Extensive evaluation of neural network models for the prediction of NO 2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki

Kukkonen, Jaakko; Partanen, Leena; Karppinen, Ari; Ruuskanen, Juhani; Junninen, Heikki; Kolehmainen, Mikko; Niska, Harri; Dorling, Stephen; Chatterton, Tim; Foxall, Rob; Cawley, Gavin

Abstract

Five neural network (NN) models, a linear statistical model and a deterministic modelling system (DET) were evaluated for the prediction of urban NO2 and PM10 concentrations. The model evaluation work considered the sequential hourly concentration time series of NO2 and PM10, which were measured at two stations in central Helsinki, from 1996 to 1999. The models utilised selected traffic flow and pre-processed meteorological variables as input data. An imputed concentration dataset was also created, in which the missing values were replaced, in order to obtain a harmonised database that is well suited for the inter-comparison of models. Three statistical criteria were adopted: the index of agreement (IA), the squared correlation coefficient (R2) and the fractional bias. The results obtained with various non-linear NN models show a good agreement with the measured concentration data for NO2; for instance, the annual mean of the IA values and their standard deviations range from 0.86±0.02 to 0.91±0.01. In the case of NO2, the non-linear NN models produce a range of model performance values that are slightly better than those by the DET. NN models generally perform better than the statistical linear model, for predicting both NO2 and PM10 concentrations. In the case of PM10, the model performance statistics of the NN models were not as good as those for NO2 over the entire range of models considered. However, the currently available NN models are neither applicable for predicting spatial concentration distributions in urban areas, nor for evaluating air pollution abatement scenarios for future years. © 2003 Elsevier Ltd. All rights reserved.

• Journal Article Type: Article
• Publication Date: Jan 1, 2003
• Journal: Atmospheric Environment
• Print ISSN: 1352-2310
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 37
• Issue: 32
• Pages: 4539-4550
• DOI: https://doi.org/10.1016/S1352-2310%2803%2900583-1
• Keywords: neural network models, NO2, PM10, Helsinki
• Public URL: https://uwe-repository.worktribe.com/output/1067388
• Publisher URL: http://dx.doi.org/10.1016/S1352-2310(03)00583-1
• Additional Information: Additional Information : Equal co-author. Arising from IST RTD project (2000 � 2002) involving 9 institutions in 5 countries developing modelling tools for improved smog management. This paper assists air quality managers in the selection of predictive tools.