An assessment of a biosensor system for the quantification of microcystins in freshwater cyanobacterial blooms.

Microcystin-producing cyanobacterial blooms are a global issue threatening drinking water supplies and recreation on lakes and beaches. Direct measurement of microcystins is the only way to ensure waters have concentrations below guideline concentrations; however, analyzing water for microcystins takes several hours to days to obtain data. We tested LightDeck Diagnostics' bead beater cell lysis and two versions of the quantification system designed to give microcystin concentrations within 20 min and compared it to the standard freeze-thaw cycle lysis method and ELISA quantification. The bead beater lyser was only 30 % effective at extracting microcystins compared to freeze-thaw. When considering freeze-thaw samples analyzed in 2021, there was good agreement between ELISA and LightDeck version 2 (n = 152; R2 = 0.868), but the LightDeck slightly underestimated microcystins (slope of 0.862). However, we found poor relationships between LightDeck version 2 and ELISA in 2022 (n = 49, slopes 0.60 to 1.6; R2 < 0.6) and LightDeck version 1 (slope = 1.77 but also a high number of less than quantifiable concentrations). After the quantification issues are resolved, combining the LightDeck system with an already-proven rapid lysis method (such as microwaving) will allow beach managers and water treatment operators to make quicker, well-informed decisions.

Reef Metabolism Monitoring Methods and Potential Applications for Coral Restoration.

Coral reef metabolism measurements have been used by scientists for decades to track reef responses to the globe's changing carbon budget and project shifts in reef function. Here, we propose that metabolism measurement tools and methods could also be used to monitor reef ecosystem change in response to coral restoration. This review paper provides a general introduction to net ecosystem metabolism and carbon chemistry for coral reef ecosystems, followed by a review of five metabolism monitoring methods with potential for application to coral reef restoration monitoring. Selected methodologies included those with measurement scales appropriate to assess outplant arrays and whole reef ecosystem outcomes associated with restoration interventions. Subsequently we discuss how water column and CO2 chemistry could be used to address coral restoration monitoring research gaps and scale up from biological, colony-level metrics to ecosystem-scale function and performance assessments. Such function-based measurements could potentially be used to inform several goal-based monitoring objectives highlighted in the Coral Reef Restoration Monitoring Guide. Lastly, this review discusses important methodological factors, such as scale, reef type, and flow environment, that should be considered when determining which metabolism monitoring technique would be most appropriate for a reef restoration project.

Testing of the Storm Water Management Model Low Impact Development Modules.

Stormwater infrastructure designers and operators rely heavily on the United States Environmental Protection Agency's Storm Water Management Model (SWMM) to simulate stormwater and wastewater infrastructure performance. Since its inception in the late 1970s, improvements and extensions have been tested and evaluated rigorously to verify the accuracy of the model. As a continuation of this progress, the main objective of this study was to quantify how accurately SWMM simulates the hydrologic activity of low impact development (LID) storm control measures. Model performance was evaluated by quantitatively comparing empirical data to model results using a multievent, multiobjective calibration method. The calibration methodology utilized the PEST software, a Parameter ESTimation tool, to determine unmeasured hydrologic parameters for SWMM's LID modules. The calibrated LID modules' Nash-Sutcliffe efficiencies averaged 0.81; average percent bias (PBIAS) -9%; average ratio of root mean square error to standard deviation of measured values 0.485; average index of agreement 0.94; and the average volume error, simulated vs. observed, was +9%. SWMM accurately predicted the timing of peak flows, but usually underestimated their magnitudes by 10%. The average volume reduction, measured outflow volume divided by inflow volume, was 48%. We had more difficulty in calibrating one study, an infiltration trench, which identified a significant limitation of the current version of the SWMM LID module; it cannot simulate lateral exfiltration of water out of the storage layers of a LID storm control measure. This limitation is especially severe for a deep LIDs, such as infiltration trenches. Nevertheless, SWMM satisfactorily simulated the hydrologic performance of eight of the nine LID practices.

Methane and Carbon Dioxide Emissions From Reservoirs: Controls and Upscaling.

Estimating carbon dioxide (CO2) and methane (CH4) emission rates from reservoirs is important for regional and national greenhouse gas inventories. A lack of methodologically consistent data sets for many parts of the world, including agriculturally intensive areas of the United States, poses a major challenge to the development of models for predicting emission rates. In this study, we used a systematic approach to measure CO2 and CH4 diffusive and ebullitive emission rates from 32 reservoirs distributed across an agricultural to forested land use gradient in the United States. We found that all reservoirs were a source of CH4 to the atmosphere, with ebullition being the dominant emission pathway in 75% of the systems. Ebullition was a negligible emission pathway for CO2, and 65% of sampled reservoirs were a net CO2 sink. Boosted regression trees (BRTs), a type of machine learning algorithm, identified reservoir morphology and watershed agricultural land use as important predictors of emission rates. We used the BRT to predict CH4 emission rates for reservoirs in the U.S. state of Ohio and estimate they are the fourth largest anthropogenic CH4 source in the state. Our work demonstrates that CH4 emission rates for reservoirs in our study region can be predicted from information in readily available national geodatabases. Expanded sampling campaigns could generate the data needed to train models for upscaling in other U.S. regions or nationally.

Effects of an Experimental Water-level Drawdown on Methane Emissions from a Eutrophic Reservoir.

Reservoirs are a globally significant source of methane (CH4) to the atmosphere. However, emission rate estimates may be biased low due to inadequate monitoring during brief periods of elevated emission rates (that is, hot moments). Here we investigate CH4 bubbling (that is, ebullition) during periods of falling water levels in a eutrophic reservoir in the Midwestern USA. We hypothesized that periods of water-level decline trigger the release of CH4-rich bubbles from the sediments and that these emissions constitute a substantial fraction of the annual CH4 flux. We explored this hypothesis by monitoring CH4 ebullition in a eutrophic reservoir over a 7-month period, which included an experimental water-level drawdown. We found that the ebullitive CH4 flux rate was among the highest ever reported for a reservoir (mean = 32.3 mg CH4 m-2 h-1). The already high ebullitive flux rates increased by factors of 1.4-77 across the nine monitoring sites during the 24-h experimental water-level drawdown, but these emissions constituted only 3% of the CH4 flux during the 7-month monitoring period due to the naturally high ebullitive CH4 flux rates that persist throughout the warm weather season. Although drawdown emissions were found to be a minor component of annual CH4 emissions in this reservoir, our findings demonstrate a link between water-level change and CH4 ebullition, suggesting that CH4 emissions may be mitigated through water-level management in some reservoirs.