摘要：Background:
Accurate estimation of historical
PM
2.5
(particle matter with an aerodynamic diameter of less than
2.5
μ
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
PM
2.5
concentrations.
Methods:
An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily
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
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
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
PM
2.5
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
×
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
PM
2.5
prediction performance of DEML [coefficients of determination
(
R
2
)
=
0.87
and root mean square error
(
RMSE
)
=
5.3
8
μ
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
PM
2.5
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.
https://doi.org/10.1289/EHP9752
