Assessment of Spatial-Temporal Characteristics of Water and Sediment Quality using Integrated Multivariate Methods
Date Issued
2015
Date
2015
Author(s)
Wang, Yeuh-Bin
Abstract
Multivariate methods are very efficient to interpret environmental monitoring data in depth. We can explore latent intension and get more worthy insight from monitoring data by using multivariate methods. This study used groundwater quality of Kinmen Island, water quality of Tamsui River, and sediment quality of Erjen River as case studies to comprehend the characteristics of water and sediment quality monitoring data by applying multivariate methods. These studies could find out the core issues to the water and sediment quality from the comprehended characteristics, and then thses studies suggested measures to improve thses issues. The first case study applied multivariate methods, involving cluster analysis (CA), factor analysis (FA), discriminate analysis (DA) and self organizing map (SOM), to interpret the spatial and temporal characteristics of groundwater quality in Kinmen. The nitrate and organic contamination is the major factor dominating groundwater quality in Kinmen. For further assessing nitrate contamination, this study used logistic regression (LR) to find that the soil type, pH, and EC have close relationship of nitrate contamination. The established LR model can be used for preliminary evaluation of nitrate contamination in groundwater. And the application of the model is to predict the probability of exceeding nitrate threshold and to draw the probability map of nitrate contamination. The model can also be applied to develop a handy tool using EC and pH for preliminary evaluation of nitrate contamination in private wells water. The secondary case study also integrated these aforementioned multivariate methods to evaluate the spatial and temporal variance of water quality in the Tamsui River. This work indicated that the water quality of Tamsui River has been improving to better status and monitoring station can be simplified. This work plotted a spatial pattern using the four latent factor scores and identified 10 redundant monitoring stations near each upstream station with the same score pattern. Finally, for further improving water quality of the Tamsui River, this study also used positive matrix factorization (PMF) to identify the ratio of contribution from the each major pollution. The result of this work can suggest Taiwan EPA adopt some measures to eliminate major pollution. The third case study explored and compared spatial characteristics of sediment quality of the Erjen River in rainy and dry season by coupling FA and SOM methods. The result of FA and SOM indicated the wastewater that discharged from metal electroplate plants polluted seriously the sediment of the Sanyegong Creek in dry season, but PAHs also polluted unusually the sediment in rainy season. The work also assessed accumulation of heavy metal by using Igeo index and found out two heavy metals, Cr and Cu, accumulated heavily in sediment. The biological risk of heavy metal was evaluated as moderate and high risk in the Erjen River, but hazardous index value of the health risk caused by heavy metal was less than 1. Furthermore, this work used positive matrix factorization method (PMF) to estimate the contribution ratio of the each major heavy metal pollution source to health risk. The geological and nonpoint source of heavy metal contributed 46% health risk in the main stream of the Erjen River and the wastewater from metal electroplate plant also contributed 46% health risk in the tributary stream of the Erjen River, the Sanyegong Creek. As to the assessment of PAHs pollution in sediment, the biological risk caused by PAHs was very little except the unusual polluted event in S6 site. But the carcinogenic risk was the 10-4 level and could be assessed as moderate health risk. The petrochemical industry complex source contributed 56% toxicity caused by PAHs in sediment of the Erjen River.
Subjects
water quality assessment
factor analysis
self organizing map
sediment quality
risk assessment
positive matrix factorization
Type
thesis