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Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network.

Document Type : Original Article

Authors
Abed Bashar Doost 1
Mohammad Saadi Mesgari 2
1 Doctoral student of civil engineering and mapping, geographic information systems, K. N. Toosi University, Tehran, Iran.
2 Associate Professor of Department of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, Iran
10.30467/nivar.2024.430255.1276
Abstract
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.
Keywords
Air pollutants
Spatial Modeling
Risk map
Deep learning
Subjects
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Nivar
Volume 48, 124-125 - Serial Number 124
September 2024
Pages 31-49

  • Receive Date 12 December 2023
  • Revise Date 27 February 2024
  • Accept Date 16 March 2024
  • Publish Date 20 March 2024