Stochastic simulation of phytoplankton biomass using eighteen years of daily data - predictability of phytoplankton growth in a large, shallow lake

Large shallow lake response to anthropogenic stressors and climate change: Missing macroinvertebrate recovery after oligotrophication (Lake Balaton, East-Central Europe)

East-Central Europe's largest shallow lake, Balaton, experienced strong eutrophication in the 1970-80s, followed by water quality improvement and oligotrophication by 2010 CE. Recently however, repeated cyanobacterial blooms occurred and warned that internal P-recycling can act similarly to external P load, therefore we need a better understanding of past water level (WL) and trophic changes in the lake. In this study we discuss the last 500-yr trophic, WL and habitat changes of the lake using paleoecological (chironomids, pollen) and geochemical (sediment chlorophyll, TOC, TS, TN, C/H ratio, major and trace element) methods. We demonstrate that the most intensive and irreversible change in the macroinvertebrate fauna occurred during the period of economic boom between the First and Second World War (∼1925-1940 CE), when large-scale built-in and leisure use of the lake has intensified. At that time, the Procladius-Microchironomus-Stempellina dominated community transformed to Procladius-Chironomus plumosus-type-Microchironomus community that coincided with land use changes, intensified erosion and water-level regulation in the lake with the maintenance of year-round high WL. This was followed by the impoverishment and population size decrease of the chironomid fauna and Procladius dominance since 1940 CE, without any recovery after 1994 CE despite the ongoing oligotrophication. Accelerated rate of change and turnover of the fauna was connected to an increase in the benthivorous fish biomass and eutrophication. The basin lost almost completely its once characteristic Stempellina species between 1927 and 1940 CE due to trophic level increase and seasonal anoxia in the Szemes Basin. Reference conditions for ecosystem improvement were assigned to 1740-1900 CE. We conclude that in spite of the ongoing oligotrophication, the re-establishment of the Procladius-Microchironomus-Stempellina assemblage is hampered, and requires fish population regulation.

Describing and simulating phytoplankton of a small and shallow reservoir using decision trees and rule-based models

Phytoplankton represents one of the most important biological components of primary production, trophic interactions, and circulation of organic matter in lakes and reservoirs. To contribute to the understanding of eutrophication processes and ecological status of the small, shallow Butoniga reservoir, a machine learning tool for induction of models in form of decision trees and rule-based models was applied on a dataset comprising physical, chemical, and biological variables measured at four stations. Two types of models were successfully elaborated, i.e., (1) model describing phytoplankton Phylum, which describes and connects phytoplankton Phylum with phytoplankton abundance and biomass, and (2) model simulating phytoplankton biomass according to environmental variables which could be used in management purposes. Such models and their presentation contribute to a better understanding of the Butoniga reservoir ecosystem functioning.

Phytoplankton Community Structure Dynamics in Relation to Water Environmental Factors in Zhalong Wetland

Phytoplankton, as the primary producer of the wetland water ecosystem's food chain, are very sensitive to environmental changes. In order to explore the significance of phytoplankton in protecting ecosystem integrity, the wetland ecosystem in Zhalong wetland, one of the most important international wetlands, was selected as the research area. For the study, 81 sampling sites were set up in the whole wetland, and phytoplankton samples and water quality environmental factors were measured in spring, summer, and autumn of 2019. The phytoplankton community structure and water environmental factors were evaluated by canonical correspondence analysis (CCA). The main research findings are as follows: a total of 292 species and variants of phytoplankton belonging to 8 phyla and 110 genera were identified within Zhalong wetland in spring, summer, and autumn 2019. The total phytoplankton abundance and biomass in summer were higher than in spring and autumn, and Cyclotella meneghiniana was the most dominant species in three seasons and three areas. The results of random forest are generally consistent with the results of CCA in spring, when the main environmental factors affecting phytoplankton were NTU and WT; the result in summer and autumn agreed with those of CCA, which awaits further study. In addition, the phytoplankton is mainly affected by WT, depth, and DO in the lake area, TP, DO, and NTU in the river area, and WT in the wetland area.

Algae-containing raw water treatment and by-products control based on ClO2 preoxidation-assisted coagulation/precipitation process

Eutrophication has become a great concern in recent years with the algae blooms in source water resulting in a serious threat posing to the safety of drinking water. Chlorine dioxide (ClO₂) has been served as an alternative oxidant for preoxidation or disinfection during drinking water treatment process due to its high oxidation efficiency and low risk of organic by-products formation. However, the generation of inorganic by-products including chlorite (ClO₂^-) and chlorate (ClO₃^-) has become a potential problem when applied in drinking water treatment. In this study, ClO₂ preoxidation-assisted coagulation/precipitation process was applied to improve the raw water quality, especially algae, turbidity, chemical oxygen demand (COD_Mn), and UV₂₅₄, and explore the formation mechanisms of inorganic by-products. It was found that the polymeric aluminum chloride (PAC) and ClO₂ have shown the best raw water treatment performance with the optimal dosage of 10 mg/L and 0.8 mg/L, respectively. Moreover, the initial pH also has exhibited a notable influence on pollutants treatment and by-products generation. Due to the adverse influence of algae and natural organic matters (NOM) and the generation of by-products, it was significant to investigate their inhibition effect on the water quality and the production of ClO₂^- and ClO₃^- in the ClO₂ preoxidation-assisted coagulation/precipitation process. Moreover, it was applicable of this process to apply for the algae-containing raw water (calculated as Chl.a lower than 50 μg/L) treatment with the ClO₂ dosage of less than 0.8 mg/L to achieve optimum treatment performance and minimum by-products generation.

Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach

Eutrophication, water quality, and environmental status of lakes is a global issue that depends not only on external loadings from industrial, agricultural, and municipal sources but often also on internal loadings from lake sediments. In the latter case, in addition to the quality and quantity of nutrients stored in sediments, their relative content may be an important factor. In the example of Lake Balaton, we jointly modeled the spatial distribution of the nutrients nitrogen (N) and phosphorus (P) and their ratio (i.e., N:P) in the sediments of the lake and then provided spatial predictions at different scales (i.e., point, basin, and entire lake) with the associated uncertainty. Our aim was to illustrate the merits of applying multivariate geostatistics when spatial modeling of more than one variable is targeted at various scales in water ecosystems. Variography confirmed that there is a spatial interdependence between the nutrients. The results revealed that multivariate geostatistics allows this interdependence to be taken into account and exploited to provide coherent and accurate spatial models. Additionally, stochastic realizations, reproducing the joint spatial variability, can be generated that allow providing spatially aggregated predictions with the associated uncertainty at various scales. Our study highlighted that it is worthy of applying multivariate geostatistics in case spatial modeling of two or more variables, which jointly vary in space, is targeted in water ecosystems.