Physicochemical properties of water were analyzed to assess the long-term water quality variations in Lake Hwajinpo, Korea. Water quality data monitored from 1998 to 2015 was divided in three periods and descriptive statistics, correlation and rotated components were deducted using statistical procedures. Based on the results of analyses, water quality patterns in three periods (1998-2003, 2004-2009 and 2013-2015) were distinguishable from each other. Water parameters, Chlorophyll-a, total phosphorus, total nitrogen, dissolved oxygen and pH, showed the highest mean values in the first period. On the other hand, conductivity and salinity in the second period and temperature and suspended solids in the third period showed the highest mean values. Principal component analysis (PCA) procedure was utilized to deduct the most significant parameters influencing water quality and observed that each period had different pattern of variables. Salinity and conductivity were the two variables highly contributing in first component/factor (F1) explaining 20.77% and 22.93% of the total variance in the first period and second period, respectively. But total phosphorus and chlorophyll-a were the two variables highly loading in F1 of the third period explaining 23.72% of total variance. These results revealed that the water quality of Lake Hwajinpo had different patterns of variations throughout the study period. Thus, PCA results could be valuable to understand the water quality status of water body and take proper steps to protect the water environment.

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This study was based on water quality data of the Lake Doam watershed, monitored from 2010 to 2013 at eight different sites with multiple physiochemical parameters. The dataset was divided into two sub-datasets, namely, non-rainy and rainy. Principal component analysis (PCA) and factor analysis (FA) techniques were applied to evaluate seasonal correlations of water quality parameters and extract the most significant parameters influencing stream water quality. The first five principal components identified by PCA techniques explained greater than 80% of the total variance for both datasets. PCA and FA results indicated that total nitrogen, nitrate nitrogen, total phosphorus, and dissolved inorganic phosphorus were the most significant parameters under the non-rainy condition. This indicates that organic and inorganic pollutants loads in the streams can be related to discharges from point sources (domestic discharges) and non-point sources (agriculture, forest) of pollution. During the rainy period, turbidity, suspended solids, nitrate nitrogen, and dissolved inorganic phosphorus were identified as the most significant parameters. Physical parameters, suspended solids, and turbidity, are related to soil erosion and runoff from the basin. Organic and inorganic pollutants during the rainy period can be linked to decayed matters, manure, and inorganic fertilizers used in farming. Thus, the results of this study suggest that principal component analysis techniques are useful for analysis and interpretation of data and identification of pollution factors, which are valuable for understanding seasonal variations in water quality for effective management.

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