International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
A Dual-Microcontroller IoT Air Quality Monitoring System with RealTime Local Alerting Kedar Gurav1
Harsh Gupta2
Abhimanyu Gitte3
1Department of Engineering
2Department of Engineering
3Department of Engineering
Science and Humanities, Science and Humanities, Science and Humanities, Vishwakarma Institute of Vishwakarma Institute of Vishwakarma Institute of Technology, Pune, India. Technology, Pune, India Technology, Pune, India ---------------------------------------------------------------------***--------------------------------------------------------------------potential failure points in critical alert functions. Systems Abstract – Air pollution is a prolonged environmental and
depending solely on cloud-based alerts stop working during network outages, which creates a safety vulnerability. [2].
public-health concern that requires affordable monitoring tools. This work presents a dual-microcontroller Internet-ofThings (IoT) air-quality monitoring system that separates time-critical alert logic from network communication. An Arduino Uno performs continuous data acquisition and threshold evaluation of an MQ135 gas sensor, while a NodeMCU ESP8266 handles Wi-Fi connectivity and cloud transmission to the Blynk platform. Local LED and buzzer alerts are controlled through a non-blocking 200 ms sampling loop, achieving sub-millisecond computational reaction after threshold breach. Experimental tests with incense smoke diffusion showed that the sensor crossed the 70 ADC alert threshold after ≈ 80 s and reached a peak of 145 ADC after ≈ 130 s. The alert deactivated after ≈ 170 s and the sensor returned to baseline in ≈ 14.5 min. Local alert functions remained operational during intentional Wi-Fi interruptions, demonstrating architecture-level stability.. Although the uncalibrated MQ135 limits measurement fidelity, the system provides an economically viable and reliable framework for educational and awareness-based air-quality monitoring applications.
1.2 Research Objectives This project is designed to address these gaps with the following objectives: (1) Design and implement a real-time air pollution monitoring system using readily available, lowcost components within an IoT framework; (2) Develop a dual-microcontroller architecture that decouples timecritical sensor acquisition and alerting from network communication tasks; (3) Integrate an MQ135 gas sensor for detection of common airborne pollutants; (4) Implement immediate local feedback mechanisms through an I 2C LCD display and audiovisual alerts; (5) Enable wireless data transmission to the Blynk IoT platform for remote monitoring; (6) Validate system functionality through controlled experimental testing.
1.3 Research Contributions The main contributions are: (1) A dual-microcontroller architecture, which was validated experimently to enhance system robustness by isolating safety-critical functions from network dependencies; (2) End-to-end system integration from sensor hardware to cloud visualization; (3) A performance validation that shows system responsiveness and operational stability.
Key Words: Air Quality Monitoring, Internet of Things, MQ135 Sensor, Arduino, NodeMCU, Real-Time Alert System, Environmental Sensing, IoT Architecture.
1. INTRODUCTION Air pollution is one of the biggest environmental threats to human health. The World Health Organization (WHO) states that it causes about 7 million premature deaths every year. We see this pollution in our cities from cars, factories, and burning fossil fuels. These activities have raised the levels of harmful pollutants like PM2.5, NO₂, SO₂, and others [1].
2. LITERATURE REVIEW 2.1 Evolution of Air Quality Monitoring Traditional air monitoring networks use expensive, hightech tools like chemiluminescence analyzers for nitrogen oxides and ultraviolet fluorescence for sulfur dioxide. While they provide highly accurate measurements, these tools cost $30,000 or more, cities can't install many of them [1]. This creates large gaps in data. Clements et al. [2] studied the switch to cheaper, "low-cost" monitors. They pointed out the
1.1 Problem Statement Although many low-cost prototypes exist, they still have several key problems. Many existing systems use a single microcontroller to both acquire sensor data and communicate with the network, leading to latency and
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