Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network.
Air pollution poses significant risks to human health and the environment, which makes it necessary to create effective strategies for air quality management. This study presents an approach for air quality management in Tehran using the Convolutional Neural Network (CNN) algorithm. The proposed met...
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I.R. of Iran Meteorological Organization
2024-03-01
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| Series: | Nīvār |
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| Online Access: | https://nivar.irimo.ir/article_192602_7ef07a445c311907bf6f9e2ab32a61a1.pdf |
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| author | Abed Bashar Doost Mohammad Saadi Mesgari |
| author_facet | Abed Bashar Doost Mohammad Saadi Mesgari |
| author_sort | Abed Bashar Doost |
| collection | DOAJ |
| description | Air pollution poses significant risks to human health and the environment, which makes it necessary to create effective strategies for air quality management. This study presents an approach for air quality management in Tehran using the Convolutional Neural Network (CNN) algorithm. The proposed method provides the possibility of spatial modeling and preparation of risk maps of two important air pollutants, namely particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10). To develop this air pollution model, the data available in the database containing the annual average of two pollutants from 2012 to 2022 were used. In this model, various parameters affecting air pollution including altitude, humidity, distance to industrial areas, normalized difference index of plants (NDVI), population density, precipitation, distance to the street, temperature, traffic volume, wind direction, and wind speed are considered. Taken and spatial modeling of two pollutants using CNN has been done. The evaluation of the model was done using different evaluation criteria, and the findings showed that the R-squared (R2) values in this model for PM2.5 and PM10 pollutants are 0.889 and 0.972, respectively. The accuracy of the risk map was evaluated using relative operating characteristic (ROC) for two pollutants, and the findings showed that the CNN model has an acceptable accuracy in producing the pollution risk map. In general, risk maps provide useful information about geographic areas with high pollution risks and help in decision-making and targeted pollution reduction efforts. |
| format | Article |
| id | doaj-art-594578d6304843968ed12a8855aea50f |
| institution | Kabale University |
| issn | 1735-0565 2645-3347 |
| language | fas |
| publishDate | 2024-03-01 |
| publisher | I.R. of Iran Meteorological Organization |
| record_format | Article |
| series | Nīvār |
| spelling | doaj-art-594578d6304843968ed12a8855aea50f2025-01-05T10:55:06ZfasI.R. of Iran Meteorological OrganizationNīvār1735-05652645-33472024-03-0148124-125314910.30467/nivar.2024.430255.1276192602Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network.Abed Bashar Doost0Mohammad Saadi Mesgari1Doctoral student of civil engineering and mapping, geographic information systems, K. N. Toosi University, Tehran, Iran.Associate Professor of Department of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, IranAir pollution poses significant risks to human health and the environment, which makes it necessary to create effective strategies for air quality management. This study presents an approach for air quality management in Tehran using the Convolutional Neural Network (CNN) algorithm. The proposed method provides the possibility of spatial modeling and preparation of risk maps of two important air pollutants, namely particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10). To develop this air pollution model, the data available in the database containing the annual average of two pollutants from 2012 to 2022 were used. In this model, various parameters affecting air pollution including altitude, humidity, distance to industrial areas, normalized difference index of plants (NDVI), population density, precipitation, distance to the street, temperature, traffic volume, wind direction, and wind speed are considered. Taken and spatial modeling of two pollutants using CNN has been done. The evaluation of the model was done using different evaluation criteria, and the findings showed that the R-squared (R2) values in this model for PM2.5 and PM10 pollutants are 0.889 and 0.972, respectively. The accuracy of the risk map was evaluated using relative operating characteristic (ROC) for two pollutants, and the findings showed that the CNN model has an acceptable accuracy in producing the pollution risk map. In general, risk maps provide useful information about geographic areas with high pollution risks and help in decision-making and targeted pollution reduction efforts.https://nivar.irimo.ir/article_192602_7ef07a445c311907bf6f9e2ab32a61a1.pdfair pollutantsspatial modelingrisk mapdeep learning |
| spellingShingle | Abed Bashar Doost Mohammad Saadi Mesgari Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. Nīvār air pollutants spatial modeling risk map deep learning |
| title | Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. |
| title_full | Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. |
| title_fullStr | Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. |
| title_full_unstemmed | Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. |
| title_short | Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. |
| title_sort | spatial modeling of airborne particles pm2 5 and pm10 in tehran city using convolutional neural network |
| topic | air pollutants spatial modeling risk map deep learning |
| url | https://nivar.irimo.ir/article_192602_7ef07a445c311907bf6f9e2ab32a61a1.pdf |
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