Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural Network
Abstract The complexity of monitoring is compounded by the environmental and health impacts linked to air pollution. The elevated expenses and intricate execution involved in measurements prompt the integration of modeling as a complementary approach alongside monitoring and surveillance efforts. Tr...
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| Format: | Article |
| Language: | English |
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Springer
2024-04-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.230309 |
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| author | Amine Ajdour Brahim Ydir Houria Bouzghiba Ishaq Dimeji Sulaymon Anas Adnane Dris Ben Hmamou Kenza Khomsi Jamal Chaoufi Gábor Géczi Radouane Leghrib |
| author_facet | Amine Ajdour Brahim Ydir Houria Bouzghiba Ishaq Dimeji Sulaymon Anas Adnane Dris Ben Hmamou Kenza Khomsi Jamal Chaoufi Gábor Géczi Radouane Leghrib |
| author_sort | Amine Ajdour |
| collection | DOAJ |
| description | Abstract The complexity of monitoring is compounded by the environmental and health impacts linked to air pollution. The elevated expenses and intricate execution involved in measurements prompt the integration of modeling as a complementary approach alongside monitoring and surveillance efforts. Transport chemistry models like CHIMERE operate deterministically, utilizing meteorological factors, emissions data, boundary conditions, and various physical processes such as transport and Horizontal Mesh-Grid to influence inputs and outputs. The findings are validated using monitoring data over different periods of 2010, 2016, and 2021 and compared with results from prior research. The initial aspect reveals: (1) Enhanced resolution increases the probability of accurate forecasts, particularly for ozone, with PM10 displaying less distinct patterns. (2) Spatial resolution has minimal impact on temperature and wind speed. (3) Planetary Boundary Layer Height (PBLH) exhibits higher sensitivity, influencing Land Use and Land Cover (LULC), primarily due to emissions, advocating for higher resolution. The second aspect demonstrates: (4) CHIMERE-Artificial Neural Network (CHIMERE-ANN) demonstrates high accuracy in predicting ozone levels for Agadir and Casablanca, achieving improved correlation coefficients of 80% and 94%, respectively, accompanied by a notable decrease in Root Mean Square Error (RMSE) to 7.5 µg m–3 and 7.4 µg m–3. (5) Implementing CHIMERE-ANN with high spatial resolution concentrations (RA3 and RC3) enhances the accuracy of pollutant concentration forecasts. The proposed model enables rapid and detailed simulation of air pollution scenarios alongside flexibility for continuous updates. |
| format | Article |
| id | doaj-art-f38f3dfef90149888f7f9e15b32aa964 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-f38f3dfef90149888f7f9e15b32aa9642025-02-09T12:24:30ZengSpringerAerosol and Air Quality Research1680-85842071-14092024-04-0124812110.4209/aaqr.230309Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural NetworkAmine Ajdour0Brahim Ydir1Houria Bouzghiba2Ishaq Dimeji Sulaymon3Anas Adnane4Dris Ben Hmamou5Kenza Khomsi6Jamal Chaoufi7Gábor Géczi8Radouane Leghrib9Doctoral School of Environmental Sciences, Hungarian University of Agriculture and Life SciencesLaboratory of Materials, Signals, Systems and Physical Modeling, Physics Department, Faculty of Sciences, Ibn Zohr UniversityDoctoral School of Environmental Sciences, Hungarian University of Agriculture and Life SciencesSand and Dust Storm Warning Regional Center, National Center for MeteorologyLaboratory of Materials, Signals, Systems and Physical Modeling, Physics Department, Faculty of Sciences, Ibn Zohr UniversityLaboratory of Materials, Signals, Systems and Physical Modeling, Physics Department, Faculty of Sciences, Ibn Zohr UniversityGeneral Directorate of Meteorology, Face Préfecture Hay HassaniLaboratory of Materials, Signals, Systems and Physical Modeling, Physics Department, Faculty of Sciences, Ibn Zohr UniversityInstitute of Environmental Sciences, Department of Environmental Analysis and Environmental Technology, Hungarian University of Agriculture and Life SciencesLaboratory of Materials, Signals, Systems and Physical Modeling, Physics Department, Faculty of Sciences, Ibn Zohr UniversityAbstract The complexity of monitoring is compounded by the environmental and health impacts linked to air pollution. The elevated expenses and intricate execution involved in measurements prompt the integration of modeling as a complementary approach alongside monitoring and surveillance efforts. Transport chemistry models like CHIMERE operate deterministically, utilizing meteorological factors, emissions data, boundary conditions, and various physical processes such as transport and Horizontal Mesh-Grid to influence inputs and outputs. The findings are validated using monitoring data over different periods of 2010, 2016, and 2021 and compared with results from prior research. The initial aspect reveals: (1) Enhanced resolution increases the probability of accurate forecasts, particularly for ozone, with PM10 displaying less distinct patterns. (2) Spatial resolution has minimal impact on temperature and wind speed. (3) Planetary Boundary Layer Height (PBLH) exhibits higher sensitivity, influencing Land Use and Land Cover (LULC), primarily due to emissions, advocating for higher resolution. The second aspect demonstrates: (4) CHIMERE-Artificial Neural Network (CHIMERE-ANN) demonstrates high accuracy in predicting ozone levels for Agadir and Casablanca, achieving improved correlation coefficients of 80% and 94%, respectively, accompanied by a notable decrease in Root Mean Square Error (RMSE) to 7.5 µg m–3 and 7.4 µg m–3. (5) Implementing CHIMERE-ANN with high spatial resolution concentrations (RA3 and RC3) enhances the accuracy of pollutant concentration forecasts. The proposed model enables rapid and detailed simulation of air pollution scenarios alongside flexibility for continuous updates.https://doi.org/10.4209/aaqr.230309Horizontal Mesh-GridEulerian Transport ModelCHIMERE-ANNOzone (O3)Particulate matter (PM10) |
| spellingShingle | Amine Ajdour Brahim Ydir Houria Bouzghiba Ishaq Dimeji Sulaymon Anas Adnane Dris Ben Hmamou Kenza Khomsi Jamal Chaoufi Gábor Géczi Radouane Leghrib Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural Network Aerosol and Air Quality Research Horizontal Mesh-Grid Eulerian Transport Model CHIMERE-ANN Ozone (O3) Particulate matter (PM10) |
| title | Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural Network |
| title_full | Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural Network |
| title_fullStr | Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural Network |
| title_full_unstemmed | Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural Network |
| title_short | Investigating Two-dimensional Horizontal Mesh Grid Effects on the Eulerian Atmospheric Transport Model Using Artificial Neural Network |
| title_sort | investigating two dimensional horizontal mesh grid effects on the eulerian atmospheric transport model using artificial neural network |
| topic | Horizontal Mesh-Grid Eulerian Transport Model CHIMERE-ANN Ozone (O3) Particulate matter (PM10) |
| url | https://doi.org/10.4209/aaqr.230309 |
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