Chapter Fiveteen Identification, Resolution and Apportionment of Contamination Sources
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Current approaches and recent developments in methods and software associated with multivariate factor analysis and related methods in the analysis of environmental data for the identification, resolution and apportionment of contamination sources are discussed and compared. The chapter first focuses on techniques to be applied in the analysis of the various factors contributing to contamination of the environment, among which we list Principal Component Analysis, and alternative methods and tools such as: Unmix, Positive Matrix Factorization (PMF) and the Multilinear Engine (ME), and Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). In cases where uncertainties were not experimentally available, the use of jackknife uncertainty estimations methods for receptor modelling is described and recommended. Time series extension of multivariate receptor modelling has been developed to account for temporal dependence in air pollution data into estimation of source compositions and uncertainty estimations. In the case of air pollution it is also of interest to estimate the average concentration of a given pollutant at the monitoring site after the air masses have travelled over a certain point (source) on the map. The use of non-parametric regression (kernel smoothing) methods proves to be useful. A method is given for source apportionment of local sources of air pollution by non-parametric regression of the concentration of a pollutant on wind speed and direction. Non-parametric methods have shown that nearby sources (such as freeways) are not always important contributors to high pollutant concentrations. Finally, a number of applications of receptor modelling techniques are presented, including source identification by the new CATT tool (Combined Aerosol Trajectory Tools). Ensemble backward trajectory techniques have been employed to identify regional origins of air pollutants subject to synoptic-scale atmospheric transport. In another application, one detailed study is reported with the analysis of the results obtained from the application of PMF and from PCA-MLRA (Principal Component Analysis with Multilinear Regression Receptor Modelling) to one dataset containing compositional PM10 data at an industrial site in Northern Spain. Even though similar results were obtained with both models, PMF achieved a higher level of detail in the apportionment of sources than PCA-MLRA. However, it was also noted that the application of PMF is more time consuming (at least 50% more time) than PCA-MLRA. 2008 Elsevier B.V. All rights reserved.
