Circularity in municipal waste systems
How can we make better use of food “waste”? We’re working with local partners to identify opportunities for synergies to divert as much organic material from landfill as possible, creating economic opportunities.
To facilitate that process, we’re conducting a material flow analysis to pinpoint where we can reduce food loss and waste at each stage of the food value chain, from raw inputs to the final consumer, and design opportunities for appropriate reuse.
We’re also planning to equip municipal waste collection trucks with advanced sensors and AI technology to audit organic waste on a household-by-household level. The resulting data could be used to generate customized reports that residents receive on their smartphones, outlining their waste patterns and providing practical suggestions on how to reduce their avoidable waste, costs and carbon footprint. Meanwhile, aggregating the data will allow us to design targeted waste-reduction interventions for particular neighbourhoods.
Finally, we’re creating a public dashboard that offers up-to-date statistics on the levels of avoidable food waste in the community, the percentage of organics contaminating other waste streams and our progress toward reduction targets.
Supply chain material flow analysis of the Guelph-Wellington food system
To identify opportunities to divert waste from landfill, we need to understand the current flow of organic material through the food supply chain, from production and processing to distribution and retailing. To that end, we’re undertaking a material flow analysis—the first study of its kind in Canada. This involves:
- Conducting a landscape analysis to determine what data is already available, what needs to be collected and how to collect it,
- Collecting material flow data and mapping the stakeholders that make up the local food system,
- Mapping the data in order to create a baseline and to identify significant areas of waste and interception points where material could be diverted for reuse,
- Using that information to engage local stakeholders in developing a shared vision to reduce and revalorize waste, and
- Developing key performance indicators and a roadmap based on that shared vision.
We’ve assembled a team of experts in food waste, material flow mapping, the circular economy, waste management, stakeholder engagement and food waste mitigation. They will be guided by an advisory panel who will review proposed work plans, methodologies and outputs to provide their comments and insights. At the current stage, our challenge is establishing the granularity of data to be collected, striking an appropriate balance between the effort required to gather data and the specificity of the resulting insights.
Leveraging food system data to help our partners innovate and respond to the changing market
In 2020 and 2021, our data collaboration efforts will focus on helping partners undertake projects that bolster food security and economic recovery in the aftermath of COVID-19. This includes identifying immediate needs and leveraging food system data to help delivery partners innovate and respond to the changing market. We’ll draw on the data collected through our material flow study, as well as other data sets shared through the Data Utility. Meanwhile, we’ll focus the Utility’s first use cases on identifying immediate gaps or excess resources that can support food security and economic recovery.
County of Wellington curbside waste audits
In preparation for the rollout of the County of Wellington green bin programme in July 2020, we conducted food waste audits to develop baseline data to understand the trends related to household food waste disposal habits throughout the County.
The work included a waste audit of 150 households divided into 15 sampling areas. The food waste from each household was weighed and sorted into two categories:
- Avoidable—food that was at one point edible, and
- Unavoidable—inedible food such as eggshells, coffee grounds and vegetable peels.
These categories were further divided into bread and baked goods, dairy, meat and fish, fruit and vegetables, dried food and other food. With the County’s new curbside green bin collection programme now underway, we plan to conduct a second audit with the same sample group to measure changes in food waste behaviour.
City of Guelph curbside waste analysis
Guelph already has GPS technology and radiofrequency identification (RFID) tags installed in the curbside collection bins distributed to residents. This allows the City to collect real-time data on waste, recycling and organics that can be tracked back to a residential address or neighbourhood. However, to get more granular data on the organic waste that residents put in their bin, our only option at this point is to conduct manual audits. This is time consuming and provides only a snapshot of waste from a sampling of households.
We see the potential to leverage emerging technologies to go much further. By equipping our waste collection trucks with sensors and machine-learning capabilities, for example, we could gather detailed geospatial information on how much residential food waste is discarded, what it consists of and how much is avoidable.
To our knowledge, no such truck-based system currently exists. However, optical detection and sorting technology within municipal waste plants has reached high levels of sophistication, while machine learning continues to improve in leaps and bounds. Through the CFE iHub, we therefore put out a challenge to researchers and entrepreneurs to develop a system to incorporate into our organic waste collection trucks.
Armed with that granular data, we’ll be able to provide feedback to households and develop targeted, evidence-based interventions. This might include the creation of a smartphone app that gives residents easy and secure access to their household waste data, generates reports on personal waste patterns and offers practical suggestions on how to reduce avoidable food waste. To ensure we reach the entire Guelph community, this app could provide information in a number of different languages.