Indoor Air Quality Assessment and Management

Indoor air quality assessment and management encompass a multidisciplinary approach to identifying, quantifying and controlling airborne contaminants within built environments. Core objectives include monitoring concentrations of particulate matter, gases and bioaerosols; understanding their sources and temporal–spatial variability; and evaluating associated health risks. Advances in sensor technologies, data analytics and exposure modelling now permit high-resolution mapping of pollutants across homes, schools and workplaces. Concurrently, epidemiological studies and burden-of-disease metrics have clarified the contribution of indoor exposures—particularly fine particulate matter—to respiratory and cardiovascular morbidity. Integrating building physics with human behaviour research has yielded dynamic infiltration models that account for ventilation, airtightness and occupant activities. Management strategies range from source control and enhanced filtration to optimised ventilation schedules and adaptive building envelopes. Emphasis on low-cost, scalable interventions—such as window-timing algorithms, retrofit air cleaners and predictive control systems—has broadened applicability in both high- and low-income settings. The global significance of indoor air quality is underscored by its impact on childhood asthma, cardiovascular disease and cognitive performance, reinforcing the need for evidence-based policies and standards. Future directions centre on harmonising international guidelines, leveraging machine learning for real-time risk forecasts and embedding air-quality considerations within sustainable building design.

Research from Nature Portfolio

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Indoor Air Quality Assessment and Management publication trend

The graph below shows the total number of articles in indoor air quality assessment and management across all publications each year (not limited to Nature Index journals).

Technical terms

Particulate matter (PM2.5): Airborne particles with aerodynamic diameter ≤2.5 µm, capable of deep lung penetration and associated with adverse health effects.

Infiltration factor: The fraction of outdoor particulate concentration that penetrates and remains suspended in indoor air under prevailing building and environmental conditions.

Bayesian neural network: A machine-learning model that incorporates probabilistic reasoning to predict pollutant levels and quantify the uncertainty of its estimates.

Spatiotemporal modelling: Analytical methods that characterise how pollutant concentrations vary over space and time, often integrating sensor data and geographic variables.

Disability-adjusted life year (DALY): A composite health metric combining years of life lost due to premature mortality and years lived with disability, used to quantify disease burden from exposures.

Source apportionment: The process of identifying and quantifying the contributions of different pollutant sources—such as cooking, traffic and infiltration—to indoor air concentrations.

References

1. The burden of disease attributable to indoor air pollutants in China from 2000 to 2017. The Lancet Planetary Health (2023).
2. Large-scale spatiotemporal deep learning predicting urban residential indoor PM2.5 concentration. Environment International (2023).
3. Assessing residential PM2.5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors. Proceedings of the National Academy of Sciences of the United States of America (2023).