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Particulate matter estimation from photochemistry: a modelling approach using neural networks and synoptic clustering

Taylor, M., Retalis, A. and Flocas, H. A. (2016) Particulate matter estimation from photochemistry: a modelling approach using neural networks and synoptic clustering. Aerosol and Air Quality Research, 16 (9). pp. 2067-2084. ISSN 2071-1409

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To link to this item DOI: 10.4209/aaqr.2015.07.0481

Abstract/Summary

We report on the development and validation of a neural network (NN) model of PM10 concentrations in terms of photochemical measurements of NO, NO_2 and O_3 and temporal parameters that include the day of the week and the day of the year with its sinusoidal variation. A long-term record (≈10 yr) from 2001–2012 (inclusive) assembled from measurements taken at 10 station nodes in the air quality monitoring network of the Greater Athens Area in Greece has been used. Eight synoptic categorizations of the circulation at 850 hPa were used to partition the data record, and to train individual NNs with Bayesian regularization using 90% of available data for different atmospheric conditions. The time series of PM10 estimates was then reconstructed from the partitioned output. As a control, a NN without synoptic clustering was trained on the same data. The remaining 10% of the data was used for testing the simulation performance. NN models with synoptic clustering achieved an average root mean square error (RMSE) ≈ 16 µg/m^3 across the station nodes with an average index of agreement (IA) of 0.71 (somewhat better than the control network whose performance statistics were RMSE ≈ 17 µg/m^3 and IA = 0.61, respectively). For routine measurements below the EU Air Quality Directive limit value of 50 µg/m^3, the average error is as low as RMSE ≈ 11 µg/m^3 across the station nodes. NN models were found to strongly outperform analogous MLR models over all station nodes.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:77173
Uncontrolled Keywords:Particulate matter, Photochemistry, Air quality, Synoptic classification, Neural networks
Publisher:Taiwan Association for Aerosol Research

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