5 Sep 2024

Next-Generation Water Quality Monitoring during Dredging Operations: Knowns, Unknowns, and Path Forward

Abstract: Water quality monitoring data are routinely collected during dredging and placement operations to address various state and federal requirements, including water quality standards, with the intention of protecting ecosystem health. However, such efforts may be limited by the lack of a standardized national strategic focus and user-friendly streamlined interfaces to interpret the data. Inconsistencies in how and what data are collected and lack of consensus on scientifically backed biological-effects thresholds make it difficult to quantify potential dredging operations impacts (or lack thereof) both within individual projects over time and across multiple projects of differing characteristics. Summarized herein is an initial effort to define a scientifically backed path forward to improve the value of current and future water quality monitoring and management decisions based on water quality data collected. The provided turbidity data were generally below applicable state thresholds for two case studies but for a third case study did periodically exceed thresholds at depth. This includes providing rationale for strategic focus on the most relevant dredging operations and projects, based on three general site-specific data categorizations: (1) sediment type, (2) dredge type, and (3) ecosystem type.

15 Jun 2021

A Novel Laboratory Method for the Detection and Identification of Cyanobacteria Using Hyperspectral Imaging: Hyperspectral Imaging for Cyanobacteria Detection

Abstract: To assist US Army Corps of Engineers resource managers in monitoring for cyanobacteria bloom events, a laboratory method using hyperspectral imaging has been developed. This method enables the rapid detection of cyanobacteria in large volumes and has the potential to be transitioned to aerial platforms for field deployment. Prior to field data collection, validation of the technology in the laboratory using monocultures was needed. This report describes the development of the detection method using hyperspectral imaging and the stability/reliability of these signatures for identification purposes. Hyperspectral signatures of different cyanobacteria were compared to evaluate spectral deviations between genera to assess the feasibility of using this imaging method in the field. Algorithms were then developed to spectrally deconvolute mixtures of cyanobacteria to determine relative abundances of each species. Last, laboratory cultures of Microcystis aeruginosa and Anabaena sp. were subjected to varying macro (nitrate and phosphate) and micro-nutrient (iron and magnesium) stressors to establish the stability of signatures within each species. Based on the findings, hyperspectral imaging can be a valuable tool for the detection and monitoring of cyanobacteria. However, it should be used with caution and only during stages of active growth for accurate identification and limited interference owing to stress.