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Environmental monitoring and assessment
Sep 05, 2023;195 (10): 1152.

Air pollution forecasting based on wireless communications: review.

Muthna J Fadhil, Sadik Kamel Gharghan, Thamir R Saeed
Author Information
  1. Muthna J Fadhil: Department of Electrical Engineering, University of Technology, Baghdad, Iraq. eee.20.12@grad.uotechnology.edu.iq.
  2. Sadik Kamel Gharghan: Middle Technical University, Electrical Engineering Technical College, Baghdad, Iraq.
  3. Thamir R Saeed: Department of Electrical Engineering, University of Technology, Baghdad, Iraq.
PMID: 37670163 DOI: 10.1007/s10661-023-11756-y

Abstract

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air pollution technique relate to various effects such as sensing accuracy, sensor drifts, and sluggish reactions to changes in pollution levels. Recently, machine learning has made it feasible to build a more intelligent, context-aware system that can anticipate events and monitor present conditions. This paper focuses on the use of environment sensors for detecting air pollution based on several types of wireless protocols, including Wi-Fi, Bluetooth, ZigBee, LoRa, Global Positioning System (GPS), and 4G/5G. Furthermore, it classifies previous published articles on the topic according to the wireless protocol and compared in terms of several performance metrics such as the adopted air pollution sensors, hardware platform, adopted algorithm, power consumption or power savings, and sensing accuracy. In addition, this work highlights the challenges and limitations facing drones during their mission for detecting air pollution. As a result, we suggest to build and implement at base station an intelligent system based on backpropagation (BP) neural networks, which provides flexibility to track and predict the true values of polluting gases in the atmosphere to overcome the above problems. Finally, this work addresses the advantages of using drones in the air pollution field.

Keywords

Air pollution Artificial intelligence Drone LoRa Real-time system Wireless sensor network

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MeSH Term

Artificial Intelligence
Environmental Monitoring
Air Pollution
Environmental Pollution
Gases

Chemicals

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