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Environmental Sampling and Laboratory Analysis
Published in G. Mattney Cole, Assessment and Remediation of Petroleum Contaminated Sites, 2018
Samples must be preserved according to specifications and are typically stored at 4°C (40°F) using either ordinary ice or “blue ice.” Ordinary ice should not be used with water sensitive samples. Internal temperatures must be measured before, during and after sample collection, and before and after shipping.
Marked blue discoloration of late winter ice and water due to autumn blooms of cyanobacteria
Published in Lake and Reservoir Management, 2022
Heather A. Haig, Amir M. Chegoonian, John-Mark Davies, Deirdre Bateson, Peter R. Leavitt
Further research is also needed to determine the extent of lake discoloration by C-phycocyanin (Arii et al. 2015). Although Sentinel 3 A/B with OLCI sensors provides approximations of C-phycocyanin and cyanobacterial abundance during summer (Mishra et al. 2019, Ogashawara 2019), ours was the first attempt to use remote-sensed images to document the presence of the pigment under late winter conditions. Here we found that intense blue discoloration at 3 field sites corresponded well to Rrs620 nm/Rrs709 nm ratios ≤0.9, but also noted that the presence of fresh snow introduced bias and overestimated the extent of blue discoloration (Supplementary material, Figure S1). Use of simple band ratios to estimate C-phycocyanin concentrations can be difficult in optically complex environments (Matthews 2011, Stumpf et al. 2016); however, its use in this instance is justified because of the absence of physical or biologically induced turbidity during the period of analysis. Nonetheless, much more extensive ground validation will be required to refine the use of satellite imagery to detect the blue-ice phenomenon, as seasonal changes in snow cover, adjacency effect, spectral characteristics of lake water, and the distribution, composition, and cause of blooms all need to be further constrained. Nonetheless, this promising first report suggests that some future ecological surprises may be detectable in near real time using the next generation of orbital platforms.
Ice cover and thermal regime in a dimictic seepage lake under climate change
Published in Inland Waters, 2018
David P. Hamilton, Madeline R. Magee, Chin H. Wu, Timothy K. Kratz
We summarized the comparison of the timing of ice-on and ice-off as well as monthly ice and snow thickness measurements (Table 3) and compared measured and simulated variations in thickness of blue ice, snow ice, and total ice (blue ice + snow ice), and snow cover (Fig. 9). Note that the model was not calibrated to observed values for water temperature and ice cover thickness but adjusted the unmeasured groundwater inflow and outflow to match simulated lake water levels with those of observed values during the study period. The model simulations of total ice thickness were in close agreement, with <10∼20% difference and a mean absolute difference of 9 (SE 0.3) cm, n = 63 (Fig. 9c). The model slightly overpredicted blue ice thickness and underpredicted snow ice thickness. In summary, DYRESM-WQ-I provided accurate simulations of phenological characteristics of the ice. Additionally, the measured and simulated ice duration at extended time scales (Fig. 10) indicate the model reliably captured the interannual variation of ice duration, and the results agreed closely with the measurements, except the extreme winter 1999–2000, which had the shortest ice cover duration in the past 22 years.