Gas Pollution: A Parametric Analysis Adopting Drone-based Evaluation and IOTs | Chapter 9 | Engineering Research: Perspectives on Recent Advances Vol. 12

 

In Nigeria, the Niger Delta region is faced with challenges of oil and gas exploitation. These activities increasingly affect human, aquatic life in the ocean, animals and the natural environment. Recent advancements in technology have introduced unmanned aerial vehicles (UAVs), commonly known as drones, as a viable and innovative solution to these challenges. This study presents a novel approach for monitoring air pollution with a drone and Internet of Things (IoT) technology. The specific objectives include developing a drone-based system capable of capturing gas pollution data, integrating various sensors to monitor environmental conditions and detect air pollutants (harmful gases) and developing a communication system for real-time data collection and dissemination. The design utilises sensors for the detection of hazardous gases and an ESP8266 module for real-time data transmission and cloud-based data presentation. The system facilitates sustainable environmental studies by providing access to areas that are hard or unsafe to reach, anytime. The research locations include Iko Town and Ukpenekang communities in Eastern Obolo LGA, Akwa Ibom State, Nigeria. Data collected during drone test flights was compared with traditional air quality monitoring stations to evaluate accuracy. The results show an affordable method for measurement of air quality in real time, especially in the challenging areas that are affected by oil and gas exploration, production and refining processes, such as the Niger Delta region of Nigeria. The findings demonstrate the feasibility of using drones and IOT for real-time environmental monitoring aimed at equipping researchers and policy makers with data to protect human lives, public health and the environment. In the course of this study, minor limitations were observed, including restricted flight duration and short range. Future research will investigate advancements in long-range communication protocols and the application of machine learning technology.

 

 

Author(s) Details

Bassey Okon
Department of Mechanical Engineering, Federal University of Technology, Ikot Abasi, Nigeria.

 

Ubong Ukommi

Department of Electrical and Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria.

 

Isaac Udoetor
Department of Electrical and Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria.

 

Enobong Akanimo
Department of Electrical and Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria.

 

Please see the book here :- https://doi.org/10.9734/bpi/erpra/v12/6960