Article ID Journal Published Year Pages File Type
6340262 Atmospheric Environment 2014 12 Pages PDF
Abstract

•We use Neural Networks to analyse the 1970-2008 time series of some chemical species.•Similar relation between chemical species are identified in different geographic regions.•For CO2,NN-MLP1 give good results with reliable projections for 4 subsequent years.•For CH4 and N2O a less reliable projection is obtained.•For SO2, NOx gap-filling of a missing data-point in the past time series can be done.

This study is the first ever analysis of the global time series 1970-2008 of the Emissions Database for Global Atmospheric Research (EDGAR) for 10 chemical species and more than 3000 subsectors with neural networks, which tries to find non-linear behaviours that several species have in common.The application of the different neural network types, suggests that General Regression Neural Networks (GRNNs) are the most suitable to train a typical Gaussian trend with a very low error level. As such, GRNNs are very suitable for filling the data points missing from the EDGAR time-series, but they are not so good at a making projections outside the time period of the database. Instead Multi Layers Perceptron (MLP) is very suitable for projecting a subsequent year to the database time period of several decades, even though MLP is characterised by a slightly higher absolute mean error than the GRNN.By means of the Principal Component Analysis (PCA), we identified which chemical substances are driven similarly by the activity data over the almost 40 years time period. In all the geographic aggregations, we observed that the emission trends of CO2, SO2 and NOx can be grouped into one cluster, and the emission trends of CH4 and the particulates into another. The best time interval for the prediction proved to be eleven years, and projections seemed to be reliable for three consecutive years following the last year of the database time-series.

Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Atmospheric Science
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