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Field sampling consisted of collection of water, suspended sediments, and bottom sediment samples from Hamilton Harbour, and water samples from external sources over 2015 and 2016. Water samples were collected from four sites across Hamilton Harbour at 1m below the surface (epilimnion), and 1m above the sediment-water interface (hypolimnion). Final effluent samples were collected from the two wastewater treatment plants that discharge into Hamilton Harbour. Water samples were collected from the steel mill, intake and surface water discharge sites, as well as from two combined sewer overflow tanks. Suspended sediment samples were collected monthly from the same four sites across Hamilton Harbour as the water samples, using sediment traps. Sediment cores were collected in July and October 2016 from multiple sites across Hamilton Harbour using a box corer and subsampling into core tubes (7.5cm in diameter, 60cm long). Field samples were analysed for dissolved silicon, and total dissolved phosphorus (water samples), as well as reactive particulate silicon (suspended solids in water samples, suspended sediment samples, and bottom sediment samples). Sediment cores were also used in sediment core incubation experiments under varying oxygen and temperature conditions to determine the seasonal flux of reactive silicon from bottom sediments to the water column in Hamilton Harbour. This data was combined with water discharge data and reactive silicon concentration data from published literature and federal and provincial monitoring programs, to create a silicon mass balance model for the time period May to November 2016. Discharge and concentration data were processed in Excel to give mean monthly values. Reactive silicon fluxes were computed by multiplying discharge by concentration, or by mass balance. Internal cycling fluxes were computed from laboratory experiments, or by mass balance.
The metabolism of rainbow, greenside, and fantail darters will be measured using intermittent flow respirometry, to determine aerobic scope and hypoxia tolerance. Gill physiological changes will be measured using histological analyses and enzyme assays relating to energetics. The sites of collection are near the wastewater treatment plant in Waterloo, Ontario. Two upstream, non-effluent receiving sites, and two sites at different distances from the effluent release downstream are considered. Sampling will be done, at least once, during spring, summer, and fall seasons.
The ASTER Global Digital Elevation Model from NASA, 1 second (30 m) were downloaded from National Aeronautics and Space Administration's (NASA) Jet Propulsion Laboratory website (https://asterweb.jpl.nasa.gov/gdem.asp). ASTER Global Digital Elevation Model was developed jointly by the NASA and Japan’s Ministry of Economy, Trade, and Industry. This global product has a 30 m resolution. The region of interest (Great Lakes) is cropped and data converted into formats ASCII and NetCDF. Data are then made available to the project collaborators on a private GitHub. Researchers interested in data can email Juliane Mai (University of Waterloo; email@example.com)
Collected data represents various water samples (surface and subsurface) taken near Yellowknife and Wekweètì, Northwest Territories. Samples were collected irregularly between July 2016 and present from both locations from nearby lakes and drive-point piezometers, filtered in field to 0.45um, and kept cold and in the dark until analyses (generally within three weeks). Water was analyzed for dissolved organic matter (DOM) concentration, DOM composition (via size-exclusion chromatography, UV-visible light absorbance, and elemental ratios), disinfection potential, and concentrations of disinfection by-products (both trihalomethane and haloacetic acids). DOM analysis was completed at the Environmental Geochemistry Laboratory, University of Waterloo, while disinfection demand and DBP concentrations were analysed at the Clean Water Lab, Dalhousie University.
IDroplet microfluidic technology enables the production of nanoliter-sized water droplets that can be produced at kHz frequencies. As a result, this technology offers tremendous potential for producing a significant amount of data, which can be used for training and testing “smart” sensors; therefore, droplet microfluidics will be used in tandem with the microwave resonator to measure water samples with different dielectric/electrical properties. The measured data can then be used to understand the accuracy and limit of detection of the sensor for different water contaminants.
To create this decision support tool, we undertook extensive literature review on how decision support systems/tools are developed and utilized related to large lake basin management. We identified key components of decision support framework and established process to develop a decision-support framework followed by a systematic tool. Furthermore, we held a workshop and stakeholder consultation to refine and further seek stakeholder inputs. To help steer the research outputs and outcomes, we are focusing on the co-creation of decision support tools to manage two major nutrient inputs: agricultural inputs and Combined Sewer Overflows (CSOs). It is imperative to obtain relevant stakeholders and decision-makers data, therefore, we sought memberships from the following working group (some already confirmed). 1. Successfully established membership in Conservation Halton as Board of Director for the year 2019-2022. This membership allows access to key Lake Ontario stakeholders and senior executives who are responsible for making decisions related to Lake Ontario. It is expected that valuable stakeholder ranking data (through Delphi methodology) will be obtained in the coming weeks. 2. Other groups that Highly Qualified Personnel has joined include Lake Erie Working Group, the Agriculture and Rural Affairs Advisory Committee, and the International Joint Commission. We continue to attend stakeholder meetings and obtained critical data that will form inputs to developing decision support framework. As an outcome of such meetings, additional stakeholders were identified, and interviews were conducted with farming communities. We established the use of Delphi method with a panel of stakeholders to formulate hypothesis related to agriculture and CSO and their relative ranking in the group setting. Currently, we are making headway progress in establishing a Driver–Pressure–State–Impact Response (DPSIR)-based decision-analysis framework at Lake Erie and Lake Ontario scale.
These data were collected to explore the impact of Alnus alnobetula shrub growth on ecosystem function and to assess the spatial variability of that impact at the taiga-tundra ecotone. These metadata are associated with five specific data sets: 1) A suite of environmental variables (soil moisture, thaw depth, nutrient availability, snow depth, and organic matter depth) paired with vegetation community composition collected between summer 2015 and spring 2017. Data were taken from Alnus and Alnus-free habitat at ten sites with S-SE facing slopes. 2) Stem map and shrub size information paired with abiotic and biotic information from the first data set. These data were collected in the summers of 2015 and 2016. 3) Field observations of seed and seedling density measured in a grid around three Alnus patches. To collect seed density, seed traps were deployed over the winter of 2016-2017. Seedlings were counted in the summer of 2017. 4) Root collar samples from Alnus individuals across Trail Valley Creek collected for dendrochronological analysis. 5) Paired measures of frost table depth, soil moisture, repeat photos of bud break, shrub density, and snow-off date collected from the spring to fall of 2017. Data were taken from Alnus and Alnus-free habitat at seven sites. All data came from within 2km of Trail Valley Creek Research Station.
This data set was developed in order to understand the role of Lake St. Clair (USA–Canada) in modulating phosphorus loads. Specifically, this research will provide information on the simulated retention of nutrients (total and soluble reactive phosphorous) during ice-free period (March 1 to October 31) in 2009 in Lake St. Clair. We will use a three-dimensional coupled hydrodynamic and biogeochemical model (ELCOM-CAEDYM). The hydrodynamic model is an Estuary, Lake and Coastal Ocean Model (ELCOM). The biogeochemical model is a Computational Aquatic Ecosystem Dynamics Model (CAEDYM). The model was calibrated and validated in its previous application to Lake St. Clair (More details on model calibration and validation can be found in Bocaniov and Scavia, 2018: Water Resources Research, 54, 3825-3840; https://doi.org/10.1029/2017WR021876). We will use this model to simulate the transport and retention of nutrients and construct the tributary-specific load-response relationships. We will also look at the mechanisms responsible for retention of nutrients such as settling and re-suspension.
ERA5, is the climate reanalysis dataset from European Centre for Medium-Range Weather Forecasts (ECMWF), which is substantially upgraded in comparison with ERA-Interim, with a spatial grid resolution of 0.25 degree (~31 km) and hourly temporal resolution, 137 vertical levels of the atmosphere, and increased amount of assimilated data using 4DVar data assimilation method. More details on ERA5 are available at https://confluence.ecmwf.int/display/CKB/ERA5+data+documentation. All ERA5 data were downloaded from the Copernicus Climate Change Service website (https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset), and is currently available on the GWF Cuizinart. The variables currently available are cloud cover (0-1), precipitation (mm), evaporation (mm), and runoff (mm) for the period of 2002-2017. Researchers interested in the data from the GWF Cuizinart can email Homa Kheyrollah Pour (University of Waterloo; firstname.lastname@example.org)