Since people spend most of their time inside buildings, indoor air quality (IAQ) remains a highlighted topic to ensure in the built environment to improve public health, especially for vulnerable users. To achieve a better indoor environment quality (IEQ), some countries’ governments or regional institutions have developed and published reference guideline values of various air pollutants to prevent the IAQ from becoming adverse to occupants. Beyond guidelines by World Health Organization (WHO), in some countries, there are specific institutional requirements on the IAQ, and others integrated it into the building regulation for the built environment. This paper is based on the literature research, summarized from previously conducted works by the authors, on the chemical reference values of IAQ-related regulations and guidelines published by several Governments or related institutions from various regions around the World. Despite these efforts at standardization and legislation, many indoor air quality monitoring activities conducted in several countries still fall short of the main indications produced. By comparing the reference values of 35 pollutants, both physical and chemical ones, which are proposed in documents from 23 regions included so far, the IAQ research and prevention actions on progress in different regions should be included in monitoring plans with guidelines/reference values in their current state. The outcome of the paper is to define the current trends and suggest some perspectives on the field of interest for improving the indoor air quality of generic spaces at an international level. It becomes evident that, at the global level, IAQ represents a complex political, social, and health challenge, which still suffers from the absence of a systematic and harmonized approach. This is not a new situation; the issue was raised more than 40 years ago, and despite efforts and a pandemic, the situation has not changed.

In residential buildings that are private, autonomous, and occupied spaces for most of the time, it is necessary to maintain good indoor air quality (IAQ), especially when there are children, elderly, or other vulnerable users. Within the development of sensors, their low-cost features with adequate accuracy and reliability, as well as Internet of Things applications, make them affordable, flexible, and feasible even for ordinary occupants to guarantee IAQ monitoring in their homes. This systematic review searched papers based on Scopus and Web of Science databases about the Low-Cost Sensors (LCS) and IoT applications in residential IAQ research, and 23 studies were included with targeted research contents. The review highlights several aspects of the active monitoring strategies in residential buildings, including the following: (1) Applying existing appropriate sensors and their target pollutants; (2) Applying micro-controller unit selection; (3) Sensors and devices’ costs and their monitoring applications; (4) Data collection and storage methods; (5) LCS calibration methods in applications. In addition, the review also discussed some possible solutions and limitations of LCS applications in residential buildings based on the applications from the included works and past device development experiences.

This study investigates indoor and outdoor air quality monitoring in a student dormitory located in northern Milan (Italy) using low-cost sensors. This research compares two monitoring periods in June and October 2024 to examine common PM_2.5 vertical patterns and differences at the building level, as well as their influence on the indoor spaces at the corresponding positions. In each period, around 30 sensors were installed at various heights and orientations across indoor and outdoor spots for 2 weeks to capture spatial variations around the building. Meanwhile, qualitative surveys on occupation presence, satisfaction, and well-being were distributed in selected rooms. The analysis of PM_2.5 data reveals that the building’s lower floors tended to have slightly higher outdoor PM_2.5 concentrations, while the upper floors generally had lower PM_2.5 indoor/outdoor (I/O) ratios, with the top-floor rooms often below 1. High outdoor humidity reduced PM infiltration, but when outdoor PM fell below 20 µg/m³ in these two periods, indoor sources became dominant, especially on the lower floors. Air pressure I/O differences had minimal impact on PM_2.5 I/O ratios, though slightly positive indoor pressure might help prevent indoor PM infiltration. Lower ventilation in Period-2 possibly contributed to more reported symptoms, especially in rooms with higher PM from shared kitchens. While outdoor air quality affects IAQ, occupant behavior—especially window opening and ventilation management—remains crucial in minimizing indoor pollutants. Users can also manage exposure by ventilating at night based on comfort and avoiding periods of high outdoor PM.

Introduction: Indoor air quality (IAQ) is a key determinant of health in enclosed environments, and it plays a relevant role in the onset of symptoms associated with sick building syndrome (SBS). In particular, student houses warrant particular attention, given that their environmental conditions are strictly controlled by the facility management through dedicated operational activities. Starting from an analysis of two facilities in Milan with continuous monitoring of air temperature, relative humidity, air pressure, CO ₂ concentration, particulate matters (PM _2.5 and PM ₁₀ ), and tVOC concentration, a cross‐sectional observational study was developed on environmental parameters and users′ symptoms. This study is aimed at evaluating the prevalence of SBS‐compatible symptoms among student residents and at analyzing their potential associations with monitored indoor environmental parameters. Methodology: The study conducted IAQ monitoring activities and surveys on users′ perception in seven fieldwork periods between May 2024 and June 2025. About 30 low‐cost sensors were deployed in each period in many rooms with different floor levels and orientations, as well as qualitative questionnaires that collected users′ perceptions of IAQ‐related symptoms, behaviors, occupancy, window openings, and so forth to support data interpretation. Results and Discussion: The analysis permitted to investigate 51 rooms for each monitoring period and to obtain in total 74 questionnaires, of which the paper examines 27 specific environmental units, that showed a stronger concordance between symptoms and IAQ anomalies. The measurements revealed temperatures frequently above comfort thresholds, relative humidity often below the comfort range, and PM _2.5 concentrations consistently exceeding reference values, while CO₂ levels generally showed acceptable limits. Meanwhile, the most frequently reported symptoms included sneezing, mucosal dryness or sore throat, headache, cough, and nasal congestion. In general, specific environmental deviations corresponded to the types of symptoms reported by occupants. Conclusions: The findings emphasize an association between altered IAQ and SBS‐compatible symptoms and point to the value of combining environmental measurements with occupant perception data, though the observational design of the study does not allow for causal conclusions. Consequently, continuous monitoring supports proactive IAQ management, and the findings highlight the need for behavioral guidance to maintain healthy buildings.

Indoor air quality (IAQ) is a major determinant of occupant health, with people spending over 90% of their time indoors. This study presents a machine learning–based classification framework for predicting self‐reported health symptoms in university dormitory residents, using continuous indoor and outdoor environmental monitoring data and occupant survey responses collected across 7 monitoring periods from May 2024 to June 2025 in 2 student accommodation facilities in Milan, Italy. From 74 occupant samples, 131 candidate predictors were derived from 5 indoor and 5 outdoor environmental parameters together with occupant‐level and contextual variables and reduced to symptom‐specific feature sets of 3–40 predictors through permutation importance. Seven symptoms each reported by at least 10 occupants (sneezing, headache, cough, dry skin, fatigue, dry/sore throat and runny nose) were modelled as independent binary classification tasks under leave‐one‐period‐out cross‐validation (LOPO‐CV), with the best classifier selected from 23 candidates per symptom based on balanced accuracy and thresholds set individually by Youden′s J index. Mean AUC across the 7 models was 0.595 (range: 0.398–0.705), with 4 symptoms exceeding AUC 0.6; dominant predictive signals were drawn from outdoor environmental statistics and distributional shape features rather than mean indoor pollutant concentrations. At a combined accuracy criterion of ≥ 5/7 symptoms correctly classified, 58.1% of occupants were correctly profiled across all 74 samples, rising to 64.3% in the symptomatic subgroup ( n = 42); excluding the 2 symptoms with the lowest model specificity raised ≥ 3/5 combined accuracy to 92.0% across the full sample and 90.0% in the symptomatic subgroup. The results demonstrate the feasibility of multisymptom environmental risk profiling and support the concept of a learning building, in which continuously updated occupant health models can inform proactive and personalised indoor environment management.