A review of air exchange rate models for air pollution exposure assessments

In-utero personal exposure to PM2.5 impacted by indoor and outdoor sources and birthweight in the MADRES cohort

BACKGROUND

In-utero exposure to outdoor particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is linked with low birthweight. However, previous results are mixed, likely due to measurement error introduced by estimating personal exposure from ambient data. This study investigated the effect of total personal PM_2.5 exposure on birthweight and whether it differed when it was more heavily impacted by sources of indoor vs outdoor origin in the MADRES cohort study.

METHODS

Personal PM_2.5 exposure was measured in 205 pregnant women in the 3rd trimester using 48 h integrated, filter-based sampling. Linear regression was used to test the association between personal PM_2.5 exposure and birthweight, adjusting for key covariates. Interactions of PM_2.5 with variables representing indoor sources of PM_2.5, home ventilation, or time spent indoors tested whether the effect of total PM_2.5 on birthweight varied when it was more impacted by sources of indoor vs outdoor origin.

RESULTS

In a sample of largely Hispanic (81%) pregnant women, total personal PM_2.5 was not significantly associated with birthweight (β = 38.6 per 1SD increase in PM_2.5; 95% CI:-21.1, 98.2). This association however, differed by home type (single family home: 156.9 (26.9, 287.0), 2-4 attached units:-16.6 (-111.9, 78.7), 5+ units:-62.6 (-184.9, 59.6), missing: 145.4 (-4.1, 294.9), interaction p = 0.028) and by household air conditioner use (none of the time: -27.6 (-101.5, 46.3) vs. some of the time: 139.9 (42.9, 237.0), interaction p = 0.008) Additionally, the effect of personal PM_2.5 on birthweight varied by time spent indoors (none or little of the time: - 45.1 (-208.3, 118.1) vs. most or all of the time: 57.1 (-7.3, 121.6), interaction p = 0.255).

CONCLUSIONS

While no significant association between total personal PM_2.5 exposure and birthweight was found, there was evidence that multi-unit housing (vs. single-family homes), candle and/or incense smoke, and greater outdoor source contributions to personal PM_2.5 were more strongly associated with lower birthweight.

Survey of residential indoor particulate matter measurements 1990-2019

We surveyed literature on measurements of indoor particulate matter in all size fractions, in residential environments free of solid fuel combustion (other than wood for recreation or space heating). Data from worldwide studies from 1990 to 2019 were assembled into the most comprehensive collection to date. Out of 2752 publications retrieved, 538 articles from 433 research projects met inclusion criteria and reported unique data, from which more than 2000 unique sets of indoor PM measurements were collected. Distributions of mean concentrations were compiled, weighted by study size. Long-term trends, the impact of non-smoking, air cleaners, and the influence of outdoor PM were also evaluated. Similar patterns of indoor PM distributions for North America and Europe could reflect similarities in the indoor environments of these regions. Greater observed variability for all regions of Asia may reflect greater heterogeneity in indoor conditions, but also low numbers of studies for some regions. Indoor PM concentrations of all size fractions were mostly stable over the survey period, with the exception of observed declines in PM2.5 in European and North American studies, and in PM10 in North America. While outdoor concentrations were correlated with indoor concentrations across studies, indoor concentrations had higher variability, illustrating a limitation of using outdoor measurements to approximate indoor PM exposures.

Occupant behavior and indoor particulate concentrations in daycare centers

'Occupant behavior' is the primary mechanism determining indoor particulate concentrations. Various indoor human activities generate particulate matter. Human-building interactions, such as window opening behavior, change the number of outdoor particulate matter introduces to the building. 'Daycare center' where young children spend considerable time has an occupant schedule distinguished from other types of buildings. In the study, we analyzed the effects of occupant behavior on indoor particle concentrations in daycare centers by on-site monitoring. The measurements were performed in four daycare centers located in Gyeonggi-do, South Korea. Optical particle counters(OPS, model 3330, TSI Inc., Shoreview, MN, USA) were used for particulate concentration monitoring. The source strengths of particles resuspended by each human activity were calculated, and their contributions to indoor particle concentrations were evaluated. Further, characteristics of human-building interactions and their corresponding impacts on indoor air quality were also analyzed. Results showed that particle resuspension was greater when occupants were awake (mean, 41.0 particles·min^-1) than when they were asleep (mean, 9.2 particles·min^-1), and the contribution of occupant status was also higher when awake (37-70% vs. 8-18%) for particles sized (0.3-10.0 μm). Analyzing five detailed human activities, vacuuming (9.8·10⁷ particles·min^-1) emitted the highest amount of particulate matter per person, followed by physical activity (4.8·10⁷ particles·min^-1), sedentary activity (1.9·10⁷ particles· min^-1), meals (1.9·10⁷ particles·min^-1), and nap time (8.1·10⁶ particles·min^-1). The study suggests that vacuuming should be avoided while children are occupied. This research also shows that children could be exposed to high daily average indoor particulate concentration (up to 1217 particles·cm^-3) when windows were opened for an extended period of time while poor outdoor air quality. These results indicate that indoor air quality can be severely degraded by opening windows without considering the level of outdoor particle concentration.

Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality

BACKGROUND

School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children.

OBJECTIVE

We investigated factors influencing classroom exposures to fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in urban schools in the northeast United States.

METHODS

Over the period of 10 y (2008-2013; 2015-2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates.

RESULTS

LASSO identified 10, 10, and 11 significant factors (p<0.05) that were associated with indoor PM2.5, BC, and NO2 exposures, respectively. The overall variability explained by these models was R2=0.679, 0.687, and 0.621 for PM2.5, BC, and NO2, respectively. Of the model's explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor PM2.5, BC, and NO2 concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of PM2.5, BC, and NO2 levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of PM2.5, BC, and NO2 concentrations, respectively.

DISCUSSION

The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children's health and well-being during their early years of development. https://doi.org/10.1289/EHP10007.

Emergency responder and public health considerations for plastic sewer lining chemical waste exposures in indoor environments

The cured-in-place pipe (CIPP) manufacturing process is used to repair buried pipes, and its waste commonly discharged into the air can enter nearby buildings. Exposure can prompt illness and the need for medical care. A mass balance model was applied to estimate indoor styrene concentrations due to intrusion of CIPP emissions through plumbing under different bathroom ventilation conditions. To better understand building contamination and recommend emergency response actions, calculations to estimate chemical intrusion through plumbing were developed. Field reports and study calculations showed that contractor-applied external pressures during plastic manufacture have and can displace plumbing trap water seals. Modeled styrene vapor concentrations that entered the building (1, 300, 1000 ppm) were similar to those measured at CIPP worksites. Modeling revealed that in some cases, bathroom exhaust fan operation during a CIPP project may increase indoor styrene concentrations due to enhanced entrainment of styrene-laden air from the sink and toilet. However, styrene concentrations decreased with increasing air leakage across the bathroom door due to reduced suction from the plumbing system. CIPP waste discharge should be treated as a hazardous material release and can pose a threat to human health. Immediate building evacuation, respiratory protection, provision of medical assistance, source elimination, and building decontamination are recommended.

The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk

Exposure to chemicals is influenced by associations between the individual's location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual's community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example.

Modeling of Flame Retardants in Typical Urban Indoor Environments in China during 2010-2030: Influence of Policy and Decoration and Implications for Human Exposure

Novel flame retardants (FRs) are of increasing concern, given growing evidence of health effects and use to replace polybrominated diphenyl ethers (PBDEs). This study modeled combined effects of use policies and decoration on indoor FRs and human exposure for 18 widely used PBDEs, organophosphate esters (OPEs), and novel brominated flame retardants in typical urban indoor environments in China. The current estimated indoor emission rates and average concentrations in air and dust of the 18 FRs were 10²-10³ ng/h, 561 ng/m³, and 1.5 × 10⁴ ng/g, respectively, with seven OPEs dominant (>69%). Different use patterns exist between China and the US and Europe. Scenarios modeled over 2010-2030 suggested that decoration would affect indoor concentrations of FRs more than use policies, and use policies were mainly responsible for shifts of FR composition. Additional use of hexabromobenzene and 2,3,4,5,6-pentabromotoluene and removal of BDE-209 would make the total human exposure to the modeled FR mixture increase after the restriction of penta- and octa-BDE but decrease after deca-BDE was banned. Better knowledge of the toxicity of substitutes is needed for a complete understanding of the health implications of such changes. Toddlers may be more affected by use changes than adults. Such studies are supportive to the management of FR use.

Estimating climate change-related impacts on outdoor air pollution infiltration

BACKGROUND

Rising temperatures due to climate change are expected to impact human adaptive response, including changes to home cooling and ventilation patterns. These changes may affect air pollution exposures via alteration in residential air exchange rates, affecting indoor infiltration of outdoor particles. We conducted a field study examining associations between particle infiltration and temperature to inform future studies of air pollution health effects.

METHODS

We measured indoor fine particulate matter (PM_2.5) in Atlanta in 60 homes (810 sampling-days). Indoor-outdoor sulfur ratios were used to estimate particle infiltration, using central site outdoor sulfur concentrations. Linear and mixed-effects models were used to examine particle infiltration ratio-temperature relationships, based on which we incorporated projected meteorological values (Representative Concentration Pathways intermediate scenario RCP 4.5) to estimate particle infiltration ratios in 20-year future (2046-2065) and past (1981-2000) scenarios.

RESULTS

The mean particle infiltration ratio in Atlanta was 0.70 ± 0.30, with a 0.21 lower ratio in summer compared to transition seasons (spring, fall). Particle infiltration ratios were 0.19 lower in houses using heating, ventilation, and air conditioning (HVAC) systems compared to those not using HVAC. We observed significant associations between particle infiltration ratios and both linear and quadratic models of ambient temperature for homes using natural ventilation and those using HVAC. Future temperature was projected to increase by 2.1 °C in Atlanta, which corresponds to an increase of 0.023 (3.9%) in particle infiltration ratios during cooler months and a decrease of 0.037 (6.2%) during warmer months.

DISCUSSION

We estimated notable changes in particle infiltration ratio in Atlanta for different 20-year periods, with differential seasonal patterns. Moreover, when stratified by HVAC usage, increases in future ambient temperature due to climate change were projected to enhance seasonal differences in PM_2.5 infiltration in Atlanta. These analyses can help minimize exposure misclassification in epidemiologic studies of PM_2.5, and provide a better understanding of the potential influence of climate change on PM_2.5 health effects.

Residential air-change rates: A critical review

Air-change rate is an important parameter influencing residential air quality. This article critically assesses the state of knowledge regarding residential air-change rates, emphasizing periods of normal occupancy. Cumulatively, about 40 prior studies have measured air-change rates in approximately 10,000 homes using tracer gases, including metabolic CO₂ . The central tendency of the air-change rates determined in these studies is reasonably described as lognormal with a geometric mean of 0.5 h^-1 and a geometric standard deviation of 2.0. However, the geometric means of individual studies vary, mainly within the range 0.2-1 h^-1 . Air-change rates also vary with time in residences. Factors influencing the air-change rate include weather (indoor-outdoor temperature difference and wind speed), the leakiness of the building envelope, and, when present, operation of mechanical ventilation systems. Occupancy-associated factors are also important, including window opening, induced exhaust from flued combustion, and use of heating and cooling systems. Empirical and methodological challenges remain to be effectively addressed. These include clarifying the time variation of air-change rates in residences during occupancy and understanding the influence of time-varying air-change rates on tracer-gas measurement techniques. Important opportunities are available to improve understanding of air-change rates and interzonal flows as factors affecting the source-to-exposure relationships for indoor air pollutants.

Real-time indoor PM2.5 monitoring in an urban cohort: Implications for exposure disparities and source control

Fine particulate matter (PM2.5) concentrations are highly variable indoors, with evidence for exposure disparities. Real-time monitoring coupled with novel statistical approaches can better characterize drivers of elevated PM2.5 indoors. We collected real-time PM2.5 data in 71 homes in an urban community of Greater Boston, Massachusetts using Alphasense OPC-N2 monitors. We estimated indoor PM2.5 concentrations of non-ambient origin using mass balance principles, and investigated their associations with indoor source activities at the 0.50 to 0.95 exposure quantiles using mixed effects quantile regressions, overall and by homeownership. On average, the majority of indoor PM2.5 concentrations were of non-ambient origin (≥77%), with a higher proportion at increasing quantiles of the exposure distribution. Major source predictors of non-ambient PM2.5 concentrations at the upper quantile (0.95) were cooking (1.4-23 μg/m3) and smoking (15 μg/m3, only among renters), with concentrations also increasing with range hood use (3.6 μg/m3) and during the heating season (5.6 μg/m3). Across quantiles, renters in multifamily housing experienced a higher proportion of PM2.5 concentrations from non-ambient sources than homeowners in single- and multifamily housing. Renters also more frequently reported cooking, smoking, spray air freshener use, and second-hand smoke exposure, and lived in units with higher air exchange rate and building density. Accounting for these factors explained observed PM2.5 exposure disparities by homeownership, particularly in the upper exposure quantiles. Our results suggest that renters in multifamily housing may experience higher PM2.5 exposures due to a combination of behavioral and building factors that are amenable to intervention.

Occupational exposure and health risks of volatile organic compounds of hotel housekeepers: Field measurements of exposure and health risks

Hotel housekeepers represent a large, low-income, predominantly minority, and high-risk workforce. Little is known about their exposure to chemicals, including volatile organic compounds (VOCs). This study evaluates VOC exposures of housekeepers, sources and factors affecting VOC levels, and provides preliminary estimates of VOC-related health risks. We utilized indoor and personal sampling at two hotels, assessed ventilation, and characterized the VOC composition of cleaning agents. Personal sampling of hotel staff showed a total target VOC concentration of 57 ± 36 µg/m3 (mean ± SD), about twice that of indoor samples. VOCs of greatest health significance included chloroform and formaldehyde. Several workers had exposure to alkanes that could cause non-cancer effects. VOC levels were negatively correlated with estimated air change rates. The composition and concentrations of the tested products and air samples helped identify possible emission sources, which included building sources (for formaldehyde), disinfection by-products in the laundry room, and cleaning products. VOC levels and the derived health risks in this study were at the lower range found in the US buildings. The excess lifetime cancer risk (average of 4.1 × 10-5 ) still indicates a need to lower exposure by reducing or removing toxic constituents, especially formaldehyde, or by increasing ventilation rates.

Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study

Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1-5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1-2) and person-to-person variability of Cout_personal, E, and D (Tiers 3-5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.

Building science approaches for vapor intrusion studies

Indoor air concentrations are susceptible to temporal and spatial variations and have long posed a challenge to characterize for vapor intrusion scientists, in part, because there was a lack of evidence to draw conclusions about the role that building and weather conditions played in altering vapor intrusion exposure risks. Importantly, a large body of evidence is available within the building science discipline that provides information to support vapor intrusion scientists in drawing connections about fate and transport processes that influence exposure risks. Modeling tools developed within the building sciences provide evidence of reported temporal and spatial variation of indoor air contaminant concentrations. In addition, these modeling tools can be useful by calculating building air exchange rates (AERs) using building specific features. Combining building science models with vapor intrusion models, new insight to facilitate decision-making by estimating indoor air concentrations and building ventilation conditions under various conditions can be gained. This review highlights existing building science research and summarizes the utility of building science models to improve vapor intrusion exposure risk assessments.

Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application ("app") that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.

Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM_2.5) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM_2.5 and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m³ increase in PM_2.5 and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R² < 1%), multivariate models began to explain some of the observed heterogeneity (R² = 13%).

Effectiveness of Using Enhanced Filters in Schools and Homes to Reduce Indoor Exposures to PM2.5 from Outdoor Sources and Subsequent Health Benefits for Children with Asthma

Filters can reduce indoor concentrations of particulate matter (PM_2.5), but their benefits have not been well-characterized. This study investigates exposure, health, and cost impacts of high efficiency filters in homes and schools, focusing on the asthma-related outcomes. Reductions in indoor exposures to PM_2.5 from outdoor sources with enhanced filters (e.g., MERV 12) are estimated using probabilistic indoor air quality models, and avoided health impacts are quantified using health impact assessment. These methods are applied using data from Detroit, Michigan, an urban region with elevated asthma rates. Replacing inefficient filters with enhanced filters in schools would reduce the PM_2.5-attributable asthma burden by 13% annually, with higher benefits for more efficient filters. Marginal costs average $63 per classroom or $32 per child with asthma per year. In homes, using efficient furnace filters or air cleaners yields 11 to 16% reductions in the asthma burden with an annualized marginal costs of $151-494 per household. Additional benefits include reductions in health risk for adults and lower exposures to other contaminants such as PM from indoor sources. On the basis of the included health outcomes, efficient filters in schools in particular is a potentially cost-efficient way to reduce the asthma-related health burden for children.

Residential inter-zonal ventilation rates for exposure modeling

Residential inter-zonal (e.g., between rooms) ventilation is comprised of fresh air infiltration in and exfiltration out of the whole house plus the "fresh" air that is entering (and exiting) the room of interest from other rooms or areas within the house. Clearly, the inter-zone ventilation rate in any room of interest will be greater than the infiltration/exfiltration ventilation rate of outdoor air for the whole house. The purpose of this study is to determine how much greater the inter-zonal ventilation rate is in typical U.S. residences compared to the whole house ventilation rate from outdoor air. The data for this statistical analysis came from HouseDB, a 1995 EPA database of residential ventilation rates. Analytical results indicate that a lognormal distribution provides the best fit to the data. Lognormal probability distribution functions (PDFs) are provided for various inter-zonal ventilation rates for comparison to the PDF for the whole house ventilation rates. All ventilation rates are expressed as air change rates per hour (ACH). These PDFs can be used as inputs to exposure models. This analysis suggests that if one were performing a deterministic analysis for unknown housing stocks in the U.S., a default mean and median ACH values of 0.4/hr and 0.3/hr, respectively, for whole house ventilation would be appropriate; and 0.7/hr and 0.6/hr, respectively, for inter-zonal ventilation.

Ventilation rates in recently constructed U.S. school classrooms

Low ventilation rates (VRs) in schools have been associated with absenteeism, poorer academic performance, and teacher dissatisfaction. We measured VRs in 37 recently constructed or renovated and mechanically ventilated U.S. schools, including LEED and EnergyStar-certified buildings, using CO₂ and the steady-state, build-up, decay, and transient mass balance methods. The transient mass balance method better matched conditions (specifically, changes in occupancy) and minimized biases seen in the other methods. During the school day, air change rates (ACRs) averaged 2.0±1.3 hour^-1 , and only 22% of classrooms met recommended minimum ventilation rates. HVAC systems were shut off at the school day close, and ACRs dropped to 0.21±0.19 hour^-1 . VRs did not differ by building type, although cost-cutting and comfort measures resulted in low VRs and potentially impaired IAQ. VRs were lower in schools that used unit ventilators or radiant heating, in smaller schools and in larger classrooms. The steady-state, build-up, and decay methods had significant limitations and biases, showing the need to confirm that these methods are appropriate. Findings highlight the need to increase VRs and to ensure that energy saving and comfort measures do not compromise ventilation and IAQ.

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing, China

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM_2.5) and to develop models to predict PM_2.5 Finf in Beijing. Eighty-eight paired indoor-outdoor PM_2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM_2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM_2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM_2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM_2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM_2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification.

IMPLICATIONS

Failure to consider the differences between indoor and outdoor PM_2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM_2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM_2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM_2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM_2.5 infiltration factor were also developed.

Characterizing the impact of projected changes in climate and air quality on human exposures to ozone

The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O₃) owing to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 US cities show differences in daily maximum 8-h (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O₃ exposures is predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O₃ exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O₃ exposure exceedance above 60 p.p.b and 70 p.p.b thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O₃ are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O₃ exposure as a result of changes in climate that could impact human health.

Probabilistic estimation of residential air exchange rates for population-based human exposure modeling

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure.

US residential building air exchange rates: new perspectives to improve decision making at vapor intrusion sites

Vapor intrusion (VI) is well-known to be difficult to characterize because indoor air (IA) concentrations exhibit considerable temporal and spatial variability in homes throughout impacted communities. To overcome this and other limitations, most VI science has focused on subsurface processes; however there is a need to understand the role of aboveground processes, especially building operation, in the context of VI exposure risks. This tutorial review focuses on building air exchange rates (AERs) and provides a review of literature related building AERs to inform decision making at VI sites. Commonly referenced AER values used by VI regulators and practitioners do not account for the variability in AER values that have been published in indoor air quality studies. The information presented herein highlights that seasonal differences, short-term weather conditions, home age and air conditioning status, which are well known to influence AERs, are also likely to influence IA concentrations at VI sites. Results of a 3D VI model in combination with relevant AER values reveal that IA concentrations can vary more than one order of magnitude due to air conditioning status and one order of magnitude due to house age. Collectively, the data presented strongly support the need to consider AERs when making decisions at VI sites.

Review and Extension of CO₂-Based Methods to Determine Ventilation Rates with Application to School Classrooms

The ventilation rate (VR) is a key parameter affecting indoor environmental quality (IEQ) and the energy consumption of buildings. This paper reviews the use of CO₂ as a “natural” tracer gas for estimating VRs, focusing on applications in school classrooms. It provides details and guidance for the steady-state, build-up, decay and transient mass balance methods. An extension to the build-up method and an analysis of the post-exercise recovery period that can increase CO₂ generation rates are presented. Measurements in four mechanically-ventilated school buildings demonstrate the methods and highlight issues affecting their applicability. VRs during the school day fell below recommended minimum levels, and VRs during evening and early morning were on the order of 0.1 h⁻¹, reflecting shutdown of the ventilation systems. The transient mass balance method was the most flexible and advantageous method given the low air change rates and dynamic occupancy patterns observed in the classrooms. While the extension to the build-up method improved stability and consistency, the accuracy of this and the steady-state method may be limited. Decay-based methods did not reflect the VR during the school day due to heating, ventilation and air conditioning (HVAC) system shutdown. Since the number of occupants in classrooms changes over the day, the VR expressed on a per person basis (e.g., L·s⁻¹·person⁻¹) depends on the occupancy metric. If occupancy measurements can be obtained, then the transient mass balance method likely will provide the most consistent and accurate results among the CO₂-based methods. Improved VR measurements can benefit many applications, including research examining the linkage between ventilation and health.

Influence of exposure differences on city-to-city heterogeneity in PM2.5-mortality associations in US cities

BACKGROUND

Multi-city population-based epidemiological studies have observed heterogeneity between city-specific fine particulate matter (PM_2.5)-mortality effect estimates. These studies typically use ambient monitoring data as a surrogate for exposure leading to potential exposure misclassification. The level of exposure misclassification can differ by city affecting the observed health effect estimate.

METHODS

The objective of this analysis is to evaluate whether previously developed residential infiltration-based city clusters can explain city-to-city heterogeneity in PM_2.5 mortality risk estimates. In a prior paper 94 cities were clustered based on residential infiltration factors (e.g. home age/size, prevalence of air conditioning (AC)), resulting in 5 clusters. For this analysis, the association between PM_2.5 and all-cause mortality was first determined in 77 cities across the United States for 2001-2005. Next, a second stage analysis was conducted evaluating the influence of cluster assignment on heterogeneity in the risk estimates.

RESULTS

Associations between a 2-day (lag 0-1 days) moving average of PM_2.5 concentrations and non-accidental mortality were determined for each city. Estimated effects ranged from -3.2 to 5.1% with a pooled estimate of 0.33% (95% CI: 0.13, 0.53) increase in mortality per 10 μg/m³ increase in PM_2.5. The second stage analysis determined that cluster assignment was marginally significant in explaining the city-to-city heterogeneity. The health effects estimates in cities with older, smaller homes with less AC (Cluster 1) and cities with newer, smaller homes with a large prevalence of AC (Cluster 3) were significantly lower than the cluster consisting of cities with older, larger homes with a small percentage of AC.

CONCLUSIONS

This is the first study that attempted to examine whether multiple exposure factors could explain the heterogeneity in PM_2.5-mortality associations. The results of this study were found to explain a small portion (6%) of this heterogeneity.

Effects of climate change on residential infiltration and air pollution exposure

Air exchange through infiltration is driven partly by indoor/outdoor temperature differences, and as climate change increases ambient temperatures, such differences could vary considerably even with small ambient temperature increments, altering patterns of exposures to both indoor and outdoor pollutants. We calculated changes in air fluxes through infiltration for prototypical detached homes in nine metropolitan areas in the United States (Atlanta, Boston, Chicago, Houston, Los Angeles, Minneapolis, New York, Phoenix, and Seattle) from 1970-2000 to 2040-2070. The Lawrence Berkeley National Laboratory model of infiltration was used in combination with climate data from eight regionally downscaled climate models from the North American Regional Climate Change Assessment Program. Averaged over all study locations, seasons, and climate models, air exchange through infiltration would decrease by ~5%. Localized increased infiltration is expected during the summer months, up to 20-30%. Seasonal and daily variability in infiltration are also expected to increase, particularly during the summer months. Diminished infiltration in future climate scenarios may be expected to increase exposure to indoor sources of air pollution, unless these ventilation reductions are otherwise compensated. Exposure to ambient air pollution, conversely, could be mitigated by lower infiltration, although peak exposure increases during summer months should be considered, as well as other mechanisms.

Lung Cancer Risk from Radon in Marcellus Shale Gas in Northeast U.S. Homes

The amount of radon in natural gas varies with its source. Little has been published about the radon from shale gas to date, making estimates of its impact on radon-induced lung cancer speculative. We measured radon in natural gas pipelines carrying gas from the Marcellus Shale in Pennsylvania and West Virginia. Radon concentrations ranged from 1,520 to 2,750 Bq/m³ (41-74 pCi/L), and the throughput-weighted average was 1,983 Bq/m³ (54 pCi/L). Potential radon exposure due to the use of Marcellus Shale gas for cooking and space heating using vent-free heaters or gas ranges in northeastern U.S. homes and apartments was assessed. Though the measured radon concentrations are higher than what has been previously reported, it is unlikely that exposure from natural gas cooking would exceed 1.2 Bq/m³ (<1% of the U.S. Environmental Protection Agency's action level). Using worst-case assumptions, we estimate the excess lifetime (70 years) lung cancer risk associated with cooking to be 1.8×10^-4 (interval spanning 95% of simulation results: 8.5×10^-5 , 3.4×10^-4 ). The risk profile for supplemental heating with unvented gas appliances is similar. Individuals using unvented gas appliances to provide primary heating may face lifetime risks as high as 3.9×10^-3 . Under current housing stock and gas consumption assumptions, expected levels of residential radon exposure due to unvented combustion of Marcellus Shale natural gas in the Northeast United States do not result in a detectable change in the lung cancer death rates.

Exposure to particulate matter in India: A synthesis of findings and future directions

Air pollution poses a critical threat to human health with ambient and household air pollution identified as key health risks in India. While there are many studies investigating concentration, composition, and health effects of air pollution, investigators are only beginning to focus on estimating or measuring personal exposure. Further, the relevance of exposures studies from the developed countries in developing countries is uncertain. This review summarizes existing research on exposure to particulate matter (PM) in India, identifies gaps and offers recommendations for future research. There are a limited number of studies focused on exposure to PM and/or associated health effects in India, but it is evident that levels of exposure are much higher than those reported in developed countries. Most studies have focused on coarse aerosols, with a few studies on fine aerosols. Additionally, most studies have focused on a handful of cities, and there are many unknowns in terms of ambient levels of PM as well as personal exposure. Given the high mortality burden associated with air pollution exposure in India, a deeper understanding of ambient pollutant levels as well as source strengths is crucial, both in urban and rural areas. Further, the attention needs to expand beyond the handful large cities that have been studied in detail.

Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses

Traffic activity encompasses the number, mix, speed and acceleration of vehicles on roadways. The temporal pattern and variation of traffic activity reflects vehicle use, congestion and safety issues, and it represents a major influence on emissions and concentrations of traffic-related air pollutants. Accurate characterization of vehicle flows is critical in analyzing and modeling urban and local-scale pollutants, especially in near-road environments and traffic corridors. This study describes methods to improve the characterization of temporal variation of traffic activity. Annual, monthly, daily and hourly temporal allocation factors (TAFs), which describe the expected temporal variation in traffic activity, were developed using four years of hourly traffic activity data recorded at 14 continuous counting stations across the Detroit, Michigan, U.S. region. Five sites also provided vehicle classification. TAF-based models provide a simple means to apportion annual average estimates of traffic volume to hourly estimates. The analysis shows the need to separate TAFs for total and commercial vehicles, and weekdays, Saturdays, Sundays and observed holidays. Using either site-specific or urban-wide TAFs, nearly all of the variation in historical traffic activity at the street scale could be explained; unexplained variation was attributed to adverse weather, traffic accidents and construction. The methods and results presented in this paper can improve air quality dispersion modeling of mobile sources, and can be used to evaluate and model temporal variation in ambient air quality monitoring data and exposure estimates.

Modeling spatial and temporal variability of residential air exchange rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h(-1) with a median of 0.64 h(-1). For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010-2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.