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Numerous very small airborne particles were observed from brief use of a common household cleaning product, highlighting the importance of indoor particle pollution sources that are unrelated to combustion. This is the first time that the rapid chemical transformation of vapors to airborne particles was observed during realistic use of consumer products, emphasizing the importance for professional cleaners and people frequently exposed to cleaning products. 

Surface cleaning using commercial disinfectants, which has recently increased during the coronavirus disease 2019 pandemic, can generate secondary indoor pollutants both in gas and aerosol phases. It can also affect indoor air quality and health, especially for workers repeatedly exposed to disinfectants. Here, we cleaned the floor of a mechanically ventilated office room using a commercial cleaner while concurrently measuring gas-phase precursors, oxidants, radicals, secondary oxidation products, and aerosols in real time; these were detected within minutes after cleaner application. During cleaning, indoor monoterpene concentrations exceeded outdoor concentrations by two orders of magnitude, increasing the rate of ozonolysis under low (<10 ppb) ozone levels. High number concentrations of freshly nucleated sub–10-nm particles (≥105 cm−3) resulted in respiratory tract deposited dose rates comparable to or exceeding that of inhalation of vehicle-associated aerosols. 

The integration of Internet of Things (IoT)-enabled sensors and building energy management systems (BEMS) into smart buildings offers a platform for real-time monitoring of myriad factors that shape indoor air quality. This study explores the application of building energy and smart thermostat data to evaluate indoor ultrafine particle dynamics (UFP, diameter ≤ 100 nm). A new framework is developed whereby a cloud-based BEMS and smart thermostats are integrated with real time UFP sensing and a material balance model to characterize UFP source and loss processes. The data-driven framework was evaluated through a field campaign conducted in an occupied net-zero energy building—the Purdue Retrofit Net-zero: Energy, Water, and Waste (ReNEWW) House. Indoor UFP source events were identified through time-resolved electrical kitchen appliance energy use profiles derived from BEMS data. This enabled determination of kitchen appliance-resolved UFP source rates and time-averaged concentrations and size distributions. BEMS and smart thermostat data were used to identify the operational mode and runtime profiles of the air handling unit and energy recovery ventilator, from which UFP source and loss rates were estimated for each mode. The framework demonstrates that equipment-level energy use data can be used to understand how occupant activities and building systems affect indoor air quality.