Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools

School-age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children's exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real-time PM₁₀, PM_2.5, NO_2, and O₃ concentrations were measured in 76 classrooms and 23 non-classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM_2.5, PM₁₀, O_3, and NO₂ microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM_2.5, PM₁₀, O_3, and NO₂ microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations.     

Factors controlling volatile organic compounds in dwellings in Melbourne,Australia

This study characterized indoor volatile organic compounds (VOCs) and investigated the effects of the dwelling characteristics, building materials, occupant activities, and environmental conditions on indoor VOC concentrations in 40 dwellings located in Melbourne, Australia, in 2008 and 2009. A total of 97 VOCs were identified. Nine VOCs, n‐butane, 2‐methylbutane, toluene, formaldehyde, acetaldehyde, d‐limonene, ethanol, 2‐propanol, and acetic acid, accounted for 68% of the sum of all VOCs. The median indoor concentrations of all VOCs were greater than those measured outdoors. The occupant density was positively associated with indoor VOC concentrations via occupant activities, including respiration and combustion. Terpenes were associated with the use of household cleaning and laundry products. A petroleum‐like indoor VOC signature of alkanes and aromatics was associated with the proximity of major roads. The indoor VOC concentrations were negatively correlated (P < 0.05) with ventilation. Levels of VOCs in these Australian dwellings were lower than those from previous studies in North America and Europe, probably due to a combination of an ongoing temporal decrease in indoor VOC concentrations and the leakier nature of Australian dwellings.     

A general mechanistic model for predicting the fate and transport of phthalates in indoor environments

A mechanistic model that considers particle dynamics and their effects on surface emissions and sorptions was developed to predict the fate and transport of phthalates in indoor environments. A controlled case study was conducted in a test house to evaluate the model. The model‐predicted evolving concentrations of benzyl butyl phthalate in indoor air and settled dust and on interior surfaces are in good agreement with measurements. Sensitivity analysis was performed to quantify the effects of parameter uncertainties on model predictions. The model was then applied to a typical residential environment to investigate the fate of di‐2‐ethylhexyl phthalate (DEHP) and the factors that affect its transport. The predicted steady‐state DEHP concentrations were 0.14 μg/m³ in indoor air and ranged from 80 to 46 000 μg/g in settled dust on various surfaces, which are generally consistent with the measurements of previous studies in homes in different countries. An increase in the mass concentration of indoor particles may significantly enhance DEHP emission and its concentrations in air and on surfaces, whereas increasing ventilation has only a limited effect in reducing DEHP in indoor air. The influence of cleaning activities on reducing DEHP concentration in indoor air and on interior surfaces was quantified, and the results showed that DEHP exposure can be reduced by frequent and effective cleaning activities and the removal of existing sources, though it may take a relatively long period of time for the levels to drop significantly. Finally, the model was adjusted to identify the relative contributions of gaseous sorption and particulate‐bound deposition to the overall uptake of semi‐volatile organic compounds (SVOCs) by indoor surfaces as functions of time and the octanol‐air partition coefficient (K_oa) of the chemical. Overall, the model clarifies the mechanisms that govern the emission of phthalates and the subsequent interactions among air, suspended particles, settled dust, and interior surfaces. This model can be easily extended to incorporate additional indoor source materials/products, sorption surfaces, particle sources, and room spaces. It can also be modified to predict the fate and transport of other SVOCs, such as phthalate‐alternative plasticizers, flame retardants, and biocides, and serves to improve our understanding of human exposure to SVOCs in indoor environments.     

Characterizing the bacterial communities in retail stores in the United States

The microorganisms present in retail environments have not been studied in detail despite the fact that these environments represent a potentially important location for exposure. In this study, HVAC filter dust samples in 13 US retail stores were collected and analyzed via pyrosequencing to characterize the indoor bacterial communities and to explore potential relationships between these communities and building and environmental parameters. Although retail stores contained a diverse bacterial community of 788 unique genera, over half of the nearly 118K sequences were attributed to the Proteobacteria phylum. Streptophyta, Bacillus, Corynebacterium, Pseudomonas, and Acinetobacter were the most prevalent genera detected. The recovered indoor airborne microbial community was statistically associated with both human oral and skin microbiota, indicating occupants are important contributors, despite a relatively low occupant density per unit volume in retail stores. Bacteria generally associated with outdoor environments were present in the indoor communities with no obvious association with air exchange rate, even when considering relative abundance. No significant association was observed between the indoor bacterial community recovered and store location, store type, or season. However, predictive functional gene profiling showed significant associations between the indoor community and season. The microbiome recovered from multiple samples collected months apart from the same building varied significantly indicating that caution is warranted when trying to characterize the bacterial community with a single sampling event.     

Case study of window opening behavior using field measurement results

Window opening behavior has significant influences on indoor environment and energy consumption in residential buildings. As a response to indoor environment, the control mechanism (window and interior door open/closed) should be studied first by comprehensive understanding of the variation of indoor environmental conditions. For this reason, a field measurement of environmental conditions was carried out in five representative apartments in Beijing from April to May in 2010. By comparison analyses of the field measurement results, major findings are as follows: (1) the concentration of room CO₂ can be the best predictor of occupant behavior, also window opening behavior, in residential buildings; (2) the variation in indoor air quality mainly results from large variation in window opening behavior; (3) apart from indoor and outdoor thermal environment, indoor air quality as well as occupants’ presence at room have also considerable effect on window opening behavior; (4) while defining window opening behavior for one room in residential building, it is necessary to take the window adjustment of its connecting room and the adjustment of the connecting door into consideration due to air diffusion between the two connecting rooms.     

Experimental and numerical study of room airflow under stratum ventilation

This paper investigates the air movement, air temperature profile and gaseous contaminant transportation in an individual office with stratum ventilation. The room temperature is elevated compared with conventional standards. The experimental investigation is carried out in an environmental chamber with the presence of heat generating rectangles used to simulate an occupant and a computer. Measurements of temperature, velocity, and CO₂ concentration are carried out for nine plumb lines in the chamber. Up to sixteen points are measured along each plumb line. The experimental data of the aforesaid three parameters of the individual office in warm condition under stratum ventilation are presented. The experimental data collected are used to validate a re-normalization group (RNG) k–? turbulence model used for the warm condition. The agreements between the predicted values and experimental results are acceptable, which demonstrates the feasibility of simulating indoor airflows at elevated room temperature under stratum ventilation by the RNG k–? turbulence model.     

Modeling the resiliency of energy‐efficient retrofits in low‐income multifamily housing

Residential energy efficiency and ventilation retrofits (eg, building weatherization, local exhaust ventilation, HVAC filtration) can influence indoor air quality (IAQ) and occupant health, but these measures’ impact varies by occupant activity. In this study, we used the multizone airflow and IAQ analysis program CONTAM to simulate the impacts of energy retrofits on indoor concentrations of PM_2.5 and NO₂ in a low‐income multifamily housing complex in Boston, Massachusetts (USA). We evaluated the differential impact of residential activities, such as low‐ and high‐emission cooking, cigarette smoking, and window opening, on IAQ across two seasons. We found that a comprehensive package of energy and ventilation retrofits was resilient to a range of occupant activities, while less holistic approaches without ventilation improvements led to increases in indoor PM_2.5 or NO₂ for some populations. In general, homes with simulated concentration increases included those with heavy cooking and no local exhaust ventilation, and smoking homes without HVAC filtration. Our analytical framework can be used to identify energy‐efficient home interventions with indoor retrofit resiliency (ie, those that provide IAQ benefits regardless of occupant activity), as well as less resilient retrofits that can be coupled with behavioral interventions (eg, smoking cessation) to provide cost‐effective, widespread benefits.     

Assessment of ventilation and indoor air pollutants in nursery and elementary schools in France

The aim of this study was to characterize the relationship between Indoor Air Quality (IAQ) and ventilation in French classrooms. Various parameters were measured over one school week, including volatile organic compounds, aldehydes, particulate matter (PM_2.5 mass concentration and number concentration), carbon dioxide (CO₂), air temperature, and relative humidity in 51 classrooms at 17 schools. The ventilation was characterized by several indicators, such as the air exchange rate, ventilation rate (VR), and air stuffiness index (ICONE), that are linked to indoor CO₂ concentration. The influences of the season (heating or non‐heating), type of school (nursery or elementary), and ventilation on the IAQ were studied. Based on the minimum value of 4.2 l/s per person required by the French legislation for mechanically ventilated classrooms, 91% of the classrooms had insufficient ventilation. The VR was significantly higher in mechanically ventilated classrooms compared with naturally ventilated rooms. The correlations between IAQ and ventilation vary according to the location of the primary source of each pollutant (outdoor vs. indoor), and for an indoor source, whether it is associated with occupant activity or continuous emission.     

Modeling of thermal environment and human response in a crowded space for tropical climate

In a crowded building space with no air conditioning, heat and moisture emissions from occupants can result in heat stress in the indoor environment, which in turn, causes thermal strain on the human body. In the present paper, a 61-node thermoregulation model is coupled with a thermal environment model of ventilated space to simulate both the thermal conditions and occupant’s responses. The coupling model is validated with experimental data at high occupant density in a thermal environmental chamber. Reasonable agreements between the prediction and measurement on thermal environmental conditions, stress level and body temperatures are achieved. The model is a useful tool for design and evaluation of non-air-conditioned building environments.     

Modeling ventilation rates in bedrooms based on building characteristics and occupant behavior

Air change rate (ACR) data obtained from the bedrooms of 500 Danish children and presented in an earlier paper were analyzed in more detail. Questionnaires distributed to the families, home inspections and interviews with the parents provided information about a broad range of residential characteristics and occupant behavior. These were tested in several linear regression models to identify the degree of effect each selected independent variable has on the total ACR. The measured ACRs are summarized by some of the most significant variables such as room volume (higher ACR in smaller rooms), number of people sleeping in the bedroom (higher ACR with more people), average window and door opening habits (higher ACR with more opening), sharing the bedroom with other family members (higher ACR in shared rooms), location of the measured room (higher ACR above ground floor), year of construction (lowest ACR in buildings from early 1970s), observed condensation on the bedroom window (higher ACR at less condensation), etc. The best-fitting model explained 46% of the variability in the air change rates. Variables related to occupant behavior were stronger predictors of ventilation rate (model R² = 0.30) than those related to building characteristics (model R² = 0.09). Although not perfectly accurate on a room-to-room basis, our best-fitting model may be useful when a rough estimate of the average air change rate for larger study populations is required in future indoor air quality models.     

Managing Exposure to Indoor Air Pollutants in Residential and Office Environments

Abstract Sources of indoor air pollutants in residential and office environments can be managed to reduce occupant exposures. Techniques for managing indoor air pollution sources include: source elimination, substitution, modification, pretreatment, and altering the amount, location, or time of use. Intelligent source management requires knowledge of the source's emission characteristics, including chemical composition, emission rates, and decay rates. In addition, knowledge of mechanical and natural outdoor air exchange rates, heating/air-conditioning duct flow rates, and local exhaust fan (e.g., kitchen, bathroom) flow rates is needed to determine pollutant concentrations. Finally, indoor air quality (IAQ) models use this information and occupant activity patterns to determine instantaneous and/or cumulative individual exposure. This paper describes a number of residential and office scenarios for various indoor air pollution sources, several ventilation conditions, and typical occupant activity patterns. IAQ model predictions of occupant exposures for these scenarios are given for selected source management options. A one-month period was used to compare exposures; thus, long-term exposure information is not presented in this paper.     

Relationship of indoor and outdoor air pollutants in a naturally ventilated historical building envelope

Concentrations of NO₂, O₃, SO₂, acetic and formic acids, HNO₃ and NH₃ were measured inside and outside a historical building, the Baroque Library Hall (BLH) in the National Library in Prague (Czech Republic). The naturally ventilated system of the building, the restriction of personnel access, reduced groups of visitors and absence of activities which could influence indoor pollutant concentrations are characteristics that make the Baroque Library Hall a suitable location to study the influence of outdoor environment on the indoor air quality. The relationship between indoor and outdoor (I/O) concentration was investigated to assess the infiltration of outdoor generated pollutants. Outdoor and indoor pollution sources were determined and, infiltration of ammonium nitrate and a shift of the equilibrium to the gas phase were the reason for the high concentration of ammonia measured inside the BLH. A significant seasonal variation was observed and interpreted as a consequence of different infiltration regime associated with indoor–outdoor temperature differences, which in addition drives dilution processes of indoor generated pollutants. Based on the indoor air quality assessment performed in the BLH with regard to human and material exposure, there is reason for concern about material preservation and in particular paper at the BLH.     

Outdoor Air Contaminants and Indoor Air Quality under Transient Conditions

The indoor concentrations of contaminants originating from outdoor sources have been measured and calculated under transient conditions. The results show that contaminants that are supplied to an office building via the ventilation system can reach considerably high concentration levels. The indoor/outdoor concentration ratio and time lag are dependent on the air change rate. In buildings with low air change rates the indoor concentration variations are smoothed out compared to buildings with high air change rates. The results from the theoretical model are compared to the results from both laboratory and field measurements and the model is verified for well mixed conditions in a 20 m³ test chamber. The model can be used to simulate different control strategies for reduction of indoor contaminant concentrations related to outdoor sources. One such control strategy is based on reduction of the outdoor air change rate during periods with peak outdoor contaminant concentrations.     

Indoor environmental quality,occupant satisfaction,and acute building‐related health symptoms in Green Mark‐certified compared with non‐certified office buildings

Indoor environmental quality (IEQ) has become an important component of green building certification schemes. While green buildings are expected to provide enhanced IEQ, higher occupant satisfaction, and less risks of occupant health when compared with non‐green buildings, the literature suggests inconsistent evidence due to diverse research design, small sample size, and weak statistical analysis. This study compared several outcomes pertinent to IEQ performance in green and non‐green office buildings in Singapore. Adopting a cross‐sectional study design, objective measurements were taken in eight green and six non‐green buildings, and satisfaction and acute health symptom risks of 367 occupants were obtained. Green buildings exhibited lower concentration of PM2.5, bacteria, and fungi and maintained temperature and humidity more consistently compared to non‐green counterparts. The mean ratings for satisfaction with temperature, humidity, lighting level, air quality, and indoor environment were higher in green buildings (with statistical significance P < 0.05). There was statistically significant reduction in risk of occupants having headache, unusual fatigue, and irritated skin in green buildings. Although matching of buildings and occupant characteristics, survey participation bias, and sampling duration (a 1‐week snapshot) of IEQ monitoring remain as limitations, this study offered positive association of green buildings with qualitatively and quantitatively measured performance of IEQ.     

Investigation of transient thermal environments

Human beings are commonly exposed to transient thermal environments in their daily life. But most of the studies on indoor thermal environment have been conducted under steady-state conditions. The aim of this paper is to summarize the investigation on human responses to transient thermal environment carried out by the indoor environment group at Tsinghua University, and to predict the possibility of using their research findings in practice. Human responses to transients have some special characteristics, which could be beneficial to environmental control. Air movement is especially effective at realizing a transient thermal environment, offsetting higher air temperature or operative temperature in warm climates. Based on the analysis of measured data, the characteristics of air movement outdoors are different from artificial air supply such as fans or air supply outlets, in the probability distribution of their velocities, turbulence intensity, and power spectrum. Based on subjective experiments, it is evident that artificial air movement, which is mainly simulated with outdoor airflow characteristics, has the highest occupant preference in warm conditions. Experimentally simulated air movement improves not only whole-body cooling, but also local cooling as from personal air supplies. Finally, it is important that introducing simulated natural air movement into the space in warm or hot conditions could significantly decrease the building's energy consumption.     

Modeling residential indoor concentrations of PM2.5, NO2, NOx,and secondhand smoke in the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) Air study

Increased outdoor concentrations of fine particulate matter (PM_2.5) and oxides of nitrogen (NO₂, NO_x) are associated with respiratory and cardiovascular morbidity in adults and children. However, people spend most of their time indoors and this is particularly true for individuals with chronic obstructive pulmonary disease (COPD). Both outdoor and indoor air pollution may accelerate lung function loss in individuals with COPD, but it is not feasible to measure indoor pollutant concentrations in all participants in large cohort studies. We aimed to understand indoor exposures in a cohort of adults (SPIROMICS Air, the SubPopulations and Intermediate Outcome Measures in COPD Study of Air pollution). We developed models for the entire cohort based on monitoring in a subset of homes, to predict mean 2-week–measured concentrations of PM_2.5, NO₂, NO_x, and nicotine, using home and behavioral questionnaire responses available in the full cohort. Models incorporating socioeconomic, meteorological, behavioral, and residential information together explained about 60% of the variation in indoor concentration of each pollutant. Cross-validated R² for best indoor prediction models ranged from 0.43 (NO_x) to 0.51 (NO₂). Models based on questionnaire responses and estimated outdoor concentrations successfully explained most variation in indoor PM_2.5, NO₂, NO_x, and nicotine concentrations.     

Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings

Artificial neural networks (ANN) were constructed to predict prevalence of building-related symptoms (BRS) of office building occupants. Six indoor air pollutants and four indoor comfort variables were used as input variables to the networks. A symptom metric was used as the measure of BRS prevalence, and employed as the output variable. Pollutant concentration, comfort variable, and occupant symptom data were obtained from the Building Assessment and Survey Evaluation study conducted by the US Environmental Protection Agency, in which all were measured concurrently. Feed-forward networks that employ back-propagation algorithm with momentum term and variable learning rate were used in ANN modeling. Root mean square error and R²$R^2$ value of the simple linear regression between observed and predicted output were used as performance measures. Among the constructed networks, the best prediction performance was observed in a one-hidden-layered network with an R²$R^2$ value of 0.56 for the test set. All constructed networks except one showed a better performance than the multiple linear regression analysis.     

Evaluation of distributed environmental control systems for improving IAQ and reducing energy consumption in office buildings

Conventional heating, ventilation, and air conditioning (HVAC) systems are incapable of providing control over individual environments or adjusting fresh air supply based on the dynamic occupancy of individual rooms in an office building. This paper introduces the concept of distributed environmental control systems (DECS) and shows that improvement in indoor air quality (IAQ) and energy efficiency can be achieved by providing required amounts of fresh air directly to the individual office spaces through distributed demand controlled ventilation (DDCV). In DDCV, fresh air is provided to each micro-environment (room or cubicle) based on input from distributed sensors (CO₂, VOC, occupancy, etc.) or intelligent scheduling techniques to provide acceptable IAQ for each occupant, rather than for groups or populations of occupants. In order to study DECS, a numerical model was developed that incorporates some of the best available models for studying building energy consumption, indoor air flow, contaminant transport and HVAC system performance. The developed model was applied to a DECS in a model office building equipped with a DDCV system. By implementing DECS/DDCV and intelligent scheduling techniques it is possible to achieve an improvement in IAQ along with a reduction in annual energy consumption compared to conventional ventilation systems.     

Relating Sick Building Symptoms to Environmental Conditions and Worker Characteristics

Abstract Recent concern has centered on “sick buildings” in which there has been an unusually high percentage of health complaints by the building's occupants. Typically, these symptoms are thought to be tied to indoor air quality characteristics, such as high levels of respirable particles or volatiles, thermal conditions, etc. In addition, recent studies have drawn connections between “sick building syndrome” (SBS) symptoms and non-environmental variables, i.e., personal and occupational factors. This paper presents a brief review of a study by Hedge et al. (1995) and additional analyses of their data. In a study of 27 air-conditioned office buildings, Hedge et al. measured nine indoor environmental conditions at various locations within each building and concurrently questioned workers on sixteen SBS symptoms and a number of other personal factors. The additional analyses presented in this paper attempt to draw formal statistical connections between SBS symptoms and both personal worker characteristics and indoor air pollutants simultaneously. The analyses were based on symptom severity response variables whic include information not only on the frequency with which an individual experienced a symptom, but also on how much the symptom disrupted the individual's work. Results from sixteen linear mixed effects models indicate that significant predictors are primarily personal and occupational in nature rather than environmental. For a number of the symptoms, additional variability attributable to buildings exists. However, any physical explanation of this variability remains unclear.     

Inhabitant actions and summer overheating risk in London dwellings

ABSTRACT

An indoor overheating assessment study of 101 London dwellings during summer 2009 is presented. The study included building surveys, indoor dry bulb temperature monitoring and a questionnaire survey on occupant behaviour, including the operation of passive and active ventilation, cooling and shading systems. A theoretical London housing stock comprising 3456 combinations of building geometry, orientations, urban patterns, fabric retrofit and external weather was simulated using the EnergyPlus thermal modelling software. A statistical meta-model of EnergyPlus was then built by regressing the independent variables (simulation input) against the dependent variables (overheating risk). The monitoring and questionnaire data were analysed to explore the relationship between self-reported behaviour and overheating, and to test the meta-model. The monitoring data indicated that London homes and, in particular, bedrooms are already at risk of overheating during hot spells under the current climate. Around 70% of respondents tended to open only one or no windows at night mainly due to security reasons. An improvement in the coefficient of determination (R²) values between measured temperature and meta-model predictions was obtained only for those dwellings where occupants reported actions that were in line with the modelling assumptions, thus highlighting the importance of occupant behaviour for overheating.