Abstract
The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.
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Conceptualization: Amisha Gangwar, Sudhakar Sing, Richa Mishra, Shiv Prakash; Methodology: Amisha Gangwar, Sudhakar Sing, Richa Mishra, Shiv Prakash; Formal analysis and investigation: Amisha Gangwar, Sudhakar Singh, Richa Mishra, Shiv Prakash; Writing - original draft preparation: Amisha Gangwar, Sudhakar Singh; Writing - review and editing: Sudhakar Singh, Richa Mishra, Shiv Prakash; Resources: Amisha Gangwar, Sudhakar Singh, Richa Mishra, Shiv Prakash; Supervision: Sudhakar Singh, Richa Mishra, Shiv Prakash.
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Appendix: List of Acronyms
Appendix: List of Acronyms
Acronym | Full Name |
---|---|
ADB | Ada Boost Algorithm |
ANN | Artificial Neural Network |
AQI | Air Quality Index |
ARIMA | Auto-Regressive Integrated Moving Average |
HMM | Hidden Markov Model |
BB | Bagging and Boosting |
BiLSTM | Bi-directional LSTM |
BMR | Bangkok Metropolitan Region |
BP | Backward Propagation |
BPNN | Back Propagation Neural Network |
CO | Carbon Monoxide |
COPD | Chronic Obstructive Pulmonary Disease |
CPCB | Central Pollution Control Board |
DES | Damped Exponential Smoothing |
DNN | Deep Neural Network |
DTR | Decision Tree Regression |
EAQI | European Air Quality Index |
EPA | Environmental Protection Agency |
ETS | Error, Trend, and Seasonality |
EU | European Union |
GBDT | Gradient Boosted Decision Trees |
GBM | Gradient Boosting Machine |
GBR | Gradient Boosting Regression |
GBR | Gradient Boosting Regressor |
GBT | Gradient-Boosted Tree |
GNB | Gaussian NB Algorithm |
HAQ | Household Air Quality |
HDFS | Hadoop Distributed File System |
IAQI | Indoor Air Quality Index |
IoT | Internet of Things |
kNN | K Nearest Neighbors |
KNR | K Neighbors Regressor |
LR | Logistic Regression |
LSTM | Long Short-Term Memory |
MAE | Mean Absolute Error |
ME | Mean Error |
ML | Machine Learning |
MLP | Multilayer Perceptron Algorithm |
MLP | Multilayer Perceptron Regression |
MLPR | Multilayer Perception Regression |
MLR | Multiple Linear Regression |
NAMP | National Air Monitoring Programs |
NAQI | National Air Quality Index |
NARX | Non-linear AutoRegression with eXogenous |
NB-IoT | NarrowBand-Internet of Things |
NO2 | Nitrogen Dioxide () |
NRMSE | Normalized Root Mean Square Error |
O3 | Ozone |
PCA | Principal Component Analysis |
PM | Particulate Matter or Particle Pollution |
PPs | Partial Plots |
RE | Relative Error |
RF | Random Forest |
RFR | Random Forest Regression |
RMSE | Root Mean Squared Error |
RNN | Recurrent Neural Network |
SES | Simple Exponential Smoothing |
SMAPE | Symmetric Mean Absolute Percentage Error |
SO2 | Sulphur Dioxide |
SVM | Support Vector Machines |
SVR | Support Vector Regression |
SVR-RBF | Support Vector Regression - Radial Basis Function |
TAQMN | Taiwan Air Quality Monitoring Network |
TEPA | Taiwan Environmental Protection Agency |
TVOC | Total Volatile Organic Compounds |
US EPA | US Environmental Protection Agency |
VIR | Variation Importance Ranking |
WHO | World Health Organisation |
XGB | Extreme Gradient Boost |
XGBoost | Extreme Gradient Boosting |
XGBRF | Random Forests in XGBoost |
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Gangwar, A., Singh, S., Mishra, R. et al. The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IoT, Big Data, and Machine Learning. Wireless Pers Commun 130, 1699–1729 (2023). https://doi.org/10.1007/s11277-023-10351-1
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DOI: https://doi.org/10.1007/s11277-023-10351-1