Geology topics

Colin S. Peake

Publications and source records attributed to Colin S. Peake.

4 recordsLinked to original sources

USGS2026

Evaluating chlorophyll fluorescence sensors as a proxy for chlorophyll a concentrations in the Illinois River, Illinois, water years 2018–24

The U.S. Geological Survey deployed instream chlorophyll fluorescence sensors on the Illinois River in Illinois to better understand the formation, dynamics, and persistence of riverine algal blooms. At five sensor locations across four U.S. Geological Survey streamgages, chlorophyll fluorescence (fChl) sensors were set to measure instantaneous fChl in micrograms per liter as chlorophyll at 15-minute intervals. These sensors estimate fChl based on the fluorescence response of algal pigments but do not directly measure chlorophyll a (Chl) concentrations or algal biomass. To evaluate the reliability of fChl as a proxy for Chl, fChl measurements were compared to laboratory-measured Chl concentrations. These Chl concentrations were obtained from water-quality samples collected at the sensor locations and analyzed using the standard chromatographic-fluorometric method with acetone extraction. A total of 209 paired fChl and Chl observations were analyzed using Pearson’s correlation and model II regression. Statistically significant, positive correlations (Pearson’s correlation coefficient of 0.68–0.95) were detected at all sensor locations, indicating that fChl generally tracked with Chl. However, model II regression detected proportional and fixed bias at three of the five locations and proportional bias at another location. At locations with proportional biases, model II regression slopes were less than 1, indicating that fChl increasingly underestimated Chl as concentrations increased; however, paired observations at high Chl concentrations were sparse. Substantial variability in bias was also observed across sensor locations with slopes ranging from 0.43 to 0.93. Overall, the results indicated that fChl correlates well with Chl in the Illinois River, but fChl may not reliably represent Chl concentrations without adjustment. For applications requiring accurate Chl estimates, a sensor location-specific surrogate model could be beneficial. These findings underscore the importance of field validation, consistent sensor placement, and careful interpretation of fChl data, particularly when reported in micrograms per liter.

Illinois, Indiana, Wisconsin↗

USGS2022

Verification of multiple phosphorus analyzers for use in surface-water applications

The U.S. Geological Survey (USGS) completed a verification study of selected commercially available phosphorus analyzers for their applicability to scientific surface-water applications. In this study, the analyzers were the Hach EZ7800 TOPHO, Hach Phosphax sc, Sea-Bird Scientific HydroCycle-PO 4 , and the YSI Inc. Alyza IQ PO4. Verification tests included laboratory trials comparing analyzer results to known standards with several known concentrations of dissolved organic matter and waste production estimates. Field trials were completed at the Vermilion River near Danville, Illinois (U.S. Geological Survey station 03339000), where analyzer-measured concentrations were compared against discrete samples across a wide range of environmental conditions from November 2020 to August 2021. Data coverage was closely tracked for analyzer malfunctions and operator errors that caused missing data. Laboratory and field trials indicated that each analyzer is a viable option for scientific surface-water studies depending on environmental conditions. Because of the complexity of the analyzers, a substantial time investiture was required to get maximum data coverage including considerable site infrastructure investments and well-trained technicians. Data coverage was closely related to each analyzer’s ability to handle elevated turbidity levels.

Illinois, Indiana↗

USGS2022

Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20

The Des Plaines River in southern Wisconsin and northern Illinois is the principal conduit for the discharge of wastewater effluent and stormwater runoff from the greater Chicago metropolitan area. In November 2017, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, installed a continuous monitoring station to measure water quality and streamflow in the Des Plaines River at Joliet, Illinois. Surrogate models encompassing continuous data and discrete water-quality samples were used to estimate loads of nitrate, total phosphorus, and suspended sediment. Comparisons to other major rivers in Illinois show that the Des Plaines River is a substantial contributor to statewide loading estimates for nitrate and total phosphorus but only a minor contributor to suspended sediment. Future loading estimates of total phosphorus could include more research into the effects of combined sewage overflows because these effects likely increased model uncertainty. The results in this report document current loadings and provide a baseline from which to assess future water-quality management decisions.

Illinois↗

USGS2021

Continuous monitoring and Bayesian estimation of nutrient and sediment loads from Illinois watersheds, for water years 2016–2020

The State of Illinois is one of the leading contributors of nitrogen, phosphorus, and suspended sediment to the Mississippi River and the Gulf of Mexico. During water years 2016–20, the U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, operated continuous monitoring stations on eight major rivers in Illinois to better quantify nutrient and sediment loadings from the State of Illinois to the Mississippi River. This report estimates nitrate, phosphorus, and suspended-sediment loadings over that period, which can provide a benchmark against which to assess future changes in loading. In addition, this report develops a new method for incorporating the uncertainty created by gaps in continuous datasets based on Bayesian machine learning. Data gaps are a common problem in continuous monitoring, and gap filling is necessary to quantify loadings and the uncertainty in loadings, which is essential if these results are to provide a benchmark for future studies. The uncertainty estimates may also be useful in an operational context, and this report provides examples of how uncertainty can be used in monitoring-network design and potentially reducing monitoring costs.

Illinois↗