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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.

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 PM

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 m

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 presented a hybrid optimization method to accurately determine the key parameters in the model: the initial emittable concentration, the diffusion coefficient, and the material/air partition coefficient (for SVOCs the surface/air partition coefficient is also included). 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 (R(2)=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 °C and 35 °C, 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.

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.

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.

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

Solid-state zinc-air batteries (SZABs) are regarded as a promising energy source for next-generation wearable electronic devices due to their high theoretical energy density and reliability. However, practical development of solid-state zinc-air batteries is hindered by the low specific areal capacity and poor contact between solid-state electrolyte and electrode caused by zinc passivation and electrolyte aging. Herein, we report a new strategy for optimizing the solid-state electrolyte/electrode interface coupling by combining porous zinc electrode and thermal-sensitive solid-state electrolyte F127 to improve the areal-capacity of solid-state zinc-air batteries. The porous Zn anode prepared by zinc electrodeposition on the zinc substrate is used to alleviate zinc passivation and improve the specific area capacity of SZAB. In addition, to enhance the contact between the solid-state electrolyte and the zinc anode, the F127 surfactant is selected as the solid-state electrolyte due to its fluidity at low temperatures. Benefiting from the porous structure and excellent contact, the SZAB using porous zinc anode and the solid-state electrolyte F127 exhibits high areal capacity of 133 mAh cm−2 at the current density of 4 mA cm−2, which is 100 times higher than that of the solid zinc-air battery using zinc foil as the anode. Moreover, the results show that the use of porous zinc electrodes is conducive to achieving high current discharge, which is critical for the practical application of solid-state zinc-air batteries.

The ability to predict indoor pollutant concentrations is an indispensable function for smart homes. In this study, we present a Long Short-Term Memory network (LSTM) model in the deep learning field, to predict the concentrations of volatile organic compounds (VOCs) in different indoor settings, and a mean absolute percentage error (MAPE) is used as a metric to evaluate the performance of the LSTM model. The selection of some key parameters on the LSTM model prediction is firstly discussed. We then analyze the concentrations of different VOCs emitted from three kinds of furniture in a controlled chamber, and concentrations of 6-methyl-5-hepten-2-one (6-MHO) and 4-oxopentanal (4-OPA) due to ozone/squalene reactions, in an occupied classroom. The model's predictions for the VOCs in the chamber tests, have all MAPE within 10%; for ozone and 6-MHO in the classroom tests, 85% of the MAPE is within 15%; and for 4-OPA, 82% of the MAPE is within 15%. The small MAPE indicates good performance. Comparison analysis reveals that the LSTM model is superior to the widely used artificial neural network (ANN) model. The LSTM approach doesn't require building complex physical or chemical models and measuring various key parameters, instead a learning network is established and the learning parameters are adjusted according to practical situations. This study demonstrates that the LSTM model is promising for the prediction of pollutant transport in various indoor environments.

Most indoor building materials and vehicle cabin materials consist of multiple layers which can emit volatile organic compounds (VOCs), and these adversely affect indoor and in-cabin air quality. Previous studies have generally targeted VOC emissions from single-layer materials, while only a few look at emissions from multi-layer products, with experimental studies being very rare. This paper uses a two-layer emission model to predict the emission characteristics of VOCs from both indoor building materials and vehicle cabin materials. A hybrid optimization approach (ant colony optimization (ACO) coupled particle swarm optimization (PSO)) is presented to determine the three key parameters for each layer of material in the model, i.e., the initial emittable concentration, the diffusion coefficient, and the partition coefficient. The results indicate that this method has significant advantages compared with previous fitting methods. In addition, independent experiments for formaldehyde emission from a variety of two-layer building materials, and acetaldehyde emission from car seat materials, were performed to validate the two-layer emission model. The good agreement between model predictions and experimental results implies that the key parameters derived for each layer of material can be applied in two-layer emission scenarios. Furthermore, we explore the influence of the three key parameters on VOC emission behaviors, which is helpful for achieving effective source control.

Rechargeable zinc-air batteries are promising candidates for energy storage due to their high energy density, environmentally friendliness, and low cost. However, such batteries are limited by the high cost and sluggish kinetics of noble metal catalysts. Here, we present a highly active bifunctional catalyst of Mn–Co–Fe–N/S@CNT, where the catalyst is synthesized by Mn, Co, and Fe oxides doped with N and S on porous carbon nanotubes. Mn–Co–Fe–N/S@CNT has higher electrocatalytic activity than the commercial catalysts of Pt/C and RuO2, demonstrating that the half-wave potential of the oxygen reduction reaction (ORR) of Mn–Co–Fe–N/S@CNT is 0.807 V (0.9 V with Pt/C), the initial potential is 0.85 V (0.789 V with Pt/C), the limiting current is 5.66 mA cm−2 at 0.2 V (5.69 mA cm−2 with Pt/C), and oxygen evolution reaction overpotential of Mn–Co–Fe–N/S@CNT is 0.386 V at 10 mA cm−2 (0.371 V with RuO2). Moreover, a rechargeable zinc-air battery using Mn–Co–Fe–N/S@CNT outputs a discharging voltage of 1.2 V and a stable cycle life of over 150 h at 10 mA cm−2.

Zinc-air batteries would be a promising candidate for energy conversion and storage because of their dense energy and effective cost. The metal nickel is extensively used for current collection and framework support of the air electrode in consideration of its outstanding anticorrosion property and conductivity . However, the reaction of zinc and nickel in zinc-air batteries has not been reported. Here, we show a novel zinc-air battery with a reverse structure of the air electrode based on Zn–Ni reaction, where nickel foam of the air electrode is oriented toward the zinc electrode, protecting the catalytic layer of the air electrode from impurity coverage and inhibiting dendrite growth. A zinc-air primary battery with the reverse air electrode exhibits a stable discharging ability of >1000 h around 1.4 V, achieving a capacity of 0.73 A h g Zn−1 and a specific energy of 874 W h kgZn−1. Moreover, a zinc-air secondary battery with the reverse air electrode obtains more than 700 h cycles at the current density of 10 mA/cm2 without battery failure.

Volatile organic compounds (VOCs) emitted from furniture severely influence indoor air quality. Prior studies have mainly focused on the emissions from building materials and vehicle cabin materials, but rarely refer to indoor furniture. Considering the complex internal structure of furniture, this study presents a two-layer numerical model to describe the emission behaviors of VOCs from wooden furniture. A multi-parameter regression method was then proposed to determine the key parameters in the furniture, i.e., the initial emittable concentration, the diffusion coefficient and the partition coefficient. Experiments were conducted in a 1 m3 environmental chamber for VOC emissions from two kinds of wooden furniture (panel furniture and solid wood furniture). The agreement between model predictions and test data (R2 = 0.97–0.99) demonstrated the effectiveness of the model and measured key parameters. Compared with the traditional single-layer model, the two-layer numerical model can characterize the whole emission process from furniture better. In addition, the impact of temperature on the key parameters for furniture emissions was explored. With an increase in temperature from 23 °C to 35 °C, the diffusion coefficients for different VOCs from different wooden furniture, increase by 200%–2993%, while partition coefficients decrease by 41%–94%. This pattern conforms with that of VOC emissions from building materials. The present study should be helpful for source characterization of furniture and the associated 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

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.

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...

We investigate source characteristics and emission dynamics of volatile organic compounds (VOCs) in a single-family house in California utilizing time- and space-resolved measurements. About 200 VOC signals, corresponding to more than 200 species, were measured during eight weeks in summer and five in winter. Spatially resolved measurements, along with tracer data, reveal that VOCs in the living space were mainly emitted directly into that space, with minor contributions from the crawlspace, attic or outdoors. Time-resolved measurements in the living space exhibited baseline levels far above outdoor levels for most VOCs; many compounds also displayed patterns of intermittent short-term enhancements (spikes) well above the indoor baseline. Compounds were categorized as "high-baseline" or "spike-dominated" based on indoor-to-outdoor concentration ratio and indoor mean-to-median ratio. Short-term spikes were associated with occupants and their activities, especially cooking. High-baseline compounds indicate continuous indoor emissions from building materials and furnishings. Indoor emission rates for high-baseline species, quantified with 2-h resolution, exhibited strong temperature dependence and were affected by air-change rates. Decomposition of wooden building materials is suggested as a major source for acetic acid, formic acid, and methanol, which together accounted for ~75% of the total continuous indoor emissions of high-baseline species. This article is protected by copyright. All rights reserved.

The emissions of volatile organic compounds (VOCs) from indoor furniture contribute significantly to poor indoor air quality. We have taken a typical machine learning approach using an artificial neural network (ANN), to predict the emission behaviors of VOCs from furniture. The gas-phase VOC concentrations from four kinds of furniture (solid wood furniture, panel furniture, soft leather furniture, soft cloth furniture) were measured in a 1 m3 chamber at different temperatures, relative humidity and ventilation rates. We then used these VOC concentration data as input for training. The trained ANN model could then be used to predict VOC concentrations at other emission time. We selected a back-propagation neural network, with 3 hidden layers, and a learning rate of 0.01. Pearson correlation analysis demonstrates that there is a strong correlation between the input datasets. We used relative deviation (RD) and mean absolute percentage error (MAPE) as the criteria for evaluating the performance of the ANN. For all of the tested VOCs from different types of furniture, the RDs between the predictions and experimental data at 150 h, are less than 15%. The MAPE values of the ANN model are within 10%. These indicate that the trained ANN model has excellent capability in predicting the VOC concentrations from furniture. The main merit of the ANN is that it doesn't need to solve the challenging problem of obtaining the key parameters when using physical models for prediction, and will thus be very useful for indoor source characterization, as well as for exposure assessment.

Among many environments, the motor vehicle cabin microenvironment has been of particular public concern. Although commuters typically spend only 5.5% of their time in vehicles, the emissions from various interior components of motor vehicles as well as emissions from exhaust fumes carried by ventilation supply air are significant sources of harmful air pollutants that could lead to unhealthy human exposure due to their high concentrations inside vehicles' cabins. This review summarizes significant findings in the literature on air quality inside vehicle cabins, including chemical species, related sources, measurement methodologies and control measures. More than 90 relevant studies performed across over 10 countries were carefully reviewed. These comprised more than 2000 individual road trips, where concentrations of numerous air pollutants were determined. Ultrafine particles, aromatic hydrocarbons, carbonyls, semi-volatile organic compounds and microbes have been identified as the primary air pollutants inside vehicle cabins. Air recirculation with high-efficiency air filter has been reported as the most effective measure to lower air pollutant concentrations. Future work should focus on investigating the health risks of exposure to various air pollutants inside different vehicles and further developing advanced air filter to improve the in-cabin air quality.

The conception of green building comes out along with the concern and attention of sustainable development of buildings. Indoor air quality is an important part of green building. How to built a healthy, suitable and energy saving building becomes a hot social and research topic of the world, especially in China. As the main source of VOCs indoor, the emission character of VOCs from building materials and furniture influences indoor air quality directly. In order to predict the pollutant concentrations indoor, we must know the emission character firstly. Therefore, this paper introduces models of VOCs emission and measurement methods to determine the emission characteristic parameters. Some possible problems during pre-evaluation of indoor air quality and related suggestion are also proposed in this paper.

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.

Emissions of volatile organic compounds (VOCs) from building materials/furniture can be characterized by three key parameters: the initial emittable concentration (C0), the diffusion coefficient (Dm), and the partition coefficient (K). These parameters provide the basis for realizing effective source control, as well as for evaluating health risks. In this study, we propose a general regression method to accurately and rapidly measure the three key parameters of VOC emissions from building materials/furniture in a ventilated chamber. This method firstly establishes the relationship between the three key parameters and the first root of the analytical solution describing VOC emissions, and this root is then determined by curve regression. Compared with previous regression methods which need to fit two or three parameters simultaneously, the main merit of the present method lies in that it just needs to fit one parameter and thus can get a unique solution. We tested panel furniture in a ventilated chamber to measure the three key parameters of some common VOCs. Results indicate that the model predictions based on the parameters determined via this new method agree well with experimental data, which validates the reliability of this proposed method. Analyzing data from the literature further demonstrates the accuracy of this method. The present method involves chamber testing under ventilated conditions only, which is consistent with the testing conditions for many standards, thus will benefit routine laboratory testing.

© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Residences represent an important site for bioaerosol exposure. We studied bioaerosol concentrations, emissions, and exposures in a single-family residence in northern California with 2 occupants using real-time instrumentation during 2 monitoring campaigns (8 weeks during August-October 2016 and 5 weeks during January-March 2017). Time- and size-resolved fluorescent biological aerosol particles (FBAP) and total airborne particles were measured in real time in the kitchen using an ultraviolet aerodynamic particle sizer (UVAPS). Time-resolved occupancy status, household activity data, air-change rates, and spatial distribution of size-resolved particles were also determined throughout the house. Occupant activities strongly influenced indoor FBAP levels. Indoor FBAP concentrations were an order of magnitude higher when the house was occupied than when the house was vacant. Applying an integral material-balance approach, geometric mean of total FBAP emissions from human activities observed to perturb indoor levels were in the range of 10-50 million particles per event. During the summer and winter campaigns, occupants spent an average of 10 and 8.5 hours per day, respectively, awake and at home. During these hours, the geometric mean daily-averaged FBAP exposure concentration (1-10 μm diameter) was similar for each subject at 40 particles/L for summer and 29 particles/L for winter.

Characterization of indoor emissions of cyclic volatile methylsiloxanes (cVMS) due to the use of personal care products is important for elucidating indoor air composition and associated health risks. This manuscript describes a mass transfer model to characterize the emission behaviors of decamethylcyclopentasiloxane (D5, the most abundant indoor cVMS) from skin lipids. A C-history method is introduced to determine the key parameters in the model, i.e., the initial concentration and diffusion coefficient of D5 inside the skin lipids. Experiments were conducted in a university classroom to examine the D5 emission behaviors by using a proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS). Data from the first class session of two typical days was applied to obtain the key parameters, which were subsequently used for predicting D5 concentrations in other class sessions. Good agreement between model predictions and experiments demonstrates the effectiveness of the model and parameter determination method. With the model, we found that the reuse of personal care products has a significant impact on the D5 emissions. In addition, the time-dependent emission rate and remaining amount of D5 inside the skin can also be calculated. These results indicate a fast decay pattern during the initial emission period, which is consistent with prior experimental studies.

The accurate and rapid determination of the emission parameters of semi-volatile organic compounds (SVOCs) from indoor materials is of great importance for estimating and controlling indoor exposure. By virtue of a simplified mass transfer model we derived, a new method called the early stage C-history method, has been developed to measure the key emission parameters: the gas phase SVOC concentration adjacent to the material surface (y0) and the convective mass transfer coefficient (hm). We validate this model using experimental data found in the literature. When compared with established methods, the new method has the following salient features: (1) rapid (the experimental time is reduced from several months to several days); (2) accurate (R2 in the range of 0.92–0.97). Further analysis shows that both features can be further improved if the test system has a smaller chamber wall/air partition coefficient. The new method is also extended to measure the key parameters of SVOCs from sorption materials. This method should prove useful for screening SVOC emission characteristics and for assessing exposure, as well as for chamber and test design.

Mass transfer is key to understanding and controlling indoor airborne organic chemical contaminants (e.g., VVOCs, VOCs, and SVOCs). In this study, we first introduce the fundamentals of mass transfer and then present a series of representative works from the past two decades, focusing on the most recent years. These works cover: (i) predicting and controlling emissions from indoor sources, (ii) determining concentrations of indoor air pollutants, (iii) estimating dermal exposure for some indoor gas-phase SVOCs, and (iv) optimizing air-purifying approaches. The mass transfer analysis spans the micro-, meso-, and macroscales and includes normal mass transfer modeling, inverse problem solving, and dimensionless analysis. These representative works have reported some novel approaches to mass transfer. Additionally, new dimensionless parameters such as the Little number and the normalized volume of clean air being completely cleaned in a given time period were proposed to better describe the general process characteristics in emissions and control of airborne organic compounds in the indoor environment. Finally, important problems that need further study are presented, reflecting the authors' perspective on the research opportunities in this area.