IoT-Based Distributed Air Quality Monitoring System Using MQ-135 Sensor, NodeMCU, Django Backend, and MongoDB

Abstract

Air pollution is a critical environmental challenge in urban cities. Traditional monitoring stations are limited, expensive, and geographically sparse. This research proposes a distributed, mobile IoT-based air quality monitoring system that uses MQ-135 gas sensors mounted on public vehicles to increase coverage area without deploying multiple stationary sensors. Each unit consists of an MQ-135 sensor, a NodeMCU (ESP8266/ESP32) WiFi module, and is connected to a Django backend, where air quality data is stored in MongoDB. The system calculates the Air Quality Index (AQI), sends real-time data to the cloud, and visualizes city-wide pollution levels. By leveraging public transport as mobile sensing units, the solution achieves high coverage with minimal cost. This paper discusses system design, sensor integration, backend architecture, cloud communication, results, and applications.

Introduction

Rapid urbanization in Indian cities has led to rising air pollution levels, while traditional government monitoring stations remain expensive, sparsely distributed, and slow to respond to localized pollution spikes. To address these limitations, this study proposes a mobile IoT-based air quality monitoring system that uses low-cost MQ-135 gas sensors mounted on public vehicles such as buses, auto-rickshaws, and taxis. As these vehicles move across the city, they collect real-time pollution data and transmit it via NodeMCU WiFi modules to a cloud-based backend.

The system integrates a Django REST API for data ingestion and processing with a MongoDB database for scalable storage of high-frequency sensor data. Real-time Air Quality Index (AQI) values are calculated and visualized through dashboards, while historical analytics support trend analysis and alerts for high-pollution zones. This architecture enables wide spatial coverage, low latency, and efficient city-scale monitoring.

A review of related work highlights gaps in existing solutions, including reliance on fixed sensors, outdated communication methods, and limited backend scalability. The proposed system addresses these gaps by leveraging public transport as a mobile sensing network and using modern web and database technologies.

Results demonstrate that the mobile deployment increases monitoring coverage by 5–10 times compared to stationary systems, with data uploads occurring every 10–20 seconds and an average end-to-end latency of 2–4 seconds. Overall, the study shows that combining mobile IoT sensing with a robust backend architecture offers a cost-effective, scalable, and real-time solution for urban air quality monitoring.

Conclusion

This research presents a scalable, distributed mobile air-quality monitoring system using MQ-135 sensors mounted on public vehicles. The combination of NodeMCU, Django backend, and MongoDB enables a real-time cloud-connected architecture for reliable AQI tracking. By replacing stationary sensors with mobile ones, the system signi cantly increases coverage with minimum investment.

References

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Copyright

Copyright © 2025 Dhanashri Abhang, Ankit Pathak, Shyam Rajput, Varad Choudhari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.