Years of life lost and morbidity cases attributable to transportation noise and air pollution : a comparative health risk assessment for Switzerland in 2010

There is growing evidence that chronic exposure to transportation related noise and air pollution affects human health. However, health burden to a country of these two pollutants have been rarely compared.; As an input for external cost quantification, we estimated the cardiorespiratory health burden from transportation related noise and air pollution in Switzerland, incorporating the most recent findings related to the health effects of noise.; Spatially resolved noise and air pollution models for the year 2010 were derived for road, rail and aircraft sources. Average day-evening-night sound level (Lden) and particulate matter (PM10) were selected as indicators, and population-weighted exposures derived by transportation source. Cause-specific exposure-response functions were derived from a meta-analysis for noise and literature review for PM10. Years of life lost (YLL) were calculated using life table methods; population attributable fraction was used for deriving attributable cases for hospitalisations, respiratory illnesses, visits to general practitioners and restricted activity days.; The mean population weighted exposure above a threshold of 48dB(A) was 8.74dB(A), 1.89dB(A) and 0.37dB(A) for road, rail and aircraft noise. Corresponding mean exposure contributions were 4.4, 0.54, 0.12μg/m(3) for PM10. We estimated that in 2010 in Switzerland transportation caused 6000 and 14,000 YLL from noise and air pollution exposure, respectively. While there were a total of 8700 cardiorespiratory hospital days attributed to air pollution exposure, estimated burden due to noise alone amounted to 22,500 hospital days.; YLL due to transportation related pollution in Switzerland is dominated by air pollution from road traffic, whereas consequences for morbidity and indicators of quality of life are dominated by noise. In terms of total external costs the burden of noise equals that of air pollution

Associations between air pollution and socioeconomic characteristics, ethnicity and age profile of neighbourhoods in England and the Netherlands

Air pollution levels are generally believed to be higher in deprived areas but associations are complex especially between sensitive population subgroups. We explore air pollution inequalities at national, regional and city level in England and the Netherlands comparing particulate matter (PM10) and nitrogen dioxide (NO2) concentrations and publicly available population characteristics (deprivation, ethnicity, proportion of children and elderly). We saw higher concentrations in the most deprived 20% of neighbourhoods in England (1.5 μg/m(3) higher PM10 and 4.4 μg/m(3) NO2). Concentrations in both countries were higher in neighbourhoods with <20% non-White (England: 3.0 μg/m(3) higher PM10 and 10.1 μg/m(3) NO2; the Netherlands: 1.1 μg/m(3) higher PM10 and 4.5 μg/m(3) NO2) after adjustment for urbanisation and other variables. Associations for some areas differed from the national results. Air pollution inequalities were mainly an urban problem suggesting measures to reduce environmental air pollution inequality should include a focus on city transport

Western European land use regression incorporating satellite- and ground-based measurements of NO2 and PM10

Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005-2007) based on <1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted R(2)) is 0.48-0.58 for NO2 and 0.22-0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R(2)) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available

Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO₂ and PM₁₀

Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO₂ and PM₁₀ LUR models for Western Europe (years: 2005–2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO₂ and PM_2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted <i>R</i>²) is 0.48–0.58 for NO₂ and 0.22–0.50 for PM₁₀. Inclusion of satellite data improved model performance (adjusted <i>R</i>²) by, on average, 0.05 for NO₂ and 0.11 for PM₁₀. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available