Optimal statistical model for forecasting ozone

Chemosphere, 2005

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1, 0, 0) · (1, 0, 1) 24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2, 1, 1) · (0, 1, 1) 24 has been developed for the rural area. In both sampling points, predictions of hourly ozone concentrations agree reasonably well with measured values. However, the prediction of hourly ozone concentrations in the rural point appears to be better than that of the urban point. The performance of ARIMA models suggests that this kind of modelling can be suitable for ozone concentrations forecasting.

Atmosphere

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The res...

Journal of the Air & Waste Management Association, 2004

A time series approach using autoregressive integrated moving average (ARIMA) modeling has been used in this study to obtain maximum daily surface ozone (O 3) concentration forecasts. The order of the fitted ARIMA model is found to be (1,0,1) for the surface O 3 data collected at the airport in Brunei Darussalam during the period July 1998-March 1999. The model forecasts of one-day-ahead maximum O 3 concentrations have been found to be reasonably close to the observed concentrations. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Fractional Bias, Normalized Mean Square Error, and Mean Absolute Percentage Error as 0.025, 0.02, and 13.14% respectively.

Polish Journal of Environmental Studies, 2021

The tropospheric ozone (O3) is known as a hazardous ambient air pollutant that adversely affects human health. Recently, many studies have been devoted to assess and monitor the O3 concentration due to its impact on society health. This study was carried out to develop a model to forecast O3 concentration. A comparison between univariate and multivariate time series to examine the most appropriate model was made. The air quality data used in this research was collected from three (3) stations namely Perai, Penang (industrial), Alor Setar, Kedah (urban) and Jerantut, Pahang (background). The selection of background station allows for comparisons to be made with stations closer to anthropogenic emissions. Based on Akaike Information Criterion (AIC), the appropriate multivariate time series to develop a forecasting model for Perai, Alor Setar and Jerantut monitoring stations were vector autoregressive VAR(3), vector autoregressive VAR(2) and vector moving average VMA(2), respectively. ...

MAUSAM

International Journal of Quality Engineering and Technology, 2013

This paper compares three methods of predicting the changes in ozone concentration: linear regression, classification and regression tree (CART) analysis, and the T-method. Using linear regression on these results, a linear equation defining the change of the independent variable versus the dependent variables is created. The strength of the relationship is assessed using the R-squared value and adjusted R-squared value. Classification and regression tree analysis uses a tree-building methodology to generate decision rules, using patterns from historical data obtained on both the dependent variable and the independent or 'predictor' variables to create a prediction model. The T-method is used to calculate an overall prediction based on the dynamic signal-to-noise ratio to obtain an overall estimate of the true value of the output for each signal member. It was found that for this nearly directly correlated dataset the T-method performed comparably to linear regression and was a better predictor than the CART method.

Stochastic Environmental Research and Risk Assessment, 2018

This paper highlights the problem of step-length selection for the one-step-ahead prediction of ozone called the data time interval. This is done using a case study-based comparison of two approaches for predicting the maximum daily values of tropospheric ozone. The first approach is the one-day-ahead prediction and the second is the prediction of the maximum values based on a multi-step-ahead iteration of one-hour predictions. Gaussian process modelling is utilised for this comparison. In particular, evolving Gaussian-process models are used that update on-line with the incoming measurement data. These sorts of models have been successfully used in the past for the prediction of ozone pollution. This paper contributes an assessment of the way that the maximum ozone values are predicted. A comparison of the daily maximum ozone values forecasted by a model based on one-day-ahead predictions with those obtained by iterated one-hour-ahead predictions of the ozone with predictions at predetermined hours of the day is given. The forecast results are in favour of the on-line model based on hourly predictions when approaching closer to the real maximum values of ozone, and in favour of the daily predictions when they are made on a daily basis.

American Journal of Environmental Sciences, 2018

Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build Autoregressive Integrated Moving Average (ARIMA) model on the average of monthly ozone data taken from three monitoring stations in Klang Valley for the period 2000 to 2010 with a total of 132 readings. Result shows that ARIMA (1,0,0)(0,1,1) 12 model was successfully applied to predict the long term trend of ozone concentrations in Klang Valley. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Root Mean Squared Error, Mean Absolute Percentage Error and Normalized Bayesian Information Criteria. The finding of a statistically significant upward trend of future ozone concentrations is a concern for human health in Klang Valley since over the last decade, ozone appears as one of the main pollutant of concern in Malaysia.

Journal of Scientific …, 2008

In this paper, lower tropospheric ozone concentration was modeled using artificial neural networks (ANNs) according to 1 day, 3 days and 7 days time periods to determine best prediction period. In model formation, data that was taken from ozone measuring stations and Government Meteorology Works Office was daily averages of last 6 months of 2003 and first 6 months of 2004. Air pollutant parameters (6) and meteorological parameters (8) were used in ANN architecture for Anatolian and European sides of Istanbul separately. Correlation factor was determined to examine model effectiveness for each time period. Weekly average prediction model has been observed with highest correlation factor and three day's correlation factor was higher than daily's.

2012

An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone's concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups.