Morgan MacNeill

Windsor, Ontario Exposure Assessment Study: Design and Methods Validation of Personal, Indoor, and Outdoor Air Pollution Monitoring

ABSTRACT The Windsor, Ontario Exposure Assessment Study evaluated the contribution of ambient air pollutants to personal and indoor exposures of adults and asthmatic children living in Windsor, Ontario, Canada. In addition, the role of personal, indoor, and outdoor air pollution exposures upon asthmatic childrens respiratory health was assessed. Several active and passive sampling methods were applied, or adapted, for personal, indoor, and outdoor residential monitoring of nitrogen dioxide, volatile organic compounds, particulate matter (PM; PM ≤ 2.5 μm [PM2.5] and ≤ 10 μm [PM10] in aerodynamic diameter),elemental carbon, ultrafine particles, ozone, air exchange rates, allergens in settled dust, and particulate-associated metals. Participants completed five consecutive days of monitoring during the winter and summer of 2005 and 2006. During 2006, in addition to undertaking the air pollution measurements, asthmatic children completed respiratory health measurements (including peak flow meter tests and exhaled breath condensate) and tracked respiratory symptoms in a diary. Extensive quality assurance and quality control steps were implemented, including the collocation of instruments at the National Air Pollution Surveillance site operated by Environment Canada and at the Michigan Department of Environmental Quality site in Allen Park, Detroit, MI. During field sampling, duplicate and blank samples were also completed and these data are reported. In total, 50 adults and 51 asthmatic children were recruited to participate, resulting in 922 participant days of data. When comparing the methods used in the study with standard reference methods, field blanks were low and bias was acceptable, with most methods being within 20% of reference methods. Duplicates were typically within less than 10% of each other, indicating that study results can be used with confidence. This paper covers study design, recruitment, methodology, time activity diary, surveys, and quality assurance and control results for the different methods used. IMPLICATIONS It is important to obtain data to identify any factors that can influence the relationships among personal, indoor, and outdoor concentrations for a range of air pollutants. Ensuring that the methods used are valid and comparable to reference methods used in typical air pollution, monitoring is crucial for data to be of use to regulators. These exposure data can then be used to develop risk management policies that reduce personal and indoor exposures to air pollutants.

Predictors of Indoor Air Concentrations in Smoking and Non-Smoking Residences

Indoor concentrations of air pollutants (benzene, toluene, formaldehyde, acetaldehyde, acrolein, nitrogen dioxide, particulate matter, elemental carbon and ozone) were measured in residences in Regina, Saskatchewan, Canada. Data were collected in 106 homes in winter and 111 homes in summer of 2007, with 71 homes participating in both seasons. In addition, data for relative humidity, temperature, air exchange rates, housing characteristics and occupants’ activities during sampling were collected. Multiple linear regression analysis was used to construct season-specific models for the air pollutants. Where smoking was a major contributor to indoor concentrations, separate models were constructed for all homes and for those homes with no cigarette smoke exposure. The housing characteristics and occupants’ activities investigated in this study explained between 11% and 53% of the variability in indoor air pollutant concentrations, with ventilation, age of home and attached garage being important predictors for many pollutants.

Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children

Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO2) and particulate matter (PM2.5) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM2.5 showed little spatial variation; therefore, daily PM2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO2 and PM2.5. These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies — in particular, for research focused on short-term exposure and health effects.

Quantifying the contribution of ambient and indoor‐generated fine particles to indoor air in residential environments

UNLABELLED Indoor fine particles (FPs) are a combination of ambient particles that have infiltrated indoors, and particles that have been generated indoors from activities such as cooking. The objective of this paper was to estimate the infiltration factor (Finf ) and the ambient/non-ambient components of indoor FPs. To do this, continuous measurements were collected indoors and outdoors for seven consecutive days in 50 non-smoking homes in Halifax, Nova Scotia in both summer and winter using DustTrak (TSI Inc) photometers. Additionally, indoor and outdoor gravimetric measurements were made for each 24-h period in each home, using Harvard impactors (HI). A computerized algorithm was developed to remove (censor) peaks due to indoor sources. The censored indoor/outdoor ratio was then used to estimate daily Finfs and to determine the ambient and non-ambient components of total indoor concentrations. Finf estimates in Halifax (daily summer median = 0.80; daily winter median = 0.55) were higher than have been reported in other parts of Canada. In both winter and summer, the majority of FP was of ambient origin (daily winter median = 59%; daily summer median = 84%). Predictors of the non-ambient component included various cooking variables, combustion sources, relative humidity, and factors influencing ventilation. This work highlights the fact that regional factors can influence the contribution of ambient particles to indoor residential concentrations. PRACTICAL IMPLICATIONS Ambient and non-ambient particles have different risk management approaches, composition, and likely toxicity. Therefore, a better understanding of their contribution to the indoor environment is important to manage the health risks associated with fine particles (FPs) effectively. As well, a better understanding of the factors Finf can help improve exposure assessment and contribute to reduced exposure misclassification in epidemiologic studies.

Fine and ultrafine particle decay rates in multiple homes.

Human exposure to particles depends on particle loss mechanisms such as deposition and filtration. Fine and ultrafine particles (FP and UFP) were measured continuously over seven consecutive days during summer and winter inside 74 homes in Edmonton, Canada. Daily average air exchange rates were also measured. FP were also measured outside each home and both FP and UFP were measured at a central monitoring station. A censoring algorithm was developed to identify indoor-generated concentrations, with the remainder representing particles infiltrating from outdoors. The resulting infiltration factors were employed to determine the continuously changing background of outdoor particles infiltrating the homes. Background-corrected indoor concentrations were then used to determine rates of removal of FP and UFP following peaks due to indoor sources. About 300 FP peaks and 400 UFP peaks had high-quality (median R(2) value >98%) exponential decay rates lasting from 30 min to 10 h. Median (interquartile range (IQR)) decay rates for UFP were 1.26 (0.82-1.83) h(-1); for FP 1.08 (0.62-1.75) h(-1). These total decay rates included, on average, about a 25% contribution from air exchange, suggesting that deposition and filtration accounted for the major portion of particle loss mechanisms in these homes. Models presented here identify and quantify effects of several factors on total decay rates, such as window opening behavior, home age, use of central furnace fans and kitchen and bathroom exhaust fans, use of air cleaners, use of air conditioners, and indoor-outdoor temperature differences. These findings will help identify ways to reduce exposure and risk.

Indoor and Outdoor Levels and Sources of Submicron Particles (PM1) at Homes in Edmonton, Canada

Exposure to submicron particles (PM1) is of interest due to their possible chronic and acute health effects. Seven consecutive 24-h PM1 samples were collected during winter and summer 2010 in a total of 74 nonsmoking homes in Edmonton, Canada. Median winter concentrations of PM1 were 2.2 μg/m(3) (interquartile range, IQR = 0.8-6.1 μg/m(3)) and 3.3 μg/m(3) (IQR = 1.5-6.9 μg/m(3)) for indoors and outdoors, respectively. In the summer, indoor (median 4.4 μg/m(3), IQR = 2.4-8.6 μg/m(3)) and outdoor (median 4.3 μg/m(3), IQR = 2.6-7.4 μg/m(3)) levels were similar. Positive matrix factorization (PMF) was applied to identify and apportion indoor and outdoor sources of elements in PM1 mass. Nine sources contributing to both indoor and outdoor PM1 concentrations were identified including secondary sulfate, soil, biomass smoke and environmental tobacco smoke (ETS), traffic, settled and mixed dust, coal combustion, road salt/road dust, and urban mixture. Three additional indoor sources were identified i.e., carpet dust, copper-rich, and silver-rich. Secondary sulfate, soil, biomass smoke and ETS contributed more than 70% (indoors: 0.29 μg/m(3), outdoors: 0.39 μg/m(3)) of measured elemental mass in PM1. These findings can aid understanding of relationships between submicron particles and health outcomes for indoor/outdoor sources.

Estimation of bias with the single‐zone assumption in measurement of residential air exchange using the perfluorocarbon tracer gas method

Residential air exchange rates (AERs) are vital in understanding the temporal and spatial drivers of indoor air quality (IAQ). Several methods to quantify AERs have been used in IAQ research, often with the assumption that the home is a single, well-mixed air zone. Since 2005, Health Canada has conducted IAQ studies across Canada in which AERs were measured using the perfluorocarbon tracer (PFT) gas method. Emitters and detectors of a single PFT gas were placed on the main floor to estimate a single-zone AER (AER1z). In three of these studies, a second set of emitters and detectors were deployed in the basement or second floor in approximately 10% of homes for a two-zone AER estimate (AER2z). In total, 287 daily pairs of AER2z and AER1z estimates were made from 35 homes across three cities. In 87% of the cases, AER2z was higher than AER1z. Overall, the AER1z estimates underestimated AER2z by approximately 16% (IQR: 5–32%). This underestimate occurred in all cities and seasons and varied in magnitude seasonally, between homes, and daily, indicating that when measuring residential air exchange using a single PFT gas, the assumption of a single well-mixed air zone very likely results in an under prediction of the AER.