## 5 Conclusions
The following abbreviations are used in this manuscript:  
UN United Nations  
ICT Information and Communication Technology  
WHO World Health Organisation  
PM\({}_{2.5}\) Particulate matter with diameter equal to 2.5 micrometers  
IoT Internet of Things  
NN Neural Network  
SLI-ESN Supplementary Leaky Integrator Echo State Network  
mRMR minimum Redundancy Maximum Relevance  
ESN Echo State Network  
LI-ESN Leaky Integrator Echo State Network  
ELM Extreme Learning Machine  
PM\({}_{10}\) Particulate matter with diameter equal to 10 micrometers  
NO\({}_{2}\) Nitrogen dioxide  
CO Carbon monoxide  
O\({}_{3}\) Ground-level ozone  
SO\({}_{2}\) Sulfur dioxide  
RMSE Root Mean Square Error  
NRMSE Normalised Root Mean Square Error  
MAE Mean Absolute Error  
SMAPE Symmetric Mean Absolute Percentage Error  
R Pearson correlation coefficient  
BLSTM Bi-directional Long Short-Term Memory  
IDW Inverse Distance Weighting  
ARIMA AutoRegressive Integrated Moving Average  
SVR Support Vector Regression  
GBDT Gradient Boosting Decision Tree  
ANN Artificial Neural Network  
RNN Recurrent Neural Network  
LSTM Long Short-Term Memory  
CNN-LSTM Convolutional Neural Network-LSTM  
MAPE Mean Absolute Percentage Error  
CBGRU Convolutional-based Bidirectional Gated Recurrent Unit  
BGRU Bidirectional Gated Recurrent Unit  
GBR Gradient Boosting Regression  
DTR Decision Tree Regression  
GRU Gated Recurrent Unit  
UCI The University of California, Irvine  
CNN Convolutional Neural Network  
SVM Support Vector MachineMKSVC Multiple kernel learning model with support vector classifier  
AQHI Air Quality Health Index  
IAQL Individual Air Quality Level  
WP Weighted Precision  
WR Weighted Recall  
WF Weighted F1-score  
PPB Parts Per Billion  
STE Spatial-Temporal Ensemble  
RT Regression Tree  
MELSA Multi-channel Ensemble Learning via Supervised Assignment  
AQI Air quality index  
RAE Relative Absolute Error  
RSE Relative Squared Error  
NMSE Normalized Mean Square Error  
DRR Discounted Ridge Regression  
IPSO Improved Particle Swarm Optimization  
PSO Particle Swarm Optimization  
CEEMD Complementary Ensemble Empirical Mode Decomposition  
PSOSA Particle Swarm Optimization and Gravitational Search Algorithm  
GRNN Generalized Regression Neural Network  
GCA Grey Correlation Analysis  
SD Secondary Decomposition  
WD Wavelet Decomposition  
VMD Variational Mode Decomposition  
SE Sample Entropy  
BA Bat Algorithm  
CC Consecutive Close  
Baseline The baseline model with standard Frobenius norm regularization  
Heavy-F The heavy model with standard Frobenius norm regularization  
Light-F The light model with standard Frobenius norm regularization  
Heavy-\(\ell\)2,1 The heavy model with \(\ell\)2,1-norm regularization  
Heavy-nuclear The heavy model with nuclear-norm regularization  
Heavy-CCL2 The heavy model with CC regularization using the \(\ell\)2-norm  
Heavy-CCL1 The heavy model with CC regularization using the \(\ell\)1-norm  
Light-\(\ell\)2,1 The light model with \(\ell\)2,1-norm regularization  
Light-nuclear The light model with nuclear-norm regularization  
Light-CCL2 The light model with CC regularization using the \(\ell\)2-norm  
Light-CCL1 The light model with CC regularization using the \(\ell\)1-norm  
LMA-AV Lansing Municipal Airport-Alsip Village  
LU-LV Lewis University-Lemont Village  
SPM Suspended Particulate Matter  
BC Black carbon  
AQ Air quality data  
MET Meteorological data  
WFD Weather forecast data  
TIF Traffic intensity features  
N/S Not Specified