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

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. [ABSTRACT FROM AUTHOR]