Association between exposure to multiple air pollutants, transportation noise and cause-specific mortality in adults in Switzerland.

BACKGROUND: Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise. METHODS: Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM2.5, PM2.5 components (Cu, Fe, S and Zn), NO2, black carbon (BC) and ozone (O3) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges. RESULTS: During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m3 PM2.5, 1.050 (1.041, 1.059) per 10 µg/m3 NO2, 1.057 (1.048, 1.067) per 0.5 × 10-5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO2, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m3 for PM2.5 and NO2, < 1.5 10-5/m BC and < 53 dB Lden total transportation noise. The O3 association was inverse for all outcomes. Including noise attenuated all outcome associations, though many remained significant. Across outcomes, noise was robust to adjustment to air pollutants (e.g. natural mortality 1.037 (1.033, 1.042) per 10 dB Lden total transportation noise, after including BC). CONCLUSION: Long-term exposure to air pollution and transportation noise in Switzerland contribute to premature mortality. Considering co-exposures revealed the importance of local traffic-related pollutants such as NO2, BC and transportation noise

Spatio-temporal variation of urban ultrafine particle number concentrations

Methods are needed to characterize short-term exposure to ultrafine particle number concentrations (UFP) for epidemiological studies on the health effects of traffic-related UFP. Our aims were to assess season-specific spatial variation of short-term (20-min) UFP within the city of Basel, Switzerland, and to develop hybrid models for predicting short-term median and mean UFP levels on sidewalks. We collected measurements of UFP for periods of 20 min (MiniDiSC particle counter) and determined traffic volume along sidewalks at 60 locations across the city, during non-rush hours in three seasons. For each monitoring location, detailed spatial characteristics were locally recorded and potential predictor variables were derived from geographic information systems (GIS). We built multivariate regression models to predict local UFP, using concurrent UFP levels measured at a suburban background station, and combinations of meteorological, temporal, GIS and observed site characteristic variables. For a subset of sites, we assessed the relationship between UFP measured on the sidewalk and at the nearby residence (i.e., home outdoor exposure on e.g. balconies). The average median 20-min UFP levels at street and urban background sites were 14,700 +/- 9100 particles cm(-3) and 9900 +/- 8600 particles cm(-3), respectively, with the highest levels occurring in winter and the lowest in summer. The most important predictor for all models was the suburban background UFP concentration, explaining 50% and 38% of the variability of the median and mean, respectively. While the models with GIS-derived variables (R-2 = 0.61) or observed site characteristics (R-2 = 0.63) predicted median UFP levels equally well, mean UFP predictions using only site characteristic variables (R-2 = 0.62) showed a better fit than models using only GIS variables (R-2 = 0.55). The best model performance was obtained by using a combination of GIS-derived variables and locally observed site characteristics (median: R-2 = 0.66; mean: R-2 = 0.65). The 20-min UFP concentrations measured at the sidewalk were strongly related (R-2 = 0.8) to the concurrent 20-min residential UFP levels nearby. Our results indicate that median UFP can be moderately predicted by means of a suburban background site and GIS-derived traffic and land use variables. In areas and regions where large-scale GIS data are not available, the spatial distribution of traffic-related UFP may be assessed reasonably well by collecting on-site short-term traffic and land-use data. (C) 2014 Elsevier Ltd. All rights reserved

Spatial and temporal variability of ultrafine particles, NO2, PM2.5, PM2.5 absorbance, PM10 and PMcoarse in Swiss study areas

Exposure to outdoor air pollutants remains an important concern in Europe, as limit values for NO2 and PM10 continue to be exceeded. Few studies have addressed the long-term spatial contrasts in PM2.5, PM absorbance, PM coarse coarse and especially ultrafine particles. This scarcity of data hampers the possibility to conduct epidemiological studies, assessing the health relevance of these markers of potentially harmful pollutants. Air pollution measurements were performed in eight geographically distinct areas of the Swiss Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) in Switzerland. NO2 was measured in all eight areas at 40 sites per area, and PM2.5, PM2.5 absorbance, PM10 and ultrafine particles (particle number concentration (PNC) and lung deposited surface area (LDSA)) were measured in 4 of these areas, at a subset of 20 out of 40 sites. Each site was sampled three times during different seasons of the year, using the same equipment, sampling protocols and the same central facilities for analysis of samples. We assessed the spatial variability between areas and between individual sites, as well as pollution contrasts between the seasons and correlations between different pollutants. Within-area spatial contrasts (defined as the ratio between the 90th and 10th percentile) were highest for NO2 (3.14), moderate for PMcoarse (2.19), PNC (2.00) and PM2.5 absorbance (1.94), and lowest for LDSA (1.63), PM2.5 (1.50) and PM10 (1.46). Concentrations in the larger cities were generally higher than in smaller towns and rural and alpine areas, and were higher in the winter than in the summer and intermediate seasons, for all pollutants. Between-area differences accounted for more variation than within-area differences for all pollutants except NO2 and PMcoarse. Despite substantial within-area contrasts for PNC and LDSA, 74.7% and 83.3% of the spatial variance was attributed to between-area variability, respectively. Coefficients of determination between long-term adjusted pollutants were high (R-2>0.70) between NO2, PM2.5 absorbance, PNC and LDSA and between PM2.5 and PM10. The measurement of spatial patterns for this large range of outdoor air pollutants will contribute to a highly standardized estimation of individual long-term exposure levels for SAPALDIA cohort participants. (C) 2015 Elsevier Ltd. All rights reserved

Land use regression models for the oxidative potential of fine particles (PM2.5) in five European areas.

Oxidative potential (OP) of particulate matter (PM) is proposed as a biologically-relevant exposure metric for studies of air pollution and health. We aimed to evaluate the spatial variability of the OP of measured PM2.5 using ascorbate (AA) and (reduced) glutathione (GSH), and develop land use regression (LUR) models to explain this spatial variability. We estimated annual average values (m(-3)) of OP(AA) and OP(GSH) for five areas (Basel, CH; Catalonia, ES; London-Oxford, UK (no OP(GSH)); the Netherlands; and Turin, IT) using PM2.5 filters. OP(AA) and OP(GSH) LUR models were developed using all monitoring sites, separately for each area and combined-areas. The same variables were then used in repeated sub-sampling of monitoring sites to test sensitivity of variable selection; new variables were offered where variables were excluded (p > .1). On average, measurements of OP(AA) and OP(GSH) were moderately correlated (maximum Pearson's maximum Pearson's R = = .7) with PM2.5 and other metrics (PM2.5absorbance, NO2, Cu, Fe). HOV (hold-out validation) R(2) for OP(AA) models was .21, .58, .45, .53, and .13 for Basel, Catalonia, London-Oxford, the Netherlands and Turin respectively. For OP(GSH), the only model achieving at least moderate performance was for the Netherlands (R(2) = .31). Combined models for OP(AA) and OP(GSH) were largely explained by study area with weak local predictors of intra-area contrasts; we therefore do not endorse them for use in epidemiologic studies. Given the moderate correlation of OP(AA) with other pollutants, the three reasonably performing LUR models for OP(AA) could be used independently of other pollutant metrics in epidemiological studies

Spatial variation of PM elemental composition between and within 20 European study areas - Results of the ESCAPE project

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. Chemicals/CAS: copper, 15158-11-9, 7440-50-8; iron, 14093-02-8, 53858-86-9, 7439-89-6; nickel, 7440-02-0; nitrogen oxide, 11104-93-1; potassium, 7440-09-7; silicon, 7440-21-3; sulfur, 13981-57-2, 7704-34-9; vanadium, 7440-62-2; zinc, 7440-66-6, 14378-32-

Spatial variation of PM elemental composition between and within 20 European study areas - Results of the ESCAPE project

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

Examining the representativeness of home outdoor PM2.5, EC, and OC estimates for daily personal exposures in southern California

Recent studies have linked acute respiratory and cardiovascular outcomes to measurements or estimates of traffic-related air pollutants at homes or schools. However, few studies have evaluated these outdoor measurements and estimates against personal exposure measurements. We compared measured and modeled home outdoor concentrations with personal measurements of traffic-related air pollutants in the Los Angeles air basin (Whittier and Riverside). Personal exposure of 63 children with asthma and 15 homes were assessed for particulate matter with an aerodynamic diameter less than 2.5 ?m (PM(2.5)), elemental carbon (EC), and organic carbon (OC) during sixteen 10-day monitoring runs. Regression models to predict daily home outdoor PM(2.5), EC, and OC were constructed using home outdoor measurements, geographical and meteorological parameters, as well as CALINE4 estimates at outdoor home sites, which represent the concentrations from local traffic sources. These home outdoor models showed the variance explained (R(2)) was 0.97 and 0.94 for PM(2.5), 0.91 and 0.83 for OC, and 0.76 and 0.87 for EC in Riverside and Whittier, respectively. The PM(2.5) outdoor estimates correlated well with the personal measurements (Riverside R(2) = 0.65 and Whittier R(2) = 0.69). However, excluding potentially inaccurate samples from Riverside, the correlation between personal exposure to carbonaceous species and home outdoor estimates in Whittier was moderate for EC (R(2) = 0.37) and poor for OC (R(2) = 0.08). The CALINE4 estimates alone were not correlated with personal measurements of EC or other pollutants. While home outdoor estimates provide good approximations for daily personal PM(2.5) exposure, they may not be adequate for estimating daily personal exposure to EC and OC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11869-010-0099-y) contains supplementary material, which is available to authorized users