As part of the EU-funded SAVIAH project, a regression-based methodology for mapping traffic-related air pollution was developed within a GIS environment. Mapping was carried out for NO2 in Amsterdam, Huddersfield and Prague. In each centre, surveys of NO2, as a marker for traffic-related pollution, were conducted using passive diffusion tubes, exposed for four 2-week periods. A GIS was also established, containing data on monitored air pollution levels, road network, traffic volume, land cover, altitude and other, locally determined, features. Data from 80 of the monitoring sites were then used to construct a regression equation, on the basis of predictor environmental variables, and the resulting equation used to map air pollution across the study area. The accuracy of the map was then assessed by comparing predicted pollution levels with monitored levels at a range of independent reference sites. Results showed that the map produced extremely good predictions of monitored pollution levels, both for individual surveys and for the mean annual concentration, with r2 0.79-0.87 across 8-10 reference points, though the accuracy of predictions for individual survey periods was more variable. In Huddersfield and Amsterdam, further monitoring also showed that the pollution map provided reliable estimates of NO2 concentrations in the following year (r2 0.59-0.86 for n 20).

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Background: Ambient particulate matter (PM) exposure is associated with respiratory and cardiovascular morbidity and mortality. To what extent such effects are different for PM obtained from different sources or locations is still unclear. This study investigated the in vitro toxicity of ambient PM collected at different sites in the Netherlands in relation to PM composition and oxidative potential. Method: PM was sampled at eight sites: three traffic sites, an underground train station, as well as a harbor, farm, steelworks, and urban background location. Coarse (2.5-10 μm), fine (< 2.5 μm) and quasi ultrafine PM (qUF; < 0.18 μm) were sampled at each site. Murine macrophages (RAW 264.7 cells) were exposed to increasing concentrations of PM from these sites (6.25-12.5-25-50-100 μg/ml; corresponding to 3.68-58.8 μg/cm²). Following overnight incubation, MTT-reduction activity (a measure of metabolic activity) and the release of pro-inflammatory markers (Tumor Necrosis Factor-alpha, TNF-α; Interleukin-6, IL-6; Macrophage Inflammatory Protein-2, MIP-2) were measured. The oxidative potential and the endotoxin content of each PM sample were determined in a DTT- and LAL-assay respectively. Multiple linear regression was used to assess the relationship between the cellular responses and PM characteristics: concentration, site, size fraction, oxidative potential and endotoxin content. Results: Most PM samples induced a concentration-dependent decrease in MTT-reduction activity and an increase in pro-inflammatory markers with the exception of the urban background and stop & go traffic samples. Fine and qUF samples of traffic locations, characterized by a high concentration of elemental and organic carbon, induced the highest pro-inflammatory activity. The pro-inflammatory response to coarse samples was associated with the endotoxin level, which was found to increase dramatically during a three-day sample concentration procedure in the laboratory. The underground samples, characterized by a high content of transition metals, showed the largest decrease in MTT-reduction activity. PM size fraction was not related to MTT-reduction activity, whereas there was a statistically significant difference in pro-inflammatory activity between Fine and qUF PM. Furthermore, there was a statistically significant negative association between PM oxidative potential and MTT-reduction activity. Conclusion: The response of RAW264.7 cells to ambient PM was markedly different using samples collected at various sites in the Netherlands that differed in their local PM emission sources. Our results are in support of other investigations showing that the chemical composition as well as oxidative potential are determinants of PM induced toxicity in vitro.

Evidence is emerging that poor mental health is associated with the environmental exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single exposures with poor mental health. To evaluate associations of combined exposure to surrounding green, air pollution and traffic noise with poor mental health. In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise exposure with poor mental health. In single exposure models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single exposure models. Associations of environmental exposures with poor mental health differed somewhat by age. Studies including only one of these three correlated exposures may overestimate the influence of poor mental health attributed to the studied exposure, while underestimating the influence of combined environmental exposures.

The number of anthropogenic chemicals, manufactured, by-products, metabolites and abiotically formed transformation products, counts to hundreds of thousands, at present. Thus, humans and wildlife are exposed to complex mixtures, never one chemical at a time and rarely with only one dominating effect. Hence there is an urgent need to develop strategies on how exposure to multiple hazardous chemicals and the combination of their effects can be assessed. A workshop, "Advancing the Assessment of Chemical Mixtures and their Risks for Human Health and the Environment" was organized in May 2018 together with Joint Research Center in Ispra, EU-funded research projects and Commission Services and relevant EU agencies. This forum for researchers and policy-makers was created to discuss and identify gaps in risk assessment and governance of chemical mixtures as well as to discuss state of the art science and future research needs. Based on the presentations and discussions at this workshop we want to bring forward the following Key Messages.