Estimating landfill leachate BOD and COD based on rainfall, ambient temperature, and waste composition: Exploration of a MARS statistical approach

- Modeled leachate BOD and COD for 27 lab-scale landfills using cyclic incomplete block design.
- Multivariate Adaptive Regression Splines (MARS) statistical approach was utilized.
- MARS captured complex relationships among rain, temperature, & waste composition.
- 100% food reactors showed BOD and COD highest concentrations.
- Time and rainfall were the most important variables impacting BOD and COD.

Municipal solid waste generation is increasing rapidly globally. Increasing storage of waste in landfills means increased production of liquid landfill leachate. Leachate contains organic and inorganic pollutants which must be treated to reduce its potential impact on surrounding water supplies. To design landfill leachate treatment, its chemical composition must be known; however, the composition of leachate can vary widely depending on waste composition. Measuring leachate constituents can be expensive and time consuming, and is not possible for a landfill under construction. A global model able to forecast leachate quality parameters based on a landfill's waste composition, ambient temperature, and rainfall rate is critically needed. This research represents a first step in development of such a model.Laboratory data on leachate biochemical and chemical oxygen demand (BOD, COD) was collected as functions of waste composition (food, paper, yard, textile), temperature (70, 85, 100 °F), and rainfall rates (2, 6, 12 mm/day), according to a statistical experimental design. Multivariate Adaptive Regression Splines (MARS) models were developed for BOD and COD, with adjusted-R2 of 0.92 and 0.95, respectively. This exploratory research demonstrated the usefulness of MARS in capturing complex relationships among waste composition, temperature, and rainfall rate in order to forecast leachate quality parameters over time.