European Study Of Cohorts For Air Pollution Effects Research Articles

A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa.

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO2, SO2, and PM10 concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO2 and SO2 were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM10 were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO2 models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO2 models were less robust with lower R2 annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R2 for PM10 models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa.

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), and nitrogen dioxide (NO2) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively). Furthermore, higher levels of NO2 and SO2 were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO2 models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO2 models were less robust with lower R2 annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R2 for PM10 models ranged from 52% to 80% while for PM2.5 models this range was 61–76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO2 concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.

Land use Regression Model for Exposure Assessment in the Mace Birth Cohort Study in Ethekwini

OPS 46: Exposure assessment to air pollution in Asia and Africa, Room 315, Floor 3, August 27, 2019, 4:30 PM - 5:30 PM Background: The Mother and Child in the Environment (MACE) birth cohort aims to determine the effects of ambient air pollution on pregnancy outcomes and childhood respiratory disorders in Durban, South Africa. A land use regression model (LUR) was developed to characterise exposure. Methods: Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), and nitrogen dioxide (NO2) were measured over a period of one year, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Results: Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.75 µg/m3, 4.18 µg/m3, 50.44 µg/m3, and 20.90 µg/m3 for NO2, SO2, PM10 and PM2.5 respectively) as compared to summer (10.51 µg/m3, 2.8 µg/m3, 20.49 µg/m3, and 8.49 µg/m3 for NO2, SO2, PM10 and PM2.5 respectively). The LUR NO2 models for annual, summer and winter explained 73%, 60% and 59% of the variance with elevation, traffic, population, open space and a regional variable being identified as important predictors. The SO2 models were less robust with lower R2 annual (37%), summer (49%) and winter (33%) with industrial and traffic variables being important predictors. The R2 for PM10 models ranged from 59-66% while for PM2.5 models this range was 62%-73% with only total length of major roads emerging as a predictor. Variables such as urban area and length of minor roads remained statistically insignificant in each model. Conclusion: These results highlight the influence of industrial and traffic emissions from major roads on air pollution concentrations. Furthermore, this is the first study in South Africa highlighting the importance of a regional variable serving as a proxy marker for industry, as areas of higher industrial activity are geographically grouped within the city.

Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

BackgroundMethods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO2) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model. MethodsSatellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO2 and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO2 concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC). ResultsThe NO2 LUR model predicted 84% of spatial variability in annual mean NO2 (RMSE: 1.2 ppb; cross-validated R2: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R2: 0.90). The annual average NO2 concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO2 cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO2 cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69. ConclusionsOur LUR models explained a high degree of spatial variability in annual mean NO2 and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO2 for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.

High resolution annual average air pollution concentration maps for the Netherlands

Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO2, NOx, PM2.5, PM2.5absorbance and PM10. The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.

The association of air pollution with congenital anomalies: An exploratory study in the northern Netherlands.

There are a growing number of reports on the association between air pollution and the risk of congenital anomalies. However, the results are inconsistent and most studies have only focused on the association of air pollution with congenital heart defects and orofacial clefts. Using an exploratory study design, we aimed to identify congenital anomalies that may be sensitive to maternal exposure to specific air pollutants during the periconceptional period. We conducted a case-control study of 7426 subjects born in the 15 years between 1999 and 2014 and registered in the European Registration of Congenital Anomalies and Twins Northern Netherlands (EUROCAT NNL). Concentrations of various air pollutants (PM10, PM2.5, PM10-2.5, NO2, NOX, absorbance) were obtained using land use regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE). We linked these data to every subject in the EUROCAT NNL registry via their full postal code. Cases were classified as children or fetuses born in the 15-year period with a major congenital anomaly that was not associated with a known monogenic or chromosomal anomaly. Cases were divided into anomaly subgroups and compared with two different control groups: control group 1 comprised children or fetuses with a known monogenic or chromosomal anomaly, while control group 2 comprised all other non-monogenic and non-chromosomal registrations. Using control group 1 (n = 1618) for analysis, we did not find any significant associations, but when we used control group 2 (ranges between n = 4299 and n = 5771) there were consistent positive associations between several air pollutants (NO2, PM2.5, PM10-2.5, absorbance) and the genital anomalies subgroup. We examined various congenital anomalies and their possible associations with a number of air pollutants in order to generate hypotheses for future research. We found that air pollution exposure was positively associated with genital anomalies, mainly driven by hypospadias. These results broaden the evidence of associations between air pollution exposure during gestation and congenital anomalies in the child. They warrant further research, which should also focus on possible underlying mechanisms.

Air pollution and incidence of cancers of the stomach and the upper aerodigestive tract in the European Study of Cohorts for Air Pollution Effects (ESCAPE).

Air pollution has been classified as carcinogenic to humans. However, to date little is known about the relevance for cancers of the stomach and upper aerodigestive tract (UADT). We investigated the association of long-term exposure to ambient air pollution with incidence of gastric and UADT cancer in 11 European cohorts. Air pollution exposure was assigned by land-use regression models for particulate matter (PM) below 10 µm (PM10 ), below 2.5 µm (PM2.5 ), between 2.5 and 10 µm (PMcoarse ), PM2.5 absorbance and nitrogen oxides (NO2 and NOX ) as well as approximated by traffic indicators. Cox regression models with adjustment for potential confounders were used for cohort-specific analyses. Combined estimates were determined with random effects meta-analyses. During average follow-up of 14.1 years of 305,551 individuals, 744 incident cases of gastric cancer and 933 of UADT cancer occurred. The hazard ratio for an increase of 5 µg/m3 of PM2.5 was 1.38 (95% CI 0.99; 1.92) for gastric and 1.05 (95% CI 0.62; 1.77) for UADT cancers. No associations were found for any of the other exposures considered. Adjustment for additional confounders and restriction to study participants with stable addresses did not influence markedly the effect estimate for PM2.5 and gastric cancer. Higher estimated risks of gastric cancer associated with PM2.5 was found in men (HR 1.98 [1.30; 3.01]) as compared to women (HR 0.85 [0.5; 1.45]). This large multicentre cohort study shows an association between long-term exposure to PM2.5 and gastric cancer, but not UADT cancers, suggesting that air pollution may contribute to gastric cancer risk.

Outdoor air pollution, exhaled 8-isoprostane and current asthma in adults: the EGEA study.

Associations between outdoor air pollution and asthma in adults are still scarce, and the underlying biological mechanisms are poorly understood. Our aim was to study the associations between 1) long-term exposure to outdoor air pollution and current asthma, 2) exhaled 8-isoprostane (8-iso; a biomarker related to oxidative stress) and current asthma, and 3) outdoor air pollution and exhaled 8-iso.Cross-sectional analyses were conducted in 608 adults (39% with current asthma) from the first follow-up of the French case-control and family study on asthma (EGEA; the Epidemiological study of the Genetic and Environmental factors of Asthma). Data on nitrogen dioxide, nitrogen oxides, particulate matter with a diameter ≤10 and ≤2.5 µm (PM10 and PM2.5), road traffic, and ozone (O3) were from ESCAPE (European Study of Cohorts for Air Pollution Effects) and IFEN (French Institute for the Environment) assessments. Models took account of city and familial dependence.The risk of current asthma increased with traffic intensity (adjusted (a)OR 1.09 (95% CI 1.00-1.18) per 5000 vehicles per day), with O3 exposure (aOR 2.04 (95% CI 1.27-3.29) per 10 µg·m-3) and with exhaled 8-iso concentration (aOR 1.50 (95% CI 1.06-2.12) per 1 pg·mL-1). Among participants without asthma, exhaled 8-iso concentration increased with PM2.5 exposure (adjusted (a)β 0.23 (95% CI 0.005-0.46) per 5 µg·m-3), and decreased with O3 and O3-summer exposures (aβ -0.20 (95% CI -0.39- -0.01) and aβ -0.52 (95% CI -0.77- -0.26) per 10 µg·m-3, respectively).Our results add new insights into a potential role of oxidative stress in the associations between outdoor air pollution and asthma in adults.

Long-Term Exposure to Ambient Air Pollution and Incidence of Postmenopausal Breast Cancer in 15 European Cohorts within the ESCAPE Project.

Background:Epidemiological evidence on the association between ambient air pollution and breast cancer risk is inconsistent.Objective:We examined the association between long-term exposure to ambient air pollution and incidence of postmenopausal breast cancer in European women.Methods:In 15 cohorts from nine European countries, individual estimates of air pollution levels at the residence were estimated by standardized land-use regression models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) and Transport related Air Pollution and Health impacts - Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) projects: particulate matter (PM) , , and in diameter (, , and , respectively); absorbance; nitrogen oxides ( and ); traffic intensity; and elemental composition of PM. We estimated cohort-specific associations between breast cancer and air pollutants using Cox regression models, adjusting for major lifestyle risk factors, and pooled cohort-specific estimates using random-effects meta-analyses.Results:Of 74,750 postmenopausal women included in the study, 3,612 developed breast cancer during 991,353 person-years of follow-up. We found positive and statistically insignificant associations between breast cancer and {hazard ratio [95% confidence interval (CI): 0.77, 1.51] per }, [1.07 (95% CI: 0.89, 1.30) per ], [1.20 (95% CI: 0.96, 1.49 per ], and [1.02 (95% CI: 0.98, 1.07 per ], and a statistically significant association with [1.04 (95% CI: 1.00, 1.08) per , ].Conclusions:We found suggestive evidence of an association between ambient air pollution and incidence of postmenopausal breast cancer in European women. https://doi.org/10.1289/EHP1742

Long-Term Air Pollution Exposure and Amyotrophic Lateral Sclerosis in Netherlands: A Population-based Case-control Study.

Background:Recently, there has been increasing evidence that exposure to air pollution is linked to neurodegenerative diseases, but little is known about the association with amyotrophic lateral sclerosis (ALS).Objectives:We investigated the association between long-term exposure to air pollution and risk of developing ALS.Methods:A population-based case–control study was conducted in Netherlands from 1 January 2006 to 1 January 2013. Data from 917 ALS patients and 2,662 controls were analyzed. Annual mean air pollution concentrations were assessed by land use regression (LUR) models developed as part of the European Study of Cohorts for Air Pollution Effects (ESCAPE). Exposure estimates included nitrogen oxides (, ), particulate matter (PM) with diameters of (), (), between and (), and absorbance. We performed conditional logistic regression analysis using two different multivariate models (model 1 adjusted for age, gender, education, smoking status, alcohol use, body mass index, and socioeconomic status; model 2 additionally adjusted for urbanization degree).Results:Risk of ALS was significantly increased for individuals in the upper exposure quartile of absorbance [; 95% confidence interval (CI): 1.27, 2.18], (; 95% CI: 1.32, 2.30), and concentrations (; 95% CI: 1.07, 1.77). These results, except for , remained significant after adjusting additionally for urbanization degree.Conclusions:Based on a large population-based case–control study, we report evidence for the association between long-term exposure to traffic-related air pollution and increased susceptibility to ALS. Our findings further support the necessity for regulatory public health interventions to combat air pollution levels and provide additional insight into the potential pathophysiology of ALS. https://doi.org/10.1289/EHP1115

Long-term exposure to ambient air pollution and incidence of brain tumor: the European Study of Cohorts for Air Pollution Effects (ESCAPE).

BackgroundEpidemiological evidence on the association between ambient air pollution and brain tumor risk is sparse and inconsistent.MethodsIn 12 cohorts from 6 European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated by standardized land-use regression models developed within the ESCAPE and TRANSPHORM projects: particulate matter (PM) ≤2.5, ≤10, and 2.5–10 μm in diameter (PM2.5, PM10, and PMcoarse), PM2.5 absorbance, nitrogen oxides (NO2 and NOx) and elemental composition of PM. We estimated cohort-specific associations of air pollutant concentrations and traffic intensity with total, malignant, and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses.ResultsOf 282194 subjects from 12 cohorts, 466 developed malignant brain tumors during 12 years of follow-up. Six of the cohorts also had data on nonmalignant brain tumor, where among 106786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically nonsignificant association between malignant brain tumor and PM2.5 absorbance (hazard ratio and 95% CI: 1.67; 0.89–3.14 per 10–5/m3), and weak positive or null associations with the other pollutants. Hazard ratio for PM2.5 absorbance (1.01; 0.38–2.71 per 10–5/m3) and all other pollutants were lower for nonmalignant than for malignant brain tumors.ConclusionWe found suggestive evidence of an association between long-term exposure to PM2.5 absorbance indicating traffic-related air pollution and malignant brain tumors, and no association with overall or nonmalignant brain tumors.

The Influence of Meteorological Factors and Atmospheric Pollutants on the Risk of Preterm Birth.

Atmospheric pollutants and meteorological conditions are suspected to be causes of preterm birth. We aimed to characterize their possible association with the risk of preterm birth (defined as birth occurring before 37 completed gestational weeks). We pooled individual data from 13 birth cohorts in 11 European countries (71,493 births from the period 1994-2011, European Study of Cohorts for Air Pollution Effects (ESCAPE)). City-specific meteorological data from routine monitors were averaged over time windows spanning from 1 week to the whole pregnancy. Atmospheric pollution measurements (nitrogen oxides and particulate matter) were combined with data from permanent monitors and land-use data into seasonally adjusted land-use regression models. Preterm birth risks associated with air pollution and meteorological factors were estimated using adjusted discrete-time Cox models. The frequency of preterm birth was 5.0%. Preterm birth risk tended to increase with first-trimester average atmospheric pressure (odds ratio per 5-mbar increase = 1.06, 95% confidence interval: 1.01, 1.11), which could not be distinguished from altitude. There was also some evidence of an increase in preterm birth risk with first-trimester average temperature in the -5°C to 15°C range, with a plateau afterwards (spline coding, P=0.08). No evidence of adverse association with atmospheric pollutants was observed. Our study lends support for an increase in preterm birth risk with atmospheric pressure.

Long-term exposure to ambient air pollution and traffic noise and incident hypertension in seven cohorts of the European study of cohorts for air pollution effects (ESCAPE).

We investigated whether traffic-related air pollution and noise are associated with incident hypertension in European cohorts. We included seven cohorts of the European study of cohorts for air pollution effects (ESCAPE). We modelled concentrations of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10), >2.5, and ≤10 µm (PMcoarse), soot (PM2.5 absorbance), and nitrogen oxides at the addresses of participants with land use regression. Residential exposure to traffic noise was modelled at the facade according to the EU Directive 2002/49/EC. We assessed hypertension as (i) self-reported and (ii) measured (systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg or intake of BP lowering medication (BPLM). We used Poisson regression with robust variance estimation to analyse associations of traffic-related exposures with incidence of hypertension, controlling for relevant confounders, and combined the results from individual studies with random-effects meta-analysis. Among 41072 participants free of self-reported hypertension at baseline, 6207 (15.1%) incident cases occurred within 5-9 years of follow-up. Incidence of self-reported hypertension was positively associated with PM2.5 (relative risk (RR) 1.22 [95%-confidence interval (CI):1.08; 1.37] per 5 µg/m³) and PM2.5 absorbance (RR 1.13 [95% CI:1.02; 1.24] per 10 - 5m - 1). These estimates decreased slightly upon adjustment for road traffic noise. Road traffic noise was weakly positively associated with the incidence of self-reported hypertension. Among 10896 participants at risk, 3549 new cases of measured hypertension occurred. We found no clear associations with measured hypertension. Long-term residential exposures to air pollution and noise are associated with increased incidence of self-reported hypertension.

Association between air pollution and rhinitis symptoms in two European cohorts

Introduction: The association between air pollution and rhinitis is scarcely studied in adults. The objective is to assess the association between modeled PM10, PM2.5 and NO2 concentrations and rhinitis in two European studies. Methods: Cross-sectional analyses were performed in adults from two multicentre cohorts: the European Community Respiratory Health Survey (ECRHS) and the French longitudinal Epidemiological Study on the Genetics and Environment of Asthma (EGEA, case-control on asthma and family study), both involved in the European Study of Cohorts for Air Pollution Effects (ESCAPE). Symptoms of rhinitis -ever sneezing, runny or blocked nose in absence of cold or flu- were self-reported by questionnaire. Annual averages of pollutants exposure were estimated at participants’ residential addresses with land use regression models. Data were analysed using logistic regression analysis. Results were adjusted on age, sex, tobacco status and city. Results: Pooled analysis was performed on 6781 adults from 17 European cities: 5586 from ECRHS (mean age=42years, 53% women) and 1195 from EGEA (mean age=42 years, 49.5% women). Prevalence of asthma ever and symptoms of rhinitis were higher in EGEA than in ECRHS (40% vs 15% for asthma ever and 60.5% vs 45.5% for symptoms of rhinitis). A negative association between PM10 and symptoms of rhinitis was found in the crude analysis (cOR(95%CI)= 0.83(0.76-0.90) per 5 µg/m3) but no association remained after adjustment (aOR=1.01(0.82-1.24)). When stratifying on asthma, crude results were significant only in non-asthmatics (cOR=0.84(0.76-0.92) and 0.94(0.75-1.17) in asthmatics) but no association remained after adjustment. Similar results were found for PM2.5. No association between NO2 and rhinitis was found (cOR: 0.98(0.95-1.01) per 10 µg/m3, aOR: 1.02(0.96-1.07)). Conclusions: As it was previously found in children, these results do not support associations between either PM10, PM2.5 or NO2 and symptoms of rhinitis.

Socioeconomic position and air pollution exposure in Western Europe: A multi-city analysis

Background: Inconsistent associations between socioeconomic position (SEP) and air pollution have been reported in Europe. We aimed to assess association between SEP and air pollution exposure at residential address across 20 European cities with homogenised SEP and air pollution indicators. Methods: Three SEP indicators, defined at individual- (education and occupation) and small area-level (unemployment rate) were assessed in three European multicenter cohorts (ECRHS, EGEA and SAPALDIA). NO2 exposure was estimated at participants’ residential addresses with land use regression models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE). Pooled and city-specific linear regressions were used to analyse associations between each SEP indicator and NO2. Heterogeneity across cities was assessed using a random-effects meta-analysis. Results: 8666 participants from 20 European cities were included. In a pooled analysis, participants with lower individual-SEP indicators tended to be less exposed to NO2. Conversely, participants living in neighbourhood with higher unemployment rate were more exposed. However, in both cases, city-specific results exhibited considerable heterogeneity across areas (I2>75%). In models with both individual- and area-SEP indicators, estimates were similar compared to the simple regression model, suggesting independent associations between individual-SEP and small area-level unemployment rate with NO2. Conclusions: At European level, associations between SEP and NO2 were heterogeneous and no geographical or urban pattern could be identified. Our results showed that individual- and area-SEP indicators capture different aspects of the SEP distribution regarding exposure to air pollution, stressing the importance of considering both in air pollution epidemiological studies.

Long-term Exposure to Ambient Air Pollution and Incidence of Brain Tumor in 12 European Cohorts: the European Study of Cohorts for Air Pollution Effects (ESCAPE)

Introduction: Experimental studies suggest that exposure to air pollution can lead to neurotoxicity and development of brain tumours in animals, but epidemiological evidence in humans is sparse. We examined the association between long-term exposure to air pollution and incidence of brain tumor in European population. Methods: We utilized 12 cohorts from Sweden, Denmark, Norway, Netherlands, Austria, and Italy for which individual estimates of air pollution levels at residence were predicted by standardized land-use regression models developed within the ESCAPE study. We had data for particulate matter (PM) ≤ 2.5 μm in diameter (PM2.5), PM ≤ 10 μm in diameter (PM10), PM with diameter 2.5-10 μm (PM2.5-10), PM2.5 absorbance, nitrogen oxides (NO2 and NOx), and traffic indicator. We estimated cohort-specific associations between brain tumor (benign and malignant) incidence and air pollutants by Cox proportional hazards model, adjusting for age, gender, calendar time, occupation, education, and socio-economic status, and pooled cohort-specific estimates using random-effects meta-analyses. Results: Of 282,194 included subjects, 643 developed incident brain tumors during 4,176,933 person-years of follow-up. We found weak inverse association between brain tumor incidence and PM2.5 (Hazard Ratio and 95% Confidence Interval: 0.90; 0.56-1.44 per 5 µg/m3), PM10 (0.95: 0.54-1.65 per 10 µg/m3), and PM2.5-10 (0.86: 0.62-1.21 per 5 µg/m3), and strong positive, but statistically insignificant association with PM2.5 absorbance (1.40; 0.79-2.49 per 10-5/m). We found no association between brain tumor incidence and NO2 (1.01; 0.84-1.20 per 10 µg/m3) or NOx (1.01; 0.86-1.19 per 20 µg/m3), but detected statistically significant positive association with traffic density on the nearest road (1.07; 1.01-1.14 per 5,000 vehicles per day). Conclusion: We found weak association between development of brain tumor and long-term exposure to traffic and soot in European population.

Long-term Exposure to Ambient Air Pollution and Incidence of Postmenopausal Breast Cancer in 15 European Cohorts: the European Study of Cohorts for Air Pollution Effects (ESCAPE)

Introduction: An association between air pollution and breast cancer risk has been suggested but epidemiological evidence is sparse and inconclusive. Aim: We examined association between long-term ...

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe.Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network.LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR2: 0.33–0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 0.47–0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area.SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.

The association of air pollution and depressed mood in 70,928 individuals from four European cohorts.

Exposure to ambient air pollution may be associated with impaired mental health, including depression. However, evidence originates mainly from animal studies and epidemiological studies in specific subgroups. We investigated the association between air pollution and depressed mood in four European general population cohorts. Data were obtained from LifeLines (the Netherlands), KORA (Germany), HUNT (Norway), and FINRISK (Finland). Residential exposure to particles (PM2.5, PM2.5absorbance, PM10) and nitrogen dioxide (NO2) was estimated using land use regression (LUR) models developed for the European Study of Cohorts for Air Pollution Effects (ESCAPE) and using European wide LUR models. Depressed mood was assessed with interviews and questionnaires. Logistic regression analyses were used to investigate the cohort specific associations between air pollution and depressed mood. A total of 70,928 participants were included in our analyses. Depressed mood ranged from 1.6% (KORA) to 11.3% (FINRISK). Cohort specific associations of the air pollutants and depressed mood showed heterogeneous results. For example, positive associations were found for NO2 in LifeLines (odds ratio [OR]=1.34; 95% CI: 1.17, 1.53 per 10 μg/m(3) increase in NO2), whereas negative associations were found in HUNT (OR=0.79; 95% CI: 0.66, 0.94 per 10 μg/m(3) increase in NO2). Our analyses of four European general population cohorts found no consistent evidence for an association between ambient air pollution and depressed mood.

Air pollution: 6.6million premature deaths in 2050!

Air pollution: 6.6million premature deaths in 2050!