Marie-Eve Héroux

Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project.

ObjectiveQuantitative estimates of air pollution health impacts have become an increasingly critical input to policy decisions. The WHO project “Health risks of air pollution in Europe—HRAPIE” was implemented to provide the evidence-based concentration–response functions for quantifying air pollution health impacts to support the 2013 revision of the air quality policy for the European Union (EU).MethodsA group of experts convened by WHO Regional Office for Europe reviewed the accumulated primary research evidence together with some commissioned reviews and recommended concentration–response functions for air pollutant–health outcome pairs for which there was sufficient evidence for a causal association.ResultsThe concentration–response functions link several indicators of mortality and morbidity with short- and long-term exposure to particulate matter, ozone and nitrogen dioxide. The project also provides guidance on the use of these functions and associated baseline health information in the cost–benefit analysis.ConclusionsThe project results provide the scientific basis for formulating policy actions to improve air quality and thereby reduce the burden of disease associated with air pollution in Europe.

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.

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.

Urinary and breast milk biomarkers to assess exposure to naphthalene in pregnant women: an investigation of personal and indoor air sources

BackgroundNaphthalene exposures for most non-occupationally exposed individuals occur primarily indoors at home. Residential indoor sources include pest control products (specifically moth balls), incomplete combustion such as cigarette smoke, woodstoves and cooking, some consumer and building products, and emissions from gasoline sources found in attached garages. The study aim was to assess naphthalene exposure in pregnant women from Canada, using air measurements and biomarkers of exposure.MethodsPregnant women residing in Ottawa, Ontario completed personal and indoor air sampling, and questionnaires. During pregnancy, pooled urine voids were collected over two 24-hour periods on a weekday and a weekend day. At 2–3 months post-birth, they provided a spot urine sample and a breast milk sample following the 24-hour air monitoring. Urines were analyzed for 1-naphthol and 2-naphthol and breast milk for naphthalene. Simple linear regression models examined associations between known naphthalene sources, air and biomarker samples.ResultsStudy recruitment rate was 11.2% resulting in 80 eligible women being included. Weekday and weekend samples were highly correlated for both personal (r = 0.83, p < 0.0001) and indoor air naphthalene (r = 0.91, p < 0.0001). Urine specific gravity (SG)-adjusted 2-naphthol concentrations collected on weekdays and weekends (r = 0.78, p < 0.001), and between pregnancy and postpartum samples (r = 0.54, p < 0.001) were correlated.Indoor and personal air naphthalene concentrations were significantly higher post-birth than during pregnancy (p < 0.0001 for signed rank tests); concurrent urine samples were not significantly different. Naphthalene in breast milk was associated with urinary 1-naphthol: a 10% increase in 1-naphthol was associated with a 1.6% increase in breast milk naphthalene (95% CI: 0.2%-3.1%). No significant associations were observed between naphthalene sources reported in self-administered questionnaires and the air or biomarker concentrations.ConclusionsMedian urinary concentrations of naphthalene metabolites tended to be similar to (1-naphthol) or lower (2-naphthol) than those reported in a Canadian survey of women of reproductive age. Only urinary 1-naphthol and naphthalene in breast milk were associated. Potential reasons for the lack of other associations include a lack of sources, varying biotransformation rates and behavioural differences over time.

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.

Response to "Quantifying the health impacts of ambient air pollutants : methodological errors must be avoided"

We thank Morfeld and Erren for their interest in our recent publication on ‘‘Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project’’ (Héroux et al. 2015). Morfeld and Erren claim that there are potential problems with the statistical approach used in our paper to measure the impact on mortality from air pollution. In fact, they state that ‘‘Greenland showed that a calculation based on RR estimates, as performed in the EU research project, does estimate excess cases numbers—but it does not estimate the number of premature cases or etiological cases’’ (Greenland 1999). Close reading of the Greenland (1999) paper reveals that he distinguishes three categories of cases occurring in the exposed, observed over a certain period of time: A0, cases which would have occurred anyway even in the absence of exposure—these would typically be estimated from the number of cases occurring in an unexposed control population; A1, cases that would have occurred anyway but were accelerated by exposure; and A2, cases which would not have occurred, ever, without exposure. The word ‘premature’ does not exist in Greenland’s paper, but we consider ‘premature’ and ‘accelerated’ to be the same here. What we usually call the attributable fraction among the exposed is equivalent to the attributable risk (RR-1)/RR which in Greenland’s paper is denoted as the etiologic fraction, (A1 ? A2)/(A0 ? A1 ? A2). And then, etiologic cases are A1 ? A2, and excess cases are A2. So, contrary to what Morfield and Erren write, the calculation as per-

Development of the WHO Environmental Noise Guidelines for the European Region: An Introduction

Following the Parma Declaration on Environment and Health adopted at the Fifth Ministerial Conference (2010), the Ministers and representatives of Member States in the WHO European Region requested the World Health Organization (WHO) to develop updated guidelines on environmental noise, and called upon all stakeholders to reduce children’s exposure to noise, including that from personal electronic devices. The WHO Environmental Noise Guidelines for the European Region will provide evidence-based policy guidance to Member States on protecting human health from noise originating from transportation (road traffic, railway and aircraft), wind turbine noise, and leisure noise in settings where people spend the majority of their time. Compared to previous WHO guidelines on noise, the most significant developments include: consideration of new evidence associating environmental noise exposure with health outcomes, such as annoyance, cardiovascular effects, obesity and metabolic effects (such as diabetes), cognitive impairment, sleep disturbance, hearing impairment and tinnitus, adverse birth outcomes, quality of life, mental health, and wellbeing; inclusion of new noise sources to reflect the current noise environment; and the use of a standardized framework (grading of recommendations, assessment, development, and evaluations: GRADE) to assess evidence and develop recommendations. The recommendations in the guidelines are underpinned by systematic reviews of evidence on several health outcomes related to environmental noise as well as evidence on interventions to reduce noise exposure and/or health outcomes. The overall body of evidence is published in this Special Issue.