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Open AccessArticle

Neural Network Model Analysis for Investigation of NO Origin in a High Mountain Site

Department of Psychological, Health & Territorial Sciences, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
Institute of Atmospheric Sciences and Climate, National Research Council of Italy, 40129 Bologna, Italy
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(2), 173;
Received: 20 November 2019 / Revised: 28 January 2020 / Accepted: 4 February 2020 / Published: 7 February 2020
(This article belongs to the Section Air Quality)
Measurements of nitrogen oxide (NO), ozone (O3), and meteorological parameters have been carried out between September and November 2013 in a high mountain site in Central Italy at the background station of Mt. Portella (2401 m a.s.l.). Three NO plumes, with concentrations up to about 10 ppb, characterized the time series. To investigate their origin, single hidden layer feedforward neural networks (FFNs) have been developed setting the NO as the output neuron. Five different simulations have been carried out maintaining the same FFNs architecture and varying the input nodes. To find the best simulations, the number of the neurons in the hidden layer varied between 1 and 40 and 30 trials models have been evaluated for each network. Using the correlation coefficient (R), the normalized mean square error (NMSE), the fractional bias (FB), the factor of 2 (FA2) and the t-student test, the FFNs results suggest that two of the three NO plumes are significantly better modeled when considering the dynamical variables (with the highest R of 0.7996) as FFNs input compare to the simulations that include as input only the photochemical indexes (with the lowest R of 0.3344). In the Mt. Portella station, transport plays a crucial role for the local NO level, as demonstrated by the back-trajectories; in fact, considering also the photochemical processes, the FFNs results suggest that transport, more than local sources or the photochemistry, can explain the observed NO plumes, as confirmed by all the statistical parameters. View Full-Text
Keywords: artificial neural network; NO; high mountain artificial neural network; NO; high mountain
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Aruffo, E.; Di Carlo, P.; Cristofanelli, P.; Bonasoni, P. Neural Network Model Analysis for Investigation of NO Origin in a High Mountain Site. Atmosphere 2020, 11, 173.

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