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.

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.

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.

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.

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.

Characterizing sources and emissions of volatile organic compounds in a northern California residence using space- and time-resolved measurements.

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 8 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-hour 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.

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.

Transient Method for Determining Indoor Chemical Concentrations Based on SPME: Model Development and Calibration.

Solid-phase microextraction (SPME) is regarded as a nonexhaustive sampling technique with a smaller extraction volume and a shorter extraction time than traditional sampling techniques and is hence widely used. The SPME sampling process is affected by the convection or diffusion effect along the coating surface, but this factor has seldom been studied. This paper derives an analytical model to characterize SPME sampling for semivolatile organic compounds (SVOCs) as well as for volatile organic compounds (VOCs) by considering the surface mass transfer process. Using this model, the chemical concentrations in a sample matrix can be conveniently calculated. In addition, the model can be used to determine the characteristic parameters (partition coefficient and diffusion coefficient) for typical SPME chemical samplings (SPME calibration). Experiments using SPME samplings of two typical SVOCs, dibutyl phthalate (DBP) in sealed chamber and di(2-ethylhexyl) phthalate (DEHP) in ventilated chamber, were performed to measure the two characteristic parameters. The experimental results demonstrated the effectiveness of the model and calibration method. Experimental data from the literature (VOCs sampled by SPME) were used to further validate the model. This study should prove useful for relatively rapid quantification of concentrations of different chemicals in various circumstances with SPME.

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.

Impact of temperature on the ratio of initial emittable concentration to total concentration for formaldehyde in building materials: theoretical correlation and validation.

The initial emittable concentration (Cm,0) is a key parameter characterizing the emission behaviors of formaldehyde from building materials, which is highly dependent on temperature but has seldom been studied. Our previous study found that Cm,0 is much less than the total concentration (C0,total, used for labeling material in many standards) of formaldehyde. Because Cm,0 and not C0,total directly determines the actual emission behaviors, we need to determine the relationship between Cm,0 and C0,total so as to use Cm,0 as a more appropriate labeling index. By applying statistical physics theory, this paper derives a novel correlation between the emittable ratio (Cm,0/C0,total) and temperature. This correlation shows that the logarithm of the emittable ratio multiplied by power of 0.5 of temperature is linearly related to the reciprocal of temperature. Emissions tests for formaldehyde from a type of medium density fiberboard over the temperature range of 25.0-80.0 °C were performed to validate the correlation. Experimental results indicated that Cm,0 (or emittable ratio) increased significantly with increasing temperature, this increase being 14-fold from 25.0 to 80.0 °C. The correlation prediction agreed well with experiments, demonstrating its effectiveness in characterizing physical emissions. This study will be helpful for predicting/controlling the emission characteristics of pollutants at various temperatures.

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.

Association between the emission rate and temperature for chemical pollutants in building materials: general correlation and understanding.

The emission rate is considered to be a good indicator of the emission characteristics of formaldehyde and volatile organic compounds (VOCs) from building materials. In contrast to the traditional approach that focused on an experimental study, this paper uses a theoretical approach to derive a new correlation to characterize the relationship between the emission rate and temperature for formaldehyde emission. This correlation shows that the logarithm of the emission rate by a power of 0.25 of the temperature is linearly related to the reciprocal of the temperature. Experimental data from the literature were used to validate the derived correlation. The good agreement between the correlation and experimental results demonstrates its reliability and effectiveness. Using the derived correlation, the emission rate at temperatures other than the test condition can be obtained, greatly facilitating engineering applications. Further analysis indicates that the temperature-related emission rate of other scenarios, i.e., the standard emission reference and semi-volatile organic compounds (SVOCs), also conforms to the same correlation as that of formaldehyde. The molecular dynamics theory is introduced to preliminarily understand this phenomenon. Our new correlation should prove useful for estimating the emission characteristics of chemicals from materials that are subject to changes in temperature.

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.

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.

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.

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.

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.

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.

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.