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

Eleonora Aruffo,Paolo Cristofanelli,Piero Di Carlo,Paolo Bonasoni

doi:10.3390/atmos11020173

Eleonora Aruffo, Paolo Cristofanelli + Show 2 more

Open Access

https://doi.org/10.3390/atmos11020173

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Abstract

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.

Highlights

Nitrogen oxides (NOx = nitrogen oxide (NO) + NO2 ) indirectly affect the tropospheric greenhouse gas level playing a crucial role in the tropospheric ozone (O3 ) budget and contributing to the formation of the secondary organic aerosols (SOAs)
The artificial neural networks (ANNs) paradigm is inspired by the biological mechanisms that occur in the human brain
It is evident that the feedforward neural network (FNN) are able to reproduce the overall NO time series in almost all the simulation conditions: considering the entire time series, we found the best simulation of the NO using all the input parameters with an R between the NO modeled and measured at about 0.89

Summary

Introduction

Nitrogen oxides (NOx = NO + NO2 ) indirectly affect the tropospheric greenhouse gas level playing a crucial role in the tropospheric ozone (O3 ) budget and contributing to the formation of the secondary organic aerosols (SOAs) Their emission into the atmosphere is mainly dominated by anthropogenic sources (fossil fuel combustion-related especially to the road transport, industrial and agricultural processes, and biomass and biofuel burning) and partially associated with natural activities (lightning, soils) [1]. Kaiser et al (2007) [8] investigated the NOx transport to Alpine Global Atmosphere Watch (GAW) stations by using a methodology based on trajectory studies They found that NOx , sampled in air masses with long residence time, principally originated from northwest of Europe, East Germany, Czech Republic and southeast Poland. We found that the model including only the meteorological parameters related to the transport (i.e., wind speed, wind direction, and pressure) gives statistically better simulations than that considering only the photochemistry

Data and Site

The Neural Network Model

Results and and Discussions

Figure

The Hysplit

Conclusions