Identification and quantification of mixed air pollutants based on homotopy method for gas sensor array

Area of Science:

• Environmental Science
• Chemical Sensing Technology
• Analytical Chemistry

Background:

• Accurate air pollutant monitoring is vital for public health and safety.
• Gas sensor arrays offer a promising approach for environmental monitoring.
• Analyzing complex sensor responses, especially to mixtures, remains a challenge.

Purpose of the Study:

• To develop an efficient method for analyzing gas sensor array responses to binary pollutant mixtures.
• To accurately recognize air pollutants and estimate their concentrations.
• To provide a globally convergent solution for nonlinear sensor response modeling.

Main Methods:

• Modeling gas sensor array responses as a system of nonlinear equations.
• Utilizing a homotopy algorithm for solving the system of nonlinear equations.
• Applying the model to real-world measurement data for methane (CH4) and sulfur dioxide (SO2).

Main Results:

• The proposed homotopy algorithm efficiently analyzes sensor array data for binary mixtures.
• The method accurately predicts concentrations of CH4 and SO2.
• Prediction results demonstrated a variation within 1% of true values for both gases.

Conclusions:

• The developed homotopy algorithm provides an effective and accurate solution for analyzing gas sensor array data.
• This method enhances the capability of gas sensor arrays for reliable air pollutant monitoring.
• The approach offers a significant improvement in the precision of pollutant concentration estimation.