An evaluation of a spectral fluorometer for monitoring chlorophyll a in New York State lakes

Characterizing the Impact of Cyanobacterial Blooms on the Photoreactivity of Surface Waters from New York Lakes: A Combined Statewide Survey and Laboratory Investigation

“… Φ app , 1 O 2 and Φ app , 3 DOM TMP * clustered together on the first predictive component axis on the OPLS loading scatter plot but were separated from Φ app , • OH on the second predictive component axis (Figure g), consistent with the propositions that FFA and TMP sampled an overlapping pool of 3 DOM*, ,, and that the photoproduction of • OH does not necessarily involve 3 DOM*-mediated pathways. , On the basis of VIP scores generated by OPLS modeling (Figure h), the six most influential predictors (i.e., those with a VIP score of >1.0) of Φ app,RI followed the order of Chl- a cyano > Chl- a > TDN > NO x –N > FI > S 290–400 . Chl- a has frequently been used for inferring algal abundance in large-scale lake assessment, , whereas Chl- a cyano represents a proxy for cyanobacterial abundance . Φ app,RI for samples containing Chl- a cyano showed stronger positive correlations with Chl- a cyano and the proportion of Chl- a cyano in Chl- a (%Chl- a cyano ; an operationally defined indicator of bloom intensity) than with Chl- a (Spearman correlation coefficient ρ = 0.423–0.669; p < 0.0001; Figures S9 and S10), which corroborated the prioritization of Chl- a cyano over Chl- a as the top predictor of Φ app,RI by OPLS modeling.…”

“…Chl- a has frequently been used for inferring algal abundance in large-scale lake assessment, 72 , 73 whereas Chl- a cyano represents a proxy for cyanobacterial abundance. 36 Φ app,RI for samples containing Chl- a cyano showed stronger positive correlations with Chl- a cyano and the proportion of Chl- a cyano in Chl- a (%Chl- a cyano ; an operationally defined indicator of bloom intensity 32 ) than with Chl- a (Spearman correlation coefficient ρ = 0.423–0.669; p < 0.0001; Figures S9 and S10 ), which corroborated the prioritization of Chl- a cyano over Chl- a as the top predictor of Φ app,RI by OPLS modeling. Two nitrogen-related parameters, TDN and NO x -N, were also ranked as highly influential predictors, pointing to the link between Φ app,RI and nitrogen loading, one of the elements implicated in the emergence of cyanobacterial blooms in lakes.…”

“…Typically, samples were taken from an open water midlake location over the deepest basin of each lake with a Kemmerer bottle submerged below the water surface and shipped on ice to the Upstate Freshwater Institute (UFI). Upon arrival at UFI, samples were analyzed in a certified laboratory for total chlorophyll a (Chl- a ) and cyanobacterial chlorophyll a (Chl- a cyano ) by a bbe Moldaenke FluoroProbe III 36 along with a suite of water quality parameters (e.g., pH, specific conductance, nitrate–nitrite nitrogen (NO x –N), total dissolved nitrogen (TDN), total dissolved phosphorus, and N:P ratio; Table S3 ). 33 Samples were then transported to Syracuse University, filtered through 0.7-μm glass fiber filters followed by 0.2-μm polyethersulfone membranes, and stored in the dark at 4 °C until analyzed for dissolved organic carbon (DOC) and optical properties (e.g., specific UV absorbance at 254 nm (SUVA 254 ), 37 E 2 : E 3 (the ratio of Napierian absorption coefficients at 250 and 365 nm), 38 spectral slope coefficient S 290–400 , 39 fluorescence index (FI), 40 freshness index (β:α), 41 and peak M:T (the ratio of microbial humic-like to protein-like DOM fluorescence); 42 Table S4 ).…”

River Control Points for Algal Productivity Revealed by Transport Analysis

“…Continuous measurements of streamflow (Figure S1 in Supporting Information S1) and chlorophyll fluorescence (fchl) between 2018 and 2020 (Platt et al, 2022) were daily averaged for use as model inputs. We estimated q L (Equation 2) to account for reachby-reach tributary contributions (Figure S2 in Supporting Information S1) and corrected fchl to chl-a concentration using a pooled regression (Foster et al, 2019;Prestigiacomo et al, 2022) although correction had negligible (slope = 1.04 ± 0.05) impact on mass balance results (Schmadel et al, 2024) (Figure S3 in Supporting Information S1). River velocity and cross-sectional area were estimated using measured streamflow (Platt et al, 2022; U.S. Geological Survey, 2022) and attributes of bed slope (Schwarz, 2019) as inputs to the reachaveraged, regression-based Jobson (1996) approach (see Figure S4 and Text S2 in Supporting Information S1 for details).…”