Multiblock analysis of environmental measurements: A case study of using Proton Induced X-ray Emission and meteorology dataset obtained from Islamabad Pakistan

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• 作者：Mohd Z. Jaafar^a ; Azmat H. Khan^b ; Shahzada Adnan^b ; Andreas Markwitz^c ; Naila Siddique^d ; Shahida Waheed^d ; Richard G. Brereton^a ; ^{r.g.brereton@bris.ac.uk"" rel=""nofollow}
• 关键词：Environmental monitoring ; Airborne particulates ; Consensus Principal Components Analysis ; Multiblock Partial Least Squares ; Procrustes analysis ; Block similarity measures
• 刊名：Chemometrics and Intelligent Laboratory Systems
• 出版年：2011
• 出版时间：May 2011
• 年：2011
• 卷：107
• 期：1
• 页码：31-43
• 全文大小：1904 K

文摘

This paper reports the analysis of a multiblock environmental dataset consisting of 176 samples collected in Islamabad Pakistan between February 2006 and August 2007. The concentrations of 32 elements in each sample were measured using Proton Induced X-ray Emission plus black carbon for both coarse and fine particulate matter. Six meteorological parameters were also recorded, namely maximum and minimum daily temperatures, humidity, rainfall, windspeed and pressure. The data were explored using Principal Components Analysis (PCA), Partial Least Squares (PLS), Consensus PCA, Multiblock PLS, Mantel test, Procrustes analysis and the R_V coefficient. Seasonal trends can be identified and interpreted. Using the elemental composition of the particulates it is possible to predict meteorological parameters. Based on the models from PLS, it is possible to use elemental composition in the airborne particulates matter (APM) to predict meteorological parameters. The results from block similarity measures show that fine APM resembles meteorological parameters better than coarse APM. Multiblock PLS models however are not better than classical PLSR. This paper also demonstrates the potential of multiblock approach in environmental monitoring.