The burden associated with ambient PM2.5 and meteorological factors in Guangzhou, China, 2012-2016: A generalized additive modeling of temporal years of life lost

Background: Daily exposure to ambient particulate matter with aerodynamic diameter <2.5 Am (PM2.5) increases deaths and is an important contributor to burden of disease in population. To better understand the disease burden associated with PM2.5, we examined the effects of PM2.5 on daily years of life lost (YLL) in Guangzhou, China. Methods: Using Guangzhou death registry, air pollution and meteorological database, we applied generalized additive models (GAM) to the relationships between YLL and PM2.5. We then adjusted the models for age, gender, seasonality and meteorological variables. We also conducted within -data prediction of YLL while setting 2012-2014 as baseline. Results: Over 2 million YLLs (800,137 YLLs for females and 1,212,040 YLLs for males) were observed during 2012-2016. YLL was higher for the elderly people. Mean daily average PM2.5 concentration was 47.3 mu g/m(3). In model comparisons, the GAM with six meteorological variables (sunshine hours, relative humidity, precipitation, atmospheric pressure, wind speed, evaporation) outperformed the others. The R-2 and total deviance were 0.542 and 53.0%, respectively. Non-linear trends were observed for PM2.5 and meteorological variables. Fitted daily YLL increased to the highest level, when PM2.5 concentration reached 134.3 g/m(3) and atmospheric pressure reached 99.4 kPa. Within-data prediction supported the fitted GAM, where low mean absolute percentage errors were observed. Conclusions: Daily PM2.5 exposure has a nonlinear effect on YLL and increased levels of PM2.5 may lead to increased YLL. This study highlights the urge to reduce ambient PM2.5 pollution in Guangzhou, in order to promote environmental health. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license