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 Harrison; Pablo Ruiz-Rudolph

doi:10.1021/acs.est.3c00720

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 Harrison, Pablo Ruiz-Rudolph^*

^*Corresponding author for this work

Earth and Environmental Sciences

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	19473-19486
Number of pages	14
Journal	Environmental Science and Technology
Volume	57
Early online date	17 Nov 2023
DOIs	https://doi.org/10.1021/acs.est.3c00720
Publication status	Published - 5 Dec 2023

Bibliographical note

This work was supported as part of the project: “Impact of Wood Burning Air Pollution on Preeclampsia and other Pregnancy Outcomes in Temuco, Chile” (DPI20140093) by CONICYT and Research Councils UK. Juana Maria Delgado-Saborit is supported by the Generalitat Valenciana─Regional Ministry of Education, Research, Culture and Sport under the Talented Researcher Support Programme─Plan GenT (CIDEGENT/2019/064). María Elisa Quinteros was supported by a doctoral scholarship by CONICYT Beca Doctorado Nacional No 21150801, Chile. Estela Blanco acknowledges ANID─MILENIO─NCS2021_013. Salvador Ayala was supported by a doctoral scholarship by ANID Chile Beca Doctorado Nacional No 21191111.

Keywords

Land use regression
Particulate matter
Levoglucosan
Soluble potassium
Wood smoke

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Quinteros, M. E., Blazquez, C., Ayala, S., Kilby, D., Cárdenas-R, J. P., Ossa, X., Rosas-Diaz, F., Stone, E. A., Blanco, E., Delgado-Saborit, J.-M., Harrison, R., & Ruiz-Rudolph, P. (2023). Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile. Environmental Science and Technology, 57, 19473-19486. https://doi.org/10.1021/acs.est.3c00720

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title = "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 (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−3for 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 totalvariance, 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, Particulate matter, Levoglucosan, Soluble potassium, Wood smoke",

author = "Quinteros, {Mar{\'i}a Elisa} and Carola Blazquez and Salvador Ayala and Dylan Kilby and C{\'a}rdenas-R, {Juan Pablo} and Ximena Ossa and Felipe Rosas-Diaz and Stone, {Elizabeth A.} and Estela Blanco and Juana-Mar{\'i}a Delgado-Saborit and Roy Harrison and Pablo Ruiz-Rudolph",

note = "This work was supported as part of the project: “Impact of Wood Burning Air Pollution on Preeclampsia and other Pregnancy Outcomes in Temuco, Chile” (DPI20140093) by CONICYT and Research Councils UK. Juana Maria Delgado-Saborit is supported by the Generalitat Valenciana─Regional Ministry of Education, Research, Culture and Sport under the Talented Researcher Support Programme─Plan GenT (CIDEGENT/2019/064). Mar{\'i}a Elisa Quinteros was supported by a doctoral scholarship by CONICYT Beca Doctorado Nacional No 21150801, Chile. Estela Blanco acknowledges ANID─MILENIO─NCS2021_013. Salvador Ayala was supported by a doctoral scholarship by ANID Chile Beca Doctorado Nacional No 21191111.",

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doi = "10.1021/acs.est.3c00720",

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Quinteros, ME, Blazquez, C, Ayala, S, Kilby, D, Cárdenas-R, JP, Ossa, X, Rosas-Diaz, F, Stone, EA, Blanco, E, Delgado-Saborit, J-M, Harrison, R & Ruiz-Rudolph, P 2023, 'Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile', Environmental Science and Technology, vol. 57, pp. 19473-19486. https://doi.org/10.1021/acs.est.3c00720

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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

AU - Ruiz-Rudolph, Pablo

N1 - This work was supported as part of the project: “Impact of Wood Burning Air Pollution on Preeclampsia and other Pregnancy Outcomes in Temuco, Chile” (DPI20140093) by CONICYT and Research Councils UK. Juana Maria Delgado-Saborit is supported by the Generalitat Valenciana─Regional Ministry of Education, Research, Culture and Sport under the Talented Researcher Support Programme─Plan GenT (CIDEGENT/2019/064). María Elisa Quinteros was supported by a doctoral scholarship by CONICYT Beca Doctorado Nacional No 21150801, Chile. Estela Blanco acknowledges ANID─MILENIO─NCS2021_013. Salvador Ayala was supported by a doctoral scholarship by ANID Chile Beca Doctorado Nacional No 21191111.

PY - 2023/12/5

Y1 - 2023/12/5

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−3for 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 totalvariance, 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−3for 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 totalvariance, 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 - Particulate matter

KW - Levoglucosan

KW - Soluble potassium

KW - Wood smoke

U2 - 10.1021/acs.est.3c00720

DO - 10.1021/acs.est.3c00720

M3 - Article

SN - 0013-936X

VL - 57

SP - 19473

EP - 19486

JO - Environmental Science and Technology

JF - Environmental Science and Technology

ER -

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

Abstract

Bibliographical note

Keywords

UN SDGs

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