Development of an electronic nose to characterize odours emitted from different stages in a wastewater treatment plant

Real-Time Monitoring of Odour Emissions at the Fenceline of a Waste Treatment Plant by Instrumental Odour Monitoring Systems: Focus on Training Methods

Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors' responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

Membrane-Based Pulsed Sampling Method for Extended Dynamic Range of Ion Mobility Spectrometry

Ion mobility spectrometry (IMS) has been widely studied and applied as an effective analytical technology for the on-site detection of volatile organic compounds (VOCs). Despite its superior selectivity compared with most gas sensors, its limited dynamic range is regarded as a major drawback, limiting its further application in quantitative measurements. In this work, we proposed a novel sample introduction method based on pulsed membrane adsorption, which effectively enhanced IMS's ability to measure analytes at higher concentrations. Taking N-methyl-2-pyrrolidone (NMP) as an example, this new sampling method expanded the dynamic range from 1 ppm to 200 ppm. The working principle and measurement strategy of this sampling method were also discussed, providing new insights for the design and application of IMS-based instruments.

Emission and spatial variation characteristics of odorous pollutants in the aerobic tank of an underground wastewater treatment plant (UWWTP) in southern China

In this study, the spatial concentration of odorous pollutants in the aerobic tank of an underground wastewater treatment plant (UWWTP) in southern China is monitored. The odour activity value, odour contribution rate, and chemical concentration contribution rate are used to evaluate the degree of contribution of odorous substances. Computational fluid dynamics (CFD) simulations of odorous pollutant diffusion are also established. The study shows that the odorous substances detected in the aerobic tank mainly included ammonia (NH3), hydrogen sulfide (H2S), trimethylamine (C3H9N), and methanethiol (CH3SH), and their concentrations are 1.160, 0.778, 0.022, and 0.0006 mg/m3, respectively. The total odour activity value of the aerobic tank is 450.72 (dimensionless), of which the odour activity value of H2S is 432.22, and the contribution rate reaches 95.9%. H2S is the main contributor to odour and a key controlled substance. The air inlets and exhaust outlets in the aerobic tank are cross-arranged at the top of the space, and the CFD model of odorous pollutant diffusion shows that the gas flow organization determines the odorous pollutant diffusion. The spatial distribution of gas flow and odorous substances in the aerobic tank is relatively uniform, and the odour collection efficiency is higher. The production flux and production coefficient of H2S in the aerobic tank are calculated as 25.831 mg/(m2·h) and 14.149 mg/t, respectively. This study determines the reasonable air supply and exhaust design of the aerobic tank, the number of odour pollutants, and the key controlled substances. These findings offer guidance and serve as useful references for the prevention and control of odour pollution in aerobic tanks of the same type of UWWTPs.

Development an electronic nose to recognize pesticides in groundwater

Timely detection of Groundwater pollution is essential to protect human health, especially for pesticide pollution. To solve this issue, we proposed a novel solution to realize the prediction of pesticide in groundwater by using the electronic nose (e-nose). The main work of this paper was divided into three steps: 1) checking whether sample was polluted by pesticides, 2) further predicting the pesticide type, brand and pollution degree when the sample was polluted by pesticides, and 3) optimizing the sensor array. Random forest was used to complete the first step, which had the best accuracy and sensitivity of 100 %. Support vector machine was applied to complete the second step, and the accuracy reaching 98.08 %. As for the third step, recursive feature elimination was used to optimize the sensor array. After optimization, the number of sensors was reduced from 26 to 8. In addition, the e-nose developed in this paper was compared with a commercial e-nose. The results showed that the cost of the developed e-nose was much lower than that of the commercial e-nose despite its slightly weaker prediction performance. Thus, this e-nose can be employed to recognize the pesticides in groundwater, and even can be integrated into the while drilling technology to realize the in-situ detection of groundwater.

Multi-dimension analysis of volatile sulfur compound emissions from an urban wastewater treatment plant

Long-term monitoring of volatile sulfur compounds (VSCs) released at the water-air interface from different treatment units of an anaerobic/oxic (A/O) wastewater treatment plant (WWTP) was carried out to assess the temporal and spatial emission characteristics of VSCs, to explore relationships between wastewater quality and VSC release. The VSC from non-aerated and aerated units were collected using dynamic and static chambers, respectively, and determined using gas chromatography. The VSC emission fluxes diminished in the order of primary sedimentation tank (PST) > anaerobic areas (ANA) > oxic section 1 (OX1). VSCs were not detected in the oxic section 2 (OX2), the oxic areas section 3 (OX3), and the final setting basin (FSB). Release capacities of VSCs descended in the order of summer > fall > spring > winter, with July, August, and September being the months with the highest VSC release capacities. VSC emission fluxes correlated well with wastewater temperatures, sulfate concentrations, and COD. VSC emission flux empirical equations based on wastewater temperature, sulfate concentrations, and COD were established. Based on the established VSC emission empirical equation, a control strategy to reduce the operating costs of deodorization facilities was proposed. This strategy is economically efficient and reduces the consumption of electrical energy.

Implementation of an electronic nose for real -time identification of odour emission peaks at a wastewater treatment plant

This paper proposes a novel approach for the real-time monitoring of odour emissions from a WasteWater Treatment Plant (WWTP) using an Instrumental Odour Monitoring System (IOMS). The plant is characterized by unpredictable odour peaks at its arrival tank (AT), generating nuisance and complaints in the population living nearby the plant. Odour peaks are most likely due to the conferment of non-identified and malodorous wastewaters coming from various industrial activities. Due to the high variability of sources collecting their wastewaters to the WWTP, a new methodology to train the IOMS, based on the use of a one-class classifier (OCC), has been exploited. The OCC enables to detect deviations from a "Normal Operating Region" (NOR), defined as to include odour concentrations levels unlikely to cause nuisance in the citizenship. Such deviations from the NOR thus should be representative of the odour peaks. The results obtained prove that the IOMS is able to detect real-time alterations of odour emissions from the AT with an accuracy on independent validation data of about 90% (CI95% 55-100%). This ability of detecting anomalous conditions at the AT of the WWTP allowed the targeted withdrawal of liquid and gas samples in correspondence of the odour peaks, then subjected to further analyses that in turn enabled to investigate their origin and take proper counteractions to mitigate the WWTP odour impact.

A critical review on odor measurement and prediction

Odor pollution has become a global environmental issue of increasing concern in recent years. Odor measurements are the basis of assessing and solving odor problems. Olfactory and chemical analysis can be used for odor and odorant measurements. Olfactory analysis reflects the subjective perception of human, and chemical analysis reveals the chemical composition of odors. As an alternative to olfactory analysis, odor prediction methods have been developed based on chemical and olfactory analysis results. The combination of olfactory and chemical analysis is the best way to control odor pollution, evaluate the performances of the technologies, and predict odor. However, there are still some limitations and obstacles for each method, their combination, and the prediction. Here, we present an overview of odor measurement and prediction. Different olfactory analysis methods (namely, the dynamic olfactometry method and the triangle odor bag method) are compared in detail, the latest revisions of the standard olfactometry methods are summarized, and the uncertainties of olfactory measurement results (i.e., the odor thresholds) are analyzed. The researches, applications, and limitations of chemical analysis and odor prediction are introduced and discussed. Finally, the development and application of odor databases and algorithms for optimizing odor measurement and prediction methods are prospected, and a preliminary framework for an odor database is proposed. This review is expected to provide insights into odor measurement and prediction.

Electronic nose application for detecting different odorants in source water: Possibility and scenario

The problem of taste and odor (T&O) in drinking water is a widespread societal concern and highlights substantial challenges related to the detection and evaluation of odor in water. In this study, the portable electronic nose PEN3, which is equipped with ten different heated metal sensors, was applied to analyze its applicability, feasibility and application scenarios for the detection of typical odorants, such as 2-methylisobornel (2-MIB), geosmin (GSM), β-cyclocitral, β-ionone, and other T&O compounds in source water, while avoiding uncertainties and instability related to manual inspection. All the T&O compounds could be effectively differentiated by principal component analysis (PCA). Linear discriminant analysis (LDA) showed that the odors varied greatly between different samples and could be effectively distinguished. As the odorant concentration increased, the sensor response intensity of the primary identification sensors R6 and R8 increased with a significant positive correlation. For Microcystis aeruginosa, an algae that produces odorants, PCA could distinguish the odors of algae at a series of densities at different concentrations. The responses of R10 showed a significant increase with increasing algae density, implying the production of more aliphatic hydrocarbons and other odor compounds. The results indicated that the electronic nose could provide a promising alternative to traditional unstable and complex detection methods for the detection of odorous substances in surface water and early warning of odor events. This study aimed to provide technical support for rapid monitoring and early warning of odorants in source water management.

Development of an electronic nose to characterize water quality parameters and odor concentration of wastewater emitted from different phases in a wastewater treatment plant

For public health consideration, it is important to ensure the wastewater discharged from wastewater treatment plant is within the regulatory limits. This problem can be effectively solved by improving the accuracy and rapid characterization of water quality parameters and odor concentration of wastewater. In this paper, we proposed a novel solution to realize the precisive analysis of water quality parameters and odor concentration of wastewater by the electronic nose device. The main work of this paper was divided into three steps: 1) recognizing wastewater samples qualitatively from different sampling points, 2) analyzing the correlation between electronic nose response signals and water quality parameters and odor concentration, and 3) predicting the odor concentration and water quality parameters quantitatively. Combined with different feature extraction methods, support vector machine and linear discriminant analysis were applied as classifiers to recognize samples at different sampling points, which reported the best recognition rate of 98.83%. Partial least squares regression was applied to complete the second step, and R² was reaching 0.992. As for the third step, ridge regression was used to predict water quality parameters and odor concentration with the RMSE less than 0.9476. Thus, electronic noses can be applied to determine water quality parameters and odor concentrations in the effluent discharged from wastewater plants.

Wastewater Treatment Plants as a Source of Malodorous Substances Hazardous to Health, Including a Case Study from Poland

Using Poland as an example, it was shown that 41.6% of the requests for intervention in 2016-2021 by Environmental Protection Inspections were related to odour nuisance. Further analysis of the statistical data confirmed that approximately 5.4% of wastewater treatment plants in the group of municipal facilities were subject to complaints. Detailed identification of the subject of odour nuisance at wastewater treatment plants identified hydrogen sulphide (H₂S), ammonia (NH₃) and volatile organic compounds (VOCs) as the most common malodorous substances within these facilities. Moreover, the concentrations of hydrogen sulphide and ammonia exceed the reference values for some substances in the air (0.02 mg/m³ for H₂S and 0.4 mg/m³ for NH₃). A thorough assessment of the properties of these substances made it clear that even in small concentrations they have a negative impact on the human body and the environment, and their degree of nuisance is described as high. In the two WWTPs analysed in Poland (WWTP 1 and WWTP 2), hydrogen sulphide concentrations were in the range of 0-41.86 mg/m³ (Long-Term Exposure Limit for H₂S is 7.0 mg/m³), ammonia 0-1.43 mg/m³ and VOCs 0.60-134.79 ppm. The values recognised for H₂S cause lacrimation, coughing, olfactory impairment, psychomotor agitation, and swelling of the cornea with photophobia. Recognition of the methods used in practice at WWTPs to reduce and control malodorous emissions indicates the possibility of protecting the environment and human health, but these solutions are ignored in most facilities due to the lack of requirements specified in legislation.

Odor emission assessment of different WWTPs with Extended Aeration Activated Sludge and Rotating Biological Contactor technologies in the province of Cordoba (Spain)

In this study, five urban WWTPs (Wastewater Treatment Plant) with different biological treatment (Extended Aeration Activated Sludge - EAAS; Rotating Biological Contactor - RBC), wastewater type (Urban; Industrial) and size, were jointly evaluated. The aim was twofold: (1) to analyze and compare their odor emissions, and (2) to identify the main causes of its generation from the relationships between physico-chemical, respirometric and olfactometric variables. The results showed that facilities with EAAS technology were more efficient than RBC, with elimination yields of organic matter higher than 90%. In olfactometric terms, sludge managements facilities (SMFs) were found to be the critical odor source in all WWTPs compared to the Inlet point (I) or Post primary treatment (PP), and for seasonal periods with ambient temperature higher than 25 °C. Moreover, the global odor emissions quantified in all SMFs revealed that facilities with EAAS (C-WWTP, V-WWTP and Z-WWTP) had a lower odor contribution (19,345, 14,800 and 11,029 ou_E/s·m², respectively) than for those with RBC technology (P-WWTP and NC-WWTP) which accounted for 19,747 ou_E/s·m² and 80,061 ou_E/s·m², respectively. In addition, chemometric analysis helped to find groupings and differences between the WWTPs considering the wastewater (71.27% of total variance explained) and sludge management (64.52% of total variance explained) lines independently. Finally, odor emissions were adequately predicted from the physico-chemical and respirometric variables in the wastewater (r² = 0.8738) and sludge (r² = 0.9373) lines, being pH, volatile acidity and temperature (wastewater line), and pH, moisture, temperature, SOUR (Specific Oxygen Uptake Rate) and OD₂₀ (Cumulative Oxygen Demand at 20 h) (sludge line) the most influential variables.

Odour impact assessment using kinetics and optimization: case studies on removal of multiple volatile organo-sulphur compounds from sewage wastewater using porous functional materials

Expanding industries and booming population have led to the increase in the installation of wastewater and sewer systems, even in close proximity to residential areas. Emissions from these installations particularly volatile organo-sulphur compounds (VOSCs) such as methyl mercaptan (CH₃SH), ethyl mercaptan (C₂H₅SH), dimethyl sulphide (CH₃SCH₃) and carbon disulphide (CS₂) are a nuisance to people even when present in small concentration. Strategies for removal involve addition of chemicals or other chemical processes which are generally expensive. Biofilters, on the other hand, consume large amount of energy and wash waters. Hence keeping commercialization in mind, it is important to develop a strategy which would be cost-effective and at the same time be effective to remove most of the odorous compounds present in these systems. In the present research work, granular activated carbons (GAC) are functionalized with alkali solution to improve the adsorption capacity. Liquid phase batch adsorption is performed with GAC and various functionalized activated carbons (FACs) with the help of raw sewage water from a local sewage water treatment plant. Concentration of odour was evaluated by two methods-olfactometry-based analysis for sensory measurement and GCMS-based analysis for analytical estimation of a specific odorous compound. The adsorption capacities of the functionalized GACs are higher primarily because of complex formation at the surface of modified GACs. Pseudo-second-order kinetic model agreed well with experimental results with the rate constant being 0.0191 mg/l min and 0.0153 mg/l min for methyl and ethyl mercaptan adsorption onto FAC-NH₃. Boyd's film diffusion along with rate kinetic model supported that chemical adsorption forms the rate-limiting step. Response surface methodology (RSM) was used to optimize the removal of VOSCs with respect to different process parameters like adsorbent amount and time. The olfactometry removal of overall odour was also optimized taking 6 factors in the Box Behnken design. Variance of analysis results indicated that all the models displayed considerable goodness of fit with R² values close to 1. Methyl mercaptan turned out to be the highest contributor to the overall odour as confirmed both from experimental and optimization study. The optimized olfactometry odour removal (77.4%) along with CH₃SH removal (80.34%), C₂H₅SH removal (59.16%), CH₃SCH₃ removal (63.21%) and CS₂ removal (71.95%) was found at optimum process conditions, with amount of adsorbent of 10.29 g, adsorption time of 2.92 h. This result indicates that methyl mercaptan (CH₃SH) is the highest odour contributing component out of the studied VOSCs. The results show promising potential for the use of activated carbon as an adsorbent for removal of odorous compounds from STPs.

Smart instrumental Odour Monitoring Station for the efficient odour emission management and control in wastewater treatment plants

Odour emission assessment in wastewater treatment plants (WWTP) is a key aspect that needs to be improved in the plant management to avoid complaints and guarantee a sustainable environment. The research presents a smart instrumental odour monitoring station (SiOMS) composed of an advanced instrumental odour monitoring system (IOMS) integrated with other measurement units, for the continuous characterization and measurement of the odour emissions, with the aim of managing the potential odour annoyance causes in real time, in order to avoid negative effects. The application and on-site validation procedure of the trained IOMS is discussed. Experimental studies have been conducted at a large-scale WWTP. Fingerprint analysis has been applied to analyze and identify the principal gaseous compounds responsible for the odour annoyance. The artificial neural network has been adopted to elaborate and dynamically update the odour monitoring classification and quantification models (OMMs) of the IOMS. The results highlight the usefulness of a real-time measurement and control system to provide continuous and different information to the plant operators, thus allowing the identification of the odour sources and the most appropriate mitigation actions to be implemented. The paper provides important information for WWTP operators, as well as for the regulating bodies, authorities, manufacturers and end-users of odour monitoring systems involved in environmental odour impact management.

The variation of odor characteristics of wastewater sludge treated by advanced anaerobic digestion (AAD) and the contribution pattern of key odorants

Identification of the odor characteristics of wastewater sludge is important in the evaluation of sludge quality and disposal options considering that sludge odor nuisance may cause major environmental issues. In this study, raw sludge and sludge cake were collected from five WWTPs applied advanced anaerobic digestion (AAD) sludge treatment process to clarify the variation of odor characteristics using sensory analysis and instrumental analysis. The electronic nose, gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS/MS) were used to profile and identify the chemical composition of key odorants. A total of 20 odorants were identified and quantified, including 6 groups of chemicals, among which volatile sulfur compounds (VSCs), indole, 3-methylindole and geosmin were identified as key odorants. The odor of the dewatered digested sludge was improved by means of changing the odor character from fecal/sulfide to earthy odor due to the reduction in VSCs concentration. The AAD and subsequent dewatering process resulted in effective removal of VSCs, which are important constituents that impact the sludge odor characteristics through synergistic effect on fecal odorants and masking effect on earthy odorants. Moreover, due to the variation of sludge quality after AAD treatment, the emission capacity of indole, 3-methylindole, and other volatiles increased, which could not be neglected for the formation of unique sludge odor.

Study on the Discrimination of Possible Error Sources That Might Affect the Quality of Volatile Organic Compounds Signature in Dairy Cattle Using an Electronic Nose

Simple Summary

In recent decades, remarkable progress in the development of electronic nose (EN) technologies, particularly for disease detection, has been accomplished through the disclosure of novel methods and associated devices, mainly for the detection of volatile organic compounds (VOCs). Herein, we assessed the ability of a novel EN technology (MENT-EGAS prototype) to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. Principal Component Analyses (PCA) evidenced the presence in the analyzed samples of sufficient information to consent the discrimination of different environmental backgrounds, feed headspaces and exhalated breath between two groups of cows fed with two different types of feed. Moreover, discrimination was also observed within the same group between exhalated breaths sampled before and after feed intake. Based on these findings, we provided evidence that the MENT-EGAS prototype can identify error sources with accuracy. Livestock precision farming technologies are powerful tools for monitoring animal health and welfare parameters in a continuous and automated way.

Abstract

Electronic nose devices (EN) have been developed for detecting volatile organic compounds (VOCs). This study aimed to assess the ability of the MENT-EGAS prototype-based EN to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. This study was performed on a dairy farm using 11 (n = 11) multiparous Holstein-Friesian cows. The cows were divided into two groups housed in two different barns: group I included six lactating cows fed with a lactating diet (LD), and group II included 5 non-lactating late pregnant cows fed with a far-off diet (FD). Each group was offered 250 g of their respective diet; 10 min later, exhalated breath was collected for VOC determination. After this sampling, 4 cows from each group were offered 250 g of pellet concentrates. Ten minutes later, the exhalated breath was collected once more. VOCs were also measured directly from the feed’s headspace, as well as from the environmental backgrounds of each. Principal component analyses (PCA) were performed and revealed clear discrimination between the two different environmental backgrounds, the two different feed headspaces, the exhalated breath of groups I and II cows, and the exhalated breath within the same group of cows before and after the feed intake. Based on these findings, we concluded that the MENT-EGAS prototype can recognize several error sources with accuracy, providing a novel EN technology that could be used in the future in precision livestock farming.

Nonlinear Dynamic Response Analysis of a Three-Stage Gear Train Based on Lightweight Calculation for Edge Equipment

Bevel gears are widely used in aerospace transmission systems as well as modern mechanical equipment. In order to meet the needs and development of aerospace, high-speed dynamic vehicles, and various defense special equipment, higher and higher requirements are made for the high precision and stability of gear transmission systems, as well as the prediction and control of noise and vibration. Considering the nonlinear factors such as comprehensive gear error and tooth side clearance, a dynamic model of the three-stage gear transmission system is established. The relevant physical parameters, geometric parameters, and load parameters in the gear system are considered random variables to obtain the stochastic vibration model. When the random part of the random parameters is much smaller than the deterministic part, the vibration differential equation is expanded into a first-order term at the mean of the random parameter vector according to the Taylor series expansion theorem, and the ordering equation is solved numerically. Based on the improved stochastic regression method, the nonlinear dynamic response analysis of the three-stage gear train is carried out. This results in a relatively stable system when the dimensionless excitation frequency is in the range of 0.716 to 0.86 and the magnitude of the dimensionless integral meshing error is < 1.089.

ZIF-X (8, 67) based nanostructures for gas-sensing applications

ZIF-8 and ZIF-67 are the most investigated zeolitic imidazolate frameworks (ZIFs) materials that have aroused enormous scientific interests in numerous areas of application including electrochemistry, gas storage, separation, and sensors by reason of their fascinating structural properties. Recently, there is a rapidly growing demand for chemical gas sensors for the detection of various analytes in widespread applications including environmental pollution monitoring, clinical analysis, wastewater analysis, industrial applications, food quality, consumer products, and automobiles. In general, the key to the development of superior gas sensors is exploring innovative sensing materials. ZIF-X (8, 67) based nanostructures have demonstrated great potential as ideal sensing materials for high-performance sensing applications. In this review, the general properties and applications of ZIF-X (8, 67) including gas storage and gas adsorption are first summarized, and then the recent progress of ZIF-X (8, 67) based nanostructures for gas-sensing applications and the structure-property correlations are summarized and analyzed.

Selectivity enhancement for metal oxide (MOX) based gas sensor using thermally modulated datasets coupled with golden section optimization and chemometric techniques

The ever-increasing demand for smart sensors for internet of things applications drove the change in outlook toward smart sensor system design. This paper focuses on using low-cost gas sensors [Metal Oxide (MOX)] for detection of more than one gas, which is otherwise complex due to poor selectivity of MOX sensors. In this work, detection of two gases, namely, ammonia (NH₃) and carbon monoxide (CO), using a single metal oxide (pristine tin oxide) sensor is demonstrated. Furthermore, chemometric based algorithms have been used to classify and quantify both gases. The present investigation uses the temperature modulated gas sensor response obtained at different concentrations for the mentioned gases. The golden section based optimization technique has been employed to obtain two different ranges of temperatures for both gases. After applying certain pre-processing techniques, the acquired data from the sensors were fed to various classification techniques, such as partial least squares (PLS) discriminant analysis, k-means, and soft independent modeling by class analogy, and 100% classification results were obtained. Furthermore, PLS regression (PLS-R) was used to perform quantitative analysis on the data using the optimized temperature ranges for both gases, and R² regression coefficients, 0.999 25 for NH₃ and 0.9399 for CO, were obtained. The results obtained from both the qualitative and quantitative analyses make our approach low-cost and smart to mitigate the cross-selectivity of metal oxide semiconductor based smart sensor design.

Ethylene dimethacrylate used as an NH3 adsorbent with high adsorption capacity and selectivity

NH₃ molecularly imprinted polymers (NH₃-MIPs) were synthesized that could successfully separate and recover NH₃ during sludge aerobic composting; however, increased toluene usage during the adsorbent preparation incurred a high cost and severe environmental risks. The purpose of this study was to reduce toluene usage by optimizing the reagent composition of NH₃-MIPs, based on maintaining a high NH₃ adsorption capacity and selectivity. Five adsorbent groups, including NH₃-MIPs, and NH₃-Ethylene dimethacrylate adsorbents (NH₃-EGDMA) with 0%, 75%, 90%, and 100% toluene reduction efficiencies, were prepared and tested for their adsorption performance. The results showed that NH₃-EGDMA with 75% toluene reduction not only had a high NH₃ adsorption capacity (104.42 mg g^-1) but also had a high separation factor for NH₃/methyl sulfide (3121) and NH₃/dimethyl disulfide (4597). The adsorption mechanism was identified as a chemical force between NH₃ and NH₃-EGDMA with a 75% toluene reduction using the analysis of the kinetic model. This study significantly reduces NH₃ adsorbent cost as well as harm to the environment during the adsorbent preparation, which was beneficial to the popularization and application of this NH₃ adsorbent.

Impact of Lateral SnO2 Nanofilm Channel Geometry on a 1024 Crossbar Chemical Sensor Array

We propose a rational strategy to fabricate thermally robust, highly integrated molecular and gas sensors utilizing a lateral SnO₂ nanofilm channel geometry on a 1024 crossbar sensor array. The proposed lateral channel geometry substantially suppresses the detrimental effects of parasitic interconnect wire resistances compared with those of a conventional vertical sandwich-type crossbar array because of its excellent resistance controllability. A conductive oxide top-contact electrode on the lateral SnO₂ nanofilm channel enhances the thermal stability at temperatures of up to 500 °C in ambient air. Integrating this lateral SnO₂ nanofilm geometry with analog circuits enables the operation of a 1024 crossbar sensor array without selector devices to avoid sneak currents. The developed 1024 crossbar sensor array system detects the local spatial distribution of the molecular gas concentration. The spatial data of molecular concentrations include molecule-specific data to distinguish various volatile molecules based on their vapor pressures. Thus, this integrated crossbar sensor array system using lateral nanofilm geometry offers a platform for robust, reliable, highly integrated molecular and gas sensors.

Full-Scale Odor Abatement Technologies in Wastewater Treatment Plants (WWTPs): A Review

The release of air pollutants from the operation of wastewater treatment plants (WWTPs) is often a cause of odor annoyance for the people living in the surrounding area. Odors have been indeed recently classified as atmospheric pollutants and are the main cause of complaints to local authorities. In this context, the implementation of effective treatment solutions is of key importance for urban water cycle management. This work presents a critical review of the state of the art of odor treatment technologies (OTTs) applied in full-scale WWTPs to address this issue. An overview of these technologies is given by discussing their strengths and weaknesses. A sensitivity analysis is presented, by considering land requirements, operational parameters and efficiencies, based on data of full-scale applications. The investment and operating costs have been reviewed with reference to the different OTTs. Biofilters and biotrickling filters represent the two most applied technologies for odor abatement at full-scale plants, due to lower costs and high removal efficiencies. An analysis of the odors emitted by the different wastewater treatment units is reported, with the aim of identifying the principal odor sources. Innovative and sustainable technologies are also presented and discussed, evaluating their potential for full-scale applicability.

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

Volatile sulfur compound emissions and health risk assessment from an A2/O wastewater treatment plant

Anoxic/anaerobic/oxic (A²/O) wastewater treatment has emerged as a major process for treatment of domestic wastewater. One of the issues with wastewater treatment plants (WWTPs) is that volatile sulfur compounds (VSCs) are discharged from them and pose numerous health risks. This study characterized VSC emissions at the water-air interface and concentrations of ambient air exposure from different treatment units in an A²/O WWTP. AERMOD modeling was used to simulate the atmospheric behaviors of discharged VSCs. Results demonstrated that VSC emission fluxes and exposure concentrations had followed a descending order of pretreatment>biological treatment>advanced treatment. Emissions were affected by sulfate concentrations and chemical oxygen demand in the wastewater, and control strategies based on these values were proposed. The AERMOD results indicated that the majority of the total hydrogen sulfide (87%) and methyl mercaptan (65%) emissions came from the primary sedimentation tank, while the majority of dimethyl sulfide (81%), carbon disulfide (84%), and dimethyl disulfide (93%) were emitted from the oxic area. MT and DMS were the main odorous components of the VSCs in ambient air based on the indicator of odor activity values. Noncancer health risks, determined by having a hazard quotient >1, of the measured VSCs were beyond acceptable limits. Overall, efforts should be made to minimize noncancer health risks as individuals are exposed to VSCs not only in treatment units but also in areas surrounding WWTPs.

Optimization of Classification Prediction Performances of an Instrumental Odour Monitoring System by Using Temperature Correction Approach

Odour emissions generated by industrial and environmental protection plants are often a cause of nuisances and consequent conflicts in exposed populations. Their control is a key action to avoid complaints. Among the odour measurement techniques, the sensory-instrumental method with the application of Instrumental Odour Monitoring Systems (IOMSs) currently represents an effective solution to allow a continuous classification and quantification of odours in real time, combining the advantages of conventional analytical and sensorial techniques. However, some aspects still need to be improved. The study presents and discusses the investigation and optimization of the operational phases of an advanced IOMS, applied for monitoring of environmental odours, with the aim of increasing their performances and reliability of the measures. Accuracy rates of over 98% were reached in terms of classification performances. The implementation of automatic correction systems for the resistance values of the measurement sensors, by considering the influence of the temperature, has been proven to be a solution to further improve the reliability of IOMS. The proposed approach was based on the application of corrective coefficients experimentally determined by analyzing the correlation between resistance values and operating conditions. The paper provides useful information for the implementation of real-time management activities by using a tailor-made software, able to increase and enlarge the IOMS fields of application.

Next-generation of instrumental odour monitoring system (IOMS) for the gaseous emissions control in complex industrial plants

Odour emissions from complex industrial plants may cause potential impacts on the surrounding areas. Consequently, the validation of effective tools for the control of the associated environmental pressures, without hindering economic growth, is strongly needed. Nowadays, senso-instrumental methods by using Instrumental Odour Emissions Systems (IOMSs) is among the most attractive tool for the continuous monitoring of environmental odours, allowing the possibility of obtaining real-time information to support the decision-making process and proactive approach. The systems complexity and scarcity of real data limited their wider full-scale employment. The study presents an advanced prototype of IOMS for the continuous classification and quantification of the odours emitted in ambient air by complex industrial plants, to continuously control the plants emissions with backwards approach. The IOMS device was designed and optimized and included the system for the automatic control of the conditions inside the measurement chamber. The designed operational procedures were presented and discussed. Results highlighted the influence of temperature and air flow rate for the measurement repeatability. Accurate prediction model was created and optimized and resulted able to distinguish 3 different industrial odour sources with accuracy approximately equal to 96%. The models were optimized thanks to the software features, which allowed to automatically apply the designed statistical procedures on the identified dataset with different pre-processing approach. The usefulness of having a fully-developed and user-friendly flexible system that allowed to select and automatically compare different settings options, including the different feature extraction methods, was demonstrated in order to identify the best prediction model.

Chemical and olfactive impacts of organic matters on odor emission patterns from the simulated construction and demolition waste landfills

The explosive increase of construction and demolition waste (CDW) caused the insufficient source separation and emergency disposal at domestic waste landfills in many developing countries. Some organic fractions were introduced to the CDW landfill process and resulted in serious odor pollution. To comprehensively explore the impacts of organic matters on odor emission patterns, five CDW landfills (OIL), with organic matters/ inert CDW components (O/I) from 5% to 30%, and the control group only with inert components (IL) or organics (OL) were simulated at the laboratory. The chemical and olfactive characters of odors were evaluated using the emission rate of 94 odorants content (ER_total), theory odor concentration (TOC_total), and e-nose concentration (ER_ENC), and their correlations with waste properties were also analyzed. It was found that the main contributors to ER_total (IL: 93.0% NH₃; OIL: 41.6% sulfides, 31.0% NH₃, 25.9% oxygenated compounds) and TOC_total (IL: 64.1% CH₃SH, 28.2% NH₃; OIL: 71.7% CH₃SH, 24.8% H₂S) changed significantly. With the rise of O/I, ER_total, TOC_total, and ER_ENC increased by 10.9, 20.6, and 2.1 times, respectively. And the organics content in CDW should be less than 10% (i.e., DOC<101.3 mg/L). The good regressions between waste properties (DOC, DN, pH) and ER_ENC- (r=0.86, 0.86, -0.88, p<0.05), TOC_total- (r=0.82, 0.79, -0.82, p<0.05) implied that the carbon sources and acidic substances relating to organics degradation might result in that increase. Besides, the correlation analysis results (ER_ENC-vs.TOC_total-, r=0.96, p<0.01; vs.ER_total-, r=0.86, p<0.05) indicated that e-nose perhaps was a reliable odor continuous monitoring tool for CDW landfills.

Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose

The composition of volatile organic compounds (VOCs) in large-scale livestock farms is complex, which seriously affects the health of livestock and is difficult to evaluate. In order to quickly analyze the pollution degree of VOCs in livestock farms, electronic nose technology was used in this study to detect and analyze the gases in pig and chicken houses, respectively. Firstly, the gas chromatography–mass spectrometry (GC–MS) and electronic nose were used to analyze the VOCs in the pig and chicken houses at different time and locations. The types and relative contents of VOCs were obtained from different livestock farms by GC–MS analysis. The sensor array response of the electronic nose showed similar results. In addition, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machine (SVM) analyses were performed on the electrical signal that was generated by the sensors of electronic nose, respectively. Finally, the classification rate of different odor sources in livestock farms was the highest (>85%), which indicates that SVM is a more effective method suitable for volatile gases recognition in livestock farms. The results have shown that the developed electronic nose sensor is a promising and feasible instrument for characterizing volatile odors in livestock farms.

Robust and Rapid Detection of Mixed Volatile Organic Compounds in Flow Through Air by a Low Cost Electronic Nose

This work aims to detect volatile organic compounds (VOC), i.e., acetone, ethanol and isopropyl alcohol (IPA) and their binary and ternary mixtures in a simulated indoor ventilation system. Four metal-oxide-semiconductor (MOS) gas sensors were chosen to form an electronic nose and it was used in a flow-through system. To speed up the detection process, transient signals were used to extracted features, as opposed to commonly used steady-state signals, which would require long time stabilization of testing parameters. Five parameters were extracted including three in phase space and two in time space. Classifier and regression models based on backpropagation neural network (BPNN) were used for the qualitative and quantitative detection of VOC mixtures. The VOCs were mixed at different ratios; ethanol and isopropyl alcohol had similar physical and chemical properties, both being challenging in terms of obtaining quantitative results. To estimate the amounts of VOC in the mixtures, the Levenberg–Marquardt algorithm was chosen in network training. When compared with the multivariate linear regression method, the BPNN-based model offered better performance on differentiating ethanol and IPA. The test accuracy of the classification was 82.6%. The concept used in this work could be readily translated for detecting closely related chemicals.

Experimental study on volatile sulfur compound inhibition using a single-chamber membrane-free microbial electrolysis cell

Odor emissions from sewer systems and wastewater treatment plants have attracted much attention due to the potential negative effects on human health. A single-chamber membrane-free microbial electrolysis cell was proposed for the removal of sulfides in a sewer system. The feasibility of the use of volatile sulfur compounds and their removal efficiency in liquid and headspace gas phases were investigated using synthetic wastewater with real sewer sediment and Ru/Ir-coated titanium electrodes. The results indicate that hydrogen sulfide and volatile organic sulfur compounds were effectively inhibited in the liquid phase upon electrochemical treatment at current densities of 1.55, 2.06, and 2.58 mA/cm², and their removal rates reached up to 86.2-100%, except for dimethyl trisulfide, the amount of which increased greatly at 1.55 mA/cm². In addition, the amount of volatile sulfur compounds in the headspace decreased greatly; however, the total theoretical odor concentration was still high, and methanethiol and ethanethiol greatly contributed to the total strength of the odor concentration due to their low odor threshold concentrations. The major pathway for sulfide removal in the single-chamber membrane-free microbial electrolysis cell is biotic oxidation, the removal rate of which was 0.4-0.5 mg/min, 4-5 times that of indirect electrochemical oxidation.

Wastewater monitoring by means of e-nose, VE-tongue, TD-GC-MS, and SPME-GC-MS

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.

Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)

The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.

Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

The odour assessment of thyme essential oils in electrospun fibre mat with a virtual sensor array data and its relation to antibacterial activity

Aims: The ability of a single-sensor gas diagnosis device (SSGDD) as a virtual sensor array data to appraise thyme essential oils (TEO) based on its quantitatively release rate from nanofibres was aimed.Methods: To form nylon 6 fragrant electrospun nanofibre, TEO was added as a natural antibacterial substance under homogeniser to make a stable emulsion.Results: The optimised nanofibre inactivated against Escherichia Coli and Staphylococcus Aureus bacteria up to >75% at once and to > 41.9% over 2-weeks period. Moreover, large differences in sensor responses to samples with experimental variables (percent TEO and storage time) and different odour intensity exist which correctly classified by discriminant function analysis.Conclusions: Odour intensity as an accessible incubator evinces the nanofibres efficiency which correlated to the antibacterial activity. With applying SSGDD technique as a quantified subjective solution, carefully odour assessment is possible and prepared mats could be demonstrated as a face-masks' promising candidate.

Measurements techniques and models to assess odor annoyance: A review

Odors have received increasing attention among atmospheric pollutants. Indeed, odor emissions are a common source of complaints, affecting the quality of life of humans and animals. The odor is a property of a mixture of different volatile chemical species (sulfur, nitrogen, and volatile organic compounds) capable of stimulating the olfaction sense sufficiently to trigger a sensation of odor. The impact of odors on the surrounding areas depends on different factors, such as the amount of odors emitted from the site, the distance from the site, weather conditions, topography, other than odors sensitivity and tolerance of the neighborhood. Due to the complexity of the odor issue, the aim of this review was to give an overview of: (i) techniques (sensorial and analytical) that can be used to determine a quantitative and qualitative characterization; (ii) air dispersion models applied for the evaluation of the spatial and temporal distribution of atmospheric pollutants in terms of concentration in air and/or deposition in the studied domain; (iii) major sources of odor nuisance (waste and livestock); (iv) mitigation actions against odor impact. Among sensorial techniques dynamic olfactometry, field inspection, and recording from residents were considered; whereas, for analytical methodologies: gas chromatography-mass spectrometry, identification of specific compounds, and electronic nose. Both kinds of techniques evaluate the odor concentration. Instead, to account for the effective impact of odors on the population, air dispersion models are used. They can provide estimates of odor levels in both current and future emission scenarios. Moreover, they can be useful to estimate the efficiency of mitigation strategies. Most of the odor control strategies involve measures oriented to prevent, control dispersion, minimize the nuisance or remove the odorants from emissions, such as adequate process design, buffer zones, odor covers, and treatment technologies.

Application of Electronic Nose for Evaluation of Wastewater Treatment Process Effects at Full-Scale WWTP

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; thus, the samples could be classified in terms of their similarity and the analyzed gas-fingerprint could be related to the pollution level expressed by physical and biochemical indicators. Principal component analysis was employed for dimensionality reduction, and cluster analysis for grouping observation purposes. Supervised learning techniques confirmed that the obtained data were applicable for the classification of wastewater at different stages of the purification process.