Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring

Abstract

Traditional methods for detecting harmful gases in air are often limited in their widespread deployment, accuracy, and real-time monitoring capabilities due to their complexity and cost. To address this challenge, optimization algorithms such as the Particle Swarm Optimization (PSO) algorithm have shown promise. The PSO algorithm, is applied to calculate the concentrations of harmful gases in air, maximizing detection accuracy. Detecting indoor gas pollution is a crucial concern due to the abundance of odors and vapors, particularly those emanating from activities such as cooking. The presence of these substances in the air poses a challenge in identifying traces of other harmful gases. This research endeavors to pioneer a novel approach characterized by heightened sensitivity, even in the presence of unidentified elements in the air. In this work, PSO algorithm is used in conjunction with Chirped Spectral Modulation (CSM) technique to increase system sensitivity to detect small traces of harmful gases inside buildings and protect the environment through early detection of pollution. The use of PSO and CSM altogether allowed for detecting carbon dioxide CO2, carbon monoxide CO, and nitrogen dioxide NO2 down to 10−6 % in volume, and sulfur dioxide SO2 down to 5×10−4 % in volume, while keeping the error below 0.1%

Keywords

• Harmful gas detection
• Atmospheric pollution monitoring
• Environmental pollution monitoring
• Indoor air quality
• Particle swarm optimization (PSO)
• Chirped spectral modulation (CSM)