AirSPEC: An IoT-empowered Air Quality Monitoring System integrated with a Machine Learning Framework to Detect and Predict defined Air Quality parameters

11/28/2021
by   Nuwan Bandara, et al.
0

The air that surrounds us is the cardinal source of respiration of all life-forms. Therefore, it is undoubtedly vital to highlight that balanced air quality is utmost important to the respiratory health of all living beings, environmental homeostasis, and even economical equilibrium. Nevertheless, a gradual deterioration of air quality has been observed in the last few decades, due to the continuous increment of polluted emissions from automobiles and industries into the atmosphere. Even though many people have scarcely acknowledged the depth of the problem, the persistent efforts of determined parties, including the World Health Organization, have consistently pushed the boundaries for a qualitatively better global air homeostasis, by facilitating technology-driven initiatives to timely detect and predict air quality in regional and global scales. However, the existing frameworks for air quality monitoring lack the capability of real-time responsiveness and flexible semantic distribution. In this paper, a novel Internet of Things framework is proposed which is easily implementable, semantically distributive, and empowered by a machine learning model. The proposed system is equipped with a NodeRED dashboard which processes, visualizes, and stores the primary sensor data that are acquired through a public air quality sensor network, and further, the dashboard is integrated with a machine-learning model to obtain temporal and geo-spatial air quality predictions. ESP8266 NodeMCU is incorporated as a subscriber to the NodeRED dashboard via a message queuing telemetry transport broker to communicate quantitative air quality data or alarming emails to the end-users through the developed web and mobile applications. Therefore, the proposed system could become highly beneficial in empowering public engagement in air quality through an unoppressive, data-driven, and semantic framework.

READ FULL TEXT

page 1

page 4

research
07/02/2023

IoT-Based Air Quality Monitoring System with Machine Learning for Accurate and Real-time Data Analysis

Air pollution in urban areas has severe consequences for both human heal...
research
05/08/2020

GASDUINO-Wireless Air Quality Monitoring System Using Internet of Things

The Health Effects Institute (HEI) reported recently that the deaths fro...
research
11/19/2018

Toward SATVAM: An IoT Network for Air Quality Monitoring

Air pollution is ranked as the second most serious risk for public healt...
research
02/15/2023

Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments

The estimation of plume cloud height is essential for air-quality transp...
research
05/29/2021

Estimating air quality co-benefits of energy transition using machine learning

Estimating health benefits of reducing fossil fuel use from improved air...
research
06/30/2021

Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope

We leverage state-of-the-art machine learning methods and a decade's wor...
research
07/08/2022

Participatory Action for Citizens' Engagement to Develop a Pro-Environmental Research Application

To understand and begin to address the challenge of air pollution in Eur...

Please sign up or login with your details

Forgot password? Click here to reset