Office‐to‐education adaptive reuse changes occupancy density, schedules, and activities without necessarily involving a redesign of the existing ventilation system. Evidence on within‐room indoor air quality and occupant perception in converted higher education buildings remains limited. This study examined a mechanically ventilated design studio at Istanbul Gelisim University through measurements in window‐side (Z1), central (Z2), and circulation (Z3) zones and a questionnaire completed by 48 users. Carbon dioxide (CO 2 ), particulate matter (PM 2.5 and PM 10 ), total volatile organic compounds (TVOCs), and grille‐level air velocity were assessed. Mean CO 2 was highest and most variable in Z2 (527 ± 114 ppm; 95th percentile = 760 ppm; maximum = 882 ppm), whereas upper‐end particulate indicators were greater in Z3. Grille velocities showed point‐level heterogeneity despite broadly similar zonal means. Exploratory point‐level comparisons did not establish statistically significant zonal differences in CO 2 , particulate indicators, or TVOC after multiplicity adjustment. Perceived air freshness was positively associated with overall environmental satisfaction (Spearman ρ = 0.475, 95% bootstrap CI 0.219–0.682; Holm‐adjusted permutation p = 0.0022). Respondents could not be linked to individual measurement times and locations, so this association does not establish an exposure–perception relationship. This single case illustrates the value and limitations of combining zone‐based monitoring with occupant assessment in a converted educational space. Sequential monitoring, sensor limitations, and the small number of spatial sampling points restrict inference. The findings support further investigation of supply air distribution and occupant experience rather than a definitive ranking of zones or proof of ventilation inadequacy.
With recent advances in IT, it has become possible to individually control the indoor environment for each room in buildings. However, there are differences in thermal preferences and local heat distribution between occupants and between. To settle such problems, personalized environmental control systems (PECS) have emerged these days as a solution, which provides the advantages of portability, easy installation, and operation as local environmental control devices. However, research on the optimal selection of cooling PECS devices still remains limited. Therefore, this study explored to develop a decision‐making framework to select optimal PECS—among typical PECS devices (i.e., desk fan, body fan, cooling cushion, and neck band)—based on individual characteristics and activities. To avoid large data requirements of conventional machine learning, the proposed framework uses association rule mining (ARM) to provide reliable thermal comfort patterns of cooling PECS from limited experimental datasets. The decision‐making framework integrates a personal characteristic‐based rule refinement and prioritization logic to reflect individual traits to effective cooling modalities. In particular, the proposed framework can simultaneously optimize several thermal comfort metrics and effectively capture the interplay between occupant‐specific attributes and localized thermal interventions of cooling PECS devices to provide actionable PECS selection guideline. The results of this study offer a data‐efficient pathway for addressing heterogeneous cooling demands and advancing human‐centric building climate control using cooling PECS.
This article presents the development and approbation of an indoor climate model for Latvian residential buildings based on long-term measurements and statistical analysis. The study analyzes 2,195,785 records of indoor air temperature and humidity collected from 36 dwellings across seven Latvian cities between 2023 and 2025, with outdoor air temperature used as the key variable. Piecewise regression revealed a distinct breakpoint at an outdoor temperature of +7.86 degrees C, below which indoor temperature remains nearly constant at approximately +21.7 degrees C due to heating system operation, showing negligible correlation with outdoor conditions. Above this threshold, a moderate correlation was observed, indicating free-running indoor behavior. Indoor relative humidity and the indoor-outdoor absolute humidity difference were best described by simple linear regressions, exhibiting moderate positive and negative correlations with outdoor temperature, respectively. Comparison with European standards (EN and DIN) demonstrated substantial deviations, particularly during the heating season, where measured indoor temperatures were higher and moisture dynamics differed from standardized assumptions. Hygrothermal simulations using the DELPHIN program showed that the standard EN 15026 indoor climate underestimated mold risk (mold index VTT 1.5), while the developed Latvian model predicted visible mold formation (mold index VTT 3.2) in an internally insulated masonry wall. The findings underline the limitations of generalized standards and emphasize the necessity of region-specific, data-driven indoor climate models for reliable building performance and durability assessment in cold climates such as Latvia.
Museums are low-ventilation environments where volatile organic compounds (VOCs) can accumulate, yet museum-specific evidence for health prioritization remains limited. We surveyed three representative museums in Beijing, Hubei, and Guangdong, with four predefined functional room types sampled with site-specific coverage over 5 consecutive days, and profiled 55 VOCs. Using a Toxicological Priority Index that integrated concentration, detection frequency, and toxicological benchmarks, we prioritized compounds and resolved site- and room-specific patterns. Aldehydes and ketones dominated total mass (> 88%), whereas descriptive room-category profiles suggested relatively higher aromatic fractions in the sampled sample-preparation room and some task-intensive spaces. Prioritization consistently highlighted o-xylene, formaldehyde, ethylbenzene, m/p-xylene, 1,2-dibromoethane, and benzene. Incremental lifetime cancer risk (ILCR) estimates based on museum measurements indicated multiple exceedances of the commonly adopted 1 & times; 10(-6) ILCR screening benchmark: six in Beijing, six in Guangdong, and seven in Hubei, with common signals from formaldehyde, benzene, and 1,2-dichloroethane. To provide contextual cumulative-risk scenarios, we combined measured museum ILCRs with province-matched residential ILCR estimates for overlapping VOCs and found scenario-based regional mean cumulative ILCRs above the benchmark. The museum-attributable fraction was substantial for several agents, including benzene and ethylbenzene in Beijing (84% and 77.2%), formaldehyde in Guangdong (27.1%), and benzene in Hubei (53%), indicating nonnegligible measured museum contributions to the contextual cumulative risk scenario. Network toxicology identified a hub of cancer-related genes centered on ribosome biogenesis and oxidative phosphorylation, offering mechanistic hypotheses for VOC-related carcinogenicity. This integrated framework links prioritization, occupational risk, and putative mechanisms to guide targeted ventilation, source control, and material selection in heritage environments.
This study analyzed 9377 indoor air samples from 27 multiuse facility types in South Korea to evaluate the nonlinear influence of temperature and relative humidity (RH) on bacterial and fungal concentrations. Although previous research often relied on linear correlations that failed to capture complex interactions, this study employed a generalized additive mixed model (GAMM) to identify the specific meteorological sensitivities of diverse building environments. Results revealed significant heterogeneity across facility types: fungal concentrations were predominantly sensitive to RH (16 facilities), whereas bacteria responded more frequently to temperature (seven facilities). Peak microbial "danger" levels were observed at approximately 55%-60% RH, confirming 60% as a critical threshold for indoor dampness and proliferation. Based on these responses, facilities were categorized into four sensitivity groups: Both, Relative humidity only, Temperature only, and None. A random forest model was applied to evaluate the predictive value of this categorization. Predictive performance significantly improved when the model focused on facilities with significant meteorological sensitivity, achieving F1-scores of 0.65 for bacteria and 0.75 for mold, compared with 0.53 for the unclassified datasets for both bacteria and mold. Conversely, facilities in the "None" category-those showing no significant meteorological response-suggest that nonmeteorological factors such as occupant activity and ventilation efficiency dominate microbial dynamics in those settings. These findings demonstrate that indoor microbial management must transition from universal standards toward facility-specific, climate-resilient strategies that account for unique building vulnerabilities.
