A Novel Cost-Effective Portable Electronic Nose for Indoor-/In-Car Air Quality Monitoring

Abstract

With today's competitive and complex environment which results from rapid industrial development, air quality monitoring is becoming a necessity. Devising devices that provide reliable, cost-effective, and fast monitoring of indoor/in-car harmful chemical compounds is of paramount importance for governments as well as individuals. Sensors array systems or commonly called electronic nose (E-nose) systems have been used in various fields of consumer applications. Owing to their versatility and ease of use, these systems can be an adequate alternative for indoor/in-car air quality monitoring. In this study a novel self-made and cost-effective electronic nose aiming at quantifying five indoor/in-car harmful gases (formaldehyde, benzene, CO, NO2, toluene), has been devised and implemented at the college of electronic and communication engineering of Chongqing University, China. A hybrid genetic algorithm support machine vector regression (GA-LSSVMR) model is used for pattern recognition and concentrations estimation. With absolute relative errors of prediction (MAREP) less than 10%, these models outperform those based on hybrid genetic algorithm backpropagation neural network regression (GA-BPNNR). Furthermore, the best regression models were embedded into the system for real-time concentration estimation; our system's predictions mostly agree with those of specific gas detectors. The product will therefore be a good alternative for indoor/in-car air quality monitoring.

Assessment of the Indoor Odour Impact in a Naturally Ventilated Room

“…This is confirmed by the increase of the international studies related to the application of these instruments in indoor monitoring [53,54,55,56,59,62,63]. This interest is due to a growing awareness of the indoor air quality of environments where people live and work [64].…”

“…Issues mainly dealt with:

The identification of a method to evaluate the performances of the electronic nose. This topic is particularly interesting internationally because of the recent formation of a technical committee dedicated to the definition of specific regulations of the electronic noses for their standardization;

The analysis of parameters related to air quality;The analysis of parameters related to water quality;The process control, as well as the verification of the efficiency of odour control systems. The indoor applications [53,54,55,56,57,58,59,60,61] are focused on the detection of the odour compounds that can worsen the air quality and possibly pose some safety problems. Only few Italian studies [62,63,64,65] focus the attention on the indoor air quality (IAQ) monitoring and safety.…”

Indoor air quality prediction systems for smart environments: A systematic review

“…Two studies [131,136] followed ARIMA and two other studies used the GRU method for IAQ prediction. Besides this, one study each focused on ANFIS [134], Kalman filter [49], GA-based SVM [114], time slicer method [106], Bayesian inference [132] and decision tree regression [119]. However, none of these studies included fuzzy logic, which otherwise offers potentials scope for forecasting problems [56,57].…”

Improving air quality management using gradient boosting based hierarchical temporal memory neural networks and fuzzy based classification based regression tree

“…So, the quality of the air must be predicted and controlled the pollutant concentration level. Tian et al, [7] uses the hybrid genetic algorithm and support vector regression tree and hybrid genetic algorithm back propagation neural network for recognizing the air quality. The system monitored the quality of the air in China, Chongqing university electronic and communication engineering department.…”