Spatiotemporal Assessment of Tropospheric Nitrogen Dioxide Changes During COVID-19 Lockdowns Using Cloud-Based Remote Sensing: Evidence from Central America.
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| Title: | Spatiotemporal Assessment of Tropospheric Nitrogen Dioxide Changes During COVID-19 Lockdowns Using Cloud-Based Remote Sensing: Evidence from Central America. |
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| Authors: | Caal Suc, Nestor Erick Anibal1,2 (AUTHOR) nestorcaalsuc@profesor.usac.edu.gt, Pacheco Gil, Henry Antonio2,3 (AUTHOR), Godoy Morales, Martha Ruthilia1,3 (AUTHOR), Lobos Morales, Víctor Manuel1,4 (AUTHOR), López Bautista, Amado Adalberto4,5 (AUTHOR), Rivas, Carlos A.1,5 (AUTHOR), Navarro-Cerrillo, Rafael María2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1850. 15p. |
| Subjects: | Satellite-based remote sensing, Atmospheric nitrogen dioxide, Stay-at-home orders, Remote sensing, Geospatial data, Air quality, Artificial satellites |
| Geographic Terms: | Central America, El Salvador, Guatemala (Guatemala), Honduras |
| Abstract: | Highlights: What are the main findings? Satellite-based observations revealed noticeable spatiotemporal variability in tropospheric NO2 concentrations across Central America during the COVID-19 period, with stronger negative variations observed in highly urbanized departments of Guatemala, El Salvador, and Honduras. The integration of Sentinel-5P TROPOMI data within Google Earth Engine enabled a consistent regional-scale assessment of atmospheric variability, highlighting heterogeneous responses among countries with different mobility restriction measures. What are the implications of the main findings? The results demonstrate the potential of cloud-based Earth observation platforms for atmospheric monitoring and air quality assessment in tropical regions characterized by limited ground-based monitoring networks. The observed regional and subnational variability in NO2 concentrations suggests that mobility restriction measures, urbanization intensity, and anthropogenic activity patterns may influence atmospheric pollution dynamics across Central America. The large-scale mobility restrictions implemented worldwide in response to the COVID-19 (SARS-CoV-2) pandemic led to short-term reductions in anthropogenic emissions, providing an opportunity to explore atmospheric pollutant responses to large-scale changes in human activity and mobility patterns. Although numerous studies have reported air quality improvements during lockdowns, most rely on ground-based monitoring networks and focus on developed regions, leaving gaps in less-studied areas such as Central America. This study evaluates spatiotemporal changes in tropospheric nitrogen dioxide (NO2) across Central America before, during, and after COVID-19 lockdowns using satellite-based remote sensing. High-resolution NO2 vertical column density (VCD) data from the TROPOMI instrument onboard Sentinel-5P were processed using Google Earth Engine. Percentage variations were calculated using the March–May 2020 lockdown period as a reference within the 2019–2021 analysis period. Results indicate reductions in NO2 across several high-density departments, particularly in Guatemala, El Salvador, and Honduras, with decreases of 20–30% and localized negative variations below −40%. In contrast, Nicaragua exhibited comparatively limited changes, while a gradual recovery in NO2 concentrations was observed during 2021. The observed patterns suggest a potential association between NO2 variability and changes in anthropogenic activity during the COVID-19 period, while also highlighting the importance of considering meteorological influences in regional atmospheric assessments. The results further demonstrate the potential of cloud-based Earth observation platforms for atmospheric monitoring in data-scarce tropical regions. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194587071 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Spatiotemporal Assessment of Tropospheric Nitrogen Dioxide Changes During COVID-19 Lockdowns Using Cloud-Based Remote Sensing: Evidence from Central America. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Caal+Suc%2C+Nestor+Erick+Anibal%22">Caal Suc, Nestor Erick Anibal</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> nestorcaalsuc@profesor.usac.edu.gt</i><br /><searchLink fieldCode="AR" term="%22Pacheco+Gil%2C+Henry+Antonio%22">Pacheco Gil, Henry Antonio</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Godoy+Morales%2C+Martha+Ruthilia%22">Godoy Morales, Martha Ruthilia</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lobos+Morales%2C+Víctor+Manuel%22">Lobos Morales, Víctor Manuel</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22López+Bautista%2C+Amado+Adalberto%22">López Bautista, Amado Adalberto</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rivas%2C+Carlos+A%2E%22">Rivas, Carlos A.</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Navarro-Cerrillo%2C+Rafael+María%22">Navarro-Cerrillo, Rafael María</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1850. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+nitrogen+dioxide%22">Atmospheric nitrogen dioxide</searchLink><br /><searchLink fieldCode="DE" term="%22Stay-at-home+orders%22">Stay-at-home orders</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Air+quality%22">Air quality</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Central+America%22">Central America</searchLink><br /><searchLink fieldCode="DE" term="%22El+Salvador%22">El Salvador</searchLink><br /><searchLink fieldCode="DE" term="%22Guatemala+%28Guatemala%29%22">Guatemala (Guatemala)</searchLink><br /><searchLink fieldCode="DE" term="%22Honduras%22">Honduras</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Satellite-based observations revealed noticeable spatiotemporal variability in tropospheric NO2 concentrations across Central America during the COVID-19 period, with stronger negative variations observed in highly urbanized departments of Guatemala, El Salvador, and Honduras. The integration of Sentinel-5P TROPOMI data within Google Earth Engine enabled a consistent regional-scale assessment of atmospheric variability, highlighting heterogeneous responses among countries with different mobility restriction measures. What are the implications of the main findings? The results demonstrate the potential of cloud-based Earth observation platforms for atmospheric monitoring and air quality assessment in tropical regions characterized by limited ground-based monitoring networks. The observed regional and subnational variability in NO2 concentrations suggests that mobility restriction measures, urbanization intensity, and anthropogenic activity patterns may influence atmospheric pollution dynamics across Central America. The large-scale mobility restrictions implemented worldwide in response to the COVID-19 (SARS-CoV-2) pandemic led to short-term reductions in anthropogenic emissions, providing an opportunity to explore atmospheric pollutant responses to large-scale changes in human activity and mobility patterns. Although numerous studies have reported air quality improvements during lockdowns, most rely on ground-based monitoring networks and focus on developed regions, leaving gaps in less-studied areas such as Central America. This study evaluates spatiotemporal changes in tropospheric nitrogen dioxide (NO2) across Central America before, during, and after COVID-19 lockdowns using satellite-based remote sensing. High-resolution NO2 vertical column density (VCD) data from the TROPOMI instrument onboard Sentinel-5P were processed using Google Earth Engine. Percentage variations were calculated using the March–May 2020 lockdown period as a reference within the 2019–2021 analysis period. Results indicate reductions in NO2 across several high-density departments, particularly in Guatemala, El Salvador, and Honduras, with decreases of 20–30% and localized negative variations below −40%. In contrast, Nicaragua exhibited comparatively limited changes, while a gradual recovery in NO2 concentrations was observed during 2021. The observed patterns suggest a potential association between NO2 variability and changes in anthropogenic activity during the COVID-19 period, while also highlighting the importance of considering meteorological influences in regional atmospheric assessments. The results further demonstrate the potential of cloud-based Earth observation platforms for atmospheric monitoring in data-scarce tropical regions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18111850 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1850 Subjects: – SubjectFull: Satellite-based remote sensing Type: general – SubjectFull: Atmospheric nitrogen dioxide Type: general – SubjectFull: Stay-at-home orders Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Geospatial data Type: general – SubjectFull: Air quality Type: general – SubjectFull: Artificial satellites Type: general – SubjectFull: Central America Type: general – SubjectFull: El Salvador Type: general – SubjectFull: Guatemala (Guatemala) Type: general – SubjectFull: Honduras Type: general Titles: – TitleFull: Spatiotemporal Assessment of Tropospheric Nitrogen Dioxide Changes During COVID-19 Lockdowns Using Cloud-Based Remote Sensing: Evidence from Central America. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Caal Suc, Nestor Erick Anibal – PersonEntity: Name: NameFull: Pacheco Gil, Henry Antonio – PersonEntity: Name: NameFull: Godoy Morales, Martha Ruthilia – PersonEntity: Name: NameFull: Lobos Morales, Víctor Manuel – PersonEntity: Name: NameFull: López Bautista, Amado Adalberto – PersonEntity: Name: NameFull: Rivas, Carlos A. – PersonEntity: Name: NameFull: Navarro-Cerrillo, Rafael María IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 11 Titles: – TitleFull: Remote Sensing Type: main |
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