Dynamic Headspace SIFT-MS Coupled with Indoor Air Modelling for Bottom-Up Estimations of Indoor Air Pollution from Cleaning

Cleaning products are frequently used in indoor environments and emit a range of volatile organic compounds (VOCs). Many of these VOCs are directly harmful to occupant health or can undergo chemical transformations to generate harmful secondary pollutants. The impact of cleaning on indoor air quality is attributable to a range of factors, including the chemical composition of cleaning product formulations. There has been an increasing trend towards 'green' cleaning products, with the assumption that these are better for our health. However, there is little evidence to suggest that air pollution from these products is less detrimental to indoor air quality compared to conventional cleaning products. In this study, a bottom-up approach was used to determine the VOC emission profiles and emission rates from 4 regular and 7 green commercially available cleaning products, based on dynamic headspace selected-ion flow-tube mass spectrometry (SIFT-MS) measurements. The INdoor CHEMical model in Python (INCHEM-Py) was used to simulate the indoor air chemistry and secondary pollutant formation which results from the use of these cleaning products under conditions relevant to the indoor environment. Results showed that cleaning product formulations were sources of VOCs such as terpenes, alcohols and aldehydes to varying extents, with no discernible difference between regular and green cleaners. Modelling of VOC emissions showed the evolution of secondary pollutants such as formaldehyde following the emission event, primarily a result of terpene-ozone chemistry. This study shows that both regular and green cleaning products can act as a source of hazardous primary and secondary pollutants in indoor environments.