Key points are not available for this paper at this time.
ABSTRACT Fine particulate matter (PM 2.5 ) poses a significant threat to human health globally. Several epidemiological studies rely on outdoor air data to estimate human exposure levels, associating them with health effects across various populations. However, it’s crucial to note that individuals typically spend most of their time indoors, where PM 2.5 data is lacking. This gap hinders comprehensive studies that require large sample sizes to identify associations and control for confounding variables. Indoor PM 2.5 plays a crucial role in human exposure assessment. Measuring indoor PM 2.5 level through stringent quality control procedures usually requires specialized sampling monitor, but this direct method is expensive, time-intensive, and impractical. Thus, there is a necessity for finding alternative approaches, such as predictive models, which can estimate indoor PM 2.5 levels where direct measurement is not feasible due to various constraints in place. This review concentrates on studies from 1997 in which we analyzed PM 2.5 sources, influencing factors, modeling techniques, and personal exposure assessments. The global PM 2.5 exposure thresholds currently in use are outlined. Outdoor and indoor predictive models, including spatial models, machine learning, physicochemical models, and others, were thoroughly evaluated. The merits and drawbacks of each model were assessed to facilitate optimal selection in future studies. Furthermore, the knowledge gap and priorities for future PM 2.5 exposure model development were identified. A holistic understanding of this field can ensure the sustainable control of PM 2.5 exposure and its risks to human health. Hence, understanding the association between outdoor and indoor PM 2.5 is the main key to assessing exposure.
Lu et al. (Sat,) studied this question.