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Review

Analyzing Urban Microclimate (UMC) Parameters and Comprehensive Review of UHI and Air Quality Interconnections

by
Lirane Kertesse Mandjoupa
1,*,†,
Kibria K. Roman
2,
Hossain Azam
1 and
Max Denis
1,†
1
School of Engineering and Applied Sciences, Department of Civil and Mechanical Engineering, University of the District of Columbia, Washington, DC 20008, USA
2
State University of New York Canton, Canton, NY 13617, USA
*
Author to whom correspondence should be addressed.
†
Authors contributed equally to this work.
Environments 2025, 12(4), 104; https://doi.org/10.3390/environments12040104
Submission received: 24 February 2025 / Revised: 20 March 2025 / Accepted: 24 March 2025 / Published: 29 March 2025
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Abstract

:
This review analyzes the relationship between Urban Heat Island (UHI) microclimate parameters and poor air quality in urban environments, focusing on how temperature variations, wind dynamics, and urban morphology influence pollutant concentrations. Experimental studies and numerical simulations emphasize the necessity of pedestrian-level sensing combined with computational fluid dynamics (CFD) simulations to accurately capture spatial variations in UHI-related parameters. Ozone concentrations have been observed to increase by up to 4 ppbv, while PM2.5 levels rise by 1–2 µg/m3 in response to UHI effects. Additionally, ANSYS Fluent 2020.R1. simulations demonstrate a 0.93 °C error in temperature predictions and a 1.35 m/s error in wind speed estimations. These findings highlight the critical role of sustainable urban planning strategies in mitigating UHI effects and improving air quality in densely populated areas.

1. Introduction

The Urban Heat Island (UHI) phenomenon refers to the higher temperatures observed in urbanized and industrialized areas compared to surrounding rural regions [1]. This temperature increase results from the absorption and retention of heat by buildings and pavement, along with heat generated by human activities. The temperature difference between urban and rural areas can range from 1 °C to 7 °C, depending on factors such as city size and surface material properties [2]. Urban Heat Islands (UHIs) increase the use of air conditioning in cool buildings, leading to higher electricity demand and energy costs, particularly in metropolitan cities [3]. Furthermore, UHIs exacerbate air pollution by increasing the formation of tropospheric (ground-level) ozone and fine particulate matter concentrations in urban air [4].
Urban Heat Island (UHI) effects manifest as air temperature differences near the ground (canopy layer) due to varying cooling patterns in urban and rural areas. This phenomenon is influenced by factors such as city/town size, geographical and temporal factors, city form, and city function, all of which contribute to elevated urban temperatures in urban areas [5]. These elevated temperatures alter urban thermal dynamics, impacting wind flow, pollution dispersion, and human comfort. Urban thermal aerodynamics particularly affect pollution dispersion in street canyons, where heated surfaces and stratified conditions trap pollutants and worsen air quality [6,7,8].
Microclimates, described as localized climatic conditions, are crucial for understanding Urban Heat Island (UHI) and air quality interactions. Urban microclimates focus on phenomena ranging from neighborhood-level variations to narrow street canyons. For instance, research by Toparler et al. [9] highlights temperature effects in urban microclimates, reporting a typical UHI intensity of 2 °C to 5 °C, while broader studies incorporate wind aerodynamics, energy demand, and pollutant dispersion [10,11,12,13].
Studies on urban microclimates focus on the UHI phenomenon and computational methods for assessing microclimate conditions. Their analysis highlights the significant influence of urban morphology—such as building density, height, and surface materials—on the outdoor thermal environment [14]. For example, densely built areas with limited vegetation experience higher heat retention and reduced nighttime cooling, exacerbating the UHI effect [15].
Beyond the complexities of urban microclimates, air pollution poses another significant environmental challenge for cities. UHIs not only elevate ground-level air temperatures but also intensify air quality issues by increasing the formation and concentration of pollutants. Common pollutants such as nitrogen oxides (NOxs), volatile organic compounds (VOCs), and carbon monoxide (CO) are emitted by vehicles, industrial activities, and residential heating systems [15]. The relationship between UHIs and air pollution is particularly critical because they frequently coexist, compounding their adverse effects on urban populations [16]. The spread and concentration of pollutants in urban areas are heavily influenced by local wind patterns, urban dynamics, and temperature gradients. Elevated temperatures associated with UHIs accelerate atmospheric chemical reactions, promoting the formation of tropospheric ozone. For example, cities experiencing UHI effects, such as Los Angeles, have recorded ozone level increases of up to 15% during heat events [17]. High temperatures can also increase NOx emissions by approximately 25% during peak heat periods [16]. Elevated ozone and fine particulate matter levels drastically impact respiratory and cardiovascular health, with the U.S. Environmental Protection Agency (EPA) estimating 1000 to 2000 premature deaths annually due to poor air quality [18].
This paper investigates the relationship between urban microclimate parameters used to evaluate the UHI effect and poor air quality. The objectives are as follows: (1) analyze the parameters utilized for assessing both UHI and air quality issues; (2) investigate measurement methods for microclimate parameters, including recent advancements with fixed weather station networks and pedestrian-level devices; and (3) explore numerical modeling approaches for studying the urban microclimate.

2. Materials and Methods

2.1. Review Method

The methodology employed in this study follows the approach outlined by [19] and is detailed in Figure 1. The key scientific databases selected were Web of Science (WoS), SCOPUS, and Google Scholar, due to their broad multidisciplinary coverage. This selection of publications prioritized research focusing on the interactions between urban microclimates and air quality. Studies centered solely on meteorological-scale microclimates such as general weather observations, without addressing street- or neighborhood-scale phenomena, were excluded. The inclusion criteria were as follows: (1) publications in English to ensure international relevance; (2) works published from 1990 to March 2024, capturing both early and recent developments in Urban Heat Island (UHI) research; (3) studies specifically addressing the relationship between UHI and air quality, while excluding health-related papers that only referenced UHI’s health impacts; (4) studies involving real-world urban observations, with wind tunnel or laboratory-based investigations omitted; and (5) for multi-city studies, the largest city mentioned was used as a reference, where easily identifiable.

2.2. Selection Approach

Figure 1 illustrates the selection strategy for collecting necessary information for the review. Step 1 involved conducting research campaigns in March 2024 for each database using a set of search terms and logical rules for title, abstract, and keywords. English language articles published from 1990 onwards were selected. Additionally, 100 review articles were considered as reference to gain insights into the current research and ensure the review remains comprehensive and up to date by filling in any outdated content. Step 2 involved processing the output from Step 1 with a data cleaning procedure, including duplicate checks and elimination of inappropriate works. By the end of Step 2, 50 publications were collected. Step 3 involved skimming the titles and abstracts of each paper. Articles that only introduced UHI as a related topic or background information instead of providing critical analyses, including those that did not include local temperatures measurements, were eliminated. Based on the selection in Step 3, 30 articles were chosen for the review in Step 4. During this step, the collection was also divided into subcollections based on their main content for further analysis and discussions, such as trend monitoring, influential factors, impacts, and mitigation strategies.
The flowchart in Figure 2 outlines the methodology for analyzing the interconnections between UHI and air quality, emphasizing the relationship between these phenomena, the measurement techniques used, and the role of numerical modeling in understanding their dynamics. This approach structured the study’s data collection and simulation efforts, ensuring a thorough exploration of UHI’s impact on urban air quality.

3. Results

3.1. Defining UHI Microclimate and Air Quality Parameters

3.1.1. UHI Microclimate Parameters: Temperature and Wind

In urban areas, the interaction between temperature and wind significantly shapes the UHI microclimate. Concrete and urban materials significantly retain more heat than natural soils, acting as thermal reservoirs. This thermal retention results in surface temperatures being 2–8 °C higher in urban areas compared to rural surroundings, a difference observed by thermal remote sensing techniques [20]. Prevailing winds are significantly disrupted by urban morphology, with buildings and infrastructure causing localized air flow variations [21]. These structures influence the urban boundary layer, which extends to heights of 1–2 km, driving turbulent heat transport and contributing to stratified wind flow [22]. During the night, the formation of a meteorological thermal inversion traps warmer air closer to the ground, preventing its upward movement. This inversion, combined with reduced Sky View Factor (SVF) in urban areas, limits radiative cooling to the sky, leading to high temperatures. Urban surfaces that have absorbed heat during the day release it at night, further elevating nighttime temperatures. As a result, urban areas can experience temperatures 1–3 °C higher than adjacent rural areas due to the combination of limited cooling and trapped heat [23]. Buildings further act as barriers, reducing heat loss by radiative cooling and reinforcing the thermal gradient between urban and rural environments, emphasizing the intricate relationship between heat retention and wind dynamics within urban settings. Over the past two decades, rapid urbanization has intensified the UHI effect globally including high power consumption as summarized in Table 1 and Table 2.
Figure 3a illustrates the data from Table 1, depicting variations in mean UHI intensity across different cities over the past two decades. Figure 3b is derived from multiple studies, incorporating peak UHI intensity, wind speed, and surface temperature variations. Notable data are summarized in Table 3.
Wind speed significantly influences the intensity and distribution of UHI effects. Lower wind speeds reduce heat dissipation and increase heat retention in urban areas, intensifying UHI effects. Light winds (0.56–2.2 m/s) amplify UHI intensity [33]. Reduced wind speeds, often caused by dense urban morphology, hinder convective cooling and increase localized heating in urban areas [20]. Figure 4 illustrates the relationship between mean UHI intensity and wind speed, derived from studies found in Table 3. The plot shows the mean UHI values corresponding to different wind speeds, along with the standard deviation. The data reveal that UHI intensity tends to peak at moderate wind speeds around 2.5 m/s, with mean UHI values ranging from 3°C to 5 °C. As wind speed increases beyond 2.5 m/s, the mean UHI intensity declines, reaching approximately 2 °C at higher wind speeds.

3.1.2. UHI Microclimate Parameter: Urban Morphology

Urban design plays an important role in shaping the UHI effect, with factors such as urban canyon dimensions, aspect ratio, building height, ventilation corridors, built-up density, site planning, and the extent of greenery influencing microclimate dynamics [34,35]. The UHI effect becomes significantly pronounced when the aspect ratio (building height to street width) of urban canyons exceeds 1.5 as it traps heat and obstructs airflow [36,37]. Table 4 summarizes the impact of urban morphology on UHI effect. Higher aspect ratios reduce air circulation, intensify heat retention, and limit the dissipation of heat, resulting in elevated temperatures during both daytime and nighttime [38]. Figure 5 shows that higher aspect ratios (H/W) correlate with increased UHI intensity. At 0.42, UHI is 2.1 °C, rising to 2.5 °C at 1.0 and reaching 3.0–4.0 °C at 2.0. The highest UHI, 5.0 °C, occurs at 2.5 as suggested in Table 4.
Urban layout significantly affects microclimate parameters such as air and surface temperatures [39], and urbanization worsens the UHI effect due to heat retention and impervious surfaces [40]. Similarly, building density and the prevalence of impervious surfaces amplify UHI by trapping heat [41]. Urban morphology influences wind patterns and solar radiation, thereby shaping microclimates [42]. Figure 6 illustrates the relationship between aspect ratio (H/W) and wind speed (m/s). Wind speed decreases as aspect ratio increases, indicating restricted airflow in denser urban environments. At an aspect ratio of 0.42, wind speed is 3 m/s, dropping to 2.0 m/s or lower at 1.0. For aspect ratios of 2.0 or higher, wind speeds range between 1.5 and 3.0 m/s, emphasizing the influence of urban geometry on ventilation.
Recent studies further highlight the impact of urban morphology on the intensity of UHI. Over 90% of the Beijing Fifth Ring Road area experiences UHI effects, particularly in summer and autumn, with the Floor Area Ratio (FAR) being the most influential factor in summer and the height of the building in autumn [43]. Furthermore, high-rise residential buildings contribute to higher outdoor temperatures compared to low-rise buildings, while open spaces play a crucial role in mitigating local temperature increases, underscoring the importance of urban planning in managing the effects of UHI of UHI [44]. Table 5 presents a summary of key review papers that examine the impact of urban morphology on the Urban Heat Island (UHI) effect.
Furthermore, dense urban layouts and high-rise buildings act as barriers, reducing wind speeds and increasing the accumulation of pollutants [47]. Urban areas with poor ventilation can experience up to 40% higher concentrations of pollutants such as PM2.5 and ozone [48].

3.1.3. Poor Air Quality in Microscale Urban Environments

Several studies have demonstrated a significant relationship between tropospheric ozone formation and high ambient temperatures, underscoring the combined effects of urban air pollution and extreme heat. Papanastasiou et al. [49] conducted a comprehensive study in Greece, monitoring air quality at six urban and suburban sites in Athens, Thessaloniki, and Volos during heat waves and normal days across the summers of 2001–2010. Their analysis, which focused on O3, NO2, and PM10 levels, along with temperature and relative humidity in city centers, revealed significant air quality degradation during heat waves. Among the pollutants, PM10 showed the highest sensitivity, increasing by 25–38%, followed by NO2 (14–29%). O3 concentrations rose conservatively, not exceeding 12%. The heavily urbanized areas, due to their dense buildings, traffic, and industrial activity, tend to have higher levels of pollution compared to the rural areas. Figure 7 depicts the microscale urban environment, emphasizing the interplay of wind patterns, pollutant dispersion, and temperature variations. Warm southern winds carry heat from lower latitudes, which increases UHI intensity in urban areas, raising temperatures and worsening air quality by trapping pollutants. In contrast, northern winds, typically colder in winter, help cool the surface, reducing UHI intensity and improving air quality by dispersing pollutants. On days with high pollution levels, UHI intensity could exceed 5 °C [50].
Table 6 summarizes key studies analyzing the relationship between UHI, microclimate parameters, and air quality. The studies utilized various methods, such as remote sensing, temperature mapping, and sensor networks, to explore correlations between urban morphology, temperature, wind patterns, and pollutant levels. Several studies have further explored the correlation between UHI intensity, pollutant levels, and urban microclimate parameters. Schaefer et al. [51] demonstrated that dense urban morphologies and street canyons significantly restrict pollution dispersion, leading to the accumulation of particulate matter. Swamy et al. [52] identified a strong correlation between UHI intensity and higher levels of secondary air pollutants, particularly ozone, using satellite and ground-based sensing technologies. However, it is known that tropospheric ozone formation is not connected only with temperature. Temperature has the highest correlation with ozone concentration, but ozone is also connected with sun brightness, NOx, VOC, and PM. Lai and Cheng [25] showed that in Taichung, Taiwan, light winds (0.56–2.2 m/s) degraded air quality and intensified UHI effects, significantly increasing concentrations of pollutants such as NO2 and CO2 ( p < 0.05 ). Similarly, Fekih et al. [53] employed low-cost sensors to assess the relationship between temperature and air quality, finding a consistent correlation between pollutant concentrations and temperature variability across urban sensor nodes. Ulpiani et al. [54] further revealed that NO2 concentrations were strongly correlated with temperature changes, while wind speed showed an inverse relationship with CO levels.
Figure 8 illustrates how urban morphology significantly influences micrometeorology within the urban canopy, which, in turn, plays an essential role in shaping local air quality. Previous studies have emphasized the critical relationship between micrometeorology and urban air pollution [20,55,56]. Among the various meteorological factors, urban wind is particularly important, as it disperses pollutants across different areas. The trajectories of these pollutants is influenced by urban morphological characteristics, which highlight the crucial role of urban morphology in the management of air quality [57]. Figure 9 highlights the interconnected features of UHI, urban morphology, and poor air quality. It shows how urban morphology amplifies localized heat effects and shows that the overlap with poor air quality emphasizes how stagnant air and elevated temperatures contribute to pollutant accumulation. The colors in the figure represent different aspects of this relationship: yellow indicates factors related to Urban Heat Island (UHI), such as microclimate, vegetation, and canopy cover; blue represents aspects of poor air quality, including pollution, aerosols, and particulate matter; and green represents the shared parameters, such as wind speed, temperature, urban morphology, and wind direction, which influence both UHI effects and air quality.

3.2. Assessing UHI Microclimate and Air Quality Parameters

Evaluating UHI Microclimate Parameters

Cities have traditionally used fixed weather stations to collect UHI-related data [58,59]. Temperature and relative humidity are commonly measured using microelectromechanical system (MEMS) sensors, which are compact, integrate amplification, and include analogue-to-digital converters (ADC) accuracy and versatility [60]. Meanwhile, wind speed is often monitored using ultrasonic anemometers, which have a broad measurement range and are not limited by mechanical components [61]. Table 7 summarizes key studies on UHI microclimate measurement methods, highlighting the measurement techniques and variables used.
These ground-based stations typically average environmental conditions city-wide; however, the limited number of these devices is insufficient to capture the full heterogeneity of urban microclimates [68,69]. To address this, innovative techniques such as wearable devices and mobile surveys have emerged. Yadav and Sharma [69] used HOBO data loggers on vehicles to monitor Delhi’s conditions but were limited to roads. Nakayoshi et al. [70] used wearables to measure thermal variables but mainly focused on physiology but excluded urban conditions.
The variability in temperature and wind speed across different studies underscores the spatial differences in urban microclimates. Soltani and Sharifi [62] found a temperature fluctuation of 5.3 ± 0.4 °C over 16.3 km, reflecting localized changes. In contrast, Taha et al. [63] reported a narrower temperature range of 2.3 to 3.3 ° C on 27 km, mainly due to the unique characteristics of each location. Wind speed variability, which generally ranged from 0.5 to 3 m/s in most studies, was wider in Taha et al. [63], showing a range of 1 to 5 m/s. These findings highlight that temperature variability is strongly influenced by the spatial scale of measurement, with smaller areas experiencing more localized fluctuations and larger areas exhibiting broader variations. Wind speed, while consistent in most studies, also tends to show greater variation over larger spatial scales. Table 8 presents the scale of study areas and the observed variability in temperature and wind speed across different urban environments. Figure 10 illustrates the relationship between city scale and temperature variability derived from Table 8, showing that as the city scale decreases, temperature fluctuations increase, peaking at 5 °C at mid-range scale.
Urban morphology plays a critical role in understanding and mitigating the Urban Heat Island (UHI) effect by influencing thermal characteristics across spatial scales. At the macro level, large-scale urban morphological factors such as land use patterns, vegetation cover, and surface albedo directly affect regional temperature distributions, often measured using satellite imagery and remote sensing [21]. Conversely, pedestrian-level measurements focus on the microscale interactions between urban form elements like building density, street canyon geometry, and surface materials, which shape localized thermal comfort and walkability [3,72]. The interplay between macro and micro scales emphasizes the need for integrated analyses, as meso-scale studies can bridge gaps by linking neighborhood-level morphologies to broader UHI patterns [73]. Table 9 summarizes key studies examining the methods and metrics of urban morphology in relation to the UHI index.

3.3. Assessing UHI Microclimate and Air Quality Parameters

3.3.1. Evaluating UHI Microclimate Parameters

Air quality measurement in urban areas involves advanced techniques and equipment designed to capture diverse pollutant concentrations and micro-meteorological conditions. Electrochemical sensors are widely used for gases like NO2 and O3 due to their high sensitivity, detecting concentrations at low parts per billion (ppb) levels. CO2 sensing often employs non-dispersive infrared (NDIR) sensors known for their long-term stability and specificity [76].
For particulate matter (PM), gravimetric methods are the standard for daily measurements, whereas laser scattering processes provide continuous monitoring [77]. Mobile monitors and equipment like the DustScan Sentinel Model 3030 and Casella ETL2000 enable real-time and location-specific data collection on pollutants such as PM2.5, NO2, O3, and CO. These monitors are strategically placed at 2 m heights within street blocks to ensure consistency and are calibrated regularly for accuracy. Statistical methods based on spatial analysis, such as Santamouris’s [71] 200 m by 200 m spatial regime, are used to quantify pollutant variation concerning urban morphological variables.

3.3.2. Urban Heat Island and Air Quality Relationship

The relationship between poor air quality and the Urban Heat Island (UHI) effect is intricately linked to urban morphology, temperature, and wind speed. Urban structures, such as dense layouts and street canyons, impede pollutant dispersion and exacerbate UHI intensity [51,78]. Elevated temperatures associated with UHI enhance secondary pollutant formation, as demonstrated by Swamy et al. [52], who found a direct correlation between UHI intensity and ozone levels.
Wind speed further mediates pollutant distribution; Lai and Cheng [25] reported that light winds (0.56–2.2 m/s) exacerbate air stagnation, increasing concentrations of NO2, CO2, and CO in urban hotspots.
Advanced sensor networks, such as those used by Fekih et al. [76], reveal significant variability in temperature and pollutant concentrations across urban areas, highlighting the influence of urban microclimates on air quality. Feizazideh and Blaschke [79] demonstrated how UHI zones with elevated land surface temperatures (LST) correlate with higher PM10 levels, emphasizing the need for integrated monitoring systems to address the combined impacts of urban morphology and climatic conditions.
Table 10 highlights the interconnection between Urban Heat Island (UHI) effects and air quality, showing that UHI intensity, quantified by temperature increases ( Δ T ranging from 1.5–4.5 °C) and reduced wind speeds (0.8–2.2 m/s), exacerbates pollutant concentrations like PM10 (30–70 µg/m3), PM2.5 (20–50 µg/m3), NO2 (15–50 µg/m3), and O3 (55–90 µg/m3). These studies emphasize the role of monitoring technologies and spatial analysis in understanding pollutant dynamics within UHI-affected zones.

3.4. Numerical Modeling of UHI Microclimate and Air Quality Parameters

Recent methodologies integrate experimental data collection and simulations from numerical models to analyze complex urban microclimates [82,87]. Experimental campaigns in urban areas have revealed spatial heterogeneity in temperature and wind flow, which have been validated using numerical models [88,89]. These studies demonstrate the utility of numerical models in addressing UHI microclimate parameters [9].

3.4.1. Computational Fluid Dynamics Software for Microclimate Modeling

Computational Fluid Dynamics (CFD) is frequently used to assess and predict urban microclimate parameters. It solves the governing equations of fluid flow and heat transfer to simulate airflow patterns and temperature distributions in urban areas. These models incorporate complex urban geometries and land use characteristics to predict microclimate variations. Urban Computational Fluid Dynamics simulations are typically performed using specialized software tailored to simulate fluid flow around complex geometries. These simulations consider various factors, including fluid properties and urban features. In recent years, CFD simulations of urban microclimates have been dominated by the commercial tool ANSYS Fluent 2025 R1, followed by OpenFOAM and ENVI-met. These three tools accounted for more than 90% of all simulations. ENVI-met (Windows-based) and OpenFOAM (Linux/Ubuntu) tools, along with related solver packages, have been widely used for simulating urban canopy models with three-dimensional complex geometries. Kadioglu and Ambrosini et al. simulated several thermodynamic parameters and analyzed a micro-scale study for measuring the interaction between microclimate and urban design [90]. Figure 11 illustrates the percentage of CFD modeling software used over the past two decades.
ENVI-met, a piece of three-dimensional computational fluid dynamics (CFD) software, has emerged as a widely used tool for analyzing urban microclimates and their interactions with environmental parameters. Its ability to simulate surface–plant–air interactions at fine resolutions makes it particularly effective in understanding the dynamics of Urban Heat Island (UHI) and pedestrian thermal comfort [91]. Recent studies integrating ENVI-met V4 with experimental monitoring have demonstrated its efficacy in assessing the impact of building density, building materials, and greenery on microclimate variations [87,92]. ENVI-met uses Reynolds–averaged Navier–Stokes (RANS) equations and turbulence kinetic energy (TKE) models to simulate wind flow, temperature, and humidity across urban environments with high spatial resolution [88]. By accounting for factors such as long-wave radiation, heat exchange, and urban morphology, the software provides a robust framework to evaluate outdoor thermal comfort and inform sustainable urban planning [9,89].
Beyond software tools, the accuracy of urban CFD models depends on several key specifications, including turbulence modeling approaches, boundary conditions, and grid resolution. Boundary conditions play an important role in CFD urban simulations. Typical boundary conditions include inflow velocity profiles based on atmospheric boundary layer characteristics, wall functions for surfaces, and radiative heat transfer models for surface energy balance calculations. To enhance accuracy, recent studies have integrated CFD models with field measurements and remose sensing data. High-resolution digital elevation models (DEMs), geographic information system (GIS) data, and LiDAR-based urban morphology datasets help improve terrain representation and surface roughness parameterization. These enhancements contribute to more reliable simulations of urban wind fields, heat fluxes, and pollutant dispersion patterns [93,94].

3.4.2. Wind and Temperature Dynamics

Some studies that have explored the relationship between urban microclimate parameters such as air temperature, wind speed, and direction using modeling tools are summarized in Table 11. Fatima et al. [95] focused on how tall buildings alter wind profiles, thereby affecting the relationship between air temperature and wind patterns in urban environments. Zoras et al. [96] employed a computational fluid dynamics (CFD) model to monitor thermal conditions in urban microclimates, emphasizing the interaction between wind speed and LST.
Yang et al. [88] evaluated the performance of ENVI-met in predicting spatial air temperature distribution, highlighting its application in urban climate studies. Brozovsky et al. [97] conducted simulations in Trondheim using steady-state RANS modeling and reported ENVI-met’s prediction errors of 1.35 m/s for wind speed and 0.93 °C for air temperature, pointing out its moderate accuracy. Berardi and Wang [98] found that ENVI-met performs better in summer than winter, with simulation errors ranging from 0.2 °C to 3.9 °C during winter days and 3.1 °C to 5.5 °C on summer days, indicating a seasonal variation in model accuracy. These studies demonstrate the effectiveness of CFD and ENVI-met in analyzing urban microclimate dynamics, although model performance can vary by season and specific parameters.

3.5. Microclimate Air Quality

The presence of urban canopies is simulated using the “annihilation method”, namely, by replacing urban surface with rural surface most typical for the surroundings [27]. The presence of urbanized land resulted in a pronounced temperature increase (up to 2 °C), wind slowdown (by up to 2 m/s), and the enhancement of the vertical turbulent diffusivity (Kv), which quantifies the vertical mixing of momentum, heat, and pollutants in the atmosphere, under all considered calculation methods (from nearly 1 up to 30 m2/s at the surface and 100 m2/s at higher elevations). This trend propagated to pollutants’ dispersion, with ozone increasing by 0.4 up to 4 ppbv (maximum 10% difference between summer and winter) and PM2.5 decreasing by up to 1–2 µg/m3 in summer and winter, respectively [82].

Coupled Simulations: Weather Research and Forecasting (WRF)

Coupled simulations have enabled comprehensive investigations across various timescales and spatial scales in urban microclimate (UMC) research. For outdoor-oriented studies, larger-scale models like the Weather Research and Forecasting (WRF) model are utilized to provide detailed boundary conditions, including solar radiation, building surface temperatures, and wind patterns in urban environments [99]. The WRF model is adaptable and has been extensively applied in UMC research. Scaled-down versions of WRF have been employed to understand the canopy effect within urban environments [100,101]. This approach offers valuable insights into how mesoscale interactions influence microlevel urban climates, contributing to more accurate urban climate predictions and improved mitigation strategies [102].
Integrating WRF with computational fluid dynamics (CFD) models has further enhanced the accuracy of urban microclimate simulations. For instance, coupling WRF with models like CityFFD enables detailed analysis of urban heatwaves and their impact on microclimates [94]. Similarly, coupling WRF with the PALM-4U microscale model facilitates realistic urban microclimate simulations, incorporating detailed urban canopy parameters [99,102]. Such coupled modeling approaches are instrumental in advancing our understanding of urban microclimates, thereby informing the development of effective mitigation strategies for urban heat islands and enhancing overall urban sustainability.

4. Discussion

The interplay between Urban Heat Island (UHI) microclimate parameters—temperature, wind speed, and urban morphology—and air quality presents a complex environmental challenge. Elevated temperatures from UHI effects accelerate atmospheric chemical reactions, leading to increased ozone formation, while reduced wind speeds in dense urban areas hinder pollutant dispersion. Urban morphology, particularly street canyon geometry, building density, and vegetation cover, directly influences pollutant retention and thermal dynamics. These factors create localized variations in air quality and thermal comfort that cannot be fully captured by traditional fixed monitoring stations.
Pedestrian-level measurements using portable sensors and mobile platforms offer a solution by providing high-resolution data that better reflect urban-scale microclimatic variations. These measurements enhance our understanding of how urban morphology modulates pollutant distribution and temperature fluctuations, which is crucial for developing effective mitigation strategies. Additionally, ground-based remote sensing technologies complement existing monitoring networks by capturing real-time fluctuations in environmental parameters, enabling more dynamic assessments of urban air quality and thermal conditions.
Numerical modeling serves as a powerful tool in integrating observational data to predict interactions between UHI effects and air quality. Computational fluid dynamics (CFD) models, along with urban climate modeling tools such as ENVI-met and OpenFOAM, have been widely used to analyze urban airflow, pollutant dispersion, and temperature distribution under different urban configurations. Coupled simulations incorporating mesoscale models like the Weather Research and Forecasting (WRF) model provide further insights by bridging micro- and regional-scale interactions. The effectiveness of these models, however, is contingent upon high-quality validation data, emphasizing the need for comprehensive observational networks that incorporate both stationary and pedestrian-scale measurements.
By combining mobile sensing technologies with advanced numerical modeling, urban planners and policymakers can implement data-driven strategies to mitigate the adverse effects of UHI and poor air quality. These approaches could include optimizing urban geometry for improved ventilation, implementing reflective and permeable materials to reduce heat accumulation, and increasing vegetation coverage to enhance cooling and air purification. A synergistic approach integrating real-time monitoring and predictive simulations is essential for designing resilient urban environments that prioritize both climate adaptation and public health.

5. Conclusions

This study examined the significant spatial and temporal variations in urban microclimates, particularly in relation to the Urban Heat Island (UHI) phenomenon and its impact on air quality. In urban areas, surface temperatures in UHI zones can reach 10–12 °C higher than surrounding rural areas, with temperature differences peaking during summer months in cities such as New York, Chicago, and Tokyo. Concurrently, pollutant concentrations, including PM10 and NO2, are consistently higher by 20–30% in UHI-affected areas compared to suburban zones. These elevated pollutant levels are linked to the intensified UHI effect, as higher temperatures promote the release of volatile organic compounds and contribute to the formation of ground-level ozone, further aggravating air pollution.
Despite these findings, current urban environmental monitoring remains limited in scope, as most studies rely on fixed, stationary monitoring stations that fail to capture variations at smaller, more localized scales. For example, research has shown discrepancies of up to 5–8 °C in temperature differences between shaded and sun-exposed areas within urban street canyons. These variations underscore the need for more granular data to fully understand the complexities of urban microclimates and the interplay between temperature, air quality, and urban geometry.
Computational fluid dynamics (CFD) simulations and numerical modeling tools such as ENVI-met and OpenFOAM have been employed to analyze airflow, temperature distribution, and pollutant dispersion in urban environments. These models have provided valuable insights into the influence of building morphology, vegetation, and material properties on microclimate variations. However, their accuracy is often constrained by the availability of high-resolution, real-world validation data. Enhancing these models with fine-scale empirical data would significantly improve predictive capabilities and the formulation of targeted mitigation strategies.
To address this gap, there is a clear need for advanced ground-based remote sensing systems, particularly at the pedestrian scale. Mobile sensor networks integrated within urban environments could provide real-time, high-resolution data that capture the dynamic nature of environmental conditions. These systems would enable more accurate measurement of temperature, particulate matter, and gaseous pollutants at the micro-scale, thereby supporting the development of more effective and localized mitigation strategies, including urban planning adjustments, the incorporation of green infrastructure, and the use of cooling techniques.

Author Contributions

Conceptualization: M.D. and L.K.M.; methodology: M.D.; software: K.K.R., L.K.M. and M.D.; validation: H.A., K.K.R. and M.D.; formal analysis: M.D., H.A. and L.K.M.; investigation: L.K.M. and M.D.; resources: M.D.; data curation: M.D. and L.K.M.; writing—original draft preparation: L.K.M. and M.D.; writing—review and editing: H.A. and M.D.; supervision: M.D.; project administration: M.D.; funding acquisition: M.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF 19 2 0120. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation herein.

Data Availability Statement

The data supporting the findings of this study are available from various sources referenced in the literature, including urban microclimate, Urban Heat Island (UHI) data, thermal imagery, and air quality measurements. These sources include books such as Urban Heat Island Mitigation by Santamouris, articles from journals like Environmental Pollution and Urban Climate, and websites such as the U.S. Environmental Protection Agency (EPA) and ENVI-met. Additionally, data can be accessed through environmental research platforms like the European Environment Agency (EEA) and government databases such as the U.S. National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Department of Energy’s (DOE) Energy Information Administration (EIA).

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Review method flowchart.
Figure 1. Review method flowchart.
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Figure 2. Methodological framework for analyzing UHI and air quality interconnection.
Figure 2. Methodological framework for analyzing UHI and air quality interconnection.
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Figure 3. (a) Mean UHI (°C) over the years. (b) Mean UHI (°C) vs. surface temperature (°C).
Figure 3. (a) Mean UHI (°C) over the years. (b) Mean UHI (°C) vs. surface temperature (°C).
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Figure 4. Mean UHI (°C) vs. wind speed (m/s).
Figure 4. Mean UHI (°C) vs. wind speed (m/s).
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Figure 5. Relationship between Urban Heat Island (UHI) Intensity and aspect ratio (H/W).
Figure 5. Relationship between Urban Heat Island (UHI) Intensity and aspect ratio (H/W).
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Figure 6. Impact of aspect ratio on wind speed.
Figure 6. Impact of aspect ratio on wind speed.
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Figure 7. Interplay between microclimate parameters and air quality.
Figure 7. Interplay between microclimate parameters and air quality.
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Figure 8. Relationship between street-level air pollution and urban fabric.
Figure 8. Relationship between street-level air pollution and urban fabric.
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Figure 9. UHI parameters interconnection with poor air quality.
Figure 9. UHI parameters interconnection with poor air quality.
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Figure 10. Temperature and wind speed variability vs. experimental site scale.
Figure 10. Temperature and wind speed variability vs. experimental site scale.
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Figure 11. Percentage of CFD modeling software used over the past two decades.
Figure 11. Percentage of CFD modeling software used over the past two decades.
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Table 1. Urban Heat Island (UHI) impacts over the past two decades.
Table 1. Urban Heat Island (UHI) impacts over the past two decades.
Ref. and YearCityMean UHI (°C)
Junk et al., 2003 [24]Trier1.2
Lai and Cheng, 2009 [25]Taichung3.0
Jin et al., 2011 [26]Shanghai2.0
Bakarman et al., 2015 [27]Riyadh2.1–3.4
Boice et al., 2018 [28]San Antonio1.5
Kaur and Somvanshi, 2022 [29]Delhi4.0
Sen and Roesler, 2020 [30]Athens3.0–5.0
Rajagopalan et al., 2014 [31]Muar2.5–3.0
Hang and Chen, 2022 [32]Guangzhou1.2–1.5
Table 2. Population size and power consumption by city.
Table 2. Population size and power consumption by city.
CityPopulation Size (Approx.)Power Consumption (GWh/year)
Trier110,000N/A
Taichung2,800,0008000
Shanghai24,150,00063,314.1
Riyadh7,600,00018,547
San Antonio1,500,00015,252.7
Delhi19,000,00023,640
Athens3,150,0007956.6
Muar300,000N/A
Guangzhou15,000,00042,000
Table 3. Urban Heat Island Intensity (UHII) and microclimate parameters.
Table 3. Urban Heat Island Intensity (UHII) and microclimate parameters.
Ref. and YearCityMean UHIIWind Speed (m/s)Temperature (°C)Aspect Ratio/Size
Bakarman et al. (2015) [27]Riyadh3.43.048 (peak)2.2 (Deep Canyon)
38 (peak)0.42 (Shallow Canyon)
2.1
Sen and Roesler (2020) [30]Athens3.01.538 to 421.0
2.0 40 to 452.0
4.02.542 to 452.5
5.0
Rajagopalan et al. (2014) [31]Muar2.52.034 to 361.0
2.5 36 to 382.0
3.0
Hang and Chen (2022) [32]Guangzhou3.01.230 to 331.0
1.5 33 to 362.0
4.0
Table 4. Mean UHI Intensity, wind speed, and aspect ratio from various studies.
Table 4. Mean UHI Intensity, wind speed, and aspect ratio from various studies.
Ref. and YearMean UHIIWind Speed (m/s)Aspect Ratio/Size
Bakarman et al. (2015) [27]3.43.02.2 (Deep Canyon)
0.42 (Shallow Canyon)
2.1
Sen and Roesler (2020) [30]3.01.51.0
4.02.02.0
5.02.52.5
Rajagopalan et al. (2014) [31]2.52.01.0
3.02.52.0
Hang and Chen (2022) [32]3.01.21.0
4.01.52.0
Table 5. Summary of papers defining the impact of urban morphology as a UHI parameter.
Table 5. Summary of papers defining the impact of urban morphology as a UHI parameter.
Review PapersKey Findings
Yang et al. (2021) [34]This study found that urban morphology influences wind patterns and solar radiation, which in turn affects the microclimate.
Zaki et al. (2020) [39]This study concluded that urban layout influences microclimate parameters (e.g., air and surface temperatures).
Tsoka et al. (2018) [40]It was reported that building layout, density, and impervious surfaces amplify UHI effects by trapping heat.
Chapman et al. (2017) [42]This study found that rapid urbanization significantly amplifies the UHI effect, as heat is retained by impervious surfaces (e.g., roads and pavements) and buildings.
Liu et al. (2023) [43]This study found that urban morphology significantly influences UHI, with over 90% of the Beijing Fifth Ring Road area experiencing UHI effects, particularly in summer and autumn. Seasonal variations were observed, with FAR most correlated with UHI in summer and building height (HIGH) in autumn.
Xiang et al. (2023) [44]This study concluded that high-rise residential buildings contribute to higher outdoor temperatures compared to low-rise buildings. Open spaces play a crucial role in reducing neighborhood temperatures, highlighting the impact of urban morphology on UHI.
Tsong et al. (2018) [45]This study established the relationship between various microclimate parameters (e.g., air temperature, wind speed, etc.) and various urban morphologies. Variations in urban morphology contribute to non-uniform temperature distribution, affecting microclimate, thus UHI effects.
Emmanuel and Fernando (2007) [46]This study provided evidence on the impacts of urban morphology on UHI intensity. It was found that building density affects air temperature, with higher albedo and density yielding cooler temperatures.
Table 6. Summary of key studies analyzing the relationship between UHI, microclimate parameters, and air quality.
Table 6. Summary of key studies analyzing the relationship between UHI, microclimate parameters, and air quality.
Review PapersKey Findings
Schaefer et al. (2021) [51]
  • This study assessed local heat stress and air quality in urban environments using remote sensing technologies and temperature mapping.
  • Dense urban morphology restricts air pollution dispersion, particularly in street canyons, leading to increased particulate matter (PM) accumulation.
Swamy et al. (2024) [52]
  • This study established the correlation between UHI intensity and pollutant levels using temperature and air quality data from satellite imagery and ground-based sensing measurements.
  • It was found that elevated temperatures are linked to higher concentrations of secondary pollutants, particularly ozone.
Feikh et al. (2020) [53]
  • This study integrated low-cost sensors for air quality measurements with temperature monitoring in urban areas to assess the correlation between air quality and UHI.
  • Temperature sensor nodes recorded varying temperature values due to their location, impacting exposure to sun and wind direction.
  • Daytime temperature difference: Average of 34.8 °C (sensor nodes) vs. 32.7 °C (reference station) with a p-value of 0.021 (statistical significance of the temperature difference between sensor nodes (34.8 °C) and the reference station (32.7 °C)).
  • Maximum RMSE for temperature: 1.7 °C.
  • PM2.5 RMSE: Low, indicating consistent readings across nodes.
  • Correlation for PM concentrations: Consistent across all nodes despite differing placements.
Ulpiani et al. (2022) [54]
  • This study investigated the inter-correlation between urban microclimate parameters and pollutant concentrations using an urban sensor network.
  • The study concluded that temperature changes are correlated with nitrogen dioxide (NO2) concentrations variability.
  • Wind speed was also found to be correlated with carbon monoxide (CO).
Table 7. Key studies on UHI microclimate measurement methods.
Table 7. Key studies on UHI microclimate measurement methods.
Ref. and YearMethod and Variables
Soltani and Sharifi (2017) [62]Automobile, Tair, RH
Taha et al. (2018) [63]Automobile, Tair, J, φ , h
Sun et al. (2019) [64]Motor Vehicle, Tair, RH
Jacobs et al. (2019) [65]Automobile, Tair, RH, WS, SR
Alonso, L. (2020) [66]Wearable, Tair, RH
Rodrigez et al. (2020) [67]Bicycle, Tair, J, φ , h
Table 8. Experimental site scale vs. temperature and wind variability.
Table 8. Experimental site scale vs. temperature and wind variability.
ReferenceScaleTemp. Variability (°C)Wind Speed Variability
Soltani and Sharifi [62]16.3 km5.3 ± 0.4Not reported
Taha et al. [63]27 km2.3–3.30.5–3
Sun et al. [64]N/A2–3.51–5
Jacobs et al. [65]306 km2 51–4
Alonso, L. [66]N/A12.8–18.2Not reported
Santamouris et al. [71]70 km9–100.5–3
Rodrigez et al. [67]22.04 km2.2–3.71–5
Table 9. Methods and metrics of urban morphology in relation to the UHI index.
Table 9. Methods and metrics of urban morphology in relation to the UHI index.
Ref. and YearRelation to UHI
Steward and Oke (2012) [57]Offers standardized framework for assessing UHI intensity.
Ng et al. (2012) [73]Shows UHI mitigation through green infrastructures.
Emmanuel and Krüger (2012) [61]Discusses urban design principles to reduce UHI effects.
Bechtel et al. (2015) [74]Utilizes remote sensing to map urban morphology and its impacts on UHI.
Weng and Fu (2016) [75]Assesses the relationship between urban morphology and surface temperature variations.
Table 10. Findings from studies on UHI and pollution dispersion.
Table 10. Findings from studies on UHI and pollution dispersion.
Ref. and YearFindings
Wang et al. (2020) [80]Investigated cooling effects of urban greenery on UHI and pollutant dispersion.
Peng et al. (2012) [81]Explored UHI effects globally using MODIS satellite data; correlations with pollutants.
Hu et al. (2017) [82]Analyzed seasonal temperature and pollution changes in UHI zones.
Chakraborty et al. (2019) [83]Assessed socioeconomic factors influencing UHI and pollution exposure.
Tong et al. (2016) [84]Investigated the relationship between building density and pollutant dispersion.
Zhao et al. (2023) [85]Discussed experimental investigations using wind and water tunnels to model multiscale, multi-physics phenomena of the urban climate, including pollutant dispersion and urban airflow.
Teutscher et al. (2025) [86]Introduced a digital urban twin framework integrating real-time meteorological data and simulations to analyze pollution dispersion, highlighting the impact of wind patterns on urban air quality.
Table 11. CFD simulation of microclimate parameters.
Table 11. CFD simulation of microclimate parameters.
Ref & YearFindings
Fatima et al. (2017) [95]Daytime simulations for single- and multilayer models were found in good correlation. Slab models identified to have the highest rate of fall in night temperature.
Zoras et al. (2014) [96]Use of CFD model to monitor the thermal conditions of urban microclimates.
Yang et al. (2013) [88]Examined the performance of ENVI-met on the prediction of spatial distribution of air temperature.
Brozovsky et al. (2021) [97]Studied Trondheim (0.16 km2) using steady RANS. NRMSE results: ANSYS Fluent 2020.R1 (3 m resolution); wind speed error: 1.35 m/s; and air temperature error: 0.93 °C
Berardi and Wang (2016) [98]ENVI-met performs better in summer than in winter. Maximum Ta difference between simulation and measurement: winter (0.2  °C–3.9 °C) and summer (3.1 °C–5.5 °C).
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Mandjoupa, L.K.; Roman, K.K.; Azam, H.; Denis, M. Analyzing Urban Microclimate (UMC) Parameters and Comprehensive Review of UHI and Air Quality Interconnections. Environments 2025, 12, 104. https://doi.org/10.3390/environments12040104

AMA Style

Mandjoupa LK, Roman KK, Azam H, Denis M. Analyzing Urban Microclimate (UMC) Parameters and Comprehensive Review of UHI and Air Quality Interconnections. Environments. 2025; 12(4):104. https://doi.org/10.3390/environments12040104

Chicago/Turabian Style

Mandjoupa, Lirane Kertesse, Kibria K. Roman, Hossain Azam, and Max Denis. 2025. "Analyzing Urban Microclimate (UMC) Parameters and Comprehensive Review of UHI and Air Quality Interconnections" Environments 12, no. 4: 104. https://doi.org/10.3390/environments12040104

APA Style

Mandjoupa, L. K., Roman, K. K., Azam, H., & Denis, M. (2025). Analyzing Urban Microclimate (UMC) Parameters and Comprehensive Review of UHI and Air Quality Interconnections. Environments, 12(4), 104. https://doi.org/10.3390/environments12040104

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