Prediction of Air Pollutants Concentration Based on Meteorological Factors in Warm and Cold Season by Artificial Neural Network and Linear Regression, Case Study: Tehran

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Farhadi Razieh; HADAVIFAR MOJTABA; MOEINADDINI MAZAHER; Amintoosi Mahmood

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Journal: Journal of Natural Environment Year:2020 | Volume:73 | Issue:1 Page(s): 115-127

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Title

Prediction of Air Pollutants Concentration Based on Meteorological Factors in Warm and Cold Season by Artificial Neural Network and Linear Regression, Case Study: Tehran

Author(s)

Farhadi Razieh | HADAVIFAR MOJTABA | MOEINADDINI MAZAHER | Amintoosi Mahmood | Issue Writer Certificate

Keywords

Air pollution forecastQ1

Carbon monoxidQ1

particulate matterQ1

Air quality indexQ1

Abstract

Tehran is the most polluted city in the country, which can have long-term and short-term effects on human health. Therefore, predicting the concentration of pollutants can be useful in prevention and control planning. There are different methods for predicting, and over the years, neural network methods have made significant progress in predicting pollution. In this study, an artificial neural network of three-layer perceptron was used to predict the concentrations of PM10, CO and Air quality index (AQI) in air in Tehran. The concentrations of pollutants were collected from the Tehran Air Quality Control Department and the weather data collected from the Office of the Iranian Meteorological Organization during 2013 and 2015. The highest correlation coefficient (R2) for PM10 pollutant was 0. 83 in warm seasons and the highest CO emission factor correlated with cold seasons (R2 = 0. 74). Finally, the highest correlation coefficient of AQI was in cold season (R2 = 0. 57). In linear regression model, the highest correlation coefficient with 0. 58 for PM10 pollutant was in hot seasons. The highest correlation coefficient in this model was for the CO pollutant (0. 33) in the cold season. Finally, the highest correlation coefficient was AOI (R2 = 0. 31) in the warm season.

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APA: Copy

Farhadi, Razieh, HADAVIFAR, MOJTABA, MOEINADDINI, MAZAHER, & Amintoosi, Mahmood. (2020). Prediction of Air Pollutants Concentration Based on Meteorological Factors in Warm and Cold Season by Artificial Neural Network and Linear Regression, Case Study: Tehran. JOURNAL OF NATURAL ENVIRONMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), 73(1 ), 115-127. SID. https://sid.ir/paper/407481/en

Vancouver: Copy

Farhadi Razieh, HADAVIFAR MOJTABA, MOEINADDINI MAZAHER, Amintoosi Mahmood. Prediction of Air Pollutants Concentration Based on Meteorological Factors in Warm and Cold Season by Artificial Neural Network and Linear Regression, Case Study: Tehran. JOURNAL OF NATURAL ENVIRONMENT (IRANIAN JOURNAL OF NATURAL RESOURCES)[Internet]. 2020;73(1 ):115-127. Available from: https://sid.ir/paper/407481/en

IEEE: Copy

Razieh Farhadi, MOJTABA HADAVIFAR, MAZAHER MOEINADDINI, and Mahmood Amintoosi, “Prediction of Air Pollutants Concentration Based on Meteorological Factors in Warm and Cold Season by Artificial Neural Network and Linear Regression, Case Study: Tehran,” JOURNAL OF NATURAL ENVIRONMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), vol. 73, no. 1 , pp. 115–127, 2020, [Online]. Available: https://sid.ir/paper/407481/en

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