Effect of short-term exposure to ambient nitrogen dioxide and particulate matter on repeated lung function measures in infancy: A South African birth cohort.

BACKGROUND: The developing lung is highly susceptible to environmental toxicants, with both short- and long-term exposure to ambient air pollutants linked to early childhood effects. This study assessed the short-term exposure effects of nitrogen dioxide (NO2) and particulate matter (PM10) on lung function in infants aged 6 weeks, 6, 12 and 24 months, the early developmental phase of child growth. METHODS: Lung function was determined by multiple breath washout and tidal breathing measurement in non-sedated infants. Individual exposure to NO2 and PM10 was determined by hybrid land use regression and dispersion modelling, with two-week average estimates (preceding the test date). Linear mixed models were used to adjust for the repeated measures design and an age*exposure interaction was introduced to obtain effect estimates for each age group. RESULTS: There were 165 infants that had lung function testing, with 82 of them having more than one test occasion. Exposure to PM10 (µg/m3) resulted in a decline in tidal volume at 6 weeks [-0.4 ml (-0.9; 0.0), p = 0.065], 6 months [-0.5 ml (-1.0; 0.0), p = 0.046] and 12 months [-0.3 ml (-0.7; 0.0), p = 0.045]. PM10 was related to an increase in respiratory rate and minute ventilation, while a decline was observed for functional residual capacity for the same age groups, though not statistically significant for these outcomes. Such associations were however less evident for exposure to NO2, with inconsistent changes observed across measurement parameters and age groups. CONCLUSION: Our study suggests that PM10 results in acute lung function impairments among infants from a low-socioeconomic setting, while the association with NO2 is less convincing.

Spatial variability in air pollution exposure in relation to socioeconomic indicators in nine European metropolitan areas: A study on environmental inequality.

A limited number of studies have addressed environmental inequality, using various study designs and methodologies and often reaching contradictory results. Following a standardized multi-city data collection process within the European project EURO-HEALTHY, we conducted an ecological study to investigate the spatial association between nitrogen dioxide (NO2), as a surrogate for traffic related air pollution, and ten socioeconomic indicators at local administrative unit level in nine European Metropolitan Areas. We applied mixed models for the associations under investigation with random intercepts per Metropolitan Area, also accounting for the spatial correlation. The stronger associations were observed between NO2 levels and population density, population born outside the European Union (EU28), total crimes per 100,000 inhabitants and unemployment rate that displayed a highly statistically significant trend of increasing concentrations with increasing levels of the indicators. Specifically, the highest vs the lowest quartile of each indicator above was associated with 48.7% (95% confidence interval (CI): 42.9%, 54.8%), 30.9% (95%CI: 22.1%, 40.2%), 19.8% (95%CI: 13.4%, 26.6%) and 15.8% (95%CI: 9.9%, 22.1%) increase in NO2 respectively. The association with population density most probably reflects the higher volume in vehicular traffic, which is the main source of NO2 in urban areas. Higher pollution levels in areas with higher percentages of people born outside EU28, crime or unemployment rates indicate that worse air quality is typically encountered in deprived European urban areas. Policy makers should consider spatial environmental inequalities to better inform actions aiming to lower urban air pollution levels that will subsequently lead to improved quality of life, public health and health equity across the population.

Particulate matter air pollution components and risk for lung cancer.

BACKGROUND: Particulate matter (PM) air pollution is a human lung carcinogen; however, the components responsible have not been identified. We assessed the associations between PM components and lung cancer incidence. METHODS: We used data from 14 cohort studies in eight European countries. We geocoded baseline addresses and assessed air pollution with land-use regression models for eight elements (Cu, Fe, K, Ni, S, Si, V and Zn) in size fractions of PM2.5 and PM10. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effect models for meta-analysis. RESULTS: The 245,782 cohort members contributed 3,229,220 person-years at risk. During follow-up (mean, 13.1 years), 1878 incident cases of lung cancer were diagnosed. In the meta-analyses, elevated hazard ratios (HRs) for lung cancer were associated with all elements except V; none was statistically significant. In analyses restricted to participants who did not change residence during follow-up, statistically significant associations were found for PM2.5 Cu (HR, 1.25; 95% CI, 1.01-1.53 per 5 ng/m(3)), PM10 Zn (1.28; 1.02-1.59 per 20 ng/m(3)), PM10 S (1.58; 1.03-2.44 per 200 ng/m(3)), PM10 Ni (1.59; 1.12-2.26 per 2 ng/m(3)) and PM10 K (1.17; 1.02-1.33 per 100 ng/m(3)). In two-pollutant models, associations between PM10 and PM2.5 and lung cancer were largely explained by PM2.5 S. CONCLUSIONS: This study indicates that the association between PM in air pollution and lung cancer can be attributed to various PM components and sources. PM containing S and Ni might be particularly important.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: Our analyses of four European general population cohorts found no consistent evidence for an association between ambient air pollution and depressed mood.

International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies.

The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p < 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)).

Air pollution and childhood leukaemia: a nationwide case-control study in Italy.

OBJECTIVES: Leukaemia is the most common cancer in children, but its aetiology is still poorly understood. We tested the hypothesis that traffic-related air pollution is associated with paediatric leukaemia because of chronic exposure to several potential carcinogens. METHODS: The Italian SETIL study (Study on the aetiology of lymphohematopoietic malignancies in children) was conducted in 14 Italian regions. All incident cases of leukaemia in children aged ≤10 years from these regions (period 1998-2001) were eligible for enrolment. Two controls per case, matched on birth date, gender and region of residence were randomly selected from the local population registries. Exposure assessment at birth residence included traffic indicators (distance to main roads and length of main roads within 100 m) and estimates of pollutants concentrations (particulate matter -PM2.5 and PM10- and gases -NO2 and O3-) from national dispersion model and land use regression models. The association between the exposure variables and leukaemia was assessed by logistic regression analyses. RESULTS: Participation rates were 91.4% among cases and 69.2% in controls; 620 cases (544 acute lymphocytic and 76 acute non-lymphocytic leukaemia) and 957 controls were included. Overall, when considering the residence at birth, 35.6% of cases and 42.4% of controls lived along busy roads, and the mean annual PM10 levels were 33.3 (SD=6.3) and 33.4 µg/m(3) (SD=6.5), respectively. No association was found, and all ORs, independent of the method of assessment and the exposure windows, were close to the null value. CONCLUSIONS: Using various exposure assessment strategies, air pollution appears not to affect the incidence of childhood leukaemia.

Mobile phone base stations and early childhood cancers: case-control study.

OBJECTIVE: To investigate the risk of early childhood cancers associated with the mother's exposure to radiofrequency from and proximity to macrocell mobile phone base stations (masts) during pregnancy. DESIGN: Case-control study. SETTING: Cancer registry and national birth register data in Great Britain. PARTICIPANTS: 1397 cases of cancer in children aged 0-4 from national cancer registry 1999-2001 and 5588 birth controls from national birth register, individually matched by sex and date of birth (four controls per case). MAIN OUTCOME MEASURES: Incidence of cancers of the brain and central nervous system, leukaemia, and non-Hodgkin's lymphomas, and all cancers combined, adjusted for small area measures of education level, socioeconomic deprivation, population density, and population mixing. RESULTS: Mean distance of registered address at birth from a macrocell base station, based on a national database of 76,890 base station antennas in 1996-2001, was similar for cases and controls (1107 (SD 1131) m v 1073 (SD 1130) m, P=0.31), as was total power output of base stations within 700 m of the address (2.89 (SD 5.9) kW v 3.00 (SD 6.0) kW, P=0.54) and modelled power density (-30.3 (SD 21.7) dBm v -29.7 (SD 21.5) dBm, P=0.41). For modelled power density at the address at birth, compared with the lowest exposure category the adjusted odds ratios were 1.01 (95% confidence interval 0.87 to 1.18) in the intermediate and 1.02 (0.88 to 1.20) in the highest exposure category for all cancers (P=0.79 for trend), 0.97 (0.69 to 1.37) and 0.76 (0.51 to 1.12), respectively, for brain and central nervous system cancers (P=0.33 for trend), and 1.16 (0.90 to 1.48) and 1.03 (0.79 to 1.34) for leukaemia and non-Hodgkin's lymphoma (P=0.51 for trend). CONCLUSIONS: There is no association between risk of early childhood cancers and estimates of the mother's exposure to mobile phone base stations during pregnancy.

A GIS-based method for modelling air pollution exposures across Europe.

A GIS-based moving window approach was developed as a means for generating high resolution air pollution maps over large geographic areas. The approach is demonstrated by modelling annual mean NO(2) pollution for the EU-15 (excluding Sweden) at the 1 km level on the basis of emissions and meteorological data. Models were developed using monitoring data from 714 background NO(2) sites for 2001 and validated by comparing predicted with observed NO(2) concentrations for a reserved set of 228 background sites. First the emission map (NO(x)) was derived by disaggregating national emissions estimates, categorised by source, to a 1 km grid, using proxies including population and road density, traffic statistics and land cover. A set of annuli was then constructed, of varying radii, and these passed over the emissions grid to derive a calibration between measured annual average concentrations at each monitoring site and distance-weighted emissions in the surrounding area, using a focalsum function. The resulting model was then used to predict concentrations at the reserved set of validation sites, and measures of performance (R(2), RMSE and fractional bias) obtained. Validation gave R(2)=0.61, RMSE=6.59 and FB=-0.01, and indicated performance equivalent to universal kriging and better than ordinary kriging and land use regression.