Air pollution at the residence of Danish adults, by socio-demographic characteristics, morbidity, and address level characteristics.

BACKGROUND: Exposure to outdoor air pollution is associated with adverse health effects. Previous studies have indicated higher levels of air pollution in socially deprived areas. AIM: To investigate associations between air pollution and socio-demographic variables, comorbidity, stress, and green space at the residence in Denmark. METHODS: We included 2,237,346 persons living in Denmark, aged 35 years or older in 2017. We used the high resolution, multi-scale DEHM/UBM/AirGIS air pollution modelling system to calculate mean concentrations of air pollution with PM2.5, elemental carbon, ultrafine particles and NO2 at residences held the preceding five years. We used nationwide registries to retrieve information about socio-demographic indicators at the individual and neighborhood levels. We used general linear regression models to analyze associations between socio-demographic indicators and air pollution at the residence. RESULTS: Individuals with high SES (income, higher white-collar worker and high educational level) and of non-Danish origin were exposed to higher levels of air pollution than individuals of low SES and of Danish origin, respectively. We found comparable levels of air pollution according to sex, stress events and morbidity. For neighborhood level SES indicators, we found high air pollution levels in neighborhoods with low SES measured as proportion of social housing, sole providers, low income and unemployment. In contrast, we found higher air pollution levels in neighborhoods with higher educational level and a low proportion of manual labor. People living in an apartment and/or with little green space had higher air pollution levels. CONCLUSION: In Denmark, high levels of residential air pollution were associated with higher individual SES and non-Danish origin. For neighborhood-level indicators of SES, no consistent pattern was observed. These results highlight the need for analyzing many different socio-demographic indicators to understand the complex associations between SES and exposure to air pollution.

Road traffic air and noise pollution exposure assessment - A review of tools and techniques.

Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05-0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures.

Residential exposure to traffic noise and health-related quality of life--a population-based study.

BACKGROUND: Few studies have investigated the association between objectively measured traffic noise and health-related quality of life. However, as traffic noise has been associated with both cardiovascular disease and diabetes, and health-issues including sleeping problems, annoyance, and stress, it seems plausible that traffic noise is associated with health-related quality of life. METHODS: Between 1999 and 2002, a cohort of 38,964 Danes filled in the short form-36 (SF-36) questionnaire. Residential exposure to road traffic and railway noise was calculated for all historical addresses for 10 years preceding the SF-36, using the Nordic prediction method. Associations between noise exposure and SF-36 summary scales and the eight sub-scales were calculated using general linear models, adjusted for age, sex, socioeconomic status, and lifestyle. RESULTS: Models adjusted for age, sex and socioeconomic factors showed that a 10 dB higher road traffic noise 1 year preceding SF-36 assessment was associated with a 0.14 lower mental component summary (MCS) score (95% confidence interval (CI) -0.26, -0.01). However, further adjustment for lifestyle factors (smoking, alcohol, and waist circumference) attenuated the association: (-0.08 (95% CI: -0.20, 0.04)). Exposure to more than 55 dB of railway noise in the same time period was borderline significantly associated with lower MCS. The physical component summary was not associated with traffic noise. CONCLUSION: The present study suggests a weak association between traffic noise exposure and the mental health component score of SF-36, which may operate through lifestyle. The magnitude of effect was, however, not clinically relevant.