Spatially Modeling the Effects of Meteorological Drivers of PM2.5 in the Eastern United States via a Local Linear Penalized Quantile Regression Estimator.

ABSTRACT: Fine particulate matter (PM2.5) poses a significant risk to human health, with long-term exposure being linked to conditions such as asthma, chronic bronchitis, lung cancer, atherosclerosis, etc. In order to improve current pollution control strategies and to better shape public policy, the development of a more comprehensive understanding of this air pollutant is necessary. To this end, this work attempts to quantify the relationship between certain meteorological drivers and the levels of PM2.5. It is expected that the set of important meteorological drivers will vary both spatially and within the conditional distribution of PM2.5 levels. To account for these characteristics, a new local linear penalized quantile regression methodology is developed. The proposed estimator uniquely select

SUBMITTER: Russell BT 

PROVIDER: S-EPMC5656298 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature