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Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies.
- Source :
- Atmosphere; 2016, Vol. 7 Issue 2, p15, 24p
- Publication Year :
- 2016
-
Abstract
- PM<subscript>10</subscript> prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM<subscript>10</subscript> prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM<subscript>10</subscript> 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 PM<subscript>10</subscript> prediction. A review of the spatial predictions of PM<subscript>10</subscript> shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM<subscript>10</subscript> in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM<subscript>10</subscript>, 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20734433
- Volume :
- 7
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Atmosphere
- Publication Type :
- Academic Journal
- Accession number :
- 113301121
- Full Text :
- https://doi.org/10.3390/atmos7020015