Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets
Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM<sub>2.5</sub>), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/14/2286 |
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| author | Johana M. Carmona Pawan Gupta Diego F. Lozano-García Ana Y. Vanoye Fabiola D. Yépez Alberto Mendoza |
| author_facet | Johana M. Carmona Pawan Gupta Diego F. Lozano-García Ana Y. Vanoye Fabiola D. Yépez Alberto Mendoza |
| author_sort | Johana M. Carmona |
| collection | DOAJ |
| description | Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM<sub>2.5</sub>), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM<sub>2.5</sub> ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM<sub>2.5</sub> ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM<sub>2.5</sub>, sea salt PM<sub>2.5</sub>, black carbon (BC), organic carbon (OC), and sulfate (SO<sub>4</sub><sup>2−</sup>) reanalysis data were identified as factors that significantly influenced PM<sub>2.5</sub> concentrations. The ENN estimated a PM<sub>2.5</sub> monthly mean of 25.62 μg m<sup>−3</sup> during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient <i>R</i> ~ 0.90; root mean square error = 1.81 μg m<sup>−3</sup>; mean absolute error = 1.31 μg m<sup>−3</sup>. Overall, the PM<sub>2.5</sub> levels were higher in winter and spring. The highest PM<sub>2.5</sub> levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM<sub>2.5</sub> was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps. |
| first_indexed | 2024-03-10T18:26:03Z |
| format | Article |
| id | doaj.art-b246fc78f3cb47dda7d1f7edb4d57514 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:26:03Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b246fc78f3cb47dda7d1f7edb4d575142023-11-20T07:00:44ZengMDPI AGRemote Sensing2072-42922020-07-011214228610.3390/rs12142286Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis DatasetsJohana M. Carmona0Pawan Gupta1Diego F. Lozano-García2Ana Y. Vanoye3Fabiola D. Yépez4Alberto Mendoza5Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, MexicoScience and Technology Institute, Universities Space Research Association (USRA), Huntsville, AL 35806, USATecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, MexicoTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, MexicoFacultad de Ingeniería Civil, Universidad Autónoma de Nuevo León, San Nicolás de los Garza 66455, MexicoTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, MexicoAerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM<sub>2.5</sub>), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM<sub>2.5</sub> ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM<sub>2.5</sub> ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM<sub>2.5</sub>, sea salt PM<sub>2.5</sub>, black carbon (BC), organic carbon (OC), and sulfate (SO<sub>4</sub><sup>2−</sup>) reanalysis data were identified as factors that significantly influenced PM<sub>2.5</sub> concentrations. The ENN estimated a PM<sub>2.5</sub> monthly mean of 25.62 μg m<sup>−3</sup> during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient <i>R</i> ~ 0.90; root mean square error = 1.81 μg m<sup>−3</sup>; mean absolute error = 1.31 μg m<sup>−3</sup>. Overall, the PM<sub>2.5</sub> levels were higher in winter and spring. The highest PM<sub>2.5</sub> levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM<sub>2.5</sub> was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.https://www.mdpi.com/2072-4292/12/14/2286MERRA-2PM<sub>2.5</sub>aerosol optical depth (AOD)air pollutionsatellite dataneural networks |
| spellingShingle | Johana M. Carmona Pawan Gupta Diego F. Lozano-García Ana Y. Vanoye Fabiola D. Yépez Alberto Mendoza Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets Remote Sensing MERRA-2 PM<sub>2.5</sub> aerosol optical depth (AOD) air pollution satellite data neural networks |
| title | Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets |
| title_full | Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets |
| title_fullStr | Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets |
| title_full_unstemmed | Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets |
| title_short | Spatial and Temporal Distribution of PM<sub>2.5</sub> Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets |
| title_sort | spatial and temporal distribution of pm sub 2 5 sub pollution over northeastern mexico application of merra 2 reanalysis datasets |
| topic | MERRA-2 PM<sub>2.5</sub> aerosol optical depth (AOD) air pollution satellite data neural networks |
| url | https://www.mdpi.com/2072-4292/12/14/2286 |
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