Search

BACKGROUND: Although previous studies have reported negative associations between exposure to air pollution and cognition, studies of the effects of prenatal and postnatal exposures in early childhood have been limited. OBJECTIVES: We sought to assess the role exposure to fine particulate matter ([Formula: see text]) during different prenatal and postnatal windows may play in children's cognitive development at school age. METHODS: Within the Brain Development and Air Pollution Ultrafine Particles in School Children (BREATHE) Project, we estimated residential [Formula: see text] exposures by land use regression for the prenatal period and first seven postnatal years of 2,221 children from Barcelona, Spain. The participants ([Formula: see text]) completed computerized tests assessing working memory, attentiveness, and conflict network during four visits in 2012–2013. We used linear mixed effects and distributed lag models to assess the period of exposure to [Formula: see text] in association with cognitive development. RESULTS: Inverse associations were identified between [Formula: see text] exposure during the fifth and sixth postnatal years and working memory, with boys showing much higher vulnerability. Regarding attention functions, exposure to higher [Formula: see text] levels during the prenatal period and from the fourth postnatal year were associated with a reduction in conflict network performance, though we found no association with attentiveness. The overall estimated cumulative effect of a [Formula: see text] increase in [Formula: see text] resulted in a reduction in the working memory [Formula: see text] score of [Formula: see text] [95% confidence interval (CI): [Formula: see text], [Formula: see text]] points and an increase in the conflict attentional network of 11.31 (95% CI: 6.05, 16.57) milliseconds, indicating a poorer performance. CONCLUSIONS: Early life exposure to [Formula: see text] was associated with a reduction in fundamental cognitive abilities, including working memory and conflict attentional network. https://doi.org/10.1289/EHP3169.

Background: Prenatal and postnatal exposure to air pollution has been linked to cognitive impairment in children, but very few studies have assessed its association with attentional function. Objectives: To evaluate the association between prenatal and postnatal exposure to nitrogen dioxide (NO2) and attentional function in children at 4-5 years of age. Methods: We used data from four regions of the Spanish INMA-Environment and Childhood-Project, a population-based birth cohort. Using land-use regression models (LUR), we estimated prenatal and postnatal NO2 levels in all of these regions at the participants' residential addresses. We assessed attentional function using the Kiddie-Conners Continuous Performance Test (K-CPT). We combined the region-specific adjusted effect estimates using random-effects meta-analysis. Results: We included 1298 children with complete data. Prenatal exposure to NO2 was associated with an impaired standard error of the hit reaction time (HRT(SE)) (increase of 1.12 ms [95% CI; 0.22 a 2.02] per 10 mu g/m(3) increase in prenatal NO2) and increased omission errors (6% [95% CI; 1.01 to 1.11] per 10 mu g/m(3) increase in prenatal NO2). Postnatal exposure to NO2 resulted in a similar but borderline significant increase of omission errors (5% [95% CI; = 0.99 to 1.11] per 10 mu g/m(3) increase in postnatal NO2). These associations did not vary markedly between regions, and were mainly observed in girls. Commission errors and lower detectability were associated with prenatal and postnatal exposure to NO2 only in some regions. Conclusions: This study indicates that higher exposure to ambient NO2, mainly during pregnancy and to a lesser extent postnatally, is associated with impaired attentional function in children at 4-5 years of age.

A few studies have found associations between the exposure to traffic-related air pollution at school and/or home and cognitive development. The impact on cognitive development of the exposure to air pollutants during commuting has not been explored. We aimed to assess the role of the exposure to traffic-related air pollutants during walking commute to school on cognitive development of children. We performed a longitudinal study of children (n = 1,234, aged 7-10 y) from 39 schools in Barcelona (Catalonia, Spain) who commuted by foot to school. Children were tested four times during a 12-month follow-up to characterize their developmental trajectories of working memory (d' of the three-back numbers test) and inattentiveness (hit reaction time standard error of the Attention Network Test). Average particulate matter =2.5 µm (PM(2.5)), Black Carbon (BC) and NO(2) concentrations were estimated using Land Use Regression for the shortest walking route to school. Differences in cognitive growth were evaluated by linear mixed effects models with age-by-pollutant interaction terms. Exposure to PM(2.5) and BC from the commutes by foot was associated with a reduction in the growth of working memory (an interquartile range increase in PM(2.5) and BC concentrations decreased the annual growth of working memory by 5.4 (95% CI [-10.2, -0.6]) and 4.6 (95% CI [-9.0, -0.1]) points, respectively). The findings for NO(2) were not conclusive and none of the pollutants were associated with inattentiveness. Efforts should be made to implement pedestrian school pathways through low traffic streets in order to increase security and minimize children's exposure to air pollutants.

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. © 2015 Elsevier Ltd.

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.

Land use regression (LUR) models have been used to model concentrations of mainly traffic-related air pollutants (nitrogen oxides (NOx), particulate matter (PM) mass or absorbance). Few LUR models are published of PM composition, whereas the interest in health effects related to particle composition is increasing. The aim of our study was to evaluate LUR models of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, and elemental and organic carbon (EC/OC) content of PM2.5. In 10 European study areas, PAH, hopanes/steranes, and EC/OC concentrations were measured at 16-40 sites per study area. LUR models for each study area were developed on the basis of annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (R2) was found for EC - 84%. The median R2 was 51% for OC, 67% for benzo[a]pyrene, and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies. © 2014 American Chemical Society.

Limited information is available on the contribution of organic components in particulate matter to health effects related to fine particles. Spatial variability of specific fine particle organic components has not been assessed with consistent methods. The aim of this paper is to assess spatial variation of organic components of fine particles within and between European study areas. Highly standardized measurements of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, elemental/organic carbon (EC/OC) and levoglucosan were performed measured in ten study areas across Europe. In each study area, measurements were conducted at street, urban and regional background sites. Three two-week samples were taken per site and the annual average levels of pollutants were calculated using continuous measurements at one background site as a reference.Substantial variations within and between the study areas were found. EC/OC and hopanes/steranes concentrations were highest in southern European study areas and lowest in northern locations. PAH concentrations were lowest in London/Oxford and highest in Copenhagen, Rome and Athens. Concentrations at street locations were higher than at background locations in all study areas and for all components. However, these differences varied considerably between study areas and components. EC had the highest median street to urban background ratio (1.62), OC the lowest (1.32). EC was highly correlated with NOx and PM2.5 absorbance in all areas, with median r=0.85 and r=0.89, respectively. The correlation between OC and other components was variable, with a median correlation of 0.65 with PM2.5 mass and a weak (0.18) correlation with σhopanes/steranes. σPAH correlated moderately with EC (r=0.59) and weakly with ∑hopanes/steranes (r=0.36).In conclusion, substantial variability was found in spatial patterns of atmospheric EC, OC, PAH and hopanes/steranes both within and between European study areas. The application of this highly standardized measurement approach across different locations will contribute to a consistent assessment of air pollutant levels and potentially contribute to understanding health effects associated with them. © 2014 Elsevier Ltd.

Estimating within-city variability in air pollution concentrations is important. Land use regression (LUR) models are able to explain such small-scale within-city variations. Transparency in LUR model development methods is important to facilitate comparison of methods between different studies. We therefore developed LUR models in a standardized way in 36 study areas in Europe for the ESCAPE (European Study of Cohorts for Air Pollution Effects) project.Nitrogen dioxide (NO2) and nitrogen oxides (NOx) were measured with Ogawa passive samplers at 40 or 80 sites in each of the 36 study areas. The spatial variation in each area was explained by LUR modelling. Centrally and locally available Geographic Information System (GIS) variables were used as potential predictors. A leave-one out cross-validation procedure was used to evaluate the model performance.There was substantial contrast in annual average NO2 and NOx concentrations within the study areas. The model explained variances (R2) of the LUR models ranged from 55% to 92% (median 82%) for NO2 and from 49% to 91% (median 78%) for NOx. For most areas the cross-validation R2 was less than 10% lower than the model R2. Small-scale traffic and population/household density were the most common predictors. The magnitude of the explained variance depended on the contrast in measured concentrations as well as availability of GIS predictors, especially traffic intensity data were important. In an additional evaluation, models in which local traffic intensity was not offered had 10% lower R2 compared to models in the same areas in which these variables were offered.Within the ESCAPE project it was possible to develop LUR models that explained a large fraction of the spatial variance in measured annual average NO2 and NOx concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies. © 2013 Elsevier Ltd.

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 PM 2.5) explaining on average between 67 and 79% of the concentration variance (R2) 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 R2 ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R2 under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE. © 2013 American Chemical Society.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM2.5, PM2.5 absorbance, PM10, and PMcoarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R2) was 71% for PM2.5 (range across study areas 35-94%). Model R2 was higher for PM2.5 absorbance (median 89%, range 56-97%) and lower for PMcoarse (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R2 results were on average 8-11% lower than model R2. Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE. © 2012 American Chemical Society.

The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concentrations between and within 20 study areas across Europe.We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between October 2008 and April 2011 using standardized methods. PMcoarse was determined as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population density, traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a reference site, which was operated year-round.Substantial concentration differences were observed between and within study areas. Concentrations for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest average PM2.5 concentrations found in Turin and the highest PMcoarse in Heraklion. Street/urban background concentration ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (average R2 = 0.80). Correlations between PM2.5 and PMcoarse were lower (average R2 = 0.39). Despite high correlations, concentration ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06.In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The highly standardized measurement of particle concentrations across Europe will contribute to a consistent assessment of health effects across Europe. © 2012 Elsevier Ltd.

The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates long-term effects of exposure to air pollution on human health in Europe. This paper documents the spatial variation of measured NO2 and NOx concentrations between and within 36 ESCAPE study areas across Europe.In all study areas NO2 and NOx were measured using standardized methods between October 2008 and April 2011. On average, 41 sites were selected per study area, including regional and urban background as well as street sites. The measurements were conducted in three different seasons, using Ogawa badges. Average concentrations for each site were calculated after adjustment for temporal variation using data obtained from a routine monitor background site.Substantial spatial variability was found in NO2 and NOx concentrations between and within study areas; 40% of the overall NO2 variance was attributable to the variability between study areas and 60% to variability within study areas. The corresponding values for NOx were 30% and 70%. The within-area spatial variability was mostly determined by differences between street and urban background concentrations. The street/urban background concentration ratio for NO2 varied between 1.09 and 3.16 across areas. The highest median concentrations were observed in Southern Europe, the lowest in Northern Europe.In conclusion, we found significant contrasts in annual average NO2 and NOx concentrations between and especially within 36 study areas across Europe. Epidemiological long-term studies should therefore consider different approaches for better characterization of the intra-urban contrasts, either by increasing of the number of monitors or by modelling. © 2012 Elsevier Ltd.