Multivariate analysis of water quality parameters in Lake Palic, Serbia

• Published in: Environmental monitoring and assessment Vol. 193; no. 7; p. 410
• Main Authors: Horvat, Mirjana, Horvat, Zoltan, Pastor, Kristian
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Ammonium; Ammonium compounds; Ammonium nitrogen; Anthropogenic factors; Aquatic birds; Aquatic habitats; Atmospheric Protection/Air Quality Control/Air Pollution; Biochemical oxygen demand; Chlorophyll; Chlorophyll a; Cluster analysis; Earth and Environmental Science; ecological balance; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental science; habitats; Identification; Lakes; Learning algorithms; Machine learning; Mathematical models; Monitoring/Environmental Analysis; Multivariate analysis; Oxygen; Oxygen demand; Oxygen requirement; Parameter estimation; Parameters; principal component analysis; Principal components analysis; Serbia; Total oxygen demand; tourists; Water analysis; water birds; Water depth; Water quality; Water quality measurements; Waterfowl; Serbia; Multivariate regression; Water quality assessment; Temporal water quality; WQI; Machine learning; PCA
• DOI: 10.1007/s10661-021-09195-8
• ISSN: 0167-6369; 1573-2959

This study presents a comprehensive investigation of water quality parameters in the fourth sector of Lake Palic in Serbia, which has a regional strategic importance. Namely, it is designated as a tourist destination. What is perhaps even more important is that its surplus water ends up in Lake Ludas, a significant habitat for migrating and aquatic bird species, and it is a RAMSAR site. The conducted analysis points to the major conclusion that the reasons for very high Chlorophyll-a values can be found in considerable anthropogenic pressures exerted on the studied area. Due to these pressures, the lake is not in ecological equilibrium. To support this conclusion, an in-depth analysis was conducted using water quality measurements for 9 years, from 2011 to 2019. The data was subject to principal component analysis (PCA) and machine learning classification algorithms that identified a seasonal character regarding the lake’s water quality. Water quality indexes (WQI) were determined using two approaches to provide a more general insight into the lake’s overall quality. Keeping in mind the large number of data gathered monthly within the Palic-Ludas Lake system, fitted models for estimating certain water quality parameters were also developed. This was accomplished via multivariate regression, resulting in a number of equations that can, using a few basic input parameters, predict values of ammonium nitrogen, Chlorophyll-a, and 5-day biological oxygen demand. The fitted models were obtained for relatively homogeneous periods within a year identified by cluster analysis.