Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations

BACKGROUND: Accurate estimation of historical PM2.5 (particle matter with an aerodynamic diameter of less than 2.5 mu m) is critical and essential for environmental health risk assessment. OBJECTIVES: The aim of this study was to develop a multiple-level stacked ensemble machine learning framework for improving the estimation of the daily ground-level PM2.5 concentrations. METHODS: An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily PM2.5 concentrations. The framework has a three-stage structure: At the first stage, four base models [gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)] were used to generate a new data set of PM2.5 concentrations for training the next-stage learners. At the second stage, three meta-models [RF, XGBoost, and Generalized Linear Model (GLM)] were used to estimate PM2.5 concentrations using a combination of the original data set and the predictions from the first-stage models. At the third stage, a nonnegative least squares (NNLS) algorithm was employed to obtain the optimal weights for PM2.5 estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily PM(2)(.5 )at each 1 km x 1 km grid cell from 2015 to 2019 across Italy. We evaluated the model performance by performing 10-fold cross-validation (CV) and compared it with five benchmark algorithms [GBM, SVM, RF, XGBoost, and Super Learner (SL)]. RESULTS: The results revealed that the PM2.5 prediction performance of DEML [coefficients of determination (R-2) = 0.87 and root mean square error (RMSE) =5.38 mu g/m(3)] was superior to any benchmark models (with R-2 of 0.51, 0.76, 0.83, 0.70, and 0.83 for GBM, SVM, RF, XGBoost, and SL approach. respectively). DEML displayed reliable performance in capturing the spatiotemporal variations of PM2.5 in Italy. DISCUSSION: The proposed DEML framework achieved an outstanding performance in PM(2)(.5 )estimation, which could be used as a tool for more accurate environmental exposure assessment.

Automated summary

A multiple-level stacked ensemble machine learning framework was developed to improve the estimation of daily ground-level PM2.5 concentrations. The framework outperformed benchmark models in predicting PM2.5 concentrations in Italy based on data from monitoring stations.