Detection and identification of toxic environmental gases have assumed paramount importance precisely in the defense, industrial and civilian security sector. Numerous methods have been developed for the sensing of toxic gases in the environment ever since surface acoustic wave (SAW) technology came into existence. Such SAW sensors called electronic nose (E-Nose) sensor use the frequency response of a delay line/resonator. SAW device is focused and given importance. The selective coating between input and output interdigital transducers (IDTs) in the SAW device is responsible for corresponding changes in operating frequency of the device for a specific gas/vapour absorbed from the environment. A suitable combination of well-designed SAW delay lines with selective coatings not only help to improve sensor sensitivity and selectivity but also leads to the minimization of false frequency alarms in the E-Nose sensor. This article presents a comprehensive review of design, development, simulation and modelling of a SAW sensor for potential sensing of toxic environmental gases.

With rapid population increases, people are facing the challenge to maintain healthy conditions. One of the challenges is air pollution. Due to industrial development and vehicle usage air pollution is becoming a high threat to human life. This air pollution forms through various toxic contaminants. This toxic contamination levels increase and cause severe damage to the living things in the environment. To identify the toxic level present in the polluted air various methods were proposed by the authors, But failed to detect the tolerance level of toxic gases. This article discusses the methods to detect toxic gasses and classify the tolerance level of gasses present in polluted air. Various sensors and different algorithms are used for classifying the tolerance level. For this purpose “Artificial Sensing Methodology” (ASM), commonly known as e-nose, is a technique for detecting harmful gases. SO2-D4, NO2-D4, MQ-135, MQ136, MQ-7, and other sensors are used in artificial sensing methods (e-nose). “Carbon monoxide, Sulfur dioxide, nitrogen dioxide, and carbon dioxide” are all detected by these sensors. The data collected by sensors is sent to the data register from there it is sent to the Machine learning Training module (ML) and the comparison is done with real-time data and trained data. If the values increase beyond the tolerance level the system will give the alarm and release the oxygen.