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Atmosphere 2016, 7(2), 15; doi:10.3390/atmos7020015

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

1
Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-10, Berlin 12165, Germany
2
Remote Sensing Research Center, Sharif University of Technology, Tehran 1458889694, Iran
*
Author to whom correspondence should be addressed.
Academic Editor: Pasquale Avino
Received: 29 September 2015 / Revised: 31 December 2015 / Accepted: 12 January 2016 / Published: 26 January 2016
(This article belongs to the Special Issue Indoor and Outdoor Air Quality)
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Abstract

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ≤ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure. View Full-Text
Keywords: PM10; spatial prediction; forecasting; spatial-temporal prediction; statistical models; PM10 predictors; urban areas PM10; spatial prediction; forecasting; spatial-temporal prediction; statistical models; PM10 predictors; urban areas
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Taheri Shahraiyni, H.; Sodoudi, S. Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies. Atmosphere 2016, 7, 15.

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