Dip coating of air purifier ceramic honeycombs with photocatalytic TiO2 nanoparticles: A case study for occupational exposure

Nanoscale TiO2 (nTiO2) is manufactured in high volumes and is of potential concern in occupational health. Here, we measured workers exposure levels while ceramic honeycombs were dip coated with liquid photoactive nanoparticle suspension and dried with an air blade. The measured nTiO2 concentration levels were used to assess process specific emission rates using a convolution theorem and to calculate inhalation dose rates of deposited nTiO2 particles. Dip coating did not result in detectable release of particles but air blade drying released fine-sized TiO2 and nTiO2 particles. nTiO2 was found in pure nTiO2 agglomerates and as individual particles deposited onto background particles. Total particle emission rates were 420×109min-1, 1.33×109μm2min-1, and 3.5mgmin-1 respirable mass. During a continued repeated process, the average exposure level was 2.5×104cm-3, 30.3μm2cm-3, <116μgm-3 for particulate matter. The TiO2 average exposure level was 4.2μgm-3, which is well below the maximum recommended exposure limit of 300μgm-3 for nTiO2 proposed by the US National Institute for Occupational Safety and Health. During an 8-hour exposure, the observed concentrations would result in a lung deposited surface area of 4.3×10-3cm2g-1 of lung tissue and 13μg of TiO2 to the trachea-bronchi, and alveolar regions. The dose levels were well below the one hundredth of the no observed effect level (NOEL1/100) of 0.11cm2g-1 for granular biodurable particles and a daily no significant risk dose level of 44μgday-1. These emission rates can be used in a mass flow model to predict the impact of process emissions on personal and environmental exposure levels.

exposure determinants can be alternatively assessed by means of mathematical models. Important exposure 169 determinants that such models should include are source strengths, dispersion of pollutants, and particle removal 170 rates by exposure and emission controls. Indoor exposure models can provide insight into exposure levels across 171 a range of environmental conditions, facilitating efficient answers to 'what if' questions and can also be useful 172 tools in understanding the dynamic behavior of aerosols under controlled conditions. 173 If implemented correctly, models can improve understanding of personal exposure, which so far has been mostly 174 based on epidemiological studies solely based on ambient air monitoring data. For example, indoor exposure 175 models can provide input data for epidemiological studies, which has been challenging because measurements in 176 indoor environments on a population-representative scale have thus far not been feasible. In addition, indoor 177 exposure models can be used for total personal exposure assessment in different and mixed daily exposure 178 scenarios, including kindergarten/school/workplace, home, mall, transit, and outdoors (Hussein et al. 2015). A full 179 daily personal exposure assessment is needed to understand which environments have most significant 180 contribution to inhalation intake, dose, and health effects. 181 Exposure models consist of four main components describing: 182 • The source term (gas and PM emissions) and transformation of pollutants during release to the 183 surroundings. 184 • Loss and transformation processes as described by the general dynamic equation for aerosol particles 185 (mass balance) and chemical reactions (energy balance). 186 • The exposure controls reducing emissions from the source (e.g. local ventilation), preventing dispersion 187 of pollutants (e.g. process chamber), reducing concentrations (e.g. portable air purifier), and use of PPE. 188 • A lung deposition model for estimating regional deposition of particles in respiratory tract during 189 inspiration and expiration. 190 statistical models of exposure determinants (AIHA, 2009). Compared to knowledge-based or statistical models, 192 mathematical mass balance models are transparent, have a physical concept to simplify reality, and may include 193 physical processes, such as transformation of pollutants (e.g. particle coagulation). Most physical indoor air 194 models are based on the general dynamic equation (Gelbard and Seinfeld, 1979), which describes the time rate of 195 change of an indoor pollutant concentration by including sources, sinks (deposition, filtration), room-to-room 196 airflows (interzonal airflows), air exchange with the outdoors, and transformation processes (e.g. Nazaroff, 1989;197 Kephalopoulos et al. where a concentration gradient near the source is described using a virtual volume with limited air exchange with 202 a far-field zone (also known as a Near-Field/Far-Field (NF/FF) model; Hemeon, 1955;Nicas, 1996; Regardless of the modeling approach, the emission source is the most critical parameter considering exposure to 216 indoor generated aerosols. The particle emission source is usually described with i) a worst-case assumption -all 217 used material is emitted and becomes airborne, ii) using a concept of dustiness index (mg kg -1 ; e.g. Schneider and 218 Jensen, 2009 and demonstrated in Levin et al., 2014), iii) by direct measurements in chamber and/or field studies. 219 The particle emissions from powder handling are dependent on the material characteristics and properties (e.g. 220 density, mechanisms and extent of aggregation and agglomeration, particle size distribution, moisture content), as 221 well as external parameters (which can be mathematically represented by e.g. a handling energy factor) that are  However, accurately connecting source parameterization concepts to measured concentrations and exposure has 225 been shown to be challenging. As an example, in a paint factory, Koivisto et al. (2015a) demonstrated that the 226 dustiness index did not predict the airborne respirable particle mass-concentrations during a pouring process very 227 well. Better knowledge of the sources and their behavior, as well as more research on potentially useful concepts 228 for representative source parameterization is needed for more accurately predicting exposure levels and mass flows 229 of pollutants . 230 The RMMs and PPE properties are relatively well studied due to regulations. Fransman  Since June 2018, this is applied to all chemicals that are manufactured or imported in quantities over 1 metric ton 243 per year within the European Union. Exposure assessment/exposure scenarios are needed on substances 244 manufactured/imported more than 10 t/a and are classified as hazardous according to EU classification, labelling 245 and packaging criteria for environmental, occupational, and consumer exposure scenarios (ECHA, 2016a). Such 246 a task is not possible to overcome only with measurements, and therefore exposure assessment relies on 247 mathematical exposure modeling. in the NF/FF ratios ranged from 0.8 to 2.8, the consequence of the error was difficult to assess due to subsequent 258 calibration of the tools with measured exposure data and the empirical modeling approach. 259 why occupational exposure assessment substances of very high concern should rely on measured exposure levels. 261 Comparison of modelling results using the ART and Stoffenmanager ® with measurements has shown that the 262 predicted exposure levels 90% confidence interval limits are typically two orders of magnitude or more (Lamb et  of substances of very high concern (ECHA, 2016b). Properly applied physical mass-balance models appear to be 266 stronger tools for case-specific exposure assessments (Table 1) analyses not only need to be standardized, but also simple, feasible and to some extent, cost-efficient. Automated 286 procedures are needed to limit time and user bias; otherwise, long-term studies are not economically feasible. 287

Measurement of relevant particle properties 288
There is a wide range of sampling techniques capable of providing information on the health relevant aerosol 289 physical properties (e.g. mass, size, surface area, structure, charge, radioactivity), chemical aspects (molecular 290 composition, solubility, elemental contents) and biological features (species, microbial viability, allergens, etc.). 291 Nevertheless, for many important particle characteristics there is still a substantial need for new instrumentation 292 to obtain data with high specificity, at high time resolution and at reasonable cost. In addition, the rapid 293 development of new measurements techniques over the last decades have not been followed by a similar 294 advancement in standardization, control and calibration of the instruments. Thus, variability between instruments 295 may be considerable. Inter-calibration and harmonization of measurement procedures have been developed further 296 in atmospheric research than in research of indoor environments through well-coordinated large research networks 297 that allow comparison between field stations at different locations around the globe. Hence, experimental 298 assessment of air quality and emissions in the built environment could probably benefit from an increased use of 299 methodologies developed for calibration and quality control in atmospheric science. 300 High time resolution, on the scale of minutes, is often required to enable source identification, not least in indoor 301 environments where temporal variability may be considerable. Aerosol morphological parameters can be 302  This measurement technique enables for determination of size-resolved aerosol effective densities, dynamic shape 306 emission sources is very limited. 308 On the other hand, high spatial resolution is needed to assess distinct physico-chemical properties. There are 309 currently rapid advancement in detection technologies that facilitates this research. Combined with energy 310 dispersive X-ray detection, electron microscopy can serve to classify and categorize airborne collected particles 311 metrics, such as number and surface-area, needs to be considered as well, depending on the aerosol particle 332 properties. For example, the total particle BET surface area (cm 2 ) instilled in rats and mice lungs was recognized 333 to correlate well with polymorphonuclear neutrophilia (PMN) for low solubility and low toxicity particles as well 334 as some transition metal oxides (Schmid and Stoeger, 2016). PMN is a strong indicator for lung inflammation and 335 forming acute phase response protein that cause plaque formation in the blood vessels causing cardiovascular (ACGHI, 1986). Although this can be used as indication of air quality, human pathogens, capable of causing illness 345 even in low concentrations, may still be present. Identification of potential pathogens that could pose a health risk 346 upon exposure and investigations of microbial diversity may therefore be crucial for assessment of health effects. 347 Specific pathogens can be measured as CFU m -3 by cultivation on selective medium or as genomic copies m -3 by 348 using molecular-based methods such as qPCR. Although, the latter lacks the ability to differentiate between 349 infectious and non-infectious organisms, it is often used for assessing exposure to non-culturable and slow-

Particle emission source descriptors and ontology 370
Reliability of an exposure assessment model depends on user inputs. Thus, an ontology including all descriptors 371 needs to be designed so that the users can identify the processes and sources with reasonable accuracy. This 372 requires agreement on emission rate assessment in biologically relevant metrics, measurement of particle 373 properties, ontology and descriptors for the processes causing emissions. A Danish EPA (Miljøprojekt nr. 1800, 374 2015) and the EU FP7 SUN project (PF7, EC-GA No. 604305) developed a preliminary structure for an particle 375 emission library for articles and products containing nanomaterials ( Table 2). The emission library development 376 continues in the EU Nano Safety Cluster task force (https://www.nanosafetycluster.eu/) by developing an ontology 377 of the parameters used to describe particle emission sources and revising the library format so that it meets the 378 i.e. process, and the emissions reporting. The GRACIOUS project (EU H2020, EC-GA No.760840) will continue 380 the work by developing rules for source read-across extrapolation for products containing nanomaterials. 381

Exposure modelling 382
A comprehensive indoor exposure model compromising both gaseous and particle emissions from outdoors via 383 ventilation, passive sources (e.g. building materials), and processes (i.e. indoor activities) can be used to 384 understand most relevant exposure determinants. Outdoor exposure levels can be estimated from regulatory of the models are based on mutual agreement rather than measured values even though the data is available (see 421   Table 3 and 4 for interzonal airflows and ventilation rate in occupational settings and residences, respectively). 422 2) Has been subjected to peer review and is generally accepted. 435 3) The rate of error is known and acceptable. Currently, there is no mandatory particle emission labeling for products or processes that people use in their 458 everyday life. This is one reason why determinants for IAQ are not well known. Systematic mapping and reporting 459 of the emission sources is needed for effective mitigation for air pollution in residences, public domains, and 460 occupational environments. One potential measure might be an emission index label for products, which is based 461 on the fraction of material released per amount of processed material (mg kg -1 ). A measure for product emissions 462 is needed to make people aware of their role in a clean ambient environment. 463 Emission libraries combined with mass balance models is applicable for finding biologically relevant components 464 for human health trough epidemiological studies. A properly designed mass balance model with well characterized 465 sources, emission controls, and activities fulfils requirements for regulatory exposure assessment and would be 466 applicable for all particles from natural or incidental sources, as well as for manufactured nanomaterials. Such 467 tools would be widely applicable for atmospheric research, epidemiological and toxicological studies, industry at 468 both occupational hygiene and safe product development, and public health and environmental professionals to 469 understand exposure determinants. Accuracy in exposure/risk assessment is needed to assure a lower probability 470 of underestimating or overestimating the human health hazards associated with product use. The advantage of not 471 underestimating exposure/risk are obvious considering the precautionary principle, but the societal costs of 472 overestimating and over-regulating risk could also be grave. 473

Investment in good air quality is an efficient way to increase quality of life in both developed and developing 475
countries. The most effective approach to improve air quality is to prevent the emissions at the source. This requires 476 knowledge of the materials, processes, and activities that cause emissions. The best approach to identify aerosol 477 emission sources are systematic measurements, which are recorded into an emission library and made widely 478 available for scientific and administrative uses. When the pollution components and particle properties are