Limited information is available on the contribution of organic components in particulate matter to health effects related to fine particles. Spatial variability of specific fine particle organic ...components has not been assessed with consistent methods. The aim of this paper is to assess spatial variation of organic components of fine particles within and between European study areas. Highly standardized measurements of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, elemental/organic carbon (EC/OC) and levoglucosan were performed measured in ten study areas across Europe. In each study area, measurements were conducted at street, urban and regional background sites. Three two-week samples were taken per site and the annual average levels of pollutants were calculated using continuous measurements at one background site as a reference.
Substantial variations within and between the study areas were found. EC/OC and hopanes/steranes concentrations were highest in southern European study areas and lowest in northern locations. PAH concentrations were lowest in London/Oxford and highest in Copenhagen, Rome and Athens. Concentrations at street locations were higher than at background locations in all study areas and for all components. However, these differences varied considerably between study areas and components. EC had the highest median street to urban background ratio (1.62), OC the lowest (1.32). EC was highly correlated with NOx and PM2.5 absorbance in all areas, with median r = 0.85 and r = 0.89, respectively. The correlation between OC and other components was variable, with a median correlation of 0.65 with PM2.5 mass and a weak (0.18) correlation with Σhopanes/steranes. ΣPAH correlated moderately with EC (r = 0.59) and weakly with ∑hopanes/steranes (r = 0.36).
In conclusion, substantial variability was found in spatial patterns of atmospheric EC, OC, PAH and hopanes/steranes both within and between European study areas. The application of this highly standardized measurement approach across different locations will contribute to a consistent assessment of air pollutant levels and potentially contribute to understanding health effects associated with them.
•We used standardized method to measure EC, OC, PAH and hopanes/steranes in 10 European areas.•The highest contrast within study areas was for EC, the lowest for OC.•Concentrations of EC, OC but not PAH were higher in Southern than in Northern European areas.•Traffic impact to PAH levels differed substantially across study areas.
Exposure misclassification in longitudinal studies of air pollution exposure and health effects can occur due to residential mobility in a study population over followup. The aim of this study was to ...investigate to what extent residential mobility during followup can be expected to cause exposure misclassification in such studies, where exposure at the baseline address is used as the main exposure assessment. The addresses for each participant in a large population-based study (N>25,000) were obtained via national registers. We used a Land Use Regression model to estimate the NOx concentration for each participant's all addresses during the entire follow-up period (in average 14.6 years) and calculated an average concentration during followup. The Land Use Regression model explained 83% of the variation in measured levels. In summary, the NOx concentration at the inclusion address was similar to the average concentration over followup with a correlation coefficient of 0.80, indicating that air pollution concentration at study inclusion address could be used as indicator of average air pollution concentrations over followup. The differences between an individual's inclusion and average follow-up mean concentration were small and seemed to be nondifferential with respect to a large range of factors and disease statuses, implying that bias due to residential mobility was small.
ObjectivesLand use regression (LUR) modelling is a popular method to estimate outdoor air pollution concentrations at the home and/or work addresses of individual subjects in epidemiological studies. ...Typically, such models are constructed using measurements from dedicated monitoring campaigns lasting up to 1 year. It is unknown to what extent such models can adequately predict concentrations in earlier or later time periods. We tested the stability of measured and modelled spatial contrasts in outdoor nitrogen dioxide (NO2) pollution across the Netherlands over 8 years.MethodsNO2 measurements were conducted at 40 locations in the Netherlands in 1999–2000. In 2007, NO2 was again measured at 144 locations, of which 35 were the same as in 1999–2000. This enabled us to compare measurements as well as model predictions between the two time periods.ResultsNO2 measurements conducted in 2007 agreed well with NO2 measurements taken in 1999–2000 at the same locations (R2=0.86). LUR models from 1999–2000 and 2007 explained 85% and 86% of observed spatial variance, respectively. The 2007 LUR model explained 77% of spatial variability in the 1999–2000 measurements and the 1999–2000 model explained 81% of variability in the 2007 measurements.ConclusionWe found good agreement between measured spatial contrasts in outdoor NO2 in 1999–2000 and 2007. LUR models predicted spatial contrast 8 years in the past (2007 model) and 8 years in the future (1999–2000 model) well. This supports the use of LUR models in epidemiological studies with health data available for a later or earlier timepoint.
Studies on the health effects of long-term average exposure to outdoor air pollution have played an important role in recent health impact assessments. Exposure assessment for epidemiological studies ...of long-term exposure to ambient air pollution remains a difficult challenge because of substantial small-scale spatial variation. Current approaches for assessing intra-urban air pollution contrasts include the use of exposure indicator variables, interpolation methods, dispersion models and land-use regression (LUR) models. LUR models have been increasingly used in the past few years. This paper provides a critical review of the different components of LUR models.
We identified 25 land-use regression studies. Land-use regression combines monitoring of air pollution at typically 20–100 locations, spread over the study area, and development of stochastic models using predictor variables usually obtained through geographic information systems (GIS). Monitoring is usually temporally limited: one to four surveys of typically one or two weeks duration. Significant predictor variables include various traffic representations, population density, land use, physical geography (e.g. altitude) and climate.
Land-use regression methods have generally been applied successfully to model annual mean concentrations of NO
2, NO
x
, PM
2.5, the soot content of PM
2.5 and VOCs in different settings, including European and North-American cities. The performance of the method in urban areas is typically better or equivalent to geo-statistical methods, such as kriging, and dispersion models.
Further developments of the land-use regression method include more focus on developing models that can be transferred to other areas, inclusion of additional predictor variables such as wind direction or emission data and further exploration of focalsum methods. Models that include a spatial and a temporal component are of interest for (e.g. birth cohort) studies that need exposure variables on a finer temporal scale. There is a strong need for validation of LUR models with personal exposure monitoring.
The South Durban (SD) area of Durban, South Africa, has a history of air pollution issues due to the juxtaposition of low-income communities with industrial areas. This study used measurements of ...oxides of nitrogen (NOx) to develop a land use regression (LUR) model to explain the spatial variation of air pollution concentrations in this area.
Ambient NOx was measured over two two-week sampling periods at 32 sites using Ogawa badges. Following the ESCAPE approach, an annual adjusted average was calculated for these results and regressed against pre-selected geographic predictor variables in a multivariate regression model. The LUR model was then applied to predict the NOx exposure of a sample of pregnant women living in South Durban.
Measured NOx levels ranged from 22.3–50.9μg/m3 with a median of 36μg/m3. The model developed accounts for 73% of the variance in ambient NOx measurements using three input variables (length of minor roads within a 1000m radius, length of major roads within a 300m radius, and area of open space within a 1000m radius). Model cross validation yielded a R2 of 0.59. Subsequent participant exposure estimates indicated exposure to ambient NOx ranged from 19.9–53.2μg/m3, with a mean of 39μg/m3.
This is the first study to develop a land use regression model that predicts ambient concentrations of NOx in a South African context. The findings of this study indicate that the participants in the South Durban are exposed to high levels of NOx that can be attributed mainly to traffic.
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•Measured NOx levels were observed to be higher during the winter sampling campaign as compared to summer.•Measured and modelled mean NOx levels were highly correlated, thus illustrating the strength of the model to accurately predict exposure at un-monitored locations.•Fewer than the recommended 40 NOx sampling sites were sufficient for LUR model development for this study.•This study indicates that ambient NOx levels are strongly influenced by levels of local traffic.
Altered expression of members of the Bcl-2 family might account for the observed apoptosis resistance to chemotherapy in acute myeloid leukemia (AML). Given the poor prognosis associated with CD34+ ...expression in AML, we studied the role of spontaneous apoptosis and apoptosis regulatory proteins in sorted CD34+ and CD34- primary AML fractions.
The expression levels of apoptosis regulatory proteins and spontaneous apoptosis were measured in primary AML samples by Western blot analysis and flow cytometry. To determine the role of CD34+ cells in apoptosis resistance, spontaneous apoptosis in serum-free conditions and apoptosis regulatory protein levels were measured in CD34+ and CD34- sorted cells from CD34+ primary AML samples.
We show that CD34+ AML fractions are more resistant to apoptosis than are corresponding CD34- AML fractions, and that this is paralleled by higher Bcl-2, Bcl-xL, Mcl-1, Pgp and lower Bax expression levels. Interestingly, as the percentage of CD34 cells increased in the primary AML sample, so too did the apoptosis resistance in the corresponding CD34- fraction, which was reflected by an increasing anti-apoptosis protein profile.
The data show that the CD34+ fraction is more resistant to apoptosis than is the corresponding CD34- fraction and secondly that the AML as a whole is more apoptosis resistant with increasing CD34 percentage.
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/m3 higher PM10 and 4.4 μg/m3 NO2). Concentrations in both countries were higher in neighbourhoods with >20% non-White (England: 3.0 μg/m3 higher PM10 and 10.1 μg/m3 NO2; the Netherlands: 1.1 μg/m3 higher PM10 and 4.5 μg/m3 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.
•Air pollution inequalities are believed to vary between subpopulations.•We explore this across two European countries at different geographical levels.•We found that air pollution inequalities are largely an urban problem.•Ethnically diverse neighbourhoods have the highest air pollution levels.•Associations vary across two countries that might be thought to be quite similar.
Air pollution inequalities are largely an urban problem and associations with deprivation and ethnicity vary even across two European countries that might be thought to be quite similar.
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 41 072 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 10 896 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.
The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and ...surroundings, nitrogen dioxide (NO
2) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.
A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO
2 concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.
Moderate agreement was found between the URBIS and LUR in calculating NO
2 concentrations (
R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (
R = 0.77 versus
R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category
industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO
2.
Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO
2 concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.
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