Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach
Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in...
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| Format: | Article |
| Language: | fas |
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Tehran University of Medical Sciences
2020-11-01
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| Series: | بهداشت و ایمنی کار |
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| Online Access: | http://jhsw.tums.ac.ir/article-1-6415-en.html |
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| author | Seyedeh Reyhaneh Shams Ali Jahani Mazaher Moeinaddini Nematallah Khorasani Saba Kalantary |
| author_facet | Seyedeh Reyhaneh Shams Ali Jahani Mazaher Moeinaddini Nematallah Khorasani Saba Kalantary |
| author_sort | Seyedeh Reyhaneh Shams |
| collection | DOAJ |
| description | Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environment and its ascending trend over the past decades, it is also essential to study and predict its quantities in the air. Forecasting ozone in the air can be further used to prevent and control pollution by authorities.
Material and Methods: Using an analytical-applied research method, this study was to predict ozone gas in this metropolitan area via daily ozone data of air quality measurement stations, traffic variables, green space, as well as time factors such as one-day time delay. In this regard, an artificial neural network (ANN) model was employed to forecast ozone concentration using the MATLAB software.
Results: The results of the ANN model were compared with a linear regression one. Correlation coefficient and root-mean-square error (RMSE) of the ANN model were subsequently compared with R2=0.734 and RMSE=0.56 as well as R2=0.608 and RMSE=11.69 regression equations.
Conclusion: It was concluded that the error in the ANN model was smaller than that in the regression one. According to the results of the sensitivity analysis of the season parameters, the length of sunshine hours had the most significant effect on the amount of ozone gas in Tehran air. |
| first_indexed | 2024-12-16T07:53:09Z |
| format | Article |
| id | doaj.art-b4d4c3294b464735b1be376fb17a61a6 |
| institution | Directory Open Access Journal |
| issn | 2251-807X 2383-2088 |
| language | fas |
| last_indexed | 2024-12-16T07:53:09Z |
| publishDate | 2020-11-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | بهداشت و ایمنی کار |
| spelling | doaj.art-b4d4c3294b464735b1be376fb17a61a62022-12-21T22:38:49ZfasTehran University of Medical Sciencesبهداشت و ایمنی کار2251-807X2383-20882020-11-01104406420Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven ApproachSeyedeh Reyhaneh Shams0Ali Jahani1Mazaher Moeinaddini2Nematallah Khorasani3Saba Kalantary4 Department of Human Environment and Environmental Pollution, College of Environment, Karaj, Iran. Department of Natural Environment and Biodiversity, College of Environment, Karaj, Iran. Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Science, Tehran, Iran Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environment and its ascending trend over the past decades, it is also essential to study and predict its quantities in the air. Forecasting ozone in the air can be further used to prevent and control pollution by authorities. Material and Methods: Using an analytical-applied research method, this study was to predict ozone gas in this metropolitan area via daily ozone data of air quality measurement stations, traffic variables, green space, as well as time factors such as one-day time delay. In this regard, an artificial neural network (ANN) model was employed to forecast ozone concentration using the MATLAB software. Results: The results of the ANN model were compared with a linear regression one. Correlation coefficient and root-mean-square error (RMSE) of the ANN model were subsequently compared with R2=0.734 and RMSE=0.56 as well as R2=0.608 and RMSE=11.69 regression equations. Conclusion: It was concluded that the error in the ANN model was smaller than that in the regression one. According to the results of the sensitivity analysis of the season parameters, the length of sunshine hours had the most significant effect on the amount of ozone gas in Tehran air.http://jhsw.tums.ac.ir/article-1-6415-en.htmlair pollutionozone gasartificial neural networkmultivariate regressionsensitivity analysis |
| spellingShingle | Seyedeh Reyhaneh Shams Ali Jahani Mazaher Moeinaddini Nematallah Khorasani Saba Kalantary Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach بهداشت و ایمنی کار air pollution ozone gas artificial neural network multivariate regression sensitivity analysis |
| title | Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach |
| title_full | Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach |
| title_fullStr | Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach |
| title_full_unstemmed | Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach |
| title_short | Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach |
| title_sort | forecasting ozone density in tehran air using a smart data driven approach |
| topic | air pollution ozone gas artificial neural network multivariate regression sensitivity analysis |
| url | http://jhsw.tums.ac.ir/article-1-6415-en.html |
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