day (10 a.m. to 4 p.m.), with average temperatures at the pergola (C) being 0.36 °C lower than at the water path rest area (A) and 1.08 °C lower than at the park center (B). The ANOVA results showed that at elevated temperatures exceeding 33 °C, the lack of shade available at the water path rest area limited its contribution to temperature reduction, with the park center (B) and the water path rest area (A) being 0.83 °C and 0.84 °C hotter, respectively, than the pergola (C). In addition to temperature reduction, this study utilized the Universal Thermal Climate Index (UTCI) and physiological equivalent temperature (PET) to assess perceived thermal comfort. The pergola area showed the most significant reduction in PET, with average values around 36.2 °C, while the small water path recorded the highest PET values, which peaked at 50.2 °C due to radiant heat from surrounding surfaces. These results suggest that while water features, such as the pergola, are effective at improving thermal comfort, their impact on broader air temperature reduction is limited to localized areas.
In conclusion, water paths and cooling fog systems contribute significantly to improving the thermal environment of urban parks. However, careful consideration is needed when considering where to locate water facilities, how to encourage comfortable use by park visitors, and the periodic maintenance and management of such facilities. In addition, more detailed information on water features, including the flow rate of the water path and the droplet size and frequency of the fog system, should be considered to show more clear results. Additionally, while the findings emphasize the potential of small-scale interventions to enhance thermal comfort, there are opportunities to further refine the methodology and strengthen the scientific rigor of this analysis. Future studies should explore additional factors, such as long-term impacts and broader urban cooling strategies, to build on these results. Careful consideration of the placement, maintenance, and integration of these facilities with green infrastructure is essential for maximizing their effectiveness in climate change adaptation.

Author Contributions

Conceptualization, T.K.K. and Y.-S.L.; methodology, Y.-S.L. and H.D.Z.; software, T.K.K. and Y.-S.L.; validation, H.D.Z. and Y.-S.L.; formal analysis, Y.-S.L. and H.D.Z.; investigation, Y.-S.L. and T.H.K.; resources, T.H.K.; data curation, Y.-S.L. and H.D.Z.; writing—original draft preparation, Y.-S.L. and H.D.Z.; writing—review and editing, T.K.K. and Y.-S.L.; visualization, Y.-S.L.; supervision, T.K.K. and T.H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This paper is based on the results of the research work “The supporting project for Climate Crisis Vulnerable Groups and Areas” (2024-001-03), conducted by the Korea Environment Institute (KEI) upon the request of the Korea Ministry of Environment.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.

Conflicts of Interest

Tae Kyung Kwon is an employee of the company Airple. Co., Ltd. The paper reflects the views of the scientists and not the company.

References

  1. Hagen, B. The role of planning in minimizing the negative impacts of global climate change. Urban Plan. 2016, 1, 13–24. [Google Scholar] [CrossRef]
  2. Lai, D.; Liu, W.; Gan, T.; Liu, K.; Chen, Q. A review of mitigating strategies to improve the thermal environment and thermal comfort in urban outdoor spaces. Sci. Total Environ. 2019, 661, 337–353. [Google Scholar] [CrossRef]
  3. Coutts, A.M.; Tapper, N.J.; Beringer, J.; Loughnan, M.; Demuzere, M. Watering our cities: The capacity for Water Sensitive Urban Design to support urban cooling and improve human thermal comfort in the Australian context. Prog. Phys. Geogr. 2013, 37, 2–28. [Google Scholar] [CrossRef]
  4. Lee, S.H.; Lee, K.S.; Jin, W.C.; Song, H.K. Effect of an urban park on air temperature differences in a central business district area. Landsc. Ecol. Eng. 2009, 5, 183–191. [Google Scholar] [CrossRef]
  5. Barradas, V.L.; Miranda, J.A.; Esperón-Rodríguez, M.; Ballinas, M. (Re) Designing Urban Parks to Maximize Urban Heat Island Mitigation by Natural Means. Forests 2022, 13, 1143. [Google Scholar] [CrossRef]
  6. Irfeey, A.M.M.; Chau, H.W.; Sumaiya, M.M.F.; Wai, C.Y.; Muttil, N.; Jamei, E. Sustainable mitigation strategies for urban heat island effects in urban areas. Sustainability 2023, 15, 10767. [Google Scholar] [CrossRef]
  7. Wolch, J.R.; Byrne, J.; Newell, J.P. Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’. Landsc. Urban Plan. 2014, 125, 234–244. [Google Scholar] [CrossRef]
  8. Low, S.; Taplin, D.; Scheld, S. Rethinking Urban Parks: Public Space and Cultural Diversity; University of Texas Press: Austin, TX, USA, 2005. [Google Scholar]
  9. Huang, K.; Zhuang, G.; Lin, Y.; Wang, Q.; Fu, J.S.; Fu, Q.; Liu, T.; Deng, C. How to improve the air quality over megacities in China: Pollution characterization and source analysis in Shanghai before, during, and after the 2010 World Expo. Atmos. Chem. Phys. 2013, 13, 5927–5942. [Google Scholar] [CrossRef]
  10. Peper, P.J.; McPherson, E.G.; Simpson, J.R.; Gardner, S.L.; Vargas, K.E.; Xiao, Q. New York City, New York Municipal Forest Resource Analysis; Technical Report; US Department of Agriculture Forest Service, Pacific Southwest Research Station, Center for Urban Forest Research: Davis, CA, USA, 2007; p. 65. [Google Scholar]
  11. Cohen, P.; Potchter, O.; Matzarakis, A.A. Daily and seasonal climatic conditions of green urban open spaces in the Mediterranean climate and their impact on human comfort. Build. Environ. 2012, 51, 285–295. [Google Scholar] [CrossRef]
  12. Kim, D.W.; Kim, J.K.; Jung, E.H. An analysis of micro-climate environmental changes followed by establishment of an urban parkfocused on the Junggu in Daegu city. J. Urban Des. Inst. Korea Urban Des. 2010, 11, 77–94. [Google Scholar]
  13. Choi, H.-A.; Lee, W.-K.; Byun, W.-H. Determining the effect of green spaces on urban heat distribution using satellite imagery. Asian J. Atmos. Environ. 2012, 6, 127–135. [Google Scholar] [CrossRef]
  14. Chang, C.-R.; Li, M.-H.; Chang, S.-D. A preliminary study on the local cool-island intensity of Taipei city parks. Landsc. Urban Plan. 2007, 80, 386–395. [Google Scholar] [CrossRef]
  15. Yan, H.; Wu, F.; Dong, L. Influence of a large urban park on the local urban thermal environment. Sci. Total Environ. 2018, 622, 882–891. [Google Scholar] [CrossRef] [PubMed]
  16. Du, H.; Song, X.; Jiang, H.; Kan, Z.; Wang, Z.; Cai, Y. Research on the cooling island effects of water body: A case study of Shanghai, China. Ecol. Indic. 2016, 67, 31–38. [Google Scholar] [CrossRef]
  17. George, N.J.; Zafiriadis, K. The impact of park trees on microclimate in urban areas. Urban. Ecosyst. 2006, 9, 195–209. [Google Scholar] [CrossRef]
  18. Ryu, N.-H.; Lee, C.-S. Effects for the thermal comfort index improvement of park woodlands and lawns in summer. J. Korean Inst. Landsc. Archit. 2014, 42, 21–30. [Google Scholar] [CrossRef]
  19. Koo, M.-A. The relationship between matter reduction and space shielding rate in urban neighborhood park. J. Korean Inst. Landsc. Archit. 2019, 47, 67–77. [Google Scholar] [CrossRef]
  20. Ahn, R.; Hong, S. Characteristics of Particulate Matter 2.5 by Type of Space of Urban Park-Focusing on the Songsanghyeon Plaza in Busan. J. Korean Inst. Landsc. Archit. 2021, 49, 37–48. [Google Scholar] [CrossRef]
  21. Lee, A.; Jeong, S.; Joo, J.; Park, C.-R.; Kim, J.; Kim, S. Potential role of urban forest in removing PM 2.5: A case study in Seoul by deep learning with satellite data. Urban Clim. 2021, 36, 100795. [Google Scholar] [CrossRef]
  22. Park, J.; Lee, P.S.H. Relationship between remotely sensed ambient PM 10 and PM 2.5 and urban Forest in Seoul, South Korea. Forests 2020, 11, 1060. [Google Scholar] [CrossRef]
  23. Hwang, K.I.; Han, B.H.; Kwark, J.I.; Park, S.C. A study on decreasing effects of ultra-fine particles (PM 2.5) by structures in a roadside buffer green—A buffer green in Songpa-gu, Seoul. J. Korean Inst. Landsc. Archit. 2018, 46, 61–75. [Google Scholar] [CrossRef]
  24. Park, J.-H.; Cho, G.-H. Influence of park size on the park cooling effect: Focused on Ilsan new town in Korea. J. Korean Inst. Landsc. Archit. 2016, 51, 247–261. [Google Scholar] [CrossRef]
  25. Yoon, M.H.; Ahn, T.M. An Application of Satellite Image Analysis to Visualize the Effects of Urban Green Areas on Temperature. J. Korean Inst. Landsc. Archit. 2009, 37, 46–53. [Google Scholar]
  26. Singkran, N. Carbon sink capacity of public parks and carbon sequestration efficiency improvements in a dense urban landscape. Environ. Monit. Assess. 2020, 194, 750. [Google Scholar] [CrossRef]
  27. Rodríguez, L.C.; Cuesta, F. The metropolitan parks of Quito as important carbon sinks. J. Urban Ecol. 2024, 10, juae005. [Google Scholar] [CrossRef]
  28. Gao, S.; Tao, P.; Zhao, Z.; Dong, X.; Li, J.; Yao, P. Estimation and Differential Analysis of the Carbon Sink Service Radius of Urban Green Spaces in the Beijing Plain Area. Sustainability 2024, 16, 1406. [Google Scholar] [CrossRef]
  29. Guo, F.; Wang, Z.; Dong, J.; Zhang, H.; Lu, X.; Lau, S.S.Y.; Miao, Y. Spatial differences in outdoor thermal comfort during the transition season in cold regions of China. Buildings 2022, 12, 720. [Google Scholar] [CrossRef]
  30. Dong, J.; Guo, R.; Lin, M.; Guo, F.; Zheng, X. Multi-objective optimization of green roof spatial layout in high-density urban areas—A case study of Xiamen Island, China. Sustain. Cities Soc. 2024, 115, 105827. [Google Scholar] [CrossRef]
  31. Yang, P.; Xiao, Z.-N.; Ye, M.-S. Cooling effect of urban parks and their relationship with urban heat islands. Atmos. Ocean. Sci. Lett. 2016, 9, 298–305. [Google Scholar] [CrossRef]
  32. Zoh, H.D.; Kwon, T. Effects of urban park on thermal comfort in summer—An analysis of microclimate data of Seoul Forest Park. J. Korean Inst. Landsc. Archit. 2022, 50, 30–41. [Google Scholar] [CrossRef]
  33. Bröde, P.; Fiala, D.; Błażejczyk, K.; Holmér, I.; Jendritzky, G.; Kampmann, B.; Tinz, B.; Havenith, G. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 2012, 56, 481–494. [Google Scholar] [CrossRef] [PubMed]
  34. Matzarakis, A.; Rutz, F.; Mayer, H. Modelling radiation fluxes in simple and complex environments—Application of the RayMan model. Int. J. Biometeorol. 2007, 51, 323–334. [Google Scholar] [CrossRef] [PubMed]
Atmosphere 15 01456 g001
Figure 1. Locations for measuring devices and aerial view.
Figure 1. Locations for measuring devices and aerial view.
Atmosphere 15 01456 g001
Atmosphere 15 01456 g002
Figure 2. (a) View of smart park from north; (b) view of smart park from south side; (c) park central zone ④; (d) water path ③; and (e) pergola ⑤(with cooling fog). AWS Location: ③④⑤.
Figure 2. (a) View of smart park from north; (b) view of smart park from south side; (c) park central zone ④; (d) water path ③; and (e) pergola ⑤(with cooling fog). AWS Location: ③④⑤.
Atmosphere 15 01456 g002
Atmosphere 15 01456 g003
Figure 3. (a) Park central zone; (b) small water path; (c) pergola; and (d) overlook of small water path.
Figure 3. (a) Park central zone; (b) small water path; (c) pergola; and (d) overlook of small water path.
Atmosphere 15 01456 g003
Atmosphere 15 01456 g004
Figure 4. (a) Comparison of hourly air temperature measurements; and (b) comparison of hourly humidity measurements.
Figure 4. (a) Comparison of hourly air temperature measurements; and (b) comparison of hourly humidity measurements.
Atmosphere 15 01456 g004
Atmosphere 15 01456 g005
Figure 5. Comparison of hourly UTCI values during 26–27 August.
Figure 5. Comparison of hourly UTCI values during 26–27 August.
Atmosphere 15 01456 g005
Atmosphere 15 01456 g006
Figure 6. Comparison of hourly air temperature during 26–27 August.
Figure 6. Comparison of hourly air temperature during 26–27 August.
Atmosphere 15 01456 g006
Atmosphere 15 01456 g007
Figure 7. Comparison of hourly PET values during 26–27 August.
Figure 7. Comparison of hourly PET values during 26–27 August.
Atmosphere 15 01456 g007
Atmosphere 15 01456 g008
Figure 8. (a) Sangju daily park usage (9 August–7 September); (b) Sangju hourly park usage (9 August–7 September).
Figure 8. (a) Sangju daily park usage (9 August–7 September); (b) Sangju hourly park usage (9 August–7 September).
Atmosphere 15 01456 g008
Atmosphere 15 01456 g009
Figure 9. Correlation between the number of observed people and measured climate data: (left) temperature; (middle) humidity; and (right) solar and UVI.
Figure 9. Correlation between the number of observed people and measured climate data: (left) temperature; (middle) humidity; and (right) solar and UVI.
Atmosphere 15 01456 g009
Table 1. Detailed monitoring plan and content (weather stations).
Table 1. Detailed monitoring plan and content (weather stations).
CategoryDetailed Plan and Content
Monitoring TargetUtilizing urban buffer green spaces and unused spaces
Data Collection ItemsMeasuring air quality, temperature, humidity, fine dust, wind speed, etc.
Monitoring System ConfigurationMicroclimate sensors, cloud servers, central monitoring system, etc.
Measurement MethodsMeasurement tools: IoT sensors, on-site application, comparison at 5 m and 15 m distance
Measurement period: approximately 28 days from July to August
Measurement time: 24 h
Measurement interval (frequency): checked every 5 min, etc.
Table 2. Detailed monitoring plan and content (thermal imaging camera).
Table 2. Detailed monitoring plan and content (thermal imaging camera).
CategoryDetailed Plan and Content
Monitoring TargetUsers of the cooling water path facility
Data Collection ItemsChanges in body heat and thermal sensation before and after use
Monitoring System ConfigurationMonitoring the effects of reduced body heat and changes in thermal sensation using a thermal imaging camera
Measurement MethodsMeasurement tools: thermal imaging camera, comparison at 1–5 m distance from users
Measurement period: based on the operation period of the water path rest area
Measurement time: noon to 3 p.m., during user activity
Measurement interval (frequency): 3 min intervals, tracking for 15 min before and after use
Table 3. Spot point temp, min temp, and max temp.
Table 3. Spot point temp, min temp, and max temp.
SectionSpot Point Temp
(°C)		Min. Temp (°C)Max. Temp (°C)
Park central zone39.738.658.8
Small water path39.135.060.1
Pergola36.832.253.3
Park with small water path33.532.262.5
Table 4. Sangju microclimate measurement ANOVA results (9 August to 7 September).
Table 4. Sangju microclimate measurement ANOVA results (9 August to 7 September).
ClassificationnAverageSDFp-ValueDuncan
Temperature
(°C)		a. Water path rest area699125.733.465.940.003 *a,c < b
b. Park center699125.863.46
c. Pergola699125.673.26
Perceived temperature (°C)a. Water path rest area699127.636.2114.04<0.001 **c < a,b
b. Park center699127.726.10
c. Pergola699127.225.52
Humidity
(%)		a. Water path rest area699187.2712.32218.31<0.001 **c < b < a
b. Park center699185.1512.29
c. Pergola699183.0411.27
Note: * indicates statistical significance at p < 0.05; ** indicates statistical significance at p < 0.01.
Table 5. Sangju temperature ANOVA results (7 August–7 September, when measured temperatures are above 33 °C).
Table 5. Sangju temperature ANOVA results (7 August–7 September, when measured temperatures are above 33 °C).
ClassificationnAverageSDFp-ValueDuncan
Temperature
(°C)		a. Water path rest area20834.300.6750.30<0.001 *c < a,b
b. Park center20834.290.63
c. Pergola20833.460.72
Note: * indicates statistical significance at p < 0.05.
Table 6. Sangju temperature ANOVA results (7 August–7 September, time between 07:00–22:00).
Table 6. Sangju temperature ANOVA results (7 August–7 September, time between 07:00–22:00).
ClassificationnAverageSDFp-ValueDuncan
Temperature
(°C)		a. Water path rest area443527.073.389.16<0.001 *c < a < b
b. Park center443527.223.35
c. Pergola443526.923.14
Note: * indicates statistical significance at p < 0.05.
Table 7. Sangju high-temperature ANOVA results (7 August–7 September, time between 10:00–14:00).
Table 7. Sangju high-temperature ANOVA results (7 August–7 September, time between 10:00–14:00).
ClassificationnAverageSDFp-ValueDuncan
Temperature
(°C)		a. Water path rest area112428.543.423.060.045 *c < a,b
b. Park center112428.563.32
c. Pergola112428.243.12
Note: * indicates statistical significance at p < 0.05.
Table 8. Sangju high-temperature ANOVA results (7 August–7 September, time between 10:00–16:00).
Table 8. Sangju high-temperature ANOVA results (7 August–7 September, time between 10:00–16:00).
ClassificationnAverageSDFp-ValueDuncan
Temperature
(°C)		a. Water path rest area173928.893.567.97<0.001 *c < a,b
b. Park center173929.973.47
c. Pergola173928.533.28
Note: * indicates statistical significance at p < 0.05.
Table 9. UTCI comparison.
Table 9. UTCI comparison.
Min UTCIMax UTCIMean UTCI
Park Central Zone30.0 °C42.5 °C36.2 °C
Pergola25.0 °C32.5 °C28.7 °C
Small Water Path30.0 °C40.5 °C35.0 °C
Table 10. Air temperature comparison.
Table 10. Air temperature comparison.
Min TemperatureMax TemperatureMean Temperature
Park Central Zone20.2 °C32.7 °C25.1 °C
Pergola20.2 °C31.4 °C24.8 °C
Small Water Path20.1 °C32.5 °C24.9 °C
Table 11. PET value comparison.
Table 11. PET value comparison.
Min PETMax PETMean PET
Park Central Zone25.2 °C50.5 °C39.6 °C
Pergola25.0 °C45.2 °C36.2 °C
Small Water Path25.5 °C50.2 °C38.8 °C
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lim, Y.-S.; Zoh, H.D.; Kim, T.H.; Kwon, T.K. Analyzing the Cooling Effects of Water Facilities in Urban Park: The Case of Sangju Namsan Park, South Korea. Atmosphere 2024, 15, 1456. https://doi.org/10.3390/atmos15121456

AMA Style

Lim Y-S, Zoh HD, Kim TH, Kwon TK. Analyzing the Cooling Effects of Water Facilities in Urban Park: The Case of Sangju Namsan Park, South Korea. Atmosphere. 2024; 15(12):1456. https://doi.org/10.3390/atmos15121456

Chicago/Turabian Style

Lim, Young-Shin, Hyunmin Daniel Zoh, Tae Hyoung Kim, and Tae Kyung Kwon. 2024. "Analyzing the Cooling Effects of Water Facilities in Urban Park: The Case of Sangju Namsan Park, South Korea" Atmosphere 15, no. 12: 1456. https://doi.org/10.3390/atmos15121456

APA Style

Lim, Y.-S., Zoh, H. D., Kim, T. H., & Kwon, T. K. (2024). Analyzing the Cooling Effects of Water Facilities in Urban Park: The Case of Sangju Namsan Park, South Korea. Atmosphere, 15(12), 1456. https://doi.org/10.3390/atmos15121456

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop