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

arXiv: https://arxiv.org/abs/2304.09574 · HTML: https://mlsanity.com/paper/2304.09574/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 88 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Undefined By Data: A Survey of Big Data Definitions](https://mlsanity.com/paper/1309.5821/) (2013)
- [AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference](https://mlsanity.com/paper/2003.12205/) (2020)

## Built on by (most cited first, 2 of 2)

- [Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning](https://mlsanity.com/paper/2403.08810/) (2024)
- [M2FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices](https://mlsanity.com/paper/2605.16375/) (2026)

## Abstract (arXiv)

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.