Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile

Abstract

Biomass burning is common in much of the world, and in some areas, residential wood-burning has increased. However, air pollution resulting from biomass burning is an important public health problem. A sampling campaign was carried out between May 2017 and July 2018 in over 64 sites in four sessions, to develop a spatio-temporal land use regression (LUR) model for fine particulate matter (PM) and wood-burning tracers levoglucosan and soluble potassium (K_sol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m^-3 for PM_2.5, 0.607 (0.538) μg m^-3 for levoglucosan, and 0.635 (0.489) μg m^-3 for K_sol. LUR models for PM_2.5, levoglucosan, and K_sol had a satisfactory performance (LOSOCV R²), explaining 88.8%, 87.4%, and 87.3% of the total variance, respectively. All models included sociodemographic predictors consistent with the pattern of use of wood-burning in homes. The models were applied to predict concentrations surfaces and to estimate exposures for an epidemiological study.

Keywords: Land use regression; Levoglucosan; Particulate matter; Soluble potassium; Wood smoke.