Predictive modeling of dining facility waste by material type across time and geography

Abstract

AbstractThe increasing rate of global solid waste generation is startling. This is exacerbating the challenge of decreasing solid waste generation to reduce disposal in landfills. U.S. Army installations offer a unique qualitative and quantitative dataset across a span of geographical locations. Modeling prediction capability of dining facility (DFAC) solid waste streams using data from 11 Army installations was investigated to demonstrate aggregate building and material type generation forecasting. Solid waste generation data were collected, quantified, and categorized for diversion potential (e.g., source reduction, reuse, recycling, composting, etc.) of materials currently landfilled. Over one week, samples from one day of DFAC operations were collected for each installation. Materials from the samples were manually separated into 22 categories, weighed, and recorded. Results identified many solid waste stream materials with diversion potential. Five material types were down selected to construct and validate the linear regression model. The material types down selection was based on available data robustness and applicability beyond military contexts. A linear regression model was constructed for five material and building type combinations to avoid multiplication factor errors of coefficients for each independent variable. Results were statistically significant (p-value ≤ 0.05) for four of five modeling combination predictions. These results demonstrate the unique capability of predicting solid waste generation for the four statistically significant model combinations. Each of the four statistically significant model combinations differed in adjusted R2 values, ranging from 0.988 to 0.996. This study provides five linear regression model combinations with predictive power that could reduce the labor- and cost-intensive process of characterizing waste streams while increasing data availability across the continental U.S. to focus targeted source reduction efforts for dining settings.