Compositional Spatio-Temporal PM2.5 Modelling in Wildfires

Joseph Sánchez-Balseca; Agustí Pérez-Foguet

doi:10.3390/atmos12101309

Compositional Spatio-Temporal PM2.5 Modelling in Wildfires

Joseph Sánchez-Balseca^*
, Agustí Pérez-Foguet

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

Wildfires are natural ecological processes that generate high levels of fine particulate matter (PM2.5) that are dispersed into the atmosphere. PM2.5 could be a potential health problem due to its size. Having adequate numerical models to predict the spatial and temporal distribution of PM2.5 helps to mitigate the impact on human health. The compositional data approach is widely used in the environmental sciences and concentration analyses (parts of a whole). This numerical approach in the modelling process avoids one common statistical problem: the spurious correlation. PM2.5 is a part of the atmospheric composition. In this way, this study developed an hourly spatio-temporal PM2.5 model based on the dynamic linear modelling framework (DLM) with a compositional approach. The results of the model are extended using a Gaussian–Mattern field. The modelling of PM2.5 using a compositional approach presented adequate quality model indices (NSE = 0.82, RMSE = 0.23, and a Pearson correlation coefficient of 0.91); however, the correlation range showed a slightly lower value than the conventional/traditional approach. The proposed method could be used in spatial prediction in places without monitoring stations.

Original language	English
Article number	1309
Journal	Atmosphere
Volume	12
Issue number	10
DOIs	https://doi.org/10.3390/atmos12101309 https://doi.org/10.3390/atmos12101309
State	Published - 7 Oct 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Funding

Joseph Sánchez Balseca is the recipient of a full scholarship from the Secretaria de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT), Ecuador. We thank the research group on Engineering Sciences and Global Development (EScGD) and the Agència de Gestió d'Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (Ref. 2017 SGR 1496). Acknowledgments: Joseph Sánchez Balseca is the recipient of a full scholarship from the Secretaria de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT), Ecuador. We thank the research group on Engineering Sciences and Global Development (EScGD) and the Agència de Gestió d'Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (Ref. 2017 SGR 1496).

Funders	Funder number
Agència de Gestió d'Ajuts Universitaris i de Recerca de la Generalitat de Catalunya	2017 SGR 1496
EScGD
Secretaría de Educación Superior, Ciencia, Tecnología e Innovación

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Air pollution
CoDa
DLM
Environmental statistics
Gaussian fields

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Cite this

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title = "Compositional Spatio-Temporal PM2.5 Modelling in Wildfires",

abstract = "Wildfires are natural ecological processes that generate high levels of fine particulate matter (PM2.5) that are dispersed into the atmosphere. PM2.5 could be a potential health problem due to its size. Having adequate numerical models to predict the spatial and temporal distribution of PM2.5 helps to mitigate the impact on human health. The compositional data approach is widely used in the environmental sciences and concentration analyses (parts of a whole). This numerical approach in the modelling process avoids one common statistical problem: the spurious correlation. PM2.5 is a part of the atmospheric composition. In this way, this study developed an hourly spatio-temporal PM2.5 model based on the dynamic linear modelling framework (DLM) with a compositional approach. The results of the model are extended using a Gaussian–Mattern field. The modelling of PM2.5 using a compositional approach presented adequate quality model indices (NSE = 0.82, RMSE = 0.23, and a Pearson correlation coefficient of 0.91); however, the correlation range showed a slightly lower value than the conventional/traditional approach. The proposed method could be used in spatial prediction in places without monitoring stations.",

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author = "Joseph S{\'a}nchez-Balseca and Agust{\'i} P{\'e}rez-Foguet",

note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2021",

month = oct,

day = "7",

doi = "10.3390/atmos12101309",

language = "English",

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AU - Pérez-Foguet, Agustí

PY - 2021/10/7

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N2 - Wildfires are natural ecological processes that generate high levels of fine particulate matter (PM2.5) that are dispersed into the atmosphere. PM2.5 could be a potential health problem due to its size. Having adequate numerical models to predict the spatial and temporal distribution of PM2.5 helps to mitigate the impact on human health. The compositional data approach is widely used in the environmental sciences and concentration analyses (parts of a whole). This numerical approach in the modelling process avoids one common statistical problem: the spurious correlation. PM2.5 is a part of the atmospheric composition. In this way, this study developed an hourly spatio-temporal PM2.5 model based on the dynamic linear modelling framework (DLM) with a compositional approach. The results of the model are extended using a Gaussian–Mattern field. The modelling of PM2.5 using a compositional approach presented adequate quality model indices (NSE = 0.82, RMSE = 0.23, and a Pearson correlation coefficient of 0.91); however, the correlation range showed a slightly lower value than the conventional/traditional approach. The proposed method could be used in spatial prediction in places without monitoring stations.

AB - Wildfires are natural ecological processes that generate high levels of fine particulate matter (PM2.5) that are dispersed into the atmosphere. PM2.5 could be a potential health problem due to its size. Having adequate numerical models to predict the spatial and temporal distribution of PM2.5 helps to mitigate the impact on human health. The compositional data approach is widely used in the environmental sciences and concentration analyses (parts of a whole). This numerical approach in the modelling process avoids one common statistical problem: the spurious correlation. PM2.5 is a part of the atmospheric composition. In this way, this study developed an hourly spatio-temporal PM2.5 model based on the dynamic linear modelling framework (DLM) with a compositional approach. The results of the model are extended using a Gaussian–Mattern field. The modelling of PM2.5 using a compositional approach presented adequate quality model indices (NSE = 0.82, RMSE = 0.23, and a Pearson correlation coefficient of 0.91); however, the correlation range showed a slightly lower value than the conventional/traditional approach. The proposed method could be used in spatial prediction in places without monitoring stations.

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KW - DLM

KW - Environmental statistics

KW - Gaussian fields

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DO - 10.3390/atmos12101309

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SN - 2073-4433

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JO - Atmosphere

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ER -

Compositional Spatio-Temporal PM2.5 Modelling in Wildfires

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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