Correlation Between Albedo and Aging of Construction Materials Impacting Urban Heat Island Effects

Abstract

The built environment influences urban quality of life, particularly through material properties and design decisions that affect thermal comfort, energy consumption, and environmental performance. Among the physical parameters shaping urban microclimates, surface reflectivity—albedo plays a central role in regulating both surface and ambient temperatures. While high-albedo materials are widely recognized for mitigating the urban heat island (UHI) effect and lowering energy demand, limited attention has been given to how material aging alters albedo and, by extension, thermal performance over time. This study investigates that relationship through field measurements conducted at 18 outdoor locations in Xanthi, Greece, across four dates with varying environmental conditions. Variables such as material color, age, and temperature were analyzed through statistical methods and linear regression. Results confirmed a strong correlation between color and albedo and identified a statistically significant relationship between aging and albedo. Additionally, the expected inverse correlation between albedo and surface temperature was reaffirmed. These findings underscore the dynamic nature of material performance and highlight the need for incorporating aging behavior into sustainable urban design. The study contributes data to the field and supports the development of long-term strategies in urban planning and maintenance aimed at preserving the reflective efficiency of surface materials.

1. Introduction

In the contemporary global context, a marked trend of rural-to-urban migration is observed across many regions of the world. As populations concentrate in urban centers, this shift places increasing pressure on the built environment, leading to a range of environmental challenges—most notably those linked to urban climate modification. One such prominent challenge is the urban heat island (UHI) effect, a phenomenon where urban areas experience higher temperatures than their rural surroundings due to human activities, dense infrastructure, and surface material properties [1,2,3,4]. This temperature disparity is not only a technical or environmental concern but also a socioeconomic one, as it directly influences the health, productivity, and comfort of urban residents. The UHI effect, which has been extensively documented in many global cities, is particularly relevant to the case of Xanthi, Greece, where the current study has conducted. Like other medium-sized Mediterranean cities [5], Xanthi faces in-creased exposure to heat-related stresses that are amplified by urban morphology and surface characteristics [6].

Within the urban microclimate, one of the most critical factors affecting thermal behavior is albedo, defined as the fraction of solar radiation reflected by a surface. Albedo plays a key role in regulating surface and ambient air temperatures, and by extension, affects thermal comfort levels and energy consumption patterns in buildings albedo is influenced by several physical characteristics of surface materials—chief among them being color [7], texture or roughness [8], and potentially, the material’s age and condition over time [9]. Despite a growing body of literature addressing the relationship between albedo and variables such as energy performance, indoor and outdoor temperature regulation, and material selection in sustainable construction, there remains a notable gap in understanding how aging processes impact albedo values in real world urban environments.

This gap is particularly significant when considering the long term performance and maintenance of reflective surfaces. While new materials may be chosen based on their high initial reflectivity, the effectiveness of such materials in mitigating heat and maintaining thermal comfort may diminish as they age. The economic, environmental, and practical implications of surface degradation—such as the accumulation of dirt, wear from weather conditions, and material fading—are still not fully understood. Very few studies offer quantitative analysis on how the albedo of materials evolves over time, or what interventions (e.g., cleaning, repainting, or resurfacing) may be needed to preserve their functionality.

A substantial body of research supports the use of high albedo materials for lowering ambient temperatures and improving energy efficiency [10,11,12]. These materials reflect a larger portion of solar radiation, helping to reduce heat absorption and sur-face temperatures in both outdoor paving and building envelopes. As a result, high albedo materials are frequently cited as cost effective and passive strategies for mitigating UHI effects and reducing cooling loads during warm periods [11,12]. However, the relationship between albedo and human thermal comfort—which includes psychological, physiological, and environmental dimensions—is far more complex. Some studies report positive outcomes: for instance, Baniassadi et al. demonstrated that highly reflective roofs can improve indoor comfort in non-air-conditioned buildings [11]. In contrast, Andreou E. [13] found that changes in albedo had no significant effect on thermal comfort as measured by PET (Physiological Equivalent Temperature). More critically, other researchers have argued that on extremely hot days, surfaces with high albedo can create uncomfortable glare and increased radiative exposure, potentially worsening thermal perception for pedestrians [12,14,15]. These conflicting findings suggest that while albedo is an important variable, it does not operate in isolation. Factors such as the geometry of the built environment, surface orientation and slope, local wind conditions, and the function of the material within the urban fabric must all be considered when evaluating thermal comfort outcomes.

Among all climatic variables, air temperature exerts the most direct influence on thermal comfort and building energy consumption [6,16]. It is, however, closely linked with other physical variables such as solar radiation [17], surface albedo [10,12], and surface temperature of the materials in question. Each of these interacts with local humidity levels and material conductivity, both of which further modulate how heat is stored and released. Importantly, surface temperatures are not only a driver of thermal discomfort but also a critical indicator for energy efficiency in urban planning. High surface temperatures may accelerate material degradation, increase cooling demands, and reduce the effectiveness of passive design strategies. In addition wind speed, air-flow patterns, and humidity all influence thermal perception and energy usage in nuanced ways [18,19]. Therefore, understanding the combined effect of these variables, particularly through quantitative correlations, becomes essential in optimizing urban climate adaptation strategies.

While many researchers have used simulation tools and empirical charts to ex-amine the relationships among these variables [12,14,20], fewer studies have focused on real world data derived from field measurements across diverse material types and aging stages. Approaches often include simulation software [6,21], comfort indices (e.g., PET or UTCI), or subjective methods such as the user surveys to assess how material choices influence comfort perception [6,14].

Numerous scientists have endeavored to establish links between the physical properties of building materials, energy consumption, temperature, and thermal comfort. While the use of high-albedo materials is demonstrated to reduce temperatures in both indoor and outdoor settings and decrease energy consumption during hot days [10,11,12]. The varying findings on its impact on thermal comfort highlight the complexity of this relationship [11,12,13,14,15].

This study seeks to advance the field by exploring underexamined correlations, specifically the relationship between material aging and albedo performance. It builds upon previous findings by introducing a novel dataset of field measurements taken in Xanthi, Greece—in Mediterranean city with climatic and architectural characteristics that make a representative case for broader application.

In addition to addressing scientific gaps, this research aligns with broader sustainability goals and established sustainability assessment frameworks. Certification systems such as LEED (Leadership in Energy and Environmental Design) explicitly account both initial and aged solar reflectivity of materials in evaluating the sustainability of urban developments [22]. Similarly, the Sustainable Sites Initiative (SITES) recommends using high reflectivity materials in hardscape designs and mandates maintenance practices, such as periodic cleaning, to retain their performance over time [23]. Integrating these insights into local planning can aid cities like Xanti in mitigating heat-related stress and supporting climate resilient infrastructure.

The central objectives of this study are twofold: first go to investigate how the physical aging of building materials influences their albedo; and second, to determine whether managing or restoring albedo through maintenance can provide meaningful bioclimatic and environmental benefits. To achieve these goals, the paper provides A theoretical foundation regarding relevant physical properties common reviews key contributions from existing literature, and presents statistical analysis data collected from 18 urban locations. The methodology, results, and conclusions aim to inform future decisions regarding urban surface materials selection, maintenance protocols, and climate responsive planning strategies.

2. Materials and Methods

The design and implementation of the study were carefully structured (Figure 1) to ensure the reliability and representativeness of the measurements conducted. The selection of the 18 measurement locations was guided by multiple criteria: proximity and accessibility for repeated monitoring, the presence of surface materials with known installation timelines, and the appropriateness of these materials in terms of their exposure to environmental conditions. These factors were essential for forming meaningful comparative groups based on material type, color and aging characteristics. All measurement points were situated within the Campus of Polytechnic School of the Democritus University of Thrace (DUTH), located near the city of Xanthi in northeastern Greece. This location offered a controlled suburban setting where various building and paving materials have been exposed to outdoor conditions for different periods, providing a richer dataset for observing age-related changes in albedo.

The 18 selected locations (

Table 1. Details of measurement points.

No.	Orientation	Color	Year of Construction (-)	Age (Years)
1	Vertical	White	1993	28
2	Vertical	White	2000	21
3	Horizontal	Black	-	-
4	Vertical	White	1995	26
5	Vertical	Beige	2000	21
6	Vertical	White	2008	13
7	Vertical	Mint	2015	6
8	Horizontal	Black	2001	20
9	Horizontal	White	2001	20
10	Horizontal	Black	2015	6
11	Horizontal	White	2015	6
12	Horizontal	Black	2021	0
13	Horizontal	White	2021	0
14	Horizontal	White	2000	21
15	Horizontal	White	2008	13
16	Horizontal	White	2015	6
19	Horizontal	White	1995	26
20	Horizontal	White	1993	28

and Figure 2) were carefully documented. Supplementary information, including photographic evidence and site specific metadata, is provided in Appendix A. An important criterion for inclusion was that the surfaces had not undergone cleaning or maintenance treatments since their installation. As a result, they were subjected to natural aging processes, which include the effects of accumulated dirt, fading, moisture exposure, biological growth (e.g., moss or mold), and material wear due to weather conditions [22]. This made them particularly suitable for investigating how aging might influence albedo and surface thermal behavior.

To ensure variability in environmental conditions, four rounds of field measurements were conducted on the following dates: 14 July 2021, 22 July 2021, 8 August 2021, and 10 September 2021 (

Table 2. Rounds of measurements.

Measurement (No.)	Date (dd/mm/yyyy)	Time (hh:mm)	Points Examined	Solar Radiation (Average Value) (W/m2)
1	14/7/2021	11:00 a.m.–3:15 p.m.	18	1057
2	22/7/2021	5:00 p.m.–5:30 p.m.	4	148
3	8/8/2021	10:40 a.m.–11:00 a.m.	4	855
4	10/9/2021	11:20 a.m.–12:30 p.m.	4	1012

). These days were selected to capture seasonal variation within the summer while maintaining relatively consistent solar elevation angles. As recorded 14 July 2021 exhibited the highest average solar radiation (1057 W/m²), indicating strong radiative forcing on materials, while 22 July 2021 had the lowest average radiation (148 W/m²), offering a contrasting scenario. This range of solar exposure was valuable for assessing how material albedo and surface temperatures respond to different irradiance levels.

To ensure consistency and reliability of measurements, all field campaigns were conducted following a standardized protocol. Measurements were performed during midday hours to minimize diurnal variability. Each surface was measured in duplicate within the same session to account for potential short-term variability, and average values were used for statistical analysis.

Environmental conditions were monitored in parallel with the material measurements. Weather during the campaigns ranged from clear-sky days with high solar exposure (14 July 2021) to partly cloudy days with reduced irradiance (22 July 2021). Wind speeds during the campaigns remained below 3.2 m/s, reducing convective cooling effects. Relative humidity ranged between 33% and 51% depending on the day, and air temperatures varied from 29 °C to 38 °C.

The surfaces examined had not been subjected to cleaning or maintenance, ensuring that their condition reflected natural aging and environmental exposure [24]. Instruments were calibrated prior to each round of measurements, and all campaigns were performed by the same operator to minimize human-induced variability.

The selected locations represent typical material applications in Mediterranean urban environments, including painted walls, asphalt pavements, and concrete slabs. This approach ensured that the findings would be broadly comparable to similar contexts across Southern European cities.

Figure 2. Satellite image with the code numbers (see Table 1) of the selected locations, all inside the DUTH Campus area: (a) Student’s Residence; (b) Department of Civil Engineering; (c) Department of Electrical and Computer Engineering and Department of Architectural Engineering [25].

Figure 2. Satellite image with the code numbers (see Table 1) of the selected locations, all inside the DUTH Campus area: (a) Student’s Residence; (b) Department of Civil Engineering; (c) Department of Electrical and Computer Engineering and Department of Architectural Engineering [25].

For each round of measurements, the following physical parameters were recorded:

• Global solar radiation (W/m²)
• Reflected radiation (W/m²)
• Air temperature (°C)
• Relative humidity (%)
• Wind speed (m/s)
• Surface temperature of materials (°C)

The instrumentation setup (Figure 3) was selected to ensure precision and consistency across all measurement sessions:

• Albedometer: LPPYRA05, Delta Ohm S.r.l., Padova, Italy. Used to calculate albedo by measuring incoming and reflected solar radiation.
• Data Logger: Stylitis-10, Symmetron S.A., Athens (Gerakas), Greece. Enabled continuous recording and storage of radiation data during fieldwork.
• Pyranometer: SL 200, KIMO Instruments, Montpon-Ménestérol, France. Calibrated device used for measuring incident solar radiation with high accuracy.
• Meteorological Sensor: Kestrel 4000 Pocket Weather Tracker, Nielsen-Kellerman (NK)/Kestrel Instruments, Boothwyn, PA, USA. A handheld tool for measuring ambient air temperature, humidity, dew point, wet bulb temperature, and wind speed in real time.
• Surface Thermometer (Thermocouple): HI 9063K, Hanna Instruments, Padova, Italy. Used to measure the temperature of the material surface through direct contact.
• Verification Thermometer (infrared): KM 814 FS, Comark Instruments (part of Testo Group), Norwich, United Kingdom. Remote laser-based device used to verify surface temperature readings and identify possible inconsistencies.

To ensure methodological consistency and comparability with international practice, albedo and meteorological measurements were conducted in alignment with established standards. Specifically, albedo measurements followed the framework of ISO 9845-1:1992 [26] and the general guidelines of the World Meteorological Organization [27] for field meteorological observations. The pyranometer and albedometer employed (LPPYRA05, Delta Ohm) are classified as compliant with ISO 9060:2018 [28]. Surface temperature data were collected using a HANNA HI 9063 K thermocouple thermometer, calibrated according to manufacturer specifications and consistent with the principles outlined in ISO 7726:1998 [29]. These methodological alignments strengthen the robustness of the measurements and facilitate comparability with other studies, and are consistent with previous field-based UHI and albedo studies [30,31,32].

After data acquisition, pre-processing was carried out to calculate derived variables, including:

• Albedo (ratio of reflected to incoming solar radiation).
• Difference Between Material Surface And Air Temperature (°C).
• Material age (calculated from the documented installation year to the measurement year) (

  Table 3. Measurements.

  No.	Age (y)	Albedo	Air Temperature (°C)	Material Surface Temperature (°C)	Difference Between Material Surface and Air Temperature (°C)	Total Solar Radiation (Watt/m2) (KIMO)	Time of Measurement (hh:mm)
  14/7/2021
  1	28	0.54	36.8	51.4	14.6	1012	11:40
  2	21	0.84	36.7	44	7.3	1012	11:30
  3	-	0.32	37.3	60.2	22.9	1040	12:05
  4	26	0.85	33.7	40.5	6.8	950	11:00
  5	21	0.5	34.4	41.4	7	1117	15:08
  6	13	0.7	37.5	39	1.5	1103	14:40
  7	6	0.63	36.7	44	7.3	1160	14:00
  8	20	0.23	36.8	46.5	9.7	1012	11:45
  9	20	0.46	34	39	5	1060	12:40
  10	6	0.25	36.1	52.6	16.5	1053	13:25
  11	6	0.48	36.1	42.2	6.1	1053	13:25
  12	0	0.09	34	57	23	1060	12:35
  13	0	0.51	34	37	3	1060	12:35
  14	21	0.45	34.4	47.2	12.8	1117	15:08
  15	13	0.59	37.5	45.2	7.7	1103	14:40
  16	6	0.46	36.7	52	15.3	1160	14:00
  19	26	0.41	33.7	43.3	9.6	950	11:00
  20	28	0.40	36.8	46.2	9.4	1012	11:45
  22/7/2021
  B4	26	0.93	31.8	29.6	−2.2	204	17:00
  B19	26	0.50	31.8	37.2	5.4	204	17:00
  B12	0	0.14	29.7	44.2	14.5	91	17:20
  B13	0	0.70	29.7	32.6	2.9	91	17:20
  8/8/2021
  C4	26	0.64	31.8	32.2	0.4	891	10:40
  C19	26	0.44	31.8	41.8	10	891	10:40
  C12	0	0.1	32.6	48.4	15.8	819	11:00
  C13	0	0.59	32.6	33.6	1	819	11:00
  10/9/2021
  D4	26	0.77	30.3	29.4	−0.9	994	11:20
  D19	26	0.45	30.3	36.8	6.5	994	11:20
  D12	0	0.06	29.5	48	18.5	1030	12:30
  D13	0	0.59	29.5	28.2	−1.3	1030	12:30

  ).

A noteworthy addition to this research was the inclusion of a new indicator: “Difference Between Material Surface and Air Temperature (°C)” (Table 3). This variable, although seemingly simple, provides crucial insight into the thermal behavior of urban materials. By quantifying the temperature gap between a surface and the surrounding air, a better understanding of the heat retention or dissipation characteristics of that surface under varying environmental conditions was gained. This differential can be used as a proxy to evaluate the thermal stress materials may impose on pedestrians or on nearby structures, and it also holds implications for urban heat island mitigation and material selection in sustainable urban planning.

This indicator also supports decision-making for maintenance practices by signaling when certain materials accumulate excessive heat due to degradation or fading. By regularly monitoring this value, municipalities could intervene (e.g., with cleaning or recoating) to restore reflective performance and improve urban thermal comfort.

Following the field campaign, the dataset was systematically processed and analyzed using IBM SPSS Statistics software (Version 26). The statistical evaluation was structured into multiple phases to progressively uncover both overarching trends and more detailed insights.

In the first phase, a global correlation analysis was performed across the entire dataset to explore initial relationships among the key variables: albedo, surface-air temperature differential, and material age. This broad analysis aimed to identify general trends and interdependencies, providing a foundational understanding of how these variables interact within the diverse urban context represented by the samples.

However, because the overall dataset included materials with diverse colors, compositions, orientations, and functions, it was necessary to account for this heterogeneity to avoid masking material-specific behaviors. Therefore, in the second phase of analysis, the dataset was reorganized into four clearly defined categories—white walls, colored walls, white paving slabs, and asphalt—based on key physical characteristics such as surface orientation (vertical or horizontal), coating type (paint, plaster, or composite), and finishing texture. This classification enabled more meaningful comparisons within and between groups, allowing the analysis to better isolate the influence of material composition, color, and age on albedo performance and temperature dynamics. This refined structure enhanced the interpretability of trends related to albedo degradation and thermal response over time, aligning with practical insights for material selection and urban heat island mitigation. To further enhance the robustness and reliability of the statistical findings, a third analytical phase focused on a subset of the data collected during the first round of field measurements on 14 July 2021. This specific day was selected due to its larger number of valid measurement points and favorable meteorological conditions, including in-tense and consistent solar radiation, minimal cloud cover, and low wind speeds. These stable environmental conditions minimized variability caused by short-term atmospheric fluctuations and ensured a more controlled evaluation of material surface behavior. Limiting the analysis to a single, well-documented day helped reduce external noise in the dataset and increased the reliability of the derived statistical relationships, particularly in evaluating the influence of aging and material properties under high-exposure conditions.

Collectively, this multi-phased analytical approach enabled both a broad over-view and a nuanced understanding of the data, supporting more accurate conclusions about the thermal and reflective behavior of urban materials in real-world contexts.

3. Results

The data collected during the field measurements underwent thorough processing and statistical evaluation to extract key indicators relevant to the scope of the research. The analysis concentrated on three core variables: albedo, the difference be-tween material surface temperature and ambient air temperature, and material age. These variables were selected for their direct relevance to the thermal behavior of urban surfaces and their potential to reveal insights into the dynamics of heat absorption and reflection under real-world conditions. By examining both their individual values and the interactions between them, the analysis aimed to uncover how these parameters are influenced by intrinsic material characteristics and prolonged environmental exposure.

To facilitate interpretation and allow for grouped statistical analysis, the 18 locations were classified into four material categories based on surface characteristics, specifically color and material composition (

Table 4. Samples sorted by material category.

White Wall	White Paving Slabs	Black Asphalt	Colored Walls
1	9	3	5
2	11	8	7
4	13	10
6	14	12
B4	15	B12
C4	16	C12
D4	19	D12
	20
	B13
	B19
	C13
	C19
	D13
	D19

) as shown in Figure 4a–d. This classification was critical because both factors are known to significantly influence albedo [7]. Grouping similar materials enabled more targeted statistical comparisons and minimized confounding factors during correlation analysis. The classification was as follows:

• White wall.
• White paving slabs.
• Black asphalt.
• Colored Wall.

This typology was developed through visual inspection during the field survey, with each category representing a combination of functional role in the built environment and surface reflectivity characteristics. The categorization not only facilitated direct comparison across similar material types but also allowed for the identification of internal trends within each group.

Such structuring proved essential for isolating the effects of specific attributes—such as color-induced albedo variations or the role of material aging in thermal performance. For example, grouping white surfaces together enabled clearer observations of how lighter materials behave thermally over time, while the inclusion of dark asphalt and colored walls provided contrasting benchmarks. Overall, this approach improved the interpretability of the results and laid the groundwork for a more nuanced understanding of the thermal and optical performance of common urban surfaces.

The mean albedo values observed for each material category were compared against reference values drawn from the scientific literature. As presented in Table 5, the study found that:

• The white wall exhibited a mean albedo value of 0.75, which is well with-in the literature range of 0.5–0.9.
• The white paving slabs showed a slightly lower albedo of 0.50, compared to the expected 0.6.
• The black asphalt recorded a mean albedo of 0.17, aligning with the re-ported range (0.05–0.20).
• The colored wall displayed a mean albedo of 0.56, marginally above the reference range of 0.4–0.5.

The mean albedo values observed for the material categories were generally consistent with values reported in the literature. For example, the mean albedo of white walls (0.75) lies well within the range of 0.5–0.9 previously documented [10,33,34], while the value for black asphalt (0.17) aligns with the 0.05–0.25 range noted [33,34,35]. Similarly, our finding for white paving slabs (0.50), though slightly lower than the 0.6 reference.

Table 5. Mean albedo values in comparison to indicative albedo values in the literature.

	Mean Albedo Value in This Study	In Literature
White wall	0.75	0.5–0.9 [10,33,34]
White paving slabs	0.50	0.6 [36]
Black asphalt	0.17	0.05–0.25 [33,34,35]
Colored Wall	0.56	0.4–0.5 [36]

Table 5. Mean albedo values in comparison to indicative albedo values in the literature.

	Mean Albedo Value in This Study	In Literature
White wall	0.75	0.5–0.9 [10,33,34]
White paving slabs	0.50	0.6 [36]
Black asphalt	0.17	0.05–0.25 [33,34,35]
Colored Wall	0.56	0.4–0.5 [36]

3.1. Statistical Analysis with All Samples

Material type and surface color emerged as highly significant predictors of albedo, corroborating established results in the literature [7]. Beyond these categorical influences, a moderate positive correlation of 0.41 (p = 0.027) was detected between material age and albedo (Figure 5a and

Table 6. Pearson correlation coefficients among material age, albedo, and thermal variables with all samples.

		Age (y)	Albedo (-)	Air Temperature (°C)	Material Surface Temperature (°C)
Albedo (-)	Pearson Correlation	0.410 *
	Sig. (2-tailed)	0.027
Air temperature (°C)	Pearson Correlation	0.144	0.037
	Sig. (2-tailed)	0.457	0.846
Material Surface Temperature (°C)	Pearson Correlation	−0.161	−0.635 **	0.580 **
	Sig. (2-tailed)	0.404	0.000	0.001
Difference Between Material Surface And Air Temperature (°C)	Pearson Correlation	−0.249	−0.763 **	0.283	0.945 **
	Sig. (2-tailed)	0.194	0.000	0.130	0.000

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

). This finding suggests that aging processes, such as surface weathering, pigment degradation, or the accumulation, and possible cleaning effects can measurably alter a material’s reflective capacity over time, with important implications for long-term thermal performance in built environments.

In addition, the analysis confirmed a strong inverse relationship between albedo and material surface temperature of −0.64 (p < 0.001) (Figure 5b and Table 6), which is consistent with Akbari et al. [37], who reported that highly reflective surfaces significantly reduce surface heating and help mitigate urban heat island intensity. Surfaces with high-er albedo values absorbed less incoming solar radiation and consequently registered lower surface temperatures. This aligns with theoretical expectations and underscores the pivotal role of albedo in reducing surface heat gain—a key mechanism in mitigating urban heat islands.

An even more pronounced negative correlation was observed between albedo and the temperature differential (surface temperature minus ambient air temperature), yielding r = −0.76 (p < 0.001) (Figure 5c and Table 6). This stronger association indicates that high-albedo materials not only remain cooler in absolute terms but also maintain a smaller temperature gradient relative to the surrounding air. Reducing this gradient contributes directly to improved pedestrian comfort, lower radiative heating of nearby structures, and enhanced effectiveness of urban cooling strategies, such as evaporative or shading interventions.

Together, these statistically significant relationships demonstrate that albedo is both an outcome of material properties, modified by age, and a driver of urban thermal behavior. Consequently, optimizing surface reflectance through material selection and maintenance protocols can be a powerful, passive approach for enhancing sustainability, reducing energy demand, and improving human comfort in urban settings.

The linear regression analysis supports these observations. When material type was used as the predictor, the model yielded a high R² value of 0.83, indicating that material properties are a strong predictor of albedo values. However, when age was introduced as predictor into the model, the R² changed only by 0.02, implying that age alone is a weaker predictor (

Table 7. Linear regression results for the effect of material type and age on albedo (R², R Square Change).

Model Summary
	R	R Square	R Square Change
1	0.912 a	0.832	0.832
2	0.923 b	0.852	0.019

^a. Predictors: (Constant), Materials. ^b. Predictors: (Constant), Materials, Age.

, Figure 6). Despite this, the inclusion of both material and age variables in the regression model improved the overall fit, demonstrating that age does contribute incrementally to variations in albedo.

These findings highlight the dominant influence of material composition and sur-face characteristics on albedo, while also acknowledging that age-related degradation has a measurable, albeit smaller, effect. This insight carries practical implications for urban heat island mitigation: while material selection is paramount, maintenance practices such as surface cleaning or repainting may be necessary to sustain high albedo over time and preserve the cooling benefits of reflective materials.

In conclusion, the results emphasize the importance of incorporating material aging effects into urban planning and climate-responsive design strategies. Selecting materials with durable reflective properties and establishing maintenance protocols can improve long-term performance, reduce energy consumption, and support urban sustainability goals.

The identified correlation between material age, albedo levels, and temperature variables in urban areas has implications for sustainability and urban heat island mitigation. These findings underscore the importance of considering material aging in sustainable urban planning to predict the impact on urban heat island effects over time.

The inclusion of the age variable in predictive models suggests its significance in influencing albedo and surface temperatures. To contribute to sustainability, urban planners should prioritize enduring high-albedo materials in development projects. Proposing regular maintenance practices, including cleaning and repainting, ensures the preservation of desired albedo levels, offering effective urban heat island mitigation. This insight emphasizes the role of painting as a strategy not only for aesthetic upkeep but also for sustaining the reflective properties of surfaces.

In summary, incorporating the impact of material aging into urban planning, prioritizing enduring high-albedo materials, and implementing regular maintenance practices may be essential for sustainable urban heat island mitigation.

3.2. Statistical Analysis by Material Category

A deeper investigation into the effects of aging on albedo for each material class was carried out through stratified Pearson correlation analyses, revealing distinct aging behaviors that could be obscured in a pooled dataset:

White walls: A weak and statistically non-significant correlation between material age and albedo was observed, indicating that vertical painted surfaces maintain their reflective performance over time in the studied area (Figure 7a). This relative stability may result from reduced soiling and runoff on walls compared with horizontal surfaces, as well as the protective effects of pigments and surface primers that resist fading and debris accumulation.

White paving slabs: A strong negative correlation of −0.71 (p = 0.004) demonstrated that pavement reflectivity deteriorates markedly with age (Figure 7b). Potential drivers include abrasive traffic wear, settled dust, and microcracking that trap dirt and reduce surface brightness. This finding highlights the need for regular cleaning or sealing programs to restore reflectance and maintain thermal performance in pedestrian zones and public plazas.

Black asphalt: A positive correlation of 0.73 (p = 0.097) indicates that asphalt surfaces may become more reflective over time (Figure 7c) result in agreement with the literature [35]. This trend could result from the gradual oxidation and volatilization of dark bitumen oils, which uncovers lighter mineral aggregates beneath the binder. Additionally, accumulated dust and fine particulates may consolidate in microcrevices, collectively enhancing surface brightness.

Across all categories, these divergent trends underscore that aging processes are highly material-dependent. Horizontal surfaces appear more vulnerable to soiling and wear, whereas certain materials—due to their composition, color, or exposure orientation—may maintain or even gain reflectivity over time. Consequently, surface maintenance strategies, such as targeted cleaning, resealing, or periodic recoating, should be tailored to the specific material type and usage context. Future research employing expanded sample sets, seasonal monitoring, and controlled aging experiments would strengthen the statistical power of these observations and inform more precise urban material guidelines.

3.3. Statistical Analysis with Same-Day Samples

To control for variability introduced by differing environmental conditions, a third statistical analysis was conducted using data only from the first measurement day (14 July 2021), which featured stable climatic conditions and the highest solar radiation levels (

Table 8. Pearson correlation coefficients among material age, albedo, and thermal variables with same-day samples (14 July 2021) under stable climatic conditions.

		Age	Albedo	Air Temperature	Surface Temperature
Albedo	Pearson Correlation	0.303
	Sig.	0.237
Air temperature	Pearson Correlation	−0.040	0.108
	Sig.	0.878	0.670
Surface temperature	Pearson Correlation	−0.179	−0.593 **	0.302
	Sig.	0.492	0.009	0.223
Difference between material surface temperature and air temperature	Pearson Correlation	−0.171	−0.646 **	0.078	0.974 **
	Sig.	0.511	0.004	0.758	0.000

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

). This approach enabled a more controlled examination of albedo’s influence under near-constant atmospheric conditions.

The results reinforced earlier findings:

• A statistically significant correlation of −0.59 was observed between albedo and material surface temperature (p = 0.009).
• A similarly strong correlation of −0.65 was found between albedo and the difference between surface and air temperature (p = 0.004).

These outcomes further validate the role of albedo in regulating surface heating and minimizing the temperature differential between urban materials and ambient air.

In contrast, however, weak correlation between albedo and air temperature was dis-covered (0.11) with significance value 0.67. A weak overall correlation between albedo and material age (r = 0.30, p = 0.24) reflects the fact that different surface types age in fundamentally different ways. Light-colored materials such as white paving slabs tend to accumulate dirt, biological growth, and surface wear, causing them to darken and lose reflectivity over time (e.g., r = −0.71 for white paving slabs). By contrast, darker surfaces like black asphalt often undergo oxidative weathering that can lighten their appearance—oxidation of the bituminous binder and exposure of lighter aggregates lead to a slight increase in reflectivity (e.g., r = +0.73 for black asphalt). As a result, aggregating all materials into a single correlation masks these opposing aging trajectories, underscoring the need to assess each material group separately when evaluating long-term albedo performance.

These results reiterate the importance of analyzing materials on a case-by-case basis when assessing their long-term albedo performance.

4. Discussion

While the present study provides valuable insights into the relationship between material aging and albedo, several limitations must be acknowledged to contextualize the findings and guide future research. These limitations arise from both methodological constraints and environmental conditions that influenced the data collection process.

A key limitation relates to the measurement technique employed for assessing albedo. The albedometer used in this study, although reliable and commonly utilized in urban environmental studies, was applied to both horizontal and vertical surfaces. This introduces an inherent challenge, particularly for vertical surfaces such as walls, which are subject to variable angles of solar incidence throughout the day and across seasons. As a result, albedo readings for vertical surfaces—specifically those categorized as white walls and colored walls—may exhibit a larger margin of error compared to more uniformly exposed horizontal surfaces like pavements or asphalt. These orientation-related discrepancies could affect the consistency and comparability of the results, potentially influencing the accuracy of derived correlations. Future studies might consider employing advanced instrumentation that accounts for surface inclination and orientation, or focus on horizontal surfaces for greater consistency.

Additionally, the relatively small and uneven sample size across material categories presents a limitation in terms of statistical power. While significant correlations were observed, the inclusion of a broader and more diverse set of samples would strengthen the generalizability of the findings. For instance, the contrasting behaviors of black asphalt and white paving slabs in relation to aging and albedo highlight the importance of material-specific analysis. However, with only a few data points per category, the reliability of these insights remains somewhat limited. Increasing the sample size, particularly for underrepresented materials, would allow for more robust statistical validation and facilitate the development of predictive models.

Another important constraint pertains to the temporal scope of the study. The measurement campaign spanned four discrete days within a single summer season, with each day characterized by differing meteorological conditions. While this allowed for a degree of environmental variability, it also limited the ability to assess long-term seasonal effects or monitor gradual aging processes. A longitudinal approach, with periodic measurements over months or years, would offer a more comprehensive under-standing of how surface properties evolve under natural weathering, pollution expo-sure, and other urban stressors.

Despite these limitations, this study contributes to a relatively underexplored area of urban environmental research. The investigation into the effects of material aging on albedo is particularly relevant in the context of climate-responsive urban planning and sustainable development. While much of the existing literature has focused on the performance of new or recently installed materials, this study emphasizes the need to consider material degradation over time, which is crucial for accurately predicting long-term thermal performance in cities.

The results of this study are consistent with prior research emphasizing the influence of albedo on surface temperature and the urban heat island effect [10,15,30,31,34]. However, unlike most previous investigations that focus on newly installed or idealized material conditions [31,38], the present work introduces the dimension of material aging, offering novel empirical insights into how long-term exposure affects reflective performance. Similar studies, such as those by Ferrari et al. [9], have focused on different materials (clay roof tiles) and lacked comparative analysis across multiple material types. This research expands on that by showing that aging effects vary by color and material composition, light-colored pavements tend to darken over time, whereas asphalt surfaces may lighten due to binder oxidation and aggregate exposure. These findings contribute new evidence to the broader discourse on sustainable urban materials and reinforce the need to account for temporal degradation in climate-responsive design and maintenance strategies.

The observed variations across materials underscore that albedo behavior is governed by the combined influence of intrinsic material composition, surface color, age, and orientation. Light-colored materials such as white walls and paving slabs initially exhibit higher albedo values but are more susceptible to degradation through soiling, biological growth, and surface erosion, which progressively reduce their reflectivity. Conversely, darker materials like asphalt demonstrate the opposite aging trajectory: their albedo tends to increase over time due to binder oxidation and exposure of lighter aggregates. Vertical surfaces (e.g., walls) generally maintain more stable albedo levels because they are less exposed to dust accumulation and mechanical wear compared to horizontal pavements. These distinctions highlight that both material type and surface position play decisive roles in determining long-term reflective performance and must therefore be considered jointly in urban climate design and maintenance strategies.

Moreover, the findings suggest that maintenance practices, such as surface cleaning, painting, or recoating, may play a key role in sustaining high-albedo performance. In particular, materials such as white paving slabs—which exhibited a marked decline in albedo with age—could benefit significantly from periodic upkeep. This insight is particularly valuable for municipal decision-makers and urban planners, as it supports the integration of maintenance schedules into thermal comfort and heat island mitigation strategies.

In summary, while the study is limited by sample size, measurement constraints, and temporal scope, it lays an important foundation for future work. By highlighting the interaction between material properties, environmental exposure, and thermal behavior, it encourages a more dynamic and realistic evaluation of albedo in the urban context. The research aspires to make a modest yet meaningful contribution to the broader scientific discourse on urban sustainability, opening pathways for more re-fined studies that can inform evidence-based policy and design interventions in climate-adaptive cities.

5. Conclusions

The results of the study confirm that albedo significantly influences the surface temperature of materials, as well as the temperature differential between surface and ambient air. These findings reinforce the critical role of surface reflectivity in regulating microclimatic conditions and mitigating the urban heat island (UHI) effect, particularly in densely built environments where heat accumulation is a growing concern due to climate change and increasing urbanization.

This study confirmed that albedo strongly influences both material surface temperature and the temperature difference between surfaces and ambient air. On average, high-albedo materials (e.g., white slabs, albedo ≈ 0.50) exhibited 21% lower surface temperatures than darker materials such as asphalt (albedo ≈ 0.17), with mean values of 40 °C vs. 51 °C. Likewise, the surface–air temperature difference was reduced by about 67%, from 17.3 °C in asphalt to 5.8 °C in white slabs.

Material type and color were confirmed as the primary determinants of albedo, while aging showed material-specific effects. White paving slabs displayed a significant negative correlation with age (r = −0.71, p = 0.004), corresponding to an approximate 27% decline in reflectivity over an exposure period of 28 years, from ~0.59 in newer pavements to ~0.43 in older ones. By contrast, asphalt showed a positive trend (r = +0.73, p = 0.097), with albedo rising from ~0.13 in newer pavements to ~0.26 in older ones—an absolute increase of 0.13 units (≈100%) over about 20 years, attributable to binder fading and aggregate exposure. White walls showed no statistically significant change with age, maintaining stable reflectivity. These outcomes highlight the dynamic nature of albedo and the need to integrate considerations of material aging and maintenance into sustainable urban design practices. While high initial albedo is beneficial, the persistence of reflective performance over time is equally crucial. The degradation of albedo in horizontal materials suggests that regular maintenance—such as surface cleaning, recoating, or material replacement—may be necessary to sustain their thermal efficiency and environmental benefits. Incorporating lifecycle thinking into material selection can improve the resilience and long-term sustainability of urban spaces.

Moreover, the findings support the development of urban material guidelines and performance standards that account for aging-related changes in albedo. This may include promoting the use of durable, low-soiling, or self-cleaning materials, particularly in high-exposure zones. From a policy perspective, integrating albedo performance in-to urban planning regulations, energy codes, and climate adaptation strategies could yield significant environmental gains, particularly in cities with hot climates or growing populations.

While the findings highlight significant relationships between albedo, material type, and aging, certain limitations must be acknowledged. First, the dataset was restricted to a relatively small number of surfaces within one geographic location, which may limit the generalizability of the results. Second, measurements were conducted over a short monitoring period, and seasonal or inter-annual variability could not be fully captured. Third, vertical surfaces such as walls may involve greater measurement uncertainty due to orientation and shading effects. Finally, correlations between albedo and microbiological or surface particle loads were based on limited data and require more extensive sampling to validate. These limitations suggest the need for longer-term and larger-scale studies to strengthen the applicability of the findings.

In summary, the study underscores that albedo is a multifaceted parameter influenced by material composition, surface color, and environmental exposure over time. Its impact on surface temperatures and urban microclimates makes it a key consideration in designing climate-responsive, energy-efficient, and livable urban environments. By addressing not only the initial properties but also the temporal evolution of materials, urban planners and designers can more effectively harness albedo as a tool for mitigating heat stress, reducing cooling energy demand, and enhancing outdoor thermal comfort in urban settings.

Future research should expand upon these findings by incorporating a larger and more diverse sample of materials, measured across multiple seasons and years, to better capture long-term aging dynamics under real urban conditions. Integrating advanced spectral and remote-sensing techniques could further enhance precision in monitoring albedo evolution. Moreover, coupling albedo data with energy simulations and urban microclimate models would help quantify the actual energy and comfort benefits of maintenance or material-selection strategies. Such interdisciplinary approaches can bridge the gap between material science and urban design, supporting evidence-based policies for resilient and climate-adaptive cities.