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Policies to reduce outdoor air pollution concentrations are often assessed on the basis of the regulated pollutants. Whether these are the most appropriate components to assess the potential health benefits is questionable, as other health-relevant pollutants may be more strongly related to traffic. The aim of this study is to compare the contrast in concentration between major roads and (sub)urban background for a large range of pollutants and to analyze the magnitude of the measured difference in the street – background for major streets with different street configurations. Measurements of PM10, PM2.5, particle number concentrations (PNC), black carbon (BC), elemental composition of PM10 and PM2.5 and NOx were conducted simultaneously in eight major streets and nine (sub)urban background locations in the Netherlands. Measurements were done six times for a week during a six month period in 2008. High contrasts between busy streets and background locations in the same city were found for chromium, copper and iron (factor 2–3). These elements were especially present in the coarse fraction of PM. In addition, high contrasts were found for BC and NOx (factor 1.8), typically indicators of direct combustion emissions. The contrast for PNC was similar to BC. NO2 contrast was lower (factor 1.5). The largest contrast was found for two street canyons and two streets with buildings at one side of the street only. The contrast between busy streets and urban background in NO2 was less than the contrast found for BC, PNC and elements indicative of non-exhaust emissions, adding evidence that NO2 is not representing (current) traffic well. The study supports a substantial role for non-exhaust emissions including brake- and tyre wear and road dust in addition to direct combustion emissions. Significant underestimation of disease burden may occur when relying too much on the regulated components.

A major issue in air pollution epidemiology is whether the associations that are found in the statistical analyses on the health effects of air pollution reflect real causal associations of single components or mixtures thereof, or just reflect statistical associations that are mainly the result of the high correlation between the separate components, one of them being the true causal factor. In a previous analysis on the relationship between daily SO2 levels and daily mortality in The Netherlands [Buringh, E., Fischer, P., Hoek, G., 2000. Is SO2 a causative factor for the PM-associated mortality risks in The Netherlands? Inhal. Toxicol. 12 (Suppl. 1), 55–60.], it was shown that the statistical significant association between daily variation in SO2 and daily mortality did not reflect a causal relation. Black Smoke levels in The Netherlands have decreased 4-fold during the 34 years in the period 1972–2006 (annual average from 27 μg m−3 to 6 μg m−3). This large decrease in concentrations enabled us to use the same approach for this component as was done earlier for SO2 to assess whether a decreasing trend in Black Smoke levels in The Netherlands is associated with an increasing trend in mortality relative risks or not. We used daily averaged Black Smoke (BS) data from 1972 to 2006. In the first two decades (1970–1990) only sparse data were available. Based on the availability of the data, we selected data from 1972 to 1974 and from 1982 to 1984 because during these two periods continuous daily measurement series were available. For the later years (1989–2006) data covering the whole of The Netherlands were available, giving a total of 24 years of daily data. Data on daily total mortality counts (excluding external causes), cardiovascular mortality and respiratory mortality for the whole population of The Netherlands were analyzed with regard to daily Black Smoke levels using generalized additive Poisson regression models (GAM). Period specific relative risk estimates were compared and differences in estimates between periods were evaluated. We found no consistent increase in relative risks for daily total and cause-specific mortality over time, despite the decreasing trend in the Black Smoke levels in The Netherlands. Average relative risks for total mortality varied over the different periods from 0.997 per 10 μg m−3 daily Black Smoke to 1.010 per 10 μg m−3. Average relative risks for cardiovascular mortality varied from 0.988 per 10 μg m−3 to 1.010 per 10 μg m−3 and for respiratory mortality from 1.000 to 1.010 per 10 μg m−3. For weekly averaged concentrations the average relative risks for total mortality varied over the different periods from 1.004 per 10 μg m−3 Black Smoke to 1.018 per 10 μg m−3. Average relative risks for cardiovascular mortality varied from 1.003 per 10 μg m−3 to 1.016 per 10 μg m−3 and for respiratory mortality from 1.000 to 1.050 per 10 μg m−3. The result of our analyses suggests that Black Smoke cannot be excluded as a potential causal agent because relative risks over time show no increasing trend despite the decreasing trend in Black Smoke concentrations.

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.

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods. Long-term exposure to nitrogen dioxide (NO2), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO2) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated. The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO2, NO, BS, and SO2 concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO2, NO, BS and SO2, respectively. We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.

Epidemiological studies of long-term air pollution effects have been hampered by difficulties in characterizing the spatial variation in air pollution. We conducted a study to assess the risk of long-term exposure to traffic-related air pollution for the development of inhalant allergy and asthma in children in Stockholm county, Munich and the Netherlands. Exposure to traffic-related air pollution was assessed through a 1-year monitoring program and regression modeling using exposure indicators. This paper documents the performance of the exposure monitoring strategy and the spatial variation of ambient particle concentrations. We measured the ambient concentration of PM2.5 and the reflectance of PM2.5 filters (‘soot’) at 40–42 sites representative of different exposure conditions of the three study populations. Each site was measured during four 14-day average sampling periods spread over one year (spring 1999 to summer 2000). In each study area, a continuous measurement site was operated to remove potential bias due to temporal variation. The selected approach was an efficient method to characterize spatial differences in annual average concentration between a large number of sites in each study area. Adjustment with data from the continuous measurement site improved the precision of the calculated annual averages, especially for PM2.5. Annual average PM2.5 concentrations ranged from 11 to 20 μg/m 3 in Munich, from 8 to 16 μg/m 3 in Stockholm and from 14 to 26 μg/m 3 in the Netherlands. Larger spatial contrasts were found for the absorption coefficient of PM2.5. PM2.5 concentrations were on average 17–18% higher at traffic sites than at urban background sites, but PM2.5 absorption coefficients at traffic sites were between 31% and 55% increased above background. This suggests that spatial variation of traffic-related air pollution may be underestimated if PM2.5 only is measured.

As part of the Small Area Variations in Air Quality and Health (SAVIAH-2) study, monitoring was carried out for a number of traffic related pollutants (mass and absorbance of PM10 and PM2.5, benzene, B(a)P and PAHs) indoors and outdoors of homes in an area of Huddersfield, England. Homes were selected on the basis of their distance from nearby major roads. Monitoring was conducted during two 2-week periods. On each occasion, pollution levels were measured in paired `proximity’ (within 50 m of the main road) and `background’ (>50 m from the main road) homes, for a 24 h period, using active pump samplers. Spatial variations in pollutant concentrations were modest and in most cases did not show significant associations either with distance from road or modelled NO2 concentrations. Neither of these commonly used measures would thus seem to provide reliable indicators for these traffic-related pollutants in this study area. Several of the measured pollutants showed significant inter-correlations, but overall absorbance of the fine particulate fraction displayed the strongest correlations, and would appear to provide the best general marker for these pollutants, perhaps reflecting the importance of diesel vehicles as a major emission source. Indoor–outdoor ratios of PM10, PM2.5 and absorbance of these fractions were below 1, but other pollutants did not show significant differences between indoor and outdoor concentrations. However, all the pollutants showed significant positive correlations between indoor and outdoor concentrations, strongest correlations being seen for the absorbance measures of the particulate fraction. This suggests that outdoor concentrations may give a useful measure of exposures to traffic-related pollutants for use in epidemiological studies.

Spatial variations in urban air pollution are of considerable significance both because of the growing evidence for associations between exposure and human health and because of the increasing requirement for action to control and reduce levels of air pollution. This study examines sources and patterns of variation in NO2, as a marker for traffic-related pollution, in four predominantly urban study areas: Amsterdam (Netherlands), Huddersfield (UK), Poznan (Poland) and Prague (Czech Republic). Data on pollution levels were collected using passive samplers, deployed in duplicate for 2 week periods on four occasions over one year. Mixed-effect modelling was used to explore the magnitude of between-survey, between-site and between-sampler and interaction effects, and to provide a measure of mean annual concentration at each sample site. Measured concentrations varied significantly between different surveys, with a tendency to be higher in winter months. Strong correlations were seen, however, between monitored concentrations in successive surveys, suggesting that the geographic pattern of variation in all areas was essentially stable over time. Between-sampler variation was seen to be small (CV generally 5–8%), indicating that the samplers provided consistent measures of NO2 concentrations. There were also strongly significant between-site and site–survey interaction effects. Between-site variation accounted for between 61% (Poznan) and 84% (Prague) of total observed variation; expressed as a coefficient of variation, between-site variation was least in Amsterdam, the smallest and most uniform study area (22%), and greatest in Prague (42%). The modelled mean annual NO2 concentration, derived from the mixed-effect model, gave a good prediction of mean annual concentration measured using passive samplers on a continuous basis, at 8–10 reference sites in each study area (r2>0.85). Based on the modelled results, it appears that WHO and national air quality standards for NO2 will be routinely exceeded at a number of sites in each study area. Given the apparent stability of the pollution surface in each area, these `hotspots’ are also likely to be persistent over time, implying the potential for relatively high risks of chronic exposure in local populations. Overall the results indicate the capability to characterise spatial variation in traffic-related pollution in urban areas using a small number of intensive surveys, with low-cost sampling devices.

In the Netherlands an excess of 1000–1400 deaths was estimated due to the hot temperatures that occurred during the 2003 summer period. We estimated the number of deaths attributable to the ozone and Particular Matter (PM10) concentrations in the summer period June–August 2003. Our calculations show that an excess of around 400–600 air pollution-related deaths may have occurred compared to an ‘average’ summer. These calculations suggest that in the Netherlands, a significant proportion of the deaths now being attributed to the hot summer weather can reasonably be expected to have been caused by air pollution.