Cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) were comprehensively coupled to explore and identify the spatial and temporal variation and potential pollution sources in coastal water quality along Macau peninsula. The results show that the 12 months could be grouped into two periods, June-September and the remaining months, and the entire area divided into two clusters, one located at the western sides, and the other on the southeast and southern sides of the Macau peninsula. Through backward stepwise DA, pH, Cl -, TSS, Color and TP, Chloride, Color, NH 4 ? , DO, COD were discriminant variables of spatial and temporal variation, with 84.82 and 76.54% correct assignments, respectively. Fecal pollution, organic pollution and soil weathering are among the major sources for coastal water quality deterioration along Macau peninsula. This study illustrates that application of multivariate statistical techniques was beneficial to gain knowledge for further optimizing the monitoring network and controlling coastal water quality along Macau peninsula.

Statistical analysis of stormwater runoff data enables general identification of runoff characteristics. Six catchments with different urban surface type including roofs, roadway, park, and residential/commercial in Macau were selected for sampling and study during the period from June 2005 to September 2006. Based on univariate statistical analysis of data sampled, major pollutants discharged from different urban surface type were identified. As for iron roof runoff, Zn is the most significant pollutant. The major pollutants from urban roadway runoff are TSS and COD. Stormwater runoff from commercial/residential and Park catchments show high level of COD, TN, and TP concentration. Principal component analysis was further done for identification of linkages between stormwater quality and urban surface types. Two potential pollution sources were identified for study catchments with different urban surface types. The first one is referred as nutrients losses, soil losses and organic pollutants discharges, the second is related to heavy metals losses. PCA was proved to be a viable tool to explain the type of pollution sources and its mechanism for different urban surface type catchments.

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