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

María Elisa Quinteros, Carola Blazquez, Salvador Ayala, Dylan Kilby, Juan Pablo Cárdenas-R, Ximena Ossa, Felipe Rosas-Diaz, Elizabeth A. Stone, Estela Blanco, Juana María Delgado-Saborit, Roy M. Harrison, Pablo Ruiz-Rudolph

Research output: Contribution to journalArticlepeer-review

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 (Ksol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m-3 for PM2.5, 0.607 (0.538) μg m-3 for levoglucosan, and 0.635 (0.489) μg m-3 for Ksol. LUR models for PM2.5, levoglucosan, and Ksol 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.

Original languageEnglish
Pages (from-to)19473-19486
Number of pages14
JournalEnvironmental Science and Technology
Volume57
Issue number48
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Land use regression
  • Levoglucosan
  • Particulate matter
  • Soluble potassium
  • Wood smoke

ASJC Scopus subject areas

  • General Chemistry
  • Environmental Chemistry

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