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

TL;DR

AirSPEC tackles real-time, location-aware air quality monitoring by integrating an IoT framework with semantic distribution and a time-series decision-tree predictor. It ingests primary data from public sensors via the airly API, processes and visualizes results in a NodeRED dashboard, and disseminates predictions to web and mobile apps through an MQTT-based ESP8266 edge node. The system predicts parameters such as $PM_{1}$, $PM_{2.5}$, and $PM_{10}$, alongside humidity, temperature, and pressure distributions, in temporal and spatial contexts. This low-cost, scalable approach enables informed public health decisions and timely alerts, enhancing public engagement with air quality data.

Abstract

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.

Figures (7)

• Figure 1: The high-level architecture of the proposed system
• Figure 2: Flow management design for $PM_1$ in the NodeRED framework. The shown flow is integrated and extended to obtain and process the remaining air quality parameters.
• Figure 3: Flow management design for deploying the machine learning model with (1) creating and extending dataset with user-requested sensor data (2) training the dataset (3) testing the model for time-series prediction
• Figure 4: The proposed NodeRED dashboard which visualizes the primary and forecast air quality and weather data
• Figure 5: The emergency email which is sent by the NodeRED framework for an end-user when the air quality index value of the requested location is above the recommended safe level