Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles.

Volatile organic compounds (VOCs) are ubiquitous in vehicle cabin environments, which can significantly impact the health of drivers and passengers, whereas quick and intelligent prediction methods are lacking. In this study, we firstly analyzed the variations of environmental parameters, VOC levels and potential sources inside a new car during 7 summer workdays, indicating that formaldehyde had the highest concentration and about one third of the measurements exceeded the standard limit for in-cabin air quality. Feature importance analysis reveals that the most important factor affecting in-cabin VOC emission behaviors is the material surface temperature rather than the air temperature. By introducing the attention mechanism and ensemble strategy, we present an LSTM-A-E deep learning model to predict the concentrations of 12 observed typical VOCs, together with other five deep learning models for comparison. By comparing the prediction-observation discrepancies and five evaluation metrics, the LSTM-A-E model demonstrates better performance, which is more consistent with field measurements. Extension of the developed model for predicting the 10-day VOC concentrations in a realistic residence further illustrates its excellent environmental adaptation. This study probes the not-well-explored in-cabin VOC dynamics via observation and deep learning approaches, facilitating rapid prediction and exposure assessment of VOCs in the vehicle micro-environment.

First-Row Transition Metals for Catalyzing Oxygen Redox.

Rechargeable zinc-air batteries are widely recognized as a highly promising technology for energy conversion and storage, offering a cost-effective and viable alternative to commercial lithium-ion batteries due to their unique advantages. However, the practical application and commercialization of zinc-air batteries are hindered by the sluggish kinetics of the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Recently, extensive research has focused on the potential of first-row transition metals (Mn, Fe, Co, Ni, and Cu) as promising alternatives to noble metals in bifunctional ORR/OER electrocatalysts, leveraging their high-efficiency electrocatalytic activity and excellent durability. This review provides a comprehensive summary of the recent advancements in the mechanisms of ORR/OER, the performance of bifunctional electrocatalysts, and the preparation strategies employed for electrocatalysts based on first-row transition metals in alkaline media for zinc-air batteries. The paper concludes by proposing several challenges and highlighting emerging research trends for the future development of bifunctional electrocatalysts based on first-row transition metals.

Characterization of chemical transport in human skin and building material.

Volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) are ubiquitous in indoor environment. They can emit from source into air, and subsequently penetrate human skin into blood through dermal uptake, causing adverse health effects. This study develops a two-layer analytical model to characterize the VOC/SVOC dermal uptake process, which is then extended to predict VOC emissions from two-layer building materials or furniture. Based on the model, the key transport parameters of chemicals in every skin or material layer are determined via a hybrid optimization method using data from experiments and literature. The measured key parameters of SVOCs for dermal uptake are more accurate than those from previous studies using empirical correlations. Moreover, the association between the absorption amount of studied chemicals into blood and age is preliminarily investigated. Further exposure analysis reveals that the contribution of dermal uptake to the total exposure can be comparable with that of inhalation for the examined SVOCs. This study makes the first attempt to accurately determine the key parameters of chemicals in skin, which is demonstrated to be critical for health risk assessment.

VOC transport in an occupied residence: Measurements and predictions via deep learning.

Monitoring and prediction of volatile organic compounds (VOCs) in realistic indoor settings are essential for source characterization, apportionment, and exposure assessment, while it has seldom been examined previously. In this study, we conducted a field campaign on ten typical VOCs in an occupied residence, and obtained the time-resolved VOC dynamics. Feature importance analysis illustrated that air change rate (ACR) has the greatest impact on the VOC concentration levels. We applied three multi-feature (temperature, relative humidity, ACR) deep learning models to predict the VOC concentrations over ten days in the residence, indicating that the long short-term memory (LSTM) model owns the best performance, with predictions the closest to the observed data, compared with the other two models, i.e., recurrent neural network (RNN) model and gated recurrent unit (GRU) model. We also found that human activities could significantly affect VOC emissions in some observed erupted peaks. Our study provides a promising pathway of estimating long-term transport characteristics and exposures of VOCs under varied conditions in realistic indoor environments via deep learning.

Machine learning approach for estimating the human-related VOC emissions in a university classroom.

Indoor air quality becomes increasingly important, partly because the COVID-19 pandemic increases the time people spend indoors. Research into the prediction of indoor volatile organic compounds (VOCs) is traditionally confined to building materials and furniture. Relatively little research focuses on estimation of human-related VOCs, which have been shown to contribute significantly to indoor air quality, especially in densely-occupied environments. This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom. The time-resolved concentrations of two typical human-related (ozone-related) VOCs in the classroom over a five-day period were analyzed, i.e., 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA). By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGboost), and least squares support vector machine (LSSVM), we find that the LSSVM approach achieves the best performance, by using multi-feature parameters (number of occupants, ozone concentration, temperature, relative humidity) as the input. The LSSVM approach is then used to predict the 4-OPA concentration, with mean absolute percentage error (MAPE) less than 5%, indicating high accuracy. By combining the LSSVM with a kernel density estimation (KDE) method, we further establish an interval prediction model, which can provide uncertainty information and viable option for decision-makers. The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors, making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.

Measurement methods and impact factors for the key parameters of VOC/SVOC emissions from materials in indoor and vehicular environments: A review.

The emissions of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) from indoor building and vehicle cabin materials can adversely affect human health. Many mechanistic models to predict the VOC/SVOC emission characteristics have been proposed. Nowadays, the main obstacle to accurate model prediction is the availability and reliability of the physical parameters used in the model, such as the initial emittable concentration, the diffusion coefficient, the partition coefficient, and the gas-phase SVOC concentration adjacent to the material surface. The purpose of this work is to review the existing methods for measuring the key parameters of VOCs/SVOCs from materials in both indoor and vehicular environments. The pros and cons of these methods are analyzed, and the available datasets found in the literature are summarized. Some methods can determine one single key parameter, while other methods can determine two or three key parameters simultaneously. The impacts of multiple factors (temperature, relative humidity, loading ratio, and air change rate) on VOC/SVOC emission behaviors are discussed. The existing measurement methods span very large spatial and time scales: the spatial scale varies from micro to macro dimensions; and the time scale in chamber tests varies from several hours to one month for VOCs, and may even span years for SVOCs. Based on the key parameters, a pre-assessment approach for indoor and vehicular air quality is introduced in this review. The approach uses the key parameters for different material combinations to pre-assess the VOC/SVOC concentrations or human exposure levels during the design stage of buildings or vehicles, which can assist designers to select appropriate materials and achieve effective source control.

Investigation on the Direct Transfer of SVOCs from Source to Settled Dust: Analytical Model and Key Parameter Determination.

Settled dust is an important medium for semivolatile organic compound (SVOC) transport indoors. Understanding the mechanism of interaction between SVOCs and settled dust can greatly improve the exposure assessment. This study develops an analytical model to elucidate the mechanism of direct contact between SVOC sources and settled dust. The model incorporates the adsorption of SVOCs onto indoor surfaces, which was ignored in previous numerical models. Based on this model, a hybrid optimization method is applied to determine the key parameters of SVOC transport, i.e., the diffusion coefficient in the dust, the dust-air partition coefficient, and the chamber surface-air partition coefficient. Experiments of direct contact between SVOC source materials containing organophosphorus flame retardants (OPFRs) and settled dust were conducted in chambers. The key parameters were determined by performing curve fitting using data collected from the OPFR chamber tests and from the literature on phthalates. The reliability and robustness of the model and measurement method are demonstrated by the high fitting accuracy and sensitivity analysis. The obtained key parameters are more accurate than those from correlations in prior studies. Further analysis indicates that dust-air partition coefficient plays an important role and the adsorption effect on surfaces cannot be neglected for SVOC transport.

A rapid and robust method to determine the key parameters of formaldehyde emissions from building and vehicle cabin materials: Principle, multi-source application and exposure assessment.

The ubiquity of formaldehyde emitted in indoor and in-cabin environments can adversely affect health. This study proposes a novel full-range C-history method to rapidly, accurately and simultaneously determine the three key parameters (initial emittable concentration, partition coefficient, diffusion coefficient) that characterize the emission behaviors of formaldehyde from indoor building and vehicle cabin materials, by means of hybrid optimization. The key parameters of formaldehyde emissions from six building materials and five vehicle cabin materials at various temperatures, were determined. Independent experiments and sensitivity analysis verify the effectiveness and robustness of the method. We also demonstrate that the determined key parameters can be used for predicting multi-source emissions from different material combinations that are widely encountered in realistic indoor and in-cabin environments. Furthermore, based on a constructed vehicle cabin and the determined key parameters, we make a first attempt to estimate the human carcinogenic potential (HCP) of formaldehyde for taxi drivers and passengers at two temperatures (25 °C, 34 °C). The HCP for taxi drivers at both temperatures exceeds 10-6 cases, indicating relatively high potential risk. This study should be helpful for pre-evaluation of indoor and in-cabin air quality, and can assist designers in selecting appropriate materials to achieve effective source control.

The association between daily-diagnosed COVID-19 morbidity and short-term exposure to PM1 is larger than associations with PM2.5 and PM10.

Exposure to particulate matter (PM) could increase both susceptibility to SARS-CoV-2 infection and severity of COVID-19 disease. Prior studies investigating associations between PM and COVID-19 morbidity have only considered PM2.5 or PM10, rather than PM1. We investigated the associations between daily-diagnosed COVID-19 morbidity and average exposures to ambient PM1 starting at 0 through 21 days before the day of diagnosis in 12 cities in China using a two-step analysis: a time-series quasi-Poisson analysis to analyze the associations in each city; and then a meta-analysis to estimate the overall association. Diagnosed morbidities and PM1 data were obtained from National Health Commission in China and China Meteorological Administration, respectively. We found association between short-term exposures to ambient PM1 with COVID-19 morbidity was significantly positive, and larger than the associations with PM2.5 and PM10. Percent increases in daily-diagnosed COVID-19 morbidity per IQR/10 PM1 for different moving averages ranged from 1.50% (-1.20%, 4.30%) to 241% (95%CI: 80.7%, 545%), with largest values for exposure windows starting at 17 days before diagnosis. Our results indicate that smaller particles are more highly associated with COVID-19 morbidity, and most of the effects from PM2.5 and PM10 on COVID-19 may be primarily due to the PM1. This study will be helpful for implementing measures and policies to control the spread of COVID-19.

Determination of the key parameters of VOCs emitted from multi-layer leather furniture using a region traversal approach.

Volatile organic compounds (VOCs) emitted from indoor materials and products are one of the main factors affecting air quality and human health. Compared with building materials and wooden furniture, leather furniture has a more complex internal structure and uneven emission surfaces. The market share of leather furniture is relatively high, while investigation on this kind of furniture is relatively rare. In this study, we develop a region traversal method to measure the three key parameters of VOC emissions from typical two-layer leather furniture, i.e., the initial emittable concentration, the diffusion coefficient, and the partition coefficient. A series of experiments examining VOC emissions from a leather sofa under different conditions, were carried out in a 1 m3 chamber. This method locks the upper and lower limits of an optimal solution through loop calculation in parameter intervals, and demonstrates high accuracy, efficiency and robustness. The good agreement (R2 > 0.95) between model predictions and experimental data confirms the reliability of this method. In addition, the influence of temperature and air exchange rate on the key parameters is explored. Results indicate that, increasing the temperature leads to an increase in Dm and a decrease in K, and that air exchange rate does not affect the key parameters, which is consistent with physical principles. The region traversal method is further applied to analyze the emission scenarios for other furniture, which is very helpful for indoor air quality pre-evaluation.

Predicting the emissions of VOCs/SVOCs in source and sink materials: Development of analytical model and determination of the key parameters.

The emissions of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) from indoor materials pose an adverse effect on people's health. In this study, a new analytical model was developed to simulate the emission behaviors for both VOCs and SVOCs under ventilated conditions. Based on this model, we further introduced a hybrid optimization method to accurately determine the key parameters in the model: the initial emittable concentration, the diffusion coefficient, the material/air partition coefficient, and the chamber surface/air partition coefficient (for SVOCs). Experiments for VOC emissions from solid wood furniture were performed to determine the key parameters. We also evaluated the hybrid optimization method with the data of flame retardant emissions from polyisocyanurate rigid foam and VOC emissions from a panel furniture in the literature. The correlation coefficients are high during the fitting process (R2 = 0.92-0.99), demonstrating effectiveness of this method. In addition, we observed that chemical properties could transfer from SVOC-type to VOC-type with the increase of temperature. The transition temperatures from SVOC-type to VOC-type for the emissions of tris(2-chloroethyl) phosphate (TCEP) and tris(1-chloro-2-propyl) phosphate (TCIPP) were determined to be about 45 ℃ and 35 ℃, respectively. The present study provides a unified modelling and methodology analysis for both VOCs and SVOCs, which should be very useful for source/sink characterization and control.

Residential building materials: An important source of ambient formaldehyde in mainland China.

This study investigates the contribution of formaldehyde from residential building materials to ambient air in mainland China. Based on 265 indoor field tests in 9 provinces, we estimate that indoor residential sources are responsible for 6.66% of the total anthropogenic formaldehyde in China's ambient air (range for 31 provinces: 1.88-18.79%). Residential building materials rank 6th among 81 anthropogenic sources (range: 2nd-10th for 31 provinces). Emission intensities show large spatial variability between and within regions due to different residential densities, emission characteristics of building materials, and indoor thermal conditions. Our findings indicate that formaldehyde from the indoor environment is a significant source of ambient formaldehyde, especially in urban areas. This study will help to more accurately evaluate exposure to ambient formaldehyde and its related pollutants, and will assist in formulating policies to protect air quality and public health.

Characterization of the off-body squalene ozonolysis on indoor surfaces.

Chemical reaction and physical transport characteristics of indoor surfaces play an important role in indoor air quality. This study presents a kinetic model to describe the reaction of ozone with squalene on indoor surfaces in a family house, by incorporating external and internal mass transfer, surface partitioning, and chemical reaction on indoor surfaces. Field experiments were performed in the family house. The first 3-days of data, collected when the house was unoccupied, are used to derive the key parameters in the model, which are then used for predicting the concentrations in other unoccupied days. Comparison of squalene oxidation products during the occupied and unoccupied periods shows that even if the house is unoccupied for several days, the indoor concentrations of 6-methyl-5-hepten-2-one (6-MHO) and 4-oxopentanal (4-OPA) remain substantial, demonstrating that surface reaction of ozone with off-body squalene can significantly impact the composition of indoor air. Model predictions of the three compounds (ozone, 6-MHO, and 4-OPA) agree well with the experimental observations for all test days. Furthermore, we make the first attempt to estimate the duration of typical polyunsaturated aldehydes (TOP, TOT, and TTT), which indicated that these compounds, as well as off-body squalene, can persist on indoor surfaces for a relatively long period in the examined residence.

Physical-Chemical Coupling Model for Characterizing the Reaction of Ozone with Squalene in Realistic Indoor Environments.

Squalene can react with indoor ozone to generate a series of volatile and semi-volatile organic compounds, some of which may be skin or respiratory irritants, causing adverse health effects. Better understanding of the ozone/squalene reaction and product transport characteristics is thus important. In this study, we developed a physical-chemical coupling model to describe the behavior of ozone/squalene reaction products, that is, 6-methyl-5-hepten-2-one (6-MHO) and 4-oxopentanal (4-OPA) in the gas phase and skin, by considering the chemical reaction and physical transport processes (external convection, internal diffusion, and surface uptake). Experiments without intervention were performed in a single-family house in California utilizing time- and space-resolved measurements. The key parameters in the model were extracted from 5 day data and then used to predict the behaviors in some other days. Predictions from the present model can reproduce the concentration profiles of the three compounds (ozone, 6-MHO, and 4-OPA) well (R2 = 0.82-0.89), indicating high accuracy of the model. Exposure analysis shows that the total amount of 6-MHO and 4-OPA entering the blood capillaries in 4 days can reach 14.6 and 30.1 µg, respectively. The contribution of different sinks to ozone removal in the tested realistic indoor environment was also analyzed.

Predicting the emission characteristics of VOCs in a simulated vehicle cabin environment based on small-scale chamber tests: Parameter determination and validation.

Volatile organic compounds (VOCs) emitted from vehicle parts and interior materials can seriously affect in-cabin air quality. Prior studies mainly focused on indoor material emissions, while studies of emissions in-cabins were relatively scarce. The emission behaviors of VOCs from vehicle cabin materials can be characterized by three key emission parameters: the initial emittable concentration (C0), diffusion coefficient (Dm), and partition coefficient (K). Based on a C-history method, we have performed a series of tests with a 30 L small-scale chamber to determine these three key emission parameters for six VOCs, benzene, toluene, ethylbenzene, xylene, formaldehyde, and acetaldehyde, from typical vehicle cabin materials, car roof upholstery, carpet, and seat. We found that acetaldehyde had the highest level in the gas-phase concentration and C0, which differs from residential indoor environments where formaldehyde is usually the most prevalent pollutant. The influence of temperature on the key emission parameters was also investigated. When the temperature rose from 25 °C to 65 °C, C0 increased by 40-640%, Dm increased by 40-170%, but K decreased by 38-71% for different material-VOC combinations. We then performed an independent validation to demonstrate the accuracy of the measured key emission parameters. Furthermore, considering that in reality, several materials coexist in vehicle cabins, we made a first attempt at applying a multi-source model to predict VOC emission behaviors in a simulated 3 m3 vehicle cabin, using the key emission parameters obtained from the small-scale chamber tests. The good agreement between the predictions and experiments (R2 = 0.82-0.99) demonstrated that the three key emission parameters measured via chamber tests can be scaled to estimate emission scenarios in realistic vehicle cabin environments. A pollution contribution analysis for the tested materials indicated that the car seat could significantly contribute to the total emissions.

Characterization of phthalates in sink and source materials: Measurement methods and the impact on exposure assessment.

The fate and transport of semi-volatile organic compounds (SVOCs) in residential environments is significantly influenced by emission and sorption processes, which can be characterized by three key parameters: the gas-phase SVOC concentration adjacent to the material surface (y0); the diffusion coefficient (Dm); and the partition coefficient (K). Accurate determination of these three key parameters is critical for investigating SVOC mass transfer principles, and for assessing human health risks. Based on the mass transfer process of phthalates in a ventilated chamber, a novel method is developed to simultaneously measure Dm and K (key sorption parameters) in sink materials. The Dm and K of four target phthalates in a common T-shirt (sink material) are determined, and compared with those reported in literature. Results demonstrate that the measured parameters are in good agreement with those previously reported (relative deviation < 20 %), validating the effectiveness of proposed method. In addition, this method can be applied to determine y0, a key parameter from source materials. Results indicate that y0 determined with this method is consistent with that measured by literature method. Finally, dermal exposure analysis is performed, showing that dermal uptake of target phthalates is greatly affected by clothes.

Short-term exposure to ambient particle gamma radioactivity is associated with increased risk for all-cause non-accidental and cardiovascular mortality.

BACKGROUND: Recent studies have found that particulate matter (PM) attached radioactivity was associated with certain adverse health effects including increased blood pressure and lung dysfunction. However, there has been no investigation on the direct effect of PM radioactivity on mortality. METHODS: Exposures to ambient PM gamma activities were determined using U.S. EPA RadNet data. Data on daily deaths were obtained from individual state Departments of Public Health. We used a generalized additive quasi-Poisson model to estimate the associations between two-day average ambient PM gamma activities (gamma2 through gamma9) with all-cause non-accidental and cardiovascular daily deaths for each of 18 US cities, for each season, adjusting for two-day average PM2.5 exposure, temperature, relative humidity, day of week and long-term trends. Subsequently, we used random-effects meta-analysis to estimate the overall effect in the 18 cities for each season. RESULTS: We found that all-cause non-accidental daily mortality in spring season was positively associated with two-day average ambient PM gamma activities in spring, with significant results for gamma2, gamma5 and gamma6. Similarly, cardiovascular daily mortality was positively associated with two-day average ambient PM gamma activities, with significant results for gamma2, gamma4, gamma5, gamma6, gamma7 and gamma9. For the spring season, each interquartile range (IQR) increase of two-day averaged ambient PM gamma activity was associated with increase in all-cause daily deaths, ranging from 0.15% (95% Confidence Interval (CI): -0.36%, 0.65%) to 1.03 (95%CI: 0.18%, 1.89%). Each IQR was also associated with increase in cardiovascular daily deaths, ranging from 0.01% (95%CI: -0.89, 0.92) to 2.95% (95%CI: 1.33, 4.59). For other seasons overall we found statistically insignificant associations of PM radioactivity with mortality. CONCLUSIONS: Our findings suggest that there are potential systemic toxic effects of inhalation of radionuclides attached to ambient air particles.

An improved mechanism-based model for predicting the long-term formaldehyde emissions from composite wood products with exposed edges and seams.

Emissions of formaldehyde from building materials and furniture can cause adverse health effects. Traditional models generally only consider emissions as a physical process that can be characterized by three key parameters: the initial emittable concentration, the diffusion coefficient and the partition coefficient. However, the physical-based model causes discrepancy in predicting long-term formaldehyde emissions for the cases where chemical reaction (i.e., hydrolysis) occurs over time. In this study, an improved mechanism-based model was developed by combining the chemical reaction process with a physical mass transfer process to more accurately predict the long-term emission behaviors. The chamber testing data of formaldehyde emissions from exposed edges and seams of a laminate flooring product made with composite wood core for about 1.5 year was used to validate the model. Results indicate that the mechanism-based model characterizes well the long-term formaldehyde emissions from the tested material. Predictions of different models further demonstrate the advantages of this improved model compared with the physical model or with empirical models. This study is the first attempt to check the feasibility of including the chemical reaction term in emission modeling and to quantitatively explore the importance of its contribution to long-term formaldehyde emissions, which includes most of the indoor emissions from materials and furniture.

Emissions of DEHP from vehicle cabin materials: parameter determination, impact factors and exposure analysis.

Semi-volatile organic compounds (SVOCs) are widely used in materials employed in vehicle interiors, causing poor in-cabin air quality. The emission characteristics of SVOCs from vehicle cabin materials can be characterized by two key parameters: the gas-phase SVOC concentration adjacent to the material surface (y0) and the convective mass transfer coefficient across the material surface (hm). Accurate determination of y0 and hm is fundamental in investigating SVOC emission principles and health risks. Considering that the steady state SVOC concentration (y) in a ventilated chamber changes with the ventilation rate (Q), we developed a varied ventilation rate (VVR) method to simultaneously measure y0 and hm for typical vehicle cabin materials. Experimental results for di(2-ethylhexyl)phthalate (DEHP) emissions from test materials indicated that the VVR method has the merits of simple operation, short testing time, and high accuracy. We also examined the influence of temperature (T) on y0 and hm, and found that both y0 and hm increase with increasing temperature. A theoretical correlation between y0 and T was then derived, indicating that the logarithm of y0T is linearly related to 1/T. Analysis based on the data from this study and from the literature validates the effectiveness of the derived correlation. Moreover, preliminary exposure analysis was performed to assess the health risk of DEHP in a vehicular environment.

Modeling the Time-Dependent Concentrations of Primary and Secondary Reaction Products of Ozone with Squalene in a University Classroom.

Volatile organic chemicals are produced from reactions of ozone with squalene in human skin oil. Both primary and secondary reaction products, i.e., 6-methyl-5-hepten-2-one (6-MHO) and 4-oxopentanal (4-OPA), have been reported in indoor occupied spaces. However, the abundance of these products indoors is a function of many variables, including the amount of ozone and occupants present as well as indoor removal processes. In this study, we develop a time-dependent kinetic model describing the behavior of ozone/squalene reaction products indoors, including the reaction process and physical adsorption process of products on indoor surfaces. The key parameters in the model were obtained by fitting time-resolved concentrations of 6-MHO, 4-OPA, and ozone in a university classroom on 1 day with multiple class sessions. The model predictions were subsequently tested against observations from four additional measurement days in the same classroom. Model predictions and experimental data agreed well (R2 = 0.87-0.92) for all test days, including ∼7 class sessions covering a range of occupants (10-70) and ozone concentrations (0.09-32 ppb), demonstrating the effectiveness of the model. Accounting for surface uptake of 6-MHO and 4-OPA significantly improved model predictions (R2 = 0.52-0.76 without surface uptake), reflecting the importance of including surface interactions to quantitatively represent product behavior in indoor environments.