TY - JOUR
T1 - Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile
AU - Quinteros, María Elisa
AU - Blazquez, Carola
AU - Ayala, Salvador
AU - Kilby, Dylan
AU - Cárdenas-R, Juan Pablo
AU - Ossa, Ximena
AU - Rosas-Diaz, Felipe
AU - Stone, Elizabeth A.
AU - Blanco, Estela
AU - Delgado-Saborit, Juana María
AU - Harrison, Roy M.
AU - Ruiz-Rudolph, Pablo
N1 - Publisher Copyright:
© 2023 American Chemical Society
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Land use regression
KW - Levoglucosan
KW - Particulate matter
KW - Soluble potassium
KW - Wood smoke
UR - https://www.scopus.com/pages/publications/85179005988
U2 - 10.1021/acs.est.3c00720
DO - 10.1021/acs.est.3c00720
M3 - Article
C2 - 37976408
AN - SCOPUS:85179005988
SN - 0013-936X
VL - 57
SP - 19473
EP - 19486
JO - Environmental Science and Technology
JF - Environmental Science and Technology
IS - 48
ER -
