Inland O 3 Production Due to Nitrogen Dioxide Transport Downwind a Coastal Urban Area: A Neural Network Assessment

Piero Chiacchiaretta, Eleonora Aruffo (), Alessandra Mascitelli, Carlo Colangeli, Sergio Palermi, Sebastiano Bianco and Piero Di Carlo
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Piero Chiacchiaretta: Department of Advanced Technologies in Medicine & Dentistry, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
Eleonora Aruffo: Department of Advanced Technologies in Medicine & Dentistry, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
Alessandra Mascitelli: Department of Advanced Technologies in Medicine & Dentistry, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
Carlo Colangeli: Arta Abruzzo Provincial District of Chieti, Via Spezioli 52, 66100 Chieti, Italy
Sergio Palermi: Arta Abruzzo Provincial District of Pescara, Viale Marconi 51, 65126 Pescara, Italy
Sebastiano Bianco: Arta Abruzzo Provincial District of Pescara, Viale Marconi 51, 65126 Pescara, Italy
Piero Di Carlo: Department of Advanced Technologies in Medicine & Dentistry, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy

Sustainability, 2024, vol. 16, issue 15, 1-12

Abstract: The tropospheric production of O 3 is complex, depending on nitrogen oxides (NO x = NO + NO 2 ), volatile organic compounds (VOCs), and solar radiation. We present a case study showing that the O 3 concentration is higher in a rural area, 14 km downwind from a coastal town in Central Italy, compared with the urban environment. The hypothesis is that the O 3 measured inland results from the photochemical processes occuring in air masses originating at the urban site, which is richer in NO x emissions, during their transport inland.To demonstrate this hypothesis, a feed forward neural network (FFNN) is used to model the O 3 measured at the rural site, comparing the modeled O 3 and the measured O 3 in different scenarios, which include both input parameters related to local O 3 production by photochemistry and input parameters associated with regional transport of O 3 precursors. The simulation results show that the local NO x concentration is not a good input to model the observed O 3 (R = 0.17); on the contrary including the wind speed and direction as input of the FFNN model, the modelled O 3 is well correlated with that measured O 3 (R = 0.82).

Keywords: nitrogen oxides; O 3; neural network; regional transport; air pollution; troposphere (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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