Understanding and controlling airborne organic compounds in the indoor environment: mass transfer analysis and applications

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.

A multisource mass transfer model for simulating VOC emissions from paints

Indoor decoration generates a large number of volatile organic compounds (VOCs), which are simultaneously released from different paints. Nevertheless, the interaction mechanism of pollutant diffusion from multisource building materials (such as primer and finish) needs to be examined. In this paper, a multisource mass transfer model for VOC emissions from different combinations of paints is established, and the analytical solution is derived. The finite difference method is used to simulate the experimental results of VOC release in the environmental chambers, and its convergence and stability are verified. Using the optimization parameters of the single-source model and the law of conservation of mass, the key parameters of the multisource mass transfer model are obtained. The results show that the established model is in excellent agreement with both experimental data and literature data. In addition, the Little number Lt is used to analyse the change trend from the initial released concentration in the single-source and multisource models.

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.

Early-Stage Emissions of Formaldehyde and Volatile Organic Compounds from Building Materials: Model Development, Evaluation, and Applications

Emissions of formaldehyde and volatile organic compounds (VOCs) from building materials may result in poor indoor air quality. The emission process can be divided into three stages over time: early, transition, and equilibrium stages. In existing studies, mass transfer models without distinguishing the early and transition stages have been widely used for characterizing the formaldehyde/VOC emissions, with three key parameters involved in these models. Many methods have been proposed for determining these parameters by fitting the corresponding models to experimental data. However, multiple groups of best-fit parameters might coexist if experimental data are obtained at the early stage (to shorten the experimental time). Therefore, we developed a novel mass transfer model to describe the early-stage emissions by assuming the building material as semi-infinite medium. The novel model indicated that the early-stage emission was governed by only two parameters, instead of three parameters, which explained the reason for the multi-solution problem of existing methods. Subsequently, the application condition of the early-stage model was clarified, showing that the early stage was very common in the emissions of formaldehyde/VOCs. Finally, a novel approach for characterizing the emissions of formaldehyde/VOCs from building materials was proposed to eliminate the negative effects of the multi-solution problem.

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.

Health effects of exposure to indoor volatile organic compounds from 1980 to 2017: A systematic review and meta-analysis

Exposure to volatile organic compounds (VOCs) indoors is thought to be associated with several adverse health effects. However, we still lack concentration-response (C-R) relationships between VOC levels in civil buildings and various health outcomes. For this paper, we conducted a systematic review and meta-analysis of observational studies to summarize related associations and C-R relationships. Four databases were searched to collect all relevant studies published between January 1980 and December 2017. A total of 39 studies were identified in the systematic review, and 32 of these were included in the meta-analysis. We found that the pooled relative risk (RR) for leukemia was 1.03 (95% CI: 1.01-1.05) per 1 μg/m³ increase of benzene and 1.25 (95%CI: 1.14-1.37) per 0.1 μg/m³ increase of butadiene. The pooled RRs for asthma were 1.08 (95% CI: 1.02-1.14), 1.02 (95% CI: 1.00-1.04), and 1.04 (95% CI: 1.02-1.06) per 1 μg/m³ increase of benzene, toluene, and p-dichlorobenzene, respectively. The pooled RR for low birth weight was 1.12 (95% CI: 1.05-1.19) per 1 μg/m³ increase of benzene. Our findings provide robust evidence for associations between benzene and leukemia, asthma, and low birth weight, as well as for health effects of some other VOCs.

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 D_m 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.

Study on the Effect of an Intermittent Ventilation Strategy on Controlling Formaldehyde Concentrations in Office Rooms

Material emission and ventilation are two aspects influencing indoor air quality. In this study, a model predictive control (MPC) strategy is proposed for intermittent ventilation system in office buildings, to achieve a healthy indoor environment. The strategy is based on a dynamic model for predicting emissions of volatile organic compounds (VOCs) from materials. The key parameters of formaldehyde from panel furniture in the model are obtained by an improved C-history method and large-scale chamber experiments. The effectiveness of the determined key parameters is validated, which are then used to predict the formaldehyde concentration variation and the pre-ventilation time in a typical office room. In addition, the influence of some main factors (i.e., vacant time, loading ratio, air change rate) on the pre-ventilation time is analyzed. Results indicate that the pre-ventilation time of the intermittent ventilation system ranges from several minutes to several hours. The pre-ventilation time decreases exponentially with the increase in the vacant time, the air change rate, and with the decrease in the loading ratio. When the loading ratio of the furniture is 0.30 m2/m3 and the vacant time is 100 days, the required pre-ventilation time approaches zero. Results further reveal that an air change rate of 2 h−1 is the most effective means for rapid removal of indoor formaldehyde for the cases studied. The proposed strategy should be helpful for achieving effective indoor pollution control.

Experimental and numerical investigation of TVOC concentrations and ventilation dilution in enclosed train cabin

Air pollution in trains is an important factor threatening human health, which has attracted more and more attention in the worldwide public health researches. In this study, one cabin of a fully enclosed train was taken as an example to conduct experimental and numerical research on pollution level and distribution characteristic of total volatile organic compound (TVOC). The results show that when the average emission rate under daily environmental conditions was taken as the reference, TVOC concentration in the cabin exceeded the limit level of Chinese Indoor Air Quality Standard by more than 4 times. The obvious pollutants accumulative phenomenon could be found at bottoms and corners under the action of airflow. Setting air inlets at the roof of the train, mean age of air ranged from 30 s to 50 s in the breathing area. The concentration of pollutants was the lowest at 2.5-5 m from the center point of the cabin structure, and the ventilation efficiency was the highest. The introduction of clean fresh air could effectively eliminate pollutants. When the complete displacement ventilation rates were 51.4 h^-1 and 28.6 h^-1, the removal efficiency of pollutants was about 0.85 and 0.48 mg/m³ per minute, respectively. This study was helpful to the improvement and optimization design of air quality and ventilation mode in trains.

Volatile Organic Compounds (VOCs) Emitted from Coated Furniture Units

Volatile organic compounds (VOCs) are the main source influencing the overall air quality of an environment. It is a well-known fact that coated furniture units, in the form of paints and varnishes, emit VOCs, reducing the air quality and resulting in significant health problems. Exposure time to such compounds is also an important parameter regarding their possible health effects. Such issues also have a greater influence when the exposure period is extended. The main objective of this study was to review some of the important factors for the emission of VOCs from coated furniture, from the perspective of material characteristics, as well as health concerns. Some methods for controlling VOC emissions to improve indoor air quality, from the point of view recent regulations and suggestions, are also presented in this work.

Assessing Human Exposure to SVOCs in Materials, Products, and Articles: A Modular Mechanistic Framework

A critical review of the current state of knowledge of chemical emissions from indoor sources, partitioning among indoor compartments, and the ensuing indoor exposure leads to a proposal for a modular mechanistic framework for predicting human exposure to semivolatile organic compounds (SVOCs). Mechanistically consistent source emission categories include solid, soft, frequent contact, applied, sprayed, and high temperature sources. Environmental compartments are the gas phase, airborne particles, settled dust, indoor surfaces, and clothing. Identified research needs are the development of dynamic emission models for several of the source emission categories and of estimation strategies for critical model parameters. The modular structure of the framework facilitates subsequent inclusion of new knowledge, other chemical classes of indoor pollutants, and additional mechanistic processes relevant to human exposure indoors. The framework may serve as the foundation for developing an open-source community model to better support collaborative research and improve access for application by stakeholders. Combining exposure estimates derived using this framework with toxicity data for different end points and toxicokinetic mechanisms will accelerate chemical risk prioritization, advance effective chemical management decisions, and protect public health.

Research Challenges and Advancements in the field of Sustainable Energy Technologies in the Built Environment

Inevitably, the 21st century has initiated a series of developments in the construction industry, leading to its digitalization and resulting in a series of innovative approaches and practices. At the same time, the construction industry, being one of the main global environment polluters, should fulfil well-established, as well as novel, sustainability requirements in order to evolve in harmony with the rising concerns on the availability of natural resources. This overview study aims to present the main developments, research, and scientific challenges in the field of sustainable construction, emphasizing the field of energy. The study aims to present a state-of-the-art scientific discussion on the sustainable built environment topic by analyzing cutting edge topics in the fields of building elements and whole building energy assessment, of indoor air quality and low carbon buildings, as well as on sustainable energy systems and smart buildings. The study also presents the state-of-the-art in existing tools which are adopted for the assessment of the sustainable built environment, including the use of digital tools and building information modelling for the energy assessment of the built environment, as well as the application of Life Cycle Assessment on building-related processes. Cross cutting issues related to the analysis of the building sector in the Industry 4.0 era, such as sustainability management topics and environmental geomatics are also discussed. The study concludes in those fields which will be of interest of the scientific community in the following years, towards achieving the goals of the sustainable development of the building sector.

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 (D_m), 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%, D_m 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.

Predicting Indoor Emissions of Cyclic Volatile Methylsiloxanes from the Use of Personal Care Products by University Students

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.

Measurements of Parameters Controlling the Emissions of Organophosphate Flame Retardants in Indoor Environments

Emission of semivolatile organic compounds (SVOCs) from source materials usually occurs very slowly in indoor environments due to their low volatility. When the SVOC emission process is controlled by external mass transfer, the gas-phase concentration in equilibrium with the material ( y₀) is used as a key parameter to simplify the source models that are based on solid-phase diffusion. A material-air-material (M-A-M) configured microchamber method was developed to rapidly measure y₀ for a polyisocyanurate rigid foam material containing organophosphate flame retardants (OPRFs). The emission test was conducted in 44 mL microchambers for target OPFRs, including tris(2-chloroethyl) phosphate (CASRN: 115-96-8), tris(1-chloro-2-propyl) phosphate (CASRN: 13674-84-5), and tris(1,3-dichloro-2-propyl) phosphate (CASRN: 13674-87-8). In addition to the microchamber emission test, two other types of tests were conducted to determine y₀ for the same foam material: OPFR diffusive tube sampling tests from the OPFR source foam using stainless-steel thermal desorption tubes and sorption tests of OPFR on an OPFR-free foam in a 53 L small chamber. Comparison of parameters obtained from the three methods suggests that the discrepancy could be caused by a combination of theoretical, experimental, and computational differences. Based on the y₀ measurements, a linear relationship between the ratio of y₀ to saturated vapor pressure concentration and material-phase mass fractions has been found for phthalates and OPFRs.

SPME-Based Ca-History Method for Measuring SVOC Diffusion Coefficients in Clothing Material

Clothes play an important role in dermal exposure to indoor semivolatile organic compounds (SVOCs). The diffusion coefficient of SVOCs in clothing material (D_m) is essential for estimating SVOC sorption by clothing material and subsequent dermal exposure to SVOCs. However, few studies have reported the measured D_m for clothing materials. In this paper, we present the solid-phase microextraction (SPME) based C_a-history method. To the best of our knowledge, this is the first try to measure D_m with known relative standard deviation (RSD). A thin sealed chamber is formed by a circular ring and two pieces of flat SVOC source materials that are tightly covered by the targeted clothing materials. D_m is obtained by applying an SVOC mass transfer model in the chamber to the history of gas-phase SVOC concentrations (C_a) in the chamber measured by SPME. D_m's of three SVOCs, di-iso-butyl phthalate (DiBP), di-n-butyl phthalate (DnBP), and tris(1-chloro-2-propyl) phosphate (TCPP), in a cotton T-shirt can be obtained within 16 days, with RSD less than 3%. This study should prove useful for measuring SVOC D_m in various sink materials. Further studies are expected to facilitate application of this method and investigate the effects of temperature, relative humidity, and clothing material on D_m.

Exposure to SVOCs from Inhaled Particles: Impact of Desorption

Inhaled semivolatile organic compounds (SVOCs) are simultaneously present in gas and particle phases. Particles desorb a fraction of their SVOCs moving through the human respiratory tract (RT). Quantifying such desorption is challenging but important since gas- and particle-phase SVOCs deposit in different locations in the RT, encountering different cell populations with varying health consequences. This paper presents a mass transfer model to quantify this desorption process in the head, tracheobronchial, and alveolar regions of the RT. The desorption of SVOCs from inhaled particles can be gauged using the ratio of particle residence time to the time required to achieve particle/gas equilibrium. Results indicate that the larger this ratio is, the more likely particles desorb the SVOCs they carry. For particles smaller than 0.5 μm diameter and SVOCs with a particle/gas partition coefficient (unitless) of 10¹⁰, accounting for desorption reduces the estimated particle-phase SVOC concentrations in the alveolar region by more than 35%; the reduction is almost 700% for 0.05 μm diameter particles. In hypothetical scenarios representing common indoor and outdoor situations, neglecting desorption significantly overestimates the concentration of ultrafine particle associated SVOCs in the alveolar region. This model is a preliminary step toward more nuanced estimates of exposure to inhaled SVOCs.

Indoor air pollutants, ventilation rate determinants and potential control strategies in Chinese dwellings: A literature review

After nearly twenty years of rapid modernization and urbanization in China, huge achievements have transformed the daily lives of the Chinese people. However, unprecedented environmental consequences in both indoor and outdoor environments have accompanied this progress and have triggered public awareness and demands for improved living standards, especially in residential environments. Indoor pollution data measured for >7000 dwellings (approximately 1/3 were newly decorated and were tested for volatile organic compound (VOC) measurements, while the rest were tested for particles, phthalates and other semi-volatile organic compounds (SVOCs), moisture/mold, inorganic gases and radon) in China within the last ten years were reviewed, summarized and compared with indoor concentration recommendations based on sensory or health end-points. Ubiquitous pollutants that exceed the concentration recommendations, including particulate matter, formaldehyde, benzene and other VOCs, moisture/mold, inorganic gases and radon, were found, indicating a common indoor air quality (IAQ) issue in Chinese dwellings. With very little prevention, oral, inhalation and dermal exposure to those pollutants at unhealthy concentration levels is almost inevitable. CO₂, VOCs, humidity and radon can serve as ventilation determinants, each with different ventilation demands and strategies, at typical occupant densities in China; and particle reduction should be a prerequisite for determining ventilation requirements. Two directional ventilation modes would have profound impacts on improving IAQ for Chinese residences are: 1) natural (or window) ventilation with an air cleaner and 2) mechanical ventilation with an air filtration unit, these two modes were reviewed and compared for their applicability and advantages and disadvantages for reducing human exposure to indoor air pollutants. In general, mode 2 can more reliably ensure good IAQ for occupants; while mode 1 is more applicable due to its low cost and low energy consumption. However, besides a roadmap, substantial efforts are still needed to develop affordable, applicable and general ventilation solutions to improve the IAQ of residential buildings in China.

A SPME-based method for rapidly and accurately measuring the characteristic parameter for DEHP emitted from PVC floorings

Semivolatile organic compounds (SVOCs) are present in many indoor materials. SVOC emissions can be characterized with a critical parameter, y₀ , the gas-phase SVOC concentration in equilibrium with the source material. To reduce the required time and improve the accuracy of existing methods for measuring y₀ , we developed a new method which uses solid-phase microextraction (SPME) to measure the concentration of an SVOC emitted by source material placed in a sealed chamber. Taking one typical indoor SVOC, di-(2-ethylhexyl) phthalate (DEHP), as the example, the experimental time was shortened from several days (even several months) to about 1 day, with relative errors of less than 5%. The measured y₀ values agree well with the results obtained by independent methods. The saturated gas-phase concentration (y_sat ) of DEHP was also measured. Based on the Clausius-Clapeyron equation, a correlation that reveals the effects of temperature, the mass fraction of DEHP in the source material, and y_sat on y₀ was established. The proposed method together with the correlation should be useful in estimating and controlling human exposure to indoor DEHP. The applicability of the present approach for other SVOCs and other SVOC source materials requires further study.

Comprehensive influence of environmental factors on the emission rate of formaldehyde and VOCs in building materials: Correlation development and exposure assessment

Temperature and relative humidity can simultaneously change in indoor environment, which significantly affect the emission rate of formaldehyde and volatile organic compounds (VOCs) from building materials. Prior studies generally focus on the single effect of temperature or relative humidity, and the combined effect is not considered. This paper investigates the comprehensive influence of temperature and relative humidity on the emission rate of pollutants from building materials. Correlation between the emission rate and the combined environmental factors is derived theoretically. Data in literature are applied to validate the effectiveness of the correlation. With the correlation, the indoor formaldehyde concentration in summer is predicted to be 1.63 times of that in winter in Beijing, which is approximately consistent with surveyed data. In addition, a novel approach is proposed to assess the human health impact due to pollutants emitted from building materials at varied temperature and relative humidity. An association between the human carcinogenic potential (HCP) and the environmental factors is obtained. By introducing a reference room model developed previously, it is calculated that the HCP of bedroom at high relative humidity (70%, 25°C) for formaldehyde exceeds 10^-4 cases, meaning high cancer health risk. This study should prove useful for evaluating the emission behaviors and the associated exposure of pollutants from building materials at varied environmental conditions.

C(m)-History Method, a Novel Approach to Simultaneously Measure Source and Sink Parameters Important for Estimating Indoor Exposures to Phthalates

The concentration of a gas-phase semivolatile organic compound (SVOC) in equilibrium with its mass-fraction in the source material, y0, and the coefficient for partitioning of an SVOC between clothing and air, K, are key parameters for estimating emission and subsequent dermal exposure to SVOCs. Most of the available methods for their determination depend on achieving steady-state in ventilated chambers. This can be time-consuming and of variable accuracy. Additionally, no existing method simultaneously determines y0 and K in a single experiment. In this paper, we present a sealed-chamber method, using early-stage concentration measurements, to simultaneously determine y0 and K. The measurement error for the method is analyzed, and the optimization of experimental parameters is explored. Using this method, y0 for phthalates (DiBP, DnBP, and DEHP) emitted by two types of PVC flooring, coupled with K values for these phthalates partitioning between a cotton T-shirt and air, were measured at 25 and 32 °C (room and skin temperatures, respectively). The measured y0 values agree well with results obtained by alternate methods. The changes of y0 and K with temperature were used to approximate the changes in enthalpy, ΔH, associated with the relevant phase changes. We conclude with suggestions for further related research.

A diffusivity model for predicting VOC diffusion in porous building materials based on fractal theory

Most building materials are porous media, and the internal diffusion coefficients of such materials have an important influences on the emission characteristics of volatile organic compounds (VOCs). The pore structure of porous building materials has a significant impact on the diffusion coefficient. However, the complex structural characteristics bring great difficulties to the model development. The existing prediction models of the diffusion coefficient are flawed and need to be improved. Using scanning electron microscope (SEM) observations and mercury intrusion porosimetry (MIP) tests of typical porous building materials, this study developed a new diffusivity model: the multistage series-connection fractal capillary-bundle (MSFC) model. The model considers the variable-diameter capillaries formed by macropores connected in series as the main mass transfer paths, and the diameter distribution of the capillary bundles obeys a fractal power law in the cross section. In addition, the tortuosity of the macrocapillary segments with different diameters is obtained by the fractal theory. Mesopores serve as the connections between the macrocapillary segments rather than as the main mass transfer paths. The theoretical results obtained using the MSFC model yielded a highly accurate prediction of the diffusion coefficients and were in a good agreement with the VOC concentration measurements in the environmental test chamber.

C-Depth Method to Determine Diffusion Coefficient and Partition Coefficient of PCB in Building Materials

Polychlorinated biphenyls (PCBs) have been found to be persistent in the environment and possibly harmful. Many buildings are characterized with high PCB concentrations. Knowledge about partitioning between primary sources and building materials is critical for exposure assessment and practical remediation of PCB contamination. This study develops a C-depth method to determine diffusion coefficient (D) and partition coefficient (K), two key parameters governing the partitioning process. For concrete, a primary material studied here, relative standard deviations of results among five data sets are 5%-22% for K and 42-66% for D. Compared with existing methods, C-depth method overcomes the inability to obtain unique estimation for nonlinear regression and does not require assumed correlations for D and K among congeners. Comparison with a more sophisticated two-term approach implies significant uncertainty for D, and smaller uncertainty for K. However, considering uncertainties associated with sampling and chemical analysis, and impact of environmental factors, the results are acceptable for engineering applications. This was supported by good agreement between model prediction and measurement. Sensitivity analysis indicated that effective diffusion distance, contacting time of materials with primary sources, and depth of measured concentrations are critical for determining D, and PCB concentration in primary sources is critical for K.