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Wastewater contains harmful chemicals and heavy metals that are known to cause various environmental and health problems. Therefore, the water quality control using sensitive, simple, fast, accurate, and portable tools is of great importance. This study aimed to evaluate the pollution parameters of wastewaters collected at different treatment stages from a wastewater treatment plant (WWTP) that treats domestic and industrial wastewaters by using an electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) combined with chemometric techniques. Water and wastewater pollution parameters determination were performed using inductively coupled plasma optical emission spectrometry for the determination of cations, and anions by using ion chromatography. Chemometric techniques, such as Principal Component Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to process the E-nose and E-tongue datasets to describe the similarities between the samples. In addition, Partial Least Squares Regression (PLSR) model was constructed using electronic sensing data to simultaneously predict the concentration values of physicochemical parameters. The obtained correlation coefficient, for training and testing sets, is higher than 0.91 for the prediction of the concentration of all physicochemical parameters, except for iron (Fe) which remains 0.84. These results suggest that simple, portable, and inexpensive tools such as electronic nose and tongue are suitable for wastewater analysis.
The presence of wastewater and air pollution has become an important risk factor for citizens, not only in terms of problems related to health risks, but also because of its negative impact on the country's image. For this reason, malodorous emission monitoring and control techniques are in high demand in urban areas and industries. The aim of this work is first to build an electronic nose (e-nose) and a Voltammetric Electronic tongue (VE-tongue) in order to study their ability to discriminate between polluted and clean environmental samples. Secondly, Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS), and Solid Phase Micro Extraction-Gas Chromatography–Mass Spectrometry (SPME-GC-MS) are utilized to explain this discrimination by identifying specific compounds from these samples. Indeed, the e-nose, consisted of metal oxide semiconductor gas sensors, is used for the assessment of the studied odorous air and headspace samples from water and wastewater sites. Moreover, the VE-tongue, based on metal electrodes, is utilized to determine the patterns of the sensor array responses, which serve as fingerprints profiles of the analyzed liquid samples. Chemometric tools, such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vector Machines (SVMs) are operated for the processing of data from the e-nose and the VE-tongue. By using the both systems, the analyses of headspace and liquid samples from the seven sites allow better discrimination. To explain the cause of the obtained discrimination, TD-GC-MS and SPME-GC-MS analyses are well performed to identify compounds related sites. According to these outcomes, the proposed e-nose and VE-tongue are proved to be rapid and valuable tools for analysis of environmental polluted matrices.
Poor air quality, particularly in urban areas, causes various diseases and degrades living standards. Air Quality could be affected by emissions of odor, Volatile Organic Compounds (VOCs), and other gases. Therefore, assessment and monitoring of odorous air quality using sensitive, simple, rapid, accurate and portable tools is very important for public health. This study aimed to characterize odor emissions to detect malfunctions in facilities and to prevent air pollution and olfactory nuisance in the environment. A gas chromatographic method, in conjunction with sensorial analysis were performed for odorous air samples analysis collected from neighborhood of Meknes city (Morocco). Advanced multivariate statistical approaches, such as Principal Components Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to describe samples similarities. The electronic nose (e-nose) data processing exhibits a satisfactory discrimination between the odorous air samples. Twenty-four VOCs with known molecular formulas were identified with Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS). A validated Partial Least Square (PLS) model foresees good calibration between e-nose measurement and TD-GCMS analysis. The finding indicates that TD-GC–MS approach in conjunction with e-nose unit could be suitable tool for environmental measurement-based odor emissions.