Blooms In Great Lakes Research Articles

Effect of nitrogen/phosphorus concentration on algal organic matter generation of the diatom Nitzschia palea: Total indicators and spectroscopic characterization

Critical algal blooms in great lakes increase the level of algal organic matters (AOMs), significantly altering the composition of natural organic matters (NOMs) in freshwater of lake. This study examined the AOM's characteristics of Nitzschia palea (N. palea), one kind of the predominant diatom and an important biomarker of water quality in the great lakes of China, to investigate the effect of AOMs on the variation of NOMs in lakes and the process of algal energy. Excitation–emission matrix fluorescence (EEM) spectroscopy, synchronous fluorescence (SF) spectroscopy and deconvolution UV–vis (D-UV) spectroscopy were utilized to characterize AOMs to study the effects of nutrient loading on the composition change of AOMs. From results, it was revealed that the phosphorus is the limiting factor for N. palea's growth and the generation of both total organic carbon and amino acids but the nitrogen is more important for the generation of carbohydrates and proteins. EEM spectra revealed differences in the composition of extracellular organic matter and intracellular organic matter. Regardless of the nitrogen and phosphorus concentrations, aromatic proteins and soluble microbial products were the main components, but the nitrogen concentration had a significant impact on their composition. The SF spectra were used to study the AOMs for the first time and identified that the protein-like substances were the major component of AOMs, creating as a result of aromatic group condensation. The D-UV spectra showed carboxylic acid and esters were the main functional groups in the EOMs, with –OCH3, –SO2NH2, –CN, –NH2, –O– and –COCH3 functional groups substituting into benzene rings.

Mapping cyanobacterial blooms in the Great Lakes using MODIS

Toxin-producing Cyanobacteria have been documented in Lake Erie and Ontario in the last several years. We developed algorithms to discriminate potentially toxic cyanobacterial blooms from other harmless phytoplankton blooms and to extract relative phycocyanin abundances from Moderate Resolution Imaging Spectrometer (MODIS) satellite data. Lee's quasi-analytical algorithm was used to calculate total absorption and backscatter from the 250 m, 500 m and 1 km bands of MODIS scenes. A non-negative least square algorithm was then utilized to discern relative concentrations of Chlorophyta (green algae), phycocyanin-rich Cyanobacteria (blue-green algae), and colored dissolved organic matter and suspended sediments combined in lake waters using published absorption spectra for these components. MODIS-derived cyanobacterial concentrations and/or bloom distributions from 10 scenes acquired in the summers of 2004 and 2005 were successfully verified against contemporaneous calibrated measurements of pigments that were acquired from measurements made using continuous fluorimetric measurements of surface water (1 m depth) from six cruises, and three additional cyanobacterial blooms reported in the scientific literature between 2002 and 2006. These results demonstrate that this methodology could be used to develop a cost-effective practical screening method for rapid detection and warning of potentially toxic cyanobacterial blooms in the lower Great Lakes.