Open AccessDOI
Air pollution model and neural network: An integrated modelling system
Armando Pelliccioni,Tiziano Tirabassi +1 more
- Vol. 031, Iss: 3, pp 253-273
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The article was published on 2008-05-01 and is currently open access. It has received 6 citations till now. The article focuses on the topics: Air quality index & Pollution.read more
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Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels
TL;DR: A meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations and provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations.
Journal ArticleDOI
Improving of local ozone forecasting by integrated models
TL;DR: Improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia shows that integrated models noticeably improve ozone forecasts and provide better alert systems.
Pahs urban concentrations maps using support vector machine
Abstract: Air pollution health effects studies are based on data collected by monitoring stations. Pollutant exposure maps greatly improve the evaluation of health effects. The study of exposure to polycyclic aromatic hydrocarbons (PAHs) in urban areas is one of the goals of the EXPAH LIFE+ Project. An integrated approach has been applied to simulate PAHs levels in the urban area of Rome. In particular, support vector machines (SVMs) were applied to reconstruct PAHs urban concentrations. Starting from PAHs results provided by a chemical transport model (CTM) FARM and observed data collected in field campaigns of PM2.5 with PAHs content between June 2011 and May 2012, SVM methods were applied to build a model able to forecast PAHs exposure. The SVM has shown excellent results in reproduction of experimental data, improving those achieved by the FARM model. Finally, the SVM has produced very congruent PAHs exposure maps.
Estimation of PAHs concentration fields in an urban area by means of Support Vector Machines
Armando Pelliccioni,Andrea Cristofari,Camillo Silibello,Monica Gherardi,Angelo Cecinato,Mafalda Lamberti +5 more
TL;DR: In this article, support vector machines (SVMs) have been used to forecast polycyclic aromatic hydrocarbons (PAHs) concentrations starting from actual measurements and then the same SVM has been used for building daily PAHs exposure maps.