Current day concentrations of ambient air pollution have been associated with a range of adverse health effects, particularly mortality and morbidity due to cardiovascular and respiratory diseases. In this review, we summarize the evidence from epidemiological studies on long-term exposure to fine and coarse particles, nitrogen dioxide (NO2) and elemental carbon on mortality from all-causes, cardiovascular disease and respiratory disease. We also summarize the findings on potentially susceptible subgroups across studies. We identified studies through a search in the databases Medline and Scopus and previous reviews until January 2013 and performed a meta-analysis if more than five studies were available for the same exposure metric. There is a significant number of new studies on long-term air pollution exposure, covering a wider geographic area, including Asia. These recent studies support associations found in previous cohort studies on PM2.5. The pooled effect estimate expressed as excess risk per 10 μg/m3 increase in PM2.5 exposure was 6% (95% CI 4, 8%) for all-cause and 11% (95% CI 5, 16%) for cardiovascular mortality. Long-term exposure to PM2.5 was more associated with mortality from cardiovascular disease (particularly ischemic heart disease) than from non-malignant respiratory diseases (pooled estimate 3% (95% CI −6, 13%)). Significant heterogeneity in PM2.5 effect estimates was found across studies, likely related to differences in particle composition, infiltration of particles indoors, population characteristics and methodological differences in exposure assessment and confounder control. All-cause mortality was significantly associated with elemental carbon (pooled estimate per 1 μg/m3 6% (95% CI 5, 7%)) and NO2 (pooled estimate per 10 μg/m3 5% (95% CI 3, 8%)), both markers of combustion sources. There was little evidence for an association between long term coarse particulate matter exposure and mortality, possibly due to the small number of studies and limitations in exposure assessment. Across studies, there was little evidence for a stronger association among women compared to men. In subjects with lower education and obese subjects a larger effect estimate for mortality related to fine PM was found, though the evidence for differences related to education has been weakened in more recent studies.

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability. This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites. For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58-0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48-0.57; EV R2 0.39-0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables. Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.

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.

The oxidative potential (OP) of particulate matter (PM) has been proposed as a more health relevant metric than PM mass. Different assays exist for measuring OP and little is known about how the different assays compare. To assess the OP of PM collected at different site types and to evaluate differences between locations, size fractions and correlation with PM mass and PM composition for different measurement methods for OP. PM2.5 and PM10 was sampled at 5 sites: an underground station, a farm, 2 traffic sites and an urban background site. Three a-cellular assays; dithiothreitol (OP(DTT)), electron spin resonance (OP(ESR)) and ascorbate depletion (OP(AA)) were used to characterize the OP of PM. The highest OP was observed at the underground, where OP of PM10 was 30 (OP(DTT)) to >600 (OP(ESR)) times higher compared to the urban background when expressed as OP/m(3) and 2-40 times when expressed as OP/μg. For the outdoor sites, samples from the farm showed significantly lower OP(ESR) and OP(AA), whereas samples from the continuous traffic site showed the highest OP for all assays. Contrasts in OP between sites were generally larger than for PM mass and were lower for OP(DTT) compared to OP(ESR) and OP(AA). Furthermore, OP(DTT)/μg was significantly higher in PM2.5 compared to PM10, whereas the reverse was the case for OP(ESR). OP(ESR) and OP(AA) were highly correlated with traffic-related PM components (i.e. EC, Fe, Cu, PAHs), whereas OP(DTT) showed the highest correlation with PM mass and OC. Contrasts in OP between sites, differences in size fractions and correlation with PM composition depended on the specific OP assay used, with OP(ESR) and OP(AA) showing the most similar results. This suggests that either OP(ESR) or OP(AA) and OP(DTT) can complement each other in providing information regarding the oxidative properties of PM, which can subsequently be used to study its health effects.

Objective: To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines. Design: Pooled analysis of eight cohorts. Setting: Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries. Participants: 325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM _2.5 ), nitrogen dioxide, ozone, and black carbon. Main outcome measures: Deaths due to natural causes and cause specific mortality. Results: Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM _2.5 , nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m ³ in PM _2.5 was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m ³ increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM _2.5 , nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m ³ an increase of 5 µg/m ³ in PM _2.5 was associated with 29.6% (14% to 47.4%) increase in natural deaths. Conclusions: Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Global Burden of Disease.