Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches.

ABSTRACT: Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental c

SUBMITTER: Brokamp C 

PROVIDER: S-EPMC5611888 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature