Bacterial concentration in residential apartments using quantitative and semi-quantitative filter forensics

Indoor environments harbor diverse microbial communities that may significantly influence human health and wellbeing. Quantifying these microbial populations is therefore crucial for evaluating indoor air quality and assessing potential health risks.
HVAC system and portable air cleaner filters are increasingly utilized as samplers to collect dust samples for estimating bioaerosols (filter forensics). When the filter forensics analysis is combined with HVAC metadata, it provides temporally and spatially integrated estimates of airborne concentration of particle bound contaminants, referred to as quantitative filter forensics (QFF).
Although QFF provides detailed information, it requires HVAC metadata (airflow rate, runtime and filter efficiency) and accurate measurements of dust mass deposited on filters, which can be technically challenging and, time consuming to collect or in some cases data may be unknown, limiting the applicability of QFF. There is therefore a need for simplified approaches that can still provide useful estimates of contaminant concentration without relying on HVAC data.
In this work, we develop and evaluate a semi-quantitative filter forensics (SQFF) framework that estimates airborne concentration without requiring HVAC metadata or filter mass measurements. The approach assumes that the amount of dust settled on the filter is strongly related to volume of air passing through the filter allowing dust concentration to serve as proxy for airborne concentrations. We compared QFF and SQFF based estimates of bacterial concentration on portable air cleaner filters in 15 residential apartments using quantitative polymerase chain reaction (qPCR). The dust concentration (SQFF) of bacteria on the filters ranged between 1.1×106 - 9.5 ×106 copies/mg dust. SQFF and QFF estimated concentrations were positively correlated with strong Spearman correlation (ρ = 0.86, p= 1.5×10-4). Reasonable correspondence between the two frameworks suggests that SQFF can provide useful screening level assessments when HVAC metadata is unavailable. More broadly, this simplified framework may expand the applicability of filter forensics for indoor air quality assessment and microbial exposure studies.