Microclimate Monitoring Using Multivariate Analysis to Identify Surface Moisture in Historic Masonry in Northern Italy

Abstract

Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators—including dew point depression, critical temperature–humidity zones, and damp effect indices—were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials.

1. Introduction

1.1. Moisture Problem in Building Heritage

Water is universally acknowledged as one of the most damaging agents impacting historic masonry structures, driving both immediate and progressive deterioration [1,2,3,4]. Water infiltration threatens structural integrity and human health, with direct consequences including material degradation (e.g., surface erosion, freeze–thaw damage, salt crystallization, and metal corrosion) and reduced thermal performance. Indirectly, persistent moisture promotes mold proliferation, elevating indoor humidity and respiratory health risks. Moisture can be conveyed into ancient buildings materials mainly through capillary rise, condensation, rainwater infiltration, leakage from water pipes, and flooding. It therefore causes or worsens all the decay processes in building materials such as rain wash, freeze–thaw effects, migration, and crystallization of soluble salts, chemical and biological attacks, and corrosion of metallic elements [5,6,7,8,9,10]. Salt deterioration is one of the most important contributors to the weathering of porous materials in historical buildings and the works of art they may enclose. This form of damage predominantly occurs through repeated crystallization–dissolution or hydration–dehydration cycles, which are strongly influenced by relative humidity (RH) and environmental temperature—highlighting the importance of environmental control as a vital mitigation strategy [11,12,13]. Beyond external environmental conditions, it is essential to analyze the internal climate of buildings to identify long-term trends in air temperature and humidity. This article defines microclimate at the scale of room air volume by examining these variables [14,15].

Cantin et al. [16] observed that the indoor microclimate in historic buildings is strongly influenced by outdoor environmental conditions. A comprehensive understanding of indoor climatic conditions is essential for assessing long-term trends in air temperature and humidity within historical buildings. This study analyzes indoor microclimate dynamics at the room scale [14,15].

Although standards such as uni 10829 (1999) [17] emphasize monitoring both the external environment and indoor microclimate as part of conservation risk assessments [18], practitioners often allocate minimal resources to pre-restoration monitoring. In many cases, monitoring periods are limited to just a few months, rarely exceeding one year [19]. These standards are primarily applied in museum settings, where artifacts such as frescoes require strict environmental control. Similarly, microclimate monitoring is more routinely implemented in historical buildings with valuable surface decorations, whereas undecorated structures receive less attention [20]. The recent research on microclimate monitoring has shifted focus toward analyzing microclimate dynamics in heritage buildings, guided by established standards and protocols [21,22,23]. However, applying universal standards across diverse historic sites presents challenges due to variations in materials, climate, and context. To understand a building’s hygrothermal behavior, it is necessary to evaluate both the internal microclimate and its relationship with the external environment [24]. This interaction helps detect risks such as surface condensation, moisture accumulation, and material degradation. The building’s response to external conditions depends on factors like heat and moisture exchange, air infiltration, thermal inertia, and the balance between surface temperature and dew point, all of which shape the indoor microclimate in historic structures. While these interactions are inherently due to the unavoidable ecosystem between indoor and outdoor environments, they can lead to critical moisture-driven decay cycles—such as freeze–thaw, salt crystallization, and mold growth—regardless of the building’s envelope performance or the presence of mechanical systems [25,26,27,28].

By systematically analyzing indoor–outdoor microclimate relationships, conservators can identify critical degradation cycles, prioritize interventions, and implement targeted preservation measures to safeguard historic structures [29,30]. Therefore, the quantitative measures of environmental stability through microclimatic indexes can identify harmful fluctuations in temperature and humidity, evaluate the buffering capacity of buildings against external conditions, predict risks of condensation and material degradation. Racca et al. [31], analyzed microclimatic conditions in the historic San Panfilo Church in Italy using various microclimatic indexes to assess indoor environmental quality and to provide a framework for assessing microclimatic conditions in similar conditions. Similarly, Metals et al. [32] monitored microclimate conditions in historical churches by analyzing temperature, relative humidity, and absolute moisture content, comparing indoor and outdoor fluctuations to assess infiltration and condensation risks. They evaluated moisture excess (Δv) thresholds (e.g., 6 g/m³) and visitor impact on indoor humidity while considering building materials, ventilation, and heating systems to determine stable microclimate preservation strategies.

Despite the well-documented risks of moisture in historic masonry, few studies have quantitatively assessed microclimatic indicators for surface moisture detection, proposing correlations between temperature, relative humidity, and the mixing ratio. This gap underscores the need for integrating key indicators through systematic monitoring, allowing for proactive identification of vulnerable zones, optimization of conservation strategies, and early intervention to prevent irreversible damage.

1.2. Aim and Novelty

The aim of this study is to analyze the contribution of the statistical metrics commonly used in the field of meteorology and climate science to understand their potential to define the conditions leading to moisture on masonry surfaces, the frequency of their occurrence in a historical building, and their validity in determining the severity of the dampness risk. The aim is not to establish universal risk thresholds for moisture-related deterioration as the impacts vary based on material composition, construction techniques, and historical significance, moving away from a one-size-fits-all approach [33].

By analyzing parameters including temperature, relative humidity, dew point, and mixing ratio, we propose some indicators and assess their effectiveness in identifying moisture frequency, persistence, and severity across 13 case studies in Northern Italy, covering the Alpine region, Po valley, and Lake Como. Comparative analysis was used to validate the limitations and strengths of each indicator, as well as explore their correlations and potential synergies.

The novelty of this research lies in bridging the gap between theoretical microclimatic indicators and practical conservation needs, avoiding prescriptive thresholds due to material and contextual variability [34].

2. Materials and Methods

2.1. Description of Case Studies

The case study selection criteria aligned with the aim of this research to assess the role of microclimate monitoring in detecting surface moisture. These cases were chosen based on their architectural significance, historical value, and susceptibility to moisture-related deterioration in Northern Italy’s variable climate. These structures represent diverse masonry typologies, ranging from medieval churches to Renaissance palaces, offering a broad spectrum of porous materials (e.g., sandstone, brick, and mortar) prone to capillary action, salt crystallization, and biological growth. Their inclusion aligns with well-documented conservation challenges in the heritage science literature [35,36,37,38]. Additionally, Northern Italy’s climatic diversity, including Alpine humidity and Po Valley fog, provides an ideal setting for multivariate microclimate analysis. Thus, these cases offer both methodological rigor and practical relevance for advancing moisture monitoring techniques in heritage conservation.

Table 1. Description of selected case studies.

Type of Space	Name	Abbrev.	Location	Orientation	Context	Construction Date	Mechanical Heating [Y/N]	Building Material
Inactive	Visconti-Venosta Palace	P-VEN	Tirano	NE-SW	Semi-dense urban	15–18th C.	N	Stone masonry, plastered
Museum	Besta Palace	P-BES	Teglio-Sondrio	N-E	Semi-dense urban	15th C.	Y	Stone masonry, plastered, frescoed
Active worship space	Collegiate Church of San Vittore Mauro	S-VITT	Poschiavo	E-W	Semi-dense urban	13–17th C.	Y	Stone masonry, frescoed
Inactive	Semadeni Palace	P-SEM	Poschiavo, Switzerland	N-S	Open field, semi-urban	19th C.	N	Stone masonry
Active worship space	San Giacomo Church	S-GIA	Como	NE- SW	Dense urban	11th C.	N	Moltrasio stone, brick, faux marble
Inactive	Palazzo Pievani (ex biblioteca)	P-PIE	Tirano	NE-SW	Semi-dense urban	12–16th C.	N	Stone masonry
Inactive, under refurbishment	Museo Etnografico di Tirano	MU-TI	Tirano	E-W	Semi-dense, semi-urban	20th C.	N	Stone masonry
Inactive	Castello Masegra	C-MAS	Sondrio	E-W	Mountainous, semi-urban	11–16th c.	N	Stone masonry, brick
Active worship space	Xenodochio di S. Perpetua	S-PER	Tirano	E-W	Suburban, mountainous	11–13th C.	N	Split stone walls, plaster
Inactive	Basilica of San Fedele	S-FED	Como	NE-SW	Dense urban	10th–12th C.	Y	Granite, marble, Moltrasio stone
Under restoration	San Romerio Church	S-R	Bursio- Switzerland	NE-SW	Open field, mountainous	17th C.	N	Stone masonry
Active worship space	San Giorgio Church	S-GIO	Mandello del Lario (LC)	NW-SE	Suburban	12th–15th C.	N	Split stone walls, lime plaster
Active worship space	Sant’Ignazio Church	S-IGN	Ponte in Valtellina	E-W	Semi-dense urban	17th C.	N	Stone masonry, plastered, frescoed

summarizes the key characteristics of each case study.

2.2. Data Collection and Processing

Non-destructive, mid- to long-term microclimate monitoring is essential for evaluating hygrothermal behavior in building heritage, which allows for repeated, cost-effective testing without compromising historic materials. As porous materials (e.g., stone, brick, mortar, wood, and frescoes) are highly sensitive to environmental fluctuations and cyclic hygrothermal stress, accurate condition assessments are essential prior to any conservation and restoration decision-making process.

This research applied microclimate monitoring across the 13 case studies over more than two years as short-term data may miss critical winter condensation or summer evaporation cycles. However, only one complete year of data (13 months for calculation purposes) per case study was selected for the purpose of this research. Appendix A shows the number and location of the data loggers and the monitoring campaign for each case study. In cases where data was missing due to probe malfunctions, no data reconstruction (interpolation methods) was performed to maintain the authenticity of the results and to accurately reflect the building’s true behavior in response to outdoor microclimatic conditions. The selection criterion prioritized continuous and uninterrupted datasets, ensuring robust climatic representation despite variations in monitoring periods across sites. HOBO U12 Temp/RH and HOBO MX1101 Temp/RH data loggers were employed, meeting the accuracy standards of ±0.2 °C for temperature and ±2.5% for relative humidity. The complete list of the data logger specifications along with their calibration is shown in Appendix B. For this study, a single representative data logger was selected from each of the 13 case studies based on the following criteria to ensure consistent and reliable microclimate monitoring (

Table 2. The selected probes and their location within the case studies.

	P-VEN	P-BES	S-VITT	P-SEM	S-GIA	P-PIE	MU-TI	C-MAS	S-PER	S-FED	S-ROM	S-GIO	S-IGN
Name	S3	S4	S03	Alcova 2	ABSIDE	S2	S 04	S03	ALTARE	IV°Navata	ABSIDE	ABSIDE	ALTARE T
Orientation	SE	N	S	S	E	SE	SE	W	W	S	NE	E	E
Floor	1st	GR	GR	1st	GR	GR	GR	GR	GR	Nave	altar	Apse	altar
H [m]	6	0.6	1.5	5	1.5	2.7	1	1	0.5	6	1.5	1.5	1.5

).

• Minimal air infiltration;
• Shielded from direct sunlight;
• Reduced ground moisture influence: Located at a sufficient height;
• Distance from cooled surfaces: Positioned away from cold bridges or thermally conductive materials (e.g., metal fixtures) to avoid localized condensation bias;
• Central representative location: Situated in zones reflecting the broader microenvironment of the structure, rather than an edge or atypical conditions.

All sensors are susceptible to an inherent margin of error; however, they can also lose accuracy over time due to factors such as improper use, exposure to harsh weather or damaging environments, or general sensor aging. In multi-sensor monitoring projects, where precision and consistency are crucial, regular calibration is vital [39]. Therefore, prior to deployment, all sensors were calibrated. Sensor placement was optimized to avoid external disturbances considering solar radiation, airflows, or thermal imbalances due to the building envelope [40]. In some cases, psychrometric analysis was conducted to assess horizontal humidity gradients (assessing both relative humidity (RH%) and specific humidity (SH; g/kg)), identifying anomalies and guiding optimal data logger positioning. Data was recorded hourly, balancing resolution with the slow moisture response of masonry, which was stored until further processing. As stated by EN 15757 [41], a long-term monitoring campaign of at least one year with a recording frequency of 1 h or less is necessary to diagnose climate-vulnerable materials. Prior to applying the data analysis, the collected microclimatic data was inspected for dataset quality and anomaly detection. Given the sensitivity of built heritage environments, ensuring data reliability is critical to avoid misleading interpretations. Outliers, such as sudden spikes caused by sensor malfunctions, temporary obstructions, or electromagnetic interference, were identified and removed using statistical and filtering techniques. The moving average filter with a 4 h interval was applied to smooth transient noise while preserving trend integrity. To distinguish between sensor noise and genuine microclimatic events (e.g., visitor-induced humidity spikes or imbalances due to use), the data was cross-referenced with external meteorological data and the installed external probe for each case study. The choice to use the installed external probe for the purpose of microclimatic monitoring was due to the distance of meteorological stations from the selected built heritage sites in order to capture localized climatic conditions (e.g., urban heat islands, humidity variations near walls, or wind patterns around historical structures). An example of the comparison between ARPA Lombardy—Regional Agency for Environmental Protection of Lombardy [42]—and the installed external probe in San Giacomo Church in Como is illustrated in Figure 1.

2.3. Metrics and Indicators

The effect of external weather conditions on indoor microclimates has been studied in numerous studies [25,43,44,45,46]. This research examined eight indicators from the relationships between T°C, RH%, dew point temperature [Tₚ], and mixing ratio [MR] in the 13 case studies, as indicated in Table 3. Initially, the temperature (°C) and relative humidity (RH%) data were calculated using statistical measures (daily averages, minimum, maximum, and standard deviations) to quantify the baseline microclimate conditions prior to the in-depth analysis.

While thermo-diffusion moisture movement due solely to temperature gradients is considered negligible [47], vapor pressure gradients (driven by temperature and humidity differences) are the dominant factors that enable the detection of moisture in porous materials. It should be noted that the importance of calculating each indicator was evaluated under theoretical conditions, which were distinct from the actual degradation state of materials in the case studies. In practice, the presence of salts, contaminants, or prior damage (e.g., micro-cracking) can significantly alter moisture behavior, modifying the relative importance of indicators. For instance, hygroscopic salts can amplify moisture uptake even at a moderate RH [48] while pre-existing damage can enable moisture transport, effectively bypassing vapor diffusion mechanisms by providing preferential pathways for liquid water infiltration [49]. Despite these complexities, the evaluation of each indicator demonstrates the progression of nearly all degradation mechanisms in porous materials.

As a direct quantification of condensation risk, Dew Point Depression (difference between the air temperature and the dew point temperature at a given pressure level) was analyzed. The dew point was calculated using the Magnus formula, a commonly adopted empirical equation in building science and conservation, which offers reliable accuracy within the range of 0 °C to 60 °C, with a marginal deviation of ±1 °C at subzero temperatures. While hygrometers can provide direct hourly measurements of dew point, their use carries a high risk of data loss and signal noise due to the extended drying time required for accurate sensor readings. The margin of 2.5 °C between dry air and dew point requires the high accuracy level of data loggers (0.2 °C, as explained in Appendix B) and the dew point calculation aligned with ASHRAE’s precautionary guidelines [50].

The interdependence of T°C and RH% has been examined through various indexes [51,52]. Within the scope of this study, a critical zone was defined as two conditions of high temperature (>25 °C) and relative humidity (>60%) and low temperature (T ≈ 0 °C) and high relative humidity (>60%), which affect salt dissolution–recrystallization and freeze–thaw damage. Consequently, the calculation of RH fluctuation is of importance for detecting prolonged high variations.

Temperature gradients alone cannot reliably indicate surface moisture in historic porous materials. Diurnal temperature fluctuations can lead to material stress, micro-cracking, and subsequent moisture ingress.

To investigate building materials’ hygrothermal buffering performance, the damp effect was proposed, which is the relationship between internal and external daily variations in RH and the mixing ratio (MR): <0 indicates uncontrolled damping, 0–0.25 indicates very low damping, 0.25–0.5 indicates low damping, 0.5–0.75 indicates moderate damping, 0.75–1 indicates high damping, and 1 indicates full damping. This calculation of RH reveals how effectively building materials buffer or dampen daily fluctuations in indoor RH compared to outdoor conditions. The MR—the mass of water vapor per unit mass of dry air (g/kg)—is a reliable measure for long-term moisture transfer analysis. Table 3 provides a summary of the indicators and their calculation and effect on surface moisture.

Table 3. Indicators used in the surface moisture analysis in the 13 case studies.

#	Param	Indicator	Calculation	Effect on Surface Humidity
1	Tₚ	Dew Point Depression	Tₐᵢᵣ − Tₚ	Small depression (≤2.5 °C) → high condensation risk [53]
2	T-RH	Critical T-RH Zones	High T (>25 °C) + High RH (>60%)	High RH → hygroscopic salts in masonry dissolve absorbing moisture from the air; salts recrystallize during diurnal low-RH cycles [54]
			T ≈ 0 °C + RH > 80%	Freeze–thaw damage in masonry
3	RH%	RH% Fluctuation Index	RHd − CMAd	High RH swings + high T → condensation risk; low T + RH swings → frost damage
4	T°C	Diurnal Temperature Fluctuation	Td − CMAd	High fluctuations → material stress → micro-cracks → moisture ingress
5	RH%	Damp Effect Index	1 − (σᵢₙ/σₒᵤ)	Low buffering (<0.5) → indoor climate closely follows outdoor swings; effective buffering (> 0.5) → indoor conditions are noticeably more stable than outdoors
6	RH%	RH Hazard Frequency	PDF of RH	Frequency > 20% → chronic mold risk
7	MR [g/kg]	Mixing Ratio Stability	1 − (σᵢₙ/σₒᵤ)	Low buffering (<0.5) → indoor climate closely follows outdoor swings; effective buffering (> 0.5) → indoor conditions are noticeably more stable than outdoors
8	MR [g/kg]	Mixing Ratio Hazard Frequency	PDF of MR	Identify persistent humidity sources

Table 3. Indicators used in the surface moisture analysis in the 13 case studies.

#	Param	Indicator	Calculation	Effect on Surface Humidity
1	Tₚ	Dew Point Depression	Tₐᵢᵣ − Tₚ	Small depression (≤2.5 °C) → high condensation risk [53]
2	T-RH	Critical T-RH Zones	High T (>25 °C) + High RH (>60%)	High RH → hygroscopic salts in masonry dissolve absorbing moisture from the air; salts recrystallize during diurnal low-RH cycles [54]
			T ≈ 0 °C + RH > 80%	Freeze–thaw damage in masonry
3	RH%	RH% Fluctuation Index	RHd − CMAd	High RH swings + high T → condensation risk; low T + RH swings → frost damage
4	T°C	Diurnal Temperature Fluctuation	Td − CMAd	High fluctuations → material stress → micro-cracks → moisture ingress
5	RH%	Damp Effect Index	1 − (σᵢₙ/σₒᵤ)	Low buffering (<0.5) → indoor climate closely follows outdoor swings; effective buffering (> 0.5) → indoor conditions are noticeably more stable than outdoors
6	RH%	RH Hazard Frequency	PDF of RH	Frequency > 20% → chronic mold risk
7	MR [g/kg]	Mixing Ratio Stability	1 − (σᵢₙ/σₒᵤ)	Low buffering (<0.5) → indoor climate closely follows outdoor swings; effective buffering (> 0.5) → indoor conditions are noticeably more stable than outdoors
8	MR [g/kg]	Mixing Ratio Hazard Frequency	PDF of MR	Identify persistent humidity sources

3. Results

3.1. Monitoring of the Microclimatic Conditions

The internal monthly average temperature (°C) values across the 13 case studies illustrates distinct climatic trends influenced by environmental and architectural factors (Figure 2). Notably, San Romerio exhibited the lowest temperature profile, attributable to its high-altitude location (1800 m) in an open mountainous setting, resulting in consistently cooler conditions throughout the year compared to the other sites. A common seasonal pattern across all the locations was the temperature peak in July and August, highlighting the influence of external climate on indoor thermal conditions. This seasonal pattern highlights the influence of external climatic conditions on internal environments. Heating systems played a crucial role in moderating winter indoor temperatures, particularly in three sites: Basilica of San Fedele, San Vittore Mauro, and Besta Palace.

To evaluate the interaction between temperature (T) and relative humidity (RH), May was selected as a reference month due to the relatively uniform temperature across all sites, allowing for meaningful comparisons of RH and moisture content (MR) under consistent thermal conditions. Figure 3 reveals patterns in internal RH across the studied sites, with San Romerio emerging as the most extreme case due to its consistently high moisture levels (73–91%). This is due to its exposed high-altitude setting, with cool temperatures and limited climate control, fostering persistent conditions of dampness. A similar pattern was observed in Masegra Castle, San Perpetua Church, Pievani Palace, and the Ethnographic Museum of Tirano. Notably, during the summer months of June, July, and August, there was a humidity increase in San Romerio, Sant’Ignazio, San Giorgio, San Fedele, San Perpetua, Pievani Palace, and the Ethnographic Museum of Tirano, necessitating the continuous monitoring of RH to prevent damage. Winter months (Jan–Mar) showed non-humid conditions (~35–36%), followed by modest humidity in spring (April: 44.14%), a summer increase (May–October: 54–61%), and a decline in late autumn (~70-56% in Nov–Dec). Such fluctuations could lead to shrinkage and internal stress, particularly affecting the wooden structural elements embedded in the buttresses [55].

3.2. Indicators Assessment

3.2.1. Dew Point Depression (DPD)

Figure 4 presents the average monthly dew point depression (DPD) values for all sites. Apart from San Romerio, which showed a significantly higher risk, only San Perpetua, San Giorgio, Ethnographic Museum, and Venosta Palace exhibited a minimal condensation risk, all remaining below 2% of the year. San Romerio again stood out with the highest risk, exhibiting condensation-prone conditions for nearly 46% of the year.

3.2.2. Critical T-RH Zones

• High T (max per year) + High RH (60–90%)

To identify cases where high temperatures coincide with elevated RH levels, the conditions during July and August were plotted (Figure 5). Notably, Santa Perpetua (20 °C-73%) and Masegra castle (23 °C-71%) had the highest RH and T values, followed by San Giorgio, Sant’Ignazio, San Fedele, Pievani, Besta Palace, and the Ethnic Museum of Tirano. San Romerio, despite its lower temperatures, recorded extremely high RH values (>80%), making it a critical case for moisture-related concerns. Overall, the data highlights a clear dichotomy between warmer sites with lower humidity and cooler sites with saturated conditions, with the latter posing higher risks for conservation challenges.

• Fluctuating T around 0 °C + High RH (>90%)

The period of low temperatures spanned December and January. During December and January—the coldest months—San Romerio again demonstrated extreme conditions, with temperatures dropping to approximately 0 °C in January and 1.4 °C in December, alongside RH values exceeding 85–88%. Furthermore, Sant’Ignazio, Santa Perpetua Church, and Pievani Palace—despite slightly higher temperatures (5–8 °C)—showed persistent dampness, which could accelerate degradation.

In contrast, San Fedele, San Vittore Mauro, and Besta Palace maintained controlled T-RH conditions due to their heating systems. However, the conditions on the days immediately after turning off the heating system should be monitored to avoid sudden drops in temperature and humidity. Figure 6 illustrates the T-RH conditions across all the case study sites during December and January.

3.2.3. RH% Fluctuation Index

Fluctuations in RH are critical due to their potential to generate internal stress within materials, leading to microcracking and cumulative degradation. Both the frequency and magnitude of these fluctuations contribute to material weakening over time [34,56]. This study followed the procedure of Bertolin et al. [57] to analyze RH fluctuations in the case studies. In this method, RH fluctuations are calculated by subtracting the 30-day central moving average (CMA) from real-time RH measurements, considering the 7th and the 93rd percentiles of historic RH fluctuations as a safe band. The data was collected as a standard 13-month monitoring period where available.

In all cases, the highest rate of fluctuations occurred in December, November, and May. Winter fluctuations are partly linked to heating systems. The RH and CMA values typically ranged between 50 and 80%, with San Romerio and Masegra showing the highest averages. The Ethnographic Museum recorded the greatest number of days outside the safe fluctuation range, likely due to increased infiltration during its refurbishment period. Figure 7 depicts the three critical cases of San Romerio, Masegra Castle, and the Ethnographic Museum of Tirano.

3.2.4. Diurnal Temperature Fluctuation (DTF)

To determine the rate of temperature fluctuation, the modified Z-score method was applied considering the 7–93 percentile, which was recorded as the RH% fluctuation index. In all cases, the period from August to December witnessed the highest rate of fluctuations, with September experiencing 23 days of high fluctuations. Pievani Palace and Venosta Palace recorded the highest temperature fluctuation rates—nearly 46% of the year—while maintaining an average temperature of 14 °C. San Fedele recorded the highest average temperature, whereas San Romerio had the lowest. Figure 8 demonstrates the T fluctuations in the critical cases of Venosta and Pievani Palace and San Romerio Church.

3.2.5. RH% Damp Effect Index

In this research, an indicator was proposed to evaluate the buffering performance of building materials in response to external fluctuations. This index evaluates the level of RH stability within the interior; however, it has a key limitation—it does not consider internal moisture sources, which may significantly influence RH behavior. For more accurate validation, additional non-destructive methods such as gravimetric testing and infrared thermography (IRT) are recommended. The results of the RH damp effect from the case studies are presented in Figure 9. Uncontrolled scenarios yielded negative values, indicating a high indoor-to-outdoor ratio (σᵢ/σₒ), likely due to internal moisture sources unrelated to microclimatic conditions. Venosta Palace had the highest number of days with low damping (very low and low), followed closely by San Perpetua and, with a more significant difference, San Giorgio. To draw more concrete conclusions, this evaluation should be cross-referenced with the physical conditions of each case study site.

3.2.6. RH Hazard Frequency

Analyzing the frequencies of relative humidity revealed various trends across the case studies. Overall, three primary patterns emerged based on probability density function (PDF) analysis. In Pattern 1 (Figure 10a), the highest RH frequency was around 80%, which was observed in seven cases including Masegra Castle and Pievani Palace, which showed similar profiles. In Pattern 2 (Figure 10b), the RH peaked around 60% and then gradually declined. Semadeni Palace exhibited the highest likelihood of having an RH around this level. In Pattern 3 (Figure 10c), San Romerio displayed a unique trend with an RH frequently exceeding 90%, suggesting a behavior that notably differed from the other sites.

3.2.7. Mixing Ratio Stability

The mixing ratio (MR) is a key metric for analyzing air mass interactions between indoor and outdoor environments. Unlike relative humidity or dew point, MR remains unchanged during temperature or pressure variations (e.g., heating, cooling, or air movement), making it ideal for tracking moisture sources. However, to fully interpret material responses, it should be accompanied by RH and dew point analyses. In this study, MR stability was investigated through the damp effect (σᵢ/σₒ ratio). The unstable condition of the buffer zone can be witnessed in Venosta Palace and San Perpetua, followed by lower occurrences of low damping in San Fedele, San Giorgio, and San Vittore Mauro. In contrast, Semadeni Palace, Masegra Castle, and Pievani Palace demonstrated the strongest material buffering behaviors. Overall, the RH and MR damp effects showed closely correlated patterns across most cases. San Romerio stood out in this comparison, showing high damping effects for both RH and MR while also facing elevated condensation risk. This was due to the stable indoor humidity (high buffering, low σᵢ/σₒ ratio) which slows evaporation. Consequently, if condensation occurs and airflow is limited, retained moisture does not dry easily, exacerbating deterioration risks. Figure 11 presents the MR damp effect classifications across six levels, ranging from “uncontrolled” to “full damping,” which were calculated daily over the course of one year.

3.2.8. Mixing Ratio (MR) Hazard Frequency

MR frequency analysis was performed considering five categories ranging from 0–3 g/kg (very dry: risk of material shrinkage and cracking) to 12–15 g/kg (humid: high risk of mold and corrosion). Figure 12 reveals that San Romerio had zero probability of having an MR > 12 g/kg due to constant air infiltration during the restoration process. In most instances, the probability of having an MR of 6 g/kg was higher than that of 12 g/kg. Pievani Palace and San Fedele stood out with the highest probabilities of having an MR exceeding 12 g/kg.

4. Discussion

Effectiveness of Proposed Indicators

The study demonstrated that multivariate microclimate indicators can effectively identify surface moisture risks in historic porous masonry, though their diagnostic power varied with environmental and material contexts. Dew point depression proved to be highly sensitive to condensation risk, particularly in unheated, high-humidity environments like San Romerio (temperature below 2.5 °C for 46.39% of hours). However, its utility was limited in thermally buffered spaces (e.g., heated San Vittore Mauro), where surface temperatures may still permit condensation.

Relying solely on DPD calculations can therefore be misleading as they do not account for localized surface temperatures or the heterogeneous thermal properties of historic building materials. These variations can produce cold spots and condensation even when the overall indoor air dew point does not suggest a significant risk. Non-destructive techniques have revealed that the moisture distribution on historic building facades is closely linked to surface temperature differences [58,59]. During gravimetric testing at Sant’Ignazio Church in May 2010, the internal environment exhibited an average RH of 75%, a mixing ratio of 6.8 g/kg, and a dew point depression (DPD) ranging between 3.5 °C and 13 °C. The moisture content, calculated as W% = (wet weight − dry weight)/dry weight, was measured to be 4.6% in Sample 2 (where the data logger was installed, as shown in Figure 13) for the lime mortar. According to experiments conducted in the Mobile Testing Unit (MTU) laboratory at the ABC Department of Politecnico di Milano, porous historic materials typically reach an equilibrium moisture content of 4–5% when the relative humidity exceeds 55%. While higher RH levels do not pose critical degradation risks when temperatures remain above 0 °C, the test conditions warrant special consideration. When measured after several rainy days with a sustained RH of 75%, Sample 2’s moisture content reached 4.6%. These results partially validate the DPD findings at Sant’Ignazio, though additional testing is required for a comprehensive verification. Notably, dew point depressions as low as 3 °C may critically affect already deteriorated surfaces, particularly lime-based mortars. While the DPD (3.5–13 °C) suggested a low condensation risk from air temperature alone, gravimetry detected moisture retention (4.6%), likely due to localized cold surfaces and salt contamination. This confirms DPD’s limitation without surface-temperature data.

The evaluation of critical temperature–humidity (T–RH) zones revealed seasonal threats. High RH levels (>60%) at elevated temperatures (>25 °C) were recorded during 23% of July and August in Lake Como sites (e.g., San Fedele) and were associated with observed salt dissolution (Figure 14). Furthermore, the summer RH increased in multiple cases (San Perpetua, Masegra Castle, Pievani Palace, etc.), suggesting that even in warmer months, moisture remains a critical concern, necessitating continuous monitoring to prevent material degradation. Models and experimental data suggest that omitting temperature considerations may result in underestimating moisture risks—particularly in materials sensitive to combined temperature and humidity variations [60]. Thus, a comprehensive assessment incorporating T–RH zones, RH fluctuation indices, and diurnal temperature variability can reveal cycles related to moisture degradation. It should be noted that heated buildings (Besta Palace, San Vittore, and San Fedele) avoided these extremes but required careful post-heating monitoring to prevent rebound humidity. In San Fedele (Figure 14), we observed persistent humidity rebound—while the plaster remained intact during winter, the underlying rising dampness issue was not fully resolved, meaning that the water source remained active. This created a cyclic wetting–drying pattern where evaporation promoted salt crystallization and material damage.

Material performance and internal buffering capacity were assessed using the RH and MR damp effect indices. The cases with low buffering (low buffering for more than 50% of a year; e.g., cases of Venosta Palace and San Perpetua Church) showed visible moisture damage during the site visit. An indoor-to-outdoor standard deviation ratio (σᵢ/σₒ) exceeding 0.5 can signal unstable conditions or high infiltration rates, as observed in the Ethnographic Museum during refurbishment. An exception to this assessment was the case of San Romerio, Brusio, in which the stable indoor MR (high damping) trapped moisture, prolonging surface wetness. This finding highlights the need to combine damping metrics with airflow analysis to distinguish between effective buffering and stagnation.

Figure 15 presents the case study assessments using a point-based ranking system: critical cases were assigned a score of 2, problematic ones a score of 1, and moderate cases were left unranked. Future research should evaluate how effectively these indicators detect surface moisture by examining their relationship with existing surface damage. The optimal methodology would involve monitoring newly restored surfaces from the onset of intervention, allowing for multi-year data collection and analysis to detect anomalies and quantify moisture risks with greater precision.

5. Conclusions

The effectiveness of microclimate indicators in predicting surface moisture risks depends on the building’s use, material properties, and climatic context. In heated spaces such as San Vittore Mauro, active climate control systems helped mitigate condensation risk, although monitoring the mixing ratio (MR) remains essential to balance humidification requirements. Conversely, unheated structures relied more heavily on dew point depression and critical T-RH zones for moisture risk assessment.

Alpine environments, characterized by prolonged cold and damp conditions, necessitated stricter dew point depression margins (<3 °C), while temperate regions such as Lake Como demanded closer scrutiny of summer RH-T interactions to manage salt dissolution risks.

While the proposed indicators effectively identified at-risk zones, their accuracy was limited by the absence of surface temperature measurements, which are critical for precise condensation prediction. Future research should integrate infrared thermography (IRT) with microclimate monitoring to capture thermal gradients and localized moisture accumulation. Material heterogeneity, particularly in salt-laden or previously repaired masonry, further complicates threshold calibration, underscoring the need for case-specific adjustments. Additionally, short-term moisture events, such as rain-driven infiltration, were inadequately represented in the annual averages. Event-based analysis could improve detection sensitivity in these cases. A multiple regression model quantifying the influence of key environmental parameters on indoor MR variation could enhance the predictive accuracy. Combining IRT with microclimate data could help identify water infiltration pathways, particularly when cross-referenced with historical restoration records. Such an approach would strengthen the link between environmental monitoring and actionable conservation strategies.

While this study establishes a multivariate framework for predicting surface moisture risks, future work should quantify the relationship between microclimatic indicators and material deterioration rates, integrating structural safety assessments to inform targeted conservation strategies.