How does air quality reflect the land cover changes: remote sensing approach using Sentinel data

Significant environmental challenges, such as urban and industrial expansion, alongside vegetation preservation, directly influence the concentrations of critical air pollutants and greenhouse gases in cities and their surroundings. The urban development and expansion process is aptly captured by classifying land use and land cover (LULC). We aimed to analyze LULC changes in an Andean area, Ecuador, and to reveal the relations of LULC classes with three air pollutants ozone ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>O</mml:mi> <mml:mn>3</mml:mn></mml:msub> </mml:math> ), nitrogen dioxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> ), and sulfur dioxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>S</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> ), using remote sensing datasets (Sentinel-5P - Sentinel 1 - Sentinel-2) across different periods. Results showed that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>S</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> is not a reliable indicator for assessing its behavior based on LULC classes, as it was difficult to distinguish between different land cover types using this pollutant. For <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> , the analysis showed a moderate distinction among LULC classes, suggesting some variability in its distribution across different land cover classes. On the other hand, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>O</mml:mi> <mml:mn>3</mml:mn></mml:msub> </mml:math> analysis shows that all land cover classes are statistically distinguishable, demonstrating that urban, shrubland, green areas, and forest classes influenced ozone distribution. These findings emphasize the importance of accurate land cover classification in understanding air pollutants' spatial distribution and dynamics. This analysis is crucial for understanding the impacts of land use and land cover changes on urban health and well-being and the effects of rapid urban expansion.

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Journal
Environmental Monitoring and Assessment
Published
2024-12-19
DOI
https://doi.org/10.1007/s10661-024-13478-1
Citations
1
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
0.31
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article

How does air quality reflect the land cover changes: remote sensing approach using Sentinel data

Danilo Mejía, Evelyn Uuemaa, Szilárd Szabó, Pamela Guamán-Pintado
1 citations
Environmental Monitoring and Assessment
Air Quality and Health Impacts
0.31
article

How does air quality reflect the land cover changes: remote sensing approach using Sentinel data

Danilo Mejía, Evelyn Uuemaa, Szilárd Szabó, Pamela Guamán-Pintado
article en
1 citations

Abstract

Significant environmental challenges, such as urban and industrial expansion, alongside vegetation preservation, directly influence the concentrations of critical air pollutants and greenhouse gases in cities and their surroundings. The urban development and expansion process is aptly captured by classifying land use and land cover (LULC). We aimed to analyze LULC changes in an Andean area, Ecuador, and to reveal the relations of LULC classes with three air pollutants ozone ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>O</mml:mi> <mml:mn>3</mml:mn></mml:msub> </mml:math> ), nitrogen dioxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> ), and sulfur dioxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>S</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> ), using remote sensing datasets (Sentinel-5P - Sentinel 1 - Sentinel-2) across different periods. Results showed that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>S</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> is not a reliable indicator for assessing its behavior based on LULC classes, as it was difficult to distinguish between different land cover types using this pollutant. For <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> , the analysis showed a moderate distinction among LULC classes, suggesting some variability in its distribution across different land cover classes. On the other hand, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>O</mml:mi> <mml:mn>3</mml:mn></mml:msub> </mml:math> analysis shows that all land cover classes are statistically distinguishable, demonstrating that urban, shrubland, green areas, and forest classes influenced ozone distribution. These findings emphasize the importance of accurate land cover classification in understanding air pollutants' spatial distribution and dynamics. This analysis is crucial for understanding the impacts of land use and land cover changes on urban health and well-being and the effects of rapid urban expansion.

Environmental Monitoring and AssessmentVol. 197(1)
University of Debrecen (HU), University of Cuenca (EC), University of Tartu (EE)
Sustainable cities and communities
Openalex Percentile: Top 35%
Air Quality and Health Impacts
0.31
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