Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Youchen Shen; Kees de Hoogh; Oliver Schmitz; Nicholas Clinton; Karin Tuxen-Bettman; Jørgen Brandt; Jesper H. Christensen; Lise M. Frohn; Camilla Geels; Derek Karssenberg; Roel Vermeulen; Gerard Hoek

doi:10.1016/j.envint.2022.107485

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Youchen Shen^*, Kees de Hoogh, Oliver Schmitz, Nicholas Clinton, Karin Tuxen-Bettman, Jørgen Brandt, Jesper H. Christensen, Lise M. Frohn, Camilla Geels, Derek Karssenberg, Roel Vermeulen, Gerard Hoek

^*Corresponding author for this work

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

Abstract

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO₂, O₃, PM_2.5 and PM₁₀ using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R² = NO₂: 0.66; O₃: 0.58; PM₁₀: 0.62; PM_2.5: 0.77), which are better than SLR (average R² = NO₂: 0.61; O₃: 0.46; PM₁₀: 0.51; PM_2.5: 0.75) and RF (average R² = NO₂: 0.64; O₃: 0.53; PM₁₀: 0.56; PM_2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R² > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.

Original language	English
Article number	107485
Pages (from-to)	1-12
Number of pages	12
Journal	Environment international
Volume	168
DOIs	https://doi.org/10.1016/j.envint.2022.107485
Publication status	Published - Oct 2022

Bibliographical note

Funding Information:
This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest.

Funding Information:
This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest.

Publisher Copyright:
© 2022 The Author(s)

Funding

This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest. This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest.

Keywords

Geographically and temporally weighted regression
Land-use regression
Random forest
Spatially varying coefficient
Spatiotemporal variation

UN SDGs

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

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Cite this

Shen, Y., de Hoogh, K., Schmitz, O., Clinton, N., Tuxen-Bettman, K., Brandt, J., Christensen, J. H., Frohn, L. M., Geels, C., Karssenberg, D., Vermeulen, R., & Hoek, G. (2022). Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression. Environment international, 168, 1-12. Article 107485. https://doi.org/10.1016/j.envint.2022.107485

@article{0b93c43074144c56ad1cd76e1846c4ef,

title = "Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression",

abstract = "Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.",

keywords = "Geographically and temporally weighted regression, Land-use regression, Random forest, Spatially varying coefficient, Spatiotemporal variation",

author = "Youchen Shen and {de Hoogh}, Kees and Oliver Schmitz and Nicholas Clinton and Karin Tuxen-Bettman and J{\o}rgen Brandt and Christensen, {Jesper H.} and Frohn, {Lise M.} and Camilla Geels and Derek Karssenberg and Roel Vermeulen and Gerard Hoek",

note = "Funding Information: This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest. Funding Information: This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest. Publisher Copyright: {\textcopyright} 2022 The Author(s)",

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doi = "10.1016/j.envint.2022.107485",

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AU - Brandt, Jørgen

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AU - Geels, Camilla

AU - Karssenberg, Derek

AU - Vermeulen, Roel

AU - Hoek, Gerard

N1 - Funding Information: This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest. Funding Information: This work was supported by EXPANSE and EXPOSOME-NL projects. The EXPANSE project is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 874627. The content of this article is not officially endorsed by the European Union. The EXPOSOME-NL project is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.004.017). The authors declare no competing financial interest. Publisher Copyright: © 2022 The Author(s)

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Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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