As new scientific evidence on health effects of air pollution is generated, air quality guidelines need to be periodically updated. The objective of this review is to support the derivation of updated guidelines by the World Health Organization (WHO) by performing a systematic review of evidence of associations between long-term exposure to particulate matter with diameter under 2.5 µm (PM_2.5) and particulate matter with diameter under 10 µm (PM₁₀), in relation to all-cause and cause-specific mortality. As there is especially uncertainty about the relationship at the low and high end of the exposure range, the review needed to provide an indication of the shape of the concentration-response function (CRF). We systematically searched MEDLINE and EMBASE from database inception to 9 October 2018. Articles were checked for eligibility by two reviewers. We included cohort and case-control studies on outdoor air pollution in human populations using individual level data. In addition to natural-cause mortality, we evaluated mortality from circulatory diseases (ischemic heart disease (IHD) and cerebrovascular disease (stroke) also specifically), respiratory diseases (Chronic Obstructive Pulmonary Disease (COPD) and acute lower respiratory infection (ALRI) also specifically) and lung cancer. A random-effect meta-analysis was performed when at least three studies were available for a specific exposure-outcome pair. Risk of bias was assessed for all included articles using a specifically developed tool coordinated by WHO. Additional analyses were performed to assess consistency across geographic region, explain heterogeneity and explore the shape of the CRF. An adapted GRADE (Grading of Recommendations Assessment, Development and Evaluation) assessment of the body of evidence was made using a specifically developed tool coordinated by WHO. A large number (N = 107) of predominantly cohort studies (N = 104) were included after screening more than 3000 abstracts. Studies were conducted globally with the majority of studies from North America (N = 62) and Europe (N = 25). More studies used PM_2.5 (N = 71) as the exposure metric than PM₁₀ (N = 42). PM_2.5 was significantly associated with all causes of death evaluated. The combined Risk Ratio (RR) for PM_2.5 and natural-cause mortality was 1.08 (95%CI 1.06, 1.09) per 10 µg/m³. Meta analyses of studies conducted at the low mean PM_2.5 levels (<25, 20, 15, 12, 10 µg/m³) yielded RRs that were similar or higher compared to the overall RR, consistent with the finding of generally linear or supra-linear CRFs in individual studies. Pooled RRs were almost identical for studies conducted in North America, Europe and Western Pacific region. PM₁₀ was significantly associated with natural-cause and most but not all causes of death. Application of the risk of bias tool showed that few studies were at a high risk of bias in any domain. Application of the adapted GRADE tool resulted in an assessment of "high certainty of evidence" for PM_2.5 with all assessed endpoints except for respiratory mortality (moderate). The evidence was rated as less certain for PM₁₀ and cause-specific mortality ("moderate" for circulatory, IHD, COPD and "low" for stroke mortality. Compared to the previous global WHO evaluation, the evidence base has increased substantially. However, studies conducted in low- and middle- income countries (LMICs) are still limited. There is clear evidence that both PM_2.5 and PM₁₀ were associated with increased mortality from all causes, cardiovascular disease, respiratory disease and lung cancer. Associations remained below the current WHO guideline exposure level of 10 µg/m³ for PM_2.5. Systematic review registration number (PROSPERO ID): CRD42018082577.

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.

Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence. The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and ozone (O3) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines. The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO2, PM2.5, BC and O3 (warm season) were 24.2 µg/m3 (19.5, 29.7), 15.4 µg/m3 (12.8, 17.3), 1.6 10-5m-1 (1.3, 1.8), and 86.6 µg/m3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m3). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m3. We did not observe associations between NO2, BC or O3 and lung cancer incidence. Long-term ambient PM2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines.

Rationale:: The pathophysiologic mechanisms of air pollution–associated exacerbation of cardiovascular events remain incompletely understood. Objective:: To assess whether ambient air pollution can be a trigger of the vulnerable plaque and heightened thrombogenicity through systemic inflammatory pathways. Methods and Results:: In Beijing AIRCHD study (Air Pollution and Cardiovascular Dysfunctions in Healthy Adults Living in Beijing), 73 healthy adults (mean±SD, 23.3±5.4 years) were followed up in 2014 to 2016. We estimated associations between air pollutants and biomarkers relevant to atherosclerotic plaque vulnerability, thrombogenicity, and inflammation using linear mixed-effects models and elucidated the biological pathways involved using mediation analyses. Receiver operating characteristic analyses were conducted to assess the ability of each biomarker to predict ambient air pollution exposures. High average concentrations of particulate matter in diameter <2.5 μm (91.8±63.8 µg/m ³ ) were observed during the study period. Significant increases in circulating biomarkers of plaque vulnerability, namely MMPs (matrix metalloproteinases; MMP-1, 2, 3, 7, 8, and 9), of 8.6% (95% CI, 0.1–17.8) to 141.4% (95% CI, 111.8–171.0) were associated with interquartile range increases in moving averages of particulate matter in diameter <2.5 μm, number concentrations of particles in sizes of 5 to 560 nm and black carbon, during the last 1 to 7 days before each participant’s clinic visit. Higher air pollutant levels were also significantly associated with decreases in TIMP (tissue inhibitors of MMPs; TIMP-1 and 2), heightened thrombogenicity (shortened prothrombin time and increases in sCD40L [soluble CD40 ligand], sCD62P [soluble P-selectin], and fibrinogen/fibrin degradation products), and elevations in systemic inflammation (IL-1β [interleukin-1β], CRP [C-reactive protein], MIP-1α/β [macrophage inflammatory protein-1α/β], sRAGE [soluble receptor for advanced glycation end products], and IGFBP [insulin-like growth factor–binding protein]-1 and 3). Receiver operating characteristic curves showed that several biomarkers can serve as robust pollutant-specific predictors with high versus low black carbon exposure (area under the receiver operating characteristic curve of 0.974 [95% CI, 0.955–0.992] for MMP-8 and 0.962 [95% CI, 0.935–0.988] for sRAGE). Mediation analysis further showed that systemic inflammation can mediate ≤46% of the changes in MMPs and thrombogenicity associated with interquartile range increases in air pollutants. Conclusions:: Our results suggest that air pollution may prompt cardiovascular events by triggering vulnerable plaque along with heightened thrombogenicity possibly through systemic inflammatory pathways.