Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

<p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone...
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Autor*in:	C. Betancourt [verfasserIn] T. T. Stomberg [verfasserIn] A.-K. Edrich [verfasserIn] A. Patnala [verfasserIn] M. G. Schultz [verfasserIn] R. Roscher [verfasserIn] J. Kowalski [verfasserIn] S. Stadtler [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Geoscientific Model Development - Copernicus Publications, 2009, 15(2022), Seite 4331-4354
Übergeordnetes Werk:	volume:15 ; year:2022 ; pages:4331-4354

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.5194/gmd-15-4331-2022

Katalog-ID:	DOAJ039693457

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spelling	10.5194/gmd-15-4331-2022 doi (DE-627)DOAJ039693457 (DE-599)DOAJ3cc58269075f4002b33d4f4561477fab DE-627 ger DE-627 rakwb eng QE1-996.5 C. Betancourt verfasserin aut Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1<span class="inline-formula"<<sup<∘</sup<</span< <span class="inline-formula"<×</span< 0.1<span class="inline-formula"<<sup<∘</sup<</span<. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.</p< <p<Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.</p< <p<We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.</p< Geology T. T. Stomberg verfasserin aut A.-K. Edrich verfasserin aut A.-K. Edrich verfasserin aut A. Patnala verfasserin aut M. G. Schultz verfasserin aut R. Roscher verfasserin aut R. Roscher verfasserin aut J. Kowalski verfasserin aut S. Stadtler verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 15(2022), Seite 4331-4354 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:15 year:2022 pages:4331-4354 https://doi.org/10.5194/gmd-15-4331-2022 kostenfrei https://doaj.org/article/3cc58269075f4002b33d4f4561477fab kostenfrei https://gmd.copernicus.org/articles/15/4331/2022/gmd-15-4331-2022.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 4331-4354
allfields_unstemmed	10.5194/gmd-15-4331-2022 doi (DE-627)DOAJ039693457 (DE-599)DOAJ3cc58269075f4002b33d4f4561477fab DE-627 ger DE-627 rakwb eng QE1-996.5 C. Betancourt verfasserin aut Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1<span class="inline-formula"<<sup<∘</sup<</span< <span class="inline-formula"<×</span< 0.1<span class="inline-formula"<<sup<∘</sup<</span<. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.</p< <p<Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.</p< <p<We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.</p< Geology T. T. Stomberg verfasserin aut A.-K. Edrich verfasserin aut A.-K. Edrich verfasserin aut A. Patnala verfasserin aut M. G. Schultz verfasserin aut R. Roscher verfasserin aut R. Roscher verfasserin aut J. Kowalski verfasserin aut S. Stadtler verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 15(2022), Seite 4331-4354 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:15 year:2022 pages:4331-4354 https://doi.org/10.5194/gmd-15-4331-2022 kostenfrei https://doaj.org/article/3cc58269075f4002b33d4f4561477fab kostenfrei https://gmd.copernicus.org/articles/15/4331/2022/gmd-15-4331-2022.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 4331-4354
allfieldsGer	10.5194/gmd-15-4331-2022 doi (DE-627)DOAJ039693457 (DE-599)DOAJ3cc58269075f4002b33d4f4561477fab DE-627 ger DE-627 rakwb eng QE1-996.5 C. Betancourt verfasserin aut Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1<span class="inline-formula"<<sup<∘</sup<</span< <span class="inline-formula"<×</span< 0.1<span class="inline-formula"<<sup<∘</sup<</span<. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.</p< <p<Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.</p< <p<We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.</p< Geology T. T. Stomberg verfasserin aut A.-K. Edrich verfasserin aut A.-K. Edrich verfasserin aut A. Patnala verfasserin aut M. G. Schultz verfasserin aut R. Roscher verfasserin aut R. Roscher verfasserin aut J. Kowalski verfasserin aut S. Stadtler verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 15(2022), Seite 4331-4354 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:15 year:2022 pages:4331-4354 https://doi.org/10.5194/gmd-15-4331-2022 kostenfrei https://doaj.org/article/3cc58269075f4002b33d4f4561477fab kostenfrei https://gmd.copernicus.org/articles/15/4331/2022/gmd-15-4331-2022.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 4331-4354
allfieldsSound	10.5194/gmd-15-4331-2022 doi (DE-627)DOAJ039693457 (DE-599)DOAJ3cc58269075f4002b33d4f4561477fab DE-627 ger DE-627 rakwb eng QE1-996.5 C. Betancourt verfasserin aut Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1<span class="inline-formula"<<sup<∘</sup<</span< <span class="inline-formula"<×</span< 0.1<span class="inline-formula"<<sup<∘</sup<</span<. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.</p< <p<Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.</p< <p<We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.</p< Geology T. T. Stomberg verfasserin aut A.-K. Edrich verfasserin aut A.-K. Edrich verfasserin aut A. Patnala verfasserin aut M. G. Schultz verfasserin aut R. Roscher verfasserin aut R. Roscher verfasserin aut J. Kowalski verfasserin aut S. Stadtler verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 15(2022), Seite 4331-4354 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:15 year:2022 pages:4331-4354 https://doi.org/10.5194/gmd-15-4331-2022 kostenfrei https://doaj.org/article/3cc58269075f4002b33d4f4561477fab kostenfrei https://gmd.copernicus.org/articles/15/4331/2022/gmd-15-4331-2022.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 4331-4354
language	English
source	In Geoscientific Model Development 15(2022), Seite 4331-4354 volume:15 year:2022 pages:4331-4354
sourceStr	In Geoscientific Model Development 15(2022), Seite 4331-4354 volume:15 year:2022 pages:4331-4354
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Geology
isfreeaccess_bool	true
container_title	Geoscientific Model Development
authorswithroles_txt_mv	C. Betancourt @@aut@@ T. T. Stomberg @@aut@@ A.-K. Edrich @@aut@@ A. Patnala @@aut@@ M. G. Schultz @@aut@@ R. Roscher @@aut@@ J. Kowalski @@aut@@ S. Stadtler @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	582024102
id	DOAJ039693457
language_de	englisch
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callnumber-first	Q - Science
author	C. Betancourt
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topic_title	QE1-996.5 Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
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title	Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
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title_full	Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
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author_browse	C. Betancourt T. T. Stomberg A.-K. Edrich A. Patnala M. G. Schultz R. Roscher J. Kowalski S. Stadtler
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title_sort	global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
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title_auth	Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
abstract	<p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1<span class="inline-formula"<<sup<∘</sup<</span< <span class="inline-formula"<×</span< 0.1<span class="inline-formula"<<sup<∘</sup<</span<. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.</p< <p<Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.</p< <p<We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.</p<
abstractGer	<p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1<span class="inline-formula"<<sup<∘</sup<</span< <span class="inline-formula"<×</span< 0.1<span class="inline-formula"<<sup<∘</sup<</span<. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.</p< <p<Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.</p< <p<We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.</p<
abstract_unstemmed	<p<Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1<span class="inline-formula"<<sup<∘</sup<</span< <span class="inline-formula"<×</span< 0.1<span class="inline-formula"<<sup<∘</sup<</span<. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.</p< <p<Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.</p< <p<We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.</p<
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Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

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