Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model

There have been increasing reports of harmful algal blooms (HABs) worldwide. However, the factors that influence cyanobacteria dominance and HAB formation can be site‐specific and idiosyncratic, making prediction challenging. The drivers of cyanobacteria blooms in Lake Paldang, South Korea, the summer climate of which is strongly affected by the East Asian monsoon, may differ from those in well‐studied North American lakes. Using the observational data sampled during the growing season in 2007–2011, a Bayesian hurdle Poisson model was developed to predict cyanobacteria abundance in the lake. The model allowed cyanobacteria absence (zero count) and nonzero cyanobacteria counts to be modeled as functions of different environmental factors. The model predictions demonstrated that the principal factor that determines the success of cyanobacteria was temperature. Combined with high temperature, increased residence time indicated by low outflow rates appeared to increase the probability of cyanobacteria occurrence. A stable water column, represented by low suspended solids, and high temperature were the requirements for high abundance of cyanobacteria. Our model results had management implications; the model can be used to forecast cyanobacteria watch or alert levels probabilistically and develop mitigation strategies of cyanobacteria blooms. Key Points A Bayesian hurdle Poisson model predicted cyanobacteria abundance Temperature, flushing rate, and water column stability were key factors The model forecasted cyanobacteria watch and alert levels probabilisticallyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106958/1/wrcr20820.pd

qPCR-Based Monitoring of 2-Methylisoborneol/Geosmin-Producing Cyanobacteria in Drinking Water Reservoirs in South Korea

Cyanobacteria can exist in water resources and produce odorants. 2-Methylisoborneol (2-MIB) and geosmin are the main odorant compounds affecting the drinking water quality in reservoirs. In this study, encoding genes 2-MIB (mic, monoterpene cyclase) and geosmin (geo, putative geosmin synthase) were investigated using newly developed primers for quantitative PCR (qPCR). Gene copy numbers were compared to 2-MIB/geosmin concentrations and cyanobacterial cell abundance. Samples were collected between July and October 2020, from four drinking water sites in South Korea. The results showed similar trends in three parameters, although the changes in the 2-MIB/geosmin concentrations followed the changes in the mic/geo copy numbers more closely than the cyanobacterial cell abundances. The number of odorant gene copies decreased from upstream to downstream. Regression analysis revealed a strong positive linear correlation between gene copy number and odorant concentration for mic (R2 = 0.8478) and geo (R2 = 0.601). In the analysis of several environmental parameters, only water temperature was positively correlated with both mic and geo. Our results demonstrated the feasibility of monitoring 2-MIB/geosmin occurrence using qPCR of their respective synthase genes. Odorant-producing, gene-based qPCR monitoring studies may contribute to improving drinking water quality management