Presentation Title

Spatial Modeling of Resource Recovery and Reuse using Sri Lankan Composting Systems as an Example

Presenter's Name(s)

Maya Fein-ColeFollow

Project Collaborators

Eric Roy (Faculty Advisor)

Abstract

The goal of this project was to develop a spatial model in ArcGIS that simulates the distribution of a recovered resource to agricultural areas where that resource can be reused to support soil fertility. We focus on Sri Lankan composting systems as an example. To mitigate biodegradable municipal solid waste buildup in open dumps, the Sri Lankan government has established a national network of compost facilities that collect organic waste from households, restaurants, and other businesses for composting. However, for approximately one third of these facilities, current market demand for compost is less than the amount of compost produced. Therefore, tools are needed to inform strategies that improve the flow of compost to the agricultural sector. A model was produced using ModelBuilder and Python that distributes compost from source cells to potential agricultural demand cells in an optimal spatial pattern, highlighting the required travel distances and the crop types on the demand side under different scenarios. This model can be applied to other similar questions concerning resource recovery and reuse at a landscape scale.

Primary Faculty Mentor Name

Eric Roy

Status

Undergraduate

Student College

Rubenstein School of Environmental and Natural Resources

Program/Major

Environmental Sciences

Primary Research Category

Food & Environment Studies

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Spatial Modeling of Resource Recovery and Reuse using Sri Lankan Composting Systems as an Example

The goal of this project was to develop a spatial model in ArcGIS that simulates the distribution of a recovered resource to agricultural areas where that resource can be reused to support soil fertility. We focus on Sri Lankan composting systems as an example. To mitigate biodegradable municipal solid waste buildup in open dumps, the Sri Lankan government has established a national network of compost facilities that collect organic waste from households, restaurants, and other businesses for composting. However, for approximately one third of these facilities, current market demand for compost is less than the amount of compost produced. Therefore, tools are needed to inform strategies that improve the flow of compost to the agricultural sector. A model was produced using ModelBuilder and Python that distributes compost from source cells to potential agricultural demand cells in an optimal spatial pattern, highlighting the required travel distances and the crop types on the demand side under different scenarios. This model can be applied to other similar questions concerning resource recovery and reuse at a landscape scale.