Background: Long-term exposure to ambient fine particulate matter (≤ 2.5 μg/m3 in aerodynamic diameter; PM2.5) is significantly associated with increased risk of premature mortality. Our goal was to provide an updated meta-analysis of all-cause and cause-specific mortality associated with exposure to PM2.5 and to better estimate the risk of death as a function of air pollution levels. Methods: We systematically searched all published cohort studies examining the association between long term exposure to PM2.5 and mortality. We applied multivariate linear random effects meta-analysis with random effects for cohort, and study within cohort. Meta-regression techniques were used to test whether study population or analytic characteristics modify the PM2.5 -mortality association and to estimate the shape of the concentration-response curve. Results: A total of 53 studies that provided 135 estimates of the quantitative association between the risk of mortality and exposure to PM2.5 were included in the meta-analysis. There were 39 studies from North America, 8 from Europe, and 6 from Asia. Since 2015, 17 studies of long-term air pollution exposure have been published, covering, wider geographic areas with a wider range of mean exposures (e.g. < 12 or > 20μg/m3). A penalized spline showed the slope decreased at higher concentrations but appeared to level off. We found that the inverse transform of average PM2.5well approximated that spline and provided a parametric estimate that fit better than a linear or logarithmic term for average PM2.5. In addition, we found that studies using space time exposure models or fixed monitors at Zip-code scale (as compared to land use regression method), or additionally controlling for area level socio-economic status, or with mean exposure less than 10 μg/m3 were associated with higher mortality effect estimates. Conclusions: This meta-analysis provides strong evidence for the adverse effect of PM2.5 on mortality, that studies with poorer exposure have lower effect size estimates, that more control for SES increases effect size estimates, and that significant effects are seen below 10 µg/m3. The concentration -response function produced here can be further applied in the global health risk assessment of air particulate matter.