Review On Ambient Air Quality Control System and Pollutants Present in Air

Abhijeet P. Thakare; Shreya J. Borkar; Gauri S. Akotkar; Kshitij S. Kale

doi:10.26706/jtfs.7.1.20260302

Abstract

Air pollution has become a critical environmental issue due to the increasing concentration of harmful gases such as carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOₓ). These pollutants negatively affect human health by disturbing the oxygen transport mechanism in the body and contributing to environmental problems like smog formation. This paper presents an Online Air Pollution Control System based on the generation of negative ions to reduce atmospheric pollution. The proposed system uses a high-voltage ionizer circuit to release negatively charged ions, which attract positively charged pollutant particles. These particles combine to form heavier clusters and settle down due to gravity, thereby reducing air pollution. The system is designed for installation on streetlights and urban infrastructure, enabling continuous air purification in open environments. Experimental results indicate a significant reduction in pollutants such as CO, CO₂, and HC, along with a slight increase in oxygen levels. The system is energy-efficient, cost-effective, and safe for long-term use, making it suitable for large-scale implementation in urban areas.

Keywords

Air Pollution Control Negative Ion Generation High-Voltage Ionizer Atmospheric Pollutant Removal Urban Air Purification

References

K. Thakare, “Online Air Pollution Control System,” Patent Application No. 201721014397.
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[1] K. Thakare, “Online Air Pollution Control System,” Patent Application No. 201721014397.

[2] K. P. Thakare, “Ambient Air Quality Control System,” PCS Product Report.

[3] S. Jiyal and R. K. Saini, “Prediction and Monitoring of Air Pollution Using Internet of Things (IoT),” in 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), Nov. 2020.

[4] S. Ali, T. Glass, B. Parr, J. Potgieter, and F. Alam, “Low Cost Sensor With IoT LoRaWAN Connectivity and Machine Learning-Based Calibration for Air Pollution Monitoring,” IEEE Transactions on Instrumentation and Measurement, vol. 70, 2020.

[5] V. K. S. Yadav et al., “Room Temperature Air Pollutants Sensors Using Printed ZnO Single-Nanowire Schottky Diodes,” IEEE Transactions on Nanotechnology, vol. 20, pp. 338–345, 2021.

[6] G. Kalaivani and P. Mayilvahanan, “Air Quality Prediction and Monitoring using Machine Learning Algorithm based IoT Sensor,” in 2021 ICCES Conference.

[7] T. Madan et al., “Air Quality Prediction using Machine Learning Algorithms – A Review,” in 2020 ICACCCN Conference.

[8] J. Yun and J. Woo, “IoT-Enabled Particulate Matter Monitoring and Forecasting Method Based on Cluster Analysis,” IEEE Internet of Things Journal, vol. 8, no. 9, 2021.

[9] P. S. Acharya et al., “Data-Driven Models Support a Vision for Over-the-Air Vehicle Emission Inspections,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, 2022.

[10] P. Ferrer-Cid et al., “Graph Learning Techniques Using Structured Data for IoT Air Pollution Monitoring Platforms,” IEEE Internet of Things Journal, vol. 8, no. 17, 2021.