2017 Finalists

Fertilizer Burn
Fertilizer Burn

Taking on the algae monster

Development of a mobile soil testing lab and mobile application that will provide real-time, in-situ soil data to the user.

The mobile lab can be fitted onto existing farming equipment, for example a tractor or trailer, and is operated without interference to other tasks that the farmer may be completing concurrently. The mobile lab may be used in localized areas or throughout an entire field depending on the user’s needs. The soil data collected during usage is stored locally on the mobile device, and synchronized to a cloud application when internet access is available.

As the farmer passes through the field, the application displays visual results from the trip that can be easily interpreted by the farmer, for example, as a heat map displaying relative highs and lows of the measured soil parameters.

Once deployed alongside existing farm equipment, such as fertilizer and lye sprayers, the mobile soil lab can be used to inform decision making tasks in the field.

The goal is to collect sufficient data, in real-time, in order to justify the precise application of chemicals onto the field. By doing so, the risk of over application (or under application) can be reduced thus saving the farmer money and increasing the potential for higher yields, while also reducing the volume of fertilizer (for example, phosphorous) in oversaturated areas that is being is conveyed into waterways and eventually reservoirs such as Lake Erie.

ImPONDerable
ImPONDerable

All Eyes on Erie 

ImPONDerable’s product is Cyano Sleuth - a citizen science monitoring kit & app, addressing 3 problems common to citizen science:

(1) Ensuring credible data - taking the inspiration from microfluidic devices used in the medical industry, the team wants to develop a small kit that contains removable cartridges with colour-change test strips. Citizens merely put water into the device, take a photo, and results are automatically reported based on the colour changes. This makes data easy to collect and easy to verify.

(2) Engaging & Motivating Citizens - instead of training an army of volunteers and holding regular social gatherings to keep them engaged & motivated, or gamifying the system, the team is instead appealing to people’s selfish interests. The tests they will perform will immediately tell them whether their water is safe to swim in or not. The solution will send push notifications to people based on their location and whether a bloom is approaching - telling them to go out and test. The team is asking citizens to collect additional data only useful to scientists (nitrogen and phosphorus) but with no additional effort.

(3) Data Accessibility – the team is going to make it easy for researchers, data collectors, and those that use Lake Erie for recreation, to get more spatially-relevant data about harmful bloom risk throughout the Lake.

PolyGone
PolyGone

Stemming the plastic tide

Development of a product to capture microfibres that shed off clothing during the washing process.

Microfibres are synthetic fibres smaller than microplastics, not captured by existing filters, and are cause for concern due to bioaccumulation. The product is a sheet made of a fine filter with a polymer coating to attract and ‘catch’ microfibres as they float through laundry water. This sheet, made from recycled material, can be easily cleaned and disposed of when no longer capturing microfibres effectively.

Microplastic recycling centres, such as TerraCycle and UpGyres, collect and recycle these tiny particles, preventing them from entering waterways, endangering aquatic species, and entering our food-chain.

The team intends for this sheet, paired with an intense social marketing and education program, to be the first phase of our program. During phase one, additional R&D will continue to perfect the product and partnerships with businesses in clothing and laundry industries will be formed. Businesses will improve their environmental and social stance through increased corporate social responsibility and consumers will become aware of the issue and our solution. Further, the sheet aides in development of an effective filter that can be attached or designed into laundry machines in future.

Phase two involves combining product development and traction from Phase one to produce this alternate filter. This will further reduce waste and save consumers time, while capturing and recycling microfibers before they enter waterways. Such filters could be mandated on future laundry models. Further research could lead to filtration at wastewater treatment plants.

SIM Labs
SIM Labs

Taking on the algae monster

Development of a fast and robust way to not only automatically identify and enumerate different species of cyanobacteria, but also predict HAB behavioural trends.

The Systematic Intelligent Monitoring (SIM) system is a proprietary AI-powered imaging system that probes micro-organisms using different spectrums of light to enable the capture of unique optical fingerprints, enabling the proprietary AI engine to generate reliable and accurate identification, enumeration, and prediction data right on-site.

This system will not only remove user bias, but the team envisions that this system will travel to the source of the water for in-situ measurements as field portable device, saving valuable time and resources.

Furthermore, collecting this data will help better understand cyanobacteria behaviour in concerned environments, which ultimately will allow to predict HABs before they occur.

Emagin
Emagin

All Eyes on Erie

The increasing impacts of climate change and urbanization are posing significant strain on Municipal wastewater collection and treatment facilities across the Great Lakes.

In fact, every year, these facilities discharge significant volumes of untreated sewage into adjacent water bodies because of insufficient capacity, aging infrastructure and legacy approaches to water management. During heavy rainfall events, sewer systems take in volumes of wastewater that overwhelm the treatment plants and storage systems. The excess flow is then diverted to outfalls into public waters. These sewer overflows (SOs) discharge pollutants – including raw sewage, floatables such as trash and personal hygiene products, fecal coliforms, phosphorus, ammonia and heavy metals directly into waterways.

EMAGIN has developed a novel artificial intelligence (AI) driven real-time event-management platform that aims to enhance the operational performance of Municipal sewage collection infrastructure. Ideally, the platform is designed to predict the heterogenous occurrence of storm events; ensure operators have sufficient information on how the sewer system will respond to these storm events; and provide real-time recommendations to optimize sewer flow routing to minimize overflow events.

The objectives of the platform are thus:

1. the minimisation of sewer overflows, with the overarching goal of mitigating environmental degradation to receiving water bodies;

2. the full utilization of existing storage, transport and treatment facilities, in order to mitigate the need for building additional infrastructure; 

3. operational readiness and optimal management of incoming flows at WWTPs.