Early warning of limit-exceeding concentrations of cyanobacteria and cyanotoxins in drinking water reservoirs by inferential modelling

Abstract

An early warning scheme is proposed that runs ensembles of inferential models for predicting the cyanobacterial population dynamics and cyanotoxin concentrations in drinking water reservoirs on a diel basis driven by in situ sonde water quality data. When the 10- to 30-day-ahead predicted concentrations of cyanobacteria cells or cyanotoxins exceed pre-defined limit values, an early warning automatically activates an action plan considering in-lake control, e.g. intermittent mixing and ad hoc water treatment in water works, respectively. Case studies of the sub-tropical Lake Wivenhoe (Australia) and the Mediterranean Vaal Reservoir (South Africa) demonstrate that ensembles of inferential models developed by the hybrid evolutionary algorithm HEA are capable of up to 30days forecasts of cyanobacteria and cyanotoxins using data collected in situ. The resulting models for Dolicospermum circinale displayed validity for up to 10days ahead, whilst concentrations of Cylindrospermopsis raciborskii and microcystins were successfully predicted up to 30days ahead. Implementing the proposed scheme for drinking water reservoirs enhances current water quality monitoring practices by solely utilising in situ monitoring data, in addition to cyanobacteria and cyanotoxin measurements. Access to routinely measured cyanotoxin data allows for development of models that predict explicitly cyanotoxin concentrations that avoid to inadvertently model and predict non-toxic cyanobacterial strains.

Evaluation of Size Structure in Freshwater Cyanobacterial Populations: Methods to Quantify Risk Associated with Changes in Biomass and Microcystin Concentrations

“…The low MC concentrations we observed in Dolichospermum spp. are in contrast to those reported [16], however they are consistent with other studies that associate this genus with saxitoxins [8] or concerns related to peak biomass events [6].…”

“…Many studies have been conducted that describe cyanobacterial populations [13] and biotoxin concentrations [6] as this variable appears to influence MC concentrations [9]. In this study of lakes with "field monocultures" of Microcystis spp.…”

“…Previous studies [5] [6] [8] have used multivariate screening models with "if-then" criteria to estimate the growth of cyanobacterial biomass and increase in microcystin concentrations. Both Recknagel [6] and Newcombe [8] considered environmental variables that influenced the growth (measured by cell counts and proliferation rates) of the "buoyant bloom formers" [8] Microcystis spp. and Anabaena spp.…”

“…The continuing need for site-specific analysis providing information for management decisions has led to the development of qualitative and quantitative techniques. These include site specific models using environmental variables that influence the biomass of cyanobacteria [5] [6], measurements of increased cyanobacteria and elevated microcystin concentrations [5] [6] [7] [8] [9], and more specifically the influence of community structure on microcystin concentrations using light microscopy [8] [9], cell counts [5] and qPCR [5] [7].…”

Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China

“…Considering the difference in environmental conditions (Joung et al, 2011;Rinta-Kanto et al, 2009), predictions by empirical models are complicated and likely site-specific. It is difficult to be generalised across relative large areas (Francy et al, 2016;Recknagel et al, 2017). Therefore, there is still a need of methodological approaches that can efficiently handle large and heterogeneous data, infer cause-and-effect relationships, and capture linear, non-linear, combinatorial, stochastic relationships among variables (Feki-Sahnoun et al, 2017).…”