Understanding and forecasting atmospheric quality parameters with the aid of ANNs

Karatzas, Kostas; Papadourakis, George M.; Kyriakidis, Ioannis

doi:10.1109/ijcnn.2008.4634159

Cited by 12 publications

(8 citation statements)

References 34 publications

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“…Adaptive filters are constituted by a transfer function with parameters that can be changed according to an optimization algorithm minimizing a cost function defined in terms of the data to be processed. They found many applications in signal processing and control problems like biomedical signal processing (Mesin et al, 2008), inverse modeling, equalization, echo cancellation (Widrow et al, 1993), and signal prediction (Karatzas et al, 2008;Corani, 2005). In this chapter, a prediction application is proposed.…”

Section: Final Remarks and Conclusionmentioning

confidence: 99%

“…In this chapter we focus on ANNs, which have been often used as a prognostic tool for air pollution (Perez et al, 2000;Božnar et al, 2004;Cecchetti et al, 2004;Slini et al, 2006;Karatzas et al, 2008). We discuss in the following an application of ANN to the regression and system identification analysis in order to predict the dynamics of local ozone concentration.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Adaptive Filtering to ForecastAir Pollution

Mesin¹,

Orione²,

Pasero³

2011

Adaptive Filtering Applications

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Air pollution is an important problem concerning the quality of living and health conditions of the population in urban areas (Pasero & Mesin, 2010). Indeed, the issue of air quality is now a major concern for many governments worldwide. Since the early 1970s, the EU has been working to improve air quality by controlling emissions of harmful substances into the atmosphere, by improving fuel quality and by integrating environmental protection requirements into the transport and energy sectors. As the result of EU legislation, much progress has been made in tackling air pollutants. However air quality continues to cause problems. As an example, photochemical smog, particularly active in sunny days, regularly exceeds a safe limit over main European metropolitan areas. EU legislation established an hourly average of 180µg/m 3 as the threshold of safe limit for ozone (Directive 2002/3/EC) beyond which authorities have to inform population. According to the indications of the Environment Commissioner Janez Potočnik (Potočnik, 2010), air pollution is responsible for 370.000 premature deaths in EU each year. Airborne particles (e.g. Particulate Matter with diameter lower or equal to 10 m, PM 10) are mainly present in pollutant emissions from industry, traffic and domestic heating. They can cause asthma, cardiovascular problems, lung cancer and premature death. The EU Directive 2008/50/EC requires Member States to ensure that certain limit values for PM 10 are met. These limits, which were to be met by 2005, impose both an annual average concentration value (40 g/m 3), and a daily concentration value (50 g/m 3) which must not be exceeded more than 35 times per calendar year (European Environmental Bureau, 2005). The World Health Organization pointed out that USA traffic fatalities are over 40.000 per year, while air pollution claims 70.000 lives annually (Air Quality Guidelines, WHO, 2006). Experimental studies carried on Oslo urban site pointed out the temporal connection between cardiopulmonary inflammatory state of sick citizens and high concentration of PM in the area, especially in winter and early spring time (Schwarze P.E. et al, 2010). The impact of air pollution is on human health and on ecosystems also, as marine, freshwater, grassland, heat or forest ecosystem. The air pollution is directly linked to acidification of forests and water ecosystems, and eutrophication of soils and waters, leading to limited supply of oxygen in rivers and lakes. The benefits of air pollution abatement in the heat and grassland ecosystem, typical European farm environment, have been estimated as two hundred millions of euro each year saved (DeSmet et al, 2007). Indeed, air pollution not only affects www.intechopen.com Adaptive Filtering Applications 344 health care systems, as we cited above, but even local economy in terms of costs of medication, absences from work, and child and old people care expenses (Brown et al., 2002). European environmental regulations demand the implementation of automatic procedures to prevent principal ...

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Section: Final Remarks and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Adaptive Filtering to ForecastAir Pollution

Mesin¹,

Orione²,

Pasero³

2011

Adaptive Filtering Applications

View full text Add to dashboard Cite

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“…Air quality information is collected from various sources and sensors to be used in developing a successful relationship which helps producing accurate prediction models. Mathematical methods and tools were introduced to solve variety of problems in system identification, control, stock market prediction and air pollution modeling [10]. The complexity of air pollution data has been broadly discussed in [15], while the usage of various modeling tools was addressed in related literature [13], [21].…”

Section: Introductionmentioning

confidence: 99%

Forecast Global Carbon Dioxide Emission using Swarm Intelligence

Abdelfatah¹,

Moktar²,

Sheta³

et al. 2013

IJCA

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The tremendous effects of air quality in large cities have been considered a severe environmental problem all over the world. Therefore, the international community agreed to develop air quality standards to monitor and control pollution rates around industrial communities. Harmful emission into the air is a sign that could extremely affect man's health, natural life and agriculture. Forecasting models is essential for predicting air quality. CO 2 emissions have been an international concern because of fossil fuels. In this study, Particle Swarm Optimization (PSO) is used for analyzing world CO 2 emission based on the global energy consumption. A parametric PSO model is developed to forecast CO 2 emission based set of attributes. They include: global oil, natural gas, coal, and primary energy consumption. A data set collected during the years 1980 and 2010 were used in this study. Experimental results show that PSO can provide good modeling results using a limited number of measurements compared to other linear models.

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“…Both developed and developing countries especially in Europe, America and Asia contribute more in world environmental air quality status (Karatzas et al, 2008). This air pollution can be found both either indoors, occur when the pollutants trapped in the buildings that could last for a long time or outdoors which the pollution located in the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Seasonal ARIMA for Forecasting Air Pollution Index: A Case Study

Lee¹,

Rahman

Suhartono

et al. 2012

American Journal of Applied Sciences

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Problem statement: Both developed and developing countries are the major reason that affects the world environment quality. In that case, without limit or warning, this pollution may affect human health, agricultural, forest species and ecosystems. Therefore, the aim of this study was to determine the monthly and seasonal variations of Air Pollution Index (API) at all monitoring stations in Johor. Approach: In this study, time series models will be discussed to analyze future air quality and used in modeling and forecasting monthly future air quality in Malaysia. A Box-Jenkins ARIMA approach was applied in order to analyze the API values in Johor. Results: In all this three stations, high values recorded at sekolah menengah pasir gudang dua (CA0001). This situation indicates that the most polluted area in Johor located in Pasir Gudang. This condition appears to be the reason that Pasir Gudang is the most developed area especially in industrial activities. Conclusion: Time series model used in forecasting is an important tool in monitoring and controlling the air quality condition. It is useful to take quick action before the situations worsen in the long run. In that case, better model performance is crucial to achieve good air quality forecasting. Moreover, the pollutants must in consideration in analysis air pollution data

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Understanding and forecasting atmospheric quality parameters with the aid of ANNs

Cited by 12 publications

References 34 publications

Nonlinear Adaptive Filtering to ForecastAir Pollution

Nonlinear Adaptive Filtering to ForecastAir Pollution

Forecast Global Carbon Dioxide Emission using Swarm Intelligence

Seasonal ARIMA for Forecasting Air Pollution Index: A Case Study

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