Different Ways to Apply a Measurement Instrument of E-Nose Type to Evaluate Ambient Air Quality with Respect to Odour Nuisance in a Vicinity of Municipal Processing Plants

Volatile Profile of Strawberry Fruits and Influence of Different Drying Methods on Their Aroma and Flavor: A Review

Strawberries are the most popular berry fruit in the world, due to their distinctive aroma, flavor, and known health properties. Because volatile substances play a large role in strawberry flavor, even little alterations can have a big impact on how the fruit tastes. Strawberries are thought to have a complex aroma. Fresh strawberry fruits contain more than 360 volatile compounds, including esters, furans, terpenes, alcohols, aldehydes, ketones, and sulfur compounds. Despite having far lower concentrations than esters, terpenoids, furanones, and sulfur compounds, all have a considerable impact on how people perceive the aroma of strawberries. With a focus on the active aroma components and the many analytical methods used to identify them, including gas chromatography, electronic nose sensing, and proton-transfer- reaction mass spectrometry, the present review's aim was to provide a summary of the relevant literature. Additionally, strawberry fruits are frequently dried to create a powder in order to increase their shelf life. Consequently, the impact of various drying techniques on strawberries' volatile profile was investigated in the current review. This review can be considered a good reference for research concerning the aroma profile of strawberries. It helps to better understand the complex aroma and flavor of strawberries and provides a guide for the effects of drying processing.

The Application of In Situ Methods to Monitor VOC Concentrations in Urban Areas—A Bibliometric Analysis and Measuring Solution Review

Urbanisation development affects urban vegetation both directly and indirectly. Since this process usually involves a dramatic change in land use, it is seen as likely to cause ecological pressure on local ecosystems. All forms of human activity, including urbanisation of areas close to residential buildings, significantly impact air quality. This study aims to identify and characterise different measurement solutions of VOCs, allowing the quantification of total and selective compounds in a direct at source (in situ) manner. Portable devices for direct testing can generally be divided into detectors, chromatographs, and electronic noses. They differ in parameters such as operating principle, sensitivity, measurement range, response time, and selectivity. Direct research allows us to obtain measurement results in a short time, which is essential from the point of view of immediate reaction in the case of high concentrations of tested compounds and the possibility of ensuring the well-being of people. The paper also attempts to compare solutions and devices available on the market and assess their application.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO₂, HCHO, and NH₃ and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.

A review of environmental odor quantification and qualification methods: The question of objectivity in sensory analysis

For several years, various issues have up surged linked to odor nuisances with impacts on health and economic concerns. As awareness grew, recent development in instrumental techniques and sensorial analysis have emerged offering efficient and complementary approaches regarding environmental odor monitoring and control. While chemical analysis faces several obstacles, the sensory approach can help overcome them. Therefore, this latter may be considered as subjective, putting the reliability of the studies at risk. This paper is a review of the most commonly sensory methodology used for quantitative and qualitative environmental assessment of odor intensity (OI), odor concentration (OC), odor nature (ON) and hedonic tone (HT). For each of these odor dimensions, the assessment techniques are presented and compared: panel characteristics are discussed; laboratory and field studies are considered and the objectivity of the results is debated. For odor quantification, the use of a reference scale for OI assessment offers less subjectivity than other techniques but at the expense of ease-of-use. For OC assessment, the use of dynamic olfactometry was shown to be the least biased. For odor qualification, the ON description was less subjective when a reference-based lexicon was used but at the expense of simplicity, cost, and lesser panel-training requirements. Only when assessing HT was subjectivity an accepted feature because it reflects the impacted communities' acceptance of odorous emissions. For all discussed dimensions, field studies were shown to be the least biased due to the absence of air sampling, except for OC, where the dispersion modeling approach also showed great potential. In conclusion, this paper offers the reader a guide for environmental odor sensory analysis with the capacity to choose among different methods depending on the study nature, expectations, and capacities.

Development of Gas Sensor Array for Methane Reforming Process Monitoring

The article presents a new method of monitoring and assessing the course of the dry methane reforming process with the use of a gas sensor array. Nine commercially available TGS chemical gas sensors were used to construct the array (seven metal oxide sensors and two electrochemical ones). Principal Component Regression (PCR) was used as a calibration method. The developed PCR models were used to determine the quantitative parameters of the methane reforming process: Inlet Molar Ratio (IMR) in the range 0.6-1.5, Outlet Molar Ratio (OMR) in the range 0.6-1.0, and Methane Conversion Level (MCL) in the range 80-95%. The tests were performed on model gas mixtures. The mean error in determining the IMR is 0.096 for the range of molar ratios 0.6-1.5. However, in the case of the process range (0.9-1.1), this error is 0.065, which is about 6.5% of the measured value. For the OMR, an average error of 0.008 was obtained (which gives about 0.8% of the measured value), while for the MCL, the average error was 0.8%. Obtained results are very promising. They show that the use of an array of non-selective chemical sensors together with an appropriately selected mathematical model can be used in the monitoring of commonly used industrial processes.

Objective odor analysis of incidentally emitted compounds using the Langage des Nez® method: application to the industrial zone of Le Havre

Environmental odor studies are usually done using two approaches: nuisance impact assessment and source identification. The latter may be done using chemical analysis or sensory analysis. While sensory analyses offer many advantages, they also face the main obstacle: odor nature description still uses conventional methods based on subjective evocations as odor descriptors. This makes the sensory method ineffective especially when the expected outcome is the source identification in the context of an industrial accident. This work wants to fulfill this gap proposing to build an objective database including the odor nature description of selected potentially emitted compounds using a promising approach: the Langage des Nez® (LdN). Using definite odorous compounds as odor referents, this work provides the odor nature description of 44 compounds, reported as potential incidentally released chemical compounds in the industrial zone of Le Havre. The city of Le Havre, France, was chosen as a model due to a history of odorous emissions of industrial origins. A trained panel described the odor of each compound using up to three referents of the LdN referents collection and attributed a score to each referent. A data analysis method was developed based on the frequency of citation of the referents and the attributed scores allowing the categorization of each compound in three types of consensus categories. The data analysis results showed that around 80% of compounds were described with a good consensus, showing the LdN as a well-adapted lexicon. This study does not point to any correlation between the chemical structures of the compounds of interest and their relative referents. When compared to conventional methods, LdN revealed a more objective and precise approach. The proposed experimental method and the results provided in this work offer the first insight for time-efficient approaches to objectively describe environmental odors, especially potentially emitted odors during incidents. This work may be supplemented by abatement and mixture effect investigations for a complete understanding of odor dispersion.

The Use of Chemical Sensors to Monitor Odour Emissions at Municipal Waste Biogas Plants

Municipal waste treatment plants are an important element of the urban area infrastructure, but also, they are a potential source of odour nuisance. Odour impact from municipal waste processing plants raises social concerns regarding the well-being of employees operating the plants and residents of nearby areas. Chemical methods involve the determination of the quantitative composition of compounds comprising odour. These methods are less costly than olfactometry, and their efficiency is not dependent on human response. The relationship between the concentration of a single odorant and its odour threshold (OT) is determined by the odour activity value (OAV) parameter. The research involved the application of a multi-gas detector, MultiRae Pro. Measurements by means of the device were conducted at three municipal waste biogas plants located in Poland. In this paper we describe the results obtained when using a detector during the technological processes, the unitary procedures conducted at the plants, and the technological regime. The determination of these relationships could be useful in the development of odour nuisance minimization procedures at treatment plants and the adjustment to them. This is of paramount importance from the viewpoint of the safety and hygiene of the employees operating the installations and the comfort of residents in the areas surrounding biogas plants. Monitoring of expressed odorant emissions allows the course of technological processes and conducted unit operations to be controlled.

A Simulation Framework for the Integration of Artificial Olfaction into Multi-Sensor Mobile Robots

The simulation of how a gas disperses in a environment is a necessary asset for the development of olfaction-based autonomous agents. A variety of simulators already exist for this purpose, but none of them allows for a sufficiently convenient integration with other types of sensing (such as vision), which hinders the development of advanced, multi-sensor olfactory robotics applications. In this work, we present a framework for the simulation of gas dispersal and sensing alongside vision by integrating GADEN, a state-of-the-art Gas Dispersion Simulator, with the Unity 3D, a video game development engine that is used in many different areas of research and helps with the creation of visually realistic, complex environments. We discuss the motivation for the development of this tool, describe its characteristics, and present some potential use cases that are based on cutting-edge research in the field of olfactory robotics.

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3-0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

The Impact of Technological Processes on Odorant Emissions at Municipal Waste Biogas Plants

Municipal waste treatment is inherently associated with odour emissions. The compounds characteristic of the processes used for this purpose, and at the same time causing a negative olfactory sensation, are organic and inorganic sulphur and nitrogen compounds. The tests were carried out at the waste management plant, which in the biological part, uses the methane fermentation process and is also equipped with an installation for the collection, treatment, and energetic use of biogas. The tests include measurements of the four odorant concentrations and emissions, i.e., volatile organic compounds (VOCs), ammonia (NH3), hydrogen sulphide (H2S), and methanethiol (CH3SH). Measurements were made using a MultiRae Pro portable gas detector sensor. The tests were carried out in ten series for twenty measurement points in each series. The results show a significant impact of technological processes on odorant emissions. The types of waste going to the plant are also important in shaping this emission. On the one hand, it relates to the waste collection system and, on the other hand, the season of year. In addition, it has been proved that the detector used during the research is a valuable tool enabling the control of technological processes in municipal waste processing plants.

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning

The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model—quantity and quality of wastewater, operational parameters, and the cost of conducting measurements.

Odorant composition of post-consumer LDPE bags originating from different collection systems

The establishment of recycling systems aiming at high-quality recyclates from post-consumer plastic waste are essential to avoid the waste of resources. One main impediment for introducing recyclates into the market is their unwanted odor. For this reason, this study aimed at determining if the collection strategy affects the odor profile of post-consumer LDPE bags. Furthermore, the effect of hot water washing, inspired by the conventional mechanical recycling procedure, on the odor of post-consumer LDPE bags was screened. More than 60 odorants were detected in LDPE bags collected in a separate plastic fraction as well as in LDPE bags from the non-separated collection by means of gas chromatography-olfactometry, and 37 of them were unequivocally identified using two-dimensional gas chromatography-mass spectrometry/olfactometry. The sensory results revealed that the type of collection affects the overall odor intensity, the hedonic tone of the odor and the odor profile. Namely, cheesy and fecal smelling odorants were predominant in the waste that had not been separated at source, whereas odorants with earthy and moldy smells showed mostly higher intensity ratings in the waste separated at source. Short chain carboxylic acids, likely originating from microbial spoilage of organic waste, were found with higher dilution factors in the mixed fraction, and could, accordingly, contribute to the observed differences. Additionally, we could show that the hot washing procedure, applied to the LDPE sample from the separate collection system, significantly reduced the overall odor intensity from 8 to 6.3 (0-10 scale). However, the washed waste still showed high smell intensity ratings.

Odour Emissions of Municipal Waste Biogas Plants—Impact of Technological Factors, Air Temperature and Humidity

Biogas plants processing municipal waste are an important part of a circular economy (energy generation from biogas and organic fertiliser production for the treatment of selectively collected biowaste). However, the technological processes taking place may be associated with odour nuisance. The paper presents the results of pilot research conducted at six municipal waste biogas plants in Poland. It shows the relations between odour intensity and concentration and the occurring meteorological and ambient conditions (air temperature and relative humidity) and technological factors at biogas plants processing municipal waste. The impact of meteorological and ambient conditions was identified by measuring air temperature and relative humidity and observing their changes. The impact of technological factors was identified by measuring odorant concentration (volatile organic compounds and ammonia) and observing their changes between individual measurement series. At most analysed biogas plants, the influence of technological factors on odour emissions took place and was clearly noted. The elements of biogas installations characterised by the highest concentration of these odorants were indicated. Special attention should be paid to the choice of technological solutions and technical and organisational measures to reduce the impact of unfavourable atmospheric conditions on odour emissions.

Simultaneous Removal of Hexane and Ethanol from Air in a Biotrickling Filter—Process Performance and Monitoring Using Electronic Nose

Biofiltration is a well-accepted method for the removal of malodorous compounds from air streams. Interestingly, the mechanisms underlying this process are not fully understood. The aim of this paper was to investigate the simultaneous removal of hydrophobic hexane with hydrophilic ethanol, resulting in the enhanced removal of hexane in the presence of ethanol. Investigations were performed in a peat-perlite packed biotrickling filter and the process performance was monitored using both gas chromatography and electronic nose techniques. The results indicate that the length as well as the efficiency of biofiltration during the start-up period depend on the feed composition, with higher efficiency obtained when hexane and ethanol were fed together from the process initiation. The experiments in the steady-state period present the biofilter performance when different ratios of hydrophilic to hydrophobic compounds were fed to the biofilter. The obtained results show the synergistic effects of the addition of a hydrophilic compound on the removal efficiency of hydrophobic hexane. The influence of the ratio of hydrophilic to hydrophobic compounds is discussed in terms of enhancing the mass transfer phenomena for hydrophobic volatile organic compounds.

Environmental odour management by artificial neural network - A review

Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are challenging to handle due to their invisibility to the naked eye and can only be felt by the human olfactory stimuli. A strategy to address this issue is by introducing an intelligent processing system to odour monitoring instrument such as artificial neural network to achieve a robust result. In this paper, a review on the application of artificial neural network for the management of environmental odours is presented. The principal factors in developing an optimum artificial neural network were identified as elements, structure and learning algorithms. The management of environmental odour has been distinguished into four aspects such as measurement, characterization, control and treatment and continuous monitoring. For each aspect, the performance of the neural network is critically evaluated emphasizing the strengths and weaknesses. This work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration, the benefits and consequences. Adopting this technique could provide a new avenue in the management of environmental odours through the use of a powerful mathematical computing tool for a more efficient and reliable outcome.

Effects of n-butanol presence, inlet loading, empty bed residence time and starvation periods on the performance of a biotrickling filter removing cyclohexane vapors from air

This paper presents the results of investigations on the removal of cyclohexane vapors from air using a peat-perlite packed biotrickling filter. Effects of basic process parameters i.e. inlet loading and empty bed residence time as well as introduction of n-butanol to the treated air stream and starvation periods on the process performance were evaluated. The results show that the introduction of hydrophilic n-butanol results in an enhanced removal of hydrophobic cyclohexane comparing to the experiments where only cyclohexane was treated. Additionally, the biotrickling filter performance after the starvation events is regained to more extent for mixed system than for the single cyclohexane. A novel and interesting element of the paper is the application of an electronic nose for the process monitoring. Obtained results are discussed in the perspective of an influence of the presence of a compound with different affinity to aqueous phase on the removal efficiency of the compound with opposite chemical properties.

Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic

Measurement and monitoring of air quality in terms of odor nuisance is an important problem. From a practical point of view, it would be most valuable to directly link the odor intensity with the results of analytical air monitoring. Such a solution is offered by electronic noses, which thanks to the possibility of holistic analysis of the gas sample, allow estimation of the odor intensity of the gas mixture. The biggest problem is the occurrence of odor interactions between the mixture components. For this reason, methods that can take into account the interaction between components of the mixture are used to analyze data from the e-nose. In the presented study, the fuzzy logic algorithm was proposed for determination of odor intensity of binary mixtures of eight odorants: n-Hexane, cyclohexane, toluene, o-xylene, trimethylamine, triethylamine, α-pinene, and β-pinene. The proposed algorithm was compared with four theoretical perceptual models: Euclidean additivity, vectorial additivity, U model, and UPL model.

Low-Power Detection of Food Preservatives by a Novel Nanowire-Based Sensor Array

Food preservatives are compounds that are used for the treatment of food to improve the shelf life. In the food industry, it is necessary to monitor all processes for both safety and quality of the product. An electronic nose (or e-nose) is a biomimetic olfactory system that could find numerous industrial applications, including food quality control. Commercial electronic noses are based on sensor arrays composed by a combination of different sensors, which include conductometric metal oxide devices. Metal oxide nanowires are considered among the most promising materials for the fabrication of novel sensing devices, which can enhance the overall performances of e-noses in food applications. The present work reports the fabrication of a novel sensor array based on SnO₂, CuO, and WO₃ nanowires deposited on top of μHPs provided by ams Sensor Solutions Germany GmbH. The array was tested for the discrimination of four typical compounds added to food products or used for their treatment to increase the shelf life: ethanol, acetone, nitrogen dioxide, and ozone. Results are very promising; the sensors array was able to operate for a long time, consuming less than 50 mW for each single sensor, and principal component analysis (PCA) confirmed that the device was able to discriminate between different compounds.

Comparative Evaluation of Selected Biological Methods for the Removal of Hydrophilic and Hydrophobic Odorous VOCs from Air

Due to increasingly stringent legal regulations as well as increasing social awareness, the removal of odorous volatile organic compounds (VOCs) from air is gaining importance. This paper presents the strategy to compare selected biological methods intended for the removal of different air pollutants, especially of odorous character. Biofiltration, biotrickling filtration and bioscrubbing technologies are evaluated in terms of their suitability for the effective removal of either hydrophilic or hydrophobic VOCs as well as typical inorganic odorous compounds. A pairwise comparison model was used to assess the performance of selected biological processes of air treatment. Process efficiency, economic, technical and environmental aspects of the treatment methods are taken into consideration. The results of the calculations reveal that biotrickling filtration is the most efficient method for the removal of hydrophilic VOCs while biofilters enable the most efficient removal of hydrophobic VOCs. Additionally, a simple approach for preliminary method selection based on a decision tree is proposed. The presented evaluation strategies may be especially helpful when considering the treatment strategy for air polluted with various types of odorous compounds.

Development of Tin Oxide-Based Nanosensors for Electronic Nose Environmental Applications

Tin oxide nanofibres (NFs) are used as nanosensors in electronic noses. Their performance is compared to that of oxide commercial chemical sensors for pollutant detection. NFs were grown by electrospinning and deposited onto silicon substrates with integrated micro-hotplates. NF morphology was characterized by scanning electron microscopy (SEM). The NFs presented high sensitivity to NO₂ at low temperature.

Protein- and Peptide-Based Biosensors in Artificial Olfaction

Animals' olfactory systems rely on proteins, olfactory receptors (ORs) and odorant-binding proteins (OBPs), as their native sensing units to detect odours. Recent advances demonstrate that these proteins can also be employed as molecular recognition units in gas-phase biosensors. In addition, the interactions between odorant molecules and ORs or OBPs are a source of inspiration for designing peptides with tunable odorant selectivity. We review recent progress in gas biosensors employing biological units (ORs, OBPs, and peptides) in light of future developments in artificial olfaction, emphasizing examples where biological components have been employed to detect gas-phase analytes.

Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses

The aim of this study is to improve the discrimination performance of electronic noses by introducing a new method for measuring the similarity of the signals obtained from the electronic nose. We constructed abstract odor factor maps (AOFMs) as the characteristic maps of odor samples by decomposition of three-way signal data array of an electronic nose. A similarity measure for two-way data was introduced to evaluate the similarities and differences of AOFMs from different samples. The method was assessed by three types of pipe and powder tobacco samples. Comparisons were made with other techniques based on PCA, SIMCA, PARAFAC and PARAFAC2. The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high VOCs release.

Monitoring of n-butanol vapors biofiltration process using an electronic nose combined with calibration models

Abstract

Malodorous odors, by definition, are unpleasant, irritating smells being a mixture of volatile chemical compounds that can be sensed at low concentrations. Due to the increasing problem of odor nuisance associated with odor sensations, and thus the need to remove them from the air, deodorization techniques are commonly used. Biofiltration is one of the methods of reducing odorants in the air stream. In the paper, the possibility of using an electronic nose as an alternative method to gas chromatography for the online monitoring and evaluation of efficiency of the n-butanol vapors biofiltration process in a transient state was investigated. Three calibration models were used in the research, i.e., multiple linear regression, principal component regression, and partial least-square regression. The obtained results were compared with the theoretical values.

Graphical abstract

Evaluation of Hydrocarbon Soil Pollution Using E-Nose

The possibility of detecting low levels of soil pollution by petroleum fuel using an electronic nose (e-nose) was studied. An attempt to distinguish between pollution caused by petrol and diesel oil, and its relation to the time elapsed since the pollution event was simultaneously performed. Ten arable soils, belonging to various soil groups from the World Reference Base (WRB), were investigated. The measurements were performed on soils that were moistened to field capacity, polluted separately with both hydrocarbons, and then allowed to dry slowly over a period of 180 days. The volatile fingerprints differed throughout the course of the experiment, and, by its end, they were similar to those of the unpolluted soils. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that the e-nose results could be used to detect soil contamination and distinguish between pollutants and contamination levels.