资源环境科技发展态势分析平台

Background:

Accurate estimation of historical ${\mathrm{PM}}_{2.5}$ (particle matter with an aerodynamic diameter of less than $2.5\mathrm{\mu }\mathrm{m}$) 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 ${\mathrm{PM}}_{2.5}$ concentrations.

Methods:

An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily ${\mathrm{PM}}_{2.5}$ 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 ${\mathrm{PM}}_{2.5}$ 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 ${\mathrm{PM}}_{2.5}$ 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 ${\mathrm{PM}}_{2.5}$ estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily ${\mathrm{PM}}_{2.5}$ at each $1\mathrm{km}\times 1\mathrm{km}$ 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 ${\mathrm{PM}}_{2.5}$ prediction performance of DEML [coefficients of determination $\left(R^2\right)=0.87$ and root mean square error $\left(\text{RMSE}\right)=5.3{8\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$] 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 ${\mathrm{PM}}_{2.5}$ in Italy.

Discussion:

The proposed DEML framework achieved an outstanding performance in ${\mathrm{PM}}_{2.5}$ estimation, which could be used as a tool for more accurate environmental exposure assessment. https://doi.org/10.1289/EHP9752