Statistical models and time series forecasting of sulfur dioxide: a case study Tehran

Hassanzadeh, Smaeyl; Hosseinibalam, F.; Alizadeh, Reza

doi:10.1007/s10661-008-0424-1

Cited by 24 publications

(12 citation statements)

References 6 publications

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“…Tehran is the capital of Iran, with a population of more than 8.5 million in 2011. The annual mean temperature is 17°C, and the annual mean rainfall is about 230 mm [37]. The highest recorded temperature was 39°C, and the lowest recorded temperature was −6°C [38].…”

Section: Study Areamentioning

confidence: 99%

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

Shahbazi

Reyhanian

Hosseini

et al. 2016

Emiss. Control Sci. Technol.

113

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Tehran, a city with 8.5 million inhabitants, has suffered from rapid and unplanned urbanization in recent years resulting in substantial environmental impacts perhaps foremost of which is poor air quality. A major source of air pollution is emissions from mobile vehicles; therefore, having an accurate and comprehensive mobile source emission inventory is essential for decision-makers to develop mitigating strategies. The aim of this study is to determine the relative contributions of specific mobile sources to key air pollutants through the development of an emissions inventory for mobile sources in the city of Tehran using the International Vehicle Emissions (IVE) model. Tehran traffic data were acquired to obtain link level emission rates, using IVE emission rates. The developed emission inventory was evaluated using Tehran gasoline sales data. The results indicate that the sources of carbon monoxide (CO), volatile organic compound (VOC), nitrogen oxide (NO x ), and sulfur oxide (SO x ) emissions are mainly passenger cars. The contribution of emissions of CO, VOCs, and particulate matter (PM) from motorcycles to the total traffic emissions is more than 15, 31, and 12 %, respectively. Despite the fact that medium and heavy-duty vehicles (minibuses, buses, and trucks) only comprise 2.4 % of the Tehran fleet, they contribute more than 41, 64, and 85 % of the NO x , SO x , and PM emissions, respectively. Analyzing the distribution of the aggregated emission of pollutants shows that emissions are mostly higher in central zones due to the high traffic rate of passenger cars, taxis, motorcycles, and buses.

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Section: Study Areamentioning

confidence: 99%

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

Shahbazi

Reyhanian

Hosseini

et al. 2016

Emiss. Control Sci. Technol.

113

View full text Add to dashboard Cite

show abstract

“…Using the autoregressive representation of the air pollution time series, we are able to analyze a wide class of environmental time series. In fact, in the literature, this assumption is often made for air pollution time series (see, e.g., [2,3,[89][90][91]). 2.…”

Section: Final Remarksmentioning

confidence: 99%

Time series clustering by a robust autoregressive metric with application to air pollution

D’Urso

Giovanni

Massari

2015

Chemometrics and Intelligent Laboratory Systems

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In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propose a robust clustering model for classifying time series. In particular, by adopting a fuzzy\ud partitioning around medoids approach, the suggested clustering model is able to define the so-called medoid time series, which is a representative time series of each cluster, and the membership degrees of each time series to the different clusters. The robustness of the proposed clustering model is guaranteed by the adoption of a suitable robust metric for time series, i.e. the so-called exponential distance measure. In this way, the clustering model is able to tolerate the presence of outlier time series in the clustering process. In particular, it is capable of neutralizing and smoothing the disruptive effect of outlier time series, preserving the original clustering structure of the dataset, by assigning to outlier time series approximately the same membership degrees across clusters. To illustrate the usefulness and effectiveness of the suggested time series clustering model, a simulation study and an application to air pollution time series are carried out. Comparison with some existing clustering procedures suggested in the literature shows several advantages of the proposed model

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“…An adaptive neurofuzzy logic method has been developed by Yildirim et al to estimate the impact of meteorological factors on sulfur dioxide pollution levels [14]. Hassanzadeh and coworkers developed statistical models and time series to forecast sulfur dioxide [15]. Li et al looked into the contribution of China's emissions to global climate forcing [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators

2018

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Sulfur dioxide is an important source of atmospheric pollution. Many countries are developing policies to reduce sulfur dioxide emissions. In this paper, a novel prediction model is proposed, which could be used to forecast sulfur dioxide emissions. To improve the modeling procedure, fractional order accumulating generation operator and fractional order reducing generation operator are introduced. Based on fractional order operators, a discrete grey model with fractional operators is developed, which also makes use of genetic algorithms to optimize the modeling parameter . The improved performance of the model is demonstrated via comparison studies with other grey models. The model is then used to predict China's sulfur dioxide emissions. The forecast result shows that the amount of sulfur dioxide emissions is steadily decreasing and the policies of sulfur dioxide reduction in China are effective. According to the current trend, by 2020, the value of China's sulfur dioxide emissions will be only 86.843% of emissions in 2015. Fractional order generation operators can be used to develop other fractional order system models.

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Statistical models and time series forecasting of sulfur dioxide: a case study Tehran

Cited by 24 publications

References 6 publications

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

Time series clustering by a robust autoregressive metric with application to air pollution

Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators

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