Anna Mölter

Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project

Background: Few studies have investigated traffic-related air pollution as a risk factor for respiratory infections during early childhood. Objectives: We aimed to investigate the association between air pollution and pneumonia, croup, and otitis media in 10 European birth cohorts—BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), MAAS (United Kingdom), PIAMA (the Netherlands), and four INMA cohorts (Spain)—and to derive combined effect estimates using meta-analysis. Methods: Parent report of physician-diagnosed pneumonia, otitis media, and croup during early childhood were assessed in relation to annual average pollutant levels [nitrogen dioxide (NO2), nitrogen oxide (NOx), particulate matter ≤ 2.5 μm (PM2.5), PM2.5 absorbance, PM10, PM2.5–10 (coarse PM)], which were estimated using land use regression models and assigned to children based on their residential address at birth. Identical protocols were used to develop regression models for each study area as part of the ESCAPE project. Logistic regression was used to calculate adjusted effect estimates for each study, and random-effects meta-analysis was used to calculate combined estimates. Results: For pneumonia, combined adjusted odds ratios (ORs) were elevated and statistically significant for all pollutants except PM2.5 (e.g., OR = 1.30; 95% CI: 1.02, 1.65 per 10-μg/m3 increase in NO2 and OR = 1.76; 95% CI: 1.00, 3.09 per 10-μg/m3 PM10). For otitis media and croup, results were generally null across all analyses except for NO2 and otitis media (OR = 1.09; 95% CI: 1.02, 1.16 per 10-μg/m3). Conclusion: Our meta-analysis of 10 European birth cohorts within the ESCAPE project found consistent evidence for an association between air pollution and pneumonia in early childhood, and some evidence for an association with otitis media. Citation: MacIntyre EA, Gehring U, Mölter A, Fuertes E, Klümper C, Krämer U, Quass U, Hoffmann B, Gascon M, Brunekreef B, Koppelman GH, Beelen R, Hoek G, Birk M, de Jongste JC, Smit HA, Cyrys J, Gruzieva O, Korek M, Bergström A, Agius RM, de Vocht F, Simpson A, Porta D, Forastiere F, Badaloni C, Cesaroni G, Esplugues A, Fernández-Somoano A, Lerxundi A, Sunyer J, Cirach M, Nieuwenhuijsen MJ, Pershagen G, Heinrich J. 2014. Air pollution and respiratory infections during early childhood: an analysis of 10 European birth cohorts within the ESCAPE project. Environ Health Perspect 122:107–113; http://dx.doi.org/10.1289/ehp.1306755

A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project

The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter <10 μm (PM10), PM2.5, coarse particulate matter and PM2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, divided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4–5 and 8–10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from individual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure (e.g. adjusted OR (95%CI) for asthma at age 8–10 years and exposure at the birth address (n=10377): 1.10 (0.81–1.49) per 10 μg·m-3 nitrogen dioxide; 0.88 (0.63–1.24) per 10 μg·m-3 PM10; 1.23 (0.78–1.95) per 5 μg·m-3 PM2.5). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. This study found no significant association between air pollution exposure and childhood asthma prevalence in five European birth cohorts. No significant association between air pollution and childhood asthma prevalence in five European birth cohorts http://ow.ly/Cdbba

Long-term exposure to PM10 and NO2 in association with lung volume and airway resistance in the MAAS birth cohort.

Background: Findings from previous studies on the effects of air pollution exposure on lung function during childhood have been inconsistent. A common limitation has been the quality of exposure data used, and few studies have modeled exposure longitudinally throughout early life. Objectives: We sought to study the long-term effects of exposure to particulate matter with an aerodynamic diameter ≤ 10 μm (PM10) and to nitrogen dioxide (NO2) on specific airway resistance (sRaw) and forced expiratory volume in 1 sec (FEV1) before and after bronchodilator treatment. Subjects were from the Manchester Asthma and Allergy Study (MAAS) birth cohort (n = 1,185). Methods: Spirometry was performed during clinic visits at ages 3, 5, 8, and 11 years. Individual-level PM10 and NO2 exposures were estimated from birth to 11 years of age through a microenvironmental exposure model. Longitudinal and cross-sectional associations were estimated using generalized estimating equations and multivariable linear regression models. Results: Lifetime exposure to PM10 and NO2 was associated with significantly less growth in FEV1 (percent predicted) over time, both before (–1.37%; 95% CI: –2.52, –0.23 for a 1-unit increase in PM10 and –0.83%; 95% CI: –1.39, –0.28 for a 1-unit increase in NO2) and after bronchodilator treatment (–3.59%; 95% CI: –5.36, –1.83 and –1.20%; 95% CI: –1.97, –0.43, respectively). We found no association between lifetime exposure and sRaw over time. Cross-sectional analyses of detailed exposure estimates for the summer and winter before 11 years of age and lung function at 11 years indicated no significant associations. Conclusions: Long-term PM10 and NO2 exposures were associated with small but statistically significant reductions in lung volume growth in children of elementary-school age. Citation: Mölter A, Agius RM, de Vocht F, Lindley S, Gerrard W, Lowe L, Belgrave D, Custovic A, Simpson A. 2013. Long-term exposure to PM10 and NO2 in association with lung volume and airway resistance in the MAAS birth cohort. Environ Health Perspect 121:1232–1238. http://dx.doi.org/10.1289/ehp.1205961

Elemental composition of particulate matter and the association with lung function.

Background: Negative effects of long-term exposure to particulate matter (PM) on lung function have been shown repeatedly. Spatial differences in the composition and toxicity of PM may explain differences in observed effect sizes between studies. Methods: We conducted a multicenter study in 5 European birth cohorts—BAMSE (Sweden), GINIplus and LISAplus (Germany), MAAS (United Kingdom), and PIAMA (The Netherlands)—for which lung function measurements were available for study subjects at the age of 6 or 8 years. Individual annual average residential exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM smaller than 2.5 &mgr;m (PM2.5) and smaller than 10 &mgr;m (PM10) was estimated using land-use regression models. Associations between air pollution and lung function were analyzed by linear regression within cohorts, adjusting for potential confounders, and then combined by random effects meta-analysis. Results: We observed small reductions in forced expiratory volume in the first second, forced vital capacity, and peak expiratory flow related to exposure to most elemental pollutants, with the most substantial negative associations found for nickel and sulfur. PM10 nickel and PM10 sulfur were associated with decreases in forced expiratory volume in the first second of 1.6% (95% confidence interval = 0.4% to 2.7%) and 2.3% (−0.1% to 4.6%) per increase in exposure of 2 and 200 ng/m3, respectively. Associations remained after adjusting for PM mass. However, associations with these elements were not evident in all cohorts, and heterogeneity of associations with exposure to various components was larger than for exposure to PM mass. Conclusions: Although we detected small adverse effects on lung function associated with annual average levels of some of the evaluated elements (particularly nickel and sulfur), lower lung function was more consistently associated with increased PM mass.

Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles

Background: Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. Objectives: We aimed to develop European and regional LUR models and to examine their transferability to areas not used for model development. Methods: We evaluated LUR models for nitrogen dioxide (NO2) and particulate matter (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) study areas across 14 European countries for PM and NO2. Models were evaluated with cross-validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. Results: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5, and 70% for PM2.5 absorbance. The HV R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 absorbance, 70%). The European NO2, PM2.5, and PM2.5 absorbance models explained a median of 59%, 48%, and 70% of within-area variability in individual areas. The transferred models predicted a modest-to-large fraction of variability in areas that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%). Conclusions: Using a large data set from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted. Citation: Wang M, Beelen R, Bellander T, Birk M, Cesaroni G, Cirach M, Cyrys J, de Hoogh K, Declercq C, Dimakopoulou K, Eeftens M, Eriksen KT, Forastiere F, Galassi C, Grivas G, Heinrich J, Hoffmann B, Ineichen A, Korek M, Lanki T, Lindley S, Modig L, Mölter A, Nafstad P, Nieuwenhuijsen MJ, Nystad W, Olsson D, Raaschou-Nielsen O, Ragettli M, Ranzi A, Stempfelet M, Sugiri D, Tsai MY, Udvardy O, Varró MJ, Vienneau D, Weinmayr G, Wolf K, Yli-Tuomi T, Hoek G, Brunekreef B. 2014. Performance of multi-city land use regression models for nitrogen dioxide and fine particles. Environ Health Perspect 122:843–849; http://dx.doi.org/10.1289/ehp.1307271

Spatial variation of PM elemental composition between and within 20 European study areas - Results of the ESCAPE project

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements. We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning. PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10. Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM.

Modelling air pollution for epidemiologic research ? Part II: Predicting temporal variation through land use regression

Over recent years land use regression (LUR) has become a frequently used method in air pollution exposure studies, as it can model intra-urban variation in pollutant concentrations at a fine spatial scale. However, very few studies have used the LUR methodology to also model the temporal variation in air pollution exposure. The aim of this study is to estimate annual mean NO(2) and PM(10) concentrations from 1996 to 2008 for Greater Manchester using land use regression models. The results from these models will be used in the Manchester Asthma and Allergy Study (MAAS) birth cohort to determine health effects of air pollution exposure. The Greater Manchester LUR model for 2005 was recalibrated using interpolated and adjusted NO(2) and PM(10) concentrations as dependent variables for 1996-2008. In addition, temporally resolved variables were available for traffic intensity and PM(10) emissions. To validate the resulting LUR models, they were applied to the locations of automatic monitoring stations and the estimated concentrations were compared against measured concentrations. The 2005 LUR models were successfully recalibrated, providing individual models for each year from 1996 to 2008. When applied to the monitoring stations the mean prediction error (MPE) for NO(2) concentrations for all stations and years was -0.8μg/m³ and the root mean squared error (RMSE) was 6.7μg/m³. For PM(10) concentrations the MPE was 0.8μg/m³ and the RMSE was 3.4μg/m³. These results indicate that it is possible to model temporal variation in air pollution through LUR with relatively small prediction errors. It is likely that most previous LUR studies did not include temporal variation, because they were based on short term monitoring campaigns and did not have historic pollution data. The advantage of this study is that it uses data from an air dispersion model, which provided concentrations for 2005 and 2010, and therefore allowed extrapolation over a longer time period.

Effects of long-term exposure to PM10 and NO2 on asthma and wheeze in a prospective birth cohort

Background Epidemiological studies on the effect of urban air pollution on childhood asthma have shown conflicting results and so far no consistent association has emerged. However, a common limitation in previous studies has been exposure misclassification leading to uncertainties in risk estimates.The aim of this study was to analyse the effects of long-term exposure to particulate matter (PM10) and nitrogen dioxide (NO2) on the prevalence of asthma and wheeze within a population-based birth cohort—the Manchester Asthma and Allergy Study (MAAS). Methods The prevalence of asthma and current wheeze within the cohort (N=1185) was determined through parental questionnaires at ages 3, 5, 8 and 11 years. The typical monthly PM10 and NO2 exposure of each child was estimated through a novel microenvironmental exposure model from birth to age 11. The association between exposure and asthma or wheeze was analysed using generalised estimating equations and multiple logistic regression. Results The range of asthma prevalence was 15.2–23.3%, with the lowest prevalence at age 3 and the highest at age 5. The prevalence of current wheeze decreased from ages 3 to 8 (23.7–18%). The mean NO2 exposure decreased from the 1st year of life (21.7 µg/m3) to the 11th year of life (16.0 µg/m3). The mean PM10 exposure showed a smaller decrease (12.8 –10.7 µg/m3). The statistical analysis showed no significant association between the exposures and either outcome. Conclusions No evidence of a significant association between long-term exposure to PM10 and NO2 and the prevalence of either asthma or wheeze was found.

The Fort Collins Commuter Study: impact of route type and transport mode on personal exposure to multiple air pollutants

Traffic-related air pollution is associated with increased mortality and morbidity, yet few studies have examined strategies to reduce individual exposure while commuting. The present study aimed to quantify how choice of mode and route type affects personal exposure to air pollutants during commuting. We analyzed within-person difference in exposures to multiple air pollutants (black carbon (BC), carbon monoxide (CO), ultrafine particle number concentration (PNC), and fine particulate matter (PM2.5)) during commutes between the home and workplace for 45 participants. Participants completed 8 days of commuting by car and bicycle on direct and alternative (reduced traffic) routes. Mean within-person exposures to BC, PM2.5, and PNC were higher when commuting by cycling than when driving, but mean CO exposure was lower when cycling. Exposures to CO and BC were reduced when commuting along alternative routes. When cumulative exposure was considered, the benefits from cycling were attenuated, in the case of CO, or exacerbated, in the case of particulate exposures, owing to the increased duration of the commute. Although choice of route can reduce mean exposure, the effect of route length and duration often offsets these reductions when cumulative exposure is considered. Furthermore, increased ventilation rate when cycling may result in a more harmful dose than inhalation at a lower ventilation rate.