Machine Learning-Based Classification of Plastic Combustion Emissions Using Multi-Sensor Data

Open burning of plastic waste releases particulate and gaseous pollutants whose profiles vary with polymer composition and combustion behavior. This study evaluated a multi-sensor machine-learning approach for classifying emissions from six common plastics: high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC). The dataset comprised 2,160 observations (360 per class). Classification used six emission variables: PM1, PM2.5, PM10, CO2, CO, and total volatile organic compounds (TVOC), while relative humidity and temperature were analyzed separately as smoke-collection-chamber conditions. Kruskal-Wallis tests showed significant differences among plastic classes for all particulate and gaseous variables. Gradient Boosting (GB), Random Forest (RF), and k-nearest neighbor (k-NN) were optimized using the same randomized hyperparameter-search framework with stratified five-fold cross-validation. GB achieved the highest performance, with a cross-validated macro-F1 of 95.95%, test accuracy of 96.76%, and test macro-F1 of 96.75%. Cross-validated permutation importance identified CO2 as the most influential predictor, followed by PM10, CO, PM2.5, PM1, and TVOC. Exhaustive evaluation of 57 feature combinations identified PM1, PM2.5, and CO2 as the most parsimonious subset; the corresponding three-feature GB model retained 96.06% test accuracy and 96.05% macro-F1. The learning curve showed a 3.26-percentage-point train-validation gap at the largest training size. These results demonstrate strong discrimination of controlled plastic-combustion emission patterns, although mixed-waste, variable-combustion, and long-term sensor-drift validation is required before field application.