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The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IoT, Big Data, and Machine Learning

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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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  • 31 March 2023

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Authors and Affiliations

  1. Department of Electronics and Communication, University of Allahabad, Prayagraj, India

    Amisha Gangwar, Sudhakar Singh, Richa Mishra & Shiv Prakash

  2. School of Information Studies, Syracuse University, New York, US

    Amisha Gangwar

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Contributions

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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