IOT Based Air Pollution Monitoring System

Abstract

Air pollution has emerged as a major environmental hazard accelerated by metropolitan expansion, fossil fuel dependency, industrial emissions, and unsustainable urban practices. This research presents a real-time, IoT-centric air quality monitoring framework engineered to measure and interpret key atmospheric pollutants including CO?, CO, NO?, and airborne particulates. The solution integrates cost-effective multi-gas sensors, embedded processing units, and cloud-based services to capture, compute, store, and distribute pollution data securely through Wi-Fi connectivity. Core system features incorporate live visual analytics, threshold-driven notifications, historical pattern modeling, and decision-support insights for environmental stakeholders. Evaluation of the prototype demonstrates high scalability, portability, reduced power consumption, and resilience for long-duration deployment, positioning the system as a viable technology for smart infrastructure, environmental governance, and urban sustainability planning.

Introduction

The text addresses the growing global problem of air pollution and its serious impacts on human health, climate, and ecosystems. Major pollution sources include urban transportation, power plants, industries, and waste incineration. While traditional air quality monitoring stations are accurate, they are expensive, stationary, and limited in coverage. To overcome these limitations, the Internet of Things (IoT) offers a cost-effective and scalable solution through distributed sensors, real-time data collection, cloud connectivity, and predictive analytics. IoT-based systems enable continuous environmental monitoring, public accessibility to data, and support proactive pollution control, aligning with smart city and sustainable development goals.

The literature review highlights several IoT-based air quality monitoring systems that use low-cost sensors and microcontrollers such as Arduino and ESP32. These systems measure harmful gases and environmental parameters, transmit data to cloud platforms, and display real-time air quality information via dashboards or mobile applications. They emphasize affordability, portability, real-time alerts, and increased public awareness, especially in highly polluted urban and industrial regions.

The proposed system architecture integrates gas sensors (MQ135 and others), particulate sensors, and temperature–humidity sensors (DHT11/22) with a microcontroller for data processing and calibration. An ESP8266 Wi-Fi module transmits the data to a cloud platform for visualization, analysis, reporting, and alert generation. The design supports future enhancements such as GPS mapping, solar power, and large-scale urban deployment, making it a practical solution for continuous air pollution monitoring and awareness creation.

Conclusion

The developed IoT-integrated monitoring architecture delivers an economical and reliable approach for continuous assessment of surrounding air quality in real time. By combining sensors like MQ-135, dust sensors, and DHT11 with Arduino and ESP8266 Wi-Fi, it measures key parameters like gas concentration, particulate matter, temperature, and humidity. Data is displayed locally and uploaded to Blynk cloud for remote monitoring. Testing shows accurate readings, stable transmission, and reliable air quality classification, enabling timely pollution awareness and action. Suitable for various applications, future enhancements like solar power, mobile alerts, and ML-based prediction can boost its potential for large-scale environmental and public health monitoring.

References

[1] Md. Abdullah Al Ahasan, Saumendu Roy, A. H. M. Saim, Rozina Akter, Md. Zakir Hossain, Arduino- Based Real Time Air Quality and Pollution Monitoring System, Volume-6, Issue-4, July 2018. [2] Ramik Rawal, Air Quality Monitoring System, Volume 9, Number 1 (2019). [3] Cynthia J., Kumaraguru, Saroja M.N., Parveen Sultana, J. Senthil, IoT-Based Real Time Air Pollution Monitoring System, Volume 11 Issue 4 October December 2019. [4] Deepansh Kulshrestha, Avishi Asati, Srashti Vyas, Sneha Sharma, Aashi Singh Bhadouria, Air Pollution Detection & Monitoring Using Internet of Things (IoT), Vol. No. 01, Issue No. 01, April 2024. [5] Yamunathangam, K. Pritheka, P. Varuna, IoT Enabled Air Pollution Monitoring and Awareness Creation System, ISSN: 2277-3878, Volume-7 Issue-4s, November 2018.

Copyright

Copyright © 2025 Anjali ., Aruna , Bhavya , Prof. S K Aurad. 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.