Aerosol processes relevant to the indoor environment simulated in a detailed chemistry and aerosol microphysics model:S.P. O’Meara1,2, G. McFiggans2 and N. Carslaw3 1National Centre for Atmospheric Science, UK 2Department of Earth and Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK 3Department of Environment and Geography, University of York, York, YO10 5NG, UK Keywords: model, aerosol, indoor air quality, indoor environment, HOMs. Associated conference topics: 4.1, 5.6 Presenting author email: [email protected]

A mathematical model (CHemistry with Aerosol Microphysics (PyCHAM)) is extended from including detailed gas-phase chemistry with dynamic gas-particle and gas-single surface (e.g. wall) partitioning to also including surface reactions, partitioning to multiple surfaces and wavelength-dependent transmission of natural light. The improved PyCHAM can simulate physicochemical processes occurring indoors. The combination of processes now simulated in PyCHAM alongside relatively detailed chemistry is novel compared to alternative models. Furthermore, PyCHAM is open-source and includes a user-friendly interface and manual (PyCHAM (2023)). Here we present the ability of PyCHAM to reproduce observations from indoor environments and provide additional insight. In particular, we compare against observations in homes that target processes affecting indoor air quality: gas-surface partitioning (e.g. Figure 1), surface reactions, light transmission through windows, particle deposition to surfaces, indoor emission of gases and particles, indoor-outdoor exchange of gases and particles. In Figure 1 of this abstract is an example of PyCHAM reproducing observations: when semi-volatile organic components (SVOC) are present on indoor surfaces and allowed to partition into the gas-phase and then the particle-phase, indoor particle concentrations of organics (separated by alkane-equivalent volatility bins (carbon number (C) 24-31)) increases as the mass concentration of particles with diameter less than 2.5 μm (PM2.5) increases. The same trend, with comparable gradient (m), is reported in observations from a recently occupied household (Lunderberg et al (2020)). As an example of the detail available from PyCHAM, we report the role of Highly Oxygenated Molecules (HOMs) on particle loading and oxidation state, since chamber studies indicate HOMs can significantly affect these properties (Kruza et al (2020)). The role of HOMs is investigated over several cases in which the following variables are changed within published ranges: surface deposition of ozone; surface reactions affecting ozone, nitrogen oxides and nitrous acid; source strength of indoor and outdoor particulates; source strength of indoor and outdoor gases

INCHEM-Py : An open source Python box model for indoor air chemistry

The INdoor CHEMical model in Python (INCHEM-Py) is an open source box model that creates and solves a system of coupled Ordinary Differential Equations (ODEs) to provide predicted concentrations of indoor air pollutants through time. It is a refactor of the indoor detailed chemical model originally developed by Carslaw (20017) with improvements in form, function, and accessibility. INCHEM-Py takes the Master Chemical Mechanism (MCM), a near explicit mechanism developed for atmospheric chemistry, and additional chemical mechanisms that have been developed specifically for indoor air. These include gas-to-particle partitioning for three of the commonly encountered terpenes indoors (limonene and alpha- and beta-pinene), improved photolysis parametrisation, indoor-outdoor air exchange, and deposition to surfaces. Typical usage of INCHEM-Py is either alongside experiment, where it can be used to gain a deeper insight into the chemistry through its ability to track a vast array of species concentrations or as a stand-alone method of investigating chemical events that occur indoors over a range of conditions. INCHEM-Py is open source, has no black box processes, and all inputs can be tracked through the model allowing for complete understanding of the system. A wide array of outputs from the model can be accessed, including species concentrations, species reactivity and production rates, photolysis values, and summations such as the total peroxy radical concentration. Custom reactions and summations can also be added by users to tailor the model to specific indoor scenarios. A full pdf manual is included within this repository, including a quick start guide, this README is not intended to cover every aspect of INCHEM-Py but should be sufficient for users to get started. A copy of the GNU General Public License v3.0 under which this project is licenced is included in this repository.This work is funded by a grant from the Alfred P. Sloan Foundation, grant number 2018-10083. Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff

Simplified speciation and atmospheric volatile organic compound emission rates from non-aerosol personal care products

Volatile organic compounds (VOCs) emitted from personal care products (PCPs) can affect indoor air quality and outdoor air quality when ventilated. In this paper, we determine a set of simplified VOC species profiles and emission rates for a range of non-aerosol PCPs. These have been constructed from individual vapor analysis from 36 products available in the UK, using equilibrium headspace analysis with selected-ion flow-tube mass spectrometry (SIFT-MS). A simplified speciation profile is created based on the observations, comprising four alcohols, two cyclic volatile siloxanes, and monoterpenes (grouped as limonene). Estimates are made for individual unit-of-activity VOC emissions for dose-usage of shampoos, shower gel, conditioner, liquid foundation, and moisturizer. We use these values as inputs to the INdoor air Detailed Chemical Model (INDCM) and compare results against real-world case-study experimental data. Activity-based emissions are then scaled based on plausible usage patterns to estimate the potential scale of annual per-person emissions for each product type (eg, 2 g limonene person−1 yr−1 from shower gels). Annual emissions from non-aerosol PCPs for the UK are then calculated (decamethylcyclopentasiloxane 0.25 ktonne yr−1 and limonene 0.15 ktonne yr−1) and these compared with the UK National Atmospheric Emissions Inventory estimates for non-aerosol cosmetics and toiletries

A Modelling Study of Indoor Air Chemistry: The Surface Interactions of Ozone and Hydrogen Peroxide

Indoor surfaces play a key role in indoor chemistry, including modification of indoor oxidant concentrations. This study utilises the INdoor CHEmical Model in Python (INCHEM-Py) to investigate the impact of surface transformations and their impact on indoor gas-phase chemistry. INCHEM-Py has been developed to simulate the surface deposition of ozone and hydrogen peroxide onto nine and six individual surfaces respectively in a typical bedroom, kitchen and office for normal indoor concentrations in the absence of household activities. The results show that 91 to 96% of these oxidants are deposited onto indoor surfaces under our simulated conditions. In the bedroom, 38 to 44% of indoor ozone and hydrogen peroxide is deposited onto soft fabric surfaces, with 41 to 54% of ozone deposition occurring on plastic surfaces in the kitchen and office. Total indoor concentrations of straight-chained aldehydes (C1-C10) ranged from 4 to 5 ppb, with nonanal having the highest individual concentration (1.7, 1.6 and 1.5 ppb in the bedroom, kitchen and office respectively), primarily as a result of emissions from plastics following ozone deposition. Aldehyde concentrations following hydrogen peroxide deposition were often less than 0.01 ppb. Understanding how reactions and deposition on different indoor surfaces impact indoor air chemistry will enable internal surface selection with a view to improving overall indoor air quality

Unexpectedly high concentrations of monoterpenes in a study of UK homes

The abundance of volatile organic compounds (VOCs) found in homes depends on many factors such as emissions, ventilation and the oxidative environment and these are evolving over time, reflecting changes in chemical use, behaviour and building design/materials. The concentrations of VOCs in 25 UK homes of varying ages, design and occupancy were quantified using continuous indoor air sampling over five days. Air was collected through low flow (1 mL min-1) constant flow restrictors into evacuated 6 L internally silica-treated canisters until the canisters reached atmospheric pressure. This was followed by thermal desorption-gas chromatography and high mass accuracy time-of-flight mass spectrometry (TD-GC-TOF/MS). A fully quantitative analysis was performed on the eight most abundant hydrocarbon-based VOCs found. Despite differences in building characteristics and occupant numbers 94% of the homes had d-limonene or α-pinene as the most abundant VOCs. The variability seen across the 25 homes in concentrations of monoterpenes indoors was considerably greater than that of species such as isoprene, benzene, toluene and xylenes. The variance in VOCs indoors appeared to be strongly influenced by occupant activities such as cleaning with 5-day average concentrations of d-limonene ranging from 18 μg m-3 to over 1400 μg m-3, a peak domestic value that is possibly the highest yet reported in the literature

Temperature driven variations in VOC emissions from plastic products and their fate indoors: a chamber experiment and modelling study

Plastic products are ubiquitous in our homes, but we know very little about emissions from these products and their subsequent impact on indoor air quality. This is the first study to systematically determine temperature-dependent emissions of volatile organic compounds from commonly used plastic consumer products found in the home. The plastic types included high-density polyethylene (HDPE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS) and polyester rubber. Plastic samples were exposed to increasing temperatures (between 18 and 28 °C) in controlled environmental chambers, connected to a proton-transfer-reaction time-of-flight mass-spectrometer (PTR-ToF-MS), where real-time emissions were detected. Average emission rates were determined and used to initialise an indoor air chemistry model (INCHEM-Py) at the highest and lowest experimental temperatures, to explore the impact these product emissions have on the indoor air chemistry. The PS tubing plastic proved to be the highest emitting polymer per surface area. Almost all selected VOC emissions were found to have a linear relationship with temperature. Upon observing the impacts of primary VOC emissions from plastics in modelled simulations, the hydroxyl radical concentration decreased by an average of 1.6 and 10 % relative to the baseline (with no plastics included) at 18 °C and 28 °C respectively. On the other hand, formaldehyde concentrations increased by 29 and 31.6 % relative to the baseline conditions at 18 °C and 28 °C respectively. The presence of plastic products indoors, therefore, has the potential to impact the indoor air quality

A modelling study of the impact of photolysis on indoor air quality

The importance of photolysis as an initiator of air chemistry outdoors is widely recognized, but its role in chemical processing indoors is often ignored. This paper uses recent experimental data to modify a detailed chemical model, using it to investigate the impacts of glass type, artificial indoor lighting, cloudiness, time of year and latitude on indoor photolysis rates and hence indoor air chemistry. Switching from an LED to an uncovered fluorescent tube light increased predicted indoor hydroxyl radical concentrations by ~13%. However, moving from glass that transmitted outdoor light at wavelengths above 380 nm to one that transmitted sunlight above 315 nm led to an increase in predicted hydroxyl radicals of more than 400%. For our studied species, including ozone, nitrogen oxides, nitrous acid, formaldehyde, and hydroxyl radicals, the latter were most sensitive to changes in indoor photolysis rates. Concentrations of nitrogen dioxide and formaldehyde were largely invariant, with exchange with outdoors and internal deposition controlling their indoor concentrations. Modern lights such as LEDs, together with low transmission glasses, will likely reduce the effects of photolysis indoors and the production of potentially harmful species. Research is needed on the health effects of different indoor air mixtures to confirm this conclusion