Assessment of spatial and temporal water quality distribution in shallow lakes: case study for Lake Ludas, Serbia

This work presents an analysis methodology for identifying spatial and temporal water quality tendencies in shallow lakes through an appropriate case study for Lake Ludas in the Republic of Serbia. In the presented example, water quality data from 2011 to 2018 was used, gathered at three sampling locations. By employing the computation of correlation coefficients between all available water quality parameters, as well as distribution fit tests, the initial 15 parameters were reduced to 7. These were used later on for determining temporal and spatial tendencies. Box plots outlined rough spatial and temporal occurrences, while the principal component analysis (PCA) was employed to present the measurements' variation in a more sophisticated manner. Hence, the PCA provided biplots that enabled a more comprehensive understanding of the measurements. Finally, the overall state of the lake's quality was evaluated using the Canadian Council of Ministers of the Environment Water Quality Index method for each sampling location, both annually and for the overall time interval, and as one single value for the entire lake. The presented research leads to several conclusions. Namely, that the systematic spatial alterations can be attributed to the inflow's water quality from the Palic-Ludas channel, located on the northern part of the lake. The lake's water quality at its southern portion is most likely influenced by a change of geological facies, wastewater contamination, fertilizer leaching or a combination of these. It was also shown that a reasonable monitoring approach should include much denser data gathering in space and time. Furthermore, the necessity of the current three sampling locations remains relevant. In fact, it would be preferred to have a shorter list of monitored parameters with denser time and space data acquisition, than having more diverse water quality evaluation at fever locations or temporally sporadic measurements.