Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach

Authors

  • Jahani, Ali Department of Natural Environment and Biodiversity, College of Environment, Karaj, Iran.
  • Kalantary, Saba Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Science, Tehran, Iran
  • Khorasani, Nematallah Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran
  • Moeinaddini, Mazaher Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran
  • Reyhaneh Shams, Seyedeh Department of Human Environment and Environmental Pollution, College of Environment, Karaj, Iran.
Abstract:

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.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

A Conditional Probability Density Function for Forecasting Ozone Air Quality Data

Probabilistic forecasts are often employed to estimate the potential for high pollutant concentrations. To develop a probabilistic forecast of ozone concentrations, we suggest that use be made of the inherent properties of seasonality and autocorrelation in 0, time series. A non-stationary, autocorrelated stochastic process is used to simulate a conditional probability density function (p.d.f.)...

full text

Forecasting Air Pollution Concentrations in Iran, Using a Hybrid Model

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

full text

Forecasting Of Tehran Stock Exchange Index by Using Data Mining Approach Based on Artificial Intelligence Algorithms

Uncertainty in the capital market means the difference between the expected values ​​and the amounts that actually occur. Designing different analytical and forecasting methods in the capital market is also less likely due to the high amount of this and the need to know future prices with greater certainty or uncertainty. In order to capitalize on the capital market, investors have always sough...

full text

Forecasting Air Pollution Concentrations in Iran, Using a Hybrid Model

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

full text

Local Real-time Forecasting of Ozone Exposure using Temperature Data

full text

Local Real-time Forecasting of Ozone Exposure using Temperature Data

Rigorous and prompt assessment of ambient ozone exposure is important for informing the public about ozone levels that may lead to adverse health effects. In this paper, we make use of hierarchical modeling to forecast 8-hour average ozone exposure. Our contribution is to show how incorporating temperature data in addition to observed ozone can significantly improve forecast accuracy, as measur...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 10  issue 4

pages  406- 420

publication date 2020-11

{@ msg @}

By following a journal you will be notified via email when a new issue of this journal is published.

Keywords

No Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023